This Handbook deals with theoretical and empirical evidence on the economics of discrimination and affirmative action ac
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Table of contents :
Acknowledgments
Contents
About the Editor
Advisory Board Members
Contributors
1 Introduction
Introduction
Analyzing Discrimination and Disadvantage
What Is Discrimination?
Inequality of Opportunity
Methodological Approaches to Understanding Discrimination
Field Experiments
Intersectionality and Discrimination
Social and Regional Dimensions of Discrimination
Dimensions of Discrimination
Affirmative Action
Miscellaneous
References
Part I: Analyzing Discrimination and Disadvantage
2 Taste-Based Discrimination
Introduction
Becker´s Theory, Extensions, and Empirical Assessments
Conceptual Frameworks
Definition and Seminal Contribution
How Can Taste-Based Discrimination Persist?
Empirical Assessments
Evidence on the Relationship Between Prejudice and Gaps
Competition and Discrimination
Customer Discrimination
Residual Approaches
Opening the Black Box of Taste-Based Discrimination: Interdisciplinary Insights
From Taste to Prejudice
From Varieties of Prejudice to Varieties of Discrimination
Implicit and Explicit Prejudice
Types of Emotions Behind Prejudice
The Stereotype Content Model
Where Does Taste Come From? Investigating the Roots of Prejudice
Prejudice and Social Cognition
Bringing the Context in: The Sociohistorical Roots of Prejudice
Back to Taste-Based Versus Statistical Discrimination: The Role of Information and Accuracy of Beliefs
Towards an Integrated Approach to Discrimination: A Multidisciplinary Perspective
How to Design Public Policies to Reduce the Consequences of Taste-Based Discrimination?
Prejudice Reduction Interventions
Reducing the Scope of Prejudice in Decision-Making
Towards Less Prejudiced Societies?
References
3 Stratification Economics
Introduction
How Stratification Economics Explains Attitudes Toward Affirmative Action
A Primer on Stratification Economics
What Stratification Economics Says About Affirmative Action
How Stratification Economics Explains Affinity for White Affirmative Action
How Stratification Economics Explains White Negativism Toward Black Affirmative Action
Conclusion
Cross-References
References
4 Transforming Gendered Labor Markets to End Discrimination
Introduction
Labor Markets as Gendered Institutions
Trends in Gender Gaps in Labor Markets
Participation and Employment Rates and Labor Force Status
Gender Earnings Gaps: Equalizing Up or Equalizing Down?
Gendered Occupational and Sectoral Segregation
Gender Inequality in Labor Markets and Income Inequality Between Households
Efficiency and Discrimination
Gender Discrimination and Inefficiency
Static and Dynamic Efficiency, Micro- and Macro-efficiency
Social Efficiency
Transformatory Strategies and Labor Market Standards
Conclusion
References
5 Theories of Discrimination: Transnational Feminism
Feminist Research and International Problems
Postcolonial Contributions
Race/Ethnicity Critiques
Diaspora Studies
The Mobility Paradigm
Transnational Feminism
References
6 Discrimination as Focal Point
Introduction
Markets and Discrimination
The Art of Creating Optimal Groups
Policy Implications
Addendum. Empirical Tests and a Normative Caveat
Conclusion
New References
References
7 Inequality of Opportunity
Introduction
Inequality of Opportunity
Affirmative Action in Higher Education Through IOp Lenses
Concluding Remarks
References
Part II: Methodological Approaches to Understanding Discrimination
8 Decompositions: Accounting for Discrimination
Introduction
Decomposing the Mean
Oaxaca-Blinder Method
Issues with the Decompositions
Treatment Effect Interpretation
Formal Identification
Going Beyond the Mean
Residual Imputations
Reweighting Methods
Conditional Quantiles
Recentered Influence Functions
Conclusion
References
9 Field Experiments: Correspondence Studies
Introduction
Measuring Discrimination in the Field
Audit Studies
Limitations of Audit Studies
Correspondence Studies
Correspondence Studies in the Labor Market
Correspondence Studies in Other Settings
Rental Markets
Retail
Academia
Beyond the Résumés
Limitations of Correspondence Studies
Implicit Association Tests
Goldberg Paradigm Experiments
List Randomization
Willingness to Pay
Consequences of Discrimination
Self-Expectancy Effects
Stereotype Threat and Underperformance
Identity and Preferences
Expectancy Effects and Self-Fulfilling Prophecies
Pygmalion and Golem Effects
Endogenous Responses to Bias
Discrimination in Politics and Inequality Across Groups
Benefits of Diversity?
Does Homogeneity Hurt or Help Productivity?
Discrimination and Corruption
Law of Small Numbers
What Affects Discrimination?
Leaders and Role Models
Does Diversity in Leadership Positions Directly Affect Discrimination?
Minority Leaders and the Attitude of the Majority
Role Models, Aspirations, and the Attitude of the Minority
Intergroup Contact
Socio-Cognitive De-Biasing Strategies
Technological De-Biasing
Conclusion
Cross-References
References
10 Lab-in-the-Field Experiments
Introduction
Insights into Behavioral Causes of Discrimination
Misperception by Observers: Shooter Bias
Emotional Bias Among Judges
Implicit Bias of Teachers Against Females in Science
Bias in Work Attribution When Signals Are Ambiguous
Observational Study of Promotion to Tenure in Economics Departments
Lab-in-the-Field Experiments on Assessing Performance
Naturalized Relations of Power
Ways to Reduce Discrimination
Dearth of Field Experiments on How to Reduce Prejudice and Discrimination
Overcoming Racial Divides with Alliances and Cooperation
Overcoming Ethnic Divides with Integrated Classrooms and Living Situations
The Influence of Radio on Ethnic Divides in Rwanda
Conclusions
Cross-References
References
11 Methodological Approaches to Understanding Discrimination: Experimental Methods - Trust, Dictator, and Ultimatum Games
Introduction
Minimal Group Paradigm
Natural Identities
Dictator Games
Trust Games
Ultimatum Games
Cross-References
References
12 Insights from Social Psychology: Racial Norms, Stereotypes, and Discrimination
Introduction
Economic Stratification and Stereotypes
Allport and Blumer on Prejudice
Norms and Stereotypes
Stereotypes and Group Position
Stereotypes, Discrimination, and Policing
Conclusion
References
13 Surveys, Big Data, and Experiments
Introduction
Definition: What Are ``Lesbian,´´ ``Gay,´´ ``Bisexual,´´ ``Transgender,´´ and ``Intersex´´?
Sexual Orientation, Gender Identity and Expression, and Sex Characteristics
Spectrums
Being Out
Operationalizing the Definitions
Asking About SOGI in Surveys
Estimating the Population Size
Data from the USA
Data from Other Countries
Nepal´s 2011 Census
Methodological Overview: How Developmental Outcomes for LGBTI People Have Been Measured Through Surveys
Nepal
Methodological Takeaways
India
Europe
Australia
Lessons Learned from Other ``Hard to Survey´´ Populations
The Potential of Big Data in Building LGBTI Knowledge
Experimental Approaches to Measure Discrimination and Exclusion
Conclusion
References
14 Can Economics Become More Reflexive? Exploring the Potential of Mixed Methods
Introduction
Economics, Ethnography, and Reflexivity
What Can Economics Learn from Ethnography?
Cognitive Empathy
Narrative Data
Machine Learning and Natural Language Processing
Studying Process
Participation: Respondent as Analyst
Conclusion
References
Part III: Intersectionality and Discrimination
15 Tackling Intersecting Inequalities: Insights from Brazil
Introduction
Objectives of the Study
Conceptualizing Intersecting Inequalities
The Evolution of Intersecting Inequalities in Brazil
From ``Illiterate Agro-Export Outpost´´ to ``Conservative Modernization´´
Shifting Constructions of Inequality in the Brazilian Context: Vertical, Horizontal, and Intersecting
From Horizontal to Vertical Inequality
From Vertical to Intersecting Inequalities
Documenting Intersecting Inequalities in Late Twentieth Century
Trends in Poverty and Inequality 2002-2013
Income Inequalities
Poverty
Labor Market Outcomes
Wage Inequalities
Labor Market Segregation by Occupation and Work Status
Inequalities by Earnings and Work Status
Access to, and Control Over, Land
Educational Outcomes
Explaining the Decline in Intersecting Inequalities
From ``Conservative Modernization´´ to ``Liberal Neo-Developmentalism´´
The Politics of Social Change
Social Movements
Democratic Practice in Formal Politics
Conclusion
Appendix
References
16 Race and Gender
Introduction
Social Constructs: Ethnicity, Gender, and Race
Ethnicity, Gender, and Race Research
Disaggregated vs. Aggregate Outcomes
Conclusion
Cross-References
References
17 Caste and Gender
Introduction
Situating Intersectionality in India
The Abrahmani-Gender Complex
``Hostile Environments´´ as an Intersectional Lens
Conclusion
Cross-References
References
Part IV: Social and Regional Dimensions of Discrimination
18 The Economic Side of Religious Discrimination in France: A Review
Introduction
Section 1: French History of Immigration
Section 2: Methodologies of Existing Studies
Job Access
Wage Gap and Unemployment
Labor Market
Housing and Rental Market
Section 3: Theme-Wise Analysis
Job Access
Labor Market Outcomes
The Housing Market
Economic Assimilation Related to Marriage and Language Skills
Return Migration from France: Failure or Success?
Section 4: Discussions and Concluding Remarks
Society´s Opinion and Drivers of Discrimination
Increase or Decrease of Discrimination?
Public Policies: Past and Present
Ways Forward in Research
Cross-References
References
19 Socioeconomic Disparities Among Racialized Immigrants in Canada
Introduction
Theoretical Constructs of Dimensions of Socioeconomic Disparities
Literature Review
Dimensions of Social Disparities
Dimensions of Economic Disparities
Conclusion and Recommendations
Cross-References
References
20 The Economics of Discrimination and Affirmative Action in South Africa
Introduction
The Institutionalization of Racial Discrimination in South Africa
Post-1994 Reforms
Analyzing Aspects of Employment and Empowerment Trends
Employment
Wages
Employment Equity Patterns Among Top Management
Higher Education Institutions
Executive and Legislative Representation
Disability
Discrimination Index
Evaluating the Evidence
Conclusion
References
21 Buraku Liberation and the Politics of Redress in Modern Japan, 1868-2002
Introduction
Prewar Outcaste Emancipation
Postwar Buraku Liberation
Conclusion
References
22 Caste and Socoieconomic Disparities in India: An Overview
Introduction
Social Reproduction of the Caste System
Human Capital and Health
Education
Health
Economic Outcomes
Consumption, Income, and Wages
Occupation
Poverty
Wealth
Affirmative Action
Education and Public Sector Employment
Politics
Regional Patterns of Caste Disparities
Conclusion
Cross-References
References
23 Indian Muslims: Varieties of Discriminations and What Affirmative Action Can Do
Trajectories of Socioeconomic and Educational Marginalization
Income: When Muslims Come Last
Education: The Widening Gap Between Muslims and Hindus
Jobs: When the Informal Sector Prevails
Youth in Higher Education
Muslims in Government and Public Sector Jobs
Politics and Policies of Affirmative Action
Timidity and Bias at the Center
Muslims in Dravidian Land
Northern and Eastern India: Failed Attempts and the Delegitimization of Pro-Muslim Policies
Muslims as Collateral Casualties of the Modi Government´s Style of Empowerment
Conclusion
Appendix
References
Part V: Dimensions of Discrimination
24 Stereotypes and the Administration of Justice
Introduction
Stereotypes
Police Stops and Searches
Conflict, Cooperation, and Clearance
Preemption and Murder
Lethal Force
Discrimination in Other Venues
Discussion
References
25 Evidence of Covariation Between Regional Implicit Bias and Socially Significant Outcomes in Healthcare, Education, and Law ...
A Brief Definition of Implicit and Explicit Attitudes and Beliefs
The Relationship of Implicit Bias and Individual Discriminatory Behavior
The Relationship of Implicit Bias and Systemic Discriminatory Behaviors: An Overview
Disparities in Education and Opportunity: Standardized Testing, School Discipline, and Economic Mobility
Disparities in Healthcare: Medicaid Spending, Death Rates, and Infant Health Outcomes
Disparities in Policing: Lethal Force and Traffic Stops
Examining Implicit Bias as the Outcome Explained by Systemic Predictors
Implications for Understanding Implicit Bias and Systemic Behaviors
Concluding Remarks
Cross-References
References
26 Disadvantage and Discrimination in Self-Employment and Entrepreneurship
Introduction
What Do the Numbers Show?
Understanding the Sources of Gaps
Demographic Characteristics
Credit Access
Customer Preferences
Bridging the Gaps
Affirmative Action in Government Contracting
Reducing Unconscious Bias
Training Programs and Role Models
Conclusion
References
27 Discrimination in Credit
Introduction
Evidence
Laws, Rules, and Institutions
Implications
Mechanisms and Methods
Conclusion
References
28 Gender-Based Discrimination in Health: Evidence from Cross-Country
Introduction
Early-Life Discrimination Within Households
Excess Female Mortality and Missing Women
Gendered Differences in Utilization of Health
Discrimination by Health Providers
Measurement Issues Around Discrimination
Micro-foundations of Gendered Institutions and Economic Development
Economic Cost of Gender-wise Discrimination in Health
Policy Insights
References
29 Dimensions of Discrimination: Discrimination in Housing
Introduction
Literature Review: Housing Discrimination Globally
Housing Discrimination in India: Theoretical Considerations, Historical Experience
Existing Literature on Housing Discrimination in India
The Experiment
Location and Sample
Names and Contact Strategy
Data and Analysis
Tracing Callers
Descriptive Statistics: Listings, Landlords, and Applicants
Listing and Landlord Characteristics
Applicant and Application Characteristics
Results
Result 1: Landlords Are Significantly Less Likely to Respond to Muslims Applicants
Result 2: Relative to UC Applicants, Muslim Applicants Receive Fewer Callbacks But Landlords Who Do Respond Make Similar Numbe...
Result 3: Differences in the Probability of Response and Count of Responses Between UC and SC/OBC Are Not Statistically Signif...
Result 4: There Is Suggestive Evidence that Landlords Who Respond to Both UC and Muslim (or SC) Applicants Are More Likely to ...
Result 5: There Is Heterogeneity By Gender and Religion of Landlord and By Size and Rental Price of the Listed Property in the...
Discussion, Interpretation, and Conclusion
References
Part VI: Affirmative Action
30 Is Positive Discrimination a Good Way to Aid Disadvantaged Ethnic Communities?
Introduction
Alternatives to Ethnicity-Based Preferential Selection
Why Not Use Other Policies to Aid Disadvantaged Ethnic Communities?
Why Not Base Preferential Selection Policies on Characteristics Other Than Ethnicity?
Optimal Structuring of Ethnicity-Based Preferences
Sphere of Applicability of Positive Discrimination Policies
Choice of Beneficiary Communities Eligible for Preferences
Configuration of Positive Discrimination Policies
Quotas Versus Preferences
Magnitude of the Preference
Sensitivity of the Selection Process
Identifiability of the Beneficiaries
Extent of Support for Underprepared Beneficiaries
Conclusion
References
31 Experimental Evidence on Affirmative Action
Introduction
Experimental Designs
How Is Affirmative Action Implemented?
Contextual Background to Affirmative Action
Baseline Inequality
Salient Identity
Can Affirmative Action Increase Representation Without Harming Efficiency?
Affirmative Action Increases Participation
Is There an Efficiency Loss?
What Is the Profile of Affirmed Winners?
Does Affirmative Action Impact Effort?
Aggregate Impacts
Are Affirmative Action Beneficiaries Penalized by Others?
Evaluation of Beneficiaries
Cooperation
Dishonest Behavior
Concluding Remarks
References
32 Does Political Affirmative Action Work, and for Whom? Theory and Evidence on India´s Scheduled Areas
Introduction
Theory and Hypotheses
Extensive Margin (Size of the Pie)
Intensive Margin (Distribution of the Pie)
Context: Identity, Quotas, and Development in India
Scheduled Areas in India
Panchayat Extension to Scheduled Areas
Quotas and Political Conflict: A Case Study of Jharkhand
Comparisons Across Indian Identity Categories
Local Government and Development
The National Rural Employment Guarantee Scheme (NREGS)
Beyond NREGS: Rural Roads and Other Public Goods
Data Construction
Empirical Strategy
Geographic Regression Discontinuity
Analysis of Balance with Census Data
The Impact of Scheduled Areas
Impacts on the National Rural Employment Guarantee Scheme (NREGS)
Impacts on the Rural Roads Program (PMGSY)
Impacts on Public Goods
Discussion: Bringing the Results Together
Investigating the Electoral Mechanism
Scheduled Areas Prior to PESA
Local Elections in Scheduled Areas
Targeted Minority Electoral Influence
Quota Overlap
Conclusion
Cross-References
References
33 Gender Quotas and Representation in Politics
Introduction
Democracy, Representation, and Diversity
Quotas As Fast Track to Equal Representation
Opportunities and Challenges to Gender Quotas
Conclusion
References
Reports
34 Caste Quotas in India
Introduction
Affirmative Action in India
Quotas in Public Sector Occupation
Quotas in Public Sector Educational Institutions
Quotas in the Legislature
Quota for Economically Weaker Sections
Demands for Reservation
Contemporary Reality
Criticism of Quotas
Conclusion
Bibliography
35 The Effectiveness of Affirmative Action Policies in South Africa
Introduction
The South African Labor Market and Discrimination
Before Democracy
Since Democracy in 1994, But Before Affirmative Action
Affirmative Action Legislation in South Africa: From Employment Equity to Broad-Based Black Economic Empowerment
Redress and Access: Affirmative Action Legislation in the 1990s
Critique and Review: A Brief Summary
Multidimensional Measures: Broad-Based Black Economic Empowerment
Another Road to B-BBEE: The Sector Transformation Charters and Codes
Amendments Since 2012: Toward Simplifying Complexity and Fostering Flexibility?
Empirical Analysis
Conclusion
Appendix
Data and Methodology
References
36 Malaysia´s New Economic Policy and Affirmative Action: A Remedy in Need of a Rethink
Introduction
Foundations, Contexts, and Frameworks
Constitutional Premises and Political Imperatives
Policy Platforms and Public Discourse
Mechanisms and Instruments
Policy Achievements, Shortfalls, and Implications
Conclusion
References
37 Stereotype Threat Experiences Across Social Groups
Discrimination and Affirmative Action: Definitions and Policy in the United States
Through the Lens of Stereotype Threat: Workplace Experiences of Discrimination and Affirmative Action
Stereotype Threat Theory
Foundational Stereotype Threat Theory Research
Consequences of Stereotype Threat Beyond Academic Performance
Social Groups, Stereotype Threat, and Affirmative Action
Stereotype Threat and Affirmative Action Research
Insights from Stereotype Threat Theory
Insight One: Misconceptions and Mischaracterizations of AA Cues Stereotype Threat
Insight Two: Creating Identity-Safe Environments in the Context of Affirmative Action
Gaining Critical Historical Knowledge About Discrimination and Social Inequities
Accurate and Strategic Messaging of AAPs
Renewing Commitments to AAPs
Concluding Thoughts
Cross-References
References
Part VII: Miscellaneous
38 Feminism as Racist Backlash: How Racism Drove the Development of Nineteenth- and Twentieth-Century Feminist Theory
Introduction
Against Savage (Black) Manhood: Elizabeth Cady Stanton, Complementarianism, and the Uniting of the White Race
The Evolutionary Origin of Patriarchy is Feminine - Gilman´s Claiming of White Supremacy as the Gift of White Womanhood
The Racial Proxemic: [W]hite Womanhood, Feminism, and the Dangers of Black Men to White Civilization
Feminist Theory´s Adoption of Subculture of Violence Criminology as a Response to Desegregation
Conclusion
References
39 Inequality and Inefficiency
References
Index
Ashwini Deshpande Editor
Handbook on Economics of Discrimination and Affirmative Action
Handbook on Economics of Discrimination and Affirmative Action
Ashwini Deshpande Editor
Handbook on Economics of Discrimination and Affirmative Action With 48 Figures and 54 Tables
Editor Ashwini Deshpande Professor of Economics Ashoka University Sonipat, Haryana, India
ISBN 978-981-19-4165-8 ISBN 978-981-19-4166-5 (eBook) https://doi.org/10.1007/978-981-19-4166-5 © Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
This volume is dedicated to my “Economics of Discrimination” students of the past two decades. Their insightful questions and critiques challenged me to explore deeper, ultimately advancing my understanding of this difficult and contentious terrain.
Acknowledgments
Thanks to the advisory board for their support in this endeavor, particularly to those who sent their contributions and suggested other contributors. Special thanks to the authors who provided excellent pieces, despite their busy schedules, not to mention the added pressure due to the pandemic. Daniel Challam deserves thanks for his early research assistance and handling of correspondence with authors and Springer.
vii
Contents
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ashwini Deshpande
Part I
1
Analyzing Discrimination and Disadvantage . . . . . . . . . . . . .
15
2
Taste-Based Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roland Rathelot and Mirna Safi
17
3
Stratification Economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lucas Hubbard and William A. Darity Jr.
49
4
Transforming Gendered Labor Markets to End Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diane R. Elson
69
5
Theories of Discrimination: Transnational Feminism Inderpal Grewal
..........
85
6
Discrimination as Focal Point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kaushik Basu
105
7
Inequality of Opportunity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Patrizio Piraino and Josefina Senese
117
Part II Methodological Approaches to Understanding Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..............
133
.................
151
Lab-in-the-Field Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Allison Demeritt and Karla Hoff
235
8
Decompositions: Accounting for Discrimination Gurleen Popli
9
Field Experiments: Correspondence Studies Marianne Bertrand and Esther Duflo
10
131
ix
x
11
12
Contents
Methodological Approaches to Understanding Discrimination: Experimental Methods – Trust, Dictator, and Ultimatum Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abhinash Borah Insights from Social Psychology: Racial Norms, Stereotypes, and Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stephanie Seguino
13
Surveys, Big Data, and Experiments . . . . . . . . . . . . . . . . . . . . . . . Dominik Koehler and Nicholas Menzies
14
Can Economics Become More Reflexive? Exploring the Potential of Mixed Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vijayendra Rao
Part III
Intersectionality and Discrimination . . . . . . . . . . . . . . . . . .
261
285 299
323
351
15
Tackling Intersecting Inequalities: Insights from Brazil Naila Kabeer and Ricardo Santos
........
353
16
Race and Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rhonda Vonshay Sharpe
407
17
Caste and Gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kalpana Kannabiran
423
Part IV 18
19
20
21
22
Social and Regional Dimensions of Discrimination . . . . . .
441
The Economic Side of Religious Discrimination in France: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christophe Jalil Nordman
443
Socioeconomic Disparities Among Racialized Immigrants in Canada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karun K. Karki, Delores V. Mullings, and Sulaimon Giwa
463
The Economics of Discrimination and Affirmative Action in South Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Imraan Valodia and Arabo K. Ewinyu
481
Buraku Liberation and the Politics of Redress in Modern Japan, 1868–2002 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Timothy Amos
499
Caste and Socoieconomic Disparities in India: An Overview . . . . . Rajesh Ramachandran
517
Contents
23
xi
Indian Muslims: Varieties of Discriminations and What Affirmative Action Can Do . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christophe Jaffrelot and Kalaiyarasan A.
Part V
Dimensions of Discrimination
.......................
24
Stereotypes and the Administration of Justice . . . . . . . . . . . . . . . . Brendan O’Flaherty and Rajiv Sethi
25
Evidence of Covariation Between Regional Implicit Bias and Socially Significant Outcomes in Healthcare, Education, and Law Enforcement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tessa E. S. Charlesworth and Mahzarin R. Banaji
26
Disadvantage and Discrimination in Self-Employment and Entrepreneurship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smriti Sharma
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565 567
593
615
27
Discrimination in Credit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sunil Mitra Kumar
28
Gender-Based Discrimination in Health: Evidence from Cross-Country . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aparajita Dasgupta
649
......
667
29
Dimensions of Discrimination: Discrimination in Housing Saugato Datta and Vikram Pathania
Part VI 30
Affirmative Action
................................
Is Positive Discrimination a Good Way to Aid Disadvantaged Ethnic Communities? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thomas E. Weisskopf
31
Experimental Evidence on Affirmative Action . . . . . . . . . . . . . . . . Véronique Gille
32
Does Political Affirmative Action Work, and for Whom? Theory and Evidence on India’s Scheduled Areas . . . . . . . . . . . . . Saad Gulzar, Nicholas Haas, and Benjamin Pasquale
633
697
699 719
729
33
Gender Quotas and Representation in Politics . . . . . . . . . . . . . . . . Vidhu Verma
761
34
Caste Quotas in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amit Thorat
779
35
The Effectiveness of Affirmative Action Policies in South Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rulof Burger, Rachel Jafta, and Dieter von Fintel
797
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36
37
Contents
Malaysia’s New Economic Policy and Affirmative Action: A Remedy in Need of a Rethink . . . . . . . . . . . . . . . . . . . . . . . . . . . Hwok-Aun Lee Stereotype Threat Experiences Across Social Groups . . . . . . . . . . Valerie Jones Taylor, C. Finn Siepser, Juan José Valladares, and Rita Knasel
Part VII 38
Miscellaneous
...................................
Feminism as Racist Backlash: How Racism Drove the Development of Nineteenth- and Twentieth-Century Feminist Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tommy J. Curry
819 841
867
869
Inequality and Inefficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pranab Bardhan
897
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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About the Editor
Ashwini Deshpande is Professor of Economics at Ashoka University, India. She is Founding Director of the Centre for Economic Data and Analysis (CEDA), Ashoka University. Prior to Ashoka University, Prof. Deshpande was affiliated to the Delhi School of Economics. She is also a Fellow of the International Economic Association, Research Fellow at IZA Institute of Labor Economics, a Fellow of the Global Labor Organization (GLO), and non-resident Senior Research Fellow of United Nations University - World Institute for Development Economics Research (UNU-WIDER), Helsinki. She has published extensively on economics of discrimination and affirmative action, focusing on caste and gender in India. Other areas of her research interest are social identity and economic outcomes, specially labor market outcomes; self-employment; early childhood development; and education. Recipient of the Exim Bank Award for outstanding dissertation in 1994; the VKRV Rao Award for Indian Economists under 45 and the SKOCH Award for Gender Economics, she is the author of “The Grammar of Caste: Economic Discrimination in Contemporary India” and “Affirmative Action in India”, published by Oxford University Press.
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Pranab Bardhan is Professor of Graduate School at the Department of Economics, University of California, Berkeley. He had been at the faculty of MIT, Indian Statistical Institute, and Delhi School of Economics before joining Berkeley. He has been Visiting Professor/Fellow at Trinity College, Cambridge; St. Catherine’s College, Oxford; and the London School of Economics. He held the Distinguished Fulbright Siena Chair at the University of Siena, Italy, in 2008–2009, and was the BP Centennial Professor at the London School of Economics from 2010 to 2011. He has worked in theoretical and empirical research on rural institutions in poor countries, on political economy of development policies, and on international trade. A part of his work is in the interdisciplinary area of economics, political science, and social anthropology. He was Chief Editor of the Journal of Development Economics during 1985–2003. He was the Co-Chair of the MacArthur Foundation-funded Network on the Effects of Inequality on Economic Performance during 1996–2007. He was awarded the degree of DSC (Honoris Causa) by the Indian Statistical Institute in 2013. Kaushik Basu is Professor of Economics and the C. Marks Professor of International Studies at Cornell University, and former Senior Vice President and Chief Economist of the World Bank (2012–2016). From December 2009 to July 2012, he served as the Chief Economic Advisor (CEA) to the Government of India at the Ministry of Finance. Till 2009, he was Chairman of the Department of Economics, and during 2006–2009, he was Director of the Center for Analytic Economics at Cornell. Earlier he was Professor of Economics at the Delhi School of Economics, where in 1992 he founded the Centre for Development Economics in Delhi and was its first Executive Director. He is currently the President of the International Economic Association (2017–2020). He was the fourth President of the Human Development and Capabilities Association, which was founded by Amartya Sen. He has held advisory posts with the ILO, the World Bank, and the Reserve Bank of India and was, for several years, member of the steering committee of the Expert Group of Development Issues xv
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set up by the Swedish Government. He has also served as member of the Board of Directors of the Exim Bank of India. A Fellow of the Econometric Society, Prof. Basu has published widely in the areas of Development Economics, Industrial Organization, Game Theory, and Welfare Economics. In May 2008, he was awarded one of India’s highest civilian awards, the Padma Bhushan, by the President of India. Haroon Bhorat is Professor of Economics and Director of the Development Policy Research Unit at the University of Cape Town. He has co-edited 5 books and published over 150 academic journal articles, book chapters, and working papers, covering labor economics, poverty, and income distribution. He recently co-edited The Oxford Companion to the Economics of South Africa. He studied at the Massachusetts Institute of Technology and was a Cornell University Research Fellow. Prof. Bhorat consults with international organizations such as the ILO, the UNDP, the World Bank, Ratings Agencies, and emerging market fund managers. He is a member of the World Bank’s Commission on Global Poverty and an IZA Research Fellow (institute for the study of labor). He holds a highly prestigious National Research Chair and is Director of the Western Cape Tourism, Trade and Investment Promotion Agency Board. Prof. Bhorat advised two previous South African Presidents and was an Economic Advisor to former Ministers of Finance. François Bourguignon is Director of Studies at the Ecole des Hautes Etudes en Sciences Sociales and is Emeritus Chair at the Paris School of Economics. Originally trained as a statistician, he earned a PhD in Economics at the University of Western Ontario and a PhD at the University of Orleans. He held the position of Director of the Paris School of Economics from 2007 to the end of January 2013. His theoretical and empirical work focuses on income distribution and redistribution in developing and developed countries. He is the author of several books and numerous articles in international economic journals. He has received, during his career, several scientific distinctions and has taught in several foreign universities. He has extensive experience in consulting with several governments and international organizations. From 2003 to 2007, he was Chief Economist and first Vice President of the World Bank in Washington. William A. Darity Jr. is the Samuel DuBois Cook Professor of Public Policy, African and African American Studies, and Economics, and is Director of the Samuel DuBois Cook Center on Social Equity at Duke University. He has served as Chair of the Department of African and African American Studies and was founding Director of the Research Network on Racial and Ethnic Inequality at Duke. Previously, he served as Director of the Institute of African American Research, Director of the Moore Undergraduate Research Apprenticeship Program,
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Director of the Undergraduate Honors Program in economics, and Director of Graduate Studies at the University of North Carolina. Prof. Darity’s research focuses on inequality by race, class, and ethnicity, stratification economics, schooling and the racial achievement gap, North-South theories of trade and development, skin shade and labor market outcomes, the economics of reparations, the Atlantic slave trade and the Industrial Revolution, the history of economics, and the social psychological effects of exposure to unemployment. He has served as Editor in Chief of the latest edition of the International Encyclopedia of the Social Sciences (Macmillan Reference, 2008) and as an Associate Editor of the new edition of the Encyclopedia of Race and Racism (2013). Bhaskar Dutta is Distinguished University Professor of Economics at Ashoka University, India. His research interests include Cooperative Game Theory, Mechanism Design, Formation of Groups and Networks, Social Choice Theory, and Development Economics. He has been Professor of Economics at the University of Warwick since 2000. He has had a long association with the Indian Statistical Institute, where he has taught during 1979–2002. He has also been a Visiting Professor in several universities including the California Institute of Technology, Universitat Autonoma de Barcelona, Universite Cergy-Pointoise, Paris, and University of Graz. He was the winner of the Mahalanobis Memorial Award of the Indian Econometric Society in 1990. He was the President of the Society for Social Choice and Welfare (2014–2016). He is also a Fellow of the Econometric Society, and the Society for Advancement of Economic Theory. He has been Chair, Standing Committee for India and South Asia, as well as a member of the Council of the Econometric Society. He is currently a member of the Council of the Game Theory Society. He has also served as consultant for the World Bank, UNDP, ILO, and ADB. He is Managing Editor of Social Choice and Welfare and Advisory Editor of Games and Economic Behavior. Diane R. Elson is a British economist, sociologist, and gender and development social scientist. She is Professor Emerita of Sociology at the University of Essex and a former Professor of Development Studies at the University of Manchester. She is noted for her work on issues of development and human rights. She was a member of the UN Millennium Project Taskforce and Advisory Committee member for the UNRISD Policy Report on Gender and Development. She is Vice President of the International Association for Feminist Economics. Her research interests include gender and fiscal policy, and gender and international trade. She is the author of the books Male Bias in the Development Process and Budgeting for Women’s Rights: Monitoring Government Budgets for Compliance with CEDAW (Concepts and Tools) and of many other publications and articles. She has also worked as Special Advisor for UNIFEM and is the Chair of the UK’s Women’s Budget Group. She is the winner of the Leontief Prize for Advancing the Frontiers of Economic Thought for 2016, along with Amit Bhaduri.
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Marc Galanter is Professor of Law Emeritus at the University of Wisconsin Law School. Previously he was the John and Rylla Bosshard Professor of Law and South Asian Studies at the University of Wisconsin-Madison and LSE Centennial Professor at the London School of Economics and Political Science. He teaches South Asian Law, Law and Social Science, Legal Profession, Religion and the Law, Contracts, Dispute Processing and Negotiations. He has authored numerous books and articles related to law, the legal profession, and the provision of legal services in India. Prof. Galanter is also an expert on the Bhopal disaster that occurred in Bhopal, India, in 1984. His collection of court documents, newspaper clippings, secondary sources, and photos form the foundation of the “Bhopal: Law, Accidents, and Disasters in India” digital collection maintained by the University of Wisconsin Law School Library. The Bhopal digital archive contains thousands of documents, videos, a timeline, and a bibliography of other works about the Bhopal disaster. Naila Kabeer is currently Professor of Gender and Development at the Gender Institute, London School of Economics and Political Science. Prior to this, she was Professor of Development Studies at the School of Oriental and African Studies (SOAS), University of London. She was also a Professorial Fellow at the Institute of Development Studies, Sussex, where she is currently an Emeritus Fellow. She is interested in various aspects of inequality and how they play out within households, labor markets, and the wider economy. She is also interested in forms of collective action by poor and marginalized groups that seek a more just distribution of power, resources, and political voice and in the relationship between individual empowerment and societal justice. She has worked under various international and bilateral organizations, such as the World Bank, UNDP, UNIFEM, NORAD, SIDA, Oxfam, and with NGOs such as BRAC, PRADAN, and Nijera Kori. She is currently on the board of the Women’s Rights Program of the Open Society Foundations, of the International Centre for Research on Women, and on the advisory committee of the ILO’s Better Works Program. She is also on the editorial committees of a number of journals including Feminist Economics, Development and Change, Gender and Development, Third World Quarterly, and the Canadian Journal of Development Studies. Katherine Newman is Provost and Executive Vice President of Academic Affairs for the University of California system and Chancellor’s Distinguished Professor of Sociology and Public Policy at UC Berkeley. She previously served as the System Chancellor for Academic Programs for the University of Massachusetts system, the James B. Knapp Dean of Arts and Sciences at Johns Hopkins University. Prior to becoming the Dean at Johns Hopkins University, Professor Newman was the Forbes Class of 1941 Professor of Sociology and Public Affairs at Princeton University and the Director of the Institute for International and Regional Studies, the Founding Dean of Social Science at the Radcliffe Institute of Advanced Study,
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and the Director of Harvard’s Multidisciplinary Program on Inequality and Social Policy. She taught for 16 years in the Department of Anthropology at Columbia University and for two years in the School of Law at the University of California, Berkeley. She is the author of 15 books on topics ranging from the sociological study of the working poor in America’s cities to school violence on a mass scale and has written extensively on the consequences of globalization for youth in Western Europe, Japan, South Africa, and the USA. Her work on labor market discrimination by caste and religion in India has been cited extensively for the empirical findings it provides from controlled experiments, observational studies, and qualitative interviews. Amartya K. Sen is the Thomas W. Lamont University Professor and Professor of Economics and Philosophy at Harvard University and was until 2004 the Master of Trinity College, Cambridge. He is also Senior Fellow at the Harvard Society of Fellows. Earlier on, he was Professor of Economics at Jadavpur University Calcutta, the Delhi School of Economics, and the London School of Economics and the Drummond Professor of Political Economy at the Oxford University. Prof. Sen has served as President of the Econometric Society, the American Economic Association, the Indian Economic Association, and the International Economic Association. He was formerly Honorary President of OXFAM and is now its Honorary Advisor. His research has ranged over social choice theory, economic theory, ethics and political philosophy, welfare economics, theory of measurement, decision theory, development economics, public health, and gender studies. Prof. Sen’s awards include Bharat Ratna (India); Commandeur de la Legion d’Honneur (France); the National Humanities Medal (USA); Ordem do Merito Cientifico (Brazil); Honorary Companion of Honour (UK); the Aztec Eagle (Mexico); the Edinburgh Medal (UK); the George Marshall Award (USA); the Eisenhower Medal (USA); and the Nobel Prize in Economics. Sukhadeo Thorat is Emeritus Professor of Economics at the Centre for the Study of Regional Development, Jawaharlal Nehru University in New Delhi, India. He was also the former Chairman of the University Grants Commission (UGC) and of the Indian Council for Social Science Research (ICSSR). As Chairman of the University Grants Commission, he assisted the then government under the UPA and Prime Minister, Dr. Manmohan Singh, in its initiatives to take higher education forward. He is also the founder of the Indian Institute of Dalit Studies and served as its Director from 2003 to 2006. His research areas include agricultural development, rural poverty, institutions and economic growth, problems of marginalized groups, economics of the caste system, caste discrimination, and poverty.
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He has been awarded an Honorary DLitt, DSc, DS (Doctor of Education), and LLD degrees by 12 universities from India and 1 university from outside India. He has published 19 books and about 100 research papers. He was awarded the Padma Shri in 2008, for his efforts in extending public service to marginalized groups, minorities, and in education. Thomas E. Weisskopf has taught at the Indian Statistical Institute, at Harvard University, and, since 1972, at the University of Michigan (UM), where he is now Professor Emeritus of Economics. From 1996 to 2001 and from 2002 to 2005, he served as Director of the UM’s Residential College. He has authored or co-authored 10 books and has published more than 100 articles in a wide range of journals in the fields of economic development, macroeconomics, comparative economic systems, political economy, and public policy. His early research focused on issues of third world development and underdevelopment, with particular attention to India. In the late 1970s, his research interests shifted to the macroeconomic problems of advanced capitalist economies; among other things, he undertook studies of trends in productivity growth and profitability from a neo-Marxian political-economic perspective. In the 1990s, he worked primarily on problems of economic transition and institutional development in the formerly socialist economies of the East, concentrating especially on the interaction between political and economic change in Russia. In his recent years of research, he has worked on discrimination and affirmative action in the comparative context of the USA and India, and on the growth of economic inequality in each of these two countries.
Contributors
Kalaiyarasan A. Brown University, Providence, RI, USA Timothy Amos School of Languages and Cultures, University of Sydney, Sydney, Australia Mahzarin R. Banaji Harvard University, Cambridge, MA, USA Pranab Bardhan University of California, Berkeley, CA, USA Kaushik Basu Department of Economics and SC Johnson College of Business, Cornell University, Ithaca, NY, USA Marianne Bertrand University of Chicago Booth School of Business, NBER, and J-PAL, Chicago, IL, USA Abhinash Borah Department of Economics, Ashoka University, Sonipat, India Rulof Burger Stellenbosch University, Stellenbosch, South Africa Tessa E. S. Charlesworth Harvard University, Cambridge, MA, USA Tommy J. Curry Philosophy, University of Edinburgh, Edinburgh, UK William A. Darity Jr. Samuel DuBois Cook Center on Social Equity, Duke University, Durham, NC, USA Samuel DuBois Cook Professor of Public Policy, African and African American Studies, and Economics, Duke University, Durham, NC, USA Aparajita Dasgupta Ashoka University, Sonipat, Rai, Haryana, India Saugato Datta ideas42, Boston, MA, USA Allison Demeritt University of Washington, Seattle, Washington, USA Ashwini Deshpande Ashoka University, Sonipat, India Esther Duflo MIT Economics Department, NBER and J-PAL, Chicago, IL, USA Diane R. Elson University of Essex, Colchester, UK
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Arabo K. Ewinyu University of the Witwatersrand, Johannesburg, South Africa Southern Centre for Inequality Studies, University of the Witwatersrand, Johannesburg, South Africa Dieter von Fintel Stellenbosch University, Stellenbosch, South Africa Véronique Gille Université Paris-Dauphine, Université PSL, LEDA, CNRS, IRD, Paris, France Sulaimon Giwa School of Social Work, Memorial University of Newfoundland, St’ Johns, NL, Canada Inderpal Grewal Yale University, New Haven, CT, USA Saad Gulzar Princeton University, Princeton, NJ, USA Nicholas Haas Aarhus University, Aarhus, Denmark Karla Hoff Columbia University, New York, NY, USA Lucas Hubbard Samuel DuBois Cook Center on Social Equity, Duke University, Durham, NC, USA Christophe Jaffrelot CERI-Sciences Po/CNRS, Paris, France King’s India Institute, King’s College, London, UK Rachel Jafta Stellenbosch University, Stellenbosch, South Africa Naila Kabeer Department of International Development, London School of Economics and Political Science, London, UK Kalpana Kannabiran Hyderabad, India Karun K. Karki School of Social Work and Human Services, University of the Fraser Valley, Abbotsford, BC, Canada Rita Knasel Lehigh University, Bethlehem, USA Dominik Koehler World Bank, Washington, DC, USA Sunil Mitra Kumar King’s India Institute and Department of International Development, King’s College London, London, UK Hwok-Aun Lee ISEAS-Yusof Ishak Institute, Singapore, Singapore Nicholas Menzies World Bank, Washington, DC, USA Delores V. Mullings School of Social Work, Memorial University of Newfoundland, St’ Johns, NL, Canada Christophe Jalil Nordman French National Research Institute for Sustainable Development (IRD), LEDa-DIAL (IRD, PSL University & CNRS) & French Institute of Pondicherry, Paris, France
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Brendan O’Flaherty Department of Economics, Columbia University, New York, NY, USA Benjamin Pasquale New York, USA Vikram Pathania University of Sussex, Falmer, UK Patrizio Piraino University of Notre Dame, Notre Dame, IN, USA Gurleen Popli Department of Economics, University of Sheffield, Sheffield, UK Rajesh Ramachandran Department of Economics, School of Business, Monash University Malaysia, Selangor, Malaysia Vijayendra Rao Development Research Group, The World Bank, Washington, DC, USA Roland Rathelot Institut Polytechnique de Paris, CREST, Palaiseau, France Mirna Safi Sciences Po, CRIS, CNRS and LIEPP, Paris, France Ricardo Santos Ricardo Santos UNU-WIDER, Helsinki, Finland Stephanie Seguino Department of Economics Fellow, Gund Institute for the Environment, University of Vermont, Burlington, VT, USA Josefina Senese Boston University, Boston, MA, USA Rajiv Sethi Department of Economics, Barnard College, Columbia University, and the Santa Fe Institute, New York, NY, USA Smriti Sharma Newcastle University Business School, Newcastle, UK Rhonda Vonshay Sharpe Women’s Institute for Science, Equity and Race, Mechanicsville, VA, USA C. Finn Siepser Lehigh University, Bethlehem, USA Valerie Jones Taylor Lehigh University, Bethlehem, USA Amit Thorat Jawaharlal Nehru University, New Delhi, India Juan José Valladares Lehigh University, Bethlehem, USA Imraan Valodia University of the Witwatersrand, Johannesburg, South Africa Vidhu Verma Centre for Political Studies, Jawaharlal Nehru University, New Delhi, India Thomas E. Weisskopf University of Michigan, Ann Arbor, MI, USA
1
Introduction Ashwini Deshpande
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Analyzing Discrimination and Disadvantage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 What Is Discrimination? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Inequality of Opportunity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Methodological Approaches to Understanding Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Field Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Intersectionality and Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Social and Regional Dimensions of Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Dimensions of Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Affirmative Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Miscellaneous . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Keywords
Discrimination · Affirmative action · Race · Racism · Caste · Religion · Gender · Feminism · Inequality · Social identity
Introduction After a long and tumultuous journey, it is both unbelievable and gratifying to finally write this introduction. The original timeline for this project was first slightly disrupted due to my move from one university to another, and then completely disrupted due to the global shutdown caused by the pandemic. It is thrilling that this volume is now ready for publication. The Covid-19 pandemic did not just disrupt the timeline; it also affected the content in that some authors were not able to deliver, and I was not able to find A. Deshpande (*) Ashoka University, Sonipat, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_54
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replacements, not for lack of trying. As a result, some important topics that were in the original table of contents are not covered here. However, this regrettable absence, which was completely outside my control, is more than compensated by the superb contributions that you are about to read. All the authors, experts in their respective fields, contributed generously and, with a few exceptions, wrote original papers for this handbook, which, in my view, makes this collection highly valuable. I hope the readers concur! The volume contains 38 chapters organized into 7 sections. Another caveat before we move forward. Even though this volume is entitled the economics of discrimination and affirmative action, several of the contributors are not economists and the chapters are not exclusively focused on an economic analysis of discrimination and affirmative action. This is because, as I learnt very early on in my forays into these topics, the problems are too complex to be captured with only one disciplinary lens. Partly this is also the result of the fact that some economist contributors were not able to deliver due to pandemic-induced distress, as well as the fact that some topics might not have been explored by economists with the kind of nuance that is required for a comprehensive understanding. The contributions from many different disciplines are a strength of this volume, as they collectively enhance our understanding of this multiheaded hydra called discrimination. Thus, the title “Economics of Discrimination and Affirmative Action” should be read more appropriately as “Discrimination and Affirmative Action Through an Economic and Interdisciplinary Lens.” As the latter is too long and unwieldy, we will stick to the original title.
Analyzing Discrimination and Disadvantage This section focuses on various frames or lenses through which we can understand discrimination. A closely linked concept is disadvantage or inequality, to fathom which we need to understand the role of the lottery of birth. Persistent and systematic discrimination can lead to phenomena such as stereotype threat, which can explain the underperformance of discriminated-against groups. Under stereotype threat (discussed below in several papers), reminders of negative stereotypes against their group push the performance of group members in the direction of the stereotype. This section helps understand the interconnections between these related but distinct concepts.
What Is Discrimination? What constitutes discriminatory behavior? For economists, discrimination is simply unequal treatment of economic agents who are otherwise equal. Why do individuals discriminate against others in market settings? Is it compatible with economic rationality? Neoclassical economists have made substantial contributions to this discussion starting with Gary Becker’s seminal 1957 book “The Economics of
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Discrimination” where he laid out the theory of taste-based discrimination. This theory focuses on individual tastes and preferences, or simply prejudice, such that economic agents prefer to associate with some persons over others. They exhibit their taste for discrimination by acting as if they were willing to pay something, either directly or in the form of reduced income to associate with some persons instead of others. The grandfather of modern economics, Kenneth Arrow, critiqued this in early 1970s and proposed the theory of “statistical discrimination” as an alternative way to understand discrimination. In the context of the labor market, statistical discrimination occurs when employers use workers’ social identity as a proxy for their productivity, in the absence of complete and costless information about their actual individual productivities. Both these theories have had a profound and enduring influence on economists’ understanding of discrimination. The missing chapter on statistical discrimination is one of the major casualties of the pandemic, but Roland Rathelot and Mirna Safi’s chapter (▶ Chap. 2, “Taste-Based Discrimination”) presents its contrast to the theory of statistical discrimination succinctly such that readers would get a sufficiently good sense of the latter. They begin with an update of Becker’s classic theory with a literature review of theoretical and empirical evidence on taste-based discrimination. They go on to link insights from Becker’s theory, which identifies prejudice or animus as the chief cause of discrimination, to insights from other disciplines, most notably social psychology which helps us understand the source of animus and how it can be changed. Their chapter opens the black box of what constitutes the taste for prejudice, as they present insights from sociology and social psychology, starting with Allport’s seminal book “The Nature of Prejudice,” moving on to implicit and explicit prejudice (which is discussed in several papers in later sections), the stereotype content model to the sociohistorical roots of prejudice. Rathelot and Safi not only make a persuasive case for a multidisciplinary understanding of discrimination but also present a review of possible policy interventions to reduce the scope of prejudice in decision-making. This chapter is followed by two chapters that question the mainstream economic theories and offer alternative theoretical frameworks to understand discrimination. Lucas Hubbard and William A. Darity, Jr (▶ Chap. 3, “Stratification Economics”) present a primer on the newly emerging subfield of “stratification economics” which they use to explain the opposition to affirmative action. Stratification economics focuses on group identities and individual positions within-group and hierarchies across groups. It offers a multidisciplinary theoretical framework for understanding discrimination by linking insights from social psychology and sociology. They argue that affirmative action policies are seen as undesirable because they are expected to improve the subaltern group’s relative position. Stephanie Seguino’s ▶ Chap. 12, “Insights from Social Psychology: Racial Norms, Stereotypes, and Discrimination” in Part II elaborates on aspects of stratification economics further. This is followed by Diane Elson’s (▶ Chap. 4, “Transforming Gendered Labor Markets to End Discrimination”) exposition of a feminist understanding of labor market discrimination, which argues that gender discrimination in labor markets
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needs to be understood not as a residual nor as irrational but as rooted in institutional structures and the unequal gender division of paid and unpaid work. Activities that people do to make a living and caring for themselves and their families fall broadly into two parts: the productive sphere, counting all economic activities, and the reproductive sphere, which consist of domestic work as well as both physical and emotional care. The productive sphere cannot operate without the smooth functioning of the reproductive sphere. She highlights why labor markets are not neutral arenas but bearers of gender, in that workers in the reproductive economy are disproportionately female and are penalized the labor market, whereas men, who mostly work in the productive sphere, are rewarded. It is this fundamental asymmetry that causes gender inequality in wages, occupational and sectoral distribution, types of contract, etc. Inderpal Grewal (▶ Chap. 5, “Theories of Discrimination: Transnational Feminism”) provides a succinct history of how the academic landscape has shifted and evolved to include experiences of non-European and non-White American women in the broader realm of women’s/gender studies and development research. It started to shift with recognition of the need for interdisciplinary analysis of gender in contrast to the “masculinist” focus of individual disciplines. Grewal explains that early development research made “‘the third world woman’ . . . a homogenous and passive recipient of Western aid and policy,” as abject subjects to be rescued by western development. The questioning of this framework under the broad umbrella of transnational feminism, which called out the tendency to blame non-Western cultures for the subordination of women, led to nuanced analysis of patriarchy. Grewal’s analysis traces the evolution of this branch through the last three decades of the twentieth century and clearly articulates why transnational feminism is so vital to the understanding of gender gaps in different parts of the world. Despite the transnational feminist critique being around for over three decades, readers would be struck by the persistence of the west/rest-dichotomy-mindset within scholars analyzing gender gaps in different parts of the world, along with a near-complete erasure of the role of colonialism in consolidating practices that led to the subordination of women. Kaushik Basu’s (▶ Chap. 6, “Discrimination as Focal Point”) examines how discrimination might arise in markets when economic agents have no innate desire to discrimination but outcomes would be discriminatory when there is some complementarity between different tasks we do. Basu shows that this kind of discrimination is a product of a free market, where social identities (race, caste, and gender) acquire the salience of a “focal point,” a concept used in game theory by Nobel laureate Thomas Schelling. Basu’s analysis suggests that removing all government regulation and barriers to free competition would not eliminate competition. Contrary to the prediction of the taste-based discrimination model, which shows how in the long run, discriminating firms would be driven out of business.
Inequality of Opportunity The next chapter (▶ Chap. 7, “Inequality of Opportunity”) steps back from specific groups to ask and answer the question: to what extent is inequality (between
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individuals or groups) a product of the accident of birth, or circumstances that the individual has no control over, and to what extent it is because of differential effort that individuals exert? An understanding of inequality of opportunity must form a part of the essential toolkit to not only understand the sources of inequality but must also inform policymaking. As Patrizio Piraino and Josefina Senese point out, while the idea of unequal opportunity or the lottery of birth is intuitively simple and appealing, translating it into practice, in particular disentangling personal effort from circumstances over which individuals truly have no control, is challenging. Additionally, effort itself is shaped by circumstances of birth. They discuss the implications of the inequality of opportunity framework for affirmative action: opportunities should be available to all and should not be denied to individuals based on factors outside their control. Again, while this might be acceptable in principle, there is massive opposition to any policy that suggests a more equitable distribution of elite positions. Piraino and Senese spell out these conundrums in the context of educational admissions.
Methodological Approaches to Understanding Discrimination Is discrimination a bit like wind, in that we might be able feel it when it is strong, but is largely invisible and not amenable to being captured? It turns out that with data, especially related to monetary aggregates like income or wages, we can measure how much of the gap between two groups can be attributed to discrimination. Gurleen Popli (▶ Chap. 8, “Decompositions: Accounting for Discrimination”) provides a detailed and meticulous exposition of the original Blinder-Oaxaca decomposition method that decomposes average gaps into an “explained” and an “unexplained” component. She follows up the discussion of average gaps with the more recent advances in the literature that look beyond averages to examine the entire distribution of outcomes of interest. Students would find this chapter particularly useful as in addition to discussing the methodology, she highlights how results should be interpreted with caution, as a decomposition is essentially an accounting exercise. Using secondary data and regression-based methods is an important way to gauge the extent of discrimination. However, there are many facets of discrimination, e.g., discrimination during the job or housing search process, that are not amenable to being captured via secondary data. These might often appear as innocuous or nondiscriminatory in the absence of any other information. For instance, if there are walk-in interviews, and an African American candidate is told there are no vacancies, this might not be immediately perceived as instance of discrimination. However, if for the same job a White candidate gets a call back, it would reveal the employer’s racial bias. To uncover these instances, we would need field experiments.
Field Experiments Marianne Bertrand and Nobel laureate Esther Duflo agreed to contribute their muchcited 2017 chapter in the Handbook of Field Experiments (▶ Chap. 9, “Field
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Experiments: Correspondence Studies”) for this volume. Their contribution, along with the paper by Allison Demeritt and Karla Hoff, introduces readers to the tremendous possibilities of lab-in-the-field experiments to understand discrimination. Bertrand and Duflo highlight the advances in the psychology literature that demonstrates the existence of unconscious or unintentional bias, discussed in greater detail in the section “Dimensions of Discrimination.” They elaborate on audit and correspondence studies to measure discrimination in the field (including a discussion of weaknesses of these methods). The chapter provides a very useful tabular summary of major correspondence studies to measure discrimination along various dimensions (race, gender, caste, sexual orientation, etc.) in a number of countries. The literature has grown larger since the publication of their original paper. They go on to discuss implicit association tests (IAT), discussed in the section “Dimensions of Discrimination,” and Goldberg’s paradigm experiments (laboratory versions of audit or correspondence studies) and papers using these methods. They go on to discuss the consequences of discrimination: stereotype threat and underperformance, self-fulfilling prophecies, and endogenous responses to bias. They also discuss a range of other issues, such as whether diversity hurts or help productivity, or whether diversity in leadership positions affects discrimination. Overall, this chapter is a gold standard reference work for anyone trying to understand the complexity of field experiments for estimating discrimination. Allison Demeritt and Karla Hoff’s ▶ Chap. 10, “Lab-in-the-Field Experiments,” focuses on examining mindsets leading to discriminatory outcomes. This approach, which lies at the intersection of social psychology and economics, is gaining steady acceptance among economists who recognize the need to understand what factors shape human thinking in order uncover aspects of discrimination that secondary data cannot measure. Their chapter provides fascinating insights into behavioral causes of discrimination which they illustrate with examples such as police shootings of unarmed Black men, emotional bias among judges, and so on. This takes us to the use of behavioral games or laboratory experiments that are widely used in understanding discrimination. Abhinash Borah’s ▶ Chap. 11, “Methodological Approaches to Understanding Discrimination: Experimental Methods – Trust, Dictator, and Ultimatum Games,” shows how individuals are often not the rational agents that economics presumes them to be, and that social identity (their own, as well as of others they are dealing with) plays a crucial role in how agents interact with one another. Borah begins with explaining the minimal group paradigm, goes on to elaborate how natural identities have been made salient in games, and ends with a discussion of dictator, trust, and ultimatum games that are widely used in the literature on discrimination. The overall insight from this chapter is the powerful result that in-group favoritism and discriminatory behavior towards the out-group guide behavior by economic agents more often than not. Stephanie Seguino (▶ Chap. 12, “Insights from Social Psychology: Racial Norms, Stereotypes, and Discrimination”) reinforces this point by emphasizing that racial stereotypes influence decision-making in ways that lead to racial inequality. She summarizes evidence from audit studies that investigate how employers react to the stigma of race and criminality to find that the stigma of being Black
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outweighs the stigma of criminality. Her chapter is a close companion to the chapter on stratification economics that appears in the section “Analyzing Discrimination and Disadvantage.” She deals with prejudice, norms and stereotypes, stereotypes and group positions, and finally applies these concepts to a discussion of policing. Dominik Koehler and Nicholas Menzies (▶ Chap. 13, “Surveys, Big Data, and Experiments”) remind us that there is insufficient quantitative data on the lives of lesbian, gay, bisexual, transgender, and intersex (LGBTI) individuals in developing countries. This not only makes the formation of appropriate policies difficult but also does not allow researchers to use existing methods in the literature to assess disparities and discrimination along the axis of sexual identity. The chapter addresses issues of defining LGBTI communities (which will allow for a more accurate numerical enumeration) and how representative samples can be drawn using big data and experimental techniques. They also highlight the importance of recognizing a spectrum when it comes to classification by sexual identity. In the final paper in this section, Vijayendra Rao (▶ Chap. 14, “Can Economics Become More Reflexive? Exploring the Potential of Mixed Methods”) exhorts economists to be more reflexive and to learn from cultural anthropology and qualitative sociology. Economics, which has always tended towards numerical and quantitative techniques, has taken a behavioral turn over the last decade (as Abhinash Borah’s chapter illustrates). Rao provides an overview of mixing qualitative and quantitative methods, a mix that has the potential to analyze questions of discrimination and development. He argues that this will also lead to more empathetic research and reduce the distance between the researcher and the researched.
Intersectionality and Discrimination This section contains three papers on intersectionality or overlapping identities: intersecting identities in Brazil by Naila Kabeer and Ricardo Santos (▶ Chap. 15, “Tackling Intersecting Inequalities: Insights from Brazil”); race and gender by Rhonda Sharpe (▶ Race and Gender); and caste and gender by Kalpana Kannabiran (▶ Chap. 17, “Caste and Gender”). Together they bring home the fact that tackling discrimination and disadvantage along one dimension is hard enough; intersection of identities makes assessment and remedies of disadvantage particularly urgent and challenging. Kabeer and Santos argue that the international development agenda has focused, often exclusively, on singular monetary measure of absolute poverty. Presenting evidence in support of their argument, they make a case for bringing the intersection between material and identity-based inequalities center stage when it comes to an assessment of poverty. They take a historical long-term view of the evolution of intersecting inequalities in Brazil. Rhonda Sharpe suggests that as the USA becomes more diverse, it will be more complex to identify the role of gender, ethnicity, and race discriminatory outcomes. However, gendered racism, which includes stereotypes about intelligence and
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ability, is not only all-pervasive but it also influences how data are collected and reported. She explains how categories like race, ethnicity, and gender are socially constructed, in that norms, behaviors, and roles are assigned to individuals representing certain identities. She presents data from the USA to show why disaggregated data are needed to appreciate the full impact of intersectionality. Kalpana Kannabiran outlines the realities of caste and gender discrimination in India, notwithstanding the constitutional guarantees to a fundamental right to nondiscrimination. She outlines how control over female sexuality was fundamental to maintaining caste purity, for which caste endogamy was essential. She focuses on historically important anti-caste campaigners who argued why the caste-gender complex needed to be dismantled in order to move towards an egalitarian society. Her focus on the history of social reform in India brings home the realization that Indian feminism is not an offshoot of Western feminism but is far older.
Social and Regional Dimensions of Discrimination This section moves to a new terrain by examining experiences of discrimination in diverse settings. This contains five chapters focusing on discrimination against Muslims in France, the Buraku in Japan, immigrants in Canada, caste disparities in India, and discrimination against Muslims in India. Christophe Jalil Nordman’s ▶ Chap. 18, “The Economic Side of Religious Discrimination in France: A Review,” on religious discrimination against Muslims in France is extremely important as it reminds us that an explicit statement and fierce practice of “liberty, equality and fraternity,” the founding principles of modern French society that foster the idea of a French national identity overriding any group identity, have not been able to transcend discrimination towards groups such as Muslims. Nordman explains how the problem is rooted in France’s colonial history. The erasure of all cultural differences in the name of assimilation has contributed to the feeling of alienation among Muslim immigrants. He presents conclusive evidence of discrimination in a variety of arenas over the last two decades. The lessons from France for the rest of the world are very important as we think of antidiscrimination policies: do we need to acknowledge difference and stress diversity or would the implementation of a uniform national identity over time result in the dissolution of group identities? Karun Karki, Delores Mullings, and Sulaimon Giwa highlight socioeconomic disparities among racialized immigrants in Canada (▶ Chap. 19, “Socioeconomic Disparities Among Racialized Immigrants in Canada”), which is one of the top immigrant host countries in the world. As it celebrates multiculturalism, how equal are the socioeconomic outcomes of immigrants relative to native (White) Canadians? The authors present data that point to disparities in a variety of domains. They highlight policy interventions, including changes in the regulatory environment, that would go a long way towards lowering barriers that immigrants face. Imraan Valodia and Arabo Ewinyu (▶ Chap. 20, “The Economics of Discrimination and Affirmative Action in South Africa”) outline why South Africa is so
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critical to the study of discrimination: it is the one of two countries whose economy has been shaped by the discrimination of the majority of the population, in contrast to the rest of the world where discrimination is directed towards minorities. It also has a unique history of the legalization of discrimination via apartheid. Valodia and Ewinyu outline a concise history of the 300-year journey of racial discrimination in South Africa, starting from the time European settlers arrived in Cape Town in 1652. They present a number of compelling statistics that show the contemporary dimensions of discrimination and inequality, but equally importantly policy efforts towards racial equality. As the authors remind us, three centuries of discrimination cannot be reversed in three decades, but there are persistent efforts. Timothy Amos (▶ Chap. 21, “Buraku Liberation and the Politics of Redress in Modern Japan, 1868–2002”) reminds us that every society has its outcastes as Japan’s Burakumin. He argues that despite a fair bit of Anglophone scholarship on the Burakumin, there is not much evidence on the politics of financial redress which has reduced the inequality between the Buraku and mainstream communities and reduced discrimination against them. His chapter presents the problem in a historical light and shows how active support by the state has alleviated the problems faced by the Burakumin and shaped a new Buraku identity. In India, certain castes were considered “untouchable” and their presence was considered polluting on account of their traditional occupations that were seen as “dirty” and degrading. Untouchability is illegal in independent India, and these castes are beneficiaries of affirmative action, listed in a group of “Scheduled Castes.” Rajesh Ramachandran (▶ Chap. 22, “Caste and Socoieconomic Disparities in India: An Overview”) examines the contemporary nature of caste disparities in several domains after giving a brief overview of the caste system. He highlights that while disparities are omnipresent, there are clear regional differences, which he shows via color-coded maps. The main conclusion is that caste disparities and discrimination are being reproduced in the present and are not simply a hangover from the past. India has many axes of discrimination and disadvantage: religion is the other important one. Christophe Jaffrelot and Kalaiyarasan A. focus on Indian Muslims as the group that suffers the largest disadvantage among all religious minorities in India (▶ Chap. 23, “Indian Muslims: Varieties of Discriminations and What Affirmative Action Can Do”). They present data on the trajectories of marginalization in a variety of domains. Their analysis of broad regional differences is instructive as they highlight differences between Dravidian territory in the south, compared to north and eastern India. Contemporary Indian politics is a key factor in the way these disparities have taken shape.
Dimensions of Discrimination This section begins with a highly significant original paper by Brendan O’Flaherty and Rajiv Sethi (▶ Chap. 24, “Stereotypes and the Administration of Justice”) on how stereotypes affect the administration of justice. While their attention is on the USA, their insights are valid globally. They begin by describing stereotypes,
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conscious (explicit) and unconscious (implicit) beliefs, concepts that are a running theme throughout this volume. They focus on police stop searches that are disproportionately targeted towards African Americans. They go on to discuss conflict, cooperation, and clearance, followed by extreme violence (murder and lethal force). The data are startling: in half the states in the USA, Black exposure to deadly force exceeds White exposure four-to-one. Their evidence brings home the point that while racial profiling with a goal towards crime reduction is often seen as legitimate (including by economists), this leads to a discriminatory and unfair judicial system with substantial harm imposed on the targets of profiling, in addition to entrapment of innocents and lowering of public trust. They persuasively argue that this harm must be taken into account when we evaluate the overall impact of stereotypes and racial profiling in the interest of “justice.” This is followed by a broader exploration of the correlation between regional implicit bias and outcomes in the arenas of healthcare, education, and law enforcement by Tessa Charlesworth and Mahzarin Banaji (▶ Chap. 25, “Evidence of Covariation Between Regional Implicit Bias and Socially Significant Outcomes in Healthcare, Education, and Law Enforcement”). Their chapter tries to answer the puzzle of how an increasing stated preference for equality coexists with persistent discrimination in all significant aspects of life – from housing to jobs to healthcare and law enforcement. Banaji’s work has drawn attention to implicit attitudes and beliefs, which might be contrary to explicit attitudes. In order to measure implicit bias, she (along with Greenwald) pioneered the implicit association test (IAT) that has been used in hundreds of studies worldwide over the last two decades to measure implicit bias that affects individuals and institutions alike. This chapter summarizes several of the studies that Charlesworth and Banaji (with co-authors in different teams) have conducted over the years which show the pervasive implications of implicit bias. Given that minorities are targets of discrimination in the labor market, could they escape discrimination by moving towards self-employment? Smriti Sharma (▶ Chap. 26, “Disadvantage and Discrimination in Self-Employment and Entrepreneurship”) explores these issues by reviewing the literature on disadvantaged minorities in entrepreneurship and self-employment. She begins by offering indicative evidence from a diverse group of countries and moves on explaining the sources of gaps. We find that discrimination in other spheres, such as credit markets, explains a part of the gaps. The conclusion is that we need targeted policies, including ways to reduce unconscious bias by decision makers. Sunil Mitra Kumar’s ▶ Chap. 27, “Discrimination in Credit,” delves deeper into one specific arena that Sharma’s analysis highlights: discrimination in credit. He provides a selective review of the literature about discrimination in credit along all axes of social identity: caste, race, and gender, which makes it clear that this kind of discrimination is ubiquitous. The section on laws, rules, and institutions is insightful as it sheds light on structural or institutional discrimination where discriminatory outcomes occur, because institutions do not consciously take into account existing structures of disparity and exclusion. The chapter delves deeper into mechanisms and methods to estimate discrimination in credit.
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Aparajita Dasgupta (▶ Chap. 28, “Gender-Based Discrimination in Health: Evidence from Cross-Country”) focuses on gender-based discrimination in health outcomes by providing cross-country evidence. It is a comprehensive and well-rounded analysis covering many dimensions: discrimination by health providers, measurement issues around discrimination, micro-foundations of gendered institutions and economic development – a section that explores drivers of discrimination. Discrimination is costly; Dasgupta summarizes literature that provides tools to measure discrimination. The key insight from Dasgupta’s analysis is that gender discrimination in healthcare within households is a key driver of overall gender-based inequality in health outcomes. This makes policy even more challenging, as it needs to focus on ways to change attitudes, norms, and behavior that are in the domain of the very private household sphere. Saugato Datta and Vikram Pathania (▶ Chap. 29, “Dimensions of Discrimination: Discrimination in Housing”) investigate discrimination in housing via an audit experiment on India’s largest real estate websites. Complementing the findings of the chapter on religious discrimination in the section “Social and Regional Dimensions of Discrimination,” their evidence shows clear discrimination against Muslims in the real estate sector, measured by callbacks to Muslim clients compared to Hindu upper caste clients. There is less clear evidence against discrimination against Scheduled Caste/Other Backward Classes (SC/OBC) clients. However, as the authors point out, these audit studies use names as signals of identity, and name recognition for SC/OBC clients might be weaker, whereas Muslim names are almost universally recognized. Their findings substantiate a number of anecdotal and journalistic accounts of even very famous film personalities reporting difficulties finding housing on account of their Muslim names.
Affirmative Action What does the presence of discrimination do to individuals who are stigmatized or negatively stereotyped? Do we need special, targeted positive discrimination policies? Thomas Weisskopf has been thinking and writing about these issues over several years. In the opening chapter in this section (▶ Chap. 30, “Is Positive Discrimination a Good Way to Aid Disadvantaged Ethnic Communities?”), he provides a clear and objective assessment of the costs and benefits of positive discrimination or affirmative action policies. He also compares the implications of group versus class based affirmative action policies, viz., is it better to target social identity groups or would it better to focus attention on individuals below a certain income cutoff? After a careful weighing of all factors, he concludes that there is a strong case for positive discrimination or affirmative action policies. Veronique Gille (▶ Chap. 31, “Experimental Evidence on Affirmative Action”) provides a deeper dive into the costs-versus-benefits issues by summarizing experimental evidence on affirmative action. One of the key criticisms of affirmative action is that it is detrimental to the idea of merit. In other words, it might achieve a
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social justice/diversity/representation goal, but it does so at the cost of efficiency. Another key issue relates to the unintended negative consequences of affirmative action. Beneficiaries might face backlash due to added stigma and hostility from their peers. Experimental evidence helps us answer these questions. Gille’s summary indicates that the efficiency cost of affirmative action to the society, if any, is small, as affirmative action enhances the participation of high-performing individuals within the beneficiary group who would not have participated at all. The backlash effect is also not as large as to make affirmative action do more harm than good. Saad Gulzaar, Nicholas Haas, and Benjamin Pasquale’s (▶ Chap. 32, ”Does Political Affirmative Action Work, and for Whom? Theory and Evidence on India’s Scheduled Areas”) comprehensive and systematic analysis of the implementation of the National Rural Employment Guarantee Scheme (NREGS) in Scheduled Areas of India (where political positions are reserved for Scheduled Tribes) throws light on whether affirmative action reduces efficiency in the provision of public goods. Based on a new dataset of 217,000 villages, they show that for their main policy of interest (NREGS), outcomes under reservation are no worse overall outcomes. Additionally, there are large gains for the targeted groups: STs receive 24.1% more workdays in Scheduled Areas. These gains come at the cost of the relatively privileged (non-SC-ST), not other disadvantaged groups. They find that reservations more closely align benefits to each group’s population share and, hence, do not overcompensate for inequalities. They find similar gains in other pro-poor programs, such as a rural roads program (Pradhan Mantri Gram Sadak Yojana). Their results (reservations not lowering efficiency and beneficiaries not displacing the more disadvantaged) echo findings from other papers1 on Indian reservations (not included in this volume). Vidhu Varma’s chapter (▶ Chap. 33, “Gender Quotas and Representation in Politics”) provides a comprehensive discussion on gender quotas and representation in politics. The discussion is across countries, as the issue of gender representation is not confined to any particular set of countries and straddles both rich and poor countries. She summarizes research and evidence on how quotas could be a fast track to equal representation. Overall, while quotas continue to be controversial, the introduction of quotas for women in legislatures and political parties has doubled the average share of female parliamentarians. She underscores the need for garnering better data on impacts, which would shed clearer light on the efficacy of quotas. Amit Thorat’s contribution (▶ Chap. 34, “Caste Quotas in India”) focuses on India and explains the motivation behind the use of quotas as a redressal mechanism. This chapter provides a good follow-up to the chapter by Rajesh Ramachandran, which provides the justification for a policy of positive discrimination targeted towards marginalized caste and tribal groups. Rulof Burger, Rachel Jaffa, and Dieter von Fintel (▶ Chap. 35, “The Effectiveness of Affirmative Action Policies in South Africa”) start their analysis from 1999,
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i.e., from the postapartheid period. They go into the details of various policies and find that affirmative action policies were successful in reducing between-group earnings gaps at the top end of the earnings distribution. Overall the gaps remain substantial. This is not surprising after Valodia and Ewinyu’s chapter which reminds us that the history of racial oppression is three centuries old. The final paper in this section by Hwok-Aun Lee (▶ Chap. 36, “Malaysia’s New Economic Policy and Affirmative Action: A Remedy in Need of a Rethink”) focuses on an instance of affirmative action that favors the politically dominant but economically disadvantaged Bumiputera majority. This policy has substantially remedied existing inequalities by increasing Bumiputera access to resources and aiding their upward mobility. However, the goal of national integration needs to be advanced, including a discussion about when it might be a good time to roll back affirmative action.
Miscellaneous This is not really a whole section but has two papers that are related to the set of complex issues discussed above but do not fit any one section neatly. The chapter by Tommy Curry critiques America’s feminism (▶ Chap. 38, “Feminism as Racist Backlash: How Racism Drove the Development of Nineteenth- and Twentieth-Century Feminist Theory”) with the argument that it has a anti-Black focus. The chapter takes the view that it is not merely blind spots that have made America’s feminism driven by Whites, but it argues that the primary racial target of White feminists has been the Black male. This argument is similar to the ones we encountered in the chapter on transnational feminism. In the concluding chapter of this volume, Pranab Bardhan’s chapter on inequality (▶ Chap. 39, “Inequality and Inefficiency”) takes a step back from social identities that this volume focuses on and reminds us why inequality is not desirable: it is not only ethically distasteful, it can also be economically harmful, belying the traditional economists’ dogma of equity-efficiency trade-off. I hope this rich selection will be widely used by students, teachers, and researchers to advance their understanding and will inspire readers to contribute to this field of knowledge through further research.
References Bertrand M, Hanna R, Mullainathan S (2010) Affirmative action in education: evidence from engineering college admissions in India. J Public Econ, Elsevier 94(1–2):16–29 Deshpande A, Weisskopf TE (2014) Does affirmative action reduce productivity? A case study of the Indian railways. World Dev 64:169–180
Part I Analyzing Discrimination and Disadvantage
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Taste-Based Discrimination Roland Rathelot and Mirna Safi
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Becker’s Theory, Extensions, and Empirical Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conceptual Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Empirical Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Opening the Black Box of Taste-Based Discrimination: Interdisciplinary Insights . . . . . . . . . . . . From Taste to Prejudice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . From Varieties of Prejudice to Varieties of Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Where Does Taste Come From? Investigating the Roots of Prejudice . . . . . . . . . . . . . . . . . . . . . . Back to Taste-Based Versus Statistical Discrimination: The Role of Information and Accuracy of Beliefs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Towards an Integrated Approach to Discrimination: A Multidisciplinary Perspective . . . . . How to Design Public Policies to Reduce the Consequences of Taste-Based Discrimination? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Prejudice Reduction Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reducing the Scope of Prejudice in Decision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Towards Less Prejudiced Societies? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18 19 19 22 26 26 27 30 33 35 36 36 38 40 41
Abstract
Becker’s (The economics of discrimination. Chicago University Press, Chicago, 1957) book introduces the concept of taste-based discrimination. This chapter first reviews a large literature in economics that aims to complement Becker’s theoretical framework and to test its empirical predictions. Then, the chapter covers the literature in several disciplines (mostly economics, social psychology, R. Rathelot (*) Institut Polytechnique de Paris, CREST, Palaiseau, France e-mail: [email protected] M. Safi Sciences Po, CRIS, CNRS and LIEPP, Paris, France e-mail: mirna.safi@sciencespo.fr © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_1
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and sociology) that defines concepts that are close to Becker’s prejudice, and question its sources. The chapter also seeks to connect taste-based discrimination with other theories of discrimination, including statistical discrimination. The final section presents policies that have been proposed to remedy taste-based discrimination and reviews the literature that evaluates these interventions. Keywords
Prejudice · Bias · Animus · Stereotype · Inaccurate beliefs · Ethnic gaps · Gender gaps
Introduction Discrimination is a pervasive issue in most markets, countries, and social spheres. Although both the phenomenon and its consequences for many discriminated populations have been thoroughly documented, researchers and policy makers are still struggling to identify its mechanisms (see Lang and Kahn-Lang Spitzer (2020); Bertrand and Duflo (2017); Neumark (2018) for the most recent reviews in economics). Taste-based discrimination, as proposed by Becker in his seminal book published in 1957, assumes that discriminating agents incur a utility cost when they interact with individuals from the group that they discriminate against. These insights have been influencing research on discrimination for more than half a century and remain a central tenet of most theoretical and empirical studies in the field. This chapter has three main objectives: (i) to update the literature review on taste-based discrimination with the most recent empirical evidence and theoretical developments; (ii) to bridge the gap between the economic theory of taste-based discrimination and the literature on prejudice in psychology, sociology, and political sciences; and (iii) to discuss the implications of the theoretical framework of tastebased discrimination on antidiscrimination policies and interventions. While scholars from different disciplinary backgrounds have extensively studied discrimination as a central mechanism of social inequality in contemporary societies, the difference between the literature in economics and the other social sciences is striking. A large part of the research in economics on taste-based discrimination aims to understand and measure the relationship between prejudice and equilibrium outcomes in various markets. Economists hence conceive and use the concept of prejudice (sometimes called animus) as a largely ad hoc penalty term in the utility function. The literature in social psychology, sociology, and political science has focused on understanding where prejudice comes from, how it is generated, and how it can be changed. If discrimination comes from prejudice, one needs to find out what the determinants of prejudice as an equilibrium variable are. Considering prejudice as a parameter that is exogenous to policies and is stable over time or across socioeconomic conditions would not make sense. If taste-based discrimination is a large contributor to overall discrimination, understanding the long-term dynamics of discrimination
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requires an understanding of how taste-based discrimination and its manifestations in cross-group differences in socioeconomic outcomes can influence the determinants of prejudice. Section “Becker’s Theory, Extensions, and Empirical Assessments” presents the canonical theory of taste-based discrimination, the way the theory has developed until now, and empirical assessments of this theory, mostly as found in the economics literature in economics. Section “Opening the Black Box of Taste-Based Discrimination: Interdisciplinary Insights” returns to the literature in social psychology, sociology, and economics that investigates the origins of prejudice. Section “How to Design Public Policies to Reduce the Consequences of Taste-Based Discrimination” presents available evidence on policies aimed at reducing the consequences of tastebased discrimination by reducing prejudice, either in individuals or in organizational procedures.
Becker’s Theory, Extensions, and Empirical Assessments Conceptual Frameworks Definition and Seminal Contribution Becker (1957) is the seminal reference on taste-based discrimination, at once defining the concept and introducing a formal theoretical framework. In the simplest version of the model which focuses on employer discrimination, employers incur a utility loss when they interact with workers belonging to a non-preferred group (i.e., a group distinguishable from any other group only in terms of characteristics such as gender, race, religion, sexual orientation, etc.). Becker derives the equilibrium in a setup without frictions where minority workers transact with the leastprejudiced employers. The difference between the wages of the preferred and non-preferred groups is determined by the prejudice of the marginal employer. Here is a simple summary of Becker’s model. Denote A the majority group, B the minority group. Firms produce F(L) where L is the number of employees. The good produced is the numeraire. Workers of groups A and B are equally productive and can receive different wages wA and wB. A firm employing LA workers from group A and LB workers from group B will have a profit equal to: ΠðLA , LB Þ ¼ FðLA þ LB Þ wA LA wB LB : Becker introduces a distinction between the profit and the utility of the employer. The utility of the employer has an additional term, due to taste-based discrimination. V ðLA , LB Þ ¼ ΠðLA , LB Þ δLB : where δ is the taste-based discrimination parameter. If δ > 0, the employer is prejudiced against minority workers. If δ ¼ 0, the employer is indifferent between employing workers of groups A and B.
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We can allow the labor supply to be elastic. Assume that the distribution of the reservation wage is the same in both groups with a cumulative distribution function of H(.). There are NA individuals in group A and NB in group B. Group B is a numerical minority, NB < NA. Labor supply at wage w is equal to NAH(w) in group A and NBH(w) in group B. Suppose first that δ is constant among firms. Employers maximize their utilities such that the optimal number of workers of both groups is: F0 ðLA þ LB Þ ¼ wA ¼ wB þ δ Workers choose to work if they are proposed a wage above their reservation wage. The equilibrium wages in both groups are determined by: wB ¼ wA δ wA ¼ F0 ðN A H ðwA Þ þ N B H ðwA δÞÞ In this simple case of a constant δ, the difference δ between wages in group A and group B makes all employers indifferent between hiring a group A and a group B worker. Because wages offered to A workers are higher than those offered to B workers, the employment rate will be higher in group A than in group B. Employers may differ in terms of their prejudice towards B workers, i.e., in terms of δ. The existence of a wage gap will drive firms with low δ to hire only B workers. Firms with high δ will hire only A workers. In this model, the existence of heterogeneity in δ causes sorting between workers and firms as well as segregation across firms. Formally, suppose that the cumulative distribution function of δ is G(.). The equilibrium is determined by wages wA , the wage gap δ*, and the number L* of jobs as determined by the equations: L Gðδ Þ ¼ N B H wA δ
L ¼ N A H wA þ N B H wA δ wA ¼ F0 ðL Þ
Firms with δ < δ* will hire B workers and those with δ > δ* will hire A workers. An important point made by Becker is that the marginal employer δ ¼ δ* is the one that sets the wage gap for the whole labor market. The wage gap does not depend on average prejudice or on the prejudice of the most prejudiced employers. Rather, it depends on the prejudice of the marginal employer, i.e., the employer on the lower tail of the distribution. While the formulation of prejudice in Becker’s model looks ad hoc, as it relies on the direct manipulation of the utility function of employers, this is not where its main contribution lies. The reason Becker’s model remains influential to this day is because it provides a mapping between the distribution of prejudice among employers and the gender gaps in labor-market outcomes.
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How Can Taste-Based Discrimination Persist? One of the main critiques against Becker’s theory was formulated by Kenneth Arrow. A new employer with no prejudice will be able to hire a minority worker and derive a profit of δ*: the current wage gap in the market. If there is free entry to the market for employers, the least prejudiced employers are able to drive out the most prejudiced ones. Thus, under perfect competition, there should eventually be enough unprejudiced employers entering the market to ultimately eradicate the wage gap in the long run. As pointed by Flinn (2015), the impact of higher competition on market wage gaps is mitigated by the assumption made by Becker that companies’ production function have decreasing returns. Decreasing returns means that there needs to be enough unprejudiced employers to eliminate wage gaps completely. Following the emergence of this critique, several papers have proposed frameworks in which taste discrimination would persist. Goldberg (1982) offers a twist on Becker’s model. Instead of suffering from a utility loss due to prejudice against minority workers, employers experience a utility boost when they interact with majority workers. Switching from discrimination to favoritism means that both neutral and nepotistic employers can survive in the long run, even in a perfectcompetition setting. Charles and Guryan (2008) question the underlying assumption that employers’ prejudice would not have an impact on their utility if they were to become workers. Essentially, they argue that prejudiced employers would choose to remain in the market because they would experience a loss in utility due to the presence of minority workers regardless of their role in the market and, therefore, have no incentive to leave it. Imperfect information is often presented as a way to preserve the persistence of taste-based discrimination in the long run. Black (1995) introduces a search model in which minority and majority workers meet firms and decide whether or not to match with them based on the offered wage and on a randomly drawn nonmonetary value for the match. Firms can be prejudiced, in which case they refuse to hire minority workers, or non-prejudiced, in which case they are indifferent. Because search is random, minority workers can meet prejudiced employers who refuse to hire them. Thus, search is de facto more costly to minority workers. As a consequence, they suffer from longer unemployment spells and exhibit lower reservations wages. Minority workers end up with lower equilibrium wages than majority workers and in worse matches on the nonmonetary amenity dimension. As in Becker’s model, prejudiced employers also suffer from their prejudice because they have to wait longer to find a match. Black makes the argument that these employers can still survive in the long run, if the model allows them to differ in their entrepreneurial abilities. There are other notable differences between Becker’s and Black’s models. For example, when the share of minority workers increases, prejudiced employers have lower profits and tend to be replaced by non-prejudiced employers. This reduces the search cost of minority workers and reduces the gaps in labor-market outcomes between the two groups. Bowlus and Eckstein (2002) build on Black’s model and perform a structural estimation to decompose the causes of the wage gap between Blacks and Whites in the USA. The main differences between their model
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and Black’s are that prejudiced employers will hire minority workers whenever their wages are below some threshold and that minority and majority workers are allowed to have different productivity levels. Interpreting empirical wage gaps through the lens of this model leads to several conclusions: (i) more than half of all the US employers experience a disutility when interacting with Black workers, (ii) on average, the disutility amounts to around 30% of White workers’ productivity, and (iii) Black workers are on average 3% less productive than White workers. In their review of the literature, Lang and Lehmann (2012) argue that the model by Black, as estimated by Bowlus and Eckstein, requires an unduly high level of prejudice to match the data. Becker presents other variations on his model in anticipation of the criticism made about the lack of persistence of discrimination in the long run. One variation has been particularly successful in the subsequent literature: customer discrimination. Even when employers have no prejudice and only care about their own profits, customers may discriminate against the goods and services produced by minority workers. Becker frames it as a difference between the money price of service p and the net price p(1 þ d ) perceived by prejudiced consumers, where d > 0 is the coefficient reflecting customer discrimination. Relatedly, Sasaki (1999) introduces customer discrimination in a search model to explain the gender gap in labor markets, and Borjas and Bronars (1989) use a search model with customer discrimination and imperfect information to explain how Blacks and Whites may self-select into self-employment.
Empirical Assessments Testing the relevance of Becker’s and others’ models of taste-based discrimination has been the subject of several strands of literature. Four of these strands are particularly important: the first one directly tests predictions from Becker’s model, the second one assesses the relationship between market competition and discrimination, the third one focuses on customer discrimination, and the fourth one uses an experimental approach to isolate the impact of prejudice. Other papers identify tastebased discrimination indirectly by ruling out other channels.
Evidence on the Relationship Between Prejudice and Gaps A relatively limited body of literature aims to test the direct empirical predictions of Becker’s model. Charles and Guryan (2008) start from the canonical model by Becker and empirically examine the predictions which posit that: (i) the prejudice of marginal employers matter more than that of the average employer, (ii) the degree of prejudice among the most prejudiced employers does not affect the wage gap, and (iii) wage gaps increase along with the share of Blacks in the labor market. The paper uses questions regarding attitudes towards a Black president and support for interracial marriage from the General Social Survey (GSS) combined with wage data from the Current Population Survey (CPS). States with higher shares of Black workers do indeed have larger wage gaps. The amount of prejudice held by
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employers occupying the lower tail of the prejudice distribution explains this wage gap, while wages do not vary with the prejudice of the most prejudiced persons in a state. Extrapolating their results, Charles and Guryan find that around a quarter of the wage gap between Blacks and Whites in the USA is due to taste-based discrimination. Carlsson and Rooth (2016) apply the same strategy to ethnic wage gaps in Sweden. They also find that attitudes of marginal employers explain the wage gap, while the average employer does not matter. Burn (2020) combines data on attitudes toward homosexuality from the GSS and wage data from the Census and the American Community Survey (ACS) and documents a correlation between managers’ prejudice and the wage penalty paid by gay men. Other papers also leverage correlations between survey measures of prejudice and ethnic gaps. Zussman (2013) starts by showing that Jewish sellers discriminate against Arab buyers in the Israeli online market for used cars using a correspondence-study type of experiment. He also runs a survey on Jewish sellers to learn about their beliefs and attitudes towards Arabs (i.e., in a similar way to Charles and Guryan (2008)) and finds that Jewish sellers hold many intolerant views towards Arabs. Interestingly, one particular statement – “the Arabs in Israel are more likely to cheat than the Jews” – allows for determining the sellers that are going to be the most likely to discriminate against Arab buyers. The paper argues that this is consistent with statistical discrimination. One could also argue that, given its correlation with anti-Arab statements, the fact that these statement jointly explain discriminatory behavior might be evidence in favor of taste-based discrimination.
Competition and Discrimination One of the predictions of Becker’s model is that taste-based discrimination should be more persistent in markets that are less competitive. Comparing markets with crosssectional data cannot identify the effect of competition on wage gaps: most of the literature leverages changes in competition within markets. Ashenfelter and Hannan (1986) correlate female employment with measures of market concentration in the US banking sector. The paper innovates by using firmlevel data as an attempt to tackle the issue that cross-industry comparisons pick up differences other than just those regarding market concentration. Berkovec et al. (1998) test whether or not mortgage lenders discriminate when they evaluate mortgage loans. If taste-based discrimination is involved, discrimination should increase in less competitive lending environments. They fail to reject the null hypothesis that no taste-based discrimination exists. Again with regards to the credit market, Cavalluzzo et al. (2002) document that firms owned by Blacks are more often denied credit than owned by Whites. They find that increases in competition in local banking markets reduce racial gaps in denial rates, which is consistent with some taste-based discrimination. Investigating racial gaps in the car loan market, Butler et al. (2022) combine tests of direct predictions of the Becker model and of the influence of competition. Their results support the existence of substantial tastebased discrimination in this market. Levine et al. (2014) also use the stepped-wedged nature of bank deregulation in the USA to investigate how racial gaps in the labor market are affected by the entry
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of new firms. They find evidence in favor of taste-based discrimination: the wage gap between Blacks and Whites decreases when a deregulation takes place. In a similar vein, Hirata and Soares (2020) look into the impact of the trade liberalization that took place in Brazil in the 1990s on the wage gap between Black and White workers. They show that the wage gap decreased more in sectors that became more exposed to foreign competition following the reforms. The gap also decreased more in sectors that were initially more concentrated. Taken together, these results suggest that, in the USA and in Brazil, higher competition has contributed to reducing racial gaps, which is consistent with the existence of taste-based discrimination in these cases. This is not the case for gender gaps in India, however. Ghani et al. (2016) study the impact of reforms aimed at increasing competition in several markets in India on female employment. They find that these reforms are not associated with large changes in female outcomes on the labor market. Another way to study the influence of competition is to look directly at its impact on firm exit as a function of how discriminatory firms are. Weber and Zulehner (2014) use Austrian data to show that firms that start with lower shares of female workers than what is typically found in their industry have lower survival rates. Pager (2016) combines data from a large-sample correspondence study to measure discrimination at the firm level with data on establishment survival. She documents a correlation between discrimination and the probability to fail within 6 years. Using an experimental approach, Doleac and Stein (2013) measure the effect of race in the online market for second-hand electronic goods, creating fake ads that are identical but for the skin color of the hand holding the item to sell. She finds that Black sellers do particularly poorly in thinner markets, a piece of evidence consistent with taste-based discrimination.
Customer Discrimination Customer discrimination has received considerable empirical attention. The main prediction of this family of models is that occupations that are more exposed to customers should suffer from higher discrimination and exhibit higher wage gaps. Kanazawa and Funk (2001) use Nielsen ratings for the US basketball games to show that viewership increases with the share of White players on the field, controlling for other determinants of viewership. Both results point to substantial customer discrimination in that market. Nunley et al. (2015) use a correspondence study to measure racial discrimination towards recent college graduates in the USA. They find higher gaps in callback rates in jobs that require more interaction with customers. Combes et al. (2016) use observational data from France, where African immigrants are both more often unemployed in general and less often employed in sectors with higher exposure to consumers. They draw two predictions from a search model with employer and consumer discrimination: (i) if higher shares of migrants imply lower ethnic gaps, then there is discrimination (i.e., either customer or employer-based) and (ii) if higher shares of migrants are positively correlated with ethnic differentials in the probability of working in consumer-exposed sectors and if there is discrimination, then there is consumer discrimination. Note that the first prediction is at odds with Becker’s, which was tested by Charles and Guryan (2008)
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but is consistent with Black (1995). They use the 1990 French Census and compare the local labor market at the commuting zone level and find support for both the existence of ethnic discrimination and the contribution of customer discrimination to ethnic employment gaps. In the context of discrimination towards Arab workers in the Israeli labor market, Bar and Zussman (2017) combine survey data and a natural experiment, which result in evidence showing that sizable customer discrimination does exist in this market.
Residual Approaches Many papers only isolate taste-based discrimination as a residual. For instance, Dobbie et al. (2018) study discrimination on the British credit market. When lenders maximize short-term profitability, they indirectly discriminate against illiquid populations for which the gap between short-term and long-term profitability is the largest. They use a test that distinguishes this source of bias from taste-based or stereotype-based discrimination in data from the UK and find empirical support for this “incentive-based” model of bias. These results indirectly show that discrimination against immigrants and older applicants is unlikely to be only explained by prejudice. Correspondence and audit studies have been providing reliable evidence for the existence and magnitude of discrimination for decades (Riach and Rich 2002; Bertrand and Duflo 2017; Neumark 2018; Pager and Shepherd 2008; Quillian and Midtboen 2021). Simple designs cannot be used directly to isolate taste-based discrimination from other potential channels. To discover the mechanism underlying discrimination, researchers resort to either heterogeneity across markets (like Doleac and Stein (2013), see supra), or more complex designs, which delineate statistical discrimination and leave taste-based discrimination as a residual. An early example is Nardinelli and Simon (1990). Controlling for players’ ability, they find that baseball cards of White players are more expensive than those of Black players. Hedegaard and Tyran (2018) hire Danish juveniles to perform a real-life task (preparing letters for a mailing) and let them choose their coworker from a pool of two different people who differ in their ethnicity (i.e., Danish-sounding versus Muslim-sounding names). Pay is based on the pair’s probability so that workers should care about choosing their coworkers carefully. They find that subjects are willing to forego an average of 8% of their earnings to avoid interacting with a coworker that has a Muslim name. Caselli and Falco (2020) use a similar design to look into ethnic discrimination of Danish households towards house-cleaning workers. They deliver leaflets that differ on cleaners’ first names, price, and number of stars attributed by fictitious previous customers. They find that Muslim-named cleaners have lower callback rates when price is the same, but lower prices allow them to bridge the gap with Danish-names workers. These papers provide clients/ coworkers with measures of productivity as an attempt to isolate the gap that would not depend on productivity, and find that subject respond to productivity proxies. However, it is difficult to rule out that at least part of productivity remains unobserved, so that part of the gap could be due to statistical rather than tastebased discrimination.
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Wozniak and MacNeill (2020) use a lab experiment simulating a labor market to randomize the amount of information given about candidates to different employers. Part of the discrimination against Black candidates, exerted both by Black and non-Black employers, is due to statistical discrimination, as selection rates are affected by the perceived reliability of the information (e.g., real versus stated performance scores) provided by candidates. The study pins down a residual part of discrimination that remained even after employers were given real performance scores. This experiment provides indirect but solid evidence in favor of taste-based discrimination (only exerted by non-Black employers) in this context. In the context of online auctions, von Essen and Karlsson (2019) randomize the moment at which bidders observe the name of the seller (i.e., either at the beginning of the bidding process or after the auction has been concluded). Winning bidders tend to provide feedback less often to sellers with foreign-sounding names in cases when names are revealed at the end of the auction rather than at the start. This is consistent with tastebased discrimination, as it indicates that prejudiced bidders exit the auction earlier when names are visible. Other papers leverage natural experiments. In Israel, testers and candidates for driving tests are randomly assigned to one another. Bar and Zussman (2019) document that testers are more likely to grant a driving license to candidates of the same ethnicity as them (i.e., Arab versus Jewish) and of the opposite sex. Lavy et al. (2018) use the random allocation of matriculation exams to graders in order to study the effect of students’ religiosity on the grades they receive from graders. They find some evidence for in-group religious bias, mostly driven by religious men, but the magnitude of these effects are small.
Opening the Black Box of Taste-Based Discrimination: Interdisciplinary Insights From Taste to Prejudice Becker conceives prejudice as an aversion or disutility that some people perceive during cross-group interactions. The use of the word taste reflects Becker’s understanding of discrimination as being rooted in individual preferences. His work does not speak much to the sources or the specific content of these preferences. The model only requires them to be utility costs, and Becker focuses mostly on the consequences for market-level outcomes of the presence of these costs. This section traces back the origins of the notion of taste and explores the literature on the sources of prejudice in interdisciplinary perspectives. A few years before Becker publishes his book, a rich literature on racial inequality emerges in sociology and social psychology. Becker mentions this literature in his book and presents his own contribution as complementary. The seminal reference of this literature is Gordon Allport’s book “The nature of prejudice.” The notion of taste used by Becker follows the concept of prejudice defined in Allport’s seminal work (1954). “Ethnic prejudice is an antipathy based upon a faulty and inflexible
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generalization. It may be felt or expressed. It may be directed toward a group as a whole, or toward an individual because he is a member of that group” (p.10). This early definition stresses the duality of the notion of prejudice: both its affective (antipathy) and its cognitive dimensions ( faulty generalization). Allport refers to these two components of prejudice using words such as “attitudes” and “beliefs” (p.12). This conceptualization of prejudice echoes both taste-based and statistical discrimination theories in economics. Unlike Becker, Allport is interested in the “roots of prejudice” and its individual and contextual determinants. Allport’s definition also suggests that the cognitive dimension precedes the emotional one; prejudice entails negative attitudes based on (erroneous) beliefs. Allport’s definition of prejudice has been implemented in surveys that aim to measure emotions or beliefs towards groups using a wide battery of questions often labeled as “attitudinal.” Such questions measure antipathy, discomfort, hostility, and animosity against minorities based on gender, race, ethnicity, migration status, sexual orientation, etc. They also capture people’s beliefs regarding the members of these groups. For example, questions like the following, from the European Social Survey, capture subjective feelings: “Now thinking of people who have come to live in [country] from another country who are of a different race or ethnic group from most [country] people. [..] please tell me how much you would mind or not mind if someone like this married a close relative of yours.” Other questions that ask individuals to report their level of agreement with statements such as “It is usually better for everyone involved if the man is the achiever outside the home and the woman takes care of the home and family,” from the General Social Survey, typically measure self-declared beliefs. Some questions are used to gather largescale internationally comparative data such as with the World Value Survey or the European Social Survey, while others are specifically adapted to national contexts, like with the modern racism scale in the USA (McConahay 1986). These measures are shown to vary across personality traits, socioeconomic, demographic, and geographic factors, and to correlate with political attitudes and electoral behaviors. For example, support for Donald Trump in the USA has consistently been shown to be associated with race-, gender-, religion-, sexual orientation-based measures of prejudice (Shook et al. 2020). One important challenge of this type of research is to adapt these measurement instruments over time when there are intense shifts in the social desirability for expressing prejudice in surveys (Bonnet et al. 2016).
From Varieties of Prejudice to Varieties of Discrimination In Becker’s model, prejudice only varies in intensity. Other disciplines, however, stress that the type of prejudice is also important in shaping intergroup relations and human interactions.
Implicit and Explicit Prejudice Following changes in the legal and cultural contexts of prejudice in modern societies (e.g., the US antidiscrimination legislation in the 1960s), distinctions started to be
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made between blatant or explicit bias versus what has been referred to as subtle, benign, or modern bias (Fiske 2002; Bobo et al. 2012). Since the 1990s, the literature has distinguished between conscious or explicit and unconscious, automatic, or implicit bias. Many studies working on different contexts show that implicit and explicit measures are correlated: consumption decisions (Maison et al. 2004), employment discrimination (Ziegert and Hanges 2005), teachers’ discrimination (Carlana 2019; Alesina et al. 2018), and discrimination in academic committees (Régner et al. 2019). The literature also highlights their distinctive underlying mechanisms. Explicit attitudes/conscious bias entail expressing aversion and endorsing negative beliefs and ideologies, while subtle/unconscious/implicit bias tends to be more associated with deeply rooted negative emotions stemming from early childhood and socialization. Phelps et al. (2000) find that implicit (but not explicit) bias measures positively covary with activation of the amygdala (i.e., the brain structure associated with your ability to feel certain emotions and perceive them in others) for Whites exposed to photos of Black persons. Some findings also suggest that changes in implicit attitudes may depend on emotional reconditioning, whereas changes in explicit attitudes may depend on more cognitive and motivational factors (Rudman et al. 2001). Implicit attitudes are also shown to be related to early intergroup experience and sociocultural background; they represent a trace of past experience that lingers in the unconscious (Greenwald et al. 2002; Rudman 2004).
Types of Emotions Behind Prejudice Beyond the different levels of expression of prejudice, research has mapped the complexity of human feelings involved in intergroup relations (Cuddy et al. 2007). The literature distinguishes between different types of “antipathy”: disgust, resentment, fear, and anger. These different emotions predict different discriminatory outcomes: disgust implies avoidance and neglect, while fear and anger are associated with more exclusionary and aggressive reactions. For instance, anger-based emotions experimentally provoked by threatening masculinity have been shown to give rise to compensatory dominance that may often take the form of violence and harassment of women as well as physical aggression (Vescio and Schermerhorn 2021). Some evidence also shows that anger is the most decisive type of emotion driving the vote for the French far-right party in reaction to the 2015 Paris terror attacks (Vasilopoulos et al. 2019). This variation in the socio-emotional evaluations at stake in intergroup relations advocates for a wide range of prejudice measurements in survey data. For example, GSS data has consistently shown that few Whites embrace African Americans on an emotional level (i.e., feeling sympathy or admiration for Blacks) despite clear decreasing trends in the expression of explicit racism (Bobo et al. 2012). Emotions involved in potentially discriminatory interactions need not be negative or antipathetic. Moderate bias often consists of withholding positive emotions from out-groups, as expressed in the form of basic liking, respect, or “cool neglect” (Fiske 2002). Intergroup bias is relative rather than absolute: if positive emotions towards the in-group are more intense than the positive emotions towards the out-group, this
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discrepancy may be the source of discrimination. Moreover, specific positive emotions such as being patronizing can be associated with durable discrimination, as shown by the rich literature on benevolent sexism (Glick et al. 2000). Research also highlights strong implicit and positive bias towards in-group members (Lowes et al. 2015; Portmann 2020).
The Stereotype Content Model If prejudice is variable in its nature, intensity, and degree of expression, is it still possible to posit a single theory of taste/prejudice-based discrimination? Research on the stereotype content model (SCM) offers a powerful synthesis. In continuity with Allport’s original insights, which consider emotions and beliefs in tandem, Suzan Fiske and colleagues have delved further into the different varieties of prejudice to uncover their stereotypical contents. The concept of stereotype, defined in social psychology as the mental tendency to perceive an individual as being a representative member of the group, is a central faculty of the brain used to navigate social diversity and complexity (Hamilton and Trolier 1986). This literature documents how, despite the wide diversity across groups and societies, people invariably stereotype others along two fundamental dimensions: warmth and competence (Fiske et al. 2002; Durante et al. 2017). The first dimension pertains to the degree to which group members are depicted as “likable” and “friendly,” while the second refers to the degree to which group members are perceived as “capable” and “assertive.” The model builds on evolutionary grounds: warmth and competence are fundamental dimensions of how one perceives others because humans are predisposed to judge strangers’ intentions to harm or help (warmth) and their capacity to act in line with their intentions (competence) (Fiske 2002). Despite these evolutionary bases, the SCM endorses a conceptualization of warmth and competence as representing “cultural beliefs” about each group. They are learned early in life and activated automatically in social interactions. Measuring the contents of stereotypes implies asking individuals about the way specific groups are portrayed by society. In that sense, it is different from measuring individuals’ own attitudes and beliefs about groups. For example, Fiske et al. (2002) ask the following question to capture different groups’ stereotypical levels of competence: “As viewed by society, how competent/confident/independent/competitive are members of this group? We are not interested in your personal beliefs, but in how you think they are viewed by others.” In addition to providing a synthetic model, the SCM is also used as a prediction tool, which can be used to scrutinize intergroup relations. The two orthogonal dimensions of stereotypes lead to a four-dimensional prejudice matrix comprised of envying the up, scorning the down, pitying the down, and in-group pride (Fiske 2011). The model also predicts that groups low on both warmth and competence dimensions tend to be the most discriminated against and subject to “dehumanization.” In many other situations, stereotyping tends to be ambivalent, which may paradoxically render its effects all the more pernicious; stereotype ambivalence is shown to correlated with greater levels of social inequality and group conflict within societies (Durante et al. 2017). This model has been proven to apply to a variety of
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intergroup perceptions (Cuddy et al. 2008), whether targets are elderly people (Cuddy et al. 2005), sexual and gender minorities (Clausell and Fiske 2005; Vaughn et al. 2017), female subgroups (Eckes 2002), racial minorities (Fiske et al. 2009; Lin et al. 2005), or immigrant populations (Kotzur et al. 2019; Lee and Fiske 2006). Recent use of the model seems to indicate that it can also help us to understand attitudes towards categorical intersections (e.g., gender-sexual orientation or racegender) as well as towards the relationship between generic and subgroup stereotypes (e.g., immigrant vs. specific national origin, women vs. housewives). While early empirical evidence is based on the US society (Fiske et al. 2002), recent studies use the model to investigate stereotype contents worldwide (Durante et al. 2017). In addition to its predictive power on attitudes, recent research tends to directly use SCM to interpret the variety of discriminatory outcomes against different minority groups across societies, as observed through experimental design (Thomas 2018; Veit et al. 2021).
Where Does Taste Come From? Investigating the Roots of Prejudice Prejudice and Social Cognition Where does prejudice come from? Allport’s answer is that “erroneous generalization” and “hostility” are a “natural and common capacity of the human mind” (Allport 1954, p. 17). The first source of prejudice would be embedded in humans’ faculty of perceiving themselves and others as members of groups. Group categorization is constitutive of our social nature. It is fundamental to human cognition and applies to the ways in which we perceive and process the complex information that we receive from our environment. The literature on social categorization documents the fact that individuals have more positive affects towards members of their own group, even in experimental designs that exclude mechanisms such as competition for resources. Research on the minimal group paradigm shows that in-group favoritism emerges rapidly after the “experimental” and arbitrary construction of groups and highlights cognitive mechanisms that trigger favoritism (Tajfel 1969). Individuals retain more detailed information about in-group versus out-group members and better remember how in-group members are similar to the self. The cognitive bases of in-group favoritism are often interpreted as beneficial to norm enforcement. Research on implicit attitudes provides some evidence that the source of in-group favoritism is rooted in selfesteem and cognitive consistency (Greenwald et al. 2002). In contrast with in-group favoritism, negative attitudes towards the out-group tend to be more sensitive to the context of intergroup interactions, e.g., competition, demographic, and cultural threat, or contact (Dovidio et al. 2010). Nonetheless, there is some evidence of negative attitudes towards minority group members even in the absence of competition. This is interpreted in terms of the cognitive salience of distinctive characteristics; distinctive features tend to receive enhanced processing, are more memorable, and exert more influence on judgments. Smaller groups are more distinctive and negative behavior is easier to remember when it comes from
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out-groups, which may explain the persistence of negative feelings and attitudes towards them (Bodenhausen and Richeson 2010; Hamilton and Sherman 1989). On the other hand, evolutionary approaches tend to interpret negative emotions towards small-group members as reflecting cognitive adaptation aimed at avoiding “poor social exchange partners” and joining “cooperative groups,” especially in the research on health stigma and diseases (Kurzban and Leary 2001). What implications does this literature have on taste-based discrimination theory? First, it means that taste-based discrimination may be observed whenever there are processes of “meaningful categorizations.” Group labeling – whether inherited, imported, or constructed – is at the core of taste-based discrimination. So rather than group A having specific emotions towards group B, it is in the very existence of groups A and B as meaningful categories in human classification that the roots of taste-based discrimination lie. For example, research shows how legal categories of migrants (such as refugee and asylum seekers, family reunion migrants, undocumented migrants) although constructed by the law, may trigger social categorization dynamics and lead to different levels of prejudice and unequal treatment across these categories (Wyszynski et al. 2020). Second, the taste-based discrimination theory does not ascertain whether taste emerges from in-group favoritism or from out-group antipathy. Comparing levels of discrimination across different minority groups, several studies suggest the existence of a hierarchy of aversion towards different out-groups (Di Stasio and Larsen 2020; Quillian et al. 2019). At the same time, research designs that allow to compare hostility toward specific minority groups to aversion to an out-group whose origin is not identified suggest that in-group preferences are also at play (Jacquemet and Yannelis 2012).
Bringing the Context in: The Sociohistorical Roots of Prejudice While social categorization is a fundamental source of intergroup prejudice, it is not capable of explaining why some groups in some contexts are disliked, stigmatized, and sometimes dehumanized. As research in history and sociology shows, prejudice is inscribed in historical and cultural contexts and tends to be instilled during early childhood and transmitted from one generation to the next. From a historical point of view, group categorization dynamics arise in specific contexts of intergroup relations (Tilly 2005). For instance, group encounters (e.g., newcomers and old settlers), imposition (e.g., police category, administrative category), negotiation (e.g., gangs, peer groups, public debates), and transfers across social settings (e.g., from the family to the workplace, or from one society to another). In these specific contexts of group interactions, social meanings of human differences are sometimes created collectively and most often transformed and altered. For example, categorizations related to humans’ phenotypic traits are variable in their salience and specific contents across historical periods and cultural settings. This is valid for skin color, eye color, weight, and many other phenotypic characteristics. A specific conception of race – which attributes a hierarchical moral worth to specific phenotypes – is relatively recent (nineteenth century) and is related to the scientific and ideological context in which Social Darwinism emerged in the West (Banton 1998). For these categories to become durable and meaningful in
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routine interactions (Tilly 1998), stable correlations with market-based outcomes are key. Sociologists refer to this process as the transition from symbolic categories to social categories (Lamont and Molnar 2002). This distinction invites us to anchor prejudice in sociohistorical contexts. Social psychologists have delved into the micro mechanisms involved in the reproduction of categorical inequality. The key idea here is that human cognition of the “social structure” is fundamentally hierarchical: the social groups that are perceived as occupying the top of the hierarchy not only enjoy better material conditions but also higher status. Status refers to the moral and symbolic value conferred to a social category because of its position in the social hierarchy. Social psychologists document how several forms of status bias affect behavior (Ridgeway 2013). While preferences for associating with in-group members are widespread, status bias more specifically refers to preferences for associating with high-status groups, which lead to the reproduction of inequalities (e.g., if men and women prefer to work for male bosses, or black and white families prefer to live in white neighborhoods). Status bias is also at stake when high-status groups react to some behaviors or situations that they perceive as threatening their group position or as “going too far.” For example, assertive, dominant women may be disliked and judged as being less employable. Status bias implies that past inequality (i.e., stemming at least partly from discrimination) is capable of sustaining prejudiced meanings of categorizations, which are likely to generate future discrimination. Payne et al. (2019) illustrate the legacy of historical discrimination and provide evidence that corroborate its social and psychological determinants. They find that the US states that were more dependent on slavery before the Civil War display higher levels of pro-White implicit bias today, among White residents, and less pro-White bias among Black residents. The association between slave populations and implicit bias is partially explained by measures of structural inequalities such as poverty, segregation, and social mobility across racial groups. History also matters to reduce prejudice. For example, Schindler and Westcott (2020) show that individuals in areas of the UK where more black troops were posted during World War II are more tolerant towards minorities 60 years after the last troops left (i.e., as measured by electoral outcomes as well as explicit and implicit attitudes towards blacks). Increasing contact with minorities within a collaborative context seems to explain the reduction in prejudice. These findings highlight the importance of history in determining current-day levels of prejudice. If the past is somehow still with us when it comes to prejudice, a key mediating mechanism lies in the intergenerational transmission of aversion and antipathy towards stigmatized groups. Research in psychology finds stable associations of prejudice with personality dispositions, such as social dominance orientation and right-wing authoritarianism, that tend to be transmitted from parents to children. Research on the development of prejudice during childhood is valuable in this regard and highlights the importance of family transmission (Duriez and Soenens 2009). While the mother’s role has been documented as crucial, recent research tends to stress the role of fathers on prejudice related to gender and sexual orientation (O’Bryan et al. 2004). Moreover, sons seem more receptive to parental prejudice
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as compared to daughters: they are particularly receptive to their fathers’ attitudes (as shown by Avdeenko and Siedler (2017) on prejudice against migrants). Qualitative and ethnographic research documents the socialization process during which such prejudiced dispositions are transmitted through narratives and ideologies, as well as through everyday practices and behaviors. Intergenerational transmission may also sustain shocks that reduce prejudice. Cultural shifts such as those driven by women’s emancipation movements or LGBTIQ struggles have a long-lasting impact because they are transmitted intergenerationally. This also seems to hold in the case of structural shocks, such as the one described in the case of Black GIs (Schindler and Westcott 2020). Using cohort analyses, the authors show that intergenerational transmission is probably the core mechanism explaining the significance of the association between the presence of black troops and implicit and explicit bias 60 years after this shock in intergroup contact.
Back to Taste-Based Versus Statistical Discrimination: The Role of Information and Accuracy of Beliefs The economics of discrimination has developed around the duality between two theories, taste-based and statistical discriminations. Taste-based discrimination is grounded on the idea that members of the discriminating group experience a disutility when they interact with individuals from the group that is discriminated against. Conversely, statistical discrimination theory is based on the premise that employers or customers have beliefs about the productivity, service quality, or any other out-group members’ characteristic relevant to the social interaction that is considered. Classic papers underpinning the statistical discrimination literature assume these beliefs to be correct, in the sense that they are equal to the actual conditional distribution of the feature of interest in each group. These two theories have traditionally been opposed – taste-based discrimination being deemed irrational and statistical discrimination rational. Recent literature in economics considers the case in which priors are incorrect. Arnold et al. (2018) show suggestive evidence in the context of gaps in bail decision in the USA for Black versus White defendants, relying on the overrepresentation of black defendants showing up in the right-hand tail of the risk distribution. Bohren et al. (2019) provide more direct evidence using a field experiment to explain the gender gap in the evaluation of content posted on Github. The existence of previous positive evaluations makes women’s posts preferred over men’s, while the opposite occurs when posters have received no prior evaluations. In the context of ethnic discrimination towards Airbnb hosts in North America and Europe, Laouénan and Rathelot (2021) study the evolution of prices posted for a property as the number of reviews left by previous users increases. They find evidence in favor of statistical discrimination, half of which could be attributed to inaccurate beliefs. This literature reveals some similarities between statistical discrimination with inaccurate beliefs and taste-based discrimination. The idea of inaccurate beliefs is close to the conceptualization of prejudice, which raises the question of the sources of these inaccurate
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beliefs/prejudice. The emerging literature on stereotypes and its increasing use in economics paves the way to the reconciliation between statistical and taste-based discrimination theories. First, this literature shifts the debate from the accurate/ inaccurate beliefs distinction to the study of the distortions that are grounded in social cognition. In other words, what really matters for discriminatory outcomes is not what the social reality is but rather how people perceive the social reality. For example, recent evidence suggests that while most stereotypes tend to be correct directionally, they have many inaccurate aspects related to underlying cognitive processes: misrepresentations of the social reality are driven by the disproportionate weight of the tales of the distribution in people’s beliefs constitution, group accentuation effects, and the effect of distinctiveness and group representativeness (Bordalo et al. 2016). These sources of inaccuracy are constitutive of stereotypes. They affect both discrimination and self-stereotyping which render them powerful in explaining a large range of gaps. Hence, inaccuracy of information about groups is anchored in the cognitive processes that are at the sources of stereotypes formation. Moreover, research on stereotype also helps bridge the gap between accurate and inaccurate beliefs on the long run. The literature on stereotype threat shows, for example, that salient stereotypes (including erroneous ones) may become accurate ex-post because people are psychologically driven to conform to them (Steele 1997). Recent research in economics suggests that conforming to stereotypes does not only stem from the psychological threat felt because of exposure to hostility, but also from “avoidance” and lack of “positive interactions,” even when minority and majority group members enter into contact (Glover et al. 2017). Research on the contents of stereotype also helps connect statistical and tastebased discrimination theories. Taste-based discrimination can be related to the stereotypical beliefs about warmth as described in the SCM model. In addition, and quite in line with Becker’s insights, competition is a key contextual variable that determines warmth. People attribute warmth to those perceived to be harmless – in the sense that they are not seen as being in direct competition with the in-group for jobs, school admissions, power, and resources. Moreover, the SCM model shows that perception of group-competence, the second component of the model, derives from the real state of the distribution of resources, or what the literature refers to as the social structure, somehow in line with the standard statistical discrimination theory. There is nonetheless a crucial difference: while statistical discrimination is concerned by the beliefs about groups’ productivity, the SCM predicts that it is status (i.e., the symbolic position in the social hierarchy) that triggers stereotypical beliefs about competence. There is hence no need to think that these beliefs are grounded in “real” differences in productivity (or other type of quality-related characteristics). In other words, people attribute competence to those perceived as holding prestigious jobs and being economically successful and not to those “known” to be more “productive.” Hence, the study of the contents of stereotypes highlights the coexistence of taste and statistical sources of discrimination in the stereotypical beliefs about groups. Finally, the focus on the role of stereotypes and their specific contents invites research on discrimination to pay specific attention to the sources of prejudice
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described in the former section. Stereotypes are not only cognitive objects; they are cultural constructions that are shaped by social norms and ideologies. They connect back to power relations inherited from the past and can carry emotional dispositions transmitted from one generation to the next and socialized within different spheres of social closure.
Towards an Integrated Approach to Discrimination: A Multidisciplinary Perspective The previous sections review studies belonging to different disciplinary traditions, which can be used to build a framework to understand the determinants of prejudice. Consider the prejudice held by an assessor belonging to group A towards members of group B. Stereotypical beliefs about competence depend on the perceived socioeconomic position of group B (i.e., status), which depends on current and past prejudice held towards this group, but can also be distorted by cognitive processes underlying stereotype formation. From an economic point of view, it is tempting to model the evolution of competence as a posterior distribution of quality (or productivity) under imperfect information. However, social psychology stresses that stereotypes are conceptualized less as individual beliefs on group B than beliefs about society’s beliefs on group B. For this reason, competence might be particularly sluggish and lagging behind group B members’ actual improvements in socioeconomic status. Warmth can be thought to be even more persistent, as it is mostly about historical and intergenerational transmission. Beyond stereotypes, one needs to specify the relationship between stereotypes and prejudice. While stereotypes may be shared in society, members of different groups do not interiorize them in the same manner: they do not form the same prejudice towards group B. Social psychology has less to say about this mapping, its determinants, and its potential persistence. Sociologists and political scientists tend to highlight that this mapping has to do with the formation and persistence of narratives, ideologies that continue to feed the definition of in-groups and out-groups in society. They also tend to insist the effect of the context of social relations and the degree of separation between groups. More intense contact may speed up the updating of both competence and warmth stereotypes. If group B’s actual socioeconomic status in society is below other groups, contact needs not improve competence. Drawing on this framework and the body of literature, it seems to us that many questions are still at least partially open. • To what extent do individual level variables (e.g., personality traits, family background, and education) affect the mapping between stereotypes and prejudice? • What is the long-term relationship between education and stereotype contents?
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• What role does the meso-context play in alleviating the role of stereotypes as sources of information (e.g., discussions with other people/colleagues during an evaluative decision, debate with people holding different opinions)? • To what extent do perceived socioeconomic gaps mediate the relationship between contact and prejudice? Understanding the determinants of prejudice may help with the evaluation of policy avenues to fight taste-based discrimination and reduce prejudice. • Policies that aim to increase contact between groups in society may only work if actual socioeconomic gaps are lower than those perceived (especially by the majority group). • Enhancing cultural shifts (destigmatization of disadvantaged groups by collective actors such as the medias, politicians, etc.) may be more effective on the long run to fight against the stickiness of the warmth component of stereotypes. The next section focuses on the literature that evaluates policies and interventions against taste-based discrimination.
How to Design Public Policies to Reduce the Consequences of Taste-Based Discrimination? Three kinds of policies have been explored to reduce taste-based discrimination: (1) reducing prejudice at the individual level, (2) implementing procedures that limit the role of prejudice in decision-making (e.g., hiring, promotion, renting, selling, etc.), and (3) relying on key actors (e.g., the media, politicians, artists) to alter the processes that feed prejudice.
Prejudice Reduction Interventions How to design interventions that curb individual prejudice? Normative approaches such as blaming, sanctioning, or even outlawing are often used, but their impact in changing people’s attitudes seems quite limited. These approaches tend to reduce the expression of prejudice rather than people’s feelings or implicit attitudes, and they mainly do so when the sanction comes from “credible” and “valued” in-group members (measured, for instance, by the number of followers on Twitter, as in Munger 2017). Messages endorsed by elite in-group members that prime common religious identity with the out-group are the most consistently effective way to reduce the spread of sectarian hate speech (Siegel and Badaan 2020). Conversely, prominent public figures who endorse negative stereotypes about minority groups may foster anti-minority attitudes, even within the least prejudiced people (Bursztyn et al. 2020). Paradoxically, the effectiveness of normative approaches is related to the
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thick nature of group boundaries and mediated by mechanisms that enhance social closure and in-group favoritism, rather than reducing out-group antipathy per se. As an alternative to normative approaches, social psychologists and behavioral economists have designed and tested interventions that aim specifically at increasing the likability of stigmatized group members. “Emotional training,” which consist in, e.g., simulation encounters or guided meditation techniques are implemented to trigger emotion-regulation strategies as to help individuals fight off their personal implicit and explicit attitudes (Paluck et al. 2021). Evidence suggests that “generating empathy” is one of the most promising avenues for countering the antipathy that is at the source of prejudice. Several experimental designs drawing on this idea have been tested: making connections with other groups (priming individual history and asking people about their family migration history as a way to reduce antiimmigration attitudes, as in Williamson et al. 2020), perspective taking (asking people to put themselves in the shoes of a refugee or a transgender person, as in Broockman and Kalla 2016; Adida et al. 2018; Kalla and Broockman 2020), increasing awareness (revealing bias in the form of feedback after an implicitassociation test as in Alesina et al. (2018), or by the dissemination of scientific findings on biases and discriminatory decisions as in Pope et al. (2018)). These studies measure direct effects on socio-emotional judgments reported in explicit or implicit attitudes. However, most of them use one type of intervention, show shortterm impacts, and are specific to particular contexts. Another type of interventions focuses on increasing social interactions between groups (Pettigrew and Tropp 2006). According to Paluck et al. (2021), interventions based on promoting contact account for one-third of all prejudice reduction research. Findings suggest that intergroup contact correlates with lower levels of intergroup prejudice. Causal assessment is yet rare, and there is a wide variation in the effects across the type of prejudice (e.g., racial versus gender) (Paluck et al. 2019). Paluck and colleagues note a shift in this literature from a focus on increasing contact within specific institutional contexts (e.g., workplaces, schools, neighborhoods) to new interventions based on enhancing real or imagined interpersonal contact. Recent studies use random assignment to groups in sports teams (Mousa 2020; Lowe 2021), classrooms (Scacco and Warren 2018; Rao 2019), student housing (Corno et al. 2019), military service (Bagues and Roth 2020; Dahl et al. 2020; Carrell et al. 2019), and other settings to provide direct evidence on the interpersonal contact effect. The impact of contact can be mediated by affective pathways (e.g., increasing empathy and decreasing anxiety as stressed by Pettigrew and Tropps’ first meta-analyses) or through cognitive and cultural mechanisms (e.g., enhancing knowledge regarding the outgroup; Boisjoly et al. (2006); Paluck et al. (2021); Bursztyn et al. (2021)). Research on gender roles also highlights the effect of counter-stereotypical contact in reducing the prevalence of stereotypes (Breda et al. 2020). Confronted with the wide heterogeneity of findings across research designs, social psychologists have implemented the interventions that are thought to be the most effective in real-life situations. Forscher et al.’s (2017) habit-breaking package combines five components: (1) stereotype replacement (i.e., identify a stereotype and consciously replace it with accurate information), (2) individuating (i.e., gather
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specific information about the individual in a way to prevent group stereotype from affecting decisions), (3) counter-stereotypical imaging (i.e., positively imagining an effective minority candidate fulfilling a position before the process of evaluation), (4) perspective taking, and (5) increasing opportunity for contact. Forscher and colleagues show that the habit-breaking package tends to change explicit and implicit attitudes. These effects seem to be more durable than those measured for single interventions (they find significant effects up to 2 months after the intervention). The durability of the effect mainly pertains to measures such as awareness, concern, and effort about discrimination, while effects on implicit biases seem to fade rapidly (Lai et al. 2016). An important limitation of the literature on prejudice reduction lies in the lack of clarity on the ways in which it connects back to research on discrimination. Most studies focus on the effects of interventions on attitudes – whether explicit, implicit, or both – as proxies for prejudice. The extent to which reducing prejudice alleviates, in its turn, discrimination is rarely assessed. The rare studies that tackle both attitudes and behavior tend to challenge the relevance of reducing prejudice as an avenue for reducing discrimination. For example, Chang et al.’s (2019) experiment on diversity training in the US firms shows a positive effect on attitudes towards women but not on behavioral outcomes (such as promotion some weeks later). Some studies show that significant effects on behavior are only measurable for the least prejudiced agents, at least regarding explicit attitudes (Alesina et al. 2018). Finally, research on attitudes consistency suggests that changes in behavior may provoke an ex-post change in attitudes because people try to shape their attitudes so that they become consistent with their behavior (Olson and Stone 2005).
Reducing the Scope of Prejudice in Decision-Making Instead of addressing prejudice in individuals, another family of interventions aims at implementing rules and procedures that reduce the role prejudice plays in decision-making. Note that this type of interventions can tackle taste-based discrimination as well as statistical discrimination. The literature in organizational sociology, management, and industrial relations has been largely informed by the experience of Equal Opportunity Policies in the USA since the 1960s (Small and Pager 2020). Dobbin et al. (2015) compare many interventions in corporations and find that those designed to control managers’ bias (e.g., mandatory diversity training, job tests, grievance systems) lead to resistance and possible backlash. This research draws on job autonomy (i.e., freedom, independence, and discretion to make decisions) and self-determination theory (i.e., the crucial role of autonomous motivation in learning and working) to explain the mixed and sometime adverse effects of initiatives that limit managerial discretion/power and consequently produce defensive reactions (Dobbin and Kalev 2016; Howell et al. 2017). Some evidence suggests that even the provision of additional information may backfire in a context where prejudice is high. In a study on the credit market, an intervention informing loan officers that have been formerly shown to discriminate against women that the
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latter have higher repayment rates led the officers to exhibit even more intense gender-based discrimination (Montoya et al. 2020). Following these findings on the mixed effects of individual-level interventions, the organizational literature tends to advocate impersonal procedures such as increasing transparency and accountability as more effective methods for fighting against discrimination (Kalev et al. 2006). “Blinding strategies” are another type of policies to reduce the scope of prejudice. Following the seminal study on blind auditions in orchestras (Goldin and Rouse 2000), different interventions have been evaluated, mostly with regards to the labor market, that use procedures designed to remove many of the signals that might trigger taste-based discrimination. Hiding applicants’ names is an example of a powerful blinding strategy, since it has the potential to entirely rule out gender-, racial-, and social class-based prejudice that can be activated by this single piece of information. Qualitative research on coping with discrimination shows that some minority candidates use this kind of strategy when concealing or downplaying racial cues in their application forms (Kang et al. 2016; Safi 2017). Experiments drawing on anonymous resumes have been conducted in several countries, and they provide mixed findings. A French study suggests that anonymous resumes benefit women but can harm ethnic minorities by preventing existing affirmative-action policies from working (Behaghel et al. 2015). A Swedish study also finds that the effects of anonymous resumes tend to be more favorable for women than for ethnic minorities (Åslund and Skans 2012). At odds with the previous two studies, Krause et al. (2012) combine several trials of anonymous resumes in Germany and show that minority candidates may benefit from the blinding process, while women may suffer from it. The effects of blinding strategies seem to be heterogeneous and dependent on context (e.g., the magnitude of discrimination in the hiring process, the existence of affirmative-action or equal-opportunity policies and practices). Beyond the hiring process, blinded versus non-blinded examinations of students display heterogeneous findings that stress the importance of the initial gender composition in the educational field (Breda and Ly 2015). Finally, the evaluation of Ban-the-Box policies in the USA, which are based on blinding the criminal record to prevent employers from asking about workers’ criminal histories, shows detrimental effects on AfroAmerican job applicants. Using a correspondence study, Agan and Starr (2017) document that racial gaps in callback rates increase when Ban-the-Box policies are enacted, largely due to statistical discrimination mechanisms that associate Blackness to criminality. These mixed findings put into perspective blinding strategies as stand-alone policies and suggest the need to better integrate them into a broader strategic framework. A third strategy is to get rid of human evaluators altogether and rely on algorithm-based decisions. Hoffman et al. (2017) study the introduction of an automatic testing technology in several firms. They observe that some managers deviate from the ranking recommended by the test. Contrary to the intuition according to which human managers use discretion to incorporate superior information, they find that these deviations lead on average to hires of lower quality. This suggests that the observed managers could be due to prejudice, and that limiting the scope for manager-driven deviations could both reduce discrimination and increase firm productivity.
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Decisions based on currently used algorithms, which rely on existing data to formulate predictions, can still result in sizable bias. This “algorithm discrimination,” which is non-prejudiced by definition, may be easier to both detect and fix, as compared to taste-based discrimination. Obermeyer et al. (2019) document massive discrimination against minority patients that is induced by a health-prediction algorithm that relies on health expenditure – a highly racially biased variable – as a proxy for the general state of one’s health. Their research justifies rethinking antidiscrimination policies within organizations so as to incorporate well-established insights from the organizational literature (e.g., via regulations and routine audits, increasing transparency in practices, enhancing accountability, etc.) for the purpose of improving algorithm-based decision-making. Research at the intersection of legal studies, organizational sociology, computational studies, and economics provides a promising avenue for building objective and transparent decision-making procedures for alleviating prejudice-based discrimination within organizations whenever it is possible to circumvent the need for human intervention (Kleinberg et al. 2020). Rose (2021) shows how seemingly race-neutral policies regarding technical rules (e.g., that forbid drugs and alcohol, require the payment of fees and fines, limit travel, etc.) for convicted offenders who serve their sentences under “community supervision” at home may in fact be a source of further racial disparities in the US justice system. More specifically, evidence from a reform enacted in the state of North Carolina indicates that harshly punishing offenders for the violation of technical rules does very little to promote compliance or close the Black-White gap in re-offending. While it is true that rule violations are correlated with the overall likelihood of re-offending, the predictive power of these violations is far weaker among Black probationers. Moreover, since Black probationers are more likely to violate these rules for a number of societal reasons, this reform disproportionately harms Black offenders and does nothing to close the racial gap in rearrests.
Towards Less Prejudiced Societies? While interventions such as raising awareness and consciousness, increasing contact, and implementing impersonal procedures clearly speak at the societal level, research is still lacking in clear theoretical and methodological frameworks that enable us to derive hypotheses on the possible effects of these interventions on societies as a whole and to test them empirically. As most meta-analyses document a wide variability in the effects of interventions, the conception of large-scale policies is structurally confronted with intense trade-offs. Such trade-offs are particularly salient when one considers the extrapolation of contact theory to the meso or macro level. Some studies suggest positive and lasting effects of increasing contact on intergroup relations as measured by level of intermarriage (Merlino et al. 2019) (i.e., through quasi-random variation in the share of black students across cohorts within the US schools). Others show that increasing contact may backfire when it triggers competition, as in the case of demographic change in relation to immigration in neighborhoods (Enos 2014). Recent evidence
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also suggests that there is a trade-off between short- and long-term effects of diversity and contact (Ramos et al. 2019). Public policy can specifically focus on building the sociopolitical environment that will trigger the positive rather than the negative effects of such interventions. Allport’s seminal work advocates for the necessity of securing equal status and collaborative framing, while also promoting for intergroup contact (Allport 1954; Pettigrew and Tropp 2008). For example, in contrast with theories regarding the corrosive effect of ethnic fragmentation on trust and social cohesion, high levels of ethnic diversity in contexts of intense social contacts can trigger nation-state building when it is a collaborative endeavor (Bazzi et al. 2019; Bagues and Roth 2020). The literature in cultural sociology can help inform public policy’s aim to create an “atmosphere” that enhances positive effect from reducing prejudice. Building on qualitative fieldwork conducted in different national and local contexts, this literature suggests that efficient antidiscrimination policies need to tackle the “narratives” and “cultural repertoires” that feed prejudice. Some studies advocate large-scale public policies that engage in destigmatization to tackle the specific contents of stereotypes surrounding stigmatized groups (Clair et al. 2016) such as the belief that “immigrants equal the welfare state” or “black people equal violent” or “boys are better in maths.” Changing narratives, removing blame, and promoting equal interactions could be the most effective long-term strategy. This literature builds on examples like the progressive destigmatization of AIDS, cultural shifts towards gender equality, and stigma surrounding obesity. In addition to cultural and meaning-making dynamics, the effectiveness of interventions that build on the role of mass media relies on their capacity to enhance empathy and intergroup contact beyond interpersonal interactions (see, for example, the effect of the Paralympic Games as in Bartsch et al. 2018). Exposure to minoritygroup, nonfictional public figures with which one can identify oneself may also be conducive to generating empathy at the societal level (Alrababa’h et al. 2021). Emerging research also highlights the importance of “role model.” Finally, the literature on the stereotype content model suggests that emotions towards other groups are truly embedded in today’s socioeconomic inequalities and the social hierarchy that derives from them. Equalizing social position through policies that promote social mobility, equal opportunity, and diversity may lead to the reduction of antipathy towards the most stigmatized groups in the long run.
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Stratification Economics A Primer and an Explanation on Opposition to Affirmative Action Lucas Hubbard and William A. Darity Jr.
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How Stratification Economics Explains Attitudes Toward Affirmative Action . . . . . . . . . . . . . . . . . A Primer on Stratification Economics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Stratification Economics Says About Affirmative Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . How Stratification Economics Explains Affinity for White Affirmative Action . . . . . . . . . . . . How Stratification Economics Explains White Negativism Toward Black Affirmative Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Abstract
In this chapter, stratification economics provides a vehicle for analyzing attitudes toward affirmative action. The chapter begins with a historical example of an American politician who relied on racial tension and the fears of the dominant group to garner support to promote his campaign. This offers an introduction to the framework and guiding elements of stratification economics – most notably, its emphasis on relative, identity-based, group status. This is followed by a discussion of elemental principles of stratification economics and a summary of a range of its prior applications to explain intergroup disparities, considering outcomes in labor markets, social markets, public good allocations, and more. L. Hubbard (*) Samuel DuBois Cook Center on Social Equity, Duke University, Durham, NC, USA e-mail: [email protected] W. A. Darity Jr. Samuel DuBois Cook Center on Social Equity, Duke University, Durham, NC, USA Samuel DuBois Cook Professor of Public Policy, African and African American Studies, and Economics, Duke University, Durham, NC, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_3
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Subsequent sections explore the consistency of this approach with the varying attitudes expressed toward various group-targeted policies in America. In particular, historical examples are gathered into two categories for analysis and exploration: policies that become desirable when they no longer risk benefitting the lower-status (subaltern) group’s status and policies that are undesirable because they are expected to improve the subaltern group’s relative position. Finally, the chapter concludes with additional thoughts on the potential for targeted and universal proposals to achieve popular support in order to advance equity. Keywords
Stratification economics · Affirmative action · Racial equity · Discrimination · Group identity · Social exclusion
Introduction With 10 days left before election day in 1990, Jesse Helms trailed in the polls. His opponent, the black democrat Harvey Gantt, was expected to benefit from the fact that a third of North Carolinian voters had grown weary of Helms, who was winding down his third term representing the state as a Republican in the US Senate. Helms went on the attack with “ads subtly priming consciousness of Gantt’s blackness” (Jamieson 1992, p. 94). His initial advertisements hit Gantt’s record on abortion, his affiliation with gay rights leaders, and his financial dealings in becoming a millionaire. These ads made various alterations to Gantt’s face and voice, shifting to black and white to heighten his hue and slowing his diction so that viewers would later identify him as “stupid” and “definitely black” (Jamieson 1992, p. 96). However, it was Helms’ next ad that proved to be a memorable haymaker. A voice over says, “You needed that job, and you were the best qualified. But they had to give it to a minority because of a racial quota.” The narrator is never shown; neither is the face of the “you” in desperate need of employment. What is shown are a pair of white hands, first holding a paper, presumed to be a resume, and then crumpling it as they realize they are not wanted. As the narrator paints the candidates’ attitudes regarding a proposed racial quota law, the hands have disappeared, but not without a closing gesture on the split screen: Note too that in the final frames Gantt is ‘for racial’ and Helms is ‘against quotas.’ Here the shot of Gantt is closer—more menacing—than that of Helms. Before this apposition, the hands have exerted control. The fist once clenched in anger is now clenched in action: crushing Teddy Kennedy’s head and about to encircle Gantt’s! (Jamieson 1992, p. 98)
Helms would go on to win the election with 52.5 percent support, with Gantt garnering a mere 38 percent of the white vote, and the creator of the 30-second spot, Alex Castellanos, would become a mainstay of Republican presidential campaigns
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in the 1990s and early 2000s, eventually earning a reputation as “one of the keenest, most cutthroat strategists in the business” (Veis and Naddaf 2007). With what came to be known simply as the “hands” ad, Castellanos had discovered something powerful about the political dynamite that could be used against preferential treatment programs like affirmative action for blacks. Not only are these programs often without wide popular support, but for those politicians reliant on the support of a dominant social group, such issues also can be used as a wedge issue to rally one’s base. A half-century of American politics has proven, beyond a doubt, that nothing motivates white voters like the fear of black folks catching up or surpassing them. Perhaps it is not surprising that white voters in America would bristle at policies that favor other racial or ethnic groups. After all this is consistent with principles of rational self-interest in the context of group identification and position. Indeed, the overwhelming white American vote for former reality TV star, Donald Trump, in the 2016 and 2020 presidential elections, ensued after his preying on anxieties related to a perceived growing racial threat and his mobilization of outright racist rhetoric. When a 2017 white supremacist march took place in Charlottesville, Virginia, leading to mayhem and death, Trump refused to condemn the racist protestors. Instead, he placed them on par with those who were resisting their presence and purpose, saying there were “very fine people on both sides,” while repeatedly flashing what appeared to be the “white power” sign. This perceived threat, of course, was nothing new; it had previously become an essential component of the “Southern strategy” – an effort to shift the white southern vote away from the Democratic party – in the wake of the civil rights movement. In 1981, political consultant Lee Atwater, who successfully led the charge by coding economic policies that appeared to be unfavorable to the working class as policies to preserve white dominance, infamously summarized the approach: By 1968 you can’t say ‘n———’—that hurts you, backfires. So you say stuff like, uh, forced busing, states’ rights, and all that stuff, and you’re getting so abstract. Now, you’re talking about cutting taxes, and all these things you’re talking about are totally economic things and a byproduct of them is, blacks get hurt worse than whites.. . . (Perlstein 2018)
Why is this strategy successful? More broadly, why does the performance and status of other groups matter so much, and how does this lead to individuals – in this case, working class whites – voting against their purported economic self-interest? Why do programs that favor certain groups only become objectionable when they do not benefit the social group in power? Stratification economics offers answers to these questions. This chapter introduces the basic tenets of stratification economics, the subspecialty of economics that blends economics, sociology, social psychology, and history to understand the feedback loops between group identity, political movements, and individual action. The framework of stratification economics enables an exploration of several case studies related to affirmative action in the United States of America. Specifically, stratification economics shows how whites have benefited
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from the existence of preferential programs in the past and why they push back against recent initiatives that bolster other groups, typically blacks. The chapter concludes with some thoughts on the potential routes toward garnering popular support for affirmative action and related policies.
How Stratification Economics Explains Attitudes Toward Affirmative Action A Primer on Stratification Economics Stratification economics fuses economic theory with sociology and social psychology to explain how group identities affect individuals’ behavior, especially when individuals consider protecting both their individual status position and the status position of the group with whom they identify. Relying on the notion that human beings care more about relative position than their absolute position, stratification economics posits, with respect to group identification, “the key relevant comparison group will be an outside racial, ethnic, gender, caste, or religious group” (Darity et al. 2017). An obvious application of stratification economics accounts for racial and ethnic disparities, which “long have been treated as a peripheral object in economics” (Darity et al. 2015). Stratification economics is sufficiently general to apply to all salient instances of group identification and inequality, whether it be on the basis of gender, caste, religious affiliation, as well as race and ethnicity, or at the intersection of combinations of these categories. Any analysis of intergroup inequalities without understanding the effects of these classifications is sorely incomplete. Consider three factors to highlight this: the racialized distribution of wealth, the significance of wealth as the key economic indicator of well-being and opportunity, and wealth’s capacity to be transferred to future generations, both directly or indirectly, to entrench advantage or disadvantage (Tippett et al. 2014). As such, acts of economic discrimination, past or present, have outsized and long-standing effects on who possesses and who is dispossessed. The principles of stratification economics, drawing heavily from Darity et al. (2015), can be summarized as follows: • Individuals behave rationally and in a self-interested fashion. • Individuals form an identity through affiliation and association with a particular social group (or groups). • Social beliefs about their group can affect their sense of affinity as well as their individual productivity and performance. Such effects include stereotype threat, stereotype lift, and stereotype boost (▶ Chap. 37, “Stereotype Threat Experiences Across Social Groups”). • Collectively, members of a particular social group (or groups) seek to maintain and extend their group’s relative performance in hierarchy, in accordance with
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Herbert Blumer’s (1958) important work on the sources of prejudice. The greater the perceived difference in status between one’s own group and another, the greater the emphasis an individual will place on their personal position in comparison with others in their social group. The narrower (or the narrowing) of the perceived difference in status between one’s own group and “the other,” the greater the emphasis an individual will place on their social group’s position in comparison with the other; colloquially, this is a backlash effect against perceived progress on the part of the subordinate group. What follows from these tenets is perhaps the most novel element of stratification economics – and its most striking divergence with standard economic theory. Typically, economics treats discrimination as an inefficient irrationality that can be cured by market competition. Gary Becker’s influential taste-based models of employer and employee discrimination lead to the conclusion “that nonproductivity-based differentials in wages must evaporate over time; both approaches deny the persistence of discrimination” (Darity et al. 2017, p. 48). In contrast, stratification economics says, because of the desire to maintain one’s position – and one’s group’s position – in a hierarchy, discrimination is not only persistent but also “rational and functional” (ibid. p. 50). Furthermore, market competition is maneuverable to sustain discriminatory outcomes. What does stratification economics reject? It rejects the conception that economic and related disparities between groups are driven by deficiencies or dysfunctional behavior on the part of the subordinate, or subaltern, group, rather than discrimination, coercion, and inherited advantages. Applying the lens of stratification economics, a number of social phenomena – which run counter to traditional economic theory – suddenly become explicable. Stratification economics provides a concise explanation for the wage gaps between black and white employees (the presence of discrimination against darker-skinned employees, or colorism, in labor markets), as well as an explanation for the employment gap itself (Goldsmith et al. 2006; Pager 2003; Bertrand and Mullainathan 2004). It explains why families invest more in the futures of lighter complexioned offspring – employers, like many agents in racialized anti-black societies, offer those individuals with greater proximity to whiteness outsized rewards for similar accomplishments – and why darker-complexioned black women have lower odds of marriage or remarriage than lighter-complexioned black women (Hordge-Freeman 2015; Rangel 2015; Hamilton et al. 2009). And in the public sphere, it has an answer for why natural disasters like Hurricane Katrina disproportionately harm black individuals and families: a dearth of public services and disaster relief for these groups – and excess services and relief for white families (Price 2008). Moreover, it is consistent with studies conducted outside the oeuvre of the framework, such as research showing why America redistributes public goods less than Europe – the United States is more racially heterogeneous, and dominant groups are less willing to provide “handouts” to subordinate groups (Alesina et al. 2001). Stratification economics also explains more subtle effects. One recent example is the recent trend of whites experiencing increases in their mortality rates – at a time when no other groups in the United States are witnessing declines in their mortality
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rates (at least until the onset of the COVID-19 pandemic). The reason? Their (false) belief that the white relative position is declining – a belief strongly connected, again, to the Trump vote. The belief yields a psycho-emotional threat that has contributed to a decline in mental health resulting in “deaths of despair” from alcohol consumption, opioid addiction, and suicide (Siddiqi et al. 2019). The framework applies neatly and with great effect to political phenomena, some of which are well-known. The “Southern strategy,” mentioned earlier, is especially relevant. The strategy follows the stratification economics framework precisely. A white voter likely will not benefit individually from a given political action, the “abstract. . .totally economic things” Atwater describes. But with its passage, the white would maintain – or even lengthen – the economic lead of his/her “group” over the competing group. And so, the individual supports politicians advancing such a policy or action. Crucially, by standard economic theory, the person may appear to be acting against his/her self-interest by voting for something that does not benefit them individually. But in promoting the relative economic status of the group to which the individual belongs, this act squarely coheres with the core theory of stratification economics. Moreover, there are benefits associated with being part of a dominant social group, regardless whether one is closer to the top or to the bottom within the group. For example, all white Americans have an advantaged position in encounters with the police and the criminal justice system vis-à-vis all black Americans. Given its potential effects on the relative economic status of dominant and subaltern groups in America, affirmative action (and similar policies that target benefits toward particular subsets of the population) is ideal for analysis through the lens of stratification economics. We undertake a preliminary look at how the principles of stratification economics can illuminate the analysis of affirmative action in the United States.
What Stratification Economics Says About Affirmative Action Coined by President John F. Kennedy in Executive Order #10925 and elucidated in subsequent orders from President Lyndon B. Johnson, “affirmative action” promised to not just “open the gates of opportunity” but also provide all citizens with “the ability to walk through those gates,” as Johnson put it in his 1965 commencement address to Howard University (Katznelson 2006). But in the years to follow, when this turn of phrase was turned into policy, it would immediately bear the brunt of staunch resistance and fierce debate. Viewed under the conditions of the larger civil rights movement, in which any progress had to withstand myriad volleys of revanchist attack, this opposition should not be surprising. More than a half-century later, its role applied in higher education – debilitated from decades of decisions that have left affirmative action as a policy justified solely as a facet of diversity – likely will soon be revisited yet again when Students for Fair Admissions v. Harvard reaches its expected destination before the Supreme Court.
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Some have proposed that attitudes toward such policies emerge from a complicated confluence of economic self-interest, social demographics, racial affect, and beliefs toward the merits of stratification (Kluegel and Smith 1983). Others presuppose that resistance to affirmative action is more related to the policies themselves. While racial animus is present in the white population, it is not responsible for all of the opposition to affirmative action (Kuklinski et al. 1997). In their analysis, they find that a majority of whites (in both the north and south) support “extra effort” made by the government to benefit black Americans, but more than three-fourths oppose “preferential treatment” for blacks. Yet the arguments waged against affirmative action are well worn territory (Darity 2013). Complaints include its violating the principles of meritocracy, its negative effect on productivity, its leading students to be mismatched in higher education and/or stigmatized, its ineffectiveness in assisting all members of the target population, and the implementation of affirmative action along racial rather than class lines. These complaints all wilt under the heat lamp of investigation. Research shows that “merit” is not an objective scale but a tipped measure, designed to favor male candidates over female peers and altered continuously to exclude black students from public institutions (Uhlmann and Cohen 2005; Cross and Slater 1996). Section III, discussing affinity for white affirmative action, details how the “meritorious” students are in fact the beneficiaries of centuries of favorable conditions and policies. Moreover, claims of decreased productivity following affirmative action initiatives have been debunked at a macrolevel (Conrad 1995). Microlevel analyses have long shown the same, as have anecdotes like that relayed in W.E.B. Du Bois’ The Philadelphia Negro, of a “crank” manager at the Midvale Steel Works breaking a pattern of exclusionary hiring to let black and white mechanics toil alongside one another in his factory: . . .he had a theory that Negroes and whites could work together as mechanics without friction or trouble. In spite of some protest he put his theory into practice, and today any one can see Negro mechanics in the same gang with white mechanics without disturbance. (Du Bois 1967, p. 129)
The “crank” was Frederick Winslow Taylor, who later became widely known as the “father of scientific management.” In short, top business minds had been aware since the 1880s that at least one form of this complaint was unfounded. Some of the latter criticisms of affirmative action detailed by Darity (2013) are worthy of particular attention, in that they possess certain kernels of truth but belie their intentions. True, an affirmative action policy may not help all people in the subaltern group; indeed, it is crafted such that it can “produce and/or enhance ‘the creamy layer’” of the subaltern middle class (ibid, p.221). True, even if affirmative action is implemented, class-based inequality will remain. Do such grumbles justify scrapping the entire policy? Why would the solution not entail supplementing affirmative action with anti-poverty programs, or (additional) affirmative action on the basis of class?
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Stratification economics provides a harsh but consistent answer to these claims, following the principles outlined in the prior section. The desire to curb or alter affirmative action policies that favor black Americans stems from a wish on the part of the dominant group (white Americans) to protect their relative economic status. If affirmative action was to achieve its goals, it would close the gap between white Americans and their subaltern peer group. Scrapping it would alleviate this threat, and shifting its focus to ameliorating class-based disparities would make it much less beneficial to the subaltern group (Darity et al. 2011). Furthermore, it is worth mentioning that the findings of Kuklinski et al. (1997) also align with stratification economics. It follows that a majority of white Americans will say that they support “extra effort” to aid black Americans if they feel that this extra effort does not put their relative status at risk. Likewise, Kluegel and Smith (1983) highlight public polling that shows the white population’s desire for shallow benefits (“help”) to be conveyed to blacks and for substantive benefits to be denied. In stratification economics parlance, anything that genuinely will close the gap between the dominant and subaltern groups will be opposed; insubstantial attempts, however, are unobjectionable and could even be valuable to the dominant group, if the public and political conclusion is that even with the provided help the subaltern group proves incorrigible. Moreover, stratification economics suggests that if the dominant group can improve its status relative to the subaltern group – if it can become more dominant – then such a proposal will receive approval. Indeed, a number of recent investigations show that opposition to targeted, group-based policies arises solely when the subaltern group benefits; then, and only then, are such policies marked with the imprimatur of “unfair” to justify their resentment. To see this in action, one needs only to consider the name of the plaintiff in the higher education court case: “Students for Fair Admissions,” the implication being that the college admission process – replete with preferential admission for offspring and relatives of alumni and standardized tests that most accurately reflect an applicant’s familial wealth – only becomes “unfair” once race is considered. The following two sections spotlight instances throughout American history that reflect this pattern. The first section consists of a series of cases in which whites have benefitted disproportionately from nominally universal policies; the second features situations in which policies that favor blacks have been instituted – and then quickly disbanded or preemptively headed off. Both cases are consistent with stratification economic theory and, contrary to the “grumbles” stated above, point to the greatest driving force of attitudes toward such policies to be racial or outgroup resentment.
How Stratification Economics Explains Affinity for White Affirmative Action Given what is known about its founding, expansion, and maintenance, it makes sense that America would strategically promote the rights of certain groups over others. For the duration of its history, the country has been dominated by a minority
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elite – originally the planter aristocracy who became America’s founding fathers – who granted rights to certain groups and not others to preempt unification and revolutionary action from the underclass. During the century and a half leading up to the American Revolution, and the subsequent near-century prior to the Civil War, the settlers and white (male) working class benefitted. The colonial government and the subsequent US federal and state governments propped them up with allotments of land, stolen from the indigenous people, and with status, gained from the stigmatization, abuse, and further dehumanization of the black population, both free and, especially, enslaved. These policies were not labelled with a special term like affirmative action because there was nothing special about them: for a very long time, it was taken as given that policies would affirm the predominantly white power structure in the country. Stokes et al. (2003) highlight the Naturalization Act of 1790 as an initial stroke of favoritism that curried no resistance. Whereas the constitution famously sidestepped the racial hierarchy in play in America, the Naturalization Act directly addressed race, allowing “any alien, being a free white person” a path to citizenship in the country. This declaration, the authors note, “was adopted without a single dissent by the first sitting U.S. Congress” (Stokes et al. 2003). In the second half of the nineteenth century, homesteading, the practice of setting aside cheap or free land for settlement, would continue to consolidate white dominance. With its synthesis of “the dignity and opportunities of free labor” and “social mobility, enterprise, and ‘progress,’” the concept became popular in the run-up to and aftermath of the Civil War (Foner 1981). The passage of the Homestead Act of 1862, along with related subsequent acts – the 1866 Southern Homestead Act, 1873 Timber Culture Act, and 1877 Desert Land Act – launched settlement of western and southern lands. Starting with the 1862 Act, 160 acres of untamed land were available at $1.25 an acre to those willing to cultivate it for 5 years. This “free land,” the sort of tangible, direct help that white Americans would bristle at in the late twentieth century and beyond, was largely viewed not as a giveaway or a handout but as an entitlement. New York Tribune editor Horace Greeley exhorted such ideas in 1842, identifying the need for the lower classes to enjoy “the Right to Labor and to receive and enjoy the honest reward of such labor. . .” (Robbins 1933). In 1846, George Henry Evans of the National Reform Association petitioned congress with the slogan “Vote Yourself a Farm”; the reformers would go on to advance the notion of “the equal right to land,” such that none would be left “dependent on another for the right to work for a living” (Fure-Slocum 1995). Some newspapers did engage in handwringing over these allocations: The Goshen Democrat feared “whole contents of European poorhouses emptied down upon our fertile West,” and the Boston Daily Mail claimed that such a recipient of free land would be a “pauper entail” (Robbins 1933). In the 1800s, homesteading as a policy had faced much southern resistance, with the fear that this expansion of small, independent farmers and their subsequent political weight would lead to greater nationwide opposition of slavery. (Indeed,
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homesteading’s brief unfeasibility was due to the fear that its implementation would eventually lessen the gaps between blacks and whites.) Following the secession of the Confederate states, however, the Homestead Act of 1862 passed resoundingly in both chambers (107 to 16 in the house, and 33 to 7 in the senate). Speaking before congress in 1862, Speaker of the House of Representatives Galusha Grow said that “there has, perhaps, never been a measure before Congress so emphatically approved by a majority of the American people.” And its effects could not be overstated, allowing the freemen who were recipients of this land to, in Grow’s words, “develop the elements of a higher and better civilization” (Anderson 2011, p. 119). One and a half million families received this land asset; Williams (2000) estimates a quarter of the US adult population has descended from this cohort. The families who benefitted from these acts, however, were not equally distributed, given the capital constraints to claim the acreage, and to the larger outlays required to build a farm once occupied (Zinn 1980; Deverell 1988). While 300 acres were distributed to about 400,000 interests, writes Greg Grandin in The End of the Myth, “this was less than half of the acreage private interests acquired through purchase. Within a decade of the act’s passage, large capitalists and regulators had laid claim to the most fertile, best irrigated, and, via railroad lines, best connected portion of public ‘free land’” (Grandin 2019, p. 110). Nominally, homesteading was open to everyone. But the Black Codes of 1865 and other methods of social control – legal or otherwise – wedged blacks geographically, preventing them from settling these western lands and protecting the dominance of white Americans. In Boom Town, his book on the history of Oklahoma City, Sam Anderson details the Oklahoma City Land Run of 1889, in which frontiersmen raced into town at high noon to make claims to the land. The description underscores the attendant pressures black Americans faced in this era of the Jim Crow South – and the west that Jim Crow Southerners would go on to settle: The Land Run was for white men. Not officially, in a legal sense, but in practice. The number of African Americans who made the Run is, like many things about that day, hard to pin down, but at least one historian guesses it was fewer than fifty. Others estimate two hundred, or somewhere near one thousand. In any case, it was a tiny fraction of the human tide— roughly 100,000 strong—that rushed in that day over the prairie. Given the opportunity the Land Run represented, especially for the poor and marginalized, that absence screams volumes. Oklahoma’s settlers were a heavily armed white mob. Black Americans would have known to proceed with caution. (Anderson 2018, p. 163)
New frontiers and their capacity to generate wealth were denied most blacks (section IV, on white negativism towards black affirmative action, details this further). Moreover, Jim Crow laws dictated their capacity to move and live where they already were. In the ten largest American cities in 1880, the typical neighborhood in which an African American lived was just 15 percent black; by 1940, their local neighborhood was 75 percent black (Rothstein 2018). This restriction of movement – the denial of what Roscoe Dunjee, publisher of The Black Dispatch in Oklahoma City, called “the right of natural expansion” – is crucial to generating patterns of segregation (Anderson 2018). Once racial
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segregation is established, many seemingly innocuous, race-neutral policies can in fact systemically advantage certain groups at the expense of others; in short, segregation turns these policies into affirmative action by a different name. James Baldwin knew this, skewering the pattern of investment that spread across America in the 1950s with his assertion that “urban renewal means negro removal.” Government-sanctioned actions throughout the twentieth century – invariably positively branded as “renewals” or “revitalizations” – would demolish black neighborhoods and public housing to make way for white investment in the downtown; the effects of such policies, then, were not far from those of destructive race riots, like the 1921 Greenwood riots in Tulsa (Anderson 2018). They achieved some amalgamation of promoting the dominant group’s economic status and weakening the subaltern group’s; regardless of the mixture, the relative status of the dominant group improved. The New Deal heightened the deleterious aspects of housing segregation. In 1933, the Home Owners’ Loan Corporation was created to rescue homeowners near default during the Great Depression, and the loan risk assessment practice of redlining – so termed for the color that would demarcate African American neighborhoods on these maps – was introduced, helping codify the seemingly inherent risks of lending to blacks. The next year, the Federal Housing Administration, or FHA, was formed to help middle-class Americans access the threshold of homeownership. Approval for applicants was similarly racially restricted and consistent: “no guarantees for mortgages to African Americans, or to whites who might lease to African Americans, regardless of the applicants’ creditworthiness” (Rothstein 2018, p. 67). From 1945 to 1959, African Americans received less than 2 percent of all federally insured home loans, a tremendous setback given the outsized role homeownership plays in wealth accrual (Hanchett 2000; Shapiro 2006). Overtly stating its whites-only preference in the appraisal process, the FHA did not outwardly purport to be race neutral. But an additional significant post-World War II policy that – despite its promise of universality – disproportionately benefitted non-black Americans was the Servicemen’s Readjustment Act of 1944, now commonly known as the GI Bill. It comprised “the most wide-ranging set of social benefits ever offered by the federal government in a single, comprehensive initiative,” with spending summing to more than $95 billion between 1944 and 1971 (Katznelson 2006, p. 113). The bill provided avenues to attend college, own homes, and engage in other wealth-building ventures and, in doing so, fostered a new middle class in the country. Both black and white veterans leapt at the chance to participate in GI Bill programs. However, John Rankin, the racist Mississippi congressional representative who chaired the Committee on World War Legislation that workshopped the bill, ensured that the federal dollars would be kept under local and state control through branches of the Veterans Administration. As a consequence, in the southern states where Jim Crow racism ran rampant, the desired funds “could be directed to the country’s poorest region while keeping its system of racial power intact” (Katznelson 2006, p. 125).
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With local control in place – white-staffed state departments and local VA centers who could discourage blacks from taking advantage of the GI Bill’s bounty – the barriers to wealth-building for blacks were born anew, and a universal program quickly became targeted to the dominant group. In Mississippi, whites filed six times as many applications for unemployment payments as black (Katznelson 2006). With most avenues of higher education – the exception being historically black colleges and universities – still closed to blacks, they were forced to turn to crowded, inferior options (“no black college had a doctoral program or a certified engineering program”) and, disproportionately, to eschew collegiate opportunities (Katznelson 2006. p. 133). Of the veterans born between 1923 and 1928, just 12 percent of blacks enrolled in college programs, while 28 percent of their white counterparts did (Katznelson 2006). Job training provided an opportunity in name only: in the first 2 years of training programs in the south, black veterans counted for less than eight percent of the total enrollment, and, rather than earning the living wage the GI Bill allotted, they were occasionally charged a fee for being trained (Katznelson 2006; Frydl 2009). Blacks found themselves the victims of occupational sorting, landed in jobs far beneath their skill level: “Carpenters became janitors; truck drivers dishwashers; communications repair experts porters” (Katznelson 2006). In addition to the housing policies which created a (white) middle class, the New Deal revamped labor laws through the National Labor Relations Act (NLRA) and Fair Labor Standards Act (FLSA), which lifted wages, limited the length of work weeks, and guaranteed the right to unionize and collectively bargain. The Democratic Party, spearheading this legislation, carefully carved out exceptions to the legislation to exclude farmworkers and maids from these crucial benefits and protections: two groups that comprised “more than 60 percent of the black labor force in the 1930s and nearly 75 percent of those who were employed in the South” (Katznelson 2006, p. 22). A 1939 poll of opinions toward the Social Security Act that provided old-age benefits and insurance against unemployment – another New Deal output that (at least initially) excluded agricultural and domestic workers from its benefits – found that 89 percent of the public approved (Amenta and Parikh 1991; Leff 1983). In short, the political resistance from the Southern Democrats targeted the widesweeping nature of these policies; a universal policy was unwelcome because of who it would include. Florida Democrat James Mark Wilcox was unequivocal when discussing the FLSA before congress in 1937: We may rest assured, therefore, that when we turn over to a federal bureau or board the power to fix wages, it will prescribe the same wage for the Negro that it prescribes for the white man. Now, such a plan might work in some sections of the United States but those of us who know the true situation know that it just will not work in the South. You cannot put the Negro and the white man on the same basis and get away with it. (Wilcox 1937)
This is the corollary to the Southern strategy that Lee Atwater described in 1981. Instead of a policy in which blacks are hurt worse than whites, the New Deal time
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and again offered policies in which whites gained more than blacks. When these policies were not targeted so, they had less support. But it was under such guidelines that New Deal policies became implemented and highly regarded. They had aimed to build a middle class, and they did; they built a white middle class, locking black Americans out of crucial mechanisms to build wealth and thus fixing their place on a lower economic rung of society.
How Stratification Economics Explains White Negativism Toward Black Affirmative Action For brief windows in American history, the black population has had glimmers of favorable treatment. In 1862, for example, it looked like early waves of freedmen wouldn’t have to wait long or move far to start generating wealth through land ownership; they might receive South Carolina parcels directly, with the undertaking of the Port Royal Experiment in the midst of the Civil War, “predicated on the principle that newly freed men and women would have an opportunity to engage in homesteading on land vacated en masse by southern planters” (Darity and Mullen 2020, p. 130). However, the attempt fell apart under “the constant threat of brutality, rife with irregular pay for the black laborers or no pay at all” (ibid., p. 134). Then General William Tecumseh Sherman’s declaration, now colloquialized as “forty acres and a mule” and first made in Special Field Orders No. 15 before being formally provisioned in an 1865 Freedmen’s Bureau bill, authorized reallocating confiscated and abandoned lands in the Confederate states, a promise that would have allocated approximately 5.3 million acres of land in three states. But for innumerable reasons – not least of which was Andrew Johnson’s occupancy of the White House, his amnesty of former Confederates, and the subsequent return of their property – a key portion of the plan became “null and void, making it impossible for ex-slaves and loyal white refugees to rent up to forty acres with an option to purchase the land and then receive the title” (Darity and Mullen 2020, p. 178). This has been the pattern for black Americans; anytime it appears that a policy might narrow the gap between themselves and the dominant group, the resistance it engenders from the dominant group – writ large or simply from its most powerful actors – means it is inevitably scuttled, shortchanged, or set up for failure. A telling instance is the first (brief, quickly aborted) instance of preferential treatment for black Americans: the operations of the Bureau of Refugees, Freedmen and Abandoned Lands – better known as the Freedmen’s Bureau – following the Civil War. Supplying necessities for those displaced by the war; establishing schools, hospitals, and other institutions; assisting in the process of (re)settlement for those displaced during the war; and providing “a government guardianship for the relief and guidance of white and black labor from a feudal agrarianism to modern farming and industry,” it was a rather unique undertaking. Du Bois, in 1935, called it “the most extraordinary and far-reaching institution of social uplift that America has ever attempted” (p. 219).
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Notably, the bureau did not simply benefit blacks. In certain states in the Deep South, “the bureau extended twice—and in some cases four times—as much relief to whites as to blacks” (Nancy Isenberg, in Grandin 2019, p. 103). Nevertheless, President Andrew Johnson decried the bureau “as a giveaway,” an ostensible double-edged sword that “was both trapping African Americans in a new form of slavery and giving African Americans preferential jobs” (Grandin 2019, p. 106). The Southern Homestead Act of 1866, championed by Oliver Otis Howard, commissioner of the Freedmen’s Bureau, tried to provide blacks with the wealthbuilding mechanism their white peers had seized. It offered up the best public land remaining in Alabama, Arkansas, Florida, Louisiana, and Mississippi to “freedmen and loyal refugees,” although the most fertile and promising land had previously been claimed prior to the war (Lanza 1990; Williams 2000). In total, only 2.9 million acres, or six percent of the total land offered, was transferred before the program was disbanded after a decade. Williams (2000) provides a dispiriting analysis of the demographic data: Estimates from a sample of homestead claims in Mississippi reveal that about 23% of claimants under the Southern Homestead Act were judged to be Black. In that sample, 35% of Black claims were successful compared to 25% of white claims (Lanza 1990). Using these percentages, 5440 of the 27,800 final patents may have been awarded to Black homesteaders. Citing Magdol (1977), only 4000 Blacks even made homestead entries under the Act (p. 160). Either way, the reality is that few homesteads were granted to Black claimants. (Williams 2000)
With its mission and focus aiding predominantly black Americans, the Freedmen’s Bureau faced much resistance. In 1866, the second Freedmen’s Bureau bill, both expanding the bureau and making it permanent, was vetoed by Johnson. A modified version of the bill would eventually pass over yet another Johnson veto, but it was indicative of the staunch resistance the bureau faced throughout its work, as Du Bois writes in Black Reconstruction: Even if it had been a perfect and well-planned machine for its mission, the planters in the main were determined to try to coerce both black labor and white, without outside interference of any sort. They proposed to enact and enforce the black codes. They were going to replace legal slavery by customary serfdom and caste. And they were going to do all this because they could not conceive of civilization in the South with free Negro workers, or Negro soldiers or voters. . .Under these circumstances, the astonishing thing is that the Bureau was able to accomplish any definite and worth-while results. . . (Du Bois 1935, p. 223–224)
Despite “haphazard” financial support throughout the bureau’s short-lived existence – it wrapped up its operations in 1872 – the committee that reviewed the bureau’s performance in 1874 commended its efforts, while noting the resistance the program faced: “No thirteen millions of dollars were ever more wisely spent; yet, from the beginning, this scheme has encountered the bitterest opposition and most relenting hate” (Du Bois 1935, p. 229).
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Moving into the latter half of the twentieth century, the honing of polling practices enables an analysis with finer granularity of which policies and proposals continue to spark resistance from the dominant group. As stratification economics predicts, attitudes toward group-based preferential treatment became more hostile as the recipients of such benefits changed. Following the advent of Executive Order #10925 that established “affirmative action” – and, after a 54-day filibuster, the passage of the Civil Rights Act of 1964 – policies targeting or disproportionately benefitting black Americans have received varying levels of support. In certain instances, such support can be inconsistent, if not contradictory. One poll from 1992, for example, showed that 44 percent of respondents thought too much was being spent on “welfare” but only 13 percent thought too much was being spent on “assistance to the poor” (Zinn 1980, p. 579). As racial animus drives much of the animosity toward programs like “welfare,” use of that term – as well as the tinged phrases “quotas” and “reverse discrimination” – can trigger a more negative response in the dominant group (Gilens 1995; Feagin and Porter 1995). More generally, public support for policies benefitting blacks shifts in proportion to the policies’ magnitude and/or directness of help. A 2019 Pew survey showed three-quarters of Americans believed it is important for “companies and organizations to promote racial and ethnic diversity in their workplace”; the same exact survey showed nearly three-quarters saying race and ethnicity should not be taken into account when considering hiring and promotions (Horowitz 2019). Diversity is all well and good, until the necessary steps toward greater inclusion are explicit and the status and privileges of the dominant group become threatened in a job market that is seen as a zero-sum game.
Conclusion Unfortunately, stratification economics does not afford much optimism for effective policy solutions. On the face of it, universal policies that disproportionately benefit the subordinate group might hold the greatest political possibility. There are caveats. Universal policies – and especially universal opportunities – are rarely truly universal. They can be easily derailed by bad actors, in the winding journey from fruition to implementation, to become targeted policies hiding behind a veneer of universality, as was the case with the GI Bill. They can be victims of the cruel calculus of bigotry, as the victims of historical injustices are then deemed lending risks and redlined out of future funding. They can be submerged in the heavy sands of historical injustice, in which barriers are overlooked – the fees that black homesteaders would have to pay, the bigotry they would face as landowners – and the moment for racial change passes. But upon closer inspection, it appears that policies often escape unflagged when they work with and calcify existing patterns of discrimination. When such policies work against the grain of such patterns, however, they become objectionable; while white privilege and discrimination has been “normalized over time, and has shaped
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everyday interactions between blacks and whites from enslavement to the present day,” what some might call “reverse discrimination” is unacceptable (Stokes et al. 2003, p. 14). The period after the Civil Rights Act of 1964 shows us that discrimination falls when “active corrective policies” are implemented (Darity 2005). But since 1968 and the civil rights era, whites in America have, in fact, largely maintained their dominant economic status, slightly widening the racial wealth gap (Kuhn et al. 2020). Despite the initial promise that affirmative action held as an “active corrective policy” for black Americans, attacks on its constitutionality through a series of Supreme Court cases have drastically weakened and broadened its focus such that most of its benefits have been shuttled to white women (Moseley-Braun 1995). It is not unreasonable to expect similar fates to befall policies that directly aim to assist subaltern groups. However, there exist a few scenarios in which gains may be made, somewhat indirectly and unpredictably. First, history has previously provided periods of shock and serendipity in which the subaltern group is the majority or finds itself in the halls of power with the capacity to write the rules for affirmative action. The direct hand that B.R. Ambedkar had in writing the post-independence Indian Constitution – and garnering support for a system akin to affirmative action to benefit the scheduled castes, scheduled tribes, and other backward classes – comes to mind (Weisskopf 2023). Indeed, in moments of great upheaval and crisis, ideas that were previously unfeasible can suddenly achieve popular support. Many of the examples discussed in this chapter – homesteading during and following the Civil War, the New Deal during the Great Depression, the civil rights bills amidst the upheaval of the 1960s – came amidst unrest. Similarly, profit-seeking opportunists have used natural disasters and fomented crises around the world in order to create volatile environments in which economic policies might be changed for their benefit (Klein 2007). While none of these examples (save, perhaps, some consequences of the provisions in the Indian Constitution) led to long-term improvements for subaltern groups, the principle – that in such moments, bold ideas become more feasible – is worth heeding. Second, it is worth considering universal policies in which relative gains for subaltern group members are overshadowed by the individual gains for dominant group members. For example, a federal job guarantee, as proposed by Paul et al. (2018), would not target any specific group, but due to the intersection of race and un/underemployment, individuals in the subaltern group would reap the most benefit. While many individuals in the dominant group will continue to resist this – those that are gainfully employed and would receive no economic benefit and would simply see their group’s relative economic status decline – it is feasible that enough lower-class individuals in the dominant group would receive a substantial direct benefit and support the policy. Indeed, initial polling suggests substantial popular support for such a program (Budryk 2020). However, this is akin to a class-based affirmative action policy. Thus, it will not alleviate all the disparities that have accrued and continue to accrue from race-based discrimination and intergenerational inherited deprivation, as Darity et al. (2011) emphasize.
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Third, one could project a “Trojan horse” scenario in which the subaltern group gains in the short term because a previously implemented policy, viewed as innocuous, has beneficial effects that were initially overlooked. For example, job training programs for blacks have more support than job quotas, as the gains they promise are less substantial and tangible (and, notably, such programs are consistent with the view that black exclusion stems from black deficiency, not from discrimination). If these programs were to be improved – perhaps by accurately predicting macroeconomic demands and retraining applicants accordingly – such that they led directly to well-compensated job offers for all in the program, the relative position of the subaltern group would improve. However, over time, it is likely that the dominant group would realize the threat it had missed and rectify, or terminate, such a program. Finally, demographic change presents us one last, Charlie Brown-esque kick at the football. If the dominant group becomes overwhelmed numerically or the subaltern population develops a majority voting bloc, then a path to approval for affirmative action might not have to garner support from white America. A number of problems arise, though, when considering the overwhelming inclination of immigrating individuals to self-identify as white (regardless of skin tone) and that the strength of the self-identity of individuals in the subaltern group scales with the penalty they face for belonging to said group (Darity et al. 2006, 2017). In other words, it is unlikely that the dominant group will disappear soon (let alone peacefully), and the agitation from the subaltern group for such affirmative action policies might weaken as the dominant group becomes less powerful and punitive. As such, stratification economics – and the balance of American history – tells us that to project a feasible route to a group-based affirmative action policy on behalf of a subaltern group in America is rather wishful. Such an outcome is not impossible, but it will require some fortune, the arrival of which will undoubtedly spark a discovery of new depth under the bedrocks of this burgeoning subfield. Given what stratification economics says about the dominant group’s resistance to threats to its status, though, perhaps the best chance of achieving popular support for a policy that lets black Americans “walk through the gates of opportunity” is if white Americans do not think it actually will help black Americans walk through the gates.
Cross-References ▶ Stereotype Threat Experiences Across Social Groups
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Amenta E, Parikh S (1991) Capitalists did not want the social security act: a critique of the ‘capitalist dominance’ thesis. Am Sociol Rev 56(1):124–129. Retrieved 15 Jan 2021, from http://www.jstor.org/stable/2095678 Anderson H (2011) That settles it: the debate and consequences of the homestead act of 1862. Hist Teach 45(1):117–137. Retrieved 15 Jan 2021, from http://www.jstor.org/stable/41304034 Anderson S (2018) Boom town: the fantastical Saga of Oklahoma City, its chaotic founding, its apocalyptic weather, its purloined basketball team, and the dream of becoming a world-class Metropolis. Broadway Books, New York Bertrand M, Mullainathan S (2004) Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. Am Econ Rev 94(4):991–1013 Blumer H (1958) Race prejudice as a sense of group position. Pacific Sociol Rev 1(1):3–7. https:// doi.org/10.2307/1388607 Budryk Z (2020) Majority of voters support a Federal jobs Guarantee Program. Retrieved 25 Jan 2021, from https://thehill.com/hilltv/468236-majority-of-voters-support-a-federal-jobs-guaran tee-program Conrad C (1995) The economic cost of affirmative action. In: Simms M (ed) The cost and benefits of affirmative action. Joint Center for Political and Economic Studies, Washington, DC Cross T, Slater R (1996) Once again, Mississippi takes aim at black higher education. J Blacks Higher Educ 12:92–96 Darity W (2005) Stratification economics: the role of intergroup inequality. J Econ Financ 29: 144–153. https://doi.org/10.1007/BF02761550 Darity W (2013) Confronting those affirmative action Grumbles. In: Wicks-Lim J (ed) Capitalism on trial: explorations in the tradition of Thomas E. Weisskopf. Edward Elgar, Cheltenham, pp 215–223 Darity W Jr, Mullen AK (2020) From here to equality: reparations for black Americans in the twenty-first century. The University of North Carolina Press, Chapel Hill Darity W Jr, Mason PL, Stewart JB (2006) The economics of identity: the origin and persistence of racial identity norms. J Econ Behav Organ 60:283–305 Darity W Jr, Deshpande A, Weisskopf T (2011) Who is eligible? Should affirmative action be group- or class-based? Am J Econ Sociol 70(1):238–268 Darity W Jr, Hamilton D, Stewart JB (2015) A tour de force in understanding intergroup inequality: an introduction to stratification economics. Rev Black Polit Econ 42(1–2):1–6. https://doi.org/ 10.1007/s12114-014-9201-2 Darity W Jr, Hamilton D, Mason P, Price G, Dávila A, Mora M, Stockly S (2017) Stratification economics: a general theory of intergroup inequality. In: Flynn A, Warren D, Wong F, Holmberg S (eds) The hidden rules of race: barriers to an inclusive economy. Cambridge University Press, Cambridge, pp 35–51 Deverell WF (1988) To loosen the safety valve: eastern workers and Western lands. West Hist Q 19(3):269–285 Du Bois W (1935) Black reconstruction in America, 1860–1880. The Free Press, New York Du Bois W (1967) The Philadelphia negro: a social study. Schocken, New York Feagin J, Porter A (1995) Affirmative action and African Americans: rhetoric and practice. Humboldt J Soc Relat 21(2):81–103. Retrieved 15 Jan 2021, from http://www.jstor.org/stable/ 23263011 Foner E (1981) Politics and ideology in the age of the civil war. Oxford University Press, Oxford, UK Frydl KJ (2009) The G.I. bill. Cambridge University Press, Cambridge, UK Fure-Slocum E (1995) Urban poverty and ‘the right to cultivate the earth’: American land reformers in the 1840s. Iowa J Cult Stud 1995:120–132 Gilens M (1995) Racial attitudes and opposition to welfare. J Polit 57(4):994–1014. Retrieved 15 Jan 2021, from http://www.jstor.org/stable/2960399 Goldsmith AH, Hamilton D, Darity W Jr (2006) Shades of discrimination: skin tone and wages. Am Econ Rev 96(2):242–245
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Grandin G (2019) The end of the myth: from the frontier to the Border Wall in the mind of America. Metropolitan Books, Henry Holt and Company, New York Hamilton D, Goldsmith AH, Darity W Jr (2009) Shedding ‘light’ on marriage: the influence of skin shade on marriage for black females. J Econ Behav Organ 72(1):30–50 Hanchett TW (2000) The other ‘Subsidized Housing’: Federal aid to suburbanization 1940s–1960s. In: Bauman JF, Biles R, Szylvian KM (eds) From tenements to the Taylor Homes: in search of an urban housing policy in twentieth century America. Pennsylvania State University Press, University Park, pp 163–179 Hordge-Freeman E (2015) The color of love: racial features, stigma, and socialization in black Brazilian families. University of Texas at Austin, Austin Horowitz J (2019) Americans see advantages and challenges in Country’s growing racial and ethnic diversity, May 08. Retrieved 15 Jan 2021, from https://www.pewsocialtrends.org/2019/05/08/ americans-see-advantages-and-challenges-in-countrys-growing-racial-and-ethnic-diversity/? utm_source¼link_newsv9&utm_campaign¼item_317006&utm_medium¼copy Jamieson KH (1992) Dirty politics: deception, distraction, and democracy. Oxford University Press, New York Katznelson I (2006) When affirmative action was white: an untold history of racial inequality in twentieth-century America. W.W. Norton, New York Klein N (2007) The shock doctrine: the rise of disaster capitalism. Henry Holt and Company, New York Kluegel JR, Smith ER (1983) Affirmative action attitudes: effects of self-interest, racial affect, and stratification beliefs on whites’ views. Soc Forces 61(3):797–824. https://doi.org/10.2307/ 2578135 Kuhn M, Schularick M, Steins UI (2020) Income and wealth inequality in America, 1949–2016. J Polit Econ 128(9):3469–3519. https://doi.org/10.1086/708815 Kuklinski J, Sniderman P, Knight K, Piazza T, Tetlock P, Lawrence G, Mellers B (1997) Racial prejudice and attitudes toward affirmative action. Am J Polit Sci 41(2):402–419. https://doi.org/ 10.2307/2111770 Lanza ML (1990) Agrarianism and reconstruction politics: the southern homestead act. Louisiana State University, Baton Rouge Leff M (1983) Taxing the ‘forgotten man’: the politics of social security finance in the new deal. J Am Hist 70(2):359–381. https://doi.org/10.2307/1900209 Magdol E (1977) A right to the land: essays on the Freedmen’s community. Greenwood Press, Westport Moseley-Braun C (1995) Affirmative action and the glass ceiling. Black Scholar 25(3):7–15 Pager D (2003) The mark of a criminal record. Am J Sociol 108(5):937–975 Paul M, Darity W Jr, Hamilton D, Zaw K (2018) A path to ending poverty by way of ending unemployment: a Federal job Guarantee. RSF: Russell Sage Foundation J Soc Sci 4(3):44–63. https://doi.org/10.7758/rsf.2018.4.3.03 Perlstein R (2018) Exclusive: Lee Atwater’s infamous 1981 interview on the Southern strategy, December 07. Retrieved 19 Dec 2020, from https://www.thenation.com/article/archive/ exclusive-lee-atwaters-infamous-1981-interview-southern-strategy/ Price GN (2008) Hurricane Katrina: was there a political economy of death? Rev Black Polit Econ 35(4):163–180. https://doi.org/10.1007/s12114-008-9033-z Rangel MA (2015) Is parental love Colorblind? Human capital accumulation within mixed families. Rev Black Polit Econ 42(1–2):57–86. https://doi.org/10.1007/s12114-014-9190-1 Robbins R (1933) Horace Greeley: land reform and unemployment, 1837–1862. Agric Hist 7(1): 18–41. Retrieved 15 Jan 2021, from http://www.jstor.org/stable/3739802 Rothstein R (2018) The color of law: a forgotten history of how our government segregated America. Liveright Publishing Corporation, of W.W. Norton, New York Shapiro TM (2006) Race, homeownership and wealth, 20 Wash U J L & Pol’y 53. https:// openscholarship.wustl.edu/law_journal_law_policy/vol20/iss1/4
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Siddiqi A, Sod-Erdene O, Hamilton D, Cottom TM, Darity W Jr (2019) Growing sense of social status threat and concomitant deaths of despair among whites. SSM - Popul Health 9:100449. https://doi.org/10.1016/j.ssmph.2019.100449 Stokes C, Lawson B, Smitherman G (2003) The language of affirmative action: history, public policy and liberalism. Black Scholar 33(3/4):14–17. Retrieved 15 Jan 2021, from http://www. jstor.org/stable/41069037 Tippett RT, Jones-DeWeever A, Rockeymoore M, Hamilton D, Darity W Jr (2014) Beyond broke: why closing the racial wealth gap is a priority for national economic security. Report prepared by Center for Global Policy Solutions and The Research Network on Ethnic and Racial Inequality at Duke University with funds provided by the Ford Foundation Uhlmann E, Cohen G (2005) Constructed criteria: redefining merit to justify discrimination. Psychol Sci 16:474–480 Veis G, Naddaf R (2007) The 50 most powerful people in D.C., August 9. Retrieved 12 Jan 2021, from https://www.gq.com/story/fifty-most-powerful-dc-washington-political-aides-journalists? currentPage¼3 Weisskopf T (2023) Affirmative action in the USA and India. In: Handbook on economics of discrimination and affirmative action. Springer, New York Wilcox JM (FL). Congressional Record 82, 2 (1937). (Text from: Congressional record permanent digital collection). Accessed 13 Feb 2021 Williams T (2000) The homestead act: a major asset-building policy in American history (working paper 00-9). Center for Social Development, St. Louis. https://openscholarship.wustl.edu/csd_ research/46/ Zinn H (1980) A people’s history of the United States. Harper, New York
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Transforming Gendered Labor Markets to End Discrimination Diane R. Elson
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Labor Markets as Gendered Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trends in Gender Gaps in Labor Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Participation and Employment Rates and Labor Force Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gender Earnings Gaps: Equalizing Up or Equalizing Down? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gendered Occupational and Sectoral Segregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gender Inequality in Labor Markets and Income Inequality Between Households . . . . . . . . . Efficiency and Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gender Discrimination and Inefficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Static and Dynamic Efficiency, Micro- and Macro-efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Social Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transformatory Strategies and Labor Market Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Abstract
This chapter sets out the foundations for a feminist understanding of labor market discrimination. It is argued that labor markets are gendered institutions operating at the intersection of the sphere of production and the sphere of reproduction. Gender discrimination in labor markets needs to be understood not as a residual nor as irrational but as rooted in institutional structures and the unequal gender division of paid and unpaid work. Data on gender gaps in labor markets needs careful interpretation as a reduction in gender gaps does not necessarily imply an improvement in women’s empowerment or well-being. The relation between discrimination and economic efficiency is complex. Labor market institutions which are biased against women may, in some circumstances, and in respect of some objectives, be micro-efficient, but they are not, in the long run, D. R. Elson (*) University of Essex, Colchester, UK e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_4
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macro-efficient, especially with respect to achievement of broader social objectives beyond short-run profitability. Labor markets can be transformed through public policy and collective action so that discrimination is eliminated in ways that promote improvements in both quality and productivity of employment. Keywords
Gender · Labor market · Discrimination · Economic efficiency
Introduction This chapter aims to complement other articles in this volume by proposing that gender discrimination in labor markets should be understood as rooted in the operation of labor markets as gendered institutions situated at the intersection of the sphere of production and the sphere of reproduction. Labor markets are structured by practices, perceptions, norms, and networks which are bearers of gender and reinforce gender inequality. Gender gaps in employment may be falling, but the social and economic forces underlying the data need careful examination before conclusions are drawn on the benefits for women. Falls may reflect equalizing down rather than equalizing up or be offset by changes in behavior within households. Mainstream economists have claimed that discrimination is inefficient and will be eliminated through competition. However, women’s disadvantage is deeply rooted in markets, and more transformatory strategies are required to end it.
Labor Markets as Gendered Institutions Mainstream economists tend to approach labor markets as neutral arenas in which buyers and sellers interact. The buyers and sellers may be differentiated by sex, and they may have different endowments and preferences. This is acknowledged to be gender discrimination in labor markets if differences in the hourly earnings cannot be accounted for by differences in variables such as education and on-the-job experience. As Humphries and Rubery (1995) shows, gender discrimination is treated as a residual, stemming from the tastes of employers, to be acknowledged as an explanation when other explanations fail. From this perspective, discrimination against women is a puzzle since it does not maximize profits. There is however a different way of approaching labor markets that begins not from the premise of a neutral arena within which particular individuals may, or may not, be prejudiced against women’s employment but rather from the idea that labor markets are institutions which are bearers of gender. The idea of social relations which are bearers of gender, though not gender-ascriptive, was first put forward by Whitehead (1979). An example of a gender-ascriptive social relation is that between a husband and wife, where the terms husband and wife ascribe male and female gender. The employer/employee relation is not gender-ascriptive in that way. But it is
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a bearer of gender in the sense that there are social stereotypes which associate masculinity with having authority over others in the workplace (being the boss) and social stereotypes about what is men’s work and women’s work. Such stereotypes are not matters of individual preference but are inscribed in social institutions. The formal and informal rules which structure the operation of labor markets are instantiations of the gender relations of the society in which the labor market is embedded. They reflect existing problems of gender domination and subordination and also the tensions, contradictions, and potential for change which is characteristic of any pattern of gender relations, no matter how unequally power is distributed. Labor legislation, government labor standard inspectorates, trade unions, professional and business networks, systems of job evaluation, systems of organization of work, and pay determination structures, all these are bearers of gender, even if no overt reference is made in their rule books and in the informal discourses that surround them, to gender difference and gender inequality. These issues have been extensively discussed in the pages of Gender, Work and Organization. Pioneering theoretical contributions were made by Acker (1990). For instance, research into pay and job grading systems shows that occupations which are predominantly undertaken by women tend to have grading systems which compress jobs into a narrow range of grades offering few opportunities for advancement. Promotion systems frequently operate on the basis of rules which differentiate those with career potential from the rest on the basis of criteria such as geographical mobility. Payment systems, no matter how much they are codified and rule-based, always have scope for discretion in their application. Research on performancerelated payment systems shows that different criteria of good performance appear to be applied to men and women, even within similar jobs. Several examples are provided by Grimshaw and Rubery (2007) in relation to structures of pay and job grading systems, promotion, and payment systems. The most fundamental way in which labor markets are gendered institutions is in the way in which they operate at the intersection of ways in which people make a living and care for themselves, their children, their relatives, and friends. Activities which make a living are recognized by economists as economic activities which should in principle be counted as part of national production. For brevity, the sum of this largely market-oriented work may be called the sphere of production. But, as feminist economists have pointed out, unpaid, un-marketed caring activities are also critical for the functioning of the sphere of production, since they reproduce, on a daily and intergenerational basis, the labor force which works in the sphere of production. For more discussion see Gardiner (1997) and Folbre and Pujol (Eds.) (1996). Moreover, feminist economists have argued that unpaid caring activities entail work, even though they are not market-oriented. For brevity, the sum of unpaid, caring work may be called the sphere of reproduction (Elson 2013). Labor markets form one of the points of intersection of these two economic spheres (Humphries and Rubery 1984). But they operate in ways that fail to acknowledge the contributions of the sphere of reproduction. Instead, they operate in ways that disadvantage those who carry out most of the work in the sphere of reproduction: women. Labor market institutions are constructed in ways that
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represent only the costs to employers of the time that employees spend on unpaid caring for others. The benefits are not represented. Thus, for instance, employees’ parenting duties are represented as liabilities and not assets to their employers. Labor market institutions are constructed in ways that reflect only the immediate costs of time off from paid work to have and rear children and care for sick relatives and friends. They are not constructed to reflect the benefits of the reproduction and maintenance of a pool of labor from which employers can select their employees. Nor do they reflect the enhanced interpersonal skills which come from parenting and managing a household. The sphere of reproduction produces benefits for the sphere of production which are externalities, not reflected in market prices and wages. Or, as feminist economist Nancy Folbre puts it, labor market institutions fail to face up to the problem of who pays for the kids? (Folbre 1994). Of course, the operation of labor market institutions cannot escape the fact that someone has to pay for the kids. (Just as there is no such thing as a free lunch, there is also no such thing as a free replenishment of the pool of labor.) Most labor market institutions are constructed on the basis that the burdens of reproducing the labor force will be, and should be, borne largely by women. For instance, arrangements for paternal leave are far less widespread than maternal leave, and where they do exist, there are many barriers to men taking up their entitlements, because promotion often depends upon showing commitment to the job and taking paternal leave may be interpreted as a sign of weak commitment to the job. Domestic responsibilities penalize women in the labor market and are a key factor in women’s weak position in terms of earnings and occupations. This has been quantified for Britain and some other European countries by Joshi (1990) and Joshi and Davies (1992). It is sometimes claimed that labor markets adapt so as to allow women to combine paid work with unpaid work – for example, part-time work and home-based work. (There is a huge literature on part-time work and flexible labor markets. A useful review of many of the issues can be found in Jenson et al. 1988.) But this kind of adaptation is generally one-sided, more designed to allow the sphere of production access to workers whose entry into the labor market is constrained by domestic responsibilities than to give weight to the contribution that women’s unpaid work makes to the sphere of production. This is revealed in the way in which these types of work typically do not have contracts which give employees any rights to paid time for meeting their responsibilities in the sphere of reproduction – rights such as maternity leave and time off for caring for sick relatives. Nor does such employment cover women’s needs when they have retired from paid work, since pension rights are also frequently not covered. Instead, the presumption appears to be that someone else (husband, children) will support such workers in their old age. Thus, labor market institutions are not only bearers of gender, but they are also reinforcers of gender inequality. But different institutional configurations give different results: some labor markets are more equal than others. Moreover, improvements can be brought about by public action, that is, by combined action by the state and by groups of active citizens (this concept of public action is developed in Dreze and Sen (1989)). Public action can build upon the potential for change in labor market institutions themselves. Such institutions are not like fortresses of stone;
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rather they are combinations of overlapping and conflicting practices, norms, and networks, whose seeming solidity at any moment masks subterranean pressures and fissures.
Trends in Gender Gaps in Labor Markets Gender inequality in employment persists, with gaps in male and female participation, employment rates, and employment status, male-female pay differentials, gender segregation by sector and occupation, and gender inequalities in contracts and conditions of work.
Participation and Employment Rates and Labor Force Status Over the twentieth century, the female labor force participation rose significantly in almost all countries. However, in the early years of the twenty-first century, between 2000 and 2018, the global female labor force participation declined from 50.9% in 2000 to 47.9% in 2018 (ILO 2019). As the rate of education enrolment has increased across the world, labor force participation rates have decreased for both young men and women. Despite falling overall rates of labor force participation, the gender gap has narrowed slightly, from 27.6 percentage points in 2000 to 26.5 percentage points in 2018 (ILO 2019). This is because the fall in female labor force participation has been slower than the fall in male labor force participation. There is variation between countries: while this gender gap has narrowed in developing and developed economies, it continues to widen in emerging economies. Note that these three categories are defined by the ILO on the basis of World Bank data. Developing economies are low-income countries with a gross national income (GNI) per capita of US$1005 or less; emerging economies are middle-income countries with a GNI per capita between US$1006 and US$ US$12,235; developed economies are defined as those with high income, with a GNI per capita of US$12,236 or more. While the widening gender gap in participation rates in emerging economies shows that women in emerging economies are still a long way from catching up with men in terms of labor market opportunities, it also reflects the fact that a growing number of young women in these countries are enrolled in formal education, which delays their entry into the labor market – gender gaps in educational attainment have shrunk considerably in these countries (ILO 2018). In developed economies, the gender gap in participation rates is being reduced as women and men in these countries have near equal educational achievements and women face less restrictive social norms regarding paid work. In developing countries, the gap is falling because of rising female participation and diminishing male participation. Diminishing male participation is associated with increasing enrolment of young men in secondary and tertiary education (ILO 2018). The extent to which the reduction in the gap in participation rates reduces gender inequality more broadly depends on the returns accruing to men’s and women’s paid
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work, and the extent to which male decreases and female increases in work in the sphere of production is offset by male increases and female decreases in work caring for family, friends, and neighbors (in the sphere of reproduction). The evidence from time budgets from a wide variety of countries in all regions of the world suggests that the gender division of work in the sphere of reproduction does not change enough to offset rising female and falling male participation in the sphere of production. The result is that women in the labor force typically endure a longer working day than their male counterparts once we add their unpaid work to their paid work (ILO 2016). Further, it is important to note that the increased participation of women in the developing countries can represent distress sales rather than free choices to take up new opportunities. It is the falling earnings of other household members which have propelled poor women into the labor market in many poor communities undergoing economic restructuring (Moser 1996; Beneria and Feldman 1992). A complicating factor is that rising female labor force participation is, in part, a statistical artefact. An unknown proportion of the rise is due to improvements in accounting for women’s economic activity – reflecting to some degree changes in perceptions of what counts as work, both the perceptions of statisticians and enumerators, and perhaps also, the perceptions of respondents to surveys. For instance, International Conference of Labour Statisticians recently reduced the scope of the definition of “employment” to refer only to activities performed for others in exchange for pay or profit. At the same time, it enhanced the definition of “work” to include also the production of services for own use, such as unpaid care work (ILO 2013). In practice, new definitions do not immediately get operationalized, and it takes time for new statistics to be reported. Nevertheless, despite improvements in statistical surveys, a great deal of women’s economic activity is still invisible. The extent to which economic activity is publicly recognized as such is very much bound up with the question of whether the person doing it is paid in cash or acts as unpaid labor in a family-based business. Statistics on labor force status tend to show a considerably higher proportion of women than of men with the status of unpaid family labor (ILO 2016). Even if a woman gets paid for her work, she may be no better off in terms of meeting the needs of herself and any children she has. Labor market participation may in itself involve additional costs – transport, clothing, accommodation, and equipment. Debt may be incurred for start-up costs, and lack of information may lead to underestimation of these costs (particularly where migration is involved). Even if significant costs of participation are not involved, women may find that once they are earning their own income, there is an offsetting reduction in income transfers from nonmarket sources, particularly from the father of their children. Folbre (1994) suggests that this is one of the key downsides of the increase in female labor force participation. Moreover, while labor market participation opens up new opportunities, it also opens up new risks. There is, for instance, a risk of entitlement failure (i.e., a failure to establish command over sufficient resources for survival) owing to loss of employment, a drop in wages, or a rise in prices (Dreze and Sen 1989). One of the
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important ways in which markets are gendered is in terms of the ways in which they handle risk (such as the risk of job loss). In general, risk-reducing mechanisms have been much more a feature of male forms of market participation – for instance, trade unions, job security rights, social insurance benefits, and business and professional associations. Labor market institutions have typically been constructed on the assumption that women employees were secondary earners who could draw upon the assets and earning of men (male partners, husbands, fathers, brothers, etc.) to cushion them against risk. These institutions have assumed that women have extended entitlements which do not have the force of law but are sanctioned by accepted norms about what is a legitimate claim (Dreze and Sen 1989). Women’s very act of participating in the labor market, however, may weaken their extended entitlements, if it involves stepping outside what have been accepted as the normal roles for women. The possibility of earning an income of their own may empower them to take more decision about their own lives – but it may also cut them off from support by male kin, leaving them on their own and newly vulnerable to market forces.
Gender Earnings Gaps: Equalizing Up or Equalizing Down? Globally, the gender pay gap is estimated to be close to 23%: annually, women working full-time earn 77% of what men working full-time earn (ILO 2016). On an average, in most countries, the gap has decreased from 21.7% to 19.8%. However, progress in reducing the gender pay gap has been slow, in spite of significant progress in women’s educational attainments as well as labor force participation rates. A reduction in earning differentials may take place through equalizing up or through equalizing down. In the case of the former, there is no absolute drop in men’s earnings (which indeed may even be rising, although at a slower rate than those of women): whereas in the latter case, men’s earnings fall absolutely (and indeed, women’s earnings may also fall, though not by as much). Equalizing up is likely to be more beneficial to women than equalizing down. For instance, it may involve lesser risk of domestic violence. But even in the case of equalizing up, the narrowing of the gender wage gap may overstate the benefits to women, because there may be countervailing moves in male-to-female income transfers. For instance, women may be expected to finance a larger proportion of household expenditure from their earnings (Chant 2007). Gender-disaggregated household income and expenditure budgets are needed to put the benefits of any narrowing of male-female earning differentials into perspective.
Gendered Occupational and Sectoral Segregation Globally, over the past two decades, occupational segregation has narrowed slightly due to the movement of women in “mixed” category of jobs (such as leadership and
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managerial roles). However, between 2000 and 2010, there has been a 0.5 percentage point decline in women’s participation in male-dominated professions (like trade jobs, machine operator, etc.). Further, female-dominated occupations remain highly feminized: women remain overrepresented as “clerical, service, and sales workers” and in “elementary occupations” (ILO 2016). This segregation has increased over the past decade due to skill-biased technological change especially in emerging economies. Moreover, women are more likely to be concentrated in the lower segments across job hierarchies in employment. Labor markets that were once characterized by secure, permanent, full-time jobs with pension rights and other benefits have changed as more and more jobs are casualized. Over the years, more women than men have found themselves in marginal, part-time work that is more precarious and unpredictable (ILO 2016).
Gender Inequality in Labor Markets and Income Inequality Between Households Contemporary patterns of economic growth which are structured by market liberalization and globalization are associated in many countries with growing inequality in the distribution of income between households (UNDP 2019). As far as many women are concerned, on the one hand, their bargaining power, in relation to the men in their households, their communities, their networks, and the organizations of their civil society, may often (though not, as we have argued, inevitably) be increasing as a result of their greater participation in labor markets. But at the same time, their households, their communities, their networks, and the organizations of their civil society are more and more at the mercy of global market forces that are out of control, as witnessed in the Asian financial crisis of 1997 and the global financial crisis of 2009 (Elson 2013).
Efficiency and Discrimination Neoclassical welfare economics favors definitions of efficiency which gloss over distributional questions and gloss over the question of who has the power to define efficiency. Instead, the focus is on productive efficiency, defined as achieving maximum output from given resources, and allocative efficiency, defined as achieving that particular form of maximum output, that particular combination of commodities, that will best satisfy given, well-defined, preferences. (The latter is described as the optimal situation for the economy to be in and is consistent with profit maximization in a perfectly competitive economy.) It is not possible to directly test whether an economy has achieved optimality, but it is assumed an economy will move in that direction if prices move closer to marginal costs and wages move closer to marginal productivity. Typically, deregulation has been seen by mainstream economists as something which will assist this and promote economic growth (e.g., see World Bank’s Doing Business Report 2015).
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Gender Discrimination and Inefficiency Mainstream economists define gender discrimination in labor markets in terms of women getting lower wages, even though they have the same productive capacities as men in terms of education and work experience. This ignores the broader structures of disadvantage, which mean that women are less likely to be educated and to be able to accumulate the same work experience as men. Discrimination against women in employment clearly violates the condition for productive efficiency as defined in mainstream economics, i.e., the allocation of given quantities of resources (in this case, given quantities of human effort of a given quality) to maximize output. Thus, it can be argued that there is an efficiency-loss-based case for interventions in labor markets to counter discrimination, such as equal opportunities legislation. But some mainstream neoclassical economists would probably contest the idea that discrimination was a major reason for gender differences in occupations and wages. They would argue that discrimination against women reduces profits for employers and misallocates resources and that market competition will eliminate such discrimination. An influential labor market textbook instructs students that no strong motive for discrimination against women has been found and concludes that: labor mobility and competition among firms will eradicate discrimination and allocate labor to its most productive uses so minimizing costs (Polachek and Siebert 1993). It is possible to provide explanations for the persistence of discrimination which take account of incomplete information. For instance, the incomplete knowledge of employers about the characteristics of potential employees – employers may systematically underestimate the productive potential of women and thus pay them less and confine them to lower-grade occupations – may result in the so-called error discrimination. But again, as Humphries and Rubery (1995) points out, neoclassical economists would argue that such mistaken behavior would lead to lower profits and therefore would not persist in competitive markets. It is possible, however, to see how such mistaken behavior might persist if the social embeddedness of economic activities is recognized, which implies that male employers are not just economic men, interested only in maximizing profits, but are likely to be interested in perpetuating their advantage in the social and political spheres of life too. Altering their perceptions of the productive potential of women may threaten male employers’ advantages in other spheres of life, outside the workplace, and thus mistaken beliefs may persist because they are part of a wider system of male power. Gender discrimination may also persist because it serves strategies for managing the production process in conditions of uncertainty. It is impossible for employers to specify an employment contract to cover every possible eventuality and to be present to monitor every worker at every stage of the production process. In this context, a variety of strategies for managing production are adopted (Sawyer 1995). Such strategies include systems of monetary incentives and disincentives (e.g., piece rates, bonuses, deductions from wages for poor quality work), systems of monitoring and enforcing discipline; and systems of building the cooperation and improving the
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skills of employees. Existing patterns of gendered power, what Breugel and Perrons (1995) call the gender order, can be mobilized in such strategies. For instance, if the prevailing gender order gives more authority to men in the wider society, then systems of monitoring and enforcing work discipline can mobilize that authority by placing men rather than women in supervisory and managerial positions. If the prevailing gender order facilitates building solidarity and team spirit among men on the basis of excluding (and even denigrating) women, then systems of building cooperation in the workplace can mobilize team spirit by excluding women, formally or informally. It is important to note that such discrimination is only efficient in the sense that it may contribute to maximizing output, given the prevailing gender order. It would be more efficient in terms of production of output to change the prevailing gender order, so that the exercise of authority was not a male preserve and so that team building did not depend on excluding and denigrating people of a different gender. This would allow everyone’s talents to be effectively mobilized and would increase productivity.
Static and Dynamic Efficiency, Micro- and Macro-efficiency The problem is that economies are currently locked into set of institutions which operate in a self-reinforcing and cumulative way to limit the potential gains from employment. One of the reasons that it is so hard to change is that people’s preferences and perceptions are to a large extent endogenous, shaped by the prevailing sets of institutions, and current rules and norms are continually reinforced (Ferber and Nelson 1993; Bowles and Gintis 2000). To discuss these issues further, it is helpful to refer to distinctions between static and dynamic efficiency and microand macro-efficiency (Humphries and Rubery 1995). Static efficiency refers to efficiency at one point in time, and discussion in this chapter so far has been related to this static concept of efficiency. Dynamic efficiency refers to efficiency over a period of time, during which various factors that have to be taken as given in the short run can be changed. Economists usually focus attention on investment to change the stock of skills and equipment to be used in paid employment. It is possible to also focus on investment to try to change the gender order. Mainstream economists already recognize that market forces are less likely to achieve dynamic efficiency than to achieve static efficiency and that discrimination against women and girls in investment in education and training is inefficient in the long run but is inclined to see the root of such discrimination in the behavior of households and families, which are not subject to competitive forces in the same way as firms. The recommended answer is usually public subsidies to the education of girls. But experience seems to show that this by itself is not dynamic enough, since it does little to challenge and transform current gendered norms and perceptions. For instance, occupational segregation remains strong despite closing of gender gaps in school enrolment and even in educational attainment (UNDP 2013). Some of the reasons for this can be explained in terms of the distinction between micro-efficiency and macro-efficiency. Micro-efficiency refers to each economic
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agent (individual, or household, or enterprise) operating as efficiently as they can in the given circumstances. Macro-efficiency refers to the efficiency of the economy as a whole. There is a strong presumption in neoclassical economics that microefficiency will lead to macro-efficiency, through market coordination based on each individual pursuing their own self-interest as they see it. But this rests on the presumption that information is shared and flows quickly and that institutions can quickly be reshaped. In other words, that history does not matter. Once these very strong assumptions are relaxed, however, it is quite possible for there to be numerous alternative outcomes, in each one of which everyone is doing as well as they can in the circumstances (in technical terms, multiple equilibria) but in many of which the whole society is not doing as well as it could. In other words, there are processes of cumulative causation which lock the society into a lower level of achievement than it could potentially reach. Labor market institutions which are biased against women may, in some circumstances and in respect of some objectives, be micro-efficient, but they are not, in the long run, macro-efficient.
Social Efficiency The definition of efficiency in economics is narrow and ignores other goals that people might have besides maximizing output of commodities, consuming commodities, and enjoying leisure – goals such as dignity, autonomy, self-respect, and pleasure in exercising skills in their work. Within the confines of neoclassical welfare economics, there is no scope for relating efficiency to empowerment goals and judging the efficiency of an economic system in terms of how effective it is in providing everyone with opportunities for self-realization (Elson 1997). There is both theoretical reasoning and empirical evidence that suggest that more inclusive, democratic, and egalitarian ways of organizing employment can both increase productivity and better serve human needs through promoting commitment and cooperative behavior, encouraging workers to share information and give of their best (e.g., Hodgson 1984; Bowles and Gintis 1993, 1997; Pagano 1991).
Transformatory Strategies and Labor Market Standards The problem is that to move the society as a whole, from the lower-level equilibrium characterized by discrimination and lower economic and social productivity to a higher-level equilibrium characterized by absence of discrimination and higher levels of economic and social productivity, requires changes in people’s perceptions and concerted and cooperative action. Without this, economic agents remain locked into an inherited set of gendered roles and behaviors in the context of labor market institutions which are bearers of a preexisting set of gender relations. In order to achieve this, there is a clear need for a transformatory employment policy; that is, a policy which helps to change people’s perceptions of what is possible, beneficial, and fair, fosters cooperative action, and strengthens women’s
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bargaining power in the workplace, the home, and the marketplace. It is in this context that labor market regulation should be seen. It is, of course, impossible to have an unregulated labor market, since all markets require some sort of authority to govern them (Sawyer 1995). The questions are: what kind of regulation? Whose interests does it serve? What kind of market institutions does it promote? What kinds of norms and perceptions does it promote? In most countries basic labor standards on health and safety, minimum pay, and rights of the labor force to organize and bargain collectively are likely to be more important for improving the employment conditions of most women than complex standards relating to equal pay for work of equal value. This does not mean there is no need for specific legislation to make discrimination against women illegal. Such legislation is critical for shaping norms and perceptions – but its immediate value for many women in many countries will be to enshrine an important principle. In practical terms, public policy to set and enforce equal standards for men and women on labor basic rights would likely have more impact for more women. There is a pressing need to challenge the idea that women do not need the same basic employment rights as men because their work is more likely to be part-time, intermittent, or home-based. Simple deterministic models of the labor market which do not allow for uncertainty and endogenous preferences tend to suggest that labor market regulation to improve women’s employment outcomes and reduce gender bias in labor market institutions is likely to reduce women’s employment opportunities. The message is that women can have better employment or more employment – but not both, because better employment means extra costs for employers or (in the case of the self-employed) for customers. The World Development Report for 2013 admits that, on the question of minimum wage legislation, there is empirical evidence suggesting both that minimum wages discourage employment and that they do not (World Bank 2013). This is not surprising. As with all attempts to establish the impact of specific policies, much will depend on the conceptual framework used to generate ideas about the counterfactual and on judgments about the extent of policy implementation and the significance of changes in non-policy variables in the same period. Conflicting empirical evidence can often be traced to conflicting theories and conflicting judgments. Insofar as extending women’s employment rights imposes additional costs on individual employers, it is possible to design policy packages which will minimize such costs. For instance, the costs of maternity leave for women workers (or paternity leave for men workers) could be financed from general taxation rather than from the profits of individual employers. Indeed, the ILO Maternity Protection Convention stipulates that individual employers should not be liable (Anker and Hein 1985). Moreover, once it is acknowledged that the output produced from a given labor force varies with the way in which the production process is managed, the way is open for the idea that raising benefits to employees can lead to increases in quality and productivity (this has led to the development within mainstream economics of efficiency wage theory – see, for instance, Weiss (1990)). As Sawyer (1995) explains, this might happen through improvements in the health and strength of workers (e.g., through better nutrition), through improvements in the willingness to
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work of workers (e.g., less shirking), or through stimulating improvements in the physical organization of production (e.g., less downtime between operations). The same argument might be made in relation to improvements in the safety of the workplace, job security, time off for family responsibilities, etc. The objection might be raised that if raising wages and improving working conditions (or prices paid to the self-employed) improve productivity and quality, then employers (or customers) would do this without any need for prompting from public policy. But economic agents have bounded rationality and make adjustments through a process of search and discovery in which there can be a good deal of inertia associated with the existence of vested interests and in which perceptions of opportunities are shaped by existing positions (Humphries and Rubery 1995). Thus, public policy to set fair minimum standards in wages and working conditions should not be assumed to be redundant: it may prompt employers and customers to recognize new opportunities for increasing quality and productivity and help to overcome inertia. Of course, there are employers and customers who are not interested in increased quality and productivity: their competitive strategy rests on production of low quality, cheap goods, and they are interested in extracting as much work as possible for minimum return, provided there is surplus labor available. Such strategies frequently impose severe costs on the workforce – ranging from stress, exhaustion, ill health and disabilities, to loss of life. These strategies may be efficient in generating short-term private profits; they are not socially efficient in achieving the wider objectives of human development. The adoption of more socially efficient strategies may however be blocked by powerful groups who, as Folbre (1995) remarks, may be willing to pay a price, in lower efficiency, for continued control over a disproportionate share of output. It is always easier for a sectional interest group to maintain its opposition to change if it can buttress its position with the argument that the status quo is more efficient, hence the importance of carefully distinguishing between different types of efficiency. Economic strategies which treat people as simply instruments of production without regard for their welfare (ignoring, for instance, risks to their health) are widely regarded as unethical, although there are debates about where the line falls between practices which are ethical and unethical. But individual businesses often argue that competitive pressures mean that ethical considerations are a luxury. The role which can be played by public policy to set standards for wages and working conditions is thus twofold: to shape perceptions and set norms about what is ethical and unethical and to create an environment in which all enterprises face similar standards and thus cannot obtain competitive advantage by paying wages which are below subsistence level and undervalue the contributions of employees and enforcing working conditions which endanger the well-being of the workforce. In a global economy, there needs to be international as well as national conventions on what fair minimum standards are. Some of these standards will vary with the context (the fair minimum wage in Bangladesh will be lower than in the United States because subsistence norms and costs differ in the two countries). But there is a case for absolute standards on matters of life or death: for instance, with respect to chemical hazards and fire hazards. The ILO conventions, agreed by representatives
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of governments, workers, and employers, are a source of international labor standards that women’s organizations are using in their organizing, though they are not, of course, panaceas. For instance, research by Women in Informal Employment: Globalizing and Organizing (WIEGO) – a research and advocacy group – notes that 20 years after the adoption of the ILO Convention on home work (1996) only ten countries have ratified the convention, too few workers have realized their rights, and the legal environment around home-based work remains uncertain. Enforcing standards is difficult, even with well-organized and resourced inspectorates, and most developing countries are far from having these. A vital role must be played by collective organizations that bring together groups of workers in trade unions or other forms of organization (whether self-employed or employees) to monitor standards and mobilize in civil society for their enforcement. It is beyond the scope of this article to review the advantages and disadvantages and successes and failures of different types of organizing, from the point of view of women. For an informative discussion of these issues, see Chhachhi and Pitten (1986) and Prugl and Tinker (1997). International conventions and national legislation can play an important role in legitimizing the activities of such groups and providing a focal point for the process of changing the perceptions of the workforce and the wider society about the rights that should be and can be enjoyed by women and men in their employment. In 2013, the first ILO Convention on paid domestic work came into force, and the demand for ratification of the convention is being used as a rallying call by organizations of domestic workers around the world (UN Women 2015). Enforcing standards is always likely to be uneven – it is easier to monitor a few large workplaces than many small ones. So it is important to address at the same time the availability of a wider range of forms of employment. The existence of a large pool of surplus labor with little or no alternative but to accept employment on whatever terms are offered is always likely to undermine the enforcement of decent labor market standards. It is important to explore alternatives to those offered in the competitive struggle for short-run profit. Important contributions to countering discrimination against women in the labor market can be made by provision of employment by the public sector. For instance, research by Fernanda Bárcia de Mattos and Sukti Dasgupta (2017) suggests that India’s National Rural Employment Guarantee Scheme (MG-NREGA) has been instrumental in ensuring paid employment for women and has a positive and significant effect on women’s control over household decisions. ILO’s Global Wage Report 2018–2019 suggests that even though there is much diversity across countries, on average, hourly gender pay gaps are lower in the public sector than in the private sector. A good example of what can be achieved through public policy is Brazil in the first decade of the twentieth century. Between 2001 and 2009, 17 million jobs were created in the country, ten million of them jobs that gave access to the social security system stimulated by the Brazilian government’s package of economic and social policies. Brazilian macroeconomic policy in this period aimed at inclusive growth; and the labor market position of women was strengthened via a doubling in the minimum wage and more investment in labor standards inspection. The outcomes included an increase in women’s labor force participation rates from 54% to 58%, with the proportion of women in jobs that provide social security cards increasing
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from 30% to 35%, and a decline in gender pay gap from 38% to 29%. Most importantly, this decrease has been secured via an increase in both men and women’s wages, a good example of equalizing up (UN Women 2015).
Conclusion Gender discrimination is deeply inscribed in the institutions and practices of labor markets, especially the ways they fail to account for the benefits to the economy of the unpaid work that women do in the sphere of reproduction and in the ways they undervalue the paid work that women do. Some gender gaps in wages have been falling, but sometimes this is a result of equalizing down rather than equalizing up. Though some mainstream economists argue that gender discrimination is inefficient, gender discrimination can have advantages for businesses, and we cannot rely on competition to eliminate it. While it is important to have specific laws that outlaw discrimination and call for equal pay for work of equal value, these laws are limited in their ability to transform labor markets in ways that can reach all women. Strong labor market standards, public policy to create decent jobs, and women’s collective organization all have vital roles to play in creating truly gender equal labor markets. Acknowledgments I thank Niharika Yadav for her very helpful assistance in the preparation of this chapter.
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Theories of Discrimination: Transnational Feminism Inderpal Grewal
Contents Feminist Research and International Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Postcolonial Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Race/Ethnicity Critiques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Diaspora Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 The Mobility Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Transnational Feminism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
Abstract
In order to explain transnational feminism, this chapter will first describe trends in research that led to this feminist intervention. This chapter will then summarize the new interdisciplinary fields of research that contributed to transnational feminism, including postcolonial studies, race/ethnicity studies, diaspora studies, and critical geography. In the final section, this chapter will turn to transnational feminism and the different approaches that are part of this field and its contributions. Keywords
Transnational · Feminism · Postcolonial · Race · Diaspora · Mobility
In the past two decades, the term transnational has become popular in both the theory and practice of research and activism. What used to be called “international” or “cross-cultural” or “global” is often termed as “transnational.” While this
I. Grewal (*) Yale University, New Haven, CT, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_5
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widespread use of the term means that its meaning can change with dissemination, there are certain broad trends in usage that can be identified. This chapter considers the feminist contributions to this debate and will trace how the term gained traction because of changes in academic research in the 1980s that came in response to social movements, new theories of global social change, and critiques of economic globalization. In academic contexts, especially in the United States, a great deal of work on transnational, even outside of feminism, has come as a paradigm shift. Transnational histories are now a particular subset of the discipline of history, focusing on histories of connection and of regions and oceans, the movements of people across national boundaries, and the making of national boundaries. Transnational work in anthropology focuses on the mobility of people and their cultures, and on the impact and effects of transnational capital that crosses national boundaries, and the making of border zones and borderlands. Geographers, especially in the field of cultural geography, have used a broader notion of mobility to understand the relation between space and place. Sociologists have begun to use this concept, especially with regard to the production of differences of power between groups of women (Patil 2011). Migration studies in social sciences has used the term to understand international migrant and refugee movements. Work in literature and art has analyzed esthetic forms that cross national boundaries and the cultural work done in and by diasporic communities (Parikh 2017). In political science, it is feminist scholars who have used the term to speak of feminist movements, UN and international projects and solidarities, and the hierarchies within them. The academic site of the production of feminist interest in transnationalism has been, for the most part, within US academia, though it is crucial to understand that its influences came from and are used in research and social movements in other regions. Starting in the 1980s, historical understandings of varieties of national and religious feminisms became resonant in research showing how different nationalisms and social movements enabled particular feminisms to flourish, understanding how, when, and why they emerge in particular ways. These shifts came, in US contexts, in the aftermath of civil rights and feminist movements and globally, with the end of European colonial rule in some places, the emergence of new forms of imperialism, combined with the recognition of non-white immigrant populations in the United States and United Kingdom. By the 1980s, as neoliberal economic globalization gained popularity, and as transnational corporations became ubiquitous, the term transnational began to be used within academic research as well. In order to explain transnational feminism, this chapter will first describe trends in research that led to this feminist intervention. This chapter will then summarize the new interdisciplinary fields of research that contributed to transnational feminism, including postcolonial, race/ethnicity studies, diaspora studies, and critical geography. In the final section, this chapter will turn to transnational feminism, the different approaches that are part of this field, and its contributions.
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Feminist Research and International Problems Feminist movements in the 1960s led to changes in academia, as researchers began to study the lives of women and to argue for changes in policy that paid attention to women’s lives. While research and teaching concerning gender and feminist movements had emerged in departments of English and Humanities in the US by the 1970s, they initially focused on histories and writings of women in the United States and Europe. In the social sciences, as feminists struggled to be visible in academic and policy spaces, they began to examine inequalities between men and women arising from the devaluation and erasure of women’s work. In fields such as economics, feminist economists addressed wage and income inequalities, though this kind of work was often marginalized. Also marginal was the study of inequality outside of Europe and the United States, and the feminist work on global inequality was also seen as outside the mainstream of social science in the 1970s and into the 1980s. However, feminist scholars continued to examine how women were ignored, erased, and devalued in many societies, including within academia. Feminist scholars across the globe joined the effort to understand how inequality, violence against women, and erasure of their contributions to arts and sciences all impacted their status and condition in their societies. Historians and political scientists examined how gender played a role in conflicts and wars and how political violence and militarisms were enabled by ideas of male power and masculinity (Enloe 2016). Women’s studies (now often termed gender studies) emerged because of the need for interdisciplinary analysis of gender, as spaces where new methodologies could be introduced (instead of the demands of the disciplines which were often masculinist) and where activists and artists would connect the academic formation to the feminist movement (Wiegman 2002). Feminists began a critique of the academic disciplines, institutions, and knowledges that excluded women and which were often patriarchal. One area of policy and work that was concerned with international issues (outside of women’s history) was in what was called Development research – the policies and projects that, beginning in the 1960s, international institutions such as the UN and World Bank (among many others, including nations) used to “modernize” the “third world.” Ester Boserup argued that Development and mechanization enabled women’s separation from waged labor leading to their devaluation (Boserup 2007). Maria Mies argued that capitalism used women for cheap labor (especially in their roles as housewives) across capitalist and socialist societies that constituted a sexual and international division of labor (Mies et al. 2014). By the 1980s, feminists argued that women had been left out of Development policy and women’s needs had been neglected, especially because Development focused on industry and science as engines of modernization. However, the subsequent inclusion of women within Development research and practice meant that even feminist research and policy came to believe that capitalism was not an impediment to development and that women could be added to development policy and be modernized. This project came to be known as Women in Development within international organizations, WID; organizations run by women from the Global South organized their own network, DAWN, Development Alternatives with Women for a New Era, to push for their
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own agendas. As Shirin Rai argues, WID ignored the social and political contexts of women’s lives (Rai 2013). Some feminist researchers criticized a Development approach that left out both colonialism and capitalism in its analysis and had relied on a normative belief in ideas of labor and productivity based on male standards. Amartya Sen and Martha Nussbaum developed the “capabilities” approach that sought more recognitions of the freedoms and rights that would allow women to succeed (Nussbaum and Sen 1993). Despite these changes, the reliance of Development on notions of modernity as the goal has been subject to critique, and even the subsequent Gender and Development approach, which tried to focus on broader social formations of gender, remained tied to notions of Western modernity as the end-goal of Development. As its critics revealed, GAD made women into unwitting targets of Western policy and global institutions that experimented on their lives without recognition of cultural realities (Kabeer 1994). Decades of Development policy have led to uneven results for women. Critics also fault Development policies for ignoring alternative ways of living and different goals that were central to many communities, including indigenous ones. Thus, some scholars see Development as a form of neocolonialism and Development Economics as a form of power whose belief in modernity and markets as progress needed to be rethought (Escobar 2011). What is relevant to the discussion of transnational feminism is that it emerged from resistance to the way that Development experts thought of the “third world woman” as a homogenous and passive recipient of Western aid and policy. In a now-classic essay, “Under Western Eyes,” Chandra Mohanty offered critical examinations of discourses of “third world women” as stereotypic and abjected subjects to be rescued by Western Development (Mohanty 1984). However, it was not just in Development studies but also in all kinds of research that simplistic ideas of non-Western women were offered. What was common in feminist research was the erasure of colonial histories and colonial policies and the tendency to blame non-Western cultures for the subordination of women. Thus, blame was placed for gendered inequalities and subordination on a culture and a patriarchy, without attention to how culture and patriarchy had been “recast,” institutionalized, and deployed by colonial regimes and nationalist patriarchies to enable colonial rule (Sangari and Vaid 1990). What was left out in such analyses was not just the imposition of Western norms for women’s lives or the homogenizing of women as the same everywhere but also how patriarchal European and American cultures presented themselves as superior and altruistic in rescuing women from their cultures; Gayatri Spivak would pithily call this a colonial project of “saving brown women from brown men” (Spivak 1988) that left out the violent and extractive history of colonial rule.
Postcolonial Contributions Postcolonial critiques such as those by Gayatri Spivak did not only emerge in the US academy but came from many movements, from projects of decolonization and critiques of contemporary colonial histories, and from many global regions and
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routes. While in the 1980s postcolonial studies scholars in the US were interested in seeing how Western biases misrepresented the lives of women and around the world, as Edward Said would argue in his groundbreaking work, Orientalism, they also worked to break down what would be called “orientalist” ideas in research (Said 1978). They hoped to dismantle the “tradition-modernity” divide that had seeped into social science, global health, international relations, and Development studies – all with profound impacts. They argued that seeing non-Western societies as “traditional” did not account for the profound changes that had created new modern ways of living everywhere. Nor did it encompass the traditional elements in Western societies. Postcolonial feminism critiqued the production of universalized categories and the overwhelming power of US and European knowledge production in the making of empire and even in anti-colonial nationalisms (Narayan 1997; Jayawardena and Zakaria 1986; Shohat 2001). Feminist postcolonial theorists, however, did address the relations across boundaries, across cultures and international domains, which produced different, gendered subjects in relation to global and other projects (national and subnational as well as regional) in historically specific ways. They examined orientalist biases in the representation of women who were not white or European and critiqued the exoticization and sexualization of female subjects (Yegenoglu 1998). Gayatri Chakravorty Spivak, for instance, suggested that the very project of representation in European culture was warped by its colonial history and the problematic concepts and categories that comprised knowledge. Her approach also engaged with thinking of the global circulation of representations of non-Western women (Spivak 1985), especially in the ways that Western women’s subjectivity was constituted by contrast with the presumed unfree nature of the Asian woman. The questions of how modernity made different subjects globally, how patriarchies differed, and how what it meant to be a women (or to have a gendered identity) varied historically and regionally were both critically rethought (Weinbaum et al. 2008). While scholars questions how Africa had been represented by colonial knowledge (Mudimbe 1988), African feminists examined how gender was different in Africa, for instance, and whether feminism could be dissociated from its Western history of representing Africa through racist notions of savagery or backwardness (Oyěwùmí 2003). Postcolonial theory was also concerned with the problems and theorizations of nationalisms and desires and difficulties of decolonization because of how colonial laws and institutions had become embedded. However, though it engaged in a critique of nationalism and nationalist histories, it did not engage as much with internationalism or with diasporic and migrant populations. While postcolonial theory brought to light the problem of development and modernization, geopolitical and national inequalities, and commensurabilities, they also relied on the integrity of national and territorial boundaries. It was the work of artists such as Trinh. T. Minhha, a Vietnamese-American filmmaker and scholar, who bridged the divide between postcolonial studies and the important area of research that comprised race/ethnic studies in the United States (Trinh 1989).
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Race/Ethnicity Critiques Trinh. T. Minh-ha’s films and writings suggested that representation of brown and black women in the United States was fundamentally also about problems in colonial esthetics and how Western cultures had come to see Asian women as sexualized and subordinated. As a woman of Vietnamese origin who came to the United States after its war, she examined how colonialism produced representations of difference, racial and cultural, which came from the history of white European anthropology; she argued that women of color artists and writers could create other representations that would resist the colonial ways of seeing. Trinh’s contributions came not just from postcolonial but also from the emerging area of activism and research that had come to be called ethnic studies in the United States. It had begun in the 1960s and took shape in US universities also as Asian American, African American, Native American, and Latino studies. Emerging from the ferment and activism of the 1960s, and with solidarity with “third world movements,” it suggested that the United States itself was a racial nation, with long histories of discrimination. Thus, the United States needed to be understood through both its racial heterogeneity and racial hierarchy. Underfunded and precarious in the academy, scholars in these fields focused on the US history of racial exclusion, seeking to be part of “third world” liberation and arguing for understanding ongoing racisms and the histories of colonization of Native American lands, use of Asian labor in the making of the country, and United States foundational economy of slavery. Engaged in both scholarship and activism, with concerns to educate population within and outside the academy, it changed the conversation in numerous fields of research to engage with racial history and its contemporary formations. For feminist research, this focus on race and racism was critical for examining exclusions by white feminists, for showing how “women of color” or black or brown feminists had to struggle for space and representation (Taylor 1998, Hartman 2008, 2019); in the process, they showed how whiteness itself was a form of privilege. Black feminism emerged as a specific historical formation, a subjectivity forged out of a history of enslavement, Jim Crow, and ongoing racisms that operated in particular ways on the bodies of black women (Combahee 1977, Collins 2008, Hull 1982). Many argued that black women should not call even themselves feminists since the term and the concept referred to notions of equality and modernity that were white and European (Walker 2003, Oyewumi 2004). The race critique in legal scholarship, entitled Critical Race Studies, revealed how the law itself was an instrument of racial power and that it was not a neutral arbiter but rather a structure that enabled the power of white privilege (Bridges 2019). All these critiques changed feminist research in the US. They broke that consensus, produced by the first generation of academic feminist scholarship in the US and UK academies, of a universal woman and a global feminism. Scholars examining the United States as a slave-holding and colonial society began to examine how race divided black and white women and how race was a reason why there could not be one feminism or unified feminist movement. Postcolonial studies feminists from Asia and Africa and the Caribbean argued that colonialism’s violence included the
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making of gendered subjects through new patriarchies and that African women, for instance, had to produce their own feminism (Oyěwùmí 2003). Other scholars, participating in the uprising for racial justice in the United Kingdom, called for analysis of feminisms as forms of racial and imperial power. Valerie Amos and Pratibha Parmar’s phrase, “imperial feminism” (Amos and Parmar 1984), and Hazel Carby’s “White Women Listen” opened the doors to critiques of Western feminism, based on questions of colonialism, empire, and the racial difference (Carby 2000). Such work led to calls for Western feminism to recognize its own patriarchal and racial formations and limits. In particular, feminists asserted that women were different because of race and culture, and so their needs were different, and colonial histories mean that there was no commonality across women globally. Black feminism, also emerging as a “womanist” rather than “feminist” politics, argued that histories of racism and slavery meant that feminism was different for black women because it involved a struggle against slavery and racism and for civil rights rather than one solely against patriarchy. Thus, a universal concept of feminism, one that said that globally all women were together in a struggle against misogyny and patriarchy, was challenged by black feminisms. Many of the United States-based feminists such as Angela Davis, bell hooks, Audre Lorde, and Gloria Anzaldúa also emphasized that race, class, and sexuality were central to the work of imperialism, capitalism, and race in producing subordination through gender. The influential collection, This Bridge Called My Back, published first in 1981, which brought together writings by “US women of color,” had also become prominent, first in radical anti-racist feminist circles in the United States and also in academic ones, and it brought out questions of racial difference among feminists of different ethnicities and races, suggesting that “women of color” was a new formation that would work against “white” feminism (Moraga 2015). Feminist scholars addressed the relation between race, class, sexuality, and nationalism in terms of the “double” or “triple” oppression caused by these multiple factors; the famous statement by the Combahee River Collective is one example of this work. Additionally, feminist theorists of color from Audre Lorde to bell hooks, to novelists and writers, emphasized the feminist critique of heteronormative family and nationalism. Critical race studies coined the notion of intersectionality, as an answer to understanding the multiple oppressions experienced by black women (Crenshaw). Kimberlé Crenshaw’s (1991) innovative use of this term was a means to address the problem of the gender-versus-race argument, by which women of color were often asked to choose politically between their racial or feminist identities. Ethnic studies also focused on a history of colonialism, particularly the colonization of the Americas and the attempt by white settlers to force Native Americans into servitude, poverty, or removal from their ancestral lands. The concept of “settler colonialism” has become critical in understanding the racial and colonial logic of “manifest destiny” of Westward expansion by European settlers. Ethnic studies also brought attention to Asian American and Latin American histories, examining histories of migration and the making of national boundaries (in Texas and California). Gloria Anzaldua suggested that borderlands were distinct spaces of racial
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history and hybridity (Anzaldúa 2012). More recently, research on Muslim American and Arab American groups has both crossed these ethnic divides (because of the long history of black Muslims in America) and produced new questions regarding the making of the “Muslim” as a racialized category in the aftermath of 9/11 (Ahmad 2002).
Diaspora Studies If the division between international and domestic, or that between the national and the international, was maintained within postcolonial and US ethnic and race studies, it was the study of diasporas, particularly of brown and black populations in the West, that revealed the gaps of so much research that took the nation-state and citizenship as unproblematic and settled concepts. Emerging out of the Birmingham School of Cultural Studies in the United Kingdom, from interventions based on race and racism, and drawing on the concept from Jewish communities, diaspora studies was a project of understanding racial Others in the West, on the one hand, and on studying their relation to nationalisms in the West, on the other. This understanding of diaspora, as against white nationalism also came from the formation of “Black Britain” as a political movement emerging from protests against racism and which brought together British Asian and Caribbean communities. In the work of scholars such as Stuart Hall, Paul Gilroy, and Hazel Carby, it also examined how class in Britain had to be understood through considerations of race and racism (Carby 1999; Hall 2018; Gilroy 1991); these scholars along with artists and activists such as Sutapa Biswas and Ingrid Pollard intervened in the formation of a new critique of esthetics based on race. The specificity of diaspora, in its distinction from the place or origin or settlement, was a key idea. In the work of Paul Gilroy, theories of nationalism were modified to suggest that racialized communities did not just assimilate but came to have a double consciousness, as W. E. B. Du Bois had argued, producing strategies of difference and identification that could resist the dominant white nation. Theorizing diasporas as being against dominant nationalisms, the academic work of Black British Cultural Studies joined with postcolonial and race critiques to examine exclusionary white nationalisms as ongoing expression of imperial power. Diaspora studies became also a way to think about the racial diversity of subjects of empire and to show how some communities remained connected across continents. In this latter approach, it linked up with studies of the Jewish diaspora to suggest that diasporas spanned continents and regions and comprised mobile populations and groups. Over the last decades, “diaspora” has become a key term within cultural criticism. It has become widely used to signal a new way of thinking about race, gender, colonialism, and globalization, and migration and enabled the study of emerging and changing cultures resulting from global migrations. Previously understood predominantly in terms of assimilation or acculturation, or as movement from home to host, migrations were seen within diaspora theories as creating racially non-white
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communities as sites of resistance, rather than within a teleology of assimilation. It has allowed researchers to think of communities as racialized communities struggling against violent white societies and connected to other similar struggles globally rather than solely in terms of their place of origin as well as their difference from both origin and place of settlement. Diaspora revealed that cultures and nations could travel and move and be heterogeneous and movable and that the boundaries of nations could not be mapped onto supposedly fixed territorial boundaries. Feminist scholars such as Saskia Sassan argue that diasporas form “countergeographies of survival” that are central to global cities and their patterns of migration and that in this survival it is important to pay attention to gendered patterns of work in relation to urban space (Sassen 2000). Avtar Brah theorized diaspora not as an identity but as a space, adding a feminist, relational, and geographical dimension to the concept and arguing that diaspora space is the site of intersection of varying formations of difference but with emphasis on gender, race, and class (Brah 1996). Emphasis on race and sexuality, nationalism, and diaspora came from the work of Chicana feminist, Gloria Anzaldua, formulating “border” theory and “borderlands” as a specific project of making racialized subjects in the United States (Anzaldúa 2012).
The Mobility Paradigm Along with an interest in diaspora, the 1980s also saw an interest in “mobility” and what came to be called critical mobility studies and the “mobilities turn” in social sciences. Epistemologies of flows (Appadurai 1996), oceanic connections (Gilroy 1993), intimacies (Stoler 2010), and historical diasporas (Chow 1993) disrupted the traditional disciplinary and interdisciplinary formations of research on the nationstate. While “mobility” was often a matter of engineering and technology research, cultural geographers and anthropologists began work on questions of epistemology and historiography in relation to mobility, not just of people but also of the movements of all sorts of aspects of society that were understood as static or fixed. In particular, this meant a rethinking of boundaries and borders of nation-states as well as of notions of settlement and nativism. Rather than focus on cultures and communities as bounded, could we think of them – as in diaspora studies – as moving? Was movement not formative of all communities, and not just some of them, so that we needed to consider migration as normative rather than exceptional? What was the historiography of movement that had been lost in the histories of settlement that became normative? Mobility research suggested that a bounded and fixed notion of the nation-state was insufficient to understand social and political formations both historically and, in the present, because its boundaries and its people were not fixed: who became a citizen, how nation-states changed by colonialisms, and how national boundaries could not be thought of as unchanging. Thus, the foundational assumptions of settlement as normative to all societies needed to be rethought, and theories of space and movement had to be considered more thoroughly. Doreen Massey’s feminist critiques of conceptualization of place and space argued that space was
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dynamic and that place was not frozen in time but was an ongoing process of making meaning of space – turning space into place. Geographers such as John Urry and Mimi Sheller argued for a sociology of mobility, of understanding different kinds of mobilities from tourism to commuting as a key to societies (Urry 2004). Tim Cresswell extended this work into a critique of “sedentarist metaphysics” that he argues pervades much of the ways in which knowledge and power operate, in that it assumes that people, nations, concepts, space, and place are settled within stable ideas of territory and that belonging and rootedness are normal, while mobility and movement are seen as suspect; he saw modernity as a move to deny that people and societies were on the move even before the modern period (Cresswell 2007). For anthropologist Liisa Malkki, this normative notion of rootedness and identity requires a theorization of mobility so that refugees were not understood as people out of place, but within a long history of the movement of people impacted by conflict or other catastrophic events (Malkki 1995). Joining with postcolonial and race critiques, researchers on mobility questioned Western modernity’s colonial claim of new or exceptional power made through narratives of exploration, encounter, globalization, migration, or travel, suggesting that such claims were made by erasing longer histories from other regions.
Transnational Feminism Transnational feminism came out of the political and social ferment of social movements and academic innovations mentioned above. Different theories of transnational feminism drew from critiques and projects of Development and from postcolonial studies the emphasis on histories and ongoing colonial and neocolonial impacts. From cultural geography, it brought the questioning of any distinction between spatial categories such as “local” or “global,” from diaspora studies, the concept of nationalisms as enabled or resisted by populations from afar and from within, as well as the focus on migration. Race/ethnic studies revealed that nations in the “West” were uneven and unequal spaces with histories and communities that stretched around the globe; migrations and displacements produced new solidarities – especially racial ones – across national boundaries. From mobility studies, transnational feminism brought questions of migration as a nation on the move, to thinking of border-crossings and borderlands as particular spaces of transnational memory and movements; finally, it enabled questioning of the notion that territorial nation-states were natural or stable and argued that social worlds and social life were produced by historically shifting connection and movement across borders. It argued against both the claim of a “global sisterhood” between women that would be united in resisting patriarchy (Morgan 1996) or the claim that non-Western patriarchies were the source of all problems for women outside the West. By the new century, transnational feminism or, as Grewal and Kaplan would call it, “transnational feminist practices” became a sprawling field of feminist research, with particular trends, approaches, and differences. There are three main strands of how “transnational feminism” is being used in academic contexts: one as an
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academic formation within research and teaching on gender that is predominantly focused on knowledge politics, disciplinary questions, and research methodology; this would be what Grewal and Kaplan would call “transnational practices” as a research analytic. A second approach is to understand this as an identity formation and as a form of international solidarity as in calling someone a “transnational feminist” or in positioning a scholar or scholar-activist or activist as being part of “transnational feminism.” A third is a focus on solidarities, inequalities and activism between feminists across national divides, and across academic and activist spaces, and there are divergent approaches to this question of connections. While there is often a blurring of the differences between these three approaches, all three usages reveal that work on transnational feminism emerged to break down binaries such as activist/academic, modern/traditional, domestic/international, and “local-global” and to understand global connections and differences. Many, though not all, of these approaches engage with the anti-colonial and anti-imperial and antiracist politics of feminist activisms that insist on understanding how imperial history and power undergirds feminist politics today, especially as they impact feminist connections and activisms across national boundaries. However, the capaciousness of the term, and its proximity to transnational capital and transnational corporations because of a shared terminology, and its focus on breaking down national boundaries have made for both critical reception and/or uneasy adoption. For some, it suggests the presence of transnational corporations, while others use it because it is precisely because it is a reminder of the context of globalized capital. Some use it as a synonym for international issues, and some as a stand-in for imperialism. But it is because of its interventions in the politics of knowledge, research, and activism that the term has gained resonance. In the anthology Scattered Hegemonies, published in 1994, coedited by Inderpal Grewal and Caren Kaplan, and also in another anthology, Feminist Genealogies, Colonial Legacies, Democratic Futures, by Chandra Talpade Mohanty and M. Jacqui Alexander (1997), all attempted to bridge understandings of subject populations in the United States with those outside it and to think about how histories of colonialism, feminism, racism, and modernity linked gender constructs and feminist movements across nations and nationalisms. Grewal and Kaplan argued that “local-global” spatial constructs erased the constitutive connections across national borders and that transnational forces were critical to making gender, albeit in different ways because of different hegemonic arrangement of power and specific histories of colonialism and imperialism. Working in a related but somewhat different framework, Mohanty and Alexander offered highly influential critiques of categories of “non-Western” women and Western development regimes, calling for transnational feminism as a response to capitalist and colonial as well as racial heteropatriarchies. They focused on the need for solidarity across national divides and the breaking down of activist/academic binaries to think about feminists collaborating for the purpose. Grewal and Kaplan saw transnational feminist approaches as a way to understand the hierarchical and asymmetrical relations among diverse feminisms created by myriad institutions and contexts globally, many of which resulted from European imperial cultures. They sought attention to the connections
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and links – rather than commonalities and differences, as in the prevailing comparative approach – between feminisms that produced power relations within them. They argued that these histories made critical differences between women so that transnational feminist practices involved unequal relations within cross-border projects; they argued that histories of modernity and colonialism created networks of power that influenced differing constructions of gender. Along with Mohanty and Alexander, they were critical of the “global sisterhood” assumption voiced by many Euro-American feminists, though others saw such a “sisterhood” as a strategic participation of feminists with their own cultural and political moralities making up a transnational public (Ong 1996). For Chandra Mohanty and Jacqui Alexander, transnational feminism enables understanding of solidarities based on racial difference that have been enabled by a critique of imperial capitalism. They focused on how racial empires have produced conditions of exploitations and how women have forged collective movements in resistance. Seeing their own work as enabled by collaboration across identities (from India and Caribbean) and understanding the divergent racial identities placed on them as they moved to the United States, Mohanty and Alexander were interested in thinking how “democratic futures” could be formed by collaborations and solidarities. With an emphasis on feminist praxis that resists the ways that postcolonial nation-states recuperate colonial gendered and sexualized hierarchies and subordination, Mohanty and Alexander called for activism that is based on resisting colonial and capitalist heteropatriarchies. They argued against “global feminism” that relies on Western norms and the need for a democracy uncoupled from capitalism and engaged with socialism and decolonization. A key aspect of transnational research focused on critiques of nationalism and the impact of colonial making of national boundaries. While some scholars in transnational research argued that the nation form was being overcome by global and regional entities and boundaries (such as the European Union), others critiqued nationalism rather than the nation form (Jayawardena and Zakaria 1986). Some saw the making of new nationalisms within transnational networks, especially in the role of diasporas in nationalist longings, suggesting that the transnational did not transcend nationalism even if it produced international and transnational alliances (Axel 2001; Mankekar 2015). Transnational approaches emphasized both the operations of transnational capital in the present and in the making of these nationalisms. Consequently, these researchers saw the national as the global-national, that is, reliant on global capital while claiming to be independent of it. Transnational feminist research was also critical of the “comparative feminism” approach that reduced the study of feminisms to nationally bounded “areas.” This transnational critique emerged out of a recognition that national comparison and comparative political and literary studies were a colonial project, seldom able to break out of the North-South binary, which was variously metaphorized as moderntraditional, advanced-backward, or mobile-static. Most so-called comparative work operated through comparisons between the Global North and Global South as separate, bounded entities and civilizations that could be mapped onto existing nation-states; transnational studies hoped to overturn this diagnosis of colonial
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epistemologies in favor of lines cutting across these boundaries and networks that influenced how gender was constructed through relations across nations. In particular, transnational feminist approaches were united in critiquing the erasure of the connections and linkages among empires and nationalisms and among the forms of power that circulated globally to create both colonialisms and anti-colonialisms. Such an understanding was important in showing how something called “foreign” was used to create something called “domestic” (Kaplan 2005), how social movements moved across national boundaries and became meaningful in other places by operations of power (Basu 2016; Savci 2021; Sameh 2019), and how boundaries and networks shift and change. It was because of this trenchant critique of empire, the power of networks (including feminist ones) and transnational capital, that the activist aspect of transnational feminism became an important formation. If some saw transnational feminism as an academic enterprise limited to theoretical debate, Richa Nagar and Amanda Lock Swarr argued for its possibilities in the practice of feminism (Nagar and Swarr 2010). They argued that the “praxis” aspect of these theories, which had applications for feminist organizing, could be understood as centering on alliances across geographies, communities, and class that would have to be long-standing and reciprocal, based on trust and relationships. They continued the focus on networks and the divides between feminists but suggested that these had to be countered by forming relationships between activists and researchers, by collective work on writing and translation across divides. They also focused on the critique of neoliberal capital and empire but suggested that connections could be made to transcend some of the hierarchies of nationalism, class, and gender in order to forge alliances. They saw themselves as doing the transnational activist feminist research that was being called for, but with the goal of making the relations that would create transnational activism across social and political borders. From a very different political and academic space, another strand of transnational research came to the forefront. Margaret Keck and Kathryn Sikkink, examining the advent of feminist organizing in policy and advocacy circles, argued in their book, Advocacy Beyond Borders, that it was feminist networks across national divides that would lead to robust policy changes at the international level, leading to changes in international policy (Keck and Sikkink 1998). They argued that a “transnational advocacy network includes those relevant actors working internationally on an issue, who are bound together by shared values, a common discourse, and dense exchanges of information and services” (Keck and Sikkink 1998, 2). Bridging theories from sociology and political science, this approach drew on research on social movements that crossed domestic and international politics to argue that emerging organizations such as NGOs (nongovernment organizations) could be activist spaces that involve the work of volunteers and create new relationships between women. While this approach did not address the histories of colonization or race that created hierarchical relations between women, it came to be a powerful approach to understanding how feminist international policy was being produced through feminist networks. Later contributions by political scientists, Myra Marx Ferree and Aili Tripp, have continued in seeing feminist practice in what they call the
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“transnational arena” as the “intersection of the international and the local” (Ferree and Tripp 2006, p. vii) that came about with the UN sponsored World Conference on Women in Beijing, which had an NGO Forum. They examine the use of organizations and treaties in shaping feminism as a “product of transnational dialogues and disagreements, coalitions and networks” (p. viii) and shaping what they call a “transnational opportunity structure,” by which they mean the organizations, policies, and networks that enable feminism to flourish in both international and local contexts. While they pay attention to inequality between women, they argue that the transnational arena provides a space for feminist organizing and representation of women from the Global South. They depart from the work of Grewal and Kaplan in the critique of “global feminism” as Western hegemony and from Mohanty and Alexander in their critique of global capitalism. Rather they see the global as providing both opportunity for feminist movements and organization to flourish in new networks across national borders. This strand of discussion and debate has been active in understanding questions of human rights and other international mechanisms used by feminists. This focus on transnational networks and activism has also emerged in the “theory” vs. praxis divide, with many researchers arguing that the “theory” approach does not address or encompass what is going on in the activist arenas. Some of these debates follow on Ferree and Tripp’s analysis to argue that global feminism is much more complex and cannot be seen as a space of a neocolonial hegemony (Mendoza 2002). They advocate for a more positive approach to feminist activism that suggests that transnational feminism is much more independent and critical of globalization processes that emerged in the 1980s and critical of the neoliberalism underlying them. Val Moghadam’s research on “transnational activist networks” suggests that these make up a “transnational public sphere” that emerged to address the inequities of globalization, particularly neoliberal capitalism and religious fundamentalisms, from a feminist perspective (Moghadam 2005). Debates on NGOs have proliferated in recent decades, as researchers have examined the nature of this field of activism that emerged at the end of the twentieth century, with some seeing these as another arm of imperialism, others understanding them as transnational activist networks, and yet others arguing that these represent the waning power of many nation-states in the Global South (Kamat 2002; Bernal and Grewal 2014; Thayer 2009). These new approaches to women-focused and feminist NGOs have included analysis of imperialism and power as central to the making of feminist networks. Importantly, it was not just gender but sexuality that came to be understood as transnational, as scholars studied how LGBTQ movements spread and how empires used sexual practices for power, as with the British-era sodomy laws that were imposed in British colonies across Asia, Africa, and the Caribbean (HRW 2008; Ngaruiya 2019). The research on transnational sexuality also engaged with the ways that sexual practices and sexual identities had a variety of histories that produced new visibilities and new terms through the work of modern nation-states and social movements. The impact of imperialism, not just in the past, was understood to be critical in the making of sexuality within national frameworks; nationalism and sexuality were seen as key aspects of histories of sexuality, nationalisms, and
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colonialisms. Critique of “global gay” formations that assumed that LGBT movements had “freed” oppressed gay people in non-Western and so-called unmodern spaces emerged along with research on sexual tourism, the politics of sexuality at the border (Luibhéid 2002), and the geopolitics of “pink-washing” that claimed that imperial states were more supportive of queer communities (Puar 2017). An important critique of “homonationalism” emerged to suggest that some of those with LGBTQ identities had embraced a nationalism that could be xenophobic and racist (Puar 2017). Within gender and sexuality research, there was an important formulation of “transnational sexualities” which included both a history of imperialism and ongoing power of the West (Alexander 2006) to inform the making of these identities. How had British-era sodomy laws criminalized sexualities and what made them continue into the present? What were the transnational impacts of new Christian evangelical missionaries who advocated also for criminalizing gay sex? How had the biomedical project of controlling the HIV/AIDS pandemic impacted sexualities? While some scholars saw terms such LGBT as Western imports (Massad 2008), others debated how to understand new visibilities created by these terms and what to make of practices of sexuality that existed beyond and in spite of the heteronormativity of colonialisms and nationalisms. The use of sexuality within the Global War on Terror by the United States was examined (Puar 2017), while others examined how HIV/AIDs created new categories of sexuality through public health practices (Dutta 2013). A third area of work was to understand the relation between migration, diaspora, and transnationalism (Manalansen 2003) and understand both the impact of border-crossing on sexual identity and the experiences of queers in diasporic spaces. All of this interest has led to discussion of transnational issues in the study of the history of sexuality (Wiesner-Hanks 2011). More recent directions in transnational feminism have built on much of the earlier work on empire, neoliberal capitalism, Development, global governance, and the rise of religious nationalism, as Anneeth Kaur Hundle suggests (Hundle et al. 2019). Jennifer Nash points out that the perception in the US academy is that transnational research is about international issues, while intersectionality seems to be concerned with the United States and questions of race (Falcón and Nash 2015). Others argue that transnationalism seems to be only about the United States; this view ignores the quite large body of work that is being done by scholars working outside the United States (Fernandes 2013). The question that remains even now is how to think race as a project of many Euro- American empires contending with disjunctive and different imperial histories and anti-colonial movements. Tina Campt and Deborah Thomas, in their special issue for Feminist Review on the theme of “Diasporic Hegemonies,” argue that a transnational feminist analytic helps in understanding the role of race and gender in diasporas (Campt and Thomas 2008). They argue that it enables a recognition of heterogenous forces that shape racism, migration, and displacement as well as the internal relations within diasporic communities; they focus on what they call the “intra-diasporic differences, asymmetries, and limits of diasporic relationality.”
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Feminist research had also used transnational approaches to think about migration and the history of colonialism as one about divergences and connections in racial formations, questioning notions of nativism and of belonging or non-belonging based on religion, race, or ethnicity (Ahmad 2017). From the robust work on migration and refugee asylum that questions these categories as constructed to create a sedentary and settled national body, to work that calls itself “critical refugee studies” and “critical security studies” (questioning the frameworks of state security and the making of human security states), transnational feminism questions the construction of nations and nationalisms, borders, and border zones (Noori 2020; Ghosh 2017). Migration research has seen a change from understanding the migrant as an interloper to seeing migration as a normative subject (influenced by mobility studies), whose mobility is not new in the modern world. It has also suggested that immigration as unidirectional move from “home” to “host” needs to be rethought in a context in which people move across multiple borders and as borders also move away from people. Furthermore, the territoriality of the postcolonial nation reveals shifting and mobile boundaries; the nineteenth-century colonial “carving” of Africa and the “partition” of South Asia and Palestine are examples, revealing that boundary control and making enable power over peoples, regions, and economies that preexisted these boundaries and now come to have transnational memories and imaginaries of previous times. Thus, it is important to think transnationally about national boundaries, to see how boundaries are a matter of power, and border zones are policed and militarized to produce a citizenry and a nation-state. Though this chapter has focused on scholarly strands and trajectories of transnational feminism, the term continues to change over time and with usage. Emerging politics and events and changing theories and methods in academia all will impact how terms are used and where they are used. The many debates within feminist research can be attributed to the tendency of feminist movements to be selfreflexive and to attend to changing social movements nationally and globally – which they must if they wish to remain relevant. What is clear is that over the decades since the study of feminism moved into academic sites in the 1970s, what was conceptualized as a feminism has now become understood as feminisms, and feminist research has shifted to understanding the histories of connections and the making of inequalities between gendered and, in addition, sexual subjects globally. Understanding how gendered subjects are produced in their transnational difference and connections remains an important task.
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Morgan R (ed) (1996) Sisterhood is global: the international women’s movement anthology, reprint edn. The Feminist Press at CUNY, New York Mudimbe VY (1988) The invention of Africa: gnosis, philosophy, and the order of knowledge, 1st edn. Indiana University Press, Bloomington Nagar R, Swarr AL (2010) Theorizing transnational feminist praxis. In: Critical transnational feminist praxis. SUNY Press, New York, pp 1–20 Narayan U (1997) Dislocating cultures: identities, traditions, and third world feminism, 1st edn. Routledge, New York Ngaruiya Njambi W (2019) What sexuality? Whose knowledge? Mapping “heterosexuality” and “homosexuality” within transnational feminisms. Gend Women’s Stud 2. https://doi.org/10. 31532/GendWomensStud.2.2.001 Noori S (2020) Navigating the Aegean Sea: smartphones, transnational activism and viapolitical in (ter)ventions in contested maritime borderzones. J Ethn Migr Stud 2020:1–17. https://doi.org/ 10.1080/1369183X.2020.1796265 Nussbaum M, Sen A (eds) (1993) The quality of life, reprint edn. Clarendon Press, Oxford, UK/New York Ong A (1996) Strategic sisterhood or sisters in solidarity? Questions of communitarianism and citizenship in Asia. Indiana J Glob Leg Stud 4:107–135 Oyewumi O (2004) African Women and Feminism: Reflecting on the Politics of Sisterhood. Trenton, N.J.: Africa World Press. https://www.amazon.com/African-Women-FeminismReflecting-Sisterhood/dp/0865436282/ref=asc_df_0865436282/?tag=hyprod-20&linkCode= df0&hvadid=312039872799&hvpos=&hvnetw=g&hvrand=762559162626175513& hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9031535& hvtargid=pla-584586051796&psc=1&tag=&ref=&adgrpid=62149175916&hvpone=& hvptwo=&hvadid=312039872799&hvpos=&hvnetw=g&hvrand=762559162626175513& hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9031535&hvtargid=pla584586051796 Parikh C (2017) Transnational feminism. In: Goyal Y (ed) The Cambridge companion to transnational American literature. Cambridge University Press, Cambridge, UK, pp 221–236 Patil V (2011) Transnational feminism in sociology: articulations, agendas, debates. Sociol Compass 5:540–550. https://doi.org/10.1111/j.1751-9020.2011.00382.x Puar JK (2017) Terrorist assemblages: homonationalism in queer times, anniversary, 10th anniversary edn. Duke University Press Books, Durham Rai SM (2013) The gender politics of development: essays in hope and despair. Zed Books, London Reilly N (2011) Doing transnational feminism, transforming human rights: the emancipatory possibilities revisited. Ir J Sociol 19:60–76. https://doi.org/10.7227/IJS.19.2.5 Said E (1978) Orientalism. Pantheon Books, New York. Sameh CZ (2019) Axis of hope: Iranian women’s rights activism across borders. University of Washington Press, Seattle Sangari K, Vaid S (1990) Recasting women: essays in Indian colonial history. Rutgers University Press, New Brunswick Savci E (2021) Queer in Translation. Durham N.C.: Duke University Press. https://www. dukeupress.edu/queer-in-translation. Sassen S (2000) Women’s burden: counter-geographies of globalization and the feminization of. . . J Int Aff 53:503–524 Shohat E (2001) Talking visions: multicultural feminism in a transnational age, Documentary sources in contemporary art. MIT Press, Cambridge, MA. Paperback – common Spivak GC (1988) Can the subaltern speak? | Gayatri Chakravorty Spivak | academic room. In: Nelson C, Grossberg L (eds) Marxism and the interpretation of culture. University of Illinois Press, Urbana, pp 271–313 Stoler AL (2010) Carnal knowledge and imperial power: race and the intimate in colonial rule. University of California Press, Berkeley. Paperback
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Discrimination as Focal Point Markets and Group Identity Kaushik Basu
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Markets and Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Art of Creating Optimal Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Addendum. Empirical Tests and a Normative Caveat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . New References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Abstract
This chapter presents a theory of discrimination for markets in which there are complementarities between different tasks. It is shown that, in such a setting, even when groups are a priori identical, employers will end up discriminating against certain groups. Group discrimination serves the purpose of creating a focal point in a market game. In this model, the free market, far from curbing discrimination, nurtures it and thereby creates the need for purposive policy intervention. It is argued that, with the rise of technology, the problem of discrimination as focal point will get more acute and we will have to think in terms of affirmative action or a system of taxation and subsidy to support groups that get excluded. Keywords
Discrimination · Focal point · Strategic complementarity · Affirmative action
K. Basu (*) Department of Economics and SC Johnson College of Business, Cornell University, Ithaca, NY, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_8
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Introduction For good or for bad, group identity matters in determining market outcomes (Akerlof and Kranton 2010; Sen 2006). Discrimination against certain groups of people and, by its converse, in favor of other groups has been common practice, observed in different societies and through different periods of history. India’s caste system, with its attendant practice of intolerance and effort to marginalize large groups of people, is an example, as is the history of apartheid in South Africa and racial discrimination and slavery in the United States. From an analyst’s point of view, they are often troubling because norms and laws often merge into each other. Some of these heinous practices were explicitly backed by the law as in the case of South African apartheid and US slavery. At other times, such as with India’s caste system through history and now (see Deshpande 2010) or with racial discrimination faced by African Americans in contemporary United States, it was not backed by the law but by social norms, customs, and individual beliefs and preferences. The focus in this chapter will be on discrimination which does not have the backing of law. As a mirror image of this, we often see certain groups benefiting from discrimination in their favor. This has been true of men through long stretches of history and even now in most societies. Similarly, in the United States, the United Kingdom, India during colonial times, or South Africa till recently, if you could choose your skin color, I would strongly recommend white. Where do these discriminatory preferences come from and why have they been so persistent? Without doubt, there must be many explanations for this, ranging from plain, simple bigotry and prejudice to various forms of statistical discrimination that economists have written extensively about.1 While not wanting to take away from those standard explanations, this chapter presents a novel argument whereby discrimination has no innate origins but arises naturally in markets where there happens to be some complementarity between the different tasks we do.2 This kind of discrimination is a product of the free market and the beliefs of ordinary people. The upshot is the group identities of people come to matter in such settings. It is, as I will show, closely linked to the idea of “focal point,” used in game theory (Schelling 1960). In particular, race, caste, and gender become important in equilibrium because they acquire the salience of the focal point. In some ways, the focal point theory of discrimination developed in this chapter is more alarming than other forms of discrimination where one can point to the source, be it human mendacity or the distortions of statistical information. In my analysis, removing government interference and allowing competition in the market to flourish do not remove discrimination, as standard economics had suggested, because it is in fact a product of precisely the free market. The model is based on sufficiently realistic assumptions to make me believe that, while other forms of discrimination no doubt occur, the focal point theory of
1 2
See Arrow (1973, 1998), Phelps (1972), Spence (1974), and Stiglitz (1974). A polar case of this in a development context occurs in Kremer (1993).
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discrimination does play an important role and, as such, deserves greater attention. In the last section, I will argue why, given trends in contemporary labor markets, this is likely to become even more important.
Markets and Discrimination A good starting point is the celebrated paper by Bertrand and Mullainathan (2004). This is often treated as the most compelling empirical demonstration of pure racial bias in labor markets. When we see discrimination, the question always arises as to whether it is bias or reflection of something else that correlates with race or gender or caste, whatever it is one is studying. Thus, if an employer hires more whites than blacks, is it really a preference for whites, or is it merely a reflection of the fact that the employer needs PhDs and white job applicants are more likely to have PhDs? Bertrand and Mullainathan corrected for this by sending out job applications with fictitious resumes to help-wanted advertisements that appeared in Chicago and Boston newspapers. It was soon evident that, controlling for all other things, candidates with white names were far more likely to get callbacks for interviews than those with black names. There were striking results, such as how having a white name is equivalent to 8 years of work experience with a black name. In brief, they had engineered the celebrated “ceteris paribus” condition that traditional economists so often talked about but were seldom able to demonstrate. And the findings were striking.3 What I wish to do here is to question whether this necessarily demonstrates racial bias. Note that for most tasks in life, to conduct them effectively, you need to successfully do other tasks. If you work for a firm’s sales department to promote sales, you need to be able to successfully interact with buyers’ groups and delivery services units. If the buyers’ groups and delivery services units try to shun you, you will not be able to do the sales work you are supposed to do well. And of course, the problem is similar for the buyers’ group. When they reach out to you, they know they will get better services from you if you are trusted and used by the sales department and the delivery services unit and likewise for the delivery services unit. They have to gauge how successful you will be with the sales department and the buyers’ group. This is where race can come to acquire significance even in the absence of any innate racial preference. If you feel Emily – a common white name – is more likely to do your task more effectively, you will prefer to hire Emily over Lakisha. If all three units do that, this becomes self-fulfilling. The white name provides a focal point in a 3
Similar results were reported by Siddique (2008) who sent out applications in India using castebased names. A paper by Thorat, Banerjee, Mishra, and Rizvi (2015) does a similar test for the home rental market in the National Capital Region, in and around Delhi, and record a similar bias against Muslims and Dalit applicants. For empirical studies, using other methodologies, which nevertheless suggest pure racial bias, see Hamilton et al. (2015) and Pager, Western, and Bonikowski (2009). For some engaging research based on legal analysis, see Sander (2006) and Coleman and Gulati (2005).
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labor market for tasks that exhibit “strategic complementarity” – economists’ term for work contexts where doing one task raises your productivity in another. Since my chapter is a methodological intrusion into that rather barren terrain between economics and sociology and is, at the same time, at deviance from the so-called Chicago school, it may be apt to quote Ken Arrow from an interview he gave to Richard Swedberg in 1988, in which he distances himself from the Beckerian approach but stresses the importance of interaction between economics and sociology: “[A] lot of the environment in which economic transactions take place is social and historical in nature. I do not know exactly how to fit these pieces together but there are, for example, accounts of how special groups have played a distinct role in, say, trade. You have Chinese middlemen in Asia; Jews at certain times; Quakers during one period; and so on. It is clearly their social characteristics that matter” (Swedberg 1990, pp. 136–137). What I am arguing is that this is true but the social characteristics may well be endogenous, the product of equilibrium, however we got there. The basic idea, which shows the relation between discrimination and focal point, can be illustrated with a simple example.4 There are two entrepreneurs, 1 and 2, who have need for certain tasks to be done, and there are n (>2) service operators or laborers who can do these tasks. Suppose, for instance, entrepreneur 1 needs a person to look after his lawn – buy and apply fertilizer, sow seeds, mow, and so on – and entrepreneur 2 wants to lend money to someone. The person who is able to borrow the money can buy fertilizers and seeds easily and so does the lawn work better. And the laborer who gets the lawn contract will be more likely to pay back loans that she takes. The entrepreneurs do not know the underlying causation of what makes a laborer more productive, to wit, the fact that if both reach out to the same laborers, they get better value. This is not unlikely in a real setting where thousands of entrepreneurs reach out to hundreds of thousands of laborers. They realize some are more productive than others and may search for markers of that without quite knowing the fundamental model that drives this. The above paragraph may be summed up as follows. Each of the two entrepreneurs picks one citizen for the task he needs to get done. If he picks a citizen who is not picked by the other entrepreneur, he gets a benefit of x, and if he picks someone the other entrepreneur also picks, he gets y. Given what we said about strategic complementarity: y>x
ð1Þ
The entrepreneurs are not aware of this strategic complementarity. All they know is that they may get x or y, without being aware of what drives the difference. The only critical assumption in this exercise is (1). All other structures of the model can be varied, and we will still get the same essential result. I should clarify that I am not claiming that strategic complementarity is always the case but simply that it is
4
A more complex and also more realistic model is developed in Basu (2015).
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Table 1 The discrimination game
109 N
N
y n
þ
W
y n
þ
B
y n
þ
W xðn1Þ n xðn1Þ n xðn1Þ n
y n
þ
y n
þ
x
B xðn1Þ n xðw1Þ w
y n
Þ þ xðn1 n
x y n
Þ þ xðb1 b
realistic in many situations, and when that happens, I want to show that a kind of discrimination happens which requires no innate bias and no differences in ability or skill across groups and arises entirely through natural market processes with the concept of the focal point playing a critical role. To convert this to a game, I need to put in a little more structure to the model. Assume that the n laborers are of two different races: w (>1) of them are whites and b (>1) blacks. Hence w þ b ¼ n. Each entrepreneur, in selecting a laborer to get his or her task done, has to use one of the following rules: no discrimination (strategy N), discrimination in favor of whites (strategy W), and discrimination in favor of blacks (strategy B). If she chooses N, it means she randomly picks one of the n citizens, with 1/n probability of each citizen being chosen. If she chooses W, it means each white person faces a probability 1/w of being selected, likewise for strategy B. It is easy to work out the payoffs of the two entrepreneurs depending on the choice each of them makes. This is displayed in the payoff matrix described below, in what I call the discrimination game. Since it is completely symmetric, there is no need to show the payoffs of both entrepreneurs. I show the payoff earned by entrepreneur 1; and, in this game, entrepreneur 2 gets the same (Table 1). To understand the payoff, let us check the top left hand box. Both entrepreneurs choose N, that is, pick a laborer with no attention to race. After one has chosen, the probability that the other person will choose the same laborer is 1/n. When that happens, each gets a payoff of y. The probability that the other entrepreneur will choose someone else is (n 1)/n. When that happens, each gets x. So the expected payoff is y/n þ x(n 1)/n. It is easy to work out the payoffs in the other boxes by a similar reasoning. It is simple to check that this game has three Nash equilibria – (N, N), (B, B), (W, W) – that is, no one discriminates, everybody discriminates in favor of whites, and everybody discriminates in favor of blacks. To check this, note that if the other person chooses N, no matter what you do, you will get the same payoff. So you cannot do better by unilaterally deviating from N. Next check that, as long as y exceeds x, as assumed, and given that, by definition, n > w, the following are true: y=w þ xðw 1Þ=w > y=n þ xðn 1Þ=n, and y=w þ xðw 1Þ=w > x: In other words, if others discriminate in favor of whites, whites will be on average more productive, and so it is in your interest to choose a white to do your task. In other words, (W, W) is a Nash equilibrium. For the same reasons, (B, B) is an equilibrium as well.
110 Table 2 The discrimination game: a special case
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N W B
N 5/4, 5/4 5/4, 5/4 5/4, 5/4
W 5/4, 5/4 3/2, 3/2 1, 1
B 5/4, 5/4 1, 1 3/2, 3/2
For those with an aversion to symbols, let me convert the above game to a society in which there are four laborers, two whites and two blacks. And suppose y ¼ 2 and x ¼ 1. By inserting these values, the above discrimination game collapses into the special case illustrated below (Table 2). The three Nash equilibria are now obvious. If others discriminate, you had better do the same. But as always with games with many equilibria, there is a need for a focal point which allows players to coordinate their behavior. What I am claiming is that in markets with strategic complementarity, as just described, race or gender or caste can be the focal point. It is important only because others think it is important. You prefer Emily to Lakisha not because you have a preference for white over black but because all of you need to zero in on some group and it so happens, for reasons of history or whatever, you have settled on whites. One important implication of this is that one popular view, namely, if you leave it all to the market, with no government regulations and intervention, racial and caste discrimination would go away, is not valid. Discrimination arises from the free market. If you want to stop discrimination, you may, in fact, need regulation and conscious affirmative action. And when we go for affirmative action, we must not indulge in the politically correct banter, so often heard, that by doing affirmative action you do not hurt your returns. The truth is that your returns may indeed be diminished by such action. The appeal has to be that even if your return drops, there are certain actions in life which ought to be indulged in for its innate moral goodness. Affirmative action may be one of those. I shall return to this in the last section.
The Art of Creating Optimal Groups As a digression, I may point out that this focal point model of discrimination can be put to some rather Machiavellian uses, such as that of creating your own group and then promoting it. And indeed, such practices are not unknown in the world. Alumni associations and fraternities are good examples of this. They are often used to promote the college or frat label. Thus, we are told how Harvard students are smarter than others, or how Berkeley students are more productive than others, or how Cornell graduates are more creative than others (this one happens to be true), and so on. What the analysis in this chapter shows is that, once such beliefs catch on, they can become self-fulfilling because that belief then serves as a focal point. Most of us, human beings, have multiple identities, race, nationality, language group, ethnicity, gender, and so on (see Sen 2006), and as I just argued, we can also create new identities (see also Basu 2011). Now, if you want to deliberately nurture the view that one of these identities is a mark of greater productivity, this model
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suggests it may be worthwhile to pick on or create a group that is relatively less populous. To prove this, consider the payoff an entrepreneur gets from the equilibrium in favor of whites. This is given by [y þ (w 1)x]/w. The payoff in an equilibrium in which blacks are favored is given by [y þ (b 1)x]/b. Let us call the former whincome, and the latter blincome. Recall b ¼ n w. Hence, as w becomes smaller, whincome rises and blincome falls. Both (W, W) and (B, B) are still Nash equilibria, but the former becomes more and more dominant as the white population becomes smaller. In brief, if you want to promote the idea that a particular group you belong to is more productive, you will be better off if you choose a small group. Among other things, this explains why raising the profile of women is such a hard task. They constitute roughly half the population. Considering the case of nationalities, if you promote the idea that the British are more productive and the idea that Chinese are more productive and people buy into this belief, the British will turn out to be even more productive in a world in which they are believed to be more productive than the Chinese will be in a world in which the Chinese are believed to be more productive. There is often surprise expressed at the fact that Britain was such a small nation that once ruled virtually the world. What is being said is that we should not be surprised. One counterargument to this needs to be kept in mind. If smaller groups are more effective, why not go all the way and proclaim that you as an individual, or one-person group, is more productive? The reason must be that as groups get too fragmented, people cannot hold information about group characteristics in their heads because there are too many groups. This can set a lower bound to how small groups can be without losing advantage. To model this formally will entail a more elaborate analysis, but the intuition behind what I am arguing should be evident. In closing this section, I want to remind the reader that while this focal point model of discrimination is very important, as with all theory, to take it to the real world and to put it to use, we must enrich it with our commonsense and reasoned intuition.5 Hence, the above theory must be combined with other ideas and our own experience before it is put to use or employed in designing policy. It is, for instance, worth reminding ourselves that productivity and even intelligence are also dependent on how a person is treated and on how society views this person’s group. Even if the discrimination is purely a focal point at work, it can leave scars on people, making the ones believed6 to be less intelligent actually behave less intelligently. Unlike in a strategic-form game where a switch from one equilibrium to another can It is not a matter to go into here, but this reference to commonsense and “reasoned intuition” is not a casual side remark. I have argued at length elsewhere that for science to be useful, we must combine it with these skills. Pure analysis of data or pure theory cannot help us help the world till we combine them with reasoned intuition (Basu 2014). 6 Among the most notable findings on this are studies by Ambady, Shih, Kim, and Pittinsky (2001) and Hoff and Pande (2006). See, also, Field and Nolen (2005), Hoff (2015), and World Bank (2015). 5
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be effected in the twinkling of an eye, in reality, these changes are likely to take time and involve economics and psychology.
Policy Implications The model presented here opens up some policy dilemmas. It should be evident from the above model that GDP or the aggregate payoff earned by all is higher when there is group discrimination. Since a certain amount of multitasking enhances productivity, it is better to have a subset of the population multitask, that is, do all the work, rather than spread tasks thinly across all. Some people may find this result troubling, namely, the fact that discrimination against a group can enhance GDP. In my view, this result is disturbing only if you consider a higher GDP to be sacrosanct. All these results suggest is that you should be willing to forego some GDP to achieve fairness and greater equity across groups. Stewart (2005) pointed out that, in contrast to vertical inequality (that between persons), the normative economics of horizontal inequality (inequality across groups) remains a rather neglected subject. Exactly how one develops this and characterizes the various trade-offs between aggregate well-being and horizontal equality can matter a lot in how choices are made, as we just saw (see also Jayadev and Reddy 2011; Subramanian 2011). Fortunately, the context is simple enough in the discussion that follows that the exact trade-offs will not matter but this is indeed a subject that deserves greater attention in the future. With this in the background, note that there are two ways of achieving equity or an equitable distribution of incomes or payoffs in the present model. The first is straightforward “affirmative action.” Incentivize employers to choose a diverse workforce so that the total work is spread equitably across all individuals. The second is to let a limited number of people do all the work and then tax them and subsidize those who did not get work. This latter will result in a higher per capita income since the workforce (i.e., the people who find work or are called upon to do tasks) will be more productive. There are social scientists who have objected to the latter on the ground that work in itself gives people dignity; so even if one were to get the same ultimate income but without having to work, this may cause a diminished sense of self. I prefer to be cautious with this argument. Through history, there have been the leisure classes – the British landed aristocracy, the Indian zamindars (a brainchild of the British landed aristocracy) – who did precious little work and lived lives of luxury, and there is no evidence of them feeling diminished by the experience. There are also cases, especially relevant to women, where voluntary nonwork is, in fact, a statement of empowerment and an act that enhances agency (Basu 2016). Basically, what people need is a sense of legitimacy for what they earn. There are contexts where to get a dole without getting to work is offensive; it is like being told that you are not fit for work. This never troubled the aristocracy and the leisure classes because they had a sense of entitlement for their ample incomes, even though where that entitlement came from is a puzzle. In a world where either one group gets
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to work or the other does and there is no essential a priori difference between the two groups, for one group to work and the other to be subsidized is a legitimate strategy. Someone has to not work to make those who work more productive; in this setting, there is no indignity in not working. It is in fact a contribution to society. There is no need to resolve the policy dilemma in this chapter, but I want to point out that this problem is going to get more acute, since with the march of technology, relatively unskilled work is steadily shrinking in the world. The share of GDP that accrues to workers as aggregate wage bill has been falling over the last four or five decades. The trend is quite alarming. As I point out in Basu (2016), in 1975, total wage bill as share of GDP in the United States, Japan, and the European Union were 61%, 77%, and 66%. Now they are 57%, 60%, and 56%, respectively (see also Karabarbounis and Neiman 2014). This trend is true almost without exception in all high and middle income countries; and this is a challenge we will have to face up to sooner rather than later. What the model developed in this chapter tells us is that we face a choice – whether to forcefully distribute the limited work thinly across the entire labor force (thereby impairing productivity) or let few people work (and be productive) and then tax them to subsidize the ones without adequate work and with all the time in the world to read philosophy and swim. In itself, the advance of technology is a matter for celebration. What makes it worrying is that, as machines and robots displace workers, the incomes of workers become profits for the owners of the machines and the robots. I have discussed in Basu (2016) how we need to take on this problem head on. What this chapter points to is an additional problem. As work becomes scarce, in markets with strategic complementarity, there will likely be an exacerbation in the problem of group discrimination, with large groups being kept out of the labor market. This chapter provided an explanation of the mechanics of how this will happen and drew attention to the kinds of policy questions that, sooner or later, we will have to confront.
Addendum. Empirical Tests and a Normative Caveat The central idea behind my chapter arose in response to the striking empirical finding of Marianne Bertrand and Sendhil Mullainathan (2004), in which they showed that, in hiring workers, people discriminate purely on the basis of race. This seemed to put an end to the debate as to whether racial discrimination was an outcome of the effort to spot talent that may not be visible but may, in equilibrium, correlate with race or a result pure preference for one race over another. The same study can be done for other group identities, such as gender or sexual orientation or religion or caste. It is the wonderful design of their experiment that establishes that this is pure discrimination and not a case of race or caste being used as a surrogate in search of other indicators that affect productivity. Moreover, we know from later experiments that these discriminations are persistent, especially the discrimination against blacks. There has been a slight abatement in discrimination against Hispanics (see Quillian et al. 2017).
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The central argument of my chapter was that this empirical finding is, however, compatible with a world in which no one has an innate propensity to discriminate. If I need to hire someone who will need to work with others, I need someone others will want to work with. This strategic complementarity gives rise to the need for a coordination device. This is closely related to the importance of networks in labor markets, the importance of which in job-market discrimination has been seen in the United States and in India (see Royster 2003; Deshpande and Newman 2007). In such circumstances, the preference for a specific race or gender can be the outcome of the need to coordinate. If others make that discrimination, I need to do it as well. We could have favored whites or blacks. There is no innate advantage in preferring any group. Either would serve the purpose. Society has multiple equilibria. Identity is a focal point. Once everyone prefers one group, it is in each individual’s interest to prefer that group. That was the central message of the chapter. I want to use this occasion to add two caveats, concerning empirical tests and the normative implications of my argument. Some of this I have discussed in Basu (2018, Chap. 5). First, it is important to realize that if discrimination in favor of some group is a coordination device, then in certain kinds of labor markets, there will not be any discrimination. What this implies is that to test whether people have an innate taste for a certain race, gender, or any other group identity, in contrast to the discrimination being a pure coordination device, we need a test more fine-grained than what Bertrand and Mullainathan (2004) and some later authors, such as Siddique (2011), have done. Note that certain kinds of work require little interaction. If I am hiring a sales agent, that person has to interact with others to do my job well. Call such jobs “interactive work.” But if I am hiring an artist to paint my portrait, it does not matter how that person interacts with others. Let us call this kind of work “solipsistic work.” If the discrimination in favor of whites is the outcome of pure racial bias, then when we hire labor for solipsistic work, we should not see any discrimination. In other words, to see how much pure racial bias there is in the labor market in a certain society, what we need to do is a two-pronged test, one in which we send out responses to a call for applicants for an obviously interactive job (sales agent, communications personnel, administrative secretary) and another where we send out applications in response to a call for people to do some solipsistic work (portrait artist, home painter, caretaker for pets). If we find positive and same level of discrimination for both types of jobs, we would have established that there is innate discriminatory preference. But if we find less discrimination in the solipsistic job market, it would show that the focal point of games is at play. My conjecture is that we will find the presence of both reasons for discrimination (innate and as a coordination) in most societies but there will be variations across nations, with some nations and societies exhibiting higher levels of innate racial and gender bias. Much of my analysis is a dry, abstract dissection of a deeply troublesome attribute of many societies, namely, the propensity to discriminate against certain groups, generally minorities.
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Second, let me emphasize that group discrimination is deeply unethical and totally objectionable, no matter what the source. There is a lot written about and covered in the social media about degrading levels of discrimination, even in our contemporary world. I may add here that while there is now some awareness of racial and gender discrimination, some of the worst and most scarring ill treatment is meted out to LGBTQ groups (see Lee Badgett 2020).
Conclusion In closing, what I want to emphasize is that the argument that a part of the discrimination may be the result of an arbitrary focal point, where each individual, including each discriminator, is a victim of the circumstance, must not be treated as a mitigation of the normative problem. What recent events around the world and especially in the United States, and the rise of the Black Lives Matter movement, have made is a reminder that the consequence of discrimination, no matter what the source and whether or not we can lay the blame at anyone’s doorstep, is abhorrent. Further, what my analysis shows is that the problem cannot be solved by leaving it all to the market. The Chicago school view that by freeing the market we would cut down discrimination is wrong. We need affirmative action.
New References Lee Badget MV (2020) The economic case for LGBT equality. Beacon Press. Basu K (2018) The republic of beliefs: a new approach to law and economics. Princeton University Press, Princeton. Deshpande A, Newman K (2007) Where the paths lead: the role of caste in postuniversity employment expectations. Econ Polit Wkly, October 13. Quillian L, Pager D, Hexel O, Midboen A (2017) Meta-analysis of field experiments shows no change in racial discrimination in hiring over time. Proc Natl Acad Sci U S A. Royster D (2003) Race and the invisible hand: how white networks exclude black men from blue-collar jobs. University of California Press, Berkeley. Siddique Z (2011) Evidence of caste-based discrimination. Labour Econ 18(S1): S146–S159.
References Akerlof G, Kranton R (2010) Identity economics: how our identities shape our work, wages, and well-being. Princeton University Press, Princeton Ambady N, Shih M, Kim A, Pittinsky TL (2001) Stereotype susceptibility in children: effects of identity activation on quantitative performance. Psychol Sci 12:371 Arrow KJ (1973) Higher education as a filter. J Public Econ 2:193–216
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Arrow KJ (1998) What has economics to say about racial discrimination? J Econ Perspect 12: 91–100 Basu A (2016) Why Can’t laziness claim agency? The neo-Malthusian streak in contemporary feminism. Presented at conference on “Malthus: food, land, people”. Cambridge University, Cambridge, UK Basu K (2011) Beyond the invisible hand: groundwork for a new economics. Princeton University Press, Princeton Basu K (2014) Randomization, causality and the role of reasoned intuition. Oxf Dev Stud 42: 455–472 Basu K (2015) Discrimination as a coordination device: markets and the emergence of identity. World Bank Policy Research working paper 7490 Basu K (2016) Globalization of labor markets and the growth prospects of nations. J Policy Model 38(4):656–669 Bertrand M, Mullainathan S (2004) Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. Am Econ Rev 94:991–1013 Coleman JE Jr, Gulati M (2005) Response to professor Sander: is it really all about the grades? N C Law Rev 84:1823–1839 Deshpande A (2010) The grammar of caste. Oxford University Press, Oxford, UK Field E, Nolen P (2005) Race and student achievement in post-apartheid South Africa, mimeo: Harvard University Hamilton D, Darity W Jr, Price AE, Sridharan V, Tippett R (2015) Umbrellas don’t make it rain: why studying and working hard isn’t enough for Black Americans. Mimeo, The New School, New York Hoff K (2015) Behavioral economics and social exclusion: can interventions overcome prejudice?. World Bank Policy Research working paper 7198 Hoff K, Pandey P (2006) Discrimination, social identity, and durable inequalities. Am Econ Rev 96: 206–211 Jayadev A, Reddy SG (2011) Inequalities and identities. SSRN working paper Karabarbounis L, Neiman B (2014) The global decline of the labor share. Q J Econ 129:61–103 Kremer M (1993) The O-ring theory of economic development. Q J Econ 108:551–575 Pager D, Western B, Bonikowski B (2009) Discrimination in a low-wage labor market a field experiment. Am Sociol Rev 74:777–799 Phelps ES (1972) The statistical theory of racism and sexism. Am Econ Rev 62:659–661 Sander RH (2006) The racial paradox of the corporate law firm. N C Law Rev 84:1755–1822 Schelling T (1960) The strategy of conflict. Harvard University Press, Cambridge, MA Sen A (2006) Identity and violence. Alfred Knopf, New York Siddique Z (2008) Caste-based discrimination: evidence and policy. Mimeo, Northwestern University Spence M (1974) Market Signaling: information transfer in hiring and related screening processes. Harvard University Press, Cambridge, MA Stewart F (2005) Horizontal inequalities: a neglected dimension of development. In: WIDER perspectives on global development. Palgrave Macmillan, New York Stiglitz J (1974) Theories of discrimination and economic policy. In: von Furstenberg G (ed) Studies in the economics of discrimination. Lexington Books, London Subramanian S (2011) Inter-group disparities in the distributional analysis of human development: concepts, measurement, and illustrative applications. Rev Black Polit Econ 38:27–52 Swedberg R (1990) Economics and sociology. Princeton University Press, Princeton Thorat S, Banerjee A, Mishra V, Rizvi F (2015) Urban rental housing market: caste and religion matters in access. Econ Polit Wkly (50):47–53 World Bank (2015) World Development Report 2015: mind, society, and behavior. The World Bank, Washington, DC
7
Inequality of Opportunity Patrizio Piraino and Josefina Senese
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inequality of Opportunity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Affirmative Action in Higher Education Through IOp Lenses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Abstract
Various circumstances beyond individual control influence the range of options available to individuals and determine what life objectives one can or cannot pursue. They also impact the translation of individual effort into valued outcomes. The inequality of opportunity (IOp) framework helps clarify the role that circumstances have on socioeconomic disparities. This chapter reviews the central insights of IOp theory. It then discusses how affirmative action can be understood as a natural redress policy from an opportunity egalitarian standpoint. Specifically, the key goal of “leveling the playing field” pursued by affirmative action policies can be seen as a form of reshuffling of opportunities from those who enjoy various types of advantages to those who face structural impediments to express their full potential. Keywords
Inequality of opportunity · Affirmative action
P. Piraino (*) University of Notre Dame, Notre Dame, IN, USA e-mail: [email protected] J. Senese Boston University, Boston, MA, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_33
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Introduction The theory of inequality of opportunity (IOp) recognizes how individual achievements are due to both the circumstances people find themselves in and the effort they exert. This is a crucial distinction, as outcome disparities may be less objectionable if they largely originate from differences in personal efforts as opposed to factors outside individuals’ control (Brunori et al. 2021; Ferreira and Peragine 2015; Kanbur and Wagstaff 2014; Ramos and Van de gaer 2012; Roemer and Ünveren 2017). The IOp framework thus brings to the fore the idea that, in order to make a statement on the “fairness” of a given level of inequality, it is necessary to look at the nature of its determinants. Observed socioeconomic inequalities need not arise from unequal opportunities. Inequality may derive from variation in preferences, work ethics, or choices individuals make. However, it is often the case that people have similar aspirations and dedicate the same amount of time and effort to pursuing their goals. Yet, some groups have a significantly lower chance of achieving those goals due to various types of group-specific barriers to economic success. It is also possible that among individuals who achieve similar socioeconomic status, some groups had to exert much more effort than others to get there. In this case, looking at an equal distribution of outcomes may not necessarily reflect an equitable one. The fundamental difference here is that outcome equality may conceal that some individuals had to earn certain milestones that more advantaged individuals could instead take for granted. To illustrate this, let us consider the distribution of educational outcomes across a cohort of students. Simply looking at inequality in test scores, or completed grades, may not be as useful to gauge the education system’s fairness as would be investigating the various determinants of educational achievement. Intuitively, if students are free to decide what they want to do with their time and choose how much effort to exert in learning, one could argue that they should be held responsible for their outcomes. On the other hand, if factors beyond students’ control affect the levels of effort exerted and its “translation” into educational achievement, it is more difficult to argue that students should bear full responsibility for their success or failure. For example, some low-income students who desire to perform well in high school may also need to work part-time to support their household. In such a situation, not only would these students invest fewer hours studying, but the time spent learning could also be less productive compared to the time invested by their classmates who do not need to work. In other words, the set of opportunities students face and the careers they can ultimately pursue are partly determined by circumstances over which students have little or no say. While intuitively simple, the IOp approach brings along many theoretical and empirical challenges. For instance, there is no consensus on the set of personal characteristics that can be deemed as beyond the domain of individual responsibility. Also, contributors to the existing literature propose various alternative empirical approaches to disentangle the relative role of effort and circumstances in determining individual outcomes. Some of these challenges are due to the fact that many
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determinants of socioeconomic success are not easily observable (e.g., motivation, aspirations, etc.). An additional empirical challenge is that what can be identified as individual effort in the data may itself be influenced by unobservable circumstances. Notwithstanding the ongoing debates in the IOp literature, this chapter argues that it is valuable to examine affirmative action policies through IOp lenses. In particular, the key objective of leveling the playing field can be seen in the IOp framework as a form of redistribution of “life chances” from those who benefit from various inherited advantages to those who face structural impediments to express their potential. The rest of the chapter proceeds as follows: section “Inequality of Opportunity” reviews the central tenets of IOp theory and discusses a number of empirical approaches to operationalize the major distinction between circumstance and efforts. Section “Affirmative Action in Higher Education Through IOp Lenses” outlines how IOp clarifies the rationale for a class of affirmative action interventions. To convey the key arguments, this chapter will use the example of policies aimed at increasing access to higher education institutions. This chapter concludes with some reflections on how IOp may be used to better motivate and design affirmative action interventions and for future academic research.
Inequality of Opportunity The starting point of IOp theory is the idea that socioeconomic hierarchies are not inherently unfair. Rather, our judgment of a given ranking in social status should be influenced by the underlying determinants of individual success and by the extent to which higher positions in the social ladder are contended on equal terms. But what do we understand as equal terms? Let us consider the example of access to selective higher education institutions. The legal requirement of nondiscriminatory practices might be met with an admission mechanism that ensures that all eligible applicants are evaluated exclusively on their prior academic qualifications. On the other hand, equality of opportunity would require that being eligible to apply is not enough and that everyone should have a genuine chance to become “qualified” (Arneson 2015). In this example, having equal opportunity implies correcting the competitive advantage that favorable circumstances grant on some applicants relative to others. Arneson (1989) maintained that “The argument for equal opportunity rather than straight equality is simply that it is morally fitting to hold individuals responsible for the foreseeable consequences of their voluntary choices, and in particular for that portion of these consequences that involves their own achievement of welfare or gain or loss of resources” (p. 88). He further contended that truly equal opportunities occur when governments act to reduce the impact of mere luck on people’s prospects for welfare, including external barriers such as educational opportunities and internal obstacles such as variances in decision-making ability. In this context, an important distinction is that between accountability and responsibility. Individual preferences can be seen as not entirely under people’s
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control, as they are influenced by resource availability (Cohen 1989). Roemer (1998) explains that whereas some lifestyle choices are clearly a person’s responsibility, others arise from factors beyond their control, and people should not be held accountable for these choices. The key point is that we should be accountable for our own poor judgment only when, given our circumstances, it was plausible to expect to have behaved better. “Thus, for example, we may decide that an adolescent is responsible for a poor attendance record at school, because she decided to cut classes frequently, but nevertheless not hold her accountable for it, by our taking steps to repair her consequent educational deficit, if her circumstances explained her behaviour” (p. 18). Roemer also notes that people’s decisions regarding effort investments can vary throughout their lifespan. Given that people tend to think and act for themselves to a lesser degree when they are younger, adolescents are more easily influenced by their social background than adults. Consequently, if adolescents and children have their own special effort range, context determines their educational choices to a greater extent (Roemer 1998). That is, one also needs to consider the stage of life of different individuals when determining whether they can be regarded as having exerted the same personal effort. The basic intuition of the IOp theoretical framework can be formalized in its most general terms by expressing a given outcome y as a function of both individual circumstances C and the efforts e invested to achieve higher levels of y:1 y ¼ f ðC, eÞ
ð1Þ
A trivial implication of Equation (1) is that individuals facing the same circumstances and applying the same amount of effort would be able to achieve the same outcome.2 Note that neither the process by which outcomes are derived nor the set of opportunities individuals face are explicitly modeled in this framework. Opportunities will need to be inferred by observing the joint distributions of circumstances, effort, and outcomes. It may be obvious to some readers that this simple conceptualization encompasses, in fact, two distinct and independent postulates: (i) The compensation principle, which proposes eliminating those inequalities in y caused by factors that are beyond individual control (ii) The reward principle, which is concerned with how to reward efforts among individuals who face similar circumstances (Brunori et al. 2021; Fleurbaey and Peragine 2013; Ramos and Van de gaer 2012)
1
The model, with some variants, was introduced by Fleurbaey (1994), Roemer (1993), and Van de Gaer (1993). After these seminal contributions, a rich literature has emerged, with both empirical and theoretical developments; see the recent surveys by Ferreira and Peragine (2016), Ramos and Van De Gaer (2016), and Roemer and Trannoy (2016). 2 Consider two identical twins, who share the same genetic endowment and household inputs, who go to the same school and undertake similar activities. If they devote equal amounts of effort in their schoolwork, they should, in theory, attain equivalent schooling outcomes.
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Any acceptable measure of IOp would need to adhere to both the compensation and reward principles. However, when these postulates are given explicit empirical formulations, they tend to collide (Bosmans and Özturk 2021). The literature has proposed two main ways to operationalize the compensation principle, resulting in two prevailing empirical approaches for measuring IOp: the ex ante and the ex post approaches. While both methodologies require several analytical choices over many empirical steps, it is sufficient for the purposes of this chapter to clarify the key distinction. In the ex ante approach, equality of opportunity is achieved when the set of opportunities is the same for all individuals, regardless of their circumstances. This amounts to formulating the compensation principle with respect to individual opportunity sets. It demands equalizing circumstances across individuals ex ante. In the IOp literature, a group of individuals endowed with the same circumstances is referred to as a type. Each type can be seen as an opportunity set for individuals facing the same circumstances. Empirically, the ex ante approach estimates inequality between opportunity sets. This is also referred to as betweentype inequality. On the other hand, in the ex post approach, equality of opportunity is achieved when individuals exerting the same level of effort obtain the same outcome. Here the compensation principle is operationalized by demanding lower inequality among individuals with the same effort but unequal outcomes – i.e., ex post compensation. The IOp literature also presents different treatments of the reward principle. Broadly speaking, different papers suggest different ways to think about outcome inequality among individuals endowed with the same circumstances. At the two extremes, there are contributions proposing complete neutrality – i.e., utilitarian reward (Van de Gaer 1993; Fleurbaey 2008) and those introducing inequalityadverse rewards (Ramos and Van de Gaer 2016). Intermediate and agnostic positions are also present in the literature (e.g., Peragine 2004; Fleurbaey and Peragine 2013). Because of these different approaches, empirical researchers have used a variety of methodologies to analyze the aggregate distributions of outcomes, efforts, and circumstances. Most papers utilize a measure of IOp that reflects the principles of (i) ex ante compensation and (ii) utilitarian reward. This is achieved by estimating some version of the between-type inequality index mentioned above (see Checchi and Peragine 2010 for a nonparametric version). This approach computes the inequality in a counterfactual distribution where all disparities are solely due to variation in observable circumstances. Let us call ÿ the outcome distribution resulting from a scenario where efforts do not play any role in the function f – i.e., 100% opportunity inequality. Once this counterfactual is created, the opportunity set faced by different individuals can be inferred by comparing the observed distribution of y to ÿ (Ferreira and Peragine 2015; Ramos and Van de gaer 2021). The closer y is to ÿ, the higher the degree of inequality of opportunity in the observed outcome distribution (Brunori et al. 2021). In other words, the level of “unfairness” in the distribution is proportional to the effect that circumstance variables (which lie beyond individual responsibility) have on individual outcomes. By defining types as groups of individuals sharing the same circumstances, this literature sometimes denotes the inequality due to personal efforts as within-group inequality and the
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inequality due to circumstances as between-group inequality—i.e., inequality of opportunity (see Checchi and Peragine 2010). It is also common in the literature to present relative IOp estimates, defined as the share of unfair inequality over total outcome inequality. For a given inequality index I, the relative IOp measure is then equal to I ðÿÞ=I ðyÞ. In order to estimate this class of IOp indices on the microdata typically available in most countries, it is necessary first to classify which observable covariates can be regarded as circumstances. This is not straightforward, as it requires a judgment on which factors can be attributed to individual responsibility as opposed to conditions beyond personal control (Ramos and Van de gaer 2012; Roemer and Trannoy 2016). Moreover, data constraints generally make it impossible to account for all relevant circumstances, which implies that the within-group component is generally treated as a residual. The literature shows that the incomplete observability of circumstances generates a downward bias in IOp estimates. For this reason, the estimated IOp levels in the literature are generally interpreted as lower bound values of the true degree of inequality of opportunity in a given outcome y (Ferreira and Peragine 2015; Kanbur and Wagstaff 2014; Ramos and Van de gaer 2021).
Affirmative Action in Higher Education Through IOp Lenses A key implication of the IOp framework is that inequality resulting from differences in efforts among individuals endowed with the same set of circumstances is consistent with equal opportunities. In such cases, an appropriate policy intervention would be for governments to adopt various forms of redistribution interventions aimed to reduce the fraction of overall inequality that we deem unfair as measured by the IOp index. Any policy aimed at equalizing opportunities, as defined above, would attempt to provide equal chances of attaining a particular outcome regardless of people’s circumstances. That is, achievements should be made accessible to everyone, and opportunities should not be denied based on factors outside people’s control – e.g., socioeconomic status, ethnicity, gender, and religion (Beckley 2002; Cestau et al. 2017; Kellough 2006; Roemer and Ünveren 2017). Translating this ideal to policy practice is far from straightforward. Eliminating barriers to valuable positions (educational institutions, high-pay jobs, promotions, etc.) by simply allowing everyone to apply and evaluating applicants on merit only would not be sufficient from an IOp point of view. This version of “meritocracy” does not consider history, context, and past and present disadvantages. In fact, this notion can contribute to perpetuating the exclusion of historically marginalized groups. Policy design can thus benefit from IOp-informed approaches that try to guarantee that everyone has a genuine chance to realize a valued outcome (Arneson 2015; Kellough 2006; Kodelja 2016; Moses 2011). From this perspective, affirmative action stands out as a particularly useful policy tool for redistributing opportunities. Affirmative action acknowledges that structural disadvantages (including discriminatory practices) have permeated the distribution
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of roles and opportunities among different groups. A wide range of measures coexists under the umbrella of affirmative action policies. The most common distinction is that between policies that involve quotas and those that set goals. Quotas are used to reserve a certain number of positions to target group applicants. Goal-based affirmative action policies use instead a scoring mechanism in selection processes that allocate different points (or set different eligibility thresholds) to different groups based on observable proxies for disadvantage. Regardless of the approach, these sets of policies aim to generate an achievement distribution in which disadvantageous circumstances are partially compensated for and individual efforts appropriately rewarded. If well-designed, such policies should have the overall effect of increasing the fraction of outcome inequality that can be attributed to personal choices relative to inherited disadvantages (Lippert-Rasmussen 2020). A domain where the introduction of affirmative action policies has resulted in highly polarized (and politicized) debates is that of access to higher education. Looking at affirmative action through IOp lenses helps appreciating a key rationale for such policies: redressing unfair advantage. That is, preferential admission policies can be seen as a tool to ensure genuine competition for valued positions. This is achieved by redistributing chances from those who have historically benefited from structural inequalities to those who have faced different types of barriers (Arneson 2015; Critzer and Rai 2000; Crosby et al. 2006; Kellough 2006; Sander and Taylor 2012). This section looks at how the theory of IOp can be used to interpret some common arguments presented by both advocates and skeptics of affirmative action policies in higher education. From an IOp perspective, the starting point is to recognize that the lower academic preparedness of certain groups of students, which decreases their likelihood of accessing higher education, is partly a result of disadvantageous circumstances rather than lack of effort. These students had to face adverse conditions earlier in their lives, which led them to be less “qualified” than their more affluent peers. Conditional on having the appropriate support, these students may be able to catch up with the students who appeared to be better prepared at the moment of admission (Arcidiacono et al. 2015; Cestau et al. 2017; Kellough 2006). A key rationale for preferential admission in higher education is thus the offsetting of this accumulated disadvantage. It is an attempt to equalize access in a context where the tools necessary for achieving admissibility are unfairly distributed across sociodemographic strata. When selective institutions implement merit-based admissions, underprivileged communities are often underrepresented. To correct for this, there are several policies that can be implemented to change the allocation mechanism of scarce academic spots. “Corrective policies” are designed to create educational opportunities that would have otherwise been lost due to various social obstacles, including discrimination (Arcidiacono et al. 2015; Fryer and Loury 2005; Tanner 2016). By pursuing a greater representation of people from underprivileged backgrounds, these policies may redistribute scarce higher education openings from advantaged groups to underprivileged ones (Arneson 2015). That is, preferential admission policies will inevitably create a group of “displaced” students. In popular media debates, this is
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sometimes labeled as a form of reverse discrimination. Applicants who are displaced by affirmative action beneficiaries may feel unfairly treated and their academic efforts not adequately rewarded. From an IOp viewpoint, it is clear that these arguments misinterpret the reward principle and fail to recognize the principle of compensation. Affirmative action is intended to equalize opportunities by correcting for advantages that some candidates have gained unfairly. Although the displaced students may not be directly responsible for the structural forces that led to other groups being disadvantaged, they nonetheless benefit from these forces. A useful example to appreciate this point is provided by the rationale for affirmative action policies in post-apartheid South Africa. While white South African students are not directly responsible for the institutional racism of the past, few of them would not acknowledge that the set of educational opportunities they benefit from is partly a legacy of that discriminatory system. The IOp framework clarifies that this is a form of advantageous circumstance that justifies compensation. It is important to note that the reshuffling of opportunity brought by affirmative action policies will only benefit a subset of underprivileged students. If affirmative action fails to acknowledge within-group differences in circumstances, it may only benefit the most privileged segments of the target population. This could have the unintended consequence of rewarding privilege, rather than need, within the beneficiary group. That is, one might argue that affirmative action fails to recognize the disadvantage of those who need compensation the most within the underprivileged groups. What is more, if the marginal admitted applicant of the beneficiary group comes from a better-off background than the marginal displaced student, affirmative action may even become regressive. This is called “mis-targeting” in the literature, with reference interventions that give preference to relatively advantaged applicants who are members of a disadvantaged group at the expense of underprivileged members of an advantaged group. Although this scenario is certainly plausible, proper policy design could help mitigate this unintended consequence (Bertrand et al. 2010). Moreover, the existing literature does not find empirical evidence that suggests significant extents of mis-targeting. For instance, Bertrand et al. (2010) studied quotas for different cast groups and their effects on the composition of engineering colleges’ admissions in India. They find that caste-based targeting redistributes education resources from the most economically advantaged to traditionally underserved groups. Similarly, Bagde et al. (2016) examined student enrollment and academic success at engineering colleges in India. The authors concluded that affirmative action increased attendance from the most disadvantaged castes. Nonetheless, both papers admitted that this policy may have excluded other underserved groups, such as female applicants. Kerr et al. (2017) examined the impact of a race-based preferential admission policy in South Africa. Using applicant and admission data from a top university, they show that race was effective as a proxy for economic disadvantage and that beneficiary students had significantly lower socioeconomic status than the displaced white students.
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A criticism sometimes raised in discussions about affirmative action in higher education is that preferential admission may stigmatize beneficiaries. Students admitted under preferential rules may be judged harsher as a result of their group identity. Their accomplishments may be undervalued or dismissed and their flaws highlighted. For instance, Deshpande (2019) investigated the stigma associated with caste-based affirmative action among Indian students. While the author found no significant differences in effort or academic attitudes between recipients and non-recipients, upper-caste students judged the performance of affirmative action beneficiaries negatively, indicating the prevalence of discriminatory attitudes toward them. While this type of risk speaks more to the prevalence of discriminatory mindsets rather than a particular policy flaw, it is plausible that an unwelcoming environment for affirmative action beneficiaries may decrease the policy effectiveness (Bertrand et al. 2010; Fischer and Massey 2007; Kerr et al. 2017). It is also possible that beneficiaries find themselves in an environment for which they are unprepared, making them feel isolated and discouraged. Minority or disadvantaged students would be admitted but be more likely to fail or drop out. Thernstrom and Thernstrom (1997) write that “when students are given a preference in admission because of their race or some other extraneous characteristic, it means that they are jumping into a competition for which their academic achievements do not qualify them and many find it hard to keep up” (pp. 405–406). For example, elite academic institutions often teach at a faster pace and assume that students are familiar with a range of topics. Affirmative action beneficiaries may find the coursework excessively challenging and perform poorly in their classes. Relative to peers with similar characteristics but who attend less selective institutions, students enrolled in selective colleges may have a lower chance of graduating. This set of arguments is referred to as the mismatch hypothesis: affirmative action may generate a mismatch between students’ precollegiate skills and institutions according to this skill distribution (Alon and Tienda 2005; Arcidiacono and Lovenheim 2016; Sander and Taylor 2012). Sander and Taylor (2012) emphasize that mismatch may have a cascade effect: affirmative action students who barely pass the freshman courses can be at an even greater disadvantage in subsequent classes, widening the achievement gap with their more advantaged peers. That is, students’ ability to persist in challenging fields/majors is harmed by their initial low credentials. Likewise, Frisancho and Krishna (2016) analyzed the trajectory of a graduating class from a prestigious engineering college in India. Compared to their same-major peers, affirmative action students did not catch up: they fell behind, and performance disparities were greater for more selective majors. While theoretically plausible and empirically true in certain contexts, the mismatch problem can be partly addressed with design adaptations such as extra support interventions both before and after admission. The support may range from targeted bootcamps or other intensive catchup courses to the availability of dedicated wellness coaches and academic mentors. These types of interventions would again be consistent with the compensation principle from an IOp perspective and would help ensure that students can succeed in high-performance contexts when given a “fair chance” – i.e., the right set of tools.
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A different type of concern with respect to the effectiveness of affirmative action in higher education relates to distortionary incentive effects. By making hard work and talent less relevant for achieving valued positions, affirmative action would lessen incentives for the beneficiaries to exert more effort. If target groups are rewarded for factors unrelated to their effort, the policy would violate a key principle of IOp theory – i.e., an achievement distribution where individual efforts are appropriately rewarded. However, the opposite can be asserted as well: minority students may respond by exerting more effort by virtue of perceiving a higher chance to be admitted in higher education. An exogenous increase in the probability of admission for a group of historically disenfranchised students increases the expected returns to the effort exerted. In other words, affirmative action creates a situation in which previously unattainable opportunities are regarded as accessible and hence worth the effort (Arcidiacono and Lovenheim 2016; Bok and Bowen 1998; Durlauf 2008; Fryer and Loury 2005; Tanner 2016). Such a scenario would clearly be consistent with the reward principle.
Concluding Remarks Inequality of opportunity postulates that disparities in socioeconomic outcomes are not necessarily unfair. Individuals’ accomplishments should be rewarded when they derive from differential degrees of personal efforts. However, hard work and motivation are not the only factors determining inequality. Various types of circumstances on which individuals have no control affect the range of options and aspirations people can pursue and impact the extent to which a personal effort translates into desirable outcomes. In other words, IOp theory acknowledges that social hierarchies are not intrinsically unjust, but the process by which status is achieved defines the fairness of a certain social ranking. Inequality is more objectionable when it results mostly from variation in factors outside individual control. Since the seminal contributions in the early 1990s, the literature on inequality of opportunity has evolved rapidly. While different theoretical approaches have been presented, most contributions agree that the normative goal is not outcome equality. The objective is an achievement distribution where efforts are appropriately rewarded and unfavorable circumstances properly compensated for. Empirically, this would translate into an outcome distribution where all observable inequalities can only be attributed to personal choices (Ramos and Van de gaer 2012). This basic intuition of IOp theory lends itself quite naturally to the domain of affirmative action policies. Indeed, affirmative action recognizes that discrimination and other exclusionary practices influence how opportunities are distributed among different social groups. Building on this awareness, it attempts to reshuffle the distribution of “opportunity sets” to compensate for unfair advantages and to reward true merit. From an IOp perspective, the goal of affirmative action policies is to ensure genuine competition when the prospects of reaching a valued goal are unequally distributed across social groups.
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Focusing on the case of preferential admission to universities, this chapter showed that while affirmative action can reduce the impact of circumstance inequities, it does not represent a silver bullet. The potential for distortionary behavioral responses as well as inadequate institutional support for beneficiaries may significantly limit the effectiveness of affirmative action in higher education admissions. Careful, theory-driven and context-conscious policy design is necessary to avoid unintended negative effects on both beneficiaries and non-beneficiaries. This chapter also argued that preferential admission alone may not be sufficient to equalize opportunities, and additional support mechanisms for beneficiaries may be required depending on the context. From an IOp perspective, affirmative action can be seen as a useful tool for decreasing inequality of opportunity. It should be part of a comprehensive system of corrective measures to improve outcomes for disadvantaged groups. As Barros Francis (1968) said more than half a century ago, “It is clear that the concept of equal [. . .] opportunity, especially in higher education, is essentially meaningless if it is removed from the matrix of the social situation” (p. 312). Yet, despite its flaws, affirmative has opened and continues to open doors. In a global context where exclusion and inequality of opportunity are still prevalent, affirmative action is a valuable step to counterbalance persistent group inequality (Arneson 2015; Moses 2011).
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Roemer JE (1993) A pragmatic theory of responsibility for the Egalitarian Planner. Philosophy & Public Affairs 22:146–166 Roemer JE (1998) Equality of opportunity. Harvard University Press, Cambridge Roemer JE, Trannoy A (2016) Equality of opportunity: theory and measurement. J Econ Lit 54: 1288–1332. https://doi.org/10.1257/jel.20151206 Roemer JE, Ünveren B (2017) Dynamic equality of opportunity. Economica 84:322–343. https:// doi.org/10.1111/ecca.12197 Sander RH, Taylor S Jr (2012) Mismatch: how affirmative action hurts students it’s intended to help, and why universities won’t admit it. Basic Books, New York Tanner J (2016) Towards lifting the burden of stereotype: affirmative action and equality of opportunity. Ethos 29:152–172 Thernstrom AM, Thernstrom S (1997) America in Black and White: One nation, indivisible. Simon & Schuster, New York Van de Gaer D (1993) Equality of opportunity and investments in human capital. Ph.D. Thesis, KULeuven
Part II Methodological Approaches to Understanding Discrimination
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Decompositions: Accounting for Discrimination Gurleen Popli
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Decomposing the Mean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oaxaca-Blinder Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Issues with the Decompositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Treatment Effect Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Formal Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Going Beyond the Mean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Residual Imputations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reweighting Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conditional Quantiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Recentered Influence Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
134 135 135 137 139 141 143 143 144 146 147 148 149
Abstract
This chapter summarizes the different regression-based decomposition methods used in the empirical literature to evaluate discrimination. Starting with the decomposition at the mean using the methods made popular in the 1970s by Oaxaca and Blinder, we discuss how the method has evolved over time to look beyond the means, taking into account the entire distribution of the outcomes of interest. We present the formal identification assumptions underlying the decomposition method and discuss cautions that should be exercised in interpreting them and their limitations. We also explain how the “unexplained gap” in the decomposition, often used as a measure of discrimination, relates to the treatment effect literature.
G. Popli (*) Department of Economics, University of Sheffield, Sheffield, UK e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_15
133
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G. Popli
Keywords
Decomposition · Counterfactual regressions · Distributions · Discrimination JEL Codes
J31 · J71 · C21
Introduction The fact that discrimination exists is not a doubt; it exists and impacts every aspect of our lives, including, but not limited to, economic outcomes (in educational opportunities and outcomes in labor and credit markets), social outcomes (network formations and residential location), health outcomes (mortality rates and access to health services), and interactions with the criminal justice system (Arrow 1998; Lang and Kahn-Lang Spitzer 2020; Small and Pager 2020). Within the discipline of economics, much of the theoretical discussion around discrimination started with the work of Becker (1957), while much of the empirical work has come from within the field of labor economics with the seminal works of Oaxaca (1973) and Blinder (1973), who used wage regressions to quantitatively assess the role of discrimination in explaining the observed wage gaps between men and women (in case of Oaxaca) and between whites and blacks (in case of Blinder, who also looked at the gender wage gaps). Since Oaxaca and Blinder’s work, the literature on regression-based decompositions has seen substantial growth in methodological advancements and their empirical applications. This chapter gives a summary of the different regression-based decomposition methods. Starting with Oaxaca and Blinder’s work, we discuss how the method has evolved over time. Two key limitations of the regression-based decompositions should be stated at the very beginning. First, all the methods discussed here follow the partial equilibrium approach. The wage gaps are decomposed into a part explained by the differences in endowments of the two groups, holding the returns to these endowments constant, and the differences in the returns to endowments across the two groups, holding their endowments constant. This assumes that we can change the endowments without impacting returns to them and vice versa. This is a strong assumption and often not valid. Second, regression-based decomposition methods are an accounting exercise. While we can know various factors contributing to the existing difference between wages (or any outcome of interest), decompositions do not tell us the underlying mechanisms. They can, however, help us confirm an existing hypothesis or form new ones. When we do regression-based decompositions, we are not trying to detect discrimination but are attempting to quantify it. While we have to be cautious in our interpretations that not all the observed wage gaps are discrimination, nothing stops us from concluding that discrimination exists and is buried in it. The way we use regression-based decompositions and the way we interpret them are critical. If done
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correctly and used correctly, we can give some accounting of the existing discriminations. Section “Decomposing the Mean” of the chapter discusses the most famous decomposition method – the Oaxaca-Blinder (OB, henceforth) method. This section will also highlight issues around interpretations, including the treatment effect interpretation, and formal assumptions for identification. Section “Going Beyond the Mean” will discuss the different decomposition methods that have been proposed in the literature since the OB work. Section “Conclusion” provides concluding comments.
Decomposing the Mean Oaxaca-Blinder Method What we refer to as the OB decomposition in economics was first employed in demography by Kitagawa (1955) and made popular in sociology by Althauser and Wigler (1972). This method has been used extensively in the empirical economics literature to study mean wage gaps between different groups in the labor market, defined over gender, race and ethnicity, age, disability, and over immigration status, among others. The decomposition has also been used to look at outcomes beyond the labor market, like consumption and expenditure differences, and inequalities over time and space (i.e., across different regions). Below we lay out the basics of this decomposition. Let Ygi be any outcome of interest, for individual i (i ¼ 1, . . ., n) belonging to group g (M, W). For ease of exposition, let the two groups be men (M) and women (W ) and the outcome of interest be wages. Let Χ gi ¼ (Xgi1, . . ., XgiK) be a vector of K covariates which are associated with the outcome of interest. We assume that the outcome of interest is continuous and linearly related to the covariates as: Y gi ¼ βg0 þ ΣKk¼1 Xgik βgk þ υgi
ð1Þ
where βg0 and βgk are parameters to be estimated, and υgi is the error term which is conditionally independent of Χ gi, such that E(υgi|Χ gi) ¼ 0. The difference in the μ means of the outcomes between the two groups, ΔO ¼ Y M Y W , where Y g is the mean outcome for group g, is given as: μ
ΔO ¼ βM0 βW0 þ μ
k
XWk βMk βWk
ΔS ðunexplainedÞ
þ
k
XMk XWk βMk
ð2Þ
μ
ΔX ðexplainedÞ
where Xgk is the mean of covariate Xgik for group g, and βg0 and βgk are the estimated parameters, intercept and slope coefficients, from the regression models estimated separately for the two groups. The mean difference is decomposed into two parts.
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G. Popli μ
The first term ΔS is the unexplained component, if the outcome of interest is wages; μ this is also referred to as the wage structure effect. The second term ΔX is the explained component; this is also referred to as the composition effect. The composition (explained) effect is the difference in wages due to differences in the observed covariates (endowments) of the individuals across the two groups. For example, the composition effect is the part of the mean gender wage gap that is explained due to observable differences in mean characteristics, XMk XWk , among the men and women, like education and labor market experience. The wage structure (unexplained) effect is the difference in mean wages due to the difference in returns to individual characteristics, βMk βWk . The difference in the intercepts, βM0 βW0, is interpreted as the part of the unexplained gap attributed to group membership. The unexplained component is the gender wage gap that is often associated with labor market discrimination, omitted variables, and unobserved heterogeneity. Using the expression given in Eq. (2), we can investigate both the aggregate and detailed decompositions. Aggregate decomposition is where we are only interested μ μ in the overall wage structure and composition effects, that is, ΔX and ΔS, which helps us understand how much of the observed gap is due to differences in endowments (composition effect) and how much of the gap is due to differences in returns to those endowments (wage structure effect). If we are interested only in aggregate decompositions, we do not even need to separately estimate the wage regressions for μ women. The unexplained component can be simply computed as, ΔS ¼ μ ΔO k XMk XWk βMk . There is, however, often an interest in detailed decomposition, where we wish to know the contribution of each individual covariate, XgiK, to both the wage structure and composition effect. For example, to understand the gender wage gap, it can be of interest to know how much of the composition gap is due to differences in education between men and women and how much is due to differences in the labor market experience between the two groups, as the policy implications from the two can be different. Similarly, we might also be interested in differences in returns to education versus differences in returns to labor market experience when looking at the wage structure effect. The OB decomposition has a counterfactual interpretation, which has been exploited in later methodological innovations. Consider, Y Ci ¼ βM0 þ ΣKk¼1 XWik βMk
ð3Þ
where Y Ci is the counterfactual wage for women obtained by using their own characteristics, but the estimated parameters are from regression for men. The counterfactual wages tell us what women’s wages would have been if they had their own characteristics, but their characteristics were rewarded as men’s are. Using the notion of counterfactual wages, we can write the decomposition in Eq. (2) as:
8
Decompositions: Accounting for Discrimination
YM YW ¼ YM Y
C
137 C
þ Y YW
μ
μ
ΔX
ΔO
ð4Þ
μ
ΔS
C
where Y is the mean counterfactual wage distribution. The first term of Eq. (4) is the explained component and the second term is the unexplained component. The unexplained component is the difference in wages that women would have received if they had their own characteristics but were treated as men are in the labor market and the actual wages of women.
Issues with the Decompositions This section discusses three main cautions that should be exercised or issues that should be kept in mind when interpreting the findings from regression-based decompositions. These cautions apply to the OB decomposition of the mean and all the extensions and methods proposed since then, including those discussed in section “Going Beyond the Mean.” First is the issue of the reference group. To understand this, let’s consider the counterfactual wages generated for women, given by Eq. (3). In this format, it is clear that men are assumed to be the reference group, and male returns to characteristics (the estimated coefficients) are assumed to be nondiscriminatory, that is, we assume that these are the returns that would prevail in the market for both men and women in the absence of discrimination. This is not an innocuous assumption; choice of reference group can alter how the observed wage gap is attributed to the effect of wage structure and composition. The choice of reference group has been discussed in the literature, with various alternatives being proposed. Cotton (1988) gives an excellent graphical illustration of the inherent assumption made when choosing one group as a reference relative to the other group and proposes using a weighted average of the estimated coefficients for the two groups as the nondiscriminatory coefficients. Neumark (1988), on the other hand, proposed using estimated coefficients from a pooled regression for the two groups. Jones and Kelley (1984) propose what is known as the three-fold decomposition: μ
ΔO ¼ βM0 βW0 þ
k
XWk βMk βWk
wage structure effect
þ
k
XMk XWk
βMk βWk
þ
k
XMk XWk βMk
composition effect
ð5Þ
interaction term
The first term, in Eq. (5), is the pure wage structure effect, as before this reflects how much of the gender wage gap results from differences in how women’s characteristics are actually valued in the market and how they would be valued if
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they had the same rates of return as of men. The second term is the pure composition effect, as before, this reflects how much more women would earn if they had the same characteristics as men, but nothing else has changed. The third term is the interaction term; this is the amount that women would gain if they had the characteristics as men and if these characteristics had returns similar to men. In the empirical analysis, this term is often small and is hard to interpret when we have multiple covariates. For a further discussion of the alternative measures proposed in the literature and a unified framework to compare them, see Oaxaca and Ransom (1994). The second issue concerns the base group or the “omitted group” problem. The omitted group problem is discussed in detail by Oaxaca and Ransom (1999) and Oaxaca (2007). This is a concern if we want to do a detailed decomposition of the wage structure (unexplained) component and have categorical covariates. If we are interested only in aggregate decompositions, then this is not a concern. To illustrate the problem, let’s assume the only covariates we have are sectors of employment: primary, manufacturing, and service. In the regression framework, we include two dummies for two sectors, and one sector is omitted as the base category. Say we arbitrarily set the primary sector (sec1) as the omitted category and include dummies for manufacturing (sec2) and service (sec3) sector in the regression equation. This gives us the wage structure effect: μ
ΔS ¼ β0 M0 β0 W0 þ
X k¼2,3 Wseck
βMseck βWseck
ð6Þ
In the presence of categorical covariates, the difference in the intercepts of the two wage regressions for the two groups can be written as: β0 M0 β0 W0 ¼ βM0 þ βMsec1 βW0 þ βWsec1 ¼ βM0 βW0 þ βMsec1 βWsec1
ð7Þ
When we have categorical covariates difference in intercepts, β0 M0 β0 W0 includes not only the gap attributed to the group membership, βM0 βW0 , but also the gap attributed to belonging to the omitted sector, βMsec1 βWsec1 . The latter will change depending on the sector that is omitted. The omitted group’s issue arises only in the wage structure effect and does not impact the composition effect; neither does this problem arise if there is only one binary dummy variable as a covariate (Oaxaca 2007). However, the problem gets complicated if we have more than one categorical covariate. Further, as Jones (1983) pointed out, the base group problem also exists in continuous covariates that do not have a natural scale, like test scores. Some solutions to the base group problem have been proposed in the literature, see Gardeazabal and Ugidos (2004) and Yun (2005), which involve the normalization of coefficients of the categorical variables. However, there is no agreement on this, these normalizations tend to be sample specific,
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and different researchers can use different sets of normalizations. Base groups should be chosen based on context and economic meaning. The third issue is of self-selection within groups and between groups. The most common example of self-selection within groups is of differential selection into the labor force by gender. Self-selection between groups arises when there is an element of choice over group membership, for example, union versus nonunion workers. In the presence of selection (whether between groups or within groups), the estimated coefficients of the wage regression are biased. There are unobserved variables that are correlated with both selection, whether it is the choice of participation in the labor force or the choice of joining unions, and wages. A solution to this problem is to estimate a selection-corrected wage regression, similar to that proposed by Heckman 1979. The selection-corrected wage regression then yields the unbiased estimates of the wage regressions and allows for subsequent decomposition. Let the selection-corrected wage regression be given as: Y gi ¼ βg0 þ ΣKk¼1 Xgik βgk þ σ g λgi þ υgi
ð8Þ
where λgi is the control variable to correct for selection, if the Heckman selection model is used, this will be the inverse mills ratio estimated from the first step of the selection model; and σ g is the estimated coefficient for the control variable. This now changes the decomposition, as the selection-corrected wage regressions have an additional term; the decomposition is now given as: μ
ΔO ¼ βM0 βW0 þ
k
XFk βMk βWk
μ
ΔS ðunexplainedÞ
þ
k
XMk XWk βMk μ
ΔX ðexplainedÞ
þ λM σ M λW σ W where the first two terms are as before and the last term λM σ M λW σ W
ð9Þ is the
difference in the wage gap attributed to differences in selection bias. How the selection term (control variable and the parameter associated with it) is attributed to the different parts of the wage decomposition has implications for interpreting discrimination; for a discussion see Neuman and Oaxaca (2004).
Treatment Effect Interpretation Fortin et al. (2011) show that the wage structure effect estimated from regressionbased decomposition has parallels with the treatment effect literature. To explain this, let us consider union workers (U ) and nonunion workers (N ). The difference in the average wages if everyone is paid according to the wage structure of union members and the average wages of everybody if they were paid according to the wage structure of nonunion members can be conceived as the average treatment
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G. Popli
effect (ATE) where the treatment is “union” membership, that is, switching workers from being nonunion members to union members. Assuming nonunion workers as the reference group, we can construct counterfactual wages for union workers, these are the wages that the union workers with characteristics XUik would earn if they were paid as nonunion workers: C
Y ¼ βN0 þ
k
XUk βNk
ð10Þ
Using this counterfactual wage, the decomposition can be written as: μ
ΔO ¼ Y U Y N ¼ Y U Y μ
ΔS
C
C
þ Y YN
ð11Þ
μ
ΔX
where the difference between the average wages of the union workers and their C counterfactual wages, Y U Y , is the wage structure effect. Under treatment effect interpretation, Y U , the average wages of the union workers, is the average wages of C the treatment group where the treatment is “union” membership; and Y is then the average wages of the treated (union) workers if they were not treated, that is, they C were nonunionized. Difference between the two, Y U Y , is the “union effect” or the average treatment effect on the treated (ATT). In this framework, the composition μ effect, ΔX , is referred to as the selection bias. The ATT interpretation of the wage structure holds for aggregate decompositions, including extensions that go beyond the mean, under the formal identification assumptions discussed below; however, this equivalence does not always hold for detailed decompositions. The issues of choice of the reference group and the base group problem remain in this interpretation as well; however, this approach gives us a way around the selection issue. For a detailed discussion of equivalence between the treatment effect interpretation for the OB decompositions, see An and Glynn (2019). For the OB decomposition, we had assumed the error term to be conditionally independent of covariates, E(υgi|Χ gi) ¼ 0. Under the treatment effect interpretation, this assumption can be replaced by a weaker assumption of “ignorability.” This helps us address some issues of selection bias. Under the assumption of ignorability, selection bias is allowed as long as it is the same for the two groups. For example, ability, which we do not observe, can be correlated with education, an observed covariate, as long as the correlation is the same in the two groups being considered. This is the “selection on observables” assumption used in the treatment literature. Under this interpretation, we do not need to calculate the composition (explained) component, once we have the unexplained component, we can compute the μ μ μ explained part as: ΔX ¼ ΔO ΔS , which reflects the difference in the distribution of the covariates and the error term. In the treatment effect literature, ATT has a causal interpretation. Even though the wage structure effect is derived under the same conditions as ATT, the causal
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interpretation often cannot be extended to the wage structure effect for two main reasons. First, in decompositions, “treatment” is not always a choice or something that can be manipulated easily. For example, while we can conceive union/nonunion membership as a choice, gender cannot be conceived as a choice or something that can be manipulated. Further, as we discussed in the selection issue, group membership is often related to unobserved variables, this means the ignorability assumption is often violated, making the causal interpretation hard. Second, as mentioned in the introduction, decompositions are a partial equilibrium approach. For example, the wage structure effects for union wage gap tells us, holding union workers characteristics (Χ Ui) same, if the returns to their characteristics were the same as that of nonunion workers (i.e., βUk ¼ βNk), then the wage structure effect would be zero. Equating the two parameters is similar to “treating” union workers as nonunion workers, but this treatment is likely to change union workers’ characteristics. When Χ gi is impacted by the treatment, we cannot obtain a causal interpretation. Unless great caution has been taken to include only those characteristics that are unlikely to be impacted by the treatment, for an example of this see Neal and Johnson (1996).
Formal Identification In this section, we lay out the minimum set of assumptions needed to identify the aggregate decomposition. There are further assumptions needed to identify detailed decompositions and some of the extensions discussed in section “Going Beyond the Mean.” For a more technical account of these assumptions, refer to Fortin et al. (2011), who set out the full set of assumptions needed for the identification for all regression-based decomposition methods. Assumption 1: Mutually exclusive groups The population of interest can be divided into two mutually exclusive groups, g (A, B). We are interested in comparing the outcome Ygi of the two groups. In line with the treatment effect literature, YAi and YBi can be interpreted as potential outcomes for individual i. If the individual is in group A (B), then we only observe YAi (YBi). Assumption 2: Structural form A worker i belonging to group g is paid according to the wage structure mg, which is a function of worker’s observable characteristics, Xgi, and unobservable characteristics, υgi: Y Ai ¼ mA ðXAi , υAi Þ and Y Bi ¼ mB ðXBi , υBi Þ
ð12Þ
In a more general framework, we can think of mg as the function linking individual characteristics and their outcomes. Under linearity: Ygi ¼ mg(Xgi, υgi) ¼ Χgiβg þ υgi. There are three reasons why wages can be different between groups. (1) The difference in the wage setting equations mA and mB. For the linear model, this
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is the difference in the returns to the observed characteristics, βA and βB, or the difference in the returns to the unobservable characteristics. (2) Differences in the distribution of the observable characteristics, Xgi, for the two groups. (3) Differences in the distribution of the unobservable characteristics, υgi, for the two groups. Assumption 3: Simple counterfactual treatment A counterfactual wage structure, mC, is said to correspond to a simple treatment when it can be assumed that mC(XBi, υBi) mA(XBi, υBi), or mC(XAi, υAi) mB(XAi, υAi). This assumption states that we can identify what the distribution of wages for groups A will be if the returns to their characteristics are similar to those of group B. This is the counterfactual we constructed for women, given by Eq. (3), and the counterfactual we constructed for union workers, given by Eq. (10). This assumption, however, also highlights that while we can identify what the distribution of wages for women (union workers) will be if they were paid as men (nonunion workers) are, we cannot identify what the distribution of wages for women (union workers) will be if there were no labor market discrimination (no unions). Assumption 4: Overlapping support No single value of observable, Xgi, or unobservable, υgi, characteristics can identify group membership. This rules out a situation where the covariates in Xgi are different across groups, or there are values that Xgi can take only for one group and not for the other. The issue of overlapping support, or common support as it is also referred to, is well recognized in the treatment effect literature. This is usually not a problem if our focus is only on the decomposition of the mean but becomes an issue when looking at distributions. An example of different covariates across groups is when let’s say we want to compare wages of immigrants and natives, where the country of origin is an essential predictor of wages for the immigrants but is not relevant for natives. Similarly, when we look at gender wage gaps, often women tend to be concentrated into occupations and or industry combinations where there will be no men. For a discussion of this and possible solutions, see Ñopo (2008). It is also possible that the range of values that certain covariates take differ by groups. This was discussed by Barsky et al. (2002), where the authors look at the role of income in explaining the black-white wealth gaps, and found that there are certain regions of income where no blacks are observed. Assumption 5: Conditional independence or ignorability Let Dgi ¼ 1{i g}, where 1{.} is the indicator function for group membership. Ignorability requires Dgi ⊥ υ j X. Intuitively, the ignorability assumption states that the distribution of unobservable characteristics, υ, given observable characteristics, X, be the same for the two groups. In case of the decomposition of the mean and linear specification, it does not require E(υgi|Χgi) ¼ 0, instead all it requires is E(υAi|ΧAi) ¼ E(υBi|ΧBi). In the treatment effects literature, this assumption is also referred to as unconfoundedness or selection on observables.
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Going Beyond the Mean There have been several extensions of the OB decomposition. In this section, we discuss some of these extensions. First, we discuss the extension proposed by Juhn et al. (1993), who give a way to account for the unobserved characteristics via a residual imputation method. Second is the reweighting method, proposed by DiNardo et al. (1996) to investigate the distribution of the unexplained differences. The third is the decomposition of the conditional distributions, based on quantile regressions, by Machado and Mata (2005). Fourth, and finally, is the decomposition of unconditional quantiles, using Recentered Influence Functions, proposed by Firpo et al. (2007, 2009). While most of the methods discussed in this chapter allow us to look beyond the mean and are good at obtaining aggregate decompositions, detailed decompositions beyond the mean remain challenging, and where possible, they come at the cost of simplicity and intuitive appeal of the OB method.
Residual Imputations The main contribution of Juhn et al. (1993), JMP henceforth, was to take into account the returns to the unobservable characteristics (also referred to as unobserved heterogeneity) explicitly in the decompositions. Starting with the regression function given by Eq. (1), when we decompose the mean gap between two groups we get Eq. (2). Residuals, υig, are not a part of the decomposition as υg ¼ 0, where υg is the mean residual of group g. However, the residuals υig are interpreted as unobservable characteristics and JMP propose a way to look at how the distributions of these unobserved characteristics differ among the two groups. JMP define the cumulative distribution of wage residuals, conditional on covariates, as θig ¼ F(υig| Xig). This gives us, υig ¼ F1 θig jXig F1 ig , which is the inverse cumulative distribution of wage residuals. Using this definition of residuals, we can rewrite Eq. (1) as: Y gi ¼ βg0 þ ΣKk¼1 Xgik βgk þ F1 ig
ð13Þ
Assuming the two groups to be men and women, there are now three potential sources of the gender wage gap, differences in the observables, X; differences in returns to observables, β; and differences in the distribution of unobservables (residuals), F1 g . To obtain the decomposition, JMP recommend generating two different counterfactuals: K 1 Y C1 i ¼ βM0 þ Σk¼1 XWik β Mk þ FiM
ð14Þ
K 1 Y C2 i ¼ βW0 þ Σk¼1 XWik βWk þ FiM
ð15Þ
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The first counterfactual, Y C1 i , gives us the counterfactual wages for women, if the observable characteristics are of women and the returns to the observables and the residual distribution are of men. The second counterfactual, Y C2 i , gives us the counterfactual wages for women, if both the observable characteristics and returns to them are of women, but the residual distribution is of men. Using the two counterfactuals, the decomposition is given as: YM YW ¼ YM Y
C1
þ Y
C1
Y
C2
The first term on the right-hand side of Eq. (16),
þ Y
C2
YW
YM Y
C1
ð16Þ , gives us the
difference in wages due to observable characteristics (the composition effect); C1 C2 , gives us the difference in wages due to returns to second term, Y Y observable characteristics (the wage structure effect); and the third term, C2 Y Y W , gives us the difference in wages due to differences in the distribution of the unobservable factors. While proving to be very useful in looking at the role of unobserved characteristics in explaining wage differentials, the JMP method has some limitations which need to be kept in mind. First, if the number of observations between the two groups does not match, it is not clear how to assign residuals from one group to another, that is, how to generate the first counterfactual, Eq. (14). The solution proposed by JMP for this is to replace the ith ranked residual from women’s residual distribution with the ith ranked residual from the residual distribution for men. Second, while theoretically, we need θig ¼ F(υig| Xig), empirically all we can get is θig ¼ F(υig), which implies independence between observed and unobserved characteristics. This can be an unrealistic assumption. Third, the decomposition is path dependent, the order in which we generate the counterfactuals can change the size of the different effects. Lastly, in the empirical analysis, the three components of the decomposition need not add up to the observed mean gap. For a full discussion of the JMP method’s limitations and possible solutions, see Lemieux (2002) and Yun (2009).
Reweighting Methods DiNardo, Fortin, and Lemieux (1996), DFL henceforth, generalized the OB method for the entire distribution of wages. We start by estimating the distribution of observed wages for the two groups, f Y g f ðYjgÞ ¼ f ðYjg, xÞhðxjgÞdx
ð17Þ
where f(Y|g) is the distribution of the wages for group g; f(Y|g, x) is the wage distribution for group g given individual characteristics, X ¼ x; and h(x|g) is the
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Decompositions: Accounting for Discrimination
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distribution of individual characteristics for group g. The empirical counterpart to Eq. (17) can be given as: ng
f Yg ¼
j¼1
K
Y g,i Y g,j , for all i ¼ 1, . . . ,ng bg
ð18Þ
where K(.) is the kernel function and ng is the number of observations in group g. The actual distributions are estimated for both groups, let’s say men and women. DFL then propose an estimation of a counterfactual distribution for one of the groups, we show it for women, defined as: f C ðY W Þ f C ðYjMÞ ¼ f ðYjM, xÞhðxjW Þdx ¼ ωðxÞf ðYjM, xÞhðxjMÞdx
ð19Þ
where f C(YW) is the counterfactual distribution of women, such that the distribution of individual characteristics is as that of women, but they are paid as men would be. The counterfactual distribution suggested by DFL is the distributional equivalent of counterfactual wage regression defined in Eq. (3). The counterfactual distribution is simply a reweighted distribution of men, where ω(x) is the reweighting function Þ defined as ωðxÞ hhððxjW xjMÞ . To estimate the counterfactual distribution, we need an estimate of the reweighting function, which using the Bayes rule can be written as: ωð x Þ ¼
PrðWjxÞ=PrðW Þ PrðMjxÞ=PrðMÞ
ð20Þ
To estimate Eq. (20), we pool the data and estimate a probit model, where the dependent variable is the binary gender variable, and the covariates are the individual characteristics, X. Pr(W| x) and Pr(M| x) are simply the predicted probabilities from the probit model, and Pr(W) and Pr(M ) are the unconditional probabilities. Once we have the estimate of the reweighting function, empirically the counterfactual distribution can be estimated as: C
f ðY M Þ ¼
nM j¼1
ωðxÞK
Y M,i Y M,j , for all i ¼ 1, . . . nM bM
ð21Þ
Once we have estimates of the actual and the counterfactual distributions, we can f estimate the distributional composition effects (ΔX ) and the wage structure effects f
(ΔS ) as: f
f
ΔX ¼ f ðY M Þ f C ðY W Þ and ΔS ¼ f C ðY W Þ f ðY W Þ
ð22Þ
The DFL method is simple to implement and intuitive and can decompose statistics other than the mean. The reweighting function proposed by them has been used in the context of many other decomposition methods, including the
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G. Popli
Recentered Influence Function approach discussed below. While aggregate decompositions are easy to do with the DFL method, detailed decompositions are still an issue. For binary variables, detailed decomposition is feasible, and the authors discuss how to do them in their 1996 paper; however, detailed decompositions for continuous covariates remain a challenge. See Butcher and DiNardo (2002) and Altonji et al. (2012) for some solutions to detailed decomposition within the DFL framework. Other methods, other than DFL, have been proposed in the literature to look at the decomposition of distribution; for one such method, see Jenkins (1994) who takes the Generalised Lorenz Curve approach to estimate both the actual distributions and the counterfactual distributions.
Conditional Quantiles While the DFL method allows us to do aggregate decomposition at the distributional level, detailed distribution beyond the mean remains a challenge. An alternative to looking at the entire distributions and doing detailed decomposition is proposed by Machado and Mata (2005), MM henceforth, who based their decomposition on conditional quantile regressions (Koenker and Bassett 1978). In this, instead of estimating a regression for the mean (OLS), we start by estimating a quantile regression for each group, men and women, given as: Qτ Y g jXg ¼ Xg βτg , τ ð0, 1Þ
ð23Þ
where βτg gives us the returns to the characteristics, X, on the τth quantile of the wage (Y ) distribution. As in the OB method, we next estimate a counterfactual distribution for women if they had their own characteristics but are paid as men would be: Qτ Y C jXW ¼ XW βτg
ð24Þ
Once we have estimates of the actual and the counterfactual quantile regressions, τ τ we can estimate the composition effects (ΔX ) and the wage structure effects (ΔS ) at different quantiles as: τ
τ
ΔX ¼ Qτ ðY M jXM Þ Qτ Y C jXW and ΔS ¼ Qτ Y C jXW Qτ ðY W jXW Þ To do the quantile decomposition, MM suggest the following simulation: 1. Sample τ from a standard uniform distribution. τ 2. Estimate the quantile regression for the τth quantile, and obtain βg .
ð25Þ
8
Decompositions: Accounting for Discrimination τ
3. Compute Qτ Y C jXW ¼ XW βM then computed as residual,
τ
and Qτ ðY W jXW Þ ¼ XW βM . The difference
between the two gives us the wage structure effect τ ΔX
147
τ (ΔS ),
composition effect is τ
¼ Qτ ðY M jXM Þ Qτ ðY W jXW Þ ΔS
4. Repeat steps 1 to 3 M times. MM method is computationally very intensive; further, while it allows for the detailed decomposition of the wage structure effect, we cannot obtain the detailed decomposition of the composition effect. Melly (2005) provides a way to reduce the computation time and do a detailed decomposition of the composition effect at the median. Chernozhukov et al. (2013) provide a further extension of the MM method, giving detailed decomposition for both the wage structure and the composition effect. A key limitation of this method is that the detailed decompositions based on conditional quantile regressions are path dependent, the order in which the various covariates are considered in decomposition can alter their contribution to the explained and the unexplained components.
Recentered Influence Functions Firpo et al. (2007, 2009) proposed a way to look at the unconditional distributions and do both the aggregate and detailed decomposition, by using Recentered Influence Function (RIF) regressions. The RIF regression for the τth quantile, qτ, of the wages, Yg, for group g, is defined as: RIF Y g , qτ ¼ qτ þ τ d g,τ =f Y g ðqτ Þ, . . . τ ð0,1Þ
(26)
where f Y g ðqτ Þ is the density function of Yg computed at quantile qτ, and dg, τ is the dummy variable taking value one if Yg qτ and zero otherwise. The RIF(Yg, qτ) has two properties that make it particularly useful; first, its expectation is the actual τth-quantile, EY[RIF(Yg, qτ)] ¼ qτ; and second, the expectation of the conditional RIF, when conditioning on the vector Xg, is also the actual τth-quantile, EX[Ey[RIF(Yg, qτ)| Xg]] ¼ qτ. Assuming RIF to be a linear function of covariates, we have, RIF Y g , qτ ¼ βτg0 þ ΣKk¼1 Xgik βτgk þ υτg
ð27Þ
where βτg is the vector of coefficients for the τth-quantile, and υτg is the error term. Given the two properties of the RIF function, Eq. (27) is the unconditional quantile regression, which is estimated separately for the two groups, men and women. The difference in the τth-quantile wage for men and women, qτ, M qτ, W, can then be decomposed as follows:
148
G. Popli τ
τ
τ
qτ,M qτ,W ¼ βM0 βW0 þ Δ
τ
k
τ
XWk βMk βWk
τ
ΔS
þ
τ
XMk XWk βMk
k
ð28Þ
τ
ΔX τ
On the left-hand side of Eq. (6), Δ , is the gap in the wages of men and women at τ the τth-quantile. The first term on the right-hand side, ΔS, is the wage structure effect τ at the τth-quantile, and the second term ΔX , is the composition effect at the τthquantile. Like the OB decomposition, we can obtain both the aggregate decomposition and the detailed decomposition, which are path independent. When the RIF is evaluated at the mean of Yg, we get OB decomposition as a special case. The RIF regressions can be biased as the assumption of linearity holds true only locally. To correct for the specification error, the RIF regression is combined with the DFL reweighting function. This requires estimating the RIF regression for women with the reweighting function such that the covariates of women have the same distribution as of men. This yields the specification error and the reweighting error, separately from the composition and the wage structure effect, respectively. Empirically these errors tend to be very small.
Conclusion Oaxaca and Blinder decomposition was first popularized in the 1970s, the use and popularity of this method have not waned over the last five decades. This chapter summarizes the OB decomposition’s main facets, its main limitations, cautions that should be exercised when using it, and the formal identification assumptions underlying it. We also link OB decomposition to the treatment effect literature. This chapter is not an exhaustive discussion of all methods of decomposition and the issues underlying them. For example, we have only focused on continuous outcomes, and not discussed the various methods that have been proposed for limited dependent variables (Fairlie 2005). Instead, this chapter’s focus has been to introduce the regression-based decompositions and highlight, up to date, key innovations in this method. This main limitation of regression-based methods, from the perspective of discrimination studies, is the interpretation of the unexplained gaps. On the one hand, not all of the unexplained gap can be discrimination, there often are unobservable factors at play making the unexplained gap an overestimation of discrimination. On the other hand, the estimated coefficients (return to endowments) from regressions already taken into account the feedback from the market, so any estimated discrimination is likely to be understated. The decomposition methodology has come a long way from the original work of Oaxaca and Blinder; increasingly we have methods
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that help us address the issues of unobserved productivity differences, self-selection, and omitted variables. In recent years, in an attempt to separate discrimination from unobserved productivity differences, self-selection, and omitted variables, there has been substantial growth in the experimental literature on discrimination (for a review, see Neumark 2018). While the experimental methods help us identify discrimination more robustly and explore the underlying mechanisms, they have their limitations. Most of the experimental literature in labor economics focuses on hiring, which may not impact earnings, whereas the decomposition methods can look at earnings directly. Acknowledgments I would like to thank Okan Yilmaz and Meng Le Zhang for their helpful comments on an earlier draft of the chapter.
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Jones FL (1983) On decomposing the wage gap: a critical comment on Blinder’s method. J Hum Resour 18:126–130 Jones FL, Kelley J (1984) Decomposing differences between groups: a cautionary note on measuring discrimination. Sociol Methods Res 12(3):323–343 Juhn C, Murphy KM, Pierce B (1993) Wage inequality and the rise in returns to skill. J Polit Econ 101(3):410–442 Kitagawa EM (1955) Components of a difference between two rates. J Am Stat Assoc 50: 1168–1194 Koenker R, Bassett G (1978) Regression Quantiles. Econometrica 46:33–50 Lang K, Kahn-Lang Spitzer A (2020) Race discrimination: an economic perspective. J Econ Perspect 34(2):68–89 Lemieux T (2002) Decomposing changes in wage distributions: a unified approach. Can J Econ 35(4):646–688 Machado JA, Mata J (2005) Counterfactual decomposition of changes in wage distributions using quantile regression. J Appl Econ 20(4):445–465 Melly B (2005) Decomposition of differences in distribution using quantile regression. Labour Econ 12(4):577–590 Neal DA, Johnson WR (1996) The role of premarket factors in black-white wage differences. J Polit Econ 104(5):869–895 Neuman S, Oaxaca RL (2004) Wage decompositions with selectivity corrected wage equations: a methodological note. J Econ Inequal 2:3–10 Neumark D (1988) Employers’ discriminatory behavior and the estimation of wage discrimination. J Hum Resour 23:279–295 Neumark D (2018) Experimental research on labor market discrimination. J Econ Lit 56(3): 799–866 Ñopo H (2008) Matching as a tool to decompose wage gaps. Rev Econ Stat 90(2):290–299 Oaxaca R (1973) Male-female wage differentials in urban labor markets. Int Econ Rev 14:693–709 Oaxaca RL (2007) The challenge of measuring labor market discrimination against women. Swedish Econ Policy Rev 14(1):199–231 Oaxaca RL, Ransom MR (1994) On discrimination and the decomposition of wage differentials. J Econ 61:5–21 Oaxaca RL, Ransom MR (1999) Identification in detailed wage decompositions. Rev Econ Stat 81(1):154–157 Small ML, Pager D (2020) Sociological perspectives on racial discrimination. J Econ Perspect 34(2):49–67 Yun MS (2005) A simple solution to the identification problem in detailed wage decompositions. Econ Inq 43:766–772 Yun MS (2009) Wage differentials, discrimination and inequality: a cautionary note on the Juhn, Murphy and Pierce Decomposition Method. Scott J Political Econ 56(1):114–122
9
Field Experiments: Correspondence Studies Marianne Bertrand and Esther Duflo
Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Measuring Discrimination in the Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Audit Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Correspondence Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Correspondence Studies in Other Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Beyond the Résumés . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Limitations of Correspondence Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implicit Association Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Goldberg Paradigm Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . List Randomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Willingness to Pay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Consequences of Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Self-Expectancy Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Expectancy Effects and Self-Fulfilling Prophecies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discrimination in Politics and Inequality Across Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Benefits of Diversity? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What Affects Discrimination? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leaders and Role Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intergroup Contact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Socio-Cognitive De-Biasing Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Technological De-Biasing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
152 157 158 160 169 172 173 178 182 183 184 187 187 192 196 197 200 200 206 209 218
This chapter was originally published in 2016 as Chapter 8 in the Handbook of Economic Field Experiments, and can be accessed at https://doi.org/10.1016/bs.hefe.2016.08.004. Re-published here with permission. M. Bertrand (*) University of Chicago Booth School of Business, NBER, and J-PAL, Chicago, IL, USA e-mail: [email protected] E. Duflo MIT Economics Department, NBER and J-PAL, Chicago, IL, USA e-mail: edufl[email protected] © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_16
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Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
Abstract
This chapter reviews the existing field experimentation literature on the prevalence of discrimination, the consequences of such discrimination, and possible approaches to undermine it. We highlight key gaps in the literature and ripe opportunities for future field work. Section “Measuring Discrimination in the Field” reviews the various experimental methods that have been employed to measure the prevalence of discrimination, most notably audit and correspondence studies; it also describes several other measurement tools commonly used in lab-based work that deserve greater consideration in field research. Section “Consequences of Discrimination” provides an overview of literature on the costs of being stereotyped or discriminated against, with a focus on selfexpectancy effects and self-fulfilling prophecies; section “Consequences of Discrimination” also discusses the thin field-based literature on the consequences of limited diversity in organizations and groups. The final section of the chapter, section “What Affects Discrimination?,” reviews evidence for policies and interventions aimed at weakening discrimination, covering role model and intergroup contact effects, as well as socio-cognitive and technological de-biasing strategies. Keywords
Field experiments · Discrimination · Implicit bias · Audit studies · Correspondence studies
Introduction Black people are less likely to be employed, more likely to be arrested by the police, and more likely to be incarcerated. Women are very scarce at the top echelon of the corporate, academic, and political ladders despite the fact that (in rich countries at least) they get better grades in school and are more likely to graduate from college. While many in the media and public opinion circles argue that discrimination is a key force in driving these patterns, showing that it is actually the case is not simple. Indeed, it has proven elusive to produce convincing evidence of discrimination using standard regression analysis methods and observational data, in the sense in which we define discrimination throughout this chapter: members of a minority group (women, blacks, Muslims, immigrants, etc.) are treated differentially (less favorably) than members of a majority group with otherwise identical characteristics in similar circumstances. However, over the last couple of decades, a rich literature in economics, sociology, political science, and psychology has leveraged experiments (in the lab and in the field) to provide convincing evidence that discrimination, thus defined, indeed exists. We begin this chapter by describing the various experimental methods that
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have been used to measure discrimination in the field. Overall, this chapter offers staggering evidence of pervasive discrimination against minority or underrepresented groups all around the world. We summarize this chapter and discuss some of its key limitations. If discrimination is as pervasive as evidence suggests, what do existing theories tell us about costs to minority groups and to society overall? The two workhorse models of discrimination in the economics literature give drastically different answers, particularly with respect to the societal consequences. In the first model, developed in Becker (1957) for the context of the labor market, some employers have a distaste for hiring members of the minority group. They may indulge this distaste by refusing to hire, say, blacks or, if they do hire them, paying them less than other employees for the same level of productivity. If the fraction of discriminating employers in the economy is sufficiently large, a wage differential will emerge in equilibrium between otherwise identically productive minority and majority employees and this wage differential will be a reflection of the distaste parameter of the marginal employer for minority workers (Becker 1957; Charles and Guryan 2008). By electing to not hire minority workers, inframargin racist employers will experience lower profits. In fact, if the conditions of perfect competition were satisfied, discriminating employers would be wiped away and taste-based discrimination would disappear.1 This “taste-based” explanation for discrimination stands in contrast with what many economists would view as a more disciplined explanation, which does not involve an ad hoc (even if intuitive) addition to the utility function (animus toward certain groups) to help rationalize a puzzling behavior. In a “statistical discrimination” model (Phelps 1972; Arrow 1973; Aigner and Cain 1977), the differential treatment of members of the minority group is due to imperfect information, and discrimination is the result of a signal extraction problem. As a profit-maximizing prospective employer, renter, or car salesman tries to infer the characteristics of a person that are relevant to the market transaction they are considering to complete with that person, they use all the information available to them. When the personspecific information is limited, group-specific membership may provide additional valuable information about expected productivity. For example, again using the labor market scenario, it may be known to employers that minority applicants are on average less productive than majority applicants. In this case, an employer who sees two applicants with similar noisy but unbiased signals of productivity should rationally favor the majority applicant to the minority one as her expected productivity is higher. While expected productivity will equal true productivity on average within each group, statistical discrimination will result in some minority workers being treated less favorably than nonminority workers of the same true productivity, i.e., will result in discrimination as defined above. In the extreme case where individual signals of productivity are totally uninformative, an employer may
1
Refusing to hire black people could be efficient if the employer knows he cannot work well with them due to his animus, but this does not take away from the fact that businesses that do this should not survive.
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rationally decide to make offers only to whites if the mean productivity among blacks does not exceed the required threshold. While taste-based discrimination is clearly inefficient (simply consider how it constrains the allocation of talent), statistical discrimination is theoretically efficient and hence more easily defendable in ethical terms under the utilitarian argument. Moreover, statistical discrimination can also be argued to be “fair” in that it treats identical people with the same expected productivity (even if not with the same actual productivity) and is not motivated by animus. In fact, many economists would most likely support allowing statistical discrimination as a good policy, even where it is now illegal (as it is, for example, in the US labor market and real estate market contexts). Unfortunately, as we discuss below, while field experiments have been overall successful at documenting that discrimination exists, they have (with a few exceptions) struggled with linking the patterns of discrimination to a specific theory. Meanwhile, psychologists have made considerable progress in their own understanding of the roots of discrimination on a largely parallel track. The theories they have advanced and the (mainly lab) experiments they have conducted have been helpful in better nailing the microfoundations of discrimination. We believe this body of work blurs the sharp line economists tend to draw between taste-based and statistical explanations. Psychologists’ work on discrimination is embedded in an immense literature that attempts to understand the roots of prejudice, widely characterized as negative evaluation of others made on the basis of their group membership. This literature has looked for the microfoundations of such negative evaluation in a wide variety of areas, including personality development, socialization, social cognition, evolutionary psychology, and neuroscience. Early scholarship in psychology viewed prejudice as a form of abnormal thinking and equated it to a psychopathology (think Adolf Hitler) that could be treated by addressing the personality disorders of the subset of the population that was “diseased.” It was only in the second half of the twentieth century that the prevalent view of prejudice among psychologists became rooted in normal thinking processes (Dovidio 2001), with socialization and social norms being viewed as dominant drivers. Influential work by Tajfel (1970; Tajfel and Turner 1979) demonstrated the key role social identity plays in the process underlying prejudice. Experimental evidence has shown that the assignment of people to groups, even if they are totally arbitrary ones and even if they do not last, is sufficient to produce favoritism for in-group members and negativity toward out group members. At the same time, evolutionary psychology has stressed the importance of social differentiation and the delineation of clear group boundaries as a way to achieve the benefits of cooperation between human beings without the risk of excessive costs, with group membership and group identity emerging as a form of contingent altruism (Brewer 1981). While in-group love might not necessarily imply out-group hate, the same factors that make allegiance with group members important provide grounds for antagonism and distrust of outsiders.
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In addition, more recent advances in the psychology literature have demonstrated the existence of unconscious, unintentional forms of bias. Modern social psychologists believe that attitudes can occur in implicit modes and that people can behave in ways that are unrelated to or even sometimes opposed to their explicit views or selfinterests (Banaji and Greenwald 1995; Bertrand et al. 2005; Dovidio et al. 1998a, b; Greenwald and Banaji 1995). Neuroscience studies have shown that different regions of the brain are activated in conscious versus unconscious processing, suggesting that unconscious processes are distinct mental activities. For example, the unconscious processing of black faces has been associated with activations of the area of the brain associated with emotions and fear while the conscious processing of the same faces increases brain activity in areas related to control and regulation. Implicit biases are more likely to drive behavior under conditions of ambiguity, high time pressures and cognitive loads, or inattentiveness to the task. Both of these dominant views of prejudice in the psychology literature – as an evolutionary phenomenon making group membership an important component of one’s social identity or as an unconscious automatic negative association triggered by exposure to out-group members – could serve as microfoundations to what the more reduced form “animus-based” models economists have worked with. More importantly, these psychological models make clear that the limited information and decision-making model that drives statistical discrimination might be itself endogenous to conscious or unconscious prejudice against the out-group members. If a social need to positively associate with one’s own group also makes the out-group members feel more distant and unknowable (Brewer 1988), an employer may not invest in collecting information on an out-group member, or decide that the individual signals of productivity for minority group members are totally uninformative, resulting in all minority group members being equally treated as unhirable. Limited de-facto contact between in-group and out-group members will imply that majority employees or coworkers will be fairly ignorant about the quality of minorities; this would mean that employing, electing, or renting to them may seem riskier which, in the presence of risk aversion, will also trigger more statistical discrimination (Aigner and Cain 1977). Unconscious bias may influence the specific criteria or formulae that are used to assess expected productivity (Uhlmann and Cohen 2005), for example, the sense of danger that is implicitly triggered by seeing a black face or reading a black name on a résumé may lead an employer to put too great a weight on docility as a work quality than would be warranted for maximum productivity. Recently, the emphasis on “fit” between a prospective employee and the company as a hiring criterion in technology jobs has raised the spectrum of a new form of subtle discrimination. Similarly, unconscious stereotypes may influence our judgment of the inputs into productivity, with the same level of assertiveness being deemed as good for productivity when coming from men but bad when coming from women (Rudman and Glick 2001). Perhaps most importantly, whether discrimination is taste-based or statistical, it may ultimately result in genuine difference between groups through self-fulfilling prophecies. If the stereotypical woman is not good at math, talented girls may become discouraged and ultimately not become good at math. If teachers or
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employers assume that students of a particular color are less smart, they will invest less in them. Thus, discrimination, whether it is taste-based or statistical, can create or exacerbate existing differences between groups. Discrimination that starts as tastebased and inefficient can easily morph into the more “justifiable” form. “Valid” stereotypes today could be the product of ambient animus, very much complicating the division between the different theories of discrimination. The chapter proceeds as follows. Section “Measuring Discrimination in the Field” is devoted to the various experimental methods that have been used in the field to measure discrimination, in particular audit and correspondence studies. Audit studies send out individuals who are matched in all observable characteristics except for the one in question (race, criminal record, etc.) to apply for jobs or make purchases; then researchers analyze the responses. Correspondence studies – which represent by far the largest share of field experiments on discrimination – do the same but control for more variables by creating fictitious applicants (often for jobs or apartments) who correspond via mail. We summarize the findings of this body of work (which clearly demonstrate the pervasiveness of discrimination) and discuss its key limitations. In this section of the chapter, we also discuss a few alternative methods to measure discrimination, many of them, such as Implicit Association Tests, Goldberg Paradigm experiments, and List Randomization, having developed in the psychology literature for use in the lab, as well as measures of willingness to pay to interact with minority group members. We argue that these alternative methods deserve more consideration by economists interested in measures of discrimination for their field research. Section “Consequences of Discrimination” reviews the work that addresses the costs of being discriminated against, or stereotyped. In particular, we review the experimental work that has studied how the threat of being viewed through the lens of a negative stereotype can have a direct negative effect on performance. We also review the experimental literature on expectancy effects, the goal of which has been to understand how stereotypes and biases against minority groups may end up being self-fulfilling. We round up the second part of the chapter by reviewing what is a surprisingly thin amount of field-based literature on the costs (and benefits) of the limited diversity in organizations and groups that directly result from discrimination. This allows us to discuss field work that has considered the consequences of discrimination not just from the perspective of the group that is discriminated against, but also from the perspective of society as a whole. The third and final section of this chapter, section “What Affects Discrimination?,” is related to the review of various interventions and policies that have been proposed to undo or weaken discrimination. This section covers topics such as the impact of role models, how contact and exposure to the minority groups may change prejudice, as well as large psychology literature on both socio-cognitive and technological de-biasing strategies. We argue that there is a lot of promising future research that is “ripe for the picking” in this area, given the large amount of theoretical and lab-based work that has not yet been taken to the field.
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Measuring Discrimination in the Field Earlier research on discrimination focused on individual-level outcome regressions, with discrimination estimated from the “minority” differential that remains unexplained after including as many proxies as possible for productivity.2 The limitations of this approach are well-known. The interpretation of the estimated “minority” coefficient is problematic due to omitted variables bias. Specifically, results of a regression analysis might suggest differential treatment by race or gender even if the decision-maker (say, an employer) never used group membership in her decision of how much to pay an employee. However, it could be the case that race or gender is correlated with other proxies for productivity that are unobservable to the researcher but observed by the employer. It is therefore impossible to conclude that the employer used group membership in her decision-making process using this method. The traditional answer has been to saturate the regression with as many productivity-relevant, individual-level characteristics as are available. But, of course, ensuring that the researcher observes all that the decision-maker observes is a hopeless task. Moreover, adding more and more controls to a regression could ultimately obscure the interpretation of the evidence. Consider the labor market context: minority workers might be best-responding to the discrimination they know to exist and could have simply sorted into industries where there is no or limited discrimination. Hence, finding no racial gap in earnings after controlling for industry or employer fixed effects in a regression may indicate that there is no discrimination at the margin, which is very different from no discrimination on average. Also, as pointed out in Guryan and Charles (2013), the variables the researcher controls for might themselves be affected by discrimination. That is, disadvantaged groups may not have access to high-quality schools because of discrimination, yet they might, given their low human capital accumulation, be paid the “fair market wage.” While one might still be tempted to conclude from this that there is no discrimination in the labor market but instead discrimination in the education market, that might not be right if the minority group’s expectations about labor market discrimination drive their educational decision. In other words, minority group members may decide to under invest in education if they expect that they will not be able to obtain labor market returns for this education. Audit and correspondence methodologies were developed to address these core limitations of the regression approach to measuring discrimination. We review below both types of studies, discuss the extent to which they address these limitations of the regression approach, and also consider new issues they create.
2
For a review of this earlier literature on the narrower topic of labor market discrimination, see Chap. 48 in Altonji and Blank (1999).
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Audit Studies In the best-known collection of audit studies exploring the extent of discrimination, Fix and Struyk (1993) describe the method as follows: Two individuals (auditors or testers) are matched for all relevant personal characteristics other than the one that is presumed to lead to discrimination, e.g. race, ethnicity, gender. They then apply for a job, a housing unit, or a mortgage, or begin to negotiate for a good or service. The results they achieve and the treatment they receive in the transaction are closely observed, documented, and analyzed to determine if the outcomes reveal patterns of differential treatment on the basis of the trait studied and/or protected by anti-discrimination laws. . .
Discrimination is said to have been detected when “auditors in the protected class are systematically treated worse than their teammates” (Yinger 1998).3 A well-known early example of the audit method is offered by Ayres and Siegelman (1995). In this study, pairs of testers (one of whom was always a white male) were trained to bargain uniformly and then were sent to negotiate for the purchase of a new automobile at randomly selected Chicago-area dealerships. Thirty-eight testers bargained for 306 cars at 153 dealerships. Testers were chosen to have average attractiveness, and both testers in a pair bargained for the same model of car, at the same dealership, usually within a few days of each other. Dealerships were selected randomly, testers were randomly assigned to dealerships, and the choice of which tester in the pair would be the first to enter the dealership was also randomly made. The testers bargained at different dealerships for a total of nine car models, following a uniform bargaining script that instructed them to focus quickly on one particular car and start negotiating over it. Testers were further instructed to tell dealers at the beginning of the bargaining that they could provide their own financing for the car. In spite of the identical approach to bargaining, Ayres and Siegelman (1995) find that white males are quoted lower prices than white women and blacks (men or women). While ancillary evidence suggests that the dealerships’ disparate treatment of women and blacks may be caused by dealers’ statistical inferences about consumers’ reservation prices, the data do not strongly support any single theory of discrimination. Another well-known audit study of the labor market is Neumark, Bank, and Van Nort (1996), which investigates the role of sex discrimination in vertical segregation among waiters and waitresses. Specifically, two male and two female college students were sent to apply in person for jobs as waiters and waitresses at 65 restaurants in Philadelphia. The restaurants were divided into high-, medium-, and low-price categories, with the goal of estimating sex differences in the receipt of job offers in each price category. The study was designed so that a male and female
3
Results from the earliest audit studies can be found in Newman (1978), McIntyre, Moberg, and Posner (1980), Galster (1990), Yinger (1986), Cross, Kenney, Mell, and Zimmerman (1990), James and DelCastillo (1991), Turner, Fix, and Struyk (1991), and Fix and Struyk (1993).
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pair applied for a job at each restaurant, and so that the male and female candidates were on average identical. The findings are consistent with discrimination against women in high-price restaurants and discrimination in women’s favor in low-price restaurants. Of the 13 job offers from high-price restaurants, 11 were made to men. In contrast, of the ten job offers from low-price restaurants, eight were made to women. In addition, information gathered from restaurants included in the study suggests that earnings are substantially higher in high-price restaurants, meaning that the apparent hiring discrimination has implications for gender-based differences in earnings among waitpersons. Results are interpreted as consistent both with employer discrimination and customer discrimination. Another interesting application of the audit method is Pager (2003) who matched pairs of individuals applying for entry-level positions, and probed the impact of a criminal record, conditional on race. The author employed two black testers who formed a team, and another pair of white testers. Within each team, one auditor was “assigned” a criminal record (this assignment was random and rotating – that is, each tester played the role of an ex-convict at some point).4 In total, 350 employers were audited. The effect of the criminal record was both statistically significant and meaningful in magnitude: 17% of attempts with whites who had a supposed criminal record received a callback, compared to 34% of tries with whites who said they had no criminal record. That is, an equally qualified white candidate was about half as likely to receive a callback if he was believed to be an ex-convict. For black applicants, the effect was notably larger: 5% of attempts with blacks who were supposedly ex-convicts received a call-back, compared to 14% of applications with blacks that had no record, meaning an equally qualified black candidate was about one-third as likely to receive a callback if he had a criminal record. Furthermore, these estimates show that a black applicant without a criminal record was about as likely to receive a callback as a white applicant with a criminal record. Most audit studies do not explicitly test which theory of discrimination has most explanatory power, even if they often informally discuss what forms of discrimination might or might not be consistent with the observed patterns in the data. A notable exception is List (2004) who recruited buyers and sellers at a sports cards market and documented that minority buyers receive lower offers when they bargain for a collectible card. One finding of List (2004) is that in this context lack of information – and the expectation that minorities are inexperienced – drives discriminatory behavior. Experienced dealers discriminate more. Among experienced buyers, final offers to minorities are similar to offers received by white men, but minorities require more time to achieve this outcome. Moreover, List tries to rule out taste-based explanations for the data by combining the field data with results from a dictator game conducted in the lab with these card dealers. He finds that nonwhite males receive roughly as many positive allocations in this game as white males and interprets this pattern as evidence for the absence of taste for discrimination.
Pager argues that, “[b]y varying which member of the pair presented himself as having a criminal record, unobserved differences within the pairs of applicants were effectively controlled for.”
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Of course, while a laboratory experiment is a useful complement to the field study, the behavior of dealers in the dictator game, on its own, does not prove that tastebased discrimination is absent during the actual market transactions.
Limitations of Audit Studies Many of the weaknesses of audit studies have been discussed in Heckman and Siegelman (1993) and Heckman (1998). First, these studies require that both members of the auditor pair be identical in all dimensions that might affect productivity in employers’ eyes, except for the trait that is being manipulated. To accomplish this, researchers typically match auditors on several characteristics (height, weight, age, dialect, dressing style, and hairdo) and train them for several days to coordinate interviewing styles. Yet, critics note that this is unlikely to erase the numerous differences that exist between the auditors in a pair. Another weakness of the audit studies is that they are not double-blind: Auditors know the purpose of the study. As Turner, Fix, and Struyk (1991) note, “The first day of training also included an introduction to employment discrimination, equal employment opportunity, and a review of project design and methodology.” This may generate conscious or subconscious motives among auditors to generate data consistent or inconsistent with their beliefs about race or gender issues. As psychologists have documented, these demand effects can be quite strong. It is very difficult to insure that auditors will not want to do “a good job.” Even a vague belief by auditors that employers treat minorities differently can result in measured differences in treatment. The possibility of such a demand effect is further magnified by the fact that auditors are not in fact seeking jobs (or trying to buy a car for themselves) and are therefore more free to let their beliefs affect the bargaining or interview process.
Correspondence Studies Correspondence studies have been developed to address some of the more obvious weaknesses of the audit method. Rather than relying on real auditors or testers that physically meet with a potential employer or potential landlord, correspondence studies rely on fictitious applicants. Specifically, in response to a job or rental advertisement, the researcher sends (many) pairs of résumés or letters of interest, one of which is assigned the perceived minority trait. Discrimination is estimated by comparing the outcomes for the fictitious applicants with and without the perceived minority trait. The most common (but not the only) way to manipulate the perceived minority trait has been through the names of the applicants (e.g., female names, African-American names, Arabic names, etc.). Outcomes studied in a correspondence study have been mainly, but not exclusively, limited to measuring call-backs by employers or landlords in response to the mailed or emailed fictitious application.5
5
See sections “Retail” and “Academia” for cases of different approaches.
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The correspondence method presents several advantages over the audit method. First, because it relies on résumés or applications by fictitious people and not real people, one can be sure to generate strict comparability across groups for all information that is seen by the employers or landlords. This guarantees that any observed differences are caused solely by the minority trait manipulation. Second, the use of paper applications insulates from demand effects. Finally, because of the relatively low marginal cost, one can send out a large number of applications. Besides providing more precise estimates, the larger sample size also allows researchers to examine the nature of the differential treatment from many more angles and hence promises to link it more closely to specific theories of discrimination.6 Although Guryan and Charles (2013) call correspondence tests a “significant methodological advance,” and a review of discrimination in the marketplace published about 15 years ago discussed only observational and audit studies (Yinger 1998); the method is actually not that new. Fictitious applications and résumés have been sent to employers in order to uncover racial or religious discrimination nearly half a century ago.7 However, the number of correspondence studies in economics has greatly increased following Bertrand and Mullainathan (2004), who study race discrimination in the labor market by sending fictitious résumés in response to helpwanted ads in Boston and Chicago newspapers. To manipulate perceived race, they randomly assigned very white-sounding names (such as Emily Walsh or Greg Baker) to half the résumés and very African-American-sounding names (such as Lakisha Washington or Jamal Jones) to the other half. In total, they responded to over 1300 employment ads in the sales, administrative support, clerical, and customer services job categories and sent out nearly 5000 résumés. They find that white names receive 50% more call-backs for interviews.
Correspondence Studies in the Labor Market The main results of labor market correspondence tests are reviewed in Table 1. As is clear from Table 1, labor market correspondence studies have by now been carried out in many countries around the world and have focused on a variety of perceived traits that can be randomized on a résumé. Below, we review some of these studies in more detail, focusing in particular on those that have attempted to go beyond simply documenting whether or not differential treatment occurs based on the manipulated traits, and move toward understanding which theory may best fit the patterns in the data. However, one of our bottom lines will be that, unfortunately, the studies have tended to be fairly close replications of Bertrand and Mullainathan (2004) for different populations or contexts. With a few exceptions, the literature has We discuss in section “Limitations of Correspondence Studies” other weaknesses that are shared by the correspondence studies, as well as added weaknesses of the correspondence method compared to the audit method. 7 See Jowell and Prescott-Clarke (1970), Jolson (1974), Hubbuck and Carter (1980), Brown and Gay (1985), and Riach and Rich (1991) for early studies. One caveat is that some of these studies fail to fully match skills between minority and nonminority résumés. 6
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Table 1 Correspondence Studies across countries Effect (callback ratio) White-toIndigenous ratio: 1.8 Low attractiveness hurts white females Employed to long-term unemployed: 1.25
Paper Galarza and Yamada (2014) Trait: ethnicity; attractiveness
Country Peru
CVs/apps 4820
Vacancies 1205
Eriksson and Rooth (2014) Trait: unemployment duration Blommaert, Coenders, and van Tubergen (2014) Trait: Arabic name
Sweden
8466
–
Netherlands
636
–
Nunley, Pugh, Romero, and Seals (2014) Trait: race
The USA
9396
–
Ghayad (2013) Trait: unemployment duration Bartoš, Bauer, Chytilová, and Matejka (2013) Trait: ethnicity (Roma, Asian, and Turkish)
The USA
3360
600
Employed-tounemployed: 1.47
Czech Rep. and Germany
274 (Czech R.) 745 (Ger.)
–
The USA
6400
1600
Czech-toVietnamese: 1.34 Lower requests for CVs if candidate is Turkish White-toMuslim: 1.58
The USA (largest 100 MSAs)
12,054
3040
Wright, Wallace, Bailey, and Hyde (2013) Trait: religion/ ethnicity Kroft, Lange, and Notowidigdo (2013) Trait: unemployment duration
Dutch-toforeign: 1.62 (unconditional ratio) no difference, if views held fixed White-toblack: 1.18 (unconditional)
1 log point change in unemployment duration: 4.7 percentage points lower callback probability
Theory No
No
No
Inconsistent with statistical discrimination, consistent with taste-based discrimination No
Consistent with attention discrimination
Consistent with theoretical models of secularization and cultural distate theory No
(continued)
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Table 1 (continued) Paper Baert, Cockx, Gheyle, and Vandamme (2013) Trait: nationality (Turkishsounding name) Bailey, Wallace, and Wright (2013) Trait: sexual orientation Ahmed, Andersson, and Hammarstedt (2013) Trait: sexual orientation Acquisti and Fong (2013) Traits: sexual orientation and religion Patacchini, Ragusa, and Zenou (2012) Traits: sexual orientation and attractiveness Kaas and Manger (2012) Trait: immigrant (race/ethnicity) Maurer-Fazio (2012) Trait: ethnicity
Jacquemet and Yannelis (2012) Trait: race / nationality
Effect (callback ratio) Flemish-toTurkish: 1.03–2.05, depending on the occupation
Country Belgium
CVs/apps 752
Vacancies 376
Theory No
The USA
4608
1536
No effect
No
Sweden
3990
–
No
The USA
4183
–
Heterosexualto-homosexual (male): 1.14 Heterosexualto-homosexual (female): 1.22 Christian-toMuslim: 1.16
Italy
2320
–
Heterosexualto-homosexual: 1.38
No
Germany
1056
528
Consistent with statistical discrimination
China
21,592
10,796
The USA
330
990
German-toTurkish: 1.29 (if no reference letter is included) Han-toMongolian: 1.36 Han-toTibetan: 2.21 English-toforeign names: 1.41 English-toblack names: 1.46
No
No
Consistent with patterns of ethnic homophily
(continued)
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Table 1 (continued) Paper Ahmed, Andersson, and Hammarstedt (2012) Trait: age Oreopoulos (2011) Trait: nationality (and race)
Country Sweden
CVs/apps 466
Vacancies –
Canada
12,910
3225
Carlsson (2011) Trait: gender Booth, Leigh, and Varganova (2011) Trait: Ethnicity Booth and Leigh (2010) Trait: gender
Sweden
3228
1614
Australia
Above 4000
–
Australia
3365
–
UK
1000+
–
Sweden
1970
985
McGinnity, Nelson, Lunn, and Quinn (2009) Trait: nationality/race
Ireland
480
240
Banerjee, Bertrand,
India
3160
371
Riach and Rich (2010) Trait: age Rooth (2009) Trait: attractiveness/ obesity
Effect (callback ratio) 31 year old to 46 year old: 3.23
Theory No
English nameto-immigrant: ranged from 1.39 to 2.71 (against Indian, Pakistani, and Chinese applicants) Female-tomale: 1.07
No
White-toItalian: 1.12 White-toChinese: 1.68 Female-tomale: 1.28 (femaledominated professions) 2.64 favoring younger candidates Nonobese/ attractive-toobese/ unattractive: ranged from 1.21 to 1.25 (but higher for some occupations) 1.8, 2.07, and 2.44 in favor of Irish and against Asians, Germans, and Africans, respectively Upper caste-toother backward
No
No
No
No
No
No
No (continued)
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Table 1 (continued) Paper Datta, and Mullainathan (2009) Traits: caste and religion Lahey (2008) Trait: age Petit (2007) Traits: age, gender, and number of children
Country
The USA
CVs/apps
Vacancies
–
France
App. 4000 942
157
Bursell (2007) Trait: ethnicity
Sweden
3552
1776
Bertrand and Mullainathan (2004) Trait: race
The USA
4870
1300+
Jolson (1974) Trait: race and religion
The USA
300
–
Effect (callback ratio) castes: 1.08 (software jobs, insignificant), 1.6 (call-center jobs) Young-toolder: 1.42 Ranged from 1.13 to 2.43 against 25-year-old, childless women Swedish-toforeign names: 1.82 White-toAfricanAmerican:1.5 (1.22 for females in sales jobs) White-toblack: 4.2 for selling positions
Theory
No No
Inconsistent with statistical discrimination No
No
failed to push the correspondence methodology to design approaches to more formally test for various theories of why differential treatment is taking place. Race, ethnicity: Studies of labor market discrimination based on race and ethnic background have been by far the most popular application of the correspondence method to date. While publication bias is always a concern, the results of correspondence studies where the trait of interest is race or ethnicity offer overwhelming evidence of discrimination in the labor market against racial and ethnic minorities. Evidence has been accumulated from nearly all continents: Latin America (e.g., Galarza and Yamada (2014) compare whites to Indigenous applicants in Peru), Asia (e.g., Maurer-Fazio (2012) compares Han, Mongolian, and Tibetan applicants in China), Australia (e.g., where Booth, Leigh, and Varganova (2011) compare whites to Chinese applicants), Europe (e.g., Baert et al. (2013) compare immigrants to nonimmigrants in Belgium), Ireland (e.g., where McGinnity et al. (2009) compare candidates with Irish names to candidates with distinctively non-Irish names), etc. Attempts to adapt the correspondence method to learn more about which theory of discrimination best fits the patterns in the data have been mainly focused on trying to provide corroborative evidence for (or against) statistical discrimination. The most
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common approach has been to investigate whether the gap in callbacks is responsive to the amount of information provided to employers about the job applicants, as was first done in Bertrand and Mullainathan (2004), in which they studied how credentials affect the racial gap in callback. In particular, Bertrand and Mullainathan (2004) experimentally varied the quality of the résumé used in response to a given ad. Higher-quality applicants had on average a little more labor market experience and fewer holes in their employment history; they were also more likely to have an e-mail address, have completed some certification degree, possess foreign language skills, or have been awarded some honors. The authors sent four résumés in response to each ad: two higher-quality and two lower-quality ones. They randomly assigned an African-American sounding name to one of the higher- and one of the lowerquality résumés. They find that whites with higher-quality résumés receive nearly 30% more callbacks than whites with lower-quality résumés. On the other hand, having a higher-quality résumé has a smaller effect for African-Americans. In other words, the gap between whites and African-Americans widens with résumé quality. While one may have expected improved credentials to alleviate employers’ fear that African-American applicants are deficient in some unobservable skills under a statistical discrimination explanation for the overall discrimination, this was not the case in their data. Bertrand and Mullainathan argue that one simple alternative model that may best explain the patterns in their data is some form of lexicographic search by employers: Employers receive so many résumés that they may use quick heuristics in reading these résumés. One such heuristic could be to simply read no further when they see an AfricanAmerican name. Thus they may never see the skills of African-American candidates and this could explain why these skills are not rewarded.
These findings are replicated in Nunley et al. (2014): Blacks received 14% fewer callbacks compared to whites, and discrimination was not mitigated when productive characteristics were added to a résumé. However, some studies report results that are more in line with the predictions of statistical discrimination models. Oreopoulos (2011) submitted 12,910 résumés in response to 3225 job postings in Canada. First, he compares (fictitious) applicants who had a foreign name, but who attended a Canadian (or foreign) university and had work experience in Canada. The callback rate is 1.39 for Canadian (English-sounding names) versus foreigners if they went to a Canadian university, and 1.43 if they went to a foreign university. However, the callback rate falls dramatically if the foreigners’ job experience was purely international (2.71 callback ratio). Moreover, if candidates who had foreign job experience and education had a Chinese last name with an English first name (Allen and Michelle Wang), their prospects on the job market improved. This raises the possibility that a fraction of the “discrimination” is statistical, for example, with employers making inference about the candidate’s English skills. Perhaps even more striking, Kaas and Manger (2012) sent out 528 pairs of applications in Germany to study the effect of a Turkish-sounding name. The German-to-Turkish callback rate was 1.29 when no reference letter was included. Discrimination was eliminated
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when a reference letter, containing indirect information about productivity (such as conscientiousness and agreeableness), was added, which the authors interpret as evidence of consistency with statistical discrimination. It is interesting that such “soft information” presented in the reference letter appears to remove the difference in callback rates even though “harder information” presented in a résumé (such as employment history or honors) does not in other studies. It would be interesting to probe this contrast further. Gender: There are fewer studies on gender, and discrimination against women at the callback stage is much less apparent in general. Some studies attempt to show whether the degree (and nature) of discrimination depends on the nature of the profession. Carlsson (2011) sent paired applications for positions of IT professionals, drivers, construction workers, sales assistants, high school teachers, restaurant workers, accountants, cleaners, preschool teachers, and nurses. Overall, women are called back slightly more often than men; in male dominated professions, males have a slight (insignificant) advantage. Booth and Leigh (2010) focused on femaledominated professions (waitstaff, data-entry, customer service, and sales jobs) and found a callback of 1.28 in favor of women. A topic of interest for future work would be to apply the correspondence method to measure the extent to which a bias exists against women with children, or against young women who may have children in the future. To our knowledge only one study, Petit (2007), studies this aspect. In order to shed light on the role of family constraints in gender discrimination, Petit sent résumés for male and female applicants, with or without children, of age 25 or 37. Discrimination against women is detected for young workers in higher skilled positions (in the French finance industry), but not among prime-age workers. Caste and Religion: Banerjee et al. (2009) study the role of caste and religion in India’s software and call-center sectors. They sent 3160 fictitious résumés with randomly allocated caste-linked surnames in response to 371 job openings in and around Delhi (India) that were advertised in major city papers and online job sites. They find no evidence of discrimination against non-upper-caste (i.e., scheduled caste, scheduled tribe, and other backward caste) applicants for software jobs. But, in the case of call-center jobs, they do find larger and significant differences between callback rates for upper-castes and other backward castes (and to a lesser extent scheduled castes) in favor of upper-castes. They find no discrimination against Muslims. The potential impact of religion on job prospects in the USA is explored by Wright et al. (2013). Affiliation with a religion was signaled through student activities that were listed on résumés.8 The control group had no religious identification in their résumé. Compared to the control group, Muslim applications were
8
It is tricky to signal only religion on a résumé. The manipulation through student activities may reveal more than just religion, an issue we will come back to in section “Beyond the Résumés.”
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24% less likely to receive at least one contact by either email or phone, and they received 33% fewer total contacts than did those from the control group. Unemployment spells: More recently, researchers have applied the correspondence model to better understand patterns of labor market discrimination against the unemployed. In Sweden, Eriksson and Rooth (2014) randomly assigned various characteristics (contemporary unemployment, past unemployment immediately after graduation, past unemployment between jobs, work experience, and number of employers). Long-term unemployment did not harm job candidates’ chances, as long as the applicant had subsequent work experience. However, if the applicant was unemployed in the preceding 9 months, their callback probability fell by 20%.9 In the USA, Ghayad (2013) finds that (current) unemployment spells longer than 6 months are particularly harmful: The rate of interview requests for résumés with similar firm experience drops 1.13 percentage points for each additional month of nonemployment up to 6 months, and once the candidate experienced 6 months of unemployment, interview requests fell by an extra 8 percentage points. Kroft, Lange, and Notowidigdo (2013) relate these results to the inference problem faced by the prospective employers. The authors not only replicate the result that longer employment duration reduces callback rate, but also show that this depends on the labor market conditions. Duration dependence is stronger in tight labor markets, suggesting that employers use the information on the length of unemployment as a signal of productivity, but recognize that the signal is less informative when the labor market conditions are weak.10 Other characteristics: Sexual Orientation and Age: Résumé studies are now also being used to try to detect discrimination in a number of less obvious domains. Literature has tried to estimate discrimination against LGBT candidates; however, most studies have focused only on lesbians and gay men.11 One of the challenges with estimating discrimination against LGBT candidates is how to provide information that identifies a candidate as a member of that minority, when telling such details are not normally solicited in job applications. In Ahmed, Andersson, and Hammarstedt (2013), which was carried out in Sweden, sexual orientation was indicated by the mention of a “spouse” of either gender in the cover letter, and voluntary work in either an LGBT rights organization (gay identity) or the Swedish Red Cross (heterosexual identity). Targeted occupations included male-dominated ones (construction worker, motor vehicle driver, sales person, and mechanic worker), female-dominated ones (shop sales assistant, preschool teacher, cleaner, restaurant worker, and nurse), and more neutral ones (high school teacher). The authors find some mild evidence of discrimination (ratio of 1.14), which ultimately 9
One caveat, as the authors acknowledge, is that not all employers necessarily view the gaps on the CVs as implying unemployment. 10 This may also explain the finding in Eriksson and Rooth (2014) (mentioned above) since that particular study was carried out between March and November 2007, i.e., during the global financial crisis. 11 To the best of our knowledge, no study has been done that specifically looks at discrimination against transgender people using the correspondence method.
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could be due to the nature of the signaling (e.g., working in LGBT rights, as opposed to the Red Cross, may be seen as a political gesture, not just revealing an identity). In Italy, Patacchini, Ragusa, and Zenou (2012) performed a correspondence study that revealed “homosexual preferences” through internships in progay advocacy groups and found higher discrimination against gay men (1.38) but not lesbian candidates. In the USA, Bailey, Wallace, and Wright (2013) find no evidence of discrimination against gay men or lesbians candidates. The issue of discrimination by age has also attracted some attention, and several papers (Ahmed et al. 2012; Lahey 2008; Riach and Rich 2010) find that younger candidates are generally preferred to older ones. A fundamental issue with this work is that it is hard to argue that age is not necessarily a proxy for productivity. Lahey (2008) tries to control for physical fitness with hobbies (e.g., racquetball is supposed to indicate fitness), but this is ultimately only moderately convincing. Finally, physical appearance has also been studied: Rooth (2009) studies obesity in the Swedish labor market and Patacchini, Ragusa, and Zenou (2012) investigate the beauty premium in Italy. Using manipulated facial photos to show an otherwise identical candidate as obese, Rooth (2009) shows there is a significantly lower callback response for obese people: Obese men had a six percentage points lower callback rate, while the callback rate for obese women was eight percentage points lower. Patacchini, Ragusa, and Zenou (2012) find a small, but significant, beauty premium for “pretty” females (2%); however, they do not find a beauty premium for men. Interestingly, the beauty premium disappears for high-skilled attractive women: Low-skilled attractive women are more likely to be called back than highskilled attractive women. On the other hand, Hamermesh and Biddle (1994) do find the existence of a beauty premium in the United States. We discuss the rationale for a beauty premium further in section “Willingness to Pay.”
Correspondence Studies in Other Settings Rental Markets Correspondence studies in the housing market have very much followed the same approach as those in the labor market. The main findings from the literature are summarized in Table 2. The rental market studies replicate, in methodology and basic results, those in the labor market. The researchers typically identify rental ads and send enquiries, manipulating the trait of interest. Discrimination against Arabic names is found in Sweden (Carlsson and Eriksson 2014; Ahmed and Hammarstedt 2008; Ahmed et al. 2010). Discrimination against blacks and other minority ethnicities in the USA is found in Ewens, Tomlin, and Wang (2014), Hanson and Hawley (2011), and Carpusor and Loges (2006). Discrimination against immigrants (particularly Muslims) is found in Italy (Baldini and Federici 2011) and Spain (Bosch et al. 2010). Discrimination against LGBT people is found in Ahmed and Hammarstedt (2009). Another popular variation, parallel to the labor market literature, has been to provide more information (e.g., job, etc.) about some of the applicants. Positive
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Table 2 Rental market papers Study Carlsson and Eriksson (2014) Trait: minority status (Arabic name) Ewens, Tomlin, and Wang (2014) Trait: race
Country Sweden
Inquiries 5827
Effect Swedish-to-Arabic (females): 1.37 Swedish-to-Arabic (males): 1.62
Theory No
The USA
14,237
White-to-black: 1.19
Bartoš, Bauer, Chytilová, and Matejka (2013) Trait: minority status (Roma or Asian name) Hanson and Hawley (2011) Trait: Race
Czech Republic and Germany
1800
Czech-to-minority: 1.27 (site available), 1.9 (pooled Asian and Roma names)
Consistent with statistical discrimination, inconsistent with taste-based discrimination Consistent with attention discrimination
The USA
9456
Consistent with statistical discrimination
Baldini and Federici (2011) Trait: immigrant status; language ability Ahmed, Andersson, and Hammarstedt (2010) Trait: minority status (Arabic name) Bosch, Carnero, and Farré (2010) Trait: immigrant status Ahmed and Hammarstedt (2009) Trait: sexual orientation Ahmed and Hammarstedt (2008) Trait: immigrant (race/ethnicity/ religion)
Italy
3676
White-to-African American: 1.12 (varied by neighborhood and unit type) Italian-to-East European: 1.24 Italian-to-Arab: 1.48
Sweden
1032
Swedish-to-Arab/ Muslim: 1.44 (no information), 1.24 (detailed information about the applicant)
No
Spain
1809
No
Sweden
408
Spanish-to-Moroccan: 1.44 (no information), 1.19 (with positive information) Straight-to-gay: 1.27
Sweden
1500
Swedish-to-Arab male: 2.17
No
No
No
(continued)
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Table 2 (continued) Study Carpusor and Loges (2006) Trait: race/ ethnicity (Arab, AfricanAmerican)
Country The USA (Los Angeles County)
Inquiries 1115
Effect White-to-Arab: 1.35 White-to-black: 1.59, conditional on hearing back, 1.98 unconditional
Theory No
information (e.g., “I do not smoke and I work full time as an architect”) tends to reduce the callback ratios between white and the minority group, while negative information (“I am a smoker and I have a less than perfect credit score”) or small spelling mistakes in the email tend to increase it.
Retail The expansion of online platforms allows researchers to use the correspondence method to also study discrimination in retail markets. There are currently much fewer such studies, but the door is wide open for more to be performed. Zussman (2013) studies the mechanisms behind ethnic discrimination in the online market for used cars in Israel. This chapter uses an innovative, two-stage approach. First, about 8000 paired emails are sent to sellers of secondhand cars. An enquiry coming from somebody with a Jewish-sounding name was 22% more likely to receive a response than an enquiry emailed by an interested buyer with an Arabsounding name. Second, a follow-up phone survey was used to elicit sellers’ attitudes about minorities to tease out potential mechanisms for this effect. The researchers found that Jewish car sellers who strongly disagree with the statement “the Arabs in Israel are more likely to cheat than the Jews” do not discriminate against the Arab buyer, while others sellers do. That is, expectations about the quality of the transactions are correlated to the differential (average) treatment of Arabs. Pope and Sydnor (2011) report evidence from peer-to-peer lending sites. They find that loan listings with an attached picture of a black individual are 25–35% less likely to receive funding than those of white individuals with similar credit profiles. Academia Milkman, Akinola, and Chugh (2012) ran a field experiment set in academia with a sample of 6548 professors. Faculty members received e-mails from fictional prospective doctoral students seeking to schedule a meeting either that day or in 1 week; students’ names signaled their race (Caucasian, African-American, Hispanic, Indian, or Chinese) and gender. When the requests were to meet in 1 week, Caucasian males were granted access to faculty members 26% more often than were women and minorities; also, compared with women and minorities, Caucasian males received
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more and faster responses. However, these patterns were essentially eliminated when prospective students requested a meeting that same day. The authors argue that their finding of a temporal discrimination effect is consistent with the idea in psychology that subtle contextual shifts can alter patterns of race- and gender-based discrimination (a topic we return to in the last section of this chapter, section “Technological De-Biasing”).
Beyond the Re´sume´s With the rise of the Internet, employers can easily find more information online about a job applicant besides their résumé. A few recent studies enrich the correspondence methodology by allowing employers to search for more (and different) information than that which would typically be available in a résumé. Given the increasing popularity of online social networks, the contribution of Acquisti and Fong (2013) is particularly interesting. They employ the correspondence method by submitting applications to job postings and extend their experiments by creating either personal websites of social networking profiles for the fictitious applicants, which allow employers to gather additional information if they wish to. The additional information that can be gleaned online about the job applicants relates to their religion and sexual orientation. The question the paper is asking is whether extra information available online but not on the résumé leads to discrimination: Would applicants whose identity is not revealed in the application, but who appear to be Muslim (versus Christian) or gay (versus straight) on a popular social network, suffer unequal treatment? To do so, they created distinct online profiles: one profile on a professional network site, and another profile on a social network site where the emphasis is on sharing photographs or leisure-related comments, not job opportunities. The profile on the professional network site was identical across treatments (even the photograph was the same). The name used by researchers (selected after careful testing) was one not commonly associated with a particular race or religion. That is, the name of the “Muslim candidate” was non-Arabic, but the candidate’s religion could be inferred after some search on the social network site. Only the profile on the social network site contained clues (e.g., Christian versus Muslim or straight versus gay). The experiment finds that only a small fraction of employers use social media to conduct additional inquiry about job candidates.12 Given the limited search efforts by employers, the effects of group membership are generally small. The total effect of trait manipulation is not statistically significant: 12.6% of applicants who appeared to be Christian received callbacks, compared to 10.9% of candidates who
12
Measuring the exact number of visits to a social or professional networking profile is not possible for several reasons. However, using Google Adwords “Keyword Tool” statistics and Professional Network “Premiere” account statistics, the authors estimate that at most one-third of the employers tried to access the profile of the candidates.
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appeared to be Muslim. About 10.6% of candidates who appeared to be straight males received callbacks, and the share of callbacks for seemingly gay males was nearly identical. The strength of this type of study is that researchers are able to study more naturally the impact of traits that traditionally are not revealed on a résumé. While some correspondence tests have tried to signal religious affiliation or sexual orientation through “extracurricular activities” described on CVs, this type of disclosure might reveal more than religion or sexual identity: The employers might be reacting to someone’s activism regarding their religion or sexuality, not their religion or sexuality per se. While Acquisti and Fong (2013) focus on the impact of gay status for males and religion, their methods could be used to study the effect of other interesting and until now mostly unexplored characteristics. For example, would the size of a candidate’s network have an effect? Would employers infer that a “popular” candidate has valuable social skills? Would attractive-seeming candidates receive more callbacks, or would attempts to “choreograph” one’s online presence be viewed as an undesirable trait? Would candidates who reveal their family status be treated differently than candidates who are more private? Clearly, online field experiments offer a rich landscape for studying “what employers want.”
Limitations of Correspondence Studies While correspondence studies address some key weaknesses of the audit methodology, they share other weaknesses with audit studies and have some unique limitations of their own. Both types of studies can only inform us about the average differences in hiring behavior. But we generally think that applicants care about the marginal response. Real job seekers are likely to adjust their behavior during the search process in a strategic manner: In other words, they will not apply for positions in a random fashion. So, while informative about discrimination on average in a given setting, correspondence and audit studies are not informative about discrimination at the margin, when real job seekers have fully optimized their job search strategy to the realities of the workforce. This is related to a criticism raised by Heckman and Siegelman in their contribution to Clear and Convincing Evidence: Measurement of Discrimination in America when they challenge the use of newspaper advertisements in audit studies, referring to previous findings that most jobs are found through direct contact with a firm, or via informal channels like family and friends: [c]ollege students masqueraded as blue collar workers seeking entry level jobs. Apart from the ethical issues involved, this raises the potentially important problem that the Urban Institute actors may not experience what actually occurs in the these labor markets among real participants. (Fix and Struyk 1993)
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Another drawback of field studies (both audit and correspondence) is that fictitious applicants typically only apply to entry-level jobs. There are a few exceptions, and some of the studies we describe above use applications to skilled and experienced positions. But the bottom line is that many jobs are never advertised, and the extent of discrimination in the workplace overall may be quite different from the discrimination that is measured at the entry point in the labor market. Yet another limitation of field studies (both audit and correspondence) is that the outcome variables that can be studied are typically very coarse. In fact, in this regard, the correspondence studies are inferior to the audit studies. Most of the time, interview invitations or rental offers (“call-back rates”) are the only outcomes captured by field experiments (one exception being Doleac and Stein (2013), who were able to track transactions – sales of iPods through local online markets – all the way to completion). Obviously, because there is no real applicant, the correspondence study methodology cannot be taken to the interview stage, job-offer stage, or wage-setting stage – or to the stage at which people sign a lease on an apartment. Theoretically, all of this can be measured when auditors are used. However, even audit studies do not allow one to track other important outcomes, such as work hours, working conditions, or promotions. The binary outcome in the typical correspondence studies (callback or not) raises important issues about how to conduct some of the analysis. What should be inferred about discrimination for the employers that do not call back any of the fictitious applicants? Is that evidence of “symmetric treatment”? Riach and Rich (2002) argue that if both the majority and minority candidate are rejected, that does not constitute evidence of equal treatment. Only with more continuous outcome variables – ones that typically are not available to the researcher, such as the ranking of the job candidates by the employer – would it be possible to resolve this tension. Both correspondence and audit studies have also raised ethical concerns. Employers’ time is bound to be a scarce resource, and researchers who carry out these studies are using it without the involved parties’ consent. A positive take on this ethical issue is List (2009) who argues that, “[w]hen the research makes participants better off, benefits society, and confers anonymity and just treatment to all subjects, the lack of informed consent seems defensible.” However, many people outside the scientific community would probably disagree. (In fact, List (2009) refers to experiments where subjects are compensated; in the case of correspondence tests, we did not come across experiments where employers were actually compensated for their time.)13
13
The method of correspondence studies has also been taken to the dating market (e.g., Ong and Wang (2015)). We do not review these contributions here because it is a bit difficult to talk about discrimination when referring to the choice of whom to date, but the ethical dilemma of putting fake applications on a dating website also seems particularly acute. As a conceptual side, it is also not at all clear that one needs to create a fictitious profile on dating websites, as it is already possible for the researchers to observe exactly the same information that the user has when making a decision. There is thus no “unobserved” variable biasing the analysis and no information to be gained from fictitious
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Another underappreciated ethical issue is that when the “applicant” declines an offer, things other than the anticipated consumption of the employer’s attention can occur. The employer may “learn” (become convinced) that applicants with the attributes similar to those of the fictitious candidate are unlikely to accept offers. If this really happens, it is possible that some real job applicants will be treated differently (possibly less favorably) due to prior communication with the researcher pretending to be a job candidate. But it also possible that after observing a rejection or two from fictitious candidates, an employer may end up having the impression that the market is tighter than they thought; screening could then become less intense, which might be beneficial for real jobless candidates (but potentially detrimental for the employers).14 A subtler criticism of the correspondence and audit methods by Heckman and Siegelman (1993) has been recently revisited by Neumark (2012). Heckman and Siegelman (1993) show that a troubling result emerges in audit or correspondence studies because the outcome of interest is not linear in productivity (as it might be for a wage offer), but instead is nonlinear. That is, we think that in the hiring process firms evaluate a job applicant’s productivity relative to a standard, and offer the applicant a job (or a callback) if the standard is met. This nonlinear relationship can raise issues for any inferences of discrimination based on callbacks if employers believe that blacks and whites differ in the variance of their unobserved productivity. Consider for example the case where employers believe that the variance of unobserved productivity is higher for whites than for blacks. The correspondence and audit methods make black and white applicants equal on observable productivity characteristic X1. However, no information is conveyed on a second, unobservable productivity-related characteristic, X2. Because an employer will offer a job interview only if it perceives or expects the sum β1X1 + X2 to be sufficiently high, when X1 is set at a low level the employer has to believe that X2 is high (or likely to be high) in order to offer an interview. Even though the employer does not observe X2, if the employer knows that the variance of X2 is higher for whites, the employer correctly concludes that whites are more likely than blacks to have a sufficiently high sum of β1X1 + X2, by virtue of the simple fact that fewer blacks have very high values of X2. Employers will therefore be less likely to offer jobs to blacks than to whites, even though the observed average of X1 is the same for blacks and whites, as is the unobserved average of X2. The opposite holds if X1 is set at a high value: In this case, the employer only needs to avoid very low values of X2, which will be more common for the higher-variance whites. In other words, Heckman and Siegelman (1993) show that, even when there are equal group averages of both observed and unobserved variables, an audit or correspondence study can generate biased estimates, with spurious evidence of discrimination in either direction, or spurious evidence of its absence. résumés. The exercise can be performed with observational data (See Fisman et al. (2008), Hitsch et al. (2010), and Banerjee et al. (2009)). This makes the ethical concern particularly salient. 14 This particular issue can be at least partially addressed by debriefing employers ex post about them having been part of a research study.
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Building constructively on this criticism, Neumark (2012) shows that if a correspondence study includes variation in observable measures of applicants’ quality that affect hiring outcomes, an unbiased estimate of discrimination can be recovered even when there are group differences in the variances of the unobservables. Neumark explains how his method can be easily implemented in any future correspondence study. All that is needed is for the résumés or applicants to include some variation in characteristics that affect the probability of being hired.15 Finally, it is remarkable that after literally dozens of correspondence studies, there has been only limited refinement of the methodology to help discriminate between different theories of the differential treatment that is being consistently observed. Employers must try their best to infer future productivity of a candidate based on limited information. That is, applicants who belong to different groups may experience different treatment even if discrimination as understood by Becker (differential treatment is motivated by prejudice) is absent and only statistical discrimination is at play. Attributes beyond those intended by the researcher may be inferred by the recipient. For example, Fryer and Levitt (2004) suggest that black names may “provide a useful signal to employers about labor market productivity after controlling for information on the résumé.” This is clearly true for age, as we noted, but this may also be true for race if the choice of a black name is a political statement by the parent, accompanied by a different attitude toward schooling and obedience. More broadly, as we already mentioned several times, even if in general employers do not see a particular identity as a sign of lower productivity (or want to discriminate based on it), they may infer something from the fact that the person is wearing it on their sleeves. After all, there was no difference in callback rate according to either religion or sexuality when the information was available to the employer online, but not directly reported in the résumé (Acquisti and Fong 2013). The only approach that has been used repeatedly by researchers to try to separate statistical from taste-based discrimination has been to compare differential gaps in outcomes between pairs of minority and nonminority applicants with weaker or stronger productivity attributes on their résumés or applications. As more productivity-relevant information is included on the résumé, average differences in unobservable characteristics between the minority and nonminority applicants are reduced, and statistical discrimination should also be reduced; however, it is clear that this remains a very indirect way to try to isolate taste-based discrimination among employers or landlords. In this regard, a recent paper that breaks the mold of the typical correspondence study and deserves particular attention is Bartoš et al. (2013). This paper is
15
The method rests on three types of assumptions. First, it is based on an assumed binary threshold model of hiring that asks whether the perceived productivity of a worker exceeds a standard. Second, it imposes a parametric assumption about the distribution of unobservables. Lastly, it relies on an additional identifying assumption that some applicant characteristics affect the perceived productivity of workers, and hence hiring, and that the effects of these characteristics on perceived productivity do not vary with group membership.
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remarkable in its ability to push the correspondence study methodology forward, think beyond the pure callback data, and refine our theories of discrimination. The paper links two important ideas: Attention is a scarce resource, and lack of information about individual candidates drives discrimination in selection decisions – or, in other words, statistical discrimination is an important factor in selection decisions. While the existing models of statistical discrimination implicitly assume that individuals are fully attentive to available information, the paper develops and tests a model in which knowledge of minority status impacts the level of attention to information about an individual and how the resulting asymmetry in acquired information across groups – denoted “attention discrimination” – can lead to discrimination. In particular, the authors argue that in markets such as the labor market where only a small share of applicants is considered above the bar for selection, negative stereotypes are predicted to lower attention. On the other hand, the effect is opposite in markets where most applicants are selected, such as the rental housing market. Bartoš et al. (2013) test for such “attention discrimination” in two field experiments: one in the labor market and one in the rental market, where they can monitor the decision-maker’s information acquisition about applicants through visits to hyperlinks containing résumés and personal websites (respectively). They created personal websites for fictitious applicants and submitted rental applications in the Czech Republic, and job applications in both Germany and the Czech Republic. The advantage of using hyperlinks to résumés and personal sites is that the researchers were able to track the exact number of visitors to the personal profile, and therefore, the share of landlords and employers who allocated additional attention to an applicant. Hence, the study was able to assess whether a minority-sounding name (1) leads to differential callbacks and (2) causes less or more search. Like the prior literature, Bartoš et al. (2013) find evidence of discrimination against minority applicants in both the housing and labor markets. Most interesting, though, are their findings regarding attention allocation. In the labor markets in both Germany and the Czech Republic, employers put more effort in opening and reading résumés of majority compared to minority candidates. In contrast, in the rental housing market, landlords acquire more information about minority compared to majority candidates through their personal sites. The findings can best be explained by a model where attention is endogenously determined by the type of the market. When the choosing entity needs to select “top candidates,” it will allocate attention to candidates belonging to the group that, according to its priors, is stronger. In markets where most candidates are accepted, some kind of a threshold rule might be used, and the choosing entity will want to eliminate the weakest candidates. In that case (e.g., a housing market), more attention would optimally be allocated to members of the group that is a priori viewed less favorably. The model implies persistence of discrimination in selection decisions, even if information about individuals is available and there are no differences in preferences. The model also implies lower returns to employment qualifications for negatively stereotyped groups (as their credentials are less likely to
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be reviewed). From a policy perspective, the model and results of this paper also highlight the crucial role of the timing of when a group attribute is revealed. Bartoš et al. (2013) represents a great example of how the résumé study infrastructure can be pushed forward to deliver deeper learning, cleaner links to specific theories of why differential treatment is taking place, and suggestions about policies that might be most effective to address it. More efforts along these lines would help revitalize this literature. We now turn to other approaches to measuring discrimination, often more “labbased” and more closely tied to a particular model of the root of discrimination.
Implicit Association Tests The Implicit Association Test (IAT) is a computer-based test that was first introduced by Greenwald, McGhee, and Schwartz (1998). Developed by social psychologists Greenwald, Nosek, and Banaji, as well as other collaborators, the IAT provides a method to indirectly measure the strength of association between two concepts. This test relies on the idea that the easier a mental task is, the quicker it can be performed. When completing an IAT, a subject is asked to classify, as rapidly as possible, concepts or objects into one of four categories with only two responses (left or right). The logic of the IAT is that it will be easier to perform the task when objects that should get the same answer (left or right) somehow “go together.”16 The typical IAT consists of seven “phases,” including practice phases to acquaint the subject with the stimuli materials and rules. Consider for example an IAT designed to assess association strengths between categories of black and white and attributes of good and bad. The practice phases are used with the materials and sorting rules. In the first, subjects would only be presented with faces as stimuli and be asked to assign white faces to one side and black faces to the other; in the second, subjects would only be presented with words as stimuli and be asked to assign pleasant words to one side and unpleasant words to the other. In the test phases, subjects are asked to simultaneously sort through stimuli representing the four concepts (black, white, good, and bad) but with again only two responses (left side or right side). In two of the test phases (the “stereotypical” test phases), items representing white and good (e.g., white faces and words such as wonderful) need to be placed on one side of the screen, and items representing the concepts black and bad (e.g., black faces and words such as horrible) on the other. In the other two test phases (the “non-stereotypical” phases), items representing the concepts of black and good need be placed on one side of the screen, and items representing the concepts of white and bad on the other. The extent to which an individual dislikes
16
See Lane et al. (2007) for an excellent introduction to IATs.
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black faces (in this case) is then measured by the difference in milliseconds in response time between the stereotypical phases and the nonstereotypical phases.17 Two broad kinds of IAT are pertinent to discrimination: If attitudes or overall preferences are the issue, the category (e.g., black/white) is associated with words that represent good/bad (as in the example we just gave). Alternatively, one may be interested in the association between a category (e.g., male/female) and a particular trait or attribute (e.g., career/family).18 The first kind is called an attitude IAT, and the second a stereotype or belief IAT. Other types include self-esteem IATs (e.g., categories are self and other, and words are either positive or negative). Since the publication of the original IAT, there have been hundreds of IAT studies, many of which try to capture attitudes that could give rise to discrimination (against black people, Muslims, women, etc.), or phenomena more akin to statistical discrimination (women and math, women and career, women and politics, etc.). The IAT has been extremely influential both within and outside academic psychology. Greenwald, McGhee, and Schwartz’s original 1998 article introducing the IAT has 6689 citations in Google Scholar, as of August 2015. The findings of IAT research on discrimination have been cited to propose changing the law, educating judges and students, etc. IATs are used increasingly as a convenient tool to measure whether attitudes respond to any particular intervention, since they can be conducted remotely, with large samples of online participants to experiments. As such, they are often used as endpoints in psychology experiments, as experimentation moves to online platforms.19 There are a number of meta-analyses, review articles, and critical papers on the use of IATs. It is not in the scope of this chapter to review all of this literature; however, the key question that is raised is what the IAT actually picks up and, relatedly, whether it is effectively associated with other predictors of discriminatory behavior, and discriminatory behavior itself. Some individual studies show promising links. For example, implicit bias predicts a more negative judgment of ambiguous actions by a black target (Rudman and Lee 2002), as well as more negative nonverbal “microbehavior” (less speaking time, less smiling, etc.) during an interaction with a black subject (McConnell and Leibold 2001). This is important, as these microbehaviors are often posited to be the channel through which implicit bias would translate into different behavior, even among people who do not report explicit discrimination. Some studies have also shown some mechanisms for those effects, e.g., showing that participants who exhibited greater implicit distaste of black people were more likely to detect aggression in a black (but not white) face (Hugenberg and
17
In practice, of course, a number of choices must be made about how to use the data, and this is reviewed in Greenwald, Banaji, and Nosek (2003). 18 For example, Nosek, Banaji, and Greenwald (2002) find stereotypical associations connecting male terms with traits related to science and a career, whereas female terms are found to be associated with liberal arts and family. 19 For examples of IATs used as end points, see Lai et al. (2014).
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Bodenhausen 2004). Only a few studies have investigated whether these differences in implicit attitudes are associated with different behaviors in the field. In Atlanta and Boston, doctors with stronger antiblack implicit attitudes were less likely to prescribe thrombolysis for myocardial infarction to African-American patients, compared to white patients (Green et al. 2007). Rooth (2010) tried to relate the behavior of recruiters in a correspondence study in Sweden (focusing on Arab-Muslim versus Christian) to recruiter-level measures of implicit discrimination they collected later. Unfortunately, they were only able to interview 26% of the recruiters they were targeting, but among those, they did find a correlation between implicit distaste of Arabs as measured in an IAT test and the tendency to not call back a resume with an Arab-Muslim name on it. An initial meta-analysis, conducted on 122 research reports, found that the IAT does seem to be capturing something about attitudes, perhaps more accurately than self-reports (Greenwald et al. 2009). They show that there is a strong correlation between implicit and more standard explicit measures. Moreover, the IAT appears to be a better predictor of actual behavior than explicit reports, particularly for sensitive subjects such as racial preferences (for which they have 32 samples with IAT measure, explicit measure, and questions about behavior). However, a more recent meta-analysis by Oswald et al. (2013) questions these initial findings. Using a larger sample (which includes newer studies as well as some studies that were omitted from the earlier meta-analysis), and a slightly different aggregation method, they find much lower correlation of the IAT with various measures of discrimination than had been initially found in the 2009 meta-analysis. Explicit measures perform equally poorly, to be sure, but not much worse. Beyond this debate (which is probably the core one to be had), IATs have been subject to a number of criticisms and questions, mainly regarding their interpretation. First, to the extent that they differ from explicit attitudes, do they reflect something “deeper” about the individuals, and are they more “true” than the selfdescription in any sense, or simply another type of attitude? Interestingly, the metaanalysis by Oswald et al. (2013) shows that there is in fact a strong correlation between different brain activities when seeing black and white faces and the IAT. This suggests that the IAT does reflect something fundamental about psychological processes. But our behavior is mediated by the social environments, exactly as our answer to a question on prejudice is mediated by this environment. So do IATs really identify prejudice or just some raw psychological “material” that we then transform? What does it mean for someone to feel that they are not prejudiced against blacks but have their IAT showing automatic white preferences (Arkes and Tetlock 2004)? On this last question, Banaji, Nosek, and Greenwald (2004) argue that conscious unbiased attitudes cannot be relied upon in all circumstances, and that IATs may capture unconscious attitudes that may be more relevant in explaining behaviors in other circumstances. Hence, they reject the idea that “if prejudice is not explicitly spoken, it cannot reflect a prejudicial feeling” (Banaji et al. 2004). In this respect, though, the low correlation between the IAT and microbehavior is a bit troubling, as this theory would suggest that the unconscious bias translates into actual acts of discrimination via unconscious behavior.
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Also, do IATs measure prevalent culture or individual attitudes? For example, if a person identifies women with family more than with career, is she making a value judgment or stating, in a sense, a fact of life? Whatever the resolution of these debates, the measured implicit attitudes vary considerably across people, and the robust correlations – between implicit and explicit attitudes, between the different IATs in similar domains – do seem to indicate IATs capture some signal about the individual. This does not mean that the IAT can be considered a reliable measure of the attitude of any particular individual (at best, it measures attitudes with considerable noise). However, it does mean that the IAT may be a good measurement tool for the propensity for groups to discriminate toward each other. In this context, whatever the predictive value of the IAT for behavior, the extent to which it is affected by a particular manipulation is of interest. As economists, we may be more interested in the extent to which attitudes can be influenced (by experiences, the environment, or specific interventions), than in their pure measurement at a point in time. Using IATs as an outcome variable also helps sidestepping the question of whether they represent any deep truth about anybody: While the signal may be noisy, to the extent that there is signal, this may be a useful measurement tool. As noted, after more than a decade of using the IAT mainly as a descriptive tool, studies in psychology started using them as outcomes. For example, Lai et al. (2014) set up a research contest on de-biasing, where teams are given a budget of 5 min to interact with participants, and the outcome is the scores on a black-white attitude IAT. In recent years, economists have also started using IATs as dependent variables. For example, Beaman et al. (2009) design and implement two IATs in West Bengal, India, to measure preferences toward female leaders, and stereotypical association of women with domestic rather than political activities. They then examine the impact of exposure to female leaders on these two measures (we will discuss the results below in section “Goldberg Paradigm Experiments”). Lane et al. (2007) provide detailed and helpful instructions on how to build an IAT. The software that is needed to construct and analyze the test (millisecond software) is available for purchase. IATs can be designed with only verbal or image stimuli for subjects who are not literate (this is what Beaman et al. (2009) use), and although they are more difficult in populations that have had no experience with computers and for older participants, they can be a very useful tool. As studies increasingly use electronic data collection methods (on tablets or notebooks), the extra cost of adding an IAT diminishes. Of course, the debate in psychology does not suggest that the IAT should be considered a “magic bullet,” suitable to replace any other measure of discrimination. In particular, it is probably not a substitute for good measures of actual behavior in policy interventions. Nevertheless, it can be an extremely useful intermediate variable, to understand the mechanisms beyond a result (in Beaman et al. (2009), the final end point of interest is actual voting), or potentially, if collected beforehand, as a covariate of interest. For example, in Glover, Pallais, and Pariente (2015), the IAT
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is used as a proxy measure of latent employer discrimination (see further details in section “Endogenous Responses to Bias”).
Goldberg Paradigm Experiments Goldberg Paradigm experiments are laboratory versions of audit or correspondence studies. They are named after a 1968 experiment by Goldberg where students graded written essays, which were identical except for the male or female name of their author (Goldberg 1968). This initial experiment demonstrated a bias: Female got lower grades unless the essay was on a feminine topic. Since then, a large literature in psychology has used the Goldberg Paradigm to identify discrimination against different groups, and in particular in the resistance to female leaders.20 In the typical lab experiment, a group of subjects is asked to review a vignette, describing the behavior of a female or male manager (for example), or witness a confederate (male or female) simulating a leadership situation. The participants are then asked to evaluate the leader’s competence, or to say whether they would have liked to have them as leader for a task they may collectively perform. Reviewing a large number of such studies, Eagly, Makhijani, and Klonsky (1992) do not find that, on average, female leaders are evaluated significantly more negatively than male leaders. However, there are some circumstances where they do find that female leaders are evaluated more negatively, for example, when the leadership was carried out in a masculine style (in particular when the leader was projected to be authoritative). This supports Eagly’s hypothesis of “role congruence”: What people dislike is when women behave in a nonfeminine way. Since strong leaders must be assertive, but women must be demure, it makes it difficult for women to be appreciated as strong leaders. The fact that the circumstances are artificial, and answers have minimal stake associated with them, makes those experiments less relevant, on their own, than field-based correspondence tests. But one advantage of the Goldberg-style experiments is that they can be easily, and finely, manipulated, which makes them good outcome measures in field research (or field experiments). They can also be easily added to a standard survey instrument. For example, Beaman et al. (2009) seek to find out how discrimination against female leaders is affected by prior exposure. They administer two Goldberg-style experiments. In one, they ask the participants to listen to a speech by a political leader, which is read either by a female or a male actor (note that it is important that there are several male and female actors). In the second one, they discuss vignette where women or men leaders make decisions that are either promale (investment in irrigation) or profemale (investment in drinking water). Each individual receives a randomly selected version of the speech and vignette. The randomization is stratified by village, and hence by prior exposure to
20 See Eagly, Makhijani, and Klonsky (1992) for a review and meta-analysis of the literature on such resistance to female leaders.
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a female leader (due to a policy of gender reservation). While this does not tell us the extent to which any single person discriminates, one can learn whether, on average, exposure to a female leader affects the extent to which individuals give lower grades to women in response to the same speech or vignette. Beaman et al. (2009) find that both men and women, but men more than women, tend to discriminate against female leaders (additional results from this study are discussed further in section “Minority Leaders and the Attitude of the Majority”).
List Randomization Like correspondence tests or Goldberg experiments, list randomization (also known as item count technique, unmatched count, or list response) does not provide a measure of individual bias but can provide an estimate of the extent of discrimination in a population. They are a way of eliciting accurate answers to questions of discrimination in the presence of social desirability bias. The idea is to present the subjects with a list of N statements which are generally noncontroversial, but could be true or false (e.g., I had coffee at breakfast; I like popcorn).21 Then, a randomly selected group of people is asked a potentially controversial statement (e.g., “I would be upset if an African-American family moved next door”) on top of the N noncontroversial statements. The subject only states the number of statements with which they agree. Comparing the fraction of yes among those who got N and those who got N þ 1 statements gives a good measure of discrimination. And clearly no one (including the interviewer) will know how a given subject answered the controversial statement. Unlike the IAT, this method will not reveal biases that are unconscious or biases that the subject wants to deny even to themselves, but it will prevent the results from being affected by social desirability bias. Early applications of list randomization to measure discrimination are Kuklinski, Cobb, and Gilens (1997a) and Kuklinski et al. (1997b). Both studies found considerable racial prejudice in the American South using list randomization techniques (though not in the North). Furthermore, they found higher level of measured discrimination using this method than using direct elicitation methods, for example, respondents are more likely to disagree with a statement such as “I am comfortable with a black family moving next door” when asked via list randomization than with a traditional survey. Likewise, Coffman, Coffman, and Ericson (2013) show that stated discrimination against gay populations is much lower in response to a direct question in the control group than when it is elicited through the list randomization method. For example, respondents were 67% more likely to express disapproval of an openly gay manager at work when the question is part of a list than when the question is asked directly.
21
As we explain below, there is a tension in the choice of those questions: For maximum precision, there should be behavior that almost everyone says yes or no to, but then they do not give any cover to the subject.
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A few papers have used the method to elicit attitudes toward presidential candidates. Kane, Craig, and Wald (2004) find no discrimination against a Jewish presidential candidate (Joe Lieberman). Martinez and Craig (2010) find that few whites in Florida seemed distressed by the possibility of having a black president. However, list randomization revealed much more opposition toward the idea of a female president than that which was reported in opinion polls (Streb et al. 2008). As noted above, several studies suggest that the randomized list technique yields different answers than direct elicitation. In a meta-analysis across 48 comparisons of direct report and list randomization, Holbrook and Krosnick (2010) found that 63% of the estimates for socially undesirable behaviors were significantly larger when elicited through list randomization. On the other hand, responses on nonsensitive behavior tend to be more similar (Tsuchiya et al. 2007). The list randomization method is, however, not without issues. As we alluded to earlier, there is a fundamental tension between precision (which would require having statements to which everybody responds yes or no) and providing “cover” to the subject (which would require the opposite). The implication is that the results from list randomization methods are often quite imprecise. Gosen (2014) has also shown results tend to systematically depend on how many noncontroversial statements are included in the list, although the opposite was found in Tsuchiya, Hirai, and Ono (2007). In summary, list randomization could be a promising method to measure discrimination as it is less subject to social desirability bias, but since few economists have used it,22 more work needs to be done to ascertain its usefulness in the field. In comparison to other indirect methods, list randomization is often more simple to administer (both for surveyors and respondents) but risks having low power (Droitcour et al. 1991). It would be interesting to see more research comparing measures of discrimination obtained through list randomization compared to an IAT or Goldberg style experiment. It would also be interesting to compare how noisy these different measures are. The fact that the list randomization method can only provide an aggregate (and not individual) measure of discrimination complicates its use as an outcome variable (say, for a randomized experiment), but no more than any of the other methods we have already discussed that also only give group-level outcomes, such as the Goldberg-style experiments.
Willingness to Pay A key prediction of Becker’s model of taste-based discrimination is that people should be willing to pay to interact with people of their own group. Somewhat
22
See Karlan and Zinman (2012) for an example of an application in economics.
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surprisingly this prediction has not given rise to a large literature trying to evaluate the willingness to pay to discriminate. As we noted, the correspondence and audit tests tend to be based on a binary measure (interview or not, hire or not). Until recently, the body of work that came closest to measuring such willingness to pay was a literature on the “beauty premium” motivated by Hamermesh and Biddle’s (1994) finding that workers with better-than-average looks earn 10–15% higher wages. Analogous to the black-wage race gap, the beauty premium could be due to the fact that more beautiful workers are more productive, say because consumers prefer to interact with beautiful people (Biddle and Hamermesh 1998; Pfann et al. 2000), or because beautiful people are more confident. Or employers may be wrong in their belief. Mobius and Rosenblat (2006) set up a laboratory experiment where undergraduates and graduates from Tucuman, Argentina, were randomly assigned into groups of “employers” and “workers.” In the experiment, “employers” had to hire “workers” to perform a maze-solving task. After a practice test (which was recorded and became the digital “resume” of the worker) and a question where the workers estimated the number of mazes they could solve in 15 min, each worker was matched to five employers, who saw either (1) just the resume, (2) the resume and a photo, (3) the resume and a phone interview, or (4) the resume plus an interview, plus the photograph.23 The employers in turn saw five workers, and for each of them decided how many mazes they thought the worker could solve. This estimate contributed to the employer’s own payment. It also entered into the calculation of the actual wage of most of their workers. Mobius and Rosenblat show that productivity at the task is not affected by beauty (as evaluated by 50 high school students on the basis of the photograph), although worker confidence is. A rise of one standard deviation in beauty increases confidence by 13–16%. However, employers are willing to pay more employees who are considered to be more beautiful: In all the treatments where they can see beauty, employers are willing to pay workers more. The premium ranges between 12% and 17% depending on the treatment. Decomposing the beauty premium by comparing treatments, the authors estimate that 15% is due to the confidence channel and 40% each through the visual and oral stereotype channels (the fact that beauty still affects wages when the employer does not see the employee but talks to her on the phone indicates that beauty is correlated with speaking skill, perhaps another feature of the beauty channel). Interestingly, employer’s estimated productivity is not affected by whether or not they know that it will actually contribute to the worker’s wage. This suggests that there is little pure taste-based discrimination in this lab experiment.
The wages of “workers” were affected by the difference between their estimation of the number of mazes and what they actually competed; therefore, they were incentivized to tell the truth.
23
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Employers give a premium to beautiful people because they believe (wrongly) that they will be more productive. There are a number of limitations of this experiment, not least of all, from the point of view of this chapter that it is a lab experiment. It is also limited to a one-shot interaction at the hiring stage. Nevertheless, it sets an interesting template for what a field experiment leveraging this methodology might look like and in particular does an excellent job laying out the various pieces that are needed to establish discrimination and understand the mechanism behind it. One paper which has recently followed in Mobius and Rosenblat’s footstep is Rao (2013), which seeks to measure the extent to which well-off kids in India discriminate against poorer kids (in order, as we will discuss in more detail in section “Intergroup Contact,” to estimate the extent to which any such discrimination is affected by forced exposure to poorer kids through an affirmative action program in education). To do so, Rao sets up an Ingenious field experiment, based around team selection for a relay race. First, students from a rich private school and a poor public school, who were all present at a sporting event to support their classmates, were randomized in different sessions with different prizes for winning the race (from Rs.50 to Rs.500, which are very high stakes). After mixing for 15 min, they watched a series of one-on-one sprints (most of them pitting a poor student against a rich one), and then each was asked to indicate on a worksheet which of the two he wanted as teammate for the relay race. After these choices were revealed, one of the choices was picked, the teams were formed, and the relay race was run. To make sure that there was a “cost” to picking out a poorer student (if students did not like them), students had to spend 2 h socializing with their teammate, which was announced prior to team selection. This experiment has a number of clever features. It presents children with a real choice, and by varying the stakes, it makes it clear how much (on average) students are willing to sacrifice to avoid interacting with a poor student. The sprint phase entirely and unambiguously reveals ability, so the setup is targeted to pick up pure taste-based discrimination (e.g., dislike of hanging out with a poor teammate). The results show that there is substantial taste-based discrimination in this context: In 19% of the cases where the poor students is the fastest, rich students prefer to pick the rich student as a teammate anyway.24 Discrimination does decline as the stakes increase: discrimination falls from 35% with the lowest stake, to 27% with the intermediate stake, and 5% in the highest stake. Fitting a structural model to the data, Rao estimates that, for students without prior exposure to poor classmates, the distaste of interacting with a poor student is worth Rs.37. That is, a student is willing to give up to Rs.37 in expected prize money to hang out with a rich student rather than a poor one.
24
In contrast, if a rich student is the fastest, he is picked 97% of the time, and among two students of the same background, the fastest is picked 98% of the time.
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Consequences of Discrimination Self-Expectancy Effects Stereotype Threat and Underperformance Models of statistical discrimination explain the differential treatment of disadvantaged groups in, say, hiring decisions, due to employers’ inability to perfectly predict a given worker’s future productivity and hence their rational decision to assign some weight to the average productivity of the worker’s racial group. For example, African-Americans as a whole are categorized as less productive than whites, and employers take this supposed average difference in productivity into account when deciding whether or not to hire any African-American job candidates, given their inability to precisely predict each specific candidate’s future productivity. While discrimination emerges under the logic above as a consequence of average differences in productivity across groups, research in social psychology has provided convincing evidence for the reverse causal channel. In particular, the simple process of categorizing or “stereotyping” some groups as less productive appears to cause these groups to be less productive. This research suggests that individuals from some groups may suffer negative performance outcomes (such as lower test scores or less engagement with academics) because of the burden of the “stereotype,” or “stereotype threat” (Steele and Aronson 1995).25 The key conjecture is that the threat of being viewed through the lens of a negative stereotype can create an anxiety that disrupts cognitive performance. In a seminal study, Steele and Aronson (1995) demonstrated in a lab setting that inducing stereotype threat – by asking test takers to indicate their race before the test – significantly undermines African-Americans’ performance on intellectual tasks. They also showed that reducing stereotype threat – by convincing test takers that the test was not being used to measure their abilities – can significantly improve AfricanAmericans’ performance, dramatically reducing the racial gap. Numerous lab studies have since replicated the effects of stereotype threat both with respect to social identities other than race (e.g., gender, income class, etc.) and with respect to mediating outcomes (such as blood pressure, heart-rate variability, performance expectations, effort, etc.).26 Defined by Steele and Aronson (1995) as a “risk of confirming, as self-characteristic, a negative stereotype about one’s group.” 26 While most of these lab studies have been conducted by social psychologists, a few have been performed by economists. For example, Dee (2009) ran a lab experiment with student-athletes and nonathlete students at Swarthmore College, randomly assigning some of them to a treatment that primed their awareness of a stereotyped identity (i.e., student-athlete). He finds that the treatment reduced the test-score performance of athletes relative to nonathletes by 14%. Also, Hoff and Pandey (2006) present evidence from a caste priming experiment in Uttar Pradesh, India. Among 321 high-caste and 321 low-caste junior high school male student volunteers, there were no caste differences in performance in an incentivized maze-solving task when caste was not publicly revealed, but making caste salient created a large and robust caste gap in performance. However, the mechanisms for the underperformance in this case seem quite different from the hypothesized 25
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The rest of the social psychology research on the stereotype threat has also focused on documenting methods that can undo or undermine the threat of the stereotype. One line of research has addressed the underlying message of the stereotype – that stereotyped individuals are inherently limited because of their group membership. Thus, if participants can be convinced that intelligence is not a fixed trait, but a malleable quality that can be increased with hard work and effort, they may be less prone to stereotyping. Levy, Stroessner, and Dweck (1998) present evidence consistent with this idea. Descriptively, they find that people holding an entity theory of intelligence (i.e., intelligence is a fixed trait) made more stereotypical trait judgments of ethnic and occupational groups than those who believed that intelligence is malleable. Moreover, in a small lab experiment, they found that manipulating implicit theories affected level of stereotyping, at least temporarily. In the experiment, 155 introductory to psychology students were randomly assigned to a fake “scientific” article that either presented evidence for an entity (fixed) or an incremental (malleable) view of personality. After reading the article, they were presented with questions in which they had to rate the extent to which a series of 15 traits accurately describe certain occupational groups (teachers, doctors, lawyers, and politicians) and ethnic groups (African-Americans, Asians, and Latinos). To try to reduce the likelihood of participants recognizing the link between the article and the questions, the researchers told participants that the questions were for a separate study, and that they would be asked questions on the content of the article later. The experiment found a small but significant effect: Those who read the article that argued for fixed personality were less likely to believe traits can change, and more likely to rate stereotypical traits as highly descriptive of their respective groups. Similarly, in a lab experiment, Aronson, Fried, and Good (2002) assign white and black students to one of three conditions to assess the impact of an intervention designed to reduce stereotype threat. In two conditions, students were asked to write a letter of encouragement to a younger student who was experiencing academic struggles. In one of these conditions, students were prompted to endorse a view of intelligence as malleable, “like a muscle” that can grow with work and effort. In the second condition, students endorsed the view that there exist different types of intelligence. The third condition served as a control condition in which students were not asked to compose a letter. Several days after the intervention, all students were asked to indicate their identification with and enjoyment of academics. Results showed that black students in particular were more likely to report enjoying and valuing education if they had written a letter endorsing malleable intelligence. In addition, grades collected 9 weeks following the intervention were significantly
mechanism in the social psychology literature. In particular, the authors find that when a nonhuman factor influencing rewards received for the maze-solving task (a random draw) was introduced, the caste gap disappeared. The results suggest that when caste identity is salient, low-caste subjects anticipate that their effort will be poorly rewarded.
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higher for blacks in the malleable intelligence condition. Whites showed a similar, though statistically marginal, effect.27 While most research on stereotype threat, and how to undo it, has taken place in the lab, a few interesting field studies have also been conducted by social psychologists. Schools, where test-taking and performance measurements are part of normal operations, have provided a natural setting for much of this field research. Good, Aronson, and Inzlicht (2003) performed a field experiment to test methods for helping female, minority, and low-income adolescents overcome the effects of stereotype threat and, consequently, improve their standardized test scores. Specifically, seventh-grade students in the experimental conditions were mentored by college students who encouraged them either to view intelligence as malleable or to attribute academic difficulties in the seventh grade to the novelty of the educational setting. Results showed that females in both experimental conditions earned significantly higher math standardized test scores than females in the control condition. Similarly, the students – who were largely black or Hispanic and low-income adolescents – in the experimental conditions earned significantly higher reading standardized test scores than students in the control condition. Blackwell, Trzesniewski, and Dweck (2007) based a field experiment on the laboratory finding on “malleable intelligence.” They randomly selected half of a group of 95 mainly African-American and Hispanic seventh graders to participate in an 8-week training on the theory of malleable intelligence based on interventions that had been successful in the lab (25 min per week, in the students’ classroom). In the control condition, students also received small group coaching, but not on this theory. Students in the experimental conditions obtained higher grades. Good, Aronson, and Harder (2008) also conducted a field experiment where they explored stereotype threat and its negation in high-level college math courses that typically serve as gateways to careers in math and science. Male and female students in the last course of an advanced university calculus sequence were given a practice test containing items similar to those found on the Graduate Record Examination (GRE) standardized test. All students were told that the test was “aimed at measuring your mathematical abilities” (stereotype threat), but half of the students additionally were assured that “this mathematics test has not shown any gender differences in performance or mathematics ability” (stereotype threat negation). Test performance was higher for women than men in the stereotype threat negation condition but was equivalent in the stereotype threat condition. In a related field study, Cohen et al. (2006) reduced the black-white GPA gap among low-income middle school students by affirming the students’ self-concepts (and presumably inoculating them from stereotype threat) at the beginning of the school term. The intervention is very light touch: Students are asked to write a series
27
It is important to note that for this experiment, while the randomized interventions took place in the lab, outcomes are measured (1) on naturally occurring tasks outside the lab and (2) quite a long time after the interventions took place; both of these features are important strengths of this experiment compared to the standard “stereotype threat” lab experiments.
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of short essays focusing on a self-affirming value. The authors first found short-term impacts, and then, most remarkably, fairly large long-term impacts: Over 2 years after the intervention, the average GPA of an African-American student who participated in the essay writing was 0.24 higher than that of the control group (Cohen et al. 2009). Most of the effects are concentrated among those who were initially low achieving. These are remarkable numbers, especially since the effects of most education interventions tend to fade. The authors speculate that the long-term benefits may be due to the fact that initial psychological state sets out a selffulfilling trajectory. Cohen’s work has since then been replicated and extended in other similar contexts, and Dweck and Cohen’s initial insights have helped jump-start a subfield of psychology called “mindset studies.” Yeager et al. (2014) experiment with “wise feedback,” an intervention where high school students are given critical feedback on their written work, alongside with a note emphasizing the high standard of the teachers and the belief that the student can succeed. The intervention reduces mistrust among African-American students and improves the quality of the final product.28
Identity and Preferences The “stereotype threat” literature can be viewed as part of a broader literature on how self-identity considerations may affect behavior and preferences of disadvantaged groups and ultimately may perpetuate gaps in economic outcomes. The same way that women may do poorly on a math test when reminded of their gender (due to the anxiety-inducing burden of the stereotype of “girls not being good at math”), they may also show low-risk preferences when reminded of their gender (if nurtured with the behavioral norm that “girls should not take risk” by gender-biased parents and/or teachers). Against the backdrop of a large literature in social psychology that has tested the self-categorization theory and the cognitive mechanisms through which it operates,29 a few recent papers in economics have leveraged the lab environment to learn more about how various social identities relate to preference parameters, such as risk, time, and social preferences. For example, Benjamin, Choi, and Strickland (2010) explore the effect of racial and gender category norms on time and risk preferences. In a laboratory setting, they study how making salient a specific aspect of one’s social identity affects subjects’ likelihood to make riskier choices, or more patient choices. From a methodological perspective, the study consists of temporarily making more salient (“priming”) a certain social category (as is done in the “stereotype threat” literature) and seeing how the subjects’ choices are affected. For example, the gender identity salience
28
A background paper written for a White House conference gives a good overview of the literature on mindset studies (Yeager et al. 2013). 29 See Reicher and Levine (1994), Forehand, Deshpandé, and Reed II (2002), and LeBoeuf, Shafir, and Bayuk (2010).
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manipulation is done through a questionnaire included in the beginning of the experiment in which subjects are asked to identify their gender and their opinion regarding living on a coed versus single-sex dormitory floor. The study uncovers some interesting patterns with respect to racial identity. For example, priming a subject’s Asian-American identity makes the subject more patient. Hence, an AsianAmerican identity might partly contribute to the higher average level of human capital accumulation in this racial group. However, making gender salient appears to have no significant effects on either men’s or women’s patience, or their level of risk aversion. Of course, it is possible that the priming performed in this experiment was too weak to temporarily affect preferences. In other words, it is difficult to affirmatively conclude from these nonresults that gender identity norms are not culturally reinforcing whatever biological differences may exist between the sex in the willingness to take risks. Another lab study aimed at assessing how social preferences are affected by gender identity is Boschini, Muren, and Persson (2009). The question under study here is whether gender identity priming affects subjects’ level of altruism. The experiment consists of comparing behavior in a dictator game for subjects whose gender identity has been primed versus not primed. The results indicate that the priming does affect behavior but only when the subjects are assigned to mixedgender groups. Moreover, the effect is driven by males: men are sensitive to priming and become less generous in a mixed-gender setting when primed with their male identity. Women do not appear to respond to the treatment. As far as we are aware of, no field experiment exists on how social identity affects preferences and behaviors outside of the mindset literature discussed above, which focuses on education and on adolescents. It seems worthwhile for future research to consider such work. Interventions might be designed to emphasize a “default” social identity that may be counterproductive for that social group’s performance against an “alternative” social identity. For example, while deciding to work hard toward completing college coursework for a young black father might be uncool because it is “acting too white,” the decision might resonate much more when his identity as a “father” is being primed. Moreover, specific interventions might be designed to simply undo or undermine the power of the social identity norms when they work toward reinforcing differences in behaviors and outcomes between groups. If women decide against applying for a job in a high-risk but also high-return occupation because of internalized conservative social norms about “what is appropriate work for a woman,” it might be possible to undermine the pull of this conservative norm with counteractive “messaging,” in the same spirit as what has been done to undermine the burden of the stereotype in the “stereotype threat” literature. Such interventions might be particularly powerful if the timing of the counteractive “messaging” is close to when women are making these important career choices (e.g., when applying for school or on a job search website, or when considering which contact in their LinkedIn network to reach out to).
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Expectancy Effects and Self-Fulfilling Prophecies Pygmalion and Golem Effects Suppose minority and majority workers have similar inherent abilities. How could differential beliefs about their abilities persist? One explanation is that employers’ beliefs that minorities are on average less productive are self-reinforcing (Arrow 1973; Coate and Loury 1993). This could happen for two reasons. First, minority and majority workers may rationally make different skill investment or effort choices in the face of the beliefs of their employers. A minority worker may see less value in investing in her skills if she knows that the employers will be slow in updating their beliefs, and hence less likely to promote her. Second, the employers themselves may invest less in the minority workers (e.g., investing in training) if they do not believe that the workers will be “up to the task.” In both cases, employers’ beliefs about minority workers will be self-fulfilling. The social psychology literature offers multiple demonstrations of such selffulfilling prophecies.30 Interesting, most of these demonstrations took place in the field. Earlier work on self-fulfilling prophecies in the social psychology focused on how heightened expectations can be self-fulfilling. In a seminal study, Rosenthal and Jacobson (1968) conducted a field experiment in a US public elementary school (Oak School). Teachers were deceived into believing that a set of one-fifth of their class were expected to develop (“blossom and spurt”) much faster than the rest, as measured by IQ points (supposedly measured by the Harvard test of inflicted acquisition). In fact, this set was randomly selected. The main outcome measure was an IQ test (Test of General Ability), administered at the start of the school year (pretest) and at 4 months (end of first semester), 8 months (end of second semester and of first year of school), and 20 months (end of second school year with a different teacher). Rosenthal and Jacobson showed that the students for whom teachers had raised expectations had faster IQ gains than control students in the same classes (the treatment children gained 12 IQ points over the course of the year, and the control children gained 8), with the biggest effect on first and second grade children by the end of the first year. Rosenthal and Jacobson dubbed this boost in achievement driven by teachers’ beliefs the “Pygmalion effect.” The Pygmalion effect in the classroom was subsequently studied intensively,31 and criticized extensively. Snow (1995) reanalyzed the data from the original
The first self-fulfilling prophecy to be investigated extensively in psychology was the experimenter effect. The experimenter effect refers to the possibility of the experimenter influencing subjects to respond to the treatment in a way that conforms to the experimenter’s expectations. Rosenthal (1963) summarized a dozen experimenter-effect studies and wondered whether similar interpersonal expectation effects occur among physicians, psychotherapists, employers, and teachers. 31 See Dusek, Hall, and Meyer (1985), Rosenthal and Rubin (1978), which was one of the first metaanalysis in psychology and was based on 345 studies, and Rosenthal 1994, which updates it for reviews. 30
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experiment, highlighting that approximately 35% of the IQ observations fall out of the normal range and that there are several observations which have rapid growth in pretest and posttest scores (e.g., increasing from 17 to 110). He finds that the expectancy effect on IQ disappears when these outlier scores are omitted. A relatively recent review of the literature (including a balanced review of the metaanalysis and the various critics) concludes that while the Pygmalion effect in the classroom is real, it is probably fairly modest (Jussim and Harber 2005). Since this early work, social psychologists have demonstrated the self-fulfilling nature of leaders’ expectations in several other field settings and have tried to better understand the underlying mechanisms. Rosenthal (1994) and Eden (1992) provide a review of much of the work in this literature. For example, Eden and Shani (1982) replicated the original design and results of Rosenthal and Jacobson (1968) in the Israeli Defense Forces. But they also concluded, based on additional survey work to complement the randomized controlled trial, that leadership behavior was a key mediator in generating the Pygmalion effect. Also using the Israeli Defense Forces as a field, Eden and Ravid (1982) interestingly combined expectancy and self-expectancy manipulations in a single study. Trainees included 60 men in the first half-year of military duty enrolled in a 7-week clerical course divided into five training groups, each instructed by a commander. To produce the Pygmalion effect, a random quarter of each instructor’s trainees were described to the instructor as having high success potential. Another random quarter were told directly by a psychologist in a brief personal interview that they had high success potential, in order to induce high self-expectancy. The remaining trainees served as controls. Learning performance was significantly higher in both high expectancy groups than in controls, confirming the Pygmalion hypothesis and the additional hypothesis that inducing high self-expectations similarly enhances trainee performance. Interestingly, while several instructors were unexpectedly relieved midway through the course, the hypothesized performance differentials continued even though the authors abstained from refreshing the expectancy induction among the substitute instructors, reflecting the possible durability of expectancy effects. Finally, Eden and Ravid (1982) also showed that equity considerations among the trainees likely played a mediating role: Trainees in both of the high expectancy conditions reported feelings of over-reward, which may have motivated them to improve their performance. While the Pygmalion literature shows the self-fulfilling nature of raising leaders’ expectations, another branch of this literature also demonstrated the self-fulfilling nature of lowering those expectations. Psychologists have dubbed this the “Golem effect.” There have been far fewer studies on the Golem effect than the Pygmalion effect, given the trickier ethical issues associated with lowering leaders’ expectations (Reynolds 2007). This challenge has led to research designs that are not quite as “clean” as those used to demonstrate the Pygmalion effect. For example, Oz and Eden (1994) randomly led treatment-assigned squad leaders (n ¼ 17) in a military unit to believe that low scores on physical fitness tests were not indicative of subordinates’ ineptitude, while control squad leaders (n ¼ 17) were not told how
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to interpret test scores. Tests indicated that low-scoring individuals in the experimental squads improved more than those in the control squads. While the researchers employed a respectable research design and were cautious to abide by ethical standards, the sample was extremely small and the researchers did not directly attempt to lower supervisors’ expectations. Given the challenge of doing research on the Golem effect, an alternative approach in the literature has been to rely on natural variation in leaders’ expectations, rather than exogenously varying those expectations. For example, Babad, Inbar, and Rosenthal (1982) studied expectation effects among physical education student-teachers. They found that pupils about whom they imparted high expectations to the instructors performed best (i.e., the standard design for a demonstration of the “Pygmalion effect”). However, they also found that pupils toward whom instructors naturally harbored low expectations performed worse than those regarding whom they had high or intermediate natural expectations, consistent with a “Golem effect.” A recent paper (Kondylis et al. 2015) demonstrates the power of self-fulfilling prophecy. In villages, either women or men were randomly selected to learn a new technology and teach it to others. Women retained more information from the training, and those who were trained by them did in fact learn more. But women ended up performing much worse in terms of the number of farmers they convinced, because other farmers perceived women as less able, and hence paid less attention to their messages.
Endogenous Responses to Bias While the Pygmalion and Golem effects demonstrate the self-fulfilling nature of leaders’ expectations about performance on that performance, they are not directly tied to discrimination. Are leaders’ biases against some groups also endogenously affecting the performance of these groups? Two recent field studies in the economics literature provide what we believe is the first field-based answers to this question. Conceptually, these studies follow a very similar research approach to that in Babad, Inbar, and Rosenthal (1982) to demonstrate the relevance of self-fulfilling prophecies as an explanation for persistent differences in performance between different groups of workers or students. Specifically, rather than “artificially” priming leaders to vary their level of bias, the analysis relies on randomly assigning those trainees to leaders who are known to have different levels of bias. To be clear, the limit of this design compared to the preferred “Pygmalion design” is that any unobserved factors that are systematically correlated to different levels of biases among leaders cannot be formally ruled out as an explanation for the findings. However, the two papers below take several ingenious steps to deal with this concern. Glover, Pallais, and Pariente (2015) studied cashiers in a French grocery store chain, a sizable share of who were of North African and Sub-Saharan African origin. They assess whether cashiers performed worse on the days when they were assigned to a manager who was more biased against their group. They measured managers’
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bias toward workers of different origins using an IAT test. The cashiers in these stores worked with different managers on different days and had virtually no control over their schedule, allowing the authors to use the quasi-randomness of the schedules to assess the causal effect of being paired with a more biased manager. To address the difficulty raised above of manager bias being correlated with some other manager characteristics that might also affect employee performance (for example, more biased managers might also be less skilled), they use a difference-in-difference methodology, comparing the change in minority workers’ performance under biased and nonbiased managers with the change in nonminority workers’ performance under these two types of managers. They find that on days when they are scheduled to work with biased managers, minority cashiers are more likely to be absent. When they do come to work, they spend less time at work; in particular, they are much less likely to stay after their shift ends, and they scan articles more slowly and take longer between customers. Glover, Pallais, and Pariente (2015) also report interesting complementary survey evidence to better understand mechanisms. They do not find that minority workers report that they dislike working with biased managers more, or that biased managers dislike them, or that biased managers make them feel less confident in their abilities. However, they do find evidence that biased managers put less effort into managing minority workers. Minority workers report that biased managers were less likely to come over to their cashier stations and that biased managers demanded less effort from them. Consistent with this, they find that the effect of manager bias grows during the contract, perhaps as workers may learn that they are not being monitored by biased managers. Lavy and Sand (2015) estimate the effect of primary school teachers’ gender biases on boys and girls’ academic achievements during middle and high school, as well as on the choice of advanced-level courses in math and sciences during high school. In particular, they tracked three cohorts of students from primary school to high school in Tel-Aviv, Israel. They measured teachers’ gender biased behavior by comparing their average marking of boys and girls in a “non-blind” classroom exam to the respective means in a “blind” national exam marked anonymously. For identification, the authors rely on the conditional random assignments of teachers and students to classes within a given grade and a primary school. They compare outcomes for students that attended the same primary school but were randomly assigned to different teachers, who have different degrees of stereotypical attitudes. They find that being assigned to a more gender-biased teacher at early stage of schooling has long-run implications for occupational choices and hence likely subsequent earnings in adulthood. Specifically, teachers’ overassessment of boys in a specific subject has a positive and significant effect on boys’ achievements in the national test on that subject administered during middle and high school, while it has a significant negative effect on girls’. In addition, assignment to primary school math teachers favoring boys over girls encourages boys and discourages girls from engagement in advanced math courses offered in high school.
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Discrimination in Politics and Inequality Across Groups A direct consequence of discrimination in politics and other leadership positions is that there are fewer members of the discriminated group with the power to act in the interests of others in their group. In a standard median voter world, the underrepresentation of women or other subordinate groups in politics would matter less, as elected politicians would endeavor to represent the interest of the median voter. But if politicians cannot commit to a particular political platform, and their group membership eventually determines the type of policies they will implement, then the lack of representation at the top means that the underrepresented groups in society will get worse outcomes (Besley and Coate 1997; Pande 2003). This would also occur if the absence of a leader means that the underrepresented groups find that they cannot express their preferences in the political arena. The best evidence on the consequences of discrimination in politics comes from studies that have evaluated what happens to the underrepresented groups when they finally gain political representation. A few observational studies have exploited exogenous shocks to representation due to close elections; a few other papers have also studied nonrandomized variation in mandates.32 There have also been a set of studies that exploit the random selection of places that have to elect a leader from a historically underrepresented group (caste, tribe, or gender) in India’s local governments. Comparing villages that were randomly selected to receive either a male or female head, Chattopadhyay and Duflo (2004) find that female leaders spend more on goods that women prefer, compared to those that men prefer. Beaman et al. (2010) replicate the results over a longer time period. Using a dataset that covers a larger number of states, they find that the results persist over time, and that investments in drinking water (a preferred good for women) continue to be higher even after the seat is not reserved anymore and women have (generally) left power.33 Iyer et al. (2012) show that greater female representation (in local governments) is related to more crimes against women; using a household-level crime victimization survey in Rajasthan, they however show that the increase is not due to an actual increase in the amount of crimes, but rather greater willingness to report such crimes. Finally, Chattopadhyay and Duflo (2004) further find that village leaders from scheduled castes invest more in scheduled caste hamlets.34
32 See Pande (2003), Clots-Figueras (2009), Clots-Figueras (2011), and Rehavi (2007) for examples. 33 Bardhan, Mookherjee, and Parra Torrado (2010) compare places before and after reservation and do not find a difference in what leaders do, but since there seems to be a lingering effect of quota on pro-female policies, this finding might not be so surprising. 34 Dunning and Nilekani (2013) find little impact of the reservation on distribution of goods by ethnic group and a strong impact of parties, but they use a regression discontinuity design strategy rather than focusing on a states where the assignment is random.
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Benefits of Diversity? Discrimination leads to less-diverse firms, legislative assemblies, etc., but does diversity in itself matter for society? What are the implications of the low diversity that discrimination may generate for the performance of organizations and society in general?
Does Homogeneity Hurt or Help Productivity? A long literature in political economy and development has tended to emphasize the cost of diversity, in particular ethnic diversity. If members of different groups do not like each other, diversity creates holdups, breeds conflicts, makes it difficult to agree on public good provision, etc. Lang (1986) proposes that the roots of discrimination are communication difficulties across different groups (what he calls “language communities”). A similar argument is made by Lazear (1999). In that view of the world, segregation arises naturally, because homogenous groups are more productive (since communication within them is faster and easier). More homogeneous groups will create a trusting environment where people can work better together. While the minority will suffer as a result, the short-run equilibrium is efficient, and policies directly aimed at increasing diversity would be socially counterproductive. The role of policy should be instead to diminish language barriers between groups (through the education system, for example). Others have emphasized the benefits of diversity, and potential drawbacks of “homophily” (or the tendency to want to associate only with people like oneself). One powerful argument is that similar people will tend to have similar information and perspectives, and if people only interact with people like themselves, lots of valuable information will be not transmitted across groups. Arguments along these lines have been made, more or less formally, in the human resources and management literature. More formally, Golub and Jackson (2012) show that, when agents in a network prefer to associate with those having similar traits (homophily), it may take a very long time for participants in a network to converge to a consensus. Ultimately, there is thus a trade-off between the cost of communication and collaboration and the benefits of diverse viewpoints, which means that diversity (and hence homophily) may in theory hurt or improve productivity (Hamilton et al. 2003; Alesina and La Ferrara 2005). While there is a large nonexperimental literature on the impact on diversity on public good provision35 and a sizable lab experimental literature,36 the field experimental literature is more nascent. There are nevertheless a few interesting recent papers that we review below. Hoogendoorn and Van Praag (2012) and Hoogendoorn, Oosterbeek, and Van Praag (2013) experimentally varied the composition of teams of undergraduate 35 36
See Alesina and Ferrara (2005) for a review of the literature on diversity. For example, Woolley et al. (2010) and Engel et al. (2014).
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student required to start a business venture as part of a class. In teams of 12, students start up, sell stock, and run a real company with a profit objective and shareholders for a year. They ran the experiment on 45 teams and 550 students. The composition of teams was varied by gender (men only, women only, or mixed) and ethnicity (the fraction of non-Dutch ethnicity varied from 20% to 90%). Students then had a year to choose their venture, elect officers, conduct meetings, produce, sell, make money, and liquidate. This was a field experiment; the program played out over a year, and the incentives to do well were very high. Students’ ability to graduate, their grades, and potentially some money were all on the line. There is a clear benefit to gender diversity in the experiment. The performance as a function of the share of women in the team is inverse U-shaped, with the peak reached when the share of women is approximately 0.55. The authors attribute this effect to greater monitoring in gender-diverse groups. This in itself is an interesting finding as this is not a mechanism that is emphasized in the theoretical literature: Perhaps when communication is too easy, workers become more complacent. For ethnic diversity, the result is more subtle: Hoogendoorn, Oosterbeek, and Van Praag find that the marginal effect of increasing diversity on performance is zero or perhaps even negative when the teams are at least 50% Dutch. However, once the teams are less than 50% Dutch, further increases in the share of other groups are associated with better performance. The authors also identify evidence for the different channels proposed in the theoretical literature (including not only higher communication costs in more diverse groups, but also more diverse knowledge in more diverse teams), but these results are not extremely precise. Hjort (2013) analyzes a natural experiment where a flower firm in Kenya randomly assigned workers to teams. Kenya offers a context with heightened ethnic tensions, and where the level of distrust among different groups may be particularly high. In the experiment, an upstream worker distributed flowers to a team of two downstream workers. The upstream worker earned w per flower packed, and the downstream worker 2w per flower packed. Hjort finds that, conditional on productivity, upstream workers distribute fewer flowers to teams when one or both are not from his ethnic group, at the cost of lower wages for him, and lower production overall. Furthermore, within mixed teams, upstream workers give more flowers to the worker from their same ethnic group. Interestingly, the output gap between homogenous and ethnically mixed teams doubled during the period in 2008 when ethnic conflict intensified. In response to this, the firm introduced team pay for the downstream workers (not randomized) and subsequently experienced an increase in the productivity of the ethnically mixed teams. Also in Kenya, Marx, Pons, and Suri (2015) randomly assigned enumerators to pairs, and each pair to a supervisor. The job of the enumerator was to make contact with a household and administer an intervention. They find that homogenous pairs have higher productivity, and they attribute that to higher trust in those teams. However, when a pair is further matched with a supervisor of the same ethnic group, the productivity is lower (not higher). The contrast between the (negative) impact of diversity in horizontal teams and the (positive) impact in vertical relationships in the Marx, Pons, and Suri (2015) experiment hints at a different potential
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negative impact of discrimination: In-group preference may create room for cheating and corruption. In their setting, the coethnic supervisor was willing to let the enumerators cheat.
Discrimination and Corruption Prendergast and Topel (1996) provide a theoretical analysis of the influence of favoritism on optimal compensation and extent of authority for the manager. The point extends further than the firm: Discrimination may lead to misallocation of resources by politicians (to members of their ethnic group), or conversely to willingness to put up with corrupt or incompetent politicians from one’s own group (rather than less corrupt ones from another group) (Key 1949; Padro i Miguel 2007). More generally, voters’ preferences for a group may diminish the role of issues in campaign, and by implication the quality of government (Dickson and Scheve 2006). Besley et al. (2013) provide nonexperimental evidence of this effect: They show that in Sweden, after the social democratic party imposed gender balance by requiring that all candidates be selected in a “zipper” pattern (one man/one woman), the quality of the male candidates greatly increased (they call this the “crisis of the mediocre man”). Experimental evidence of the link between homophily and the quality of politicians or corruption level is rare. One interesting experiment took place in Uttar Pradesh, India’s most populous state, where the rise in caste-based politics has been accompanied by a staggering criminalization of politics (Banerjee et al. 2010). On the eve of the 2007 election, 206 of the sitting members of the legislative assemblies had a criminal case pending against them (Banerjee and Pande 2009). Prior to the 2007 election, the authors conducted a field experiment in which villages were randomly selected to receive nonpartisan voter mobilization campaigns (street plays, puppet shows, or discussions). One type of campaign encouraged citizens to vote on issues not of caste, while the other encouraged them to not vote for a corrupt candidate. They found that the caste campaign led to a reduction of the (reported) votes on caste, and to a reduction in the vote share going to candidates with a criminal record. It thus seems that successfully reducing discrimination (in this case, to be more precise, reducing lower caste group members’ tendency to systematically discriminate against higher caste candidates) does lead to an improvement in the quality of elected leaders. The natural experiment in India discussed in section “Discrimination in Politics and Inequality Across Groups” that introduced quotas for women in politics shed interesting light on this question as well. In the short run at least, reservation for women politicians reduced bribe taking (Beaman et al. 2010). Of course, in the short run, quotas do not increase competition (since on the contrary the pool is reduced to women only, whereas it was initially open to women and men) and the observed reduction of corruption could be due to inherent characteristics of women, or to their lack of experience. However, quotas do tend to increase political competition in the medium run, because once a woman leaves office and her seat is open, she (or her relative) has the option to run again, but the field is now more open to competition
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than if she were a traditional incumbent. Moreover, when Banerjee et al. (2013) collected data on what happens in previously reserved places, they found that female incumbents whose seats became free were less likely to run than male incumbents whose seat became free, but that this effect disappeared when they considered not only the incumbent, but also the incumbent and their family. In other words, the probability to elect someone from the incumbent’s dynasty remains the same in places that just have experienced a quota or not. Also, they found that the probability of reelection of someone from the incumbent’s family is more sensitive to past performance in the previously reserved places. Thus, the best politicians’ dynasties are more likely to be reelected after reservation, and the worst ones less likely to. To the extent this effect persists, it does suggest that policies that constrain voters to vote outside of their “comfort” zone may improve the quality of the decision-making process overall even after these constraints are lifted.
Law of Small Numbers Even if discrimination does not lead to outright corruption, it may restrict the pool of available candidates. Research shows that the leader quality matters both for firms (Bertrand and Schoar 2003) and for countries (Jones and Olken 2005). If discrimination implies that leaders have to be selected from a relatively small pool, it reduces the chance that the most talented person will be picked, and it thus may have negative productivity consequences. The empirical evidence (even nonrandomized) on any such consequence of discrimination is thin at best: Ahern and Dittmar (2012) and Matsa and Miller (2013) examine the impact of the Norway 2006 law which mandated a gender quota in corporate board seats. They both find negative consequences on profitability and stock prices. However, these are short-run impacts. It could be that women are temporally less effective because they are less experienced, or that they maximize something else other than short-run shareholder value, which may turn out to be profitable in the long run. Unfortunately, we do not see an experiment on this, nor can we think of an obvious design for one. But this would be a very intriguing avenue for further research.
What Affects Discrimination? Leaders and Role Models One effect of discrimination against a group is that few leaders emerge from it into the mainstream. This has potentially three consequences. First, mechanically, fewer people from this group are in a position to make a decision regarding others. To the extent that leaders discriminate against members from other groups, discrimination will persist. Second, the majority group may be reinforced in their belief that the minority group is incapable of success, since they have rarely, if ever, observed success of the minority in practice. And third, members of the minority group may
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then feel that either they are incapable of succeeding, or that the world is rigged against them, and there is no point in even trying.37 Given all this, discrimination could be lessened by forcing exposure to leaders from groups that are traditionally discriminated against, which is often achieved through quotas. This section reviews the evidence for this and also points out the gaps.
Does Diversity in Leadership Positions Directly Affect Discrimination? Mechanically, discrimination may breed discrimination, because the decisionmaking power is concentrated with the majority group. For example, if managers are mostly males, they may tend to favor other males in their recruiting or promotion decisions. This may happen because they themselves discriminate (consciously or unconsciously) or because they know more males and are more likely to promote or hire people they know or who are more similar to them.38 This tendency is part of the rationale for requiring a certain fraction of women on corporate boards, in academia, or in appointment, evaluation, and promotion committees. It is, however, not obvious that minority group leaders, or committees that contain such minority leaders, would necessarily favor others from the minority: Faced with their own discrimination, they may feel the need to go to lengths to avoid being perceived as biased. In several observational studies, women were not inclined to judge other women more favorably than men.39 In group decisions, there may also be a response of other members of the committee, who may try to “undo” any agenda they perceive (rightly or wrongly) the minority group members to have. The empirical evidence of the impact of minority representation on selection committees largely comes from a series of very interesting papers by Bagues, Zinovyeva, and their coauthors. Bagues and Esteve-Volart (2010) examine the impact of the gender composition of the evaluation committee for the entry exam in the Spanish judiciary on the success of women in that exam. A causal study is made possible by the fact that people are randomly assigned to a committee. They find that women are less likely to succeed at the exam when the committee they are assigned to has more women, while the opposite is true for male candidates. Additional evidence in the study suggest that these results might be driven at least in part by the fact that female evaluators tend to overestimate the quality of male candidates. Zinovyeva and Bagues (2011) and Bagues, Sylos-Labini, and Zinovyeva (2014) present interesting evidence from randomized academic evaluation committees in Spain and Italy, respectively. In both countries, candidates for promotion appear in 37
See Lockwood and Kunda (1997). Bagues and Perez-Villadoniga (2012) provide evidence from entry exams into the judiciary in Spain that support the latter effect; Bagues and Zinovyeva (2015) show that the former effect also applies in the case of academic promotions. 39 See Booth and Leigh (2010) for an audit study in Australia, where they find no interaction between the gender on the résumé and the gender of the recruiter. See also Broder (1993) for similar evidence in the context of NSF proposal reviews, and Abrevaya and Hamermesh (2012) for referee reports. 38
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front of a centralized committee to be qualified. Files are assigned to randomly composed committees. In the Spanish case, the authors find no impact of an additional female committee member on the promotion likelihood of female candidates. In the Italian case, they find a negative effect: In a five-member committee, with each additional female member added to the committee, the success rate of female applicants relative to that of male applicants decreases by around two percentage points. Analyzing the voting records, they find that both (1) the same female candidate is scored on more harshly by females than by males, and (2) male committee members grade female candidates more harshly when there are women on the committee, perhaps because they are trying to compensate for a perceived bias in favor of women on such committees (even though in reality the opposite appears to be true given (1)). This evidence on academic and recruitment committees is striking and suggests that some type of affirmative action may in fact hurt promising female candidates. It would be interesting to see if it also carries through in other settings, such as management or political decisions. Bursell (2007) is an audit study that makes some progress in this direction (although the specific comparison it focuses on is itself not experimental). He sent 3552 applications to 1776 jobs in Sweden, including applications to more skilled positions, such as senior/high school teachers, IT-professionals, economists, and engineers, and compares, among other things, the callback rates for applicants with Swedish-sounding and non-Swedish-sounding names according to the name of the CEO. He finds, consistent with the evidence above, that when the “CEO of a company has a foreign sounding name, the applicants with a Swedish sounding name have a 2.4 times higher probability to receive a call-back. If the CEO has a Swedish sounding name, the probability is 1.7 times higher” (Bursell 2007).
Minority Leaders and the Attitude of the Majority Even if there is no direct effect of having women or minority members in on leadership decisions, it could still affect discrimination against the minority because those minority individuals in leadership positions will change the beliefs, or precision of the beliefs, of the majority about the competence of the minority group. In a working paper version of Beaman et al. (2009), the authors propose a model where taste and statistical discrimination reinforce each other. Suppose that there are strong tastes (or social norms) against having a female leader. Then, it is very likely that citizens have never observed a female leader in action. This makes female leaders riskier as a group: Even if citizens believe that female leaders are equally competent on average, they have much more precise priors about male leaders, and to the extent they are risk averse, this will lead them to avoid women leaders. This is of course reinforced if citizens start with the prior that women are less competent: They will never have the occasion to find out that in fact they are wrong. In this world, forcing exposure to minority leaders (political leaders, board members, colleagues in academic departments, students at top colleges, etc.) will have a persistent negative impact on discrimination, even if it does not affect the underlying
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taste for the community, simply by affecting beliefs about the competence of the minority group.40 The impact might be reinforced if the image of what constitutes a good leader also evolves in response to what people see. Eagly and Karau’s (2002) “role incongruity” theory stipulates that one reason why people prefer male leaders is that the traits associated with leadership (strength, assertiveness) are not traits that are associated with women under prescriptive gender norms (such as being nice, accommodating, etc.). Yet, as people get to see many (not just one or two token) women leaders who are strong, but also nice, or who have an effective, but also more accommodating, leadership style, they may change their attitude toward female leaders. As inconsistencies between the female gender stereotype and qualities associated with leadership diminish, so will prejudice toward female leaders. Of course, a potential force in the opposite direction is the possibility of backlash against minority leaders, in particular if there is a perception that they got into their leadership role because of special treatment (Coate and Loury 1993). Beaman et al. (2010) study a natural experiment in the context of local electoral politics in India and are able to provide more evidence for the mechanism underlying the persistent effect of temporary affirmative action policies. In a context where local village councils are randomly selected, by rotation, to be forced to elect female leaders, the authors show that after a cycle of reservation (and even more when the same place happened to be reserved for a woman for two cycles in a row), more women run, and are elected, on unreserved seats. While there could be many reasons for this (including the fact that women may have become more willing to run, or that networks of women may have been created), they provide evidence that it is probably at least in part due to a change in attitude in the villages that were previously subjected to reservation. They collect evidence on attitudes in various ways: with a Goldberg-type experiment, and with two IATs, one for like or dislike for female leader (a more “hardwired” attitude that their model takes no stance on) and one for a stereotype associating women with domestic activities and men with leadership activities (in the spirit of the assessing whether “role incongruity” effects diminished). They find that the experience with the past quota does not affect preferences (as measured by the taste IAT), although it tends to harden stated preferences against women in leadership. However, citizens (particularly men) update on measures of perception of women’s competence. For example, their rating of a speech pronounced in female voice converges to that of a speech given by a male voice if they have been exposed to a quota either in this cycle or in the previous cycle. Moreover, the stereotypical IAT also shows a decrease of the stereotype that associates women with domestic activity rather than with leadership. This provides reasonably strong field evidence that exposure to role models from another group In an observational study, Miller (2014) finds evidence consistent with such effects in the context of affirmative action programs in the USA. US government contractors are forced to hire minority workers. Miller finds that, after an establishment is no longer subject to such affirmative action because it stops being a government contractor, the black employment share nevertheless continues to grow.
40
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affect attitudes. The evidence seems quite robust: Bhavnani (2009) also finds that women continue to be more likely to be elected after a seat was reserved for a while (in urban Maharashtra). Banerjee et al. (2013) also find, in Rajasthan, that women are more likely to run (and win) on a previously reserved seat. Although there is a vast laboratory literature that test the role-incongruity theory and its implications, and laboratory studies that show, for example, that college students asked to screen candidates for a typical male job (e.g., finance manager trainee) are less likely to discriminate against a female résumé after reading an editorial documenting women’s success in this type of job (Heilman and Martell 1986), we are not aware of field experiments that investigate these types of effect in other contexts (e.g., exposure to a female or black manager, minority teachers, etc.). It would be valuable to establish whether such impact on the majority’s attitude can also be documented in some of these other contexts.
Role Models, Aspirations, and the Attitude of the Minority Leaders issued from disadvantaged groups, in addition to their direct decisionmaking power and to the effect they may have on the opinion of the majority, could also serve as role models and trailblazers. They might affect the attitudes of the minority group about their own ability to succeed, or their aspirations to do so. Seeing successful women or blacks may lessen stereotype threat (as discussed in section “Minority Leaders and the Attitude of the Majority”), or the belief among those groups that society is rigged against them so there is no point in trying anyway. In both cases, exposure to role models may increase effort and lead to better outcomes for the minority, even without direct changes in the majority attitude (though this could of course trigger subsequent change in the majority’s beliefs and attitudes as well). However, as noted by Lockwood and Kunda (1997), these positive effects might be moderated by how fixed minority group members view their ability, and how personally relevant and attainable they consider the achievement of the role models. As in the case of the impact of exposure on the attitudes of the majority, there is both a descriptive and a laboratory literature on this question. The observational literature looks for correlation between outcomes (e.g., teen pregnancy) and measure of stereotyping (e.g., IAT) to naturally occurring exposure (e.g., black teachers). For example, Dasgupta and Asgari (2004) show that women who have been exposed to female teachers and role models are more likely to associate women and leadership. A laboratory experiment literature explores the extent to which exposure to stereotypically feminine role model in a science, technology, engineering, and math (STEM) career (Betz and Sekaquaptewa 2012) or exposure to a nonstereotypical computer science role model (Cheryan et al. 2011) increases the likelihood that girls will present themselves as interested in STEM. Interestingly, and maybe in line with the cautionary note in Lockwood and Kunda (1997), these studies show that a role model that simply belongs to the minority group might not be sufficient, and that the “type” of role model appears to matter significantly. Cheryan et al. (2011) find that exposure to a nonstereotypical computer science role model (e.g., someone who dresses fashionably, enjoys sports and hanging out
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with friends, and watches “normal” TV shows, such as The Office) through a “getting to know each other” task in the lab increases female subjects’ beliefs of succeeding in the field, and that this is true regardless of the gender of that role model. Similarly to the discussion in section “Identity and Preferences” on “social identity,” the authors argue that this is because women feel the stereotypical characteristics of a computer science major (e.g. social isolation, obsession with computers, and social awkwardness) do not align with what they see as their female gender role (e.g., helping others, having social skills, and attending to physical appearance), and a nonstereotypical role model, whether man or woman, can thus influence young girls’ preferences and beliefs. On the other hand, Betz and Sekaquaptewa (2012) find that counterstereotypic-yet-feminine STEM role models (as signaled by characteristics such as wears makeup and pink clothes, enjoys reading fashion magazines, etc.) discourage middle school girls’ success expectations in STEM relative to gender-neutral STEM role models (as signaled by characteristics such as wears dark-colored clothes and glasses, enjoys reading books, etc.). The authors find that this is particularly the case for girls who did not identify with STEM subjects and conclude that this subgroup of girls viewed the combination of both STEM-success and femininity as unattainable. Reviewing either literature fully is outside the scope of this chapter, but the mixed results of the lab experiments provide interesting directions for field research. Here again, however, the field experiment work so far appears to be quite limited. Beaman et al. (2012) study the same randomized natural experiment for women in leadership positions in India and look at the impact on girls’ educational attainment and career aspirations. They give evidence of impact on parents’ hopes for daughters. Compared to never-reserved villages, parents in reserved villages were more likely to state that they would like their girl to graduate or study beyond the secondary school level, and more likely to state that they would like their daughter to have a career. Parental aspirations for boys did not change. Furthermore, in villages with reservation, girls were more likely to stay in middle school, which cannot be directly attributed to any direct action of the leader because middle schools are not under their jurisdiction. This is therefore strongly suggestive of a causality running from role model to change in aspirations and actual change in behavior. Overall, this literature seems to us surprisingly thin, compared to the larger literature on “horizontal exposure” (e.g., roommates or classmates) which we discuss below in section “Intergroup Contact.” Part of the explanation for this is practical: There is probably more naturally occurring variation in peer groups than in supervisors, leaders, or teachers. Another issue is that, in many settings, female teachers or leaders may take actions that can translate directly into behavioral changes for female students (or trainees) even absent any effect on aspirations, so that isolating the impact on minority aspirations is tricky. While this was not the case in the quota experiment in India, it could have been. Nevertheless, we suspect that the lack of more field studies in this area is also a reflection of too little attention devoted to this important and exciting topic, and that much more probably can be done to explore how exposure to role models affects minority groups’ aspirations.
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Intergroup Contact Allport (1954) is often credited with the development of the contact hypothesis, also known as Intergroup Contact Theory. The premise of Allport’s theory is that, under appropriate conditions, interpersonal contact is one of the most effective ways to reduce prejudice. The theory, which was originally devised for encounters across racial and ethnic lines, states that if majority group members have the opportunity to communicate with minority group members, they are able to understand and appreciate them. As a result of this new appreciation and understanding, prejudice should diminish. Allport’s proposal was that properly managed contact between the groups should reduce prejudice and lead to better interactions. In particular, Allport held that reduced prejudice will result when four features of the contact situation are present: equal status between the groups in the situation, common goals, intergroup cooperation, and the support of authorities, law, or custom. Much of the psychology literature on the contact hypothesis has focused on lab experiments that have helped refine Allport’s original theory. An unresolved issue in psychology is whether specific conditions for the contact situation are needed to ensure that contact will have the theorized effect. For example, is it important for the contact to take place in a cooperative environment with peers of equal status (e.g., two roommates in a university dorm working together on a math homework) for contact to be effective at reducing discrimination? In a meta-analysis combining observational and experimental studies of the intergroup contact theory, Pettigrew and Tropp (2000) find that intergroup contact reduces prejudice in 94% of the 515 studies reviewed. Their meta-analysis also suggests that the contact effect generalizes to a broad range of minority groups (not just racial and ethnic minorities but also the elderly, the mentally ill, LGBT, etc.) as well as a broad range of contact settings (schools, homes, etc.). Pettigrew and Tropp (2000) also assess whether the optimal conditions for contact stated by Allport are necessary for positive contact outcomes. They find that the inverse relationship between contact and prejudice persists, though not as strongly, even when the contact situation is not structured to match Allport’s conditions. Hence, while Allport’s conditions may not be necessary for prejudice reduction, some combinations of them might be relevant. Psychologists are still debating and investigating the specific negative factors that may prevent intergroup contact from diminishing prejudice. While much of the experimental research on the contact hypothesis has taken place in the lab, there have also been quite a few field experiments. Green et al. (forthcoming) identify 56 field experiments. The best known field work within economics has focused on contact between college roommates. Sacerdote (2000) was the first to exploit the random assignment of roommates at college for a study of peer effects on test scores. More relevant to us, Boisjoly et al. (2006) leveraged random roommate assignment at Harvard to study the impact of shared experiences at college on opinions about the appropriateness of keeping affirmative action policies. They find that white students who are randomly assigned African-American roommates are significantly more likely to endorse affirmative action. Hence, mixing with African-Americans tends to make individuals
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more empathetic to them. They also find that white students who were assigned roommates from any minority group are more likely to continue to interact socially with members of other ethnic groups after their first year. What remains unclear from Boisjoly et al. (2006) is whether contact to a minority roommate reduced stereotype or bias. Empathy might increase even if bias is unaffected. Burns, Corno, and La Ferrara (2015) leveraged the same design to get at this question, and hence their paper is closest to a field test of the contact hypothesis. Specifically, they exploit random assignments of roommates in double rooms at the University of Cape Town to investigate whether having a roommate of a different race affects inter-ethnic attitudes, but also cooperative behavior and academic performance. They find that living with a roommate from a different race significantly reduces prejudice toward members of that group, as measured by an IAT. The reduction in stereotype is accompanied by a more general tendency to cooperate, as measured in a Prisoner’s dilemma game, but smaller effects on trust, as measured in a trust game. The paper also reports interesting results on grades. Black students that are assigned a nonblack roommate experience higher GPAs; white students that are assigned a nonwhite roommate experience lower GPAs. Related findings are reported in Shook and Fazio (2008). Participants were white freshmen who had been randomly assigned to either a white or an African-American roommate in a university college dormitory system. Students participated in two sessions during the first two and the last 2 weeks of their first quarter on campus. During these sessions, they answered questions about their satisfaction and involvement with their roommates and completed an inventory of intergroup anxiety, as well as an IAT test. Automatically activated racial attitudes (as measured with the IAT) and intergroup anxiety improved over time among students in interracial rooms, but not among students in same-race rooms. However, participants in interracial rooms reported less satisfaction and less involvement with their roommates than did participants in same-race rooms. Several field experiments in psychology have also examined contact effects between classmates. In particular, psychologists have looked at cooperative learning techniques – which are designed so that students must teach to one another and learn from one another and place a strong emphasis on the academic learning success of each member of the group – and tested whether these help reduce prejudice (Johnson and Johnson 1989; Slavin 1995). The rational is that because cooperative learning encourages positive social interactions among students of diverse racial and ethnic backgrounds, it creates some of the conditions hypothesized in Allport as beneficial to reducing discrimination: as students work cooperatively, they have the opportunity to judge each other on merits rather than stereotypes. Slavin and Cooper (1999) provide a review of the field evidence on cooperative learning, which has been generally supportive of cooperative learning being a useful tool to improve intergroup relations. For example, Slavin (1977) and Slavin (1979) study one particular approach to cooperative learning, called Student Teams Achievement Divisions. Under this approach, the teacher presents a lesson, and students then study worksheets in four-member teams. Following this, students take individual quizzes, and team
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scores are computed based on the degree to which each student has improved over his or her own past record. The team scores are published in newsletters. He finds that the students who had experienced such cooperative learning over periods of 10 to 12 weeks gained more cross-racial friendships than did control students. In a follow-up one year later, Slavin (1979) found that the students that experienced cooperative learning named an average of 2.4 friends outside their own race, compared to an average of less than one in the control group. Also, Slavin and Oickle (1981) found significant gains in Whites’ friendships toward AfricanAmericans as a consequence of using the same cooperative learning method, but, interestingly, no difference in African-American friendships toward Whites. A recent paper in economics also brings the contact hypothesis to the classroom, but under conditions that are not tailored to be as optimal as possible for prejudice reduction to occur, at least as hypothesized by Allport. Starting in 2007, some elite private schools in Delhi were required to offer places to poor students. Rao (2013) exploits this policy change and uses a combination of experimental and administrative data to study whether exposure of rich students (from 14 private schools in New Delhi) to poorer students affects (1) tastes for socially interacting with or discriminating against the poor, (2) generosity and prosocial behavior, and (3) learning and classroom behavior. Core to his identification strategy is a comparison of outcomes for treated and non-treated student cohorts within a school. Rao also exploits a second identification strategy that is closer to a randomized design. Some schools in his sample used the alphabetic order of first name to assign students to study groups and study partners. Hence, in those schools, the number of poor children with names similar to a given rich student provides plausibly exogenous variation in personal interactions with a poor student. This second identification strategy is obviously more appealing as a test of the contact hypothesis, because it focuses more centrally on changes in personal interactions between students, and rules out other confounds (such as changes in teacher behavior, changes in the curriculum, etc.). Rao (2013) finds that economically diverse classrooms cause wealthy students to discriminate less against other poor children outside school. As discussed in section “Willingness to Pay,” Rao’s approach to measure discrimination is quite unique. First, he relies on a field experiment in which rich participants select teammates for a relay race and are forced to reveal how they trade-off more-athletic poor students versus less-athletic rich students. Using this measure of discrimination, Rao finds that exposure to poor students at school reduces discrimination by 12 percentage points. Rao also conducts a second field experiment. He invites students to attend a play date at a school for poor students, and elicit incentivized measures of their willingness to accept. He finds that having poor classmates makes students more willing to attend the play dates with poor children. In particular, it reduced the average size of the incentive that is required to attend the play date by 19%. Having a poor study partner (e.g. contact alone) explains 70% of the increase in this “willingness to play.” When Rao (2013) turns to how exposure to poor students affects pro-social behavior and learning in the classroom, he finds that having poor classmates makes students more prosocial, as measured by their history of volunteering for
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charitable causes at school, as well as their behavior in dictator games conducted in the lab. The findings reveal that exposure to poor students does not just make rich students more charitable towards the poor; instead, it affects generosity and notions of fairness more generally. Finally, Rao shows that exposure to poor classmates has limited effects on the wealthy students’ test scores: while he detects marginally significant but meaningful decreases in rich students’ English test scores, he finds no effects on Hindi or Math scores, or on a combined index over all subjects. The studies reviewed above suggest that that inter-group contact is an effective tool to reduce prejudice, even though more work remains to be done to ascertain the specific conditions under which contact will be most effective. Yet, some recent work in psychology (Dixon et al. 2012) suggests new angles through which the contact hypothesis should be evaluated, and more specifically, the possibility that its impact on the ultimate goal of achieving a more inclusive society might be less obvious than what would immediately appear. One of the observations made under this new line of work is that prior research on the contact hypothesis has paid little attention on how the minority group reacts to contact, with the focus being on how contact changes prejudice level among majority group members. In this context, Dixon et al. (2012) mention a few observational studies suggesting that, while majority group members may demand more social change towards inclusiveness subsequent to inter-group contact, minority group members may actually become less demanding of social change, as they perceive that discrimination and social injustice have lessened. A few recent studies (Saguy et al. 2009; Dovidio et al. 2009; Glasford and Calcagno 2012) provide lab results consistent with this observational data, with minority group members under the contact condition appearing lulled into believing that the majority group is more just-minded than it really is. If these effects are real, one can easily imagine how contact may backfire at the societal level, with the theoretically more powerful advocates for the minority group (for example, African-Americans at Ivy League Universities experiencing positive inter-group contact) decreasing their level of political activism. At the very least, this provocative new research in psychology suggests that future field work on the inter-group contact hypothesis should be more systematic in collecting evidence on how minority group members react to contact, and broadening the definition of a successful intervention outcome.
Socio-Cognitive De-Biasing Strategies In the absence of direct contact, is it possible to teach individuals to become less biased against the minority group? We start with the discussion of a field experiment in Rajasthan, India, which offers a cautionary tale about how easy it might be to simply tell people to overcome their stereotypes. Banerjee et al. (2013) set up a large-scale randomized experiment designed to test whether citizens can learn from others’ experiences about the quality of female leaders. This is an environment where, we have already shown, there is a large bias against the ability of women to be decision-makers. Using high-quality
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street theater troupes, they set up a street play followed by a discussion of the performance of local leaders a few weeks ahead of the 2010 panchayat (local government) election. Following up on the work we discussed previously in section “Minority Leaders and the Attitude of the Majority,” which showed that direct experience with a female leader does change attitude towards female leaders (and willingness to vote for one), this study sought to test whether the process could be accelerated by providing citizens objective information that, in fact, women and men are about equally good at carrying out a key task in the local government. The experiment took place in 382 panchayats: in randomly selected ones, a street play emphasized the importance of the local leader in making key decisions, and encouraged citizens to vote for a competent leader. It then showed information on the average performance of all leaders in providing employment under the flagship employment guarantee scheme. In another group of villages, the play and the information was almost the same, but the script of the play emphasized the fact that citizens are often biased against women leaders, but that women also can be good leaders. The statistics provided on leader performance were also disaggregated by gender (as it turns out, women do about as well as men in the sample districts). There are two main results: First, the play and information campaign, when it does not emphasize gender, does appear to move priors. More candidates enter and the incumbent is less likely to enter and to win. For example, the incumbent vote share declines by 6 percentage points (or a remarkable 60%) in villages where the general campaign was run. Moreover, the vote share for the incumbent become more sensitive to past performance in places where the gender-neutral campaign was run. Second, however, these effects disappear in places where the campaign introduces the “gender” theme: in those villages, there is very little effect of the intervention on any outcomes (including on the probability that a female runs or wins, or on the vote share for women). It is as if, when citizens understood that the campaign was about convincing them to consider women, they lost interest. These findings underscore the challenge of fighting discrimination in an environment where discrimination is rife.41 It is possible that this experiment failed because it did not pay enough attention to the structure of the bias and ways to overcome it. Over the last 20 years, social psychologists have designed and tested in the laboratory setting a series of strategies to reduce bias and stereotypical thinking. These include (following the categorization in Paluck and Green (2009)): consciousness-raising, targeting emotions through perspective-taking, targeting value consistency and self-worth, expert opinion and accountability interventions, as well as re-, de- and cross-categorization techniques. Consciousness-raising strategies are inspired by the large body of work (in particular the IAT literature) suggesting that prejudice can operate without the
41
Note that the effect of reservation in this sample on the probability that a woman runs or wins after the reservation is cancelled is still positive, as in West Bengal or Mumbai: so the results are not due to the fact that people in Rajasthan are so hell bent against women that they cannot learn about them. It just appears they cannot learn about them from this intervention.
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person’s awareness or endorsement of it. The most promising consciousness-raising strategies emerging from the psychology literature to date include counter-stereotype training and approach-avoidance training. For example, in Kawakami et al. (2000), lab subjects received extensive training in negating specific stereotypical thinking towards elderly people and skinheads (young individuals with closed-cropped or shaven heads who typically wear heavy boots, are often part of the working-class, and stereotypically perceived as aggressive). In the elderly stereotype negation condition, subjects were instructed to respond “NO” on the trials in which they saw a picture of an elderly person paired with an elderly stereotypic trait and “YES” when they saw a picture of an elderly person with a non-stereotypic trait. In the skinhead stereotype negation condition, subjects were to respond “NO” on trials in which they saw a picture of a skinhead paired with a skinhead stereotypic trait and “YES” on trials in which they saw a picture of a skinhead paired with a non-stereotypic trait. Kawakami et al. (2000) show that such training in negating stereotypes was able to reduce the stereotypical activation. These results were obtained even when participants were no longer instructed to “not stereotype,” and, importantly, for stereotypic traits that were not directly involved in the negation training phase. This reduced activation level was still clearly visible 24 h following the training session.42 Dasgupta and Greenwald (2001) report on two experiments where they examined whether exposure to pictures of admired and disliked exemplars can reduce automatic preference for white over black Americans and younger over older people. In Experiment 1, participants were exposed to either admired black (e.g., Denzel Washington) and disliked white individuals (e.g., Jeffrey Dahmer), disliked black (e.g., Mike Tyson) and admired white (e.g., Tom Hanks) individuals, or nonracial exemplars. Immediately after exemplar exposure and 24 h later, they completed an IAT that assessed automatic racial attitudes and two explicit attitude measures. Exposure to admired black and disliked white exemplars significantly weakened automatic prowhite attitudes for 24 h beyond the treatment but did not affect explicit racial attitudes. Experiment 2 provided a replication using automatic age-related attitudes. Also, Wittenbrink, Judd, and Park (1997) examined the effects of watching videos of African-Americans situated either at a convivial outdoor barbecue or at a gang-related incident. Situating African-Americans in a positive setting produced lower implicit bias scores. Kawakami, Dovidio, and Van Kamp (2007a) perform another lab experiment on negating stereotypical associations but focus on outcomes that are closer to those we might wish to affect in the real world. Participants first underwent gender counterstereotype training, by pairing male faces with words like “sensitive” and female faces with words like “strong.” They next evaluated four applications (résumés and cover letters) for a position as “chairperson of a District Doctor’s Association.” All of the applicants were qualified, but two had male names and two had female names
42
Two follow-up studies outside of Kawakami’s lab have partially replicated but partially qualified the original findings. See Gawronski et al. (2008).
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(counterbalanced so that half the participants saw a particular résumé with a male name and the other half saw that same résumé with a female name). The evaluation of applicants involved two separate stages: judging the applicants along 16 different dimensions (eight stereotypically masculine traits like “risk-taker” and eight feminine traits like “helpful”) and then simply choosing the best candidate. Some participants made the trait judgments first and chose the best candidate second, while other participants completed the two tasks in the opposite order. Among participants who had received no training, only 35% chose a woman for the job. In contrast, among participants who had undergone counter-stereotype training, 61% chose a woman.43 Kawakami et al. (2007b) also found that participants can change their implicit biases and unreflective social behaviors through “approach-avoidance” conditioning. In this study, white and Asian participants repeatedly pulled a joystick toward themselves when they saw black faces and pushed it away when they saw white faces. In pulling the joystick in, it was as if participants were bringing the perceived image closer, or “approaching” it. This training significantly reduced participants’ implicit bias on the IAT. The same “approach-avoidance” conditioning training has also been shown to be promising (in the lab) to deal with the stereotype threat. Kawakami et al. (2008) report the beneficial effects for female undergraduates of repeatedly “approaching” math-related images (“e.g., calculators, equations”). After the training, those who initially reported that they did not like math and were not good at it tended to identify with and prefer math on implicit measures, as well as to answer more questions on a math test. In a series of follow-up studies, Forbes and Schmader (2010) replicated Kawakami et al. (2008), but built a longer delay (24–30 h) between the de-biasing training and the math test, and also compared the relative effectiveness of approach-avoidance training with counter-stereotype training. They found that gender-math counter-stereotype training seemed more effective than approach-avoidance training. Women trained to associate the phrase “women are good at” with math-related words exhibited increased working memory as well as improved performance on math questions from the GRE (a graduate-level standardized test). Because emotional states can influence the expression of prejudice, psychologists have hypothesized that interventions that encourage the perceiver to experience the emotions of the minority group might be effective de-biasing strategies. What does it feel like to have your intelligence automatically questioned, or to be trailed by
43
Interestingly, these effects were only observed when the task of choosing the best candidate came second, after the trait evaluation. When this choice task was first, only 37% of those who had undergone the training chose a female candidate. A similar pattern emerged when the order of the tasks was switched, in that participants were consistently biased on the first task and de-biased on the second, regardless of which task actually came first. One possible explanation for this effect is that participants seem to recognize that the researchers are trying to debias them, and then try to correct for this perceived influence by deliberately responding in more stereotypical ways, at least at first. Once they have an opportunity to explicitly counteract the debiasing, they stop trying to resist the training and then the effects emerge. Subsequently, they respond in counterstereotypical ways.
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detectives each time you walk into a store? Perspective-taking involves stepping into the shoes of a stereotyped person, and can be useful in assessing the emotional impact on individuals who are constantly being stereotyped in negative ways. There are now multiple studies attesting to the merits of perspective taking as a strategy for reducing intergroup bias. Some have linked perspective-taking to decreased activation and application of negative group stereotypes (Galinsky and Moskowitz 2000; Todd et al. 2011); others have shown that adopting the perspective of a particular out-group target leads to more positive evaluations of other individual members of the target’s group (Shih et al. 2009) and of the target’s group as a whole (Stephan and Finlay 1999). For example, Todd et al. (2011) conducted a series of lab experiments examining the impact of perspective-taking on several outcomes: automatic evaluations, approach-avoidance reactions, and behaviors displayed during face-to-face interactions. In one of the experiments, participants watched a video depicting a series of discriminatory acts directed toward a black man versus a white man. As they watched the video, participants either adopted the black man’s perspective or they attempted to remain objective and detached (control group). The researchers included two different perspective-taking conditions in this experiment. Some participants tried to imagine the black man’s thoughts, feelings, and experiences (other condition) as they watched the video; others tried to imagine their own thoughts, feelings, and experiences as if they were in the black man’s situation (self condition). After watching the video, participants completed a variant of the IAT that assesses automatic evaluations of black relative to white Americans. Subjects in both of the perspective-taking conditions (other and self conditions) exhibited significantly weaker pro-white bias than the control subjects. Strategies targeting value-consistency and self-worth rely on the theory that individuals’ desire to maintain consistency between valued cognitions and behaviors or protect their self-worth may be leveraged to lead them to repress their prejudice (Paluck and Green 2009). De-biasing strategies in this area have leveraged cognitive dissonance and self-affirmation theories. For example, in a lab experiment, Leippe and Eisenstadt (1994) apply cognitive dissonance theory to get subjects to see prejudice as inconsistent with their own values: College students softened their antiblack positions on social policies and reported more egalitarian attitudes and beliefs after agreeing to write a public statement in favor of problack policies. Also, Fein and Spencer (1997) report that lab subjects who have “self-affirmed” by circling values that were most important to them were more likely to give positive ratings to a Jewish job candidate.44 A body of research in social psychology suggests that prejudice and discrimination might also be influenced by expert opinion and greater accountability to others for one’s beliefs and behaviors. Levy, Stroessner, and Dweck (1998) show that telling lab subjects that experts believe that personality is malleable reduces stereotyping against minority groups. Dobbs and Crano (2001) report that subjects allocated more points to a fictitious out-group when they were required to justify their 44
Note that participants who were Jewish were excluded from this part of the study.
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allocations to others; similarly, Bodenhausen, Kramer, and Süsser (1994) show that students involved in a school disciplinary case were less likely to stereotype the student if they believed they would be accountable to their peers for their evaluation of the case. Individuating is another socio-cognitive de-biasing strategy that involves gathering very specific information about a person’s background, tastes, hobbies, and family, so that one’s judgments will be based on the particulars of that person, rather than on group characteristics. This approach is grounded in the social identity and categorization literatures and essentially is a de-categorization effort, where subjects are instructed to focus on the individual rather than the group (Brewer 1988; Fiske and Neuberg 1990). Lebrecht et al. (2009) provide an interesting take on the individuation exercise. In their study, two groups of Caucasian subjects were exposed equally to the same African-American faces in a training protocol run over five sessions. In the individuation condition, subjects learned to discriminate between African-American faces; specifically, they received “expertise training” with other-race faces – defined by the authors as a procedure that improves observers’ ability to individuate objects within the training domain and hence reduce the degree to which other-race faces are stereotyped. In contrast, in the categorization condition, subjects learned to categorize faces as African-American or not. Subjects in the individuation condition, but not in the categorization condition, showed improved discrimination of African-American faces with training. Also, subjects in the individuation condition, but not the categorization condition, showed a reduction in their implicit racial bias. For the individuation condition only, the degree to which an individual subject’s implicit racial bias decreased was significantly correlated with the degree of improvement that the subject showed in their ability to differentiate African-American faces. Other de-biasing strategies inspired by the social identity and categorization literatures include re-categorization and crossed-categorization techniques, where participants are encouraged to think of people from different groups as part of one subordinate group using cues such as same shirt colors or shared prizes, or participants are made aware of their common membership in a third group. Such re-categorization and cross-categorization efforts have shown some success in reducing favoritism for the in-group and improving cooperation between groups (Dovidio and Gaertner 2000; Gaertner et al. 1999). An exciting recent study in the socio-cognitive de-biasing area is Lai et al. (2014), who sought to determine the effectiveness of various methods for reducing implicit bias. Structured as a research contest, teams of scholars were given 5 min in which to enact interventions that they believed would reduce implicit preferences for whites compared to blacks, as measured by an IAT, with the goal of attaining IAT scores that reflect a lack of implicit preference for either of the two groups. Teams submitted 18 interventions that were tested approximately two times across three studies, totaling 11,868 nonblack participants. Half of the interventions were effective at reducing the implicit bias that favors whites over blacks. Most effective were the following interventions: (1) participating in a sports game in which all of the teammates were black while the opposing team was all-white and engaged in unfair
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play, and being subsequently instructed to recall how their black teammates helped them while their white opponents did not; (2) reading a graphic story in which one is asked to place oneself in the role of the victim who is assaulted by a white man and rescued by a black man; (3) practicing an IAT with counterstereotypic Black (e.g., Michael Jordan, Martin Luther King, Jr.) and counterstereotypic White (e.g., Timothy McVeigh, Jeffrey Dahmer) exemplars. A concern one may have about the relevance of this lab evidence for the field is that it can only document fairly short-term effects (up to 24 h), and hence might be of limited relevance to the real world. However, even such a short time frame might be relevant to some important decisions that have been shown to be subject to bias, such as human resource managers’ decision on whether to pass on a given résumé, or teachers’ grading decisions. Therefore, we believe that even short-term effects could be of real-world relevance. What this lab evidence does not allow us to assess, however, is how these shortterm impacts would differ if the same person (e.g., an HR manager) was repeatedly exposed to such de-biasing strategies (e.g., every time he or she sits down to start reviewing résumés, or grading exams). Some other de-biasing work in psychology has taken seriously this concern about one-shot, short-term interventions and has asked whether related strategies can be built to produce enduring reductions in bias. Work by Devine and a series of co-authors is of particular interest. Devine (1989) proposes a habit-breaking approach to prejudice reduction and likens implicit biases to deeply entrenched habits developed through socialization experiences. “Breaking the habit” of implicit bias therefore requires learning about the contexts that activate the bias and how to replace the biased responses with responses that reflect one’s non-prejudiced goals. Devine and colleagues (Devine and Monteith 1993; Plant and Devine 2009) argue that the motivation to break the prejudice habit stems from two sources. First, people must be aware of their biases and they must also be concerned about the consequences of their biases before they will be motivated to exert effort to eliminate them. Second, people need to know when biased responses are likely to occur and how to replace those biased responses with ones more consistent with their goals. Devine et al. (2012) develop and test a longer-term intervention to help people reduce implicit biases and “break the prejudice habit.” The participants were 91 nonblack introductory psychology students, who completed a 12-week longitudinal study for course credit. The key elements of the intervention were as follows. First, to ensure awareness of their bias, all participants completed a measure of implicit bias and received feedback about their level of bias. People assigned to the treatment group were also presented with a bias education and training program, the goals of which were to evoke a general concern about implicit biases and train people to eliminate them. The program lasted 45 min. The education component likened the expression of implicit biases to a habit and provided information linking implicit bias to discriminatory behaviors across a wide range of settings (e.g., interpersonal, employment, and health). The training component described how to apply a variety of bias reduction strategies in daily life. The training section presented participants with a wide array of strategies (covering many of the
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strategies discussed below, such as taking the perspective of stigmatized others, imagining counterstereotypic examples, training in negating stereotypical associations and individuation) as well as opportunities to engage in positive interactions with members of the minority group (e.g., inter-group contact). This enabled participants to flexibly choose the strategies most applicable to different situations in their lives. Following the intervention, treated participants had lower IAT scores than control group participants at 4 and 8 weeks after the intervention; moreover, the effects at 4 and 8 weeks were not systematically different from each other, indicating that the reduction in implicit race bias persisted over time. These data provide the first evidence that a controlled, randomized intervention can produce enduring reductions in implicit bias. The intervention created no changes in the participants’ reported racial attitudes, but it did affect participants’ concern about discrimination and their awareness of their personal bias. Also, concerns about discrimination emerged as a moderator for the interventions’ effects. The intervention appears to have raised concerns about discrimination at week 2, and the biggest reduction in implicit bias in the treatment group was among those subjects who experienced growing concerns. Despite the large amount of both theoretical and lab-based work in psychology on these various socio-cognitive de-biasing techniques, it is remarkable how few evaluations of these techniques have been performed in the field. Paluck and Green (2009) perform a thorough literature search of the randomized field evidence on the de-biasing techniques listed above. While the number of field experiments they identify is non-trivial (71), much of the work they survey is not directly guided by the psychology literature or directory transposable into the specific lab-based strategies reviewed above. Moreover, very few of the existing field studies are designed to track changes in behavior outcome measures. The modal existing field study also involves a very short-term follow-up (often within the day) and takes place in a classroom setting with a student population, hence quite “lablike” even if not explicitly in the lab. By far most common have been interventions relying on various forms of entertainment (books, movies, cartoons, etc.) to create a persuasive narrative aimed at altering stereotypical thinking. In many cases though, the entertainment content is not based on the specific psychological theories that have guided the lab work, and it is hence difficult to make a direct link between the lab and the field evidence. For example, Paluck and Green (2009) identify several randomized field experiments performed in schools to measure the impact of reading on prejudice. This work suggests reduction in self-reported bias associated with reading content that portrays contact between children who are similar to the studied population and children of different race (e.g., intergroup friendship), as well as reading content that emphasizes a minority characters’ individual characteristics rather than group membership (e.g., individuating). But is also possible that reading interventions might be effective because of the emotional reaction they induce through perspective-taking (e.g., putting oneself in the shoes of the minority character in a book), or because they are a channel to communicate social norms (e.g., descriptions of what others are doing and hence what the reader should do).
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Paluck and Green (2009) also identify a few instruction-based (rather than narrative-based) field interventions. In this case again, though, the content of the interventions is rarely directly guided by the lab evidence, and a lack of theoretical foundations may explain in part a lack of impressive findings. One exception is Lustig (2003), which evaluates a training program in Israel that aims to encourage perspective-taking and empathy to reduce prejudice against Palestinians among Jewish twelfth graders. Lustig (2003) reports encouraging findings among Jewish students who were asked to write an essay about the Israeli-Palestinian conflict from the Palestinian viewpoint. The randomized field studies designed to directly test consciousness-raising, value consistency and self-worth, as well as re-, de- and cross-categorization techniques can essentially be counted on one hand.45 All have been performed on student populations and have produced mixed results. At the same time, we are confident that hundreds of anti-prejudice interventions directly inspired by the lab-based literature described above must be taking place yearly not only in schools but also in business and government settings, but are not being rigorously evaluated. It would be of first-order importance for researchers to strike up partnerships with organizations interested in better understanding the value of the diversity training programs they are investing resources in, both in terms of their immediate impact on bias and their ultimate impact on organizational performance. Human resource departments, police departments, and courtrooms are only a few of the possible real-world settings where a much-needed field validation of this large lab-based literature could be performed. For example, the U.S. Department of Justice is funding the development of a curriculum for police staff that reflects on the Fair and Impartial Policing perspective. This training program applies the modern science of bias to policing: it trains officers on the effect of implicit bias and gives them the information and skills they need to reduce and manage their biases. The curriculum addresses not just racial/ ethnic bias, but biases based on other factors such as gender, sexual orientation, religion, socio-economic status, etc. Officers are taught skills, inspired by the lab-tested methods described above to reduce and manage their own biases. Social psychologists from around the nation who conduct the research on human biases are members of the team that help design the curriculum. While this program has been implemented with various target audiences (recruits/patrol officers, first line supervisors, mid-level managers, command staff and law enforcement trainers), to our knowledge it has not been the subject of a rigorous evaluation. As another example, there has been much discussion in the recent years about how the socio-debiasing techniques described above could be used to de-bias judges and jurors. Kang et al. (2012) discuss possible ways to import these techniques to the courtroom. They argue that:
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See Houlette et al. (2004) for re-categorization, Rokeach (1971) and Rokeach (1973) for value consistency, Katz and Zalk (1978) and Katz (2000) for cognitive retraining, and Lustig (2003) for perspective-taking.
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In chambers and the courtroom buildings, photographs, posters, screen savers, pamphlets, and decorations ought to be used that bring to mind counter-typical exemplars or associations for participants in the trial process . . . for jurors, then, . . . the hope would be that by reminding them of counter-typical associations, we might momentarily activate different mental patterns while in the courthouse and reduce the impact of implicit biases on their decision-making.
Also, Elek and Agor (2014) show how de-biasing strategies could be feasibly brought to the courtroom with simple alterations to the standard instructions delivered by the judge to the jury, such as including a recognition of the universality of bias and explicit encouragement of perspective-taking.
Technological De-Biasing Stanovich and West’s (2000) distinction between System 1 and System 2 cognitive functioning provides a useful framework for organizing both what scholars have learned to date about effective strategies for improving decision-making and future efforts to uncover improvement strategies. System 1 refers to our intuitive system, which is typically fast, automatic, effortless, implicit, and emotional. System 2 refers to reasoning that is slower, conscious, effortful, explicit, and logical. People often lack important information regarding a decision, fail to notice available information, face time and cost constraints, and maintain a relatively small amount of information in their usable memory. The busier people are, the more they have on their minds, and the more time constraints they face, the more likely they will be to rely on System 1 thinking. In the many situations where we know that decision biases are likely to plague us (e.g., when evaluating diverse job candidates, estimating our percent contribution to a group project, choosing between spending and saving, etc.), relying exclusively on System 1 thinking is likely to lead us to make errors. Also, when the basis for judgment is somewhat vague (e.g., situations that call for discretion, cases that involve the application of new, unfamiliar laws, etc.), biased judgments are more likely. Without more explicit, concrete criteria for decisionmaking, individuals tend to disambiguate the situation using whatever information is most easily accessible – including stereotypes (Dovidio and Gaertner 2000; Johnson et al. 1995). Similarly, certain emotional states (anger, disgust) can exacerbate implicit bias in judgments of stigmatized group members, even if the source of the negative emotion has nothing to do with the current situation or with the issue of social groups or stereotypes more broadly (DeSteno et al. 2004; Dasgupta et al. 2009). Interestingly, and perhaps counterintuitively, happiness may also produce more stereotypic judgments, though the exact reasoning for this is unclear and the stereotypic judgements can be consciously controlled if the person is motivated to do so (Bodenhausen et al. 1994). Circumstances that are tiring (e.g., long hours, fatigue), stressful (e.g., heavy, backlogged, or very diverse caseloads; loud construction noise; threats to physical
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safety; popular or political pressure about a particular decision; emergency or crisis situations), or otherwise distracting, can adversely affect judicial performance (Eells and Robert Showalter 1994; Hartley and Adams 1974; Keinan 1987). Specifically, situations that involve time pressure (Van Knippenberg et al. 1999), that force a decision-maker to form complex judgments relatively quickly (Bodenhausen and Lichtenstein 1987), or in which the decision-maker is distracted and cannot fully attend to incoming information (Gilbert and Gregory Hixon 1991; Sherman et al. 1998) all limit the ability to fully process case information. Decision-makers who are rushed, stressed, distracted, or pressured are more likely to apply stereotypes – recalling facts in ways biased by stereotypes and making more stereotypic judgments – than decisionmakers whose cognitive abilities are not similarly constrained. For instance, Correll et al. (2002) have used videogames in the lab to assess the effect of race on shoot/do not shoot decisions of targets that are either holding guns or holding nonthreatening objects. While subjects are instructed to shoot the armed targets and not shoot the unarmed targets, subjects make errors and these errors are systematically correlated with the race of the target: They disproportionately shoot unarmed blacks and do not shoot armed Whites. Subsequent work has shown this “shooter bias” to be exacerbated when respondents are tired (Ma et al. 2013), rushed (Payne 2006), or cannot see well (Payne et al. 2005). Some of these circumstances are unavoidable during actual policing. However, any staffing and scheduling steps that minimize officer fatigue could also curb some of these racial disparities. Danziger, Levav, and Avnaim-Pesso’s (2011) field study of sequential parole decisions made by experienced judges provides another interesting illustration. Their sample is 1112 parole board hearings in Israeli prisons, over a ten-month period. The rulings were made by eight Jewish-Israeli judges, with an average of 22 years of judging behind them. Every day, each judge considers between 14 and 35 cases, spending around 6 min on each decision. They take two food breaks that divide their day into three sessions. All of these details, from the decision to the times of the breaks, are duly recorded. They record the judges’ two daily food breaks, which result in segmenting the deliberations of the day into three distinct “decision sessions.” They find that the percentage of favorable rulings drops gradually from 65% to nearly zero within each decision session and returns abruptly to 65% after a break. The researchers attribute their results to the repetitive decision-making tasks draining the judges’ mental resources. When drained, the judges start suffering from “choice overload” and start opting for the easiest default choice, which is to deny the prisoner’s request. The more decisions a judge has made, the more drained he or she is, and the more likely the judge will make the default choice. Taking a break replenishes him or her. However, the researchers did not find any evidence that the timing of the decision affected discrimination: judges treated the prisoners equally regardless of their gender and ethnicity, as well as the severity of their crime. Casey et al. (2012) study how one could build on this knowledge of what triggers System 1 versus System 2 thinking to help technologically de-bias the courtroom. For example, the authors discuss how jurors might be allowed more time on cases in which implicit bias might be a concern by, for example, spending more time reviewing the facts of the case before committing to a decision; similarly, courts
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may review areas in which judges and other decision-makers are likely to be overburdened and consider options (e.g., reorganizing court calendars) for modifying procedures to provide more time for decision-making. Also, jurors may be asked to commit to decision-making criteria before reviewing case-specific information and courts may develop protocols that identify potential sources of ambiguity. Furthermore, courtrooms could consider the pros (e.g., more understanding of issues) and cons (e.g., familiarity may lead to less deliberative processing) of using judges with special expertise to handle cases with greater ambiguity. A lot of the possible strategies that Casey et al. (2012) discuss for the courtroom setting could naturally be applied to other real-world setting where biases in decision-making have been documented. Another strategy for moving toward System 2 thinking might be, in settings where data exists on past inputs to and outcomes from a particular decision-making process, to have decision-makers construct a linear model, or a formula that weights and sums the relevant predictor variables to reach a quantitative forecast about the outcome. Researchers have found that linear models produce predictions that are superior to those of experts across an impressive array of domains (Dawes 1971).46 In general, the use of linear or more complex machine learning models can help decision-makers avoid the pitfalls of many judgment biases, yet this method has only been tested in a small subset of the potentially relevant domains. With better knowledge of why discrimination occurs in a particular setting, it will become easier to design appropriate technological de-biasing strategies. As we discussed earlier in section “Limitations of Correspondence Studies,” Bartoš et al. (2013) convincingly demonstrate racial gaps in attention allocation by HR managers. Once they see a minority name on a résumé, they pay less attention to that résumé. These findings confirm the merit of requiring separate rankings of applicants from non-minority and minority groups (or across gender lines) followed by a comparison of leading candidates across the groups. One can think of this rule as providing quotas in the pre-selection process. We do not know of any systematic evaluation of such a strategy. Also, since the earlier a decision-maker learns a group attribute, such as name, the larger the asymmetry in attention to subsequent information such as education or qualification, the findings in Bartoš et al. (2013) strengthens the case for suppressing the signals of a group attribute during the part of the selection process. This particular technological approach has been receiving quite a lot of attention from policymakers in the recent years and has been evaluated in the field. In particular, the large number of correspondence studies have raised interest in the possibility of using “blind” hiring procedures. In some recruiting circumstances, the full hiring process can take place anonymously. Goldin and Rouse (2000) famously showed that American orchestras conducting blind auditions hired more women. In most
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The value of linear models in hiring, admissions, and selection decisions is highlighted by research that Moore et al. (2010) conducted on the interpretation of grades by graduate admission officers.
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other cases, though, only the first stage of the recruitment is made anonymous: this is the case in anonymous application procedures, such as the masking of identifying characteristics in résumés at the first selection stage. In several European countries, pilot studies of the impact of such anonymization of résumés have been conducted, including relatively large-scale field experiments in France, the Netherlands, Sweden and Germany. These experiments are summarized in Krause, Rinne, and Zimmermann (2012b). Only a subset were truly randomized and we focus our discussion on this subset.47 In 2010 and 2011, the French government initiated an experiment, which was implemented by the French public employment service. It involved about 1000 firms in eight local labor markets and it lasted in total for about 10 months (Behaghel et al. 2014). Among volunteer firms, résumés were either transmitted anonymously or nonanonymously. The experiment’s main findings can be summarized as follows. First, women benefit from higher callback rates with anonymous job applications – at least if they compete with male applicants for a job. Second, and most interestingly, migrants and residents of deprived neighborhoods suffer from anonymous job applications. Their callback rates are lower with anonymous job applications than with standard applications. This adverse effect on minority candidates is the exact opposite effect to what policymakers had hoped, and a surprising result given existing evidence from correspondence testing in France (Duguet et al. 2010), which shows discrimination against minority candidates for some jobs, no discrimination for others, but never discrimination against majority candidates. Behaghel, Crépon, and Le Barbanchon (2014) explain these surprising results by the selfselection of firms that agreed to participate in the field experiment. Among firms that were contacted to participate in the experiment, 62% accepted the invitation. While participating firms were very similar to refusing firms in most observable dimensions, there was one significant exception: Participating firms tended to interview and hire relatively more minority candidates (when using standard résumés). The anonymization therefore prevented selected firms from treating minority candidates more favorably during the experiment. Hence, the results of the experiment cannot be viewed as representative of what anonymization might have achieved if it had been mandated to all firms. Methodologically, this paper offers a valuable illustration of one danger when trying to generalize the findings of a field experiment. External 47
Åslund and Skans (2012) analyze an experiment conducted in parts of the local administration of the Swedish city of Gothenburg between 2004 and 2006. Based on a difference-in-differences approach, the authors find that anonymous job applications increase the chances of an interview invitation for both women and applicants of non-Western origin when compared to standard applications. These increased chances for minority candidates in the first stage also translated into a higher job offer arrival rate for women, but not for migrants. In the Netherlands, two experiments took place in the public administration of one major Dutch city in 2006 and 2007 (Bøg and Kranendonk 2011). The experiments focused on ethnic minorities. The lower call-back rate for minority candidates with standard applications disappears with anonymous job applications. With regard to job offers, however, the authors do not detect any differences between minority and majority candidates – irrespective of whether or not their résumés are treated anonymously.
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validity is far from guaranteed if there is sizable room for selection or self-selection of subjects into the experiment (Heckman 1992; Allcott 2015). Another large-scale randomized field experiment took place in Germany in early 2010 (Krause et al. 2012a). The publication of a correspondence testing study for Germany (Kaas and Manger 2012) triggered a lively public debate about discrimination in the hiring decisions of German firms.48 Against this background, the Federal Anti-Discrimination Agency initiated a field experiment with anonymous job applications in Germany to investigate their potential in combating hiring discrimination. This experiment was also subject to selection in participation, with eight organizations voluntarily joining the experiment. The characteristics that were made anonymous include the applicant’s name and contact details, gender, nationality, date and place of birth, disability, marital status and the applicant’s picture.49 Unlike the French study, the authors find that the anonymization leads to less discrimination against minority groups. Moreover, anonymizing applications is not too difficult administratively, with standardized application forms that are completed by the applicants appearing as the most effective and efficient way to make applications anonymous.
Conclusion We have organized this chapter along three overarching themes: the measurement of discrimination, the consequences of discrimination, and factors and policies that may help undermine it. It is apparent from our review of the existing field experiments under each of these themes that there remain more unanswered or unexplored questions than there are settled ones. By far the bulk of the field experiments that have been conducted in this area relate to the measurement of discrimination using the correspondence method. This body of work has demonstrated how remarkably pervasive the differential treatment of minority groups is throughout the world (at least in the labor market and rental market). These studies, most often focusing on a single minority group in a single country, have been important in generating debates in the local media and local public opinion and, from that perspective, each has added value. In many fields of inquiry, researchers shy away from replication, but this is refreshingly not the case here – most likely because demonstrating differential treatment in the given country The study finds that applicants with a Turkish-sounding name are on average 14 percentage points less likely to receive an invitation for a job interview than applicants with a German-sounding name who are otherwise similar. In small- and medium-sized firms, this difference is even larger and amounts to 24 percentage points. 49 The study was further designed to assess the practicality of different methods to remove identifiers from applications; practicality was assessed from interviews with the HR specialists at the firms. Four methods were considered: (1) standardized application forms in which sensitive information is not included; (2) refinements of existing online application forms such that sensitive information is disabled; (3) copying applicant’s nonsensitive information into another document; and (4) blacking out sensitive information in the original application documents. 48
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seemed a sufficiently important goal. On the other hand, researchers’ ability to push the correspondence method further to go beyond pure measurement of differential treatment has been more limited. Disappointingly, there has been minimal methodological innovation in the way correspondence studies are being carried out. The main innovation might have been in leveraging the method to study differential treatment across other characteristics than race, gender or ethnicity, such as in the set of recent studies using the method to study discrimination against the long-term unemployed. While one might conclude from this that the correspondence method might have reached its full potential, recent papers such as Bartoš et al. (2013) which demonstrate how it can be used to study the dynamics of discrimination (endogenous attention allocation in this case) suggest there remain unexplored avenues for more creative uses. Perhaps because so much of economists’ attention has been devoted to using field experiments to measure the extent of discrimination, there has been much less activity in designing creative ways to better document either its consequences or ways to undermine it. The dearth of field-based evidence on these last two themes is particularly striking given the rich theoretical and lab-based literatures (mainly in psychology) that such work could build upon. On the topic of consequence of discrimination, we are heartened to see a few recent papers such as Glover et al. (2015) that develop a creative field design to demonstrate how discrimination can be self-perpetuating. We believe that the last theme in our chapter, interventions to undermine discrimination, is particularly ripe for more field experimentation. It is striking that most of the research in economics on this question has centered around the contact hypothesis and exposure effects, while so many other strategies to de-bias people have been proposed by psychologists and evaluated in the lab. We strongly encourage researchers to take on this work in the near future. Creating more partnerships with organizations that are willing to provide the testing ground for different de-biasing strategies will be particularly useful for this work to move forward. More generally, while field experiments in the last decade have been instrumental in documenting the prevalence of discrimination, field experiments in the future decade should aim to play as large of a role in isolating effective methods to combat it.
Cross-References ▶ Evidence of Covariation Between Regional Implicit Bias and Socially Significant Outcomes in Healthcare, Education, and Law Enforcement ▶ Stereotypes and the Administration of Justice
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Tajfel H (1970) Experiments in intergroup discrimination. Sci Am 223(5):96–102 Tajfel H, Turner JC (1979) An integrative theory of intergroup conflict. Soc Psychol Intergroup Relat 33(47):74 Todd AR, Bodenhausen GV, Richeson JA, Galinsky AD (2011) Perspective taking combats automatic expressions of racial bias. J Pers Soc Psychol 100(6):1027 Tsuchiya T, Hirai Y, Ono S (2007) A study of properties of the item count technique. Public Opin Q 71:253–272 Turner MA, Fix M, Struyk RJ (1991) Opportunities denied, opportunities diminished: racial discrimination in hiring. The Urban Institute Uhlmann EL, Cohen GL (2005) Constructed criteria redefining merit to justify discrimination. Psychol Sci 16(6):474–480 Van Knippenberg AD, Dijksterhuis AP, Vermeulen D (1999) Judgement and memory of a criminal act: the effects of stereotypes and cognitive load. Eur J Soc Psychol 29(2–3):191–201 Wittenbrink B, Judd CM, Park B (1997) Evidence for racial prejudice at the implicit level and its relationship with questionnaire measurements. J Pers Soc Psychol 72:262–274 Woolley AW, Chabris CF, Pentland A, Hashmi N, Malone TW (2010) Evidence for a collective intelligence factor in the performance of human groups. Science 330(6004):686–688 Wright BRE, Wallace M, Bailey J, Hyde A (2013) Religious affiliation and hiring discrimination in New England: a field experiment. Res Soc Stratif Mobil 34:111–126 Yeager DS, Paunesku D, Walton GM, Dweck CS (2013) How can we instill productive mindsets at scale? A review of the evidence and an initial R&D agenda. In: White paper for White House meeting on “Excellence in education: the importance of academic mindsets” Yeager DS, Purdie-Vaughns V, Garcia J, Apfel N, Brzustoski P, Master A, Hessert WT, Williams ME, Cohen GL (2014) Breaking the cycle of mistrust: wise interventions to provide critical feedback across the racial divide. J Exp Psychol Gen 143:804–824 Yinger J (1986) Measuring racial discrimination with fair housing audits: caught in the act. Am Econ Rev 76:881–893 Yinger J (1998) Evidence on discrimination in consumer markets. J Econ Perspect 12:23–40 Zinovyeva N, Bagues M (2011) Does gender matter for academic promotion? Evidence from a randomized natural experiment. IZA discussion paper 5537 Zussman A (2013) Ethnic discrimination: lessons from the Israeli online market for used cars. Econ J 123(572):F433–F468
Lab-in-the-Field Experiments Insights into Discrimination and Ways to Reduce It
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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Insights into Behavioral Causes of Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Misperception by Observers: Shooter Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emotional Bias Among Judges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Implicit Bias of Teachers Against Females in Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bias in Work Attribution When Signals Are Ambiguous . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Naturalized Relations of Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ways to Reduce Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dearth of Field Experiments on How to Reduce Prejudice and Discrimination . . . . . . . . . . . Overcoming Racial Divides with Alliances and Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overcoming Ethnic Divides with Integrated Classrooms and Living Situations . . . . . . . . . . The Influence of Radio on Ethnic Divides in Rwanda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Abstract
Lab-in-the-field experiments have played a major role in identifying behavioral causes of discrimination and ways to reduce discrimination. Such experiments have made it possible to isolate, among the many causes of discrimination, how cognitive mechanisms such as stereotyping, implicit bias, and in-group favoritism contribute to unequal experiences and opportunities. Evidence from natural experiments shows that mandates and incentives, established norms, and the likely desires of judges, teachers, and tenure committees to be fair do not “fix” the biases revealed in lab-in-the-field experiments. To perceive is to categorize, A. Demeritt University of Washington, Seattle, Washington, USA K. Hoff (*) Columbia University, New York, NY, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_18
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and the prototypes and cultural meanings associated with a category affect perception and judgments and can lead to discrimination. The phenomenon of cultural association that drives discrimination is the central focus of this chapter. Hoff, Demeritt, and Stiglitz (The other invisible hand: The power of culture to spur or stymie progress. Manuscript in preparation for Columbia University Press, 2022) call it schematic discrimination and define it as discrimination based on widely shared cultural schemas or “mental models” about specific types of people. It is distinct from taste-based and statistical discrimination because it may be neither consciously chosen nor efficient, and it is inclusive of, but broader than, implicit discrimination because it can occur either at or below the level of consciousness. Lab-in-the-field experiments have been used to evaluate ways to reduce schematic discrimination. Interventions that give people the experience of collaborative intergroup contact have substantially reduced schematic discrimination across a variety of social contexts. Keywords
Implicit bias · Cognitive resources · Cultural mental models · Schematic discrimination · Shooter bias · Stereotype
Introduction Imagine a country with a majority and a minority ethnic group. We’ll call the two groups “M” and “mn.” Members of the majority have limited interaction with members of the minority. Neither likes the other, but the majority is particularly likely to project anger toward the minority and to discriminate against them, since the majority sees the minority as undeserving of their country’s bounty. Now imagine that a single member of the minority hits it big: he lands on one of the country’s most beloved soccer teams and becomes its star player. Almost immediately, discrimination against the minority group declines in regions where people are strident fans of the soccer team. This isn’t rational, since the minority group contains significant diversity: one person – no matter how good his dribbling or finishing skills – does not change the probability of what other members of the hated mn group are bound to be “like.” Nevertheless, the attitudes and behaviors of many people in group M shift as a result of a single individual in group mn. Like other exemplars of diverse categories, the soccer player has an extraordinary power: he is but one of millions, but he changes the perception and likeability of an entire group of people. Sound unrealistic? It’s not; the story is actually true. It describes what happened in Liverpool when a Muslim footballer, Mohamed Salah, joined the UK’s Liverpool football club in 2017 and became its star member. (Soccer in the USA is called football in Europe.) The Muslim prayer that Salah performed after scoring made it clear to the public that he was Muslim, and his devotion to his faith both on and off the field brought Islamic practices into the spotlight. In Britain, Muslims are one of the most discriminated-against groups and the object of about half of all hate crimes.
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Analysis of police data found that hate crimes in the county containing Liverpool decreased by 16% after Salah joined the Liverpool team. Analysis of Twitter posts found that prejudiced speech against Muslims by Liverpool soccer fans fell to half the rate they were before Salah joined the Liverpool team (Alrababa’h et al. 2021). After one of Salah’s successful matches, his fans sang: If he scores another few Then I’ll be Muslim, too. If he’s good enough for you, He’s good enough for me. Sitting in a mosque, That’s where I wanna be.
Salah single-handedly changed the stereotype of Muslims and increased how much British society valued people belonging to the category of Muslim. How did this happen? This chapter examines lab-in-the-field experiments that try to get “inside the minds” of individuals to shed light on some of the mechanisms driving discrimination and the policies that can reduce it. Lab-in-the-field experiments are distinguished by three features: (1) a standardized paradigm for analyzing a problem, e.g., a behavioral game, a memory test, or an evaluation of an image; (2) participants who are representative of a relevant population, e.g., the experiments might draw on personnel officers to understand labor market discrimination or on teachers to study discrimination in education; and (3) a naturalistic setting to try to replicate a real decision-making context. Membership in a group is generally correlated with many factors – for example, race is often correlated with income and education, as well as dress and demeanor. This makes it difficult to isolate the factors driving discrimination where differences in treatment are observed. The advantage of lab-in-the-field experiments for assessing discrimination is that they are able to isolate the effect of membership in a group. These experiments permit analysts to compare, for example, how individuals treat White compared to Black men in situations where the distribution of all features except race is the same in the two groups. The study of discrimination in economics has evolved as the field has broadened its understanding of human decision-making. In standard economics, decisionmakers are rational actors with exogenous preferences. Rational actors, by definition, can commit only two kinds of discrimination – taste-based and statistical. This is because rational actors costlessly make all rational calculations and never process information incorrectly. In the late twentieth century, the psychologists Daniel Kahneman and Amos Tversky introduced into economics a new model of the decision-maker – an intuitive actor. This “behavioral” decision-maker thinks intuitively, automatically, emotionally, and in terms of associations (or “fast”), not rationally (or “slow”) (Kahneman 2003). This decision-maker may discriminate without deliberately choosing to and without awareness of his bias. Unconscious discrimination is called implicit discrimination (Bertrand et al. 2005).
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In the twenty-first century, researchers documented a kind of discrimination that cannot be reduced to an expression of preferences as economists understand them (as in taste-based discrimination), since it affects perception. Nor is it reducible to implicit bias, since it can influence conscious thinking and may not go away even when individuals are thinking deliberately (“slow”). This form of discrimination arises from individuals’ use of cultural mental models or “schemas” regarding what people belonging to certain groups are “like.” Cultural environments, including organizations, clubs, communities, and states, develop many social constructs that members absorb as they are socialized into the culture. Some of these constructs create associations between individuals belonging to particular groups and the traits that these individuals are thought to have. For example, people often think of librarians as quiet, rule-bound types and of computer programmers as nerdy young males. Of course, neither of these constructs (stereotypes) is universally true. Nevertheless, they creep into thinking – thus, one might feel surprised to meet an outgoing and carefree librarian or a socially skilled, older, and female tech entrepreneur. Social constructs bring cultural associations, meanings, and values into both conscious and unconscious thinking processes. Recognition of this kind of discrimination reflects cross-disciplinary work – not only with psychology but also with sociology, anthropology, cognitive science, and political science. Psychologists have long recognized that people see the world through categories (Bruner 1957). Humans are born with a cognitive system for categorizing the social world into groups (Kinzler and Spelke 2007). But which categories a society constructs – such as Muslim/Christian, “White”/“Black,” or straight/gay – and what meanings emerge for the categories depend on history, social structure, and institutions (e.g., Acharya et al. 2016; Alesina et al. 2013; Hoff and Stiglitz 2010). Humans without any exposure to the culture of a particular group who were dropped into their world would notice physical differences between people, but it is the sociocultural workings of a society that create most of the categories and other “tools of thinking” (such as stereotypes) that people in a culture use. No matter how “slow” and deliberately people think, the cultural categories and other conceptual tools that they use to organize their thoughts influence what they see and how they think. A wide literature on discrimination driven by cultural mental models exists (e.g., Beaman et al. 2009; Rao 2019; Lowe 2021; Sarsons et al. 2021), but there is no widely used term for it. Hoff, Demeritt, and Stiglitz (2022) call this form of discrimination schematic discrimination. Its defining feature is that it is unconscious or conscious discrimination based on cultural mental models (equivalently, cultural schemas). Individuals adopt and internalize many of the mental models of the communities that they are a part of (Berger and Luckmann 1966). The mental models affect how they process information and the meanings that they give to their encounters with the world (Bruner 1990; DiMaggio 1997). People come to believe that certain associations between categories and traits are relevant (even fundamental) to a particular class of decision – such as hiring, criminal sentencing, or evaluation on a test. Correll et al. (2015) invented the term “stereotypic vision” to
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capture the way stereotypes affect visual processing – an example of this appears in the next section. Recognizing the role of cultural mental models in how humans process information and construe situations makes clear why new exemplars will influence behavior. When an individual has new exemplars of a category – such as Salah as an exemplar of a Muslim – the meaning of the category changes. Schematic discrimination might be described as coding a person by a category that he fits into and behaving toward him as if the code summarizes his traits. The coding means that the decision-maker no longer thinks about the person as an individual but rather as a member of the given category. The decision-maker will not expend high effort to see or find counter-stereotypical information (von Hippel et al. 1993; Correll et al. 2015). Lab-in-the field experiments show how labeling a person by gender, race, or social class may change how the person’s performance is evaluated (e.g., in the Goldberg paradigm in Beaman et al. 2009) or, literally, how the person is seen. Jennifer Eberhardt and her colleagues (Eberhardt et al. 2003) asked White college students to draw, as carefully as they could, a picture of a face while its image was onscreen. The images had been created by using software that morphed a color photograph of a Black man’s face with a White man’s face. For half of the students, the man was described as White; for the other half, he was described as Black. The labels led individuals to draw different kinds of pictures. Figure 1 shows drawings by two of the students. These students are not representative of the group but instead of the subset of participants who were identified from their responses to a survey as holding the belief that individuals’ traits are fixed, not malleable. It is this group whose drawings reflected most strongly the racial labels given to the faces. The bottom left figure was drawn in a case where the face at the top was labeled Black; the bottom right figure was drawn in a case where the face at the top was labeled White. The figures illustrate that seeing is not a passive process. Instead, seeing is a constructive process that entails selection and use of social concepts and categories, such as “Black” and “White.” The remainder of this chapter has two parts. The first part examines the implications of social constructions for discrimination. The second part looks at ways to reduce discrimination.
Insights into Behavioral Causes of Discrimination Misperception by Observers: Shooter Bias A long series of highly publicized police killings of unarmed Black men has generated research on how individuals make decisions to shoot. The psychologist Joshua Correll and colleagues invented a videogame simulation of these high-stakes situations. They recruited college students, community members, and police officers to participate in lab experiments to assess whether systematic misperceptions lead to the unjustified shooting of young Black men (Correll et al. 2002, 2006, 2015). In the
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Fig. 1 Sample drawings by two university students of the face at the top of this figure when it was labeled as the face of a Black man or a White man. (Source: Eberhardt et al. (2003))
videogame, young men unexpectedly appear on the screen, one at a time, holding either a gun or an innocuous object, such as a wallet. The objects that the men in the videogame are holding are small but discernable if one focuses on them. The men are either African American or White, and half of each group are holding a gun. The goal of the player of the videogame is to shoot all the armed targets and not shoot any of the unarmed targets. The results of about 20 studies with community members and college students consistently show the same bias, called shooter bias: participants shot unarmed targets more frequently when they were Black and failed to shoot armed targets more frequently when they were White. White and African American participants
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displayed equivalent levels of shooter bias. As measured by brain activity, the more that participants differentiated the targets by race and processed Black targets as if they were threats, the greater their shooter bias in the videogame. Correll et al. (2015) tracked participants’ eye gaze during the shooter task. The center of the retina produces the highest visual resolution of any point on the retina. As focus moves away from the center of the retina, visual acuity decreases steeply. Stereotypes influenced visual processing. Participants responded to stereotypic targets before attaining the visual clarity they achieved for counter-stereotypic targets. In other words, visual clarity was a choice (but very likely one that was made unconsciously) that helps explain why participants were more likely to shoot an unarmed target if he was Black rather than White (the percentages were 16.4% for Black targets and 12.6% for White targets, p ¼ 0.03). For armed targets, the pattern was reversed: more participants chose not to shoot if an armed target was White (12.0%) rather than Black (9.2) (p ¼ 0.03). Stereotypes had a top-down influence on visual processing; they reduced the probability that individuals focused on the small object in a target’s hand before acting on a decision to shoot or not shoot the target. Police officers receive extensive training with firearms throughout their careers. The training promotes their ability to focus on the central features of a situation and ignore irrelevant features. Unlike the community members, the police who participated in the shooter task experiment did not show bias in their decisions to shoot or not shoot: their expertise eliminated the impact of race on the decision to shoot a man who appeared on a computer screen with a small, identifiable object in his hand. However, the police were affected in another way – speed: they fired at an armed target more quickly if he was Black, and they decided not to shoot an unarmed target more quickly if he was White. Do experts always exert the extra cognitive effort needed to marshal the attention and control needed for unbiased judgment? The next section describes a natural experiment that shows that they do not.
Emotional Bias Among Judges This section considers a natural experiment that reveals how emotions amplify the power of stereotypes to induce discrimination. It shows how upset losses in college football games influenced the sentences that judges imposed on Black juveniles convicted of crimes. If one thinks of the naturally occurring data as the outcome of an experiment, then the “treatment” is an unexpected loss by a prominent football team in the state. The natural experiment has key features of a lab-in-the-field experiment: participants face similar cases (a juvenile accused for the first time of a crime) and make a one-dimensional decision (the length of the sentence). The advantage of this experiment being natural rather than lab-in-the-field is that the incentives to make the “right” decision are enormous. The stakes bear on the deepest values of most of the participants: justice, the freedom of the defendant, and the legal order. The outcomes show whether the incentives that decision-makers face in real life are enough to “fix” the biases that occur in lab-in-the-field experiments.
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First, a bit of background: there is strong loyalty to sports teams in much of the world. In the USA, around 200 million viewers tune in to watch the regular college football season. Upset losses (i.e., losses by a team when it was expected to win) lead many fans to become angry and upset. Evidence of the strength of this reaction is that an upset loss in the USA leads to a 10% increase in the rate of at-home violence by men against their wives or girlfriends (Card and Dahl 2011). In contrast, losses that are expected, as well as wins (both expected and unexpected), have little-to-no impact on domestic violence. In Louisiana, devotion to the Louisiana State University football team is ingrained in the culture. Weddings and other big social events are often scheduled around the dates of LSU games. Ozkan Eren and Naci Mocan (2018) investigated the effect of upset losses of the LSU football team on the sentences that judges gave juveniles in Louisiana. Cases are randomly assigned to judges in each district court. The judge determines a sentence length for each convicted juvenile, regardless of whether he/she is placed in custody or ponrobation. The data used in this study is the length of the sentences meted out to first-time delinquents, age 10–17, who were convicted of a single offense and sentenced between 1996 and 2012. The judges had broad discretion in sentencing except in cases of murder and rape, and so those cases were excluded. This left a sample of about 10,000 cases. The authors define an upset loss as a loss of a game that the Las Vegas point spread favors the football team to win by four or more points. Controlling for this point spread, the outcome of a college football game is as good as random (Card and Dahl): losses are not correlated with bad weather, economic shocks, or anything else likely to put people in a bad mood. Thus, the researchers could use upset losses of the LSU team to tease out how being angry and upset amplifies the power of widely shared stereotypes to induce discrimination. Compared to periods during the regular season when the team had no games, in a week immediately following an upset loss, the judges discriminated against Black juveniles. The sentences imposed on Black juveniles increased, on average, by 43 days. This is an 8% increase in the average sentence of 514 days. The reaction of judges was stronger when they had stronger ties to LSU (it was their alma mater) and when the game was more consequential (it would have a big effect on the team’s chance of obtaining a national championship). The impact of an upset loss for White defendants was one-tenth as large (about 5 days) and statistically not different from zero. The effects on sentencing decisions persisted over the entire week following a Saturday game, but didn’t carry over to the following week. Non-LSU games had no impact on judges’ behavior. When LSU had not experienced an upset loss, Black juveniles did not receive longer sentences than White juveniles. The race of the judge also had no impact on the sentences he gave. What can explain the emergence of discrimination in the week immediately following an upset loss? A plausible explanation is that judges were upset, so they unconsciously looked for someone to punish. A cognitively easy target was a negatively stereotyped group – Black juveniles. The title of the paper sums up the result: “Emotional judges and unlucky juveniles.”
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Implicit Bias of Teachers Against Females in Science Stereotypes entail cognitive associations between categories – such as a race or gender – and attributes, such as violence or intelligence (Dovidio et al. 1986; Gaertner and McLaughlin 1983). This insight led to the development of the Implicit Association Test (IAT). It is an imperfect yet useful means of assessing implicit attitudes without relying on self-reports (Greenwald et al. 1998). In many cases, IAT scores predict prejudice and discrimination. Michela Carlana (2019) took the IAT to the field of junior high school teachers in Italy to assess whether their implicit bias about gender and science affected their students’ achievement. The IAT entails sorting words into one of the two buckets as quickly as possible. Each bucket is for two categories. Subjects complete two versions of the test – one with and one without the stereotypical alignment of the categories. In the GenderScience IAT, the screen with the stereotypical alignment is: Male
Female
Scientific fields
Humanities
The screen with the counter-stereotypical alignment is: Male
Female
Humanities
Scientific fields
Most people perform the sorting task faster and with fewer errors in the stereotypical alignment. People who hold the stereotype will tend naturally to collapse the two categories on each side of the upper screen shown above into one category: left becomes “male and academic fields associated with men”; right becomes “female and academic fields associated with women.” An implicit bias against females in science shows up as a difference between the time it takes an individual to complete the stereotypical and the counter-stereotypical versions of the IAT. The larger the gap, the greater the individual’s bias. For a person who does not have an implicit gender-science association, the difference in time and accuracy in performance between the two conditions is predicted to be zero. Two features of the Italian junior high school system made it possible to isolate the effect of long-run exposure to teachers biased against women in science. First, the assignment of a teacher to a class of students is as good as random and is not changed for the entire 3 years of junior high school. Second, classes are balanced: an effort is made to have a similar ability distribution of students within each class. With the strong support of the junior high school principals, 80% of all math and literature teachers working in 91 junior high schools in Italy completed the GenderScience IAT. These teachers taught a total of 47,000 students. Carlana found that
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female students’ scores on a standardized math test declined if their math teachers held the stereotype against women in science, as measured by the IAT. (There was no effect on the test scores of male students.) Having a biased teacher caused one-sixth of the gender gap on the standardized test. At the end of junior high school, students in Italy decide whether to enter an academic or a vocational high school. Math teacher bias against females in science increased by 11% the likelihood that a female student self-selected into a vocational high school. One mechanism through which gender-biased science teachers influenced their female students was to lower their self-confidence in math.
Bias in Work Attribution When Signals Are Ambiguous Joint projects are increasingly important in the workplace (Lazear and Shaw 2007). How to assign credit for joint work is, thus, a growing issue. Employers cannot perfectly observe the contribution of each member of a group to the group’s output. Instead, they have to infer each person’s contribution. Evidence of bias in the attribution of work accomplishments is suggested by two observational studies of the top economics departments in US universities and by a lab-in-the-field experiment in the USA and India (Sarsons 2017; Sarsons et al. 2021). The observational studies and lab-in-the-field experiment are described below.
Observational Study of Promotion to Tenure in Economics Departments In the economics departments of the top 30 PhD-granting universities in the USA, the gender gap in getting tenure is large: 75% of men and only 52% of women are granted tenure (Sarsons 2017, Table 1). Papers published in economics journals are the main basis for tenure decisions. Objective indicators of the quality of the papers are the journal rankings and the number of times a paper has been cited. Since there is no gender gap in either the number or the quality of candidates’ publications, the gender gap in promotion to tenure is puzzling. The obvious explanation might seem to be taste-based discrimination. But the evidence does not support this explanation. Men and women with the same number of single-authored papers were granted tenure with the same probability. If some employers have a distaste for tenuring women, one should see women who write solo-authored papers disproportionately denied tenure. The gender gap arises in the treatment of coauthored papers. Women who coauthor with men have lower tenure rates than men with a similar number and quality of coauthored and solo-authored papers. In the top 35 US PhD-granting universities, one additional coauthored paper is correlated with a 7.4% increase in men’s tenure probability, but only a 4.7% increase in women’s tenure probability (Sarsons et al. 2021, Table 2). The gender gap is especially pronounced for papers that a woman coauthors with one or more men. For men, it largely does not matter whether their papers are solo-authored or coauthored. The difference in the treatment of women’s and men’s coauthored papers accounts for 65% of the gender gap in
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promotion. In this analysis, average paper quality, citations, primary field, PhD institution, and seniority of coauthors are controlled for. The results of the observational study suggest discrimination. But they do not prove it, since there are possible confounds: it could be that tenure committees observe each coauthor’s contribution to a paper and know that, on average, the women up for tenure have contributed less than the male coauthors. To isolate the influence of gender on the assignment of credit for performance in group work, Sarsons and colleagues designed and implemented a lab-in-the-field experiment in which (in one of the treatments) players could perfectly observe each worker’s contribution to the team’s output.
Lab-in-the-Field Experiments on Assessing Performance The experiment has two steps. In step 1, university students (called “job candidates”) complete tasks individually and are individually rewarded. (The tasks are vocabulary search and numerical search tasks, for which prior experiments showed that there is little or no gender difference in performance.) Then the job candidates are randomly put into male-female pairs. In step 2, human resource workers from the USA and India (called “recruiters”) choose a job candidate from a pair under one of the two conditions: either they see the individual scores of the male member and female member of the pair or they see the sum of their scores. (If the woman has scored, say, 4 out of 5 and the man has scored 3 out of 5, the recruiters would see the score 7 out of 10 for the pair.) The recruiters know that there was no interaction between workers and that they were randomly paired. Recruiters also know that their payoffs in the game will depend on the productivity of the job candidates they choose. Thus, the recruiters have an incentive to recruit the more productive member of the pair. Under the first condition, where recruiters received clear signals of individual performance, they make unbiased use of the scores. A candidate’s gender does not affect the recruiters’ choice. This mirrors the finding that men and women receive equal credit for solo-authored papers in real tenure decisions in the top 35 US economics departments. But under the second condition, where recruiters see only the sum of a man’s and a woman’s score, male recruiters are more likely to pick the male candidate, and female recruiters are more likely to pick the female candidate. The odds ratio for an unbiased decision-maker would be 1. But the actual odds ratio that a male recruiter chooses a female candidate ranges from 0.72 to 0.92 (depending on the task and the controls); the odds ratio that a female recruiter chooses a female job candidate ranges from 1.2 to 1.4. The individual scores that make up the joint score are analogous to each person’s contribution to a coauthored research paper. Since most economics professors are men, the results of the lab-in-the-field experiment can explain economics department tenure outcomes as the result of in-group favoritism in the assignment of credit for group work. The finding implies that gender bias in male-dominated fields tends to perpetuate male domination of the fields. This lab-in-the-field experiment did not, however, test for schematic discrimination, since the tasks of the job candidates
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(vocabulary search and numerical search tasks) are not culturally categorized as men’s or women’s tasks. Ambiguous signals of performance are common in many settings and require decision-makers to make inferences, which can introduce bias. Gabriela Farfan, Alaka Holla, and Renos Vakis (2021) investigated whether teachers’ judgments of a student’s ability were biased by the student’s socioeconomic status (SES). Teachers at public schools in Lima, Peru, were randomly assigned to watch one of the two videos that portrayed a fourth-grader, called Diego, playing at home and in his neighborhood playground. In one of the videos, Diego was portrayed as from a low-SES household: he played in a low-income housing development, and his parents were described as high school-educated, blue-collar workers. In the other video, Diego was portrayed as from a high-SES household: he played in a middleclass neighborhood, and his parents were described as college-educated professionals. All the participants in the experiment then watched the same video of Diego taking an oral exam. His performance gave ambiguous signals: he answered some difficult questions correctly and some easy questions incorrectly. The teachers who had earlier seen the video of low-SES Diego judged his test performance to be below the fourth-grade level, while those who had seen the video of high-SES Diego judged his performance to be close to fifth-grade level. Associations with SES functioned like a lens through which participants saw Diego. It changed what they noticed and remembered about his performance on the oral exam.
Naturalized Relations of Power One of the ways that powerful and oppressive groups have historically legitimized their acts of oppression is by creating categories of people and representing them as ranked according to a natural hierarchy. Skin color was not initially an organizing principle in the colonies that became the USA (see Hoff and Stiglitz 2010, Online Appendix). The Africans who were sold into slavery and brought to the Americas were members of many different ethnic groups with distinct languages and no shared identity. In the colonies that became the USA, all individuals, including enslaved people, were initially accorded some rights and some obligations: they could sue or be sued in court, had to do penance in the parish church if they had illegitimate children, and could earn money of their own. Slave codes, based on the principle that enslaved persons were property without any rights of their own, were enacted by the US colonies only beginning in the late seventeenth century. The codes were a response to the large increase in the profitability of slave labor and the threat to the wealthy elite of alliances between poor, landless descendants of Euro-Americans and African Americans. To maintain slavery, the elite adopted a “divide-and-conquer strategy,” creating a sharply different legal regime for Euro-Americans and African Americans. The slave codes forbade schooling, church attendance, and land ownership to slaves. In some parts of the South, a person who taught a slave to read and
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write could be punished by death. The codes also forbade marriage across the “color line”: the two groups were to be kept in different worlds. Out of this creation of ranked categories of people, the fiction emerged of the natural discontinuity between a superior “White” race descended from Europeans and an inferior race of darkskinned people descended from Africans. The caste system in South Asia was also designed to support the exercise of power by elite groups. Although it had its origins in ancient India, it was transformed by ruling elites under the Mughal Empire and the British Empire. Under the British Empire, caste for the first time became a rigid system of division in South Asia (Dirks 2001), even though there are no discernible natural physical markers of caste (Deshpande 2011). Like African Americans under the slave codes, the lowest castes – historically called Untouchables and today called Dalits – were not allowed to own land or sit inside a schoolhouse. They are also denied the right to draw water from wells used by the upper castes (to emphasize the idea that Untouchables were polluted). The Constitution of India, which came into force in 1950, made Untouchability illegal. Evidence of a new social order is visible to every schoolchild in the stipends publicly distributed to Dalits to encourage school enrollment and in the broad participation of Dalits in the political process. Yet children are also likely to encounter the traditional order of caste, segregation, and untouchability in their own experiences, through the fables they learn, and in the continued insults and atrocities levelled against upwardly mobile Dalits. The two social orders coexist in uneasy tension in India – the legal one in which “we are all equal now,” as some villagers remark, and another in which caste retains a firm grip on the mind. To assess the impact of caste identity on individuals’ performance, Hoff and Pandey (2006, 2014) undertook a lab-in-the-field experiment in a north Indian state (Uttar Pradesh) where the high castes are particularly dominant. Hoff and Pandey recruited 6th and 7th grade male students from the low castes (Dalits) and the high castes and taught them how to solve mazes. Performance in the experiment was measured by the number of mazes solved in two 15-minute sessions. The participants were paid one rupee for each maze they solved. The participants were randomly assigned to treatments that varied the salience of caste identity. In some treatments, participants sat in a classroom with a total of three high- and three low-caste boys. In this setting, low-caste boys solved on average as many mazes as high-caste boys as long as the participants’ castes were kept private. But in the treatments in which caste was publicly revealed, low-caste boys solved more than 20% fewer mazes than they did when caste was kept private. Publicly revealing caste increased the proportion of low-caste children who could not solve a single maze from 1% to 11% (Hoff and Pandey 2014, Fig. 6). This is the main channel through which making caste salient impaired the performance of the low caste. This is an example of stereotype threat, which has been widely studied for race and gender. There was one treatment in which the manipulation of caste cues reduced the performance of the high-caste boys. This was the treatment in which they sat in a high-
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caste segregated classroom and their caste identity was revealed. Segregating the high caste increased the proportion of high-caste boys who learned how to solve mazes, but significantly and substantially reduced the number of mazes that they solved (Hoff and Pandey 2014, Fig. 4). All available evidence – from the proportion of the high-caste boys who failed to learn how to solve mazes (which declined from 8% to 2%) to a direct test of self-confidence in another study (Hoff and Pandey 2005) – shows that the caste-segregated treatment did not impair the self-confidence of high-caste boys. The most plausible interpretation of the decline in high-caste performance in the caste-segregated treatment is an “entitlement effect”: segregation is a marker of high-caste dominance and evokes a sense of entitlement, reducing the need that the high-caste individuals feel to excel. As the Indian sociologist André Béteille notes, the caste system assigns social preeminence by birth, not performance (Béteille 2011, II [2003], p. 11). As of this writing, caste segregation remains in many villages a marker of high-caste privilege. It is also an issue over which high and low castes struggle. In a national survey in India in 2006, 45% of Dalits in the Hindi-speaking belt of India reported that some streets in their villages were off-limits to Dalits (Girard 2018). The lab-in-the-field experiment on the effect of priming a stigmatized identity on maze-solving was replicated in Beijing, China (Afridi et al. 2015). The subjects were elementary schoolchildren from two social categories: (a) households classified as urban Beijing households (a privileged category) and (b) households classified as rural non-Beijing (a disadvantaged category in Beijing). The household registration system in China, known as hukou, classifies citizens based on the birthplace of their parents or grandparents. It favors those who are categorized as local urban residents in housing, jobs, access to schools, and public benefits. Unlike categories of gender, class, and caste, hukou is a transparently social creation. The experimental findings show that priming hukou shifts performance in ways that mirror the way that social groups are ranked. In this sense, social identity “makes up people.” When the negative stereotype of a group lowers the performance of group members, there can be a self-reinforcing vicious circle over time. Hoff and Stiglitz (2010) present a model in which an effect of a negatively stereotyped identity is to bias individuals’ perceptions of their own performance: the members of the “inferior” group give themselves less credit than they otherwise would; this lowers their self-confidence relative to that of other groups, which lowers their performance relative to other groups and appears to validate the belief in the ranking of groups. Hoff and Stiglitz call this an “equilibrium fiction,” since the objective evidence sustains a false belief. Such fictions can make a society rigid.
Ways to Reduce Discrimination As the studies in the previous section demonstrate, prejudice and discrimination are prevalent in many societies and affect many different types of people. A pressing question is what to do about it. The remainder of this chapter addresses this question.
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Dearth of Field Experiments on How to Reduce Prejudice and Discrimination Are there means to influence individuals’ schema-based biases? Literature reviews in the area of prejudice and discrimination show that the limitations of existing studies preclude a conclusive answer to this question (Paluck and Green 2009b; Bertrand and Duflo 2017; Baldassarri and Abascal 2017; Paluck et al. 2019). In their 2009 review, which is notable for its breadth and methodological focus, Paluck and Green (2009b) found that only a small fraction of the hundreds of studies they reviewed were able to speak to “what works” to reduce prejudice in real-world settings. The vast majority used non-experimental or quasi-experimental methods unable to establish causality, if the methods were evaluated at all. Of even the best studies that Paluck and Green (2009b) include in their analysis, many evaluated interventions that lasted a day or less, more than 80% used non-representative samples as participants, the majority lacked behavioral outcome measures, and half had fewer than 100 participants and thus lacked statistical power. All in all, the authors conclude that there is a paucity of research to support scientific inference and generalization (p. 357). The entire genres of interventions developed in scientific laboratories, non-profits, government bureaus, and consulting firms – such as diversity and sensitivity training – have never been subjected to rigorous evaluation. Particularly given the dearth of field experimental work, lab-in-the-field experiments have an important role to play in shedding light on how to reduce discrimination in the real world.
Overcoming Racial Divides with Alliances and Cooperation Designing interventions to reduce discrimination requires an understanding of its roots. This chapter focuses on schematic discrimination. Recall that it is based not on hate or rational assessments, but instead on the cultural associations with categories into which people are put and the salience of the categories. Central questions are: Why do individuals categorize others by race and ethnicity? When are these categories salient? Until recently, race (like age and sex) was assumed to be a “primitive” dimension on which people individuate others – something humans use to encode or categorize people across all social contexts. But studies in the twenty-first century undermine this belief. The psychologists Kurzban, Tooby, and Cosmides (2001) hypothesized that the ease with which modern people encode race is a byproduct of cognitive machinery adapted to a different skill: detecting coalitions, alliances, and other patterns of cooperation.1 To test the idea that coalitional allegiance is what the
1
The hypothesis is that humans have a biological trait to encode people by their coalitions, alliances, and other patterns of cooperation, but not by their race. The ability to quickly compute and revise coalitional cues would have helped early humans negotiate the social world successfully. Since it gave individuals a survival advantage, the trait would have been selected for in human evolution.
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mind is actually encoding when it registers race, Kurzban et al. used a tool called a memory confusion protocol. It is a surprise memory test in which mistakes shed light on whether and how the test-taker is categorizing people. Here is how they used the tool. The participants in the experiment, who were college students (primarily Euro-American and Asian American), were told that two basketball teams had been in a fight. Subjects viewed photos of the eight members of the two teams along with statements that each man had made. The statements provided enough information for participants to infer which of the two teams an individual was on – they served as “cues” to coalition. As each team was comprised of two Black men and two White men, race was not associated with alliance. The heart of the protocol was a surprise memory test in which the participants viewed the photos again and were asked to recall who said what. Because people more easily confuse individuals whom they have coded as members of the same category than those coded as belonging to different categories (e.g., a citizen of Verona is more likely to confuse a Capulet with a Capulet than a Capulet with a Montague and vice versa), the pattern of misattributions in the memory test reveals the participants’ categorization schemes. When all of the eight men were dressed identically, subjects encoded the men by race twice as strongly as they encoded them by coalition. That is, they were more likely to misattribute a statement to someone of the same race as the actual speaker than to someone who was on the same team. But something striking happened when researchers manipulated the photos to give the team members colored uniforms signifying their team membership (a visual cue akin to skin color). Subjects then encoded the team coalition twice as strongly as they did race. This result suggests that racial perception and categorization are not fundamental (by contrast, sex encoding was shown to be robust in a nearly identical set of experiments). Instead, the extent to which humans perceive and categorize individuals by race depends on whether race predicts social relationships. The results of these experiments bear further investigation. First is the issue of replication: one recent study replicated the results (Pietraszewski 2021), but another obtained a substantially lower effect (Voorspoels et al. 2014). Second, the participants in these studies were college students. While this population represents an important part of the “field,” since racial discrimination is a problem on college campuses, racial encoding within this group is likely to be less ingrained and more malleable than in other populations. Third, the domain of basketball is strongly racially integrated and very rivalrous compared to other settings. Cueing this context likely facilitated subjects’ cognitive downshifting of race and upshifting of coalitions based on shirt color. Whether race could be “erased” (Kurzban et al. 2001) as easily in other arenas remains to be seen. Still, a key implication is that by changing the experiences people have and the alliances they observe, it is possible to start changing the significance of race. Recent lab-in-the-field experiments provide support for the “alliance” hypothesis, which holds that the root of ethnic and racial discrimination lies in a lack of exposure
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to interethnic cooperation. In Spain, university students in two port cities with ethnolinguistic diversity – Bilbao in the north and Valencia in the east – played a nested public goods game (Espinosa et al. 2019). The game is designed to discern the breadth of the social circle (local, global) within which people are willing to contribute to the collective good. In both cities, subjects readily contributed to a local public good that would benefit their co-ethnics. But whereas ethnic diversity increased contributions to a global public good by 38% in Valencia, it decreased contributions by 27% in Bilbao. A second experiment revealed that the mechanism was lower expectations of cooperation among the subjects in Bilbao. The authors point to the different histories of the north and east of Spain to explain this result. Although both cities are integrated across ethnic groups, Bilbao (in the north) has a history of political conflict, whereas Valencia (in the east) does not. In the north, residents’ day-to-day experiences led them to believe that individuals outside their ethnic group would not cooperate. In the east, historical experiences gave residents a more optimistic lens on interethnic cooperation that influenced how they played the game. Similar results regarding the effect of cooperative experiences were obtained in Mumbai, India, a city notorious for ethnic violence. Tusicisny (2017) had Hindu and Muslim slum-dwellers, many of whom had been implicated in ethnic riots, play a public goods game. He used the game not only to measure cooperation but also to manipulate it – he randomly assigned subjects to experience cooperation, or a lack of cooperation, with outgroup members before their own behavior was measured. Experiencing a short and superficial cooperative experience with an outgroup member caused ethnically mixed groups to produce as many public goods as homogeneous ones. Surprisingly, the effect was just as strong among voters of two extremist parties implicated in ethnic riots. Saumitra Jha’s (2013) analysis of Indian towns yielded the same conclusion about collaborative intergroup contact using historical data. Port cities that developed institutions to promote interethnic cooperation in the medieval period had less religious violence even centuries after the institutions were abolished. Taken together, these experiments lend credence to the view that race and ethnicity are not fundamental to human relations; rather, it may be a lifetime of experience in a society in which race is a predictor of coalition and cooperation (e.g., who interacts with whom, who likes whom, who shares with whom) that causes people to “see” race so strongly. In these experiments, the malleability of behavior and what might be called “preferences” for cooperating versus discriminating is striking and violates the assumptions of standard economics. Yet it is not surprising from the perspective of behavioral economics: humans’ bounded rationality and the influence of cultural categories on thinking lead people to revert to the use of stereotypes and other associations at the slightest need to determine who is “in” and who is “out.” The studies suggest that creating opportunities for people to experience, or even merely to witness, cooperation between historically divided groups can reduce
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discrimination by changing the associations that individuals have in mind. Such processes may help explain why, for example, even a brief exposure to cross-ethnic participatory politics in a war-torn country can change patterns of social cooperation and improve cohesion in ways that persist after a development program ends (e.g., Fearon et al. 2009). Indeed, the most methodologically robust studies on intergroup contact suggest that cooperation can decrease prejudice and discrimination (Paluck et al. 2019; Lowe 2021). The remainder of this chapter will discuss lab-in-the-field experiments that shed light on how policy can reduce discrimination. Can it change the lenses that people use to see the world?
Overcoming Ethnic Divides with Integrated Classrooms and Living Situations Several studies have used lab-in-the-field methodology to study the effect of intergroup contact in classroom and living settings. The studies arrive at conclusions that are mostly optimistic. Like the studies discussed above, they underline the importance of cooperative experiences and expectations for reducing discrimination. In Bosnia-Herzegovina, four high schools in the city of Mostar had a history of segregation by two main ethno-religious groups: Muslim Bosniaks and Catholic Croats. After the war, which killed or displaced millions, two of the four schools were partially integrated, and students were randomly assigned to a school. Researchers had students play a public goods game in ethnically homogeneous and heterogeneous groupings (Alexander and Christia 2011). Mixed-ethnicity groupings decreased students’ contributions only among students attending segregated schools. In other words, those attending desegregated schools exhibited a greater propensity for intergroup cooperation. Although the study did not look inside the schools to understand how the experience of integrated schooling might have affected students’ minds, a plausible explanation is that the integrated schools generated positive interactions that improved cooperative expectations among students. Another piece of evidence from the study supports this view: students attending integrated schools made higher contributions to the public good when they were able to sanction one another in the game. Institutions reduced the potentially adverse effects of diversity by ensuring that non-cooperators could be punished. These results suggest that ethnic discrimination does indeed have roots in expectations about cooperation. Gautam Rao (2019) found a similar pattern when he examined how wealthy students’ preferences and behaviors were influenced by interactions with poor students. A legal case in India led to a court order requiring 395 private schools to reserve 20% of their seats for students from poor households and to integrate these students into the same classrooms as the students from high-income families. Welloff students who were schooled with poor classmates were more charitable as measured by their volunteer activity. In a dictator game, they shared more of the money they were given than did students schooled only with their well-off peers, and this was true regardless of whether they played against a rich or poor partner. In other
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words, the experience of having poor classmates powerfully shaped fundamental notions of fairness. In a fascinating elaboration, students were asked to pick team members for an incentivized relay race. This allowed Rao to put a price on discrimination: when well-off students had to choose between more-athletic poor students and less-athletic rich students, past exposure to poor classmates reduced well-off students’ discriminatory behaviors by 12 percentage points. A key aspect of Rao’s study is that the significant behavior change of the well-off students came about not simply from being in classrooms with poor students, but through the mixing that occurred in small study groups – a setting that particularly emphasized interaction and cooperation. Implicit Association Tests (IATs), discussed above, are another powerful tool used in lab-in-the-field experiments to study the impact on implicit attitudes of living with, or near, members of racial and ethnic outgroups. Corno et al. (2022) found that random assignment to a Black roommate in a South African university reduced White students’ negative stereotypes of Black people and increased inter-racial friendships while also improving Black students’ academic outcomes. Barnhardt (2009) examined the consequences of a policy that caused Muslim and Hindu families to be housed together in a public housing complex in India. A range of levels of diversity within four-unit complexes allowed her to show that greater exposure to Muslim households improved Hindus’ explicit attitudes about Muslims and reduced the implicit bias of Hindu children against Muslims. Scacco and Warren (2018) reached a less optimistic conclusion about the impact of interactions in classroom settings, and yet the policy implications of their study are the same as those of the studies discussed above. The authors conducted a remarkable field experiment in the riot-prone city of Kaduna, Nigeria, that gave more than 800 young Christian and Muslim men 16 weeks of valuable computer training. They randomized recruitment into the program, assignment to a religiously homogeneous or heterogeneous classroom, and assignment to a co-religious or non-co-religious learning partner. Then they measured discrimination through two behavioral games: a dictator game and a destruction game. The dictator game measures altruism and norms of fairness, and the destruction game is its mirror image – it was designed to mimic aspects of riot behavior in which participants can choose to obtain a small personal benefit at a large cost to another person. At the end of the training, students assigned to mixed classrooms discriminated significantly less against outgroup members than subjects assigned to homogeneous classrooms. But there was a catch: students in mixed classrooms did not actually lessen their discrimination compared to a third group of control students who didn’t participate in the program at all. Rather, students in homogeneous classrooms actually began discriminating more after they finished the training. Unlike in the previous studies, where social contact increased cooperation, in this study, it seems that time spent with in-group members increased discrimination against outgroup members. The study thus yields the same policy implication as the prior studies but reaches it for a different reason: mixed groups may be helpful because they reduce opportunities for in-group bonding that strengthens discrimination.
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The Influence of Radio on Ethnic Divides in Rwanda A final example demonstrates that even fictional examples of intergroup cooperation can change the associations that inform behavior. In Rwanda, colonial practices divided a population along ethnic lines and fostered resentment between the two groups, known as Hutus and Tutsis. In 1994, the tensions erupted in a civil war. As many as one million people and a shocking 70% of the Tutsi population were slaughtered in just a few months. In terms of the percentage of the population killed, it was one of the worst genocides in recorded history. Analysis of a natural experiment demonstrated that a radio station that broadcast inflammatory messages calling for the extermination of the Tutsi minority incited violence (Yanagizawa-Drott 2014). A field experiment demonstrated that an edutainment radio soap opera could shift attitudes in the other direction by changing people’s perception of appropriate social norms regarding marriage and trust with outgroup members (Paluck and Green 2009a). In a series of lab-in-the-field experiments, Blouin and Mukand (2019) provided more precise evidence of the soap opera’s impact on interethnic relations. They used a number of cutting-edge methodological tools to investigate whether people living in areas that received the soap opera radio emissions exhibited systematically different behaviors than those living in areas that did not. The first test was an “ethnic salience” test (similar to the memory confusion protocol mentioned earlier) in which subjects viewed pictures of Hutu and Tutsi men along with statements about each (e.g., “This person owns a blue bicycle,” or “This person really likes bananas but dislikes guava”). Then subjects encounter a surprise recall test asking them to identify which statements were made by which individuals. The idea is that people living in communities where ethnicity is very relevant are more likely to notice (“encode”) ethnicity while observing the picture/statement pairs. Then, in the recall test, those individuals are expected to make more “within ethnicity” than “between ethnicity” mistakes – that is, they will be more likely to confuse a Hutu with another Hutu than with a Tutsi. The test’s critical measure is the ratio of “within category” to “total” misattributions; higher ratios are thought to reveal a higher level of ethnic identification in a community. The test showed that ethnic relevance was lower in regions that had received the emissions of the government’s soap opera; exposed individuals were 10–13 percentage points less likely to categorize others on the basis of their ethnicity. The soap opera appears to have altered private behavior, as well as public behaviors. One of the key criticisms levelled at Rwanda’s nation-building campaign is that it is doing little to change interethnic sentiments; detractors argue that people simply mask their true feelings and pretend to get along in order to avert government attention. The researchers showed this was unlikely to be the case. They asked villagers to play a trust game. Two people play the game together. One person is randomly selected to be the sender and the other to be the receiver, and players are told the rules of the game. Player 1 is given the money equivalent of about a day’s wages and told to transfer as much as she’d like to Player 2 and keep the rest. An assistant matches the amount transferred, and then Player 2 chooses how much of
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that sum to keep and transfers the remainder back to Player 1. Thus, in this game, if Player 2 is trustworthy, both players gain if Player 1 makes a large transfer to Player 2. In the public version of the game, Player 1 knows that the amount she transfers will be made public and thus reveal how trusting she is. In the private version of the game, Player 1 knows that her transfer will never be made public by the experimenters; thus, she need not fear community disapproval if she shows that she has low trust. Villagers living in regions not exposed to the soap opera offered on average 25% more in the public version of the game than they did in the private version of the game. But there was no such gap in offers made by villagers in regions that had received the soap opera emissions. Moreover, on average, interethnic trust offers in the private trust game were 47% higher in areas that had received the emissions from Radio Rwanda. The results of these experiments, like the others described in this section, suggest that our mental models of “other” racial and ethnic groups are malleable and depend on our experiences. This is a critical insight for policy. If ethnic preferences shift with experiences, changing the experiences that people have as a result of public institutions and policies has the potential to improve ethnic relations. The studies support Kurzban et al.’s (2001) view that what looks like ethnic discrimination may in fact be discrimination based on perceived cooperative potential. Direct experience with an outgroup, or a means of punishing non-contributors, reduced discrimination against outgroup members in all of the studies examined here. While the universe of high-quality research designs does not yet provide sufficient evidence to know whether, and to what depth, intergroup contact might reliably reduce prejudice (Paluck et al. 2019), the findings of existing lab-in-the-field studies support a cautiously optimistic outlook.
Conclusions This chapter shows how lab-in-the-field experiments have revealed the influence of categories, stereotypes, and prior experiences on perception and discrimination. Sociologists, anthropologists, and psychologists have long known that humans categorize everything and everyone they see. Until recently, behavioral scientists across disciplines lacked a scientific way to identify the conceptual structures in human minds, and, therefore, economics took limited account of the large role of categorization in discrimination. In the rational actor models of discrimination – i.e., taste-based and statistical discrimination – it is difficult to see how policies can reduce discrimination and improve relations between groups in society. Much has changed with the advent of a behavioral model of discrimination and with new methods that precisely measure attitudes and stereotypes. Lab-in-the-field experiments that use memory tests, IATs, and behavioral games can quantify bias and predict discrimination. Humans see the world through socially created lenses in which people are categorized and associated with traits that are emblematic of the category in the observers’ minds. These associations may persist indefinitely without an exogenous shock. Social scientists in the twenty-first century can study the biases
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Table 1 Three kinds of discrimination
Conscious only Both conscious and unconscious/ implicit Biased perception Updating of beliefs
Rational actors Taste-based discrimination ✓
Statistical discrimination
Intuitive actors with cultural mental models Schematic discrimination
✓ ✓
✓ ✓
Yes, but with Bayesian updating, the process may be slow
Sources
Exogenous, unexplained
Imperfect information about individuals in the presence of known and validated differences between demographic groups
Consequences
Discrimination based on preferences
Discrimination stemming from a desire to maximize efficiency. Increases in information about individuals and/or reductions in group-level differences reduce statistical discrimination
Updating may be slow or may not occur at all unless there is emotional engagement that evokes empathy Historical events; social patterns and social constructs; a desire to wield power over other groups; the desire to hold positive attitudes about one’s own group Discrimination stemming from cultural associations. It occurs at or below the level of consciousness and may be unintended. It is reduced by helping people experience collaborative intergroup contact, which tends to increase intergroup empathy and subjective expectations of intergroup cooperation
in human judgments and perceptions scientifically. Lab-in-the-field experiments help them get inside the heads of decision-makers to identify the causal mechanisms that drive discrimination. Lab-in-the-field experiments are a central tool to study how discrimination can be reduced by creating new exemplars and interactions that overturn existing associations and promote cooperation across group divides. Table 1 presents a typology of discrimination: taste-based, statistical, and schematic. This chapter has emphasized schematic discrimination. In most of the examples, categories, stereotypes, narratives, and identities function as lenses that mediate human experience. The lenses influence discrimination by shaping perception and construal.
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Cross-References ▶ Discrimination as Focal Point ▶ Experimental Evidence on Affirmative Action ▶ Taste-Based Discrimination
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Methodological Approaches to Understanding Discrimination: Experimental Methods – Trust, Dictator, and Ultimatum Games
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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Minimal Group Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Natural Identities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dictator Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Trust Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ultimatum Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Abstract
The chapter provides an overview of the literature that uses experimental implementations of the dictator, ultimatum, and trust games to study patterns of discrimination. The focus is on work using lab-induced and natural identities to study group-sensitive behavior. Methodological issues along with results and insights are emphasized. Keywords
Dictator game · Ultimatum game · Trust game · Discrimination · Identities · Experiments
A. Borah (*) Department of Economics, Ashoka University, Sonipat, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_17
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Introduction Although nothing in the way of the foundations of rational choice theory demands that individual behavior be exclusively selfish, research and teaching in the neoclassical tradition has proceeded mostly with the assumption of selfish behavior as the benchmark. That this is a descriptively inaccurate assumption has never been in doubt. However, early criticisms of this approach were often met with the response that this is a useful approximation of human behavior and, as such, a reasonable working assumption. Whether the last claim is valid or not, of course, is an empirical question. The dictator, ultimatum, and trust games, and the elicitation of behavior in them through lab and field experiments have formed the discipline’s most tried and tested methodology in answering this empirical question – as it turns out markedly in the negative. Together, they have provided strong evidence suggesting that a considerable fraction of decision makers have socially minded preferences, or social preferences, for short. One reason behind the appeal of these games and their popularity in the literature is their simplicity. All three are two-player games. In the dictator game, one of the players, appropriately termed the dictator, is endowed with a fixed amount of a desirable divisible good (say money). It is up to the dictator to decide a split of the endowment between herself and the other player, i.e., decide what amount of the endowment, if any at all, she wants to give to the other player. In essence, therefore, the dictator game is simply a decision problem. The earliest experimental evidence of this game in this form comes from Forsythe et al. (1994) and, subsequently, numerous other experiments have been run based on it. These experiments show that, on average, dictators tend to give about 20% of the endowment to the other player (Camerer 2011). Given that players are matched anonymously in these experiments, the evidence suggests that individuals do not care about just their own selfish outcomes, but their preferences extend to concerns like fairness and altruism. The story gets a little more involved in the ultimatum game. In it, one of the players (the proposer) makes an offer to split the endowment with the other player (the responder). The responder then has to decide if she wants to accept or reject the proposal. In the former case, both players get the specified amounts under the offer, but in the latter, both walk away with nothing. The first set of experimental results for this game came from Güth, Schmittberger, and Schwarze (1982). In general, one finds that the median offers of proposers are around 40–50% and mean offers are 30–40%. Offers of 40–50% are rarely rejected. Offers below 20% or so are rejected about half the time (Camerer 2011). The interpretation of outcomes in the ultimatum game is a little more subtle than the dictator game. Here, positive offers need not necessarily signify concern for the other person’s outcomes in a deep sense. Instead, it may simply capture a rational response to the fact that she may reject small offers. On the other hand, rejection of positive offers by the responder signifies social attitudes. Presumably, such offers are rejected because the responder has distributional concerns and cares about the fairness of the allocation.
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As far as the trust game goes, in its standard form, the first player (the investor) once again is given an endowment. She can send the second player (the trustee) any fraction of that endowment. The amount sent is multiplied by the experimenter (typically by a factor of three). The second player then has to decide how much of the multiplied amount to return to the first player. This simple game, first introduced by Berg, Dickhaut, and McCabe (1995), provides an elegant way to measure trust and trustworthiness – specifically, the amount sent by the first player measures trust, while the amount returned by the second player measures trustworthiness or reciprocity. Berg, Dickhaut, and McCabe (1995) found that investors invested about 50% of their endowment, whereas the average amount returned by the trustee was about 95% of the invested amount. In the experimental implementation of these games, in their standard form (as mentioned above), details about the identities of the two players – for instance, their gender, religion, race, etc. – remain unspecified and anonymous. The only identity attributed to subjects in them is the superordinate one of simply being human. Therefore, the results that we get in them provide us an insight into the social attitudes of individuals when they view these interactions at the level of dealing with abstract individuals. However, real-world interactions involving the expression of attitudes like altruism, fairness, trust, and reciprocity are often not viewed through the prism of abstract individuals. Individuals in such interactions have identities associated with them. Very often such identities are perceived through the prism of an “us” versus “them” or an ingroup–outgroup divide, because of which a decision maker’s social attitudes may vary depending on the identity of the individual/s she is interacting with. If it is someone from her perceived ingroup, positive sentiments may be particularly heightened. On the other hand, if it is someone from her outgroup, these sentiments may be much more subdued or nonexistent. In other words, such attitudes and sentiments may have a discriminatory nature to them. Luckily, the structure of all three games is flexible enough that experimenters can incorporate such aspects of identity into them to study groupsensitive and discriminatory attitudes. When incorporating identities in such experiments, there are two main design choices that experimenters have drawn on. The first is to endogenously create artificial identity categories as part of the design of the experiment. The most prominent approach in this regard is the minimal group paradigm. The other is to use naturally existing real-world identities like gender, caste, religion, ethnicity, nationality, and the like. When this second approach is used, experimenters often rely on a technique called priming to make such identities salient in the experiment. This technique involves exposing subjects to some feature of the identity sought to be invoked through text or other audiovisual means or by making them perform a task to make this aspect of their identity salient. The chapter starts in section “Minimal Group Paradigm” with a discussion of the minimal group paradigm and how it provides insights on intergroup discrimination. To illustrate how this approach has been used in experiments that use simple games of the type discussed here, we will focus in detail on a leading paper by Chen and Li (2009) that uses this approach. Section “Natural Identities” highlights how natural
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identities have been used and made salient in experiments that have employed these games. After that, Sections “Dictator Games,” “Trust Games,” and “Ultimatum Games” will discuss more learnings and insights about discrimination developed in experiments using, respectively, the dictator, trust, and ultimatum games.
Minimal Group Paradigm The minimal group paradigm (MGP) originated in social psychology. As is well known, social psychologists have for long emphasized that many aspects of individual behavior and personality may be socially determined. Specifically, to them, the notion of a group or group identity is not a superfluous concept in the sense of simply being a by-product of individual interaction, interests, and interdependence. Rather, groups exert a constitutive psychological influence on individual attitudes and behavior. In making this case, social psychologists have assembled an impressive body of evidence. The minimal group paradigm studies by Tajfel et al. (1971) are a leading example of this. They were designed with the purpose of identifying the minimal conditions for intergroup discrimination. In their original studies, they wanted to add variables incrementally to see at what point group favoritism would occur. To that end, the groups in their control setting were so stripped down and (apparently) empty of psychological significance that no discrimination was expected. In particular, subjects were divided into groups on the basis of trivial, ad-hoc, and random criteria such as, for instance, the result of a coin-flip. The group classification in this control setting became popularly known as the minimal group. The conditions under which a group is minimal include: (1) random nonoverlapping group assignments based on trivial tasks; (2) no interactions between subjects; (3) anonymous group membership; and (4) no link between chooser’s self-interest and her choices (Tajfel and Turner 1986; Chen and Li 2009). Despite this meaningless categorization, the authors found surprising evidence for ingroup favoritism and discriminatory behavior towards the outgroup. In many of the experiments, subjects were asked to anonymously divide a fixed sum of money between a member from their ingroup and one from their outgroup, who remained anonymous except for their group membership. In such settings, subjects chose to allocate as much as 70% to the ingroup member. As Turner writes, “it appears that imposing social categorizations on people (even on an explicitly random basis) produces discriminatory intergroup behavior, intragroup cohesion in the form of more positive attitudes toward and more reported liking of ingroup than outgroup members, ethnocentric biases in the perception, evaluation, and memory of ingroup and outgroup, and an altruistic orientation toward ingroup members” (Turner 1985, pp. 83–84). In these minimal group experiments, the choice problem facing the decision maker is a pure allocation task of dividing a sum of money between two other individuals. Her choice has no bearing on her own outcomes. Chen and Li (2009) extend this class of experiments to relax this assumption. They elicit group-sensitive social attitudes based on choices in simple games of the type we have been discussing here which feature nontrivial trade-offs between own outcomes and
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those of others. In so doing, they synthesize the social psychology approach with that of the social preferences literature in economics. Since this work has important conceptual lessons, we will now delve into it at some length and focus on the design of the experiment before going on to their findings. The experiment had five treatments and one control. In the treatment sessions, there were four stages. The first was a group assignment stage. The second was a collective problem solving stage among members within a group to enhance the salience of group identities. The third was an other-other allocation stage in the spirit of the MGP studies. The fourth was a set of two-person games that included the dictator game and other games that measured reciprocity. While subjects in different treatments participated in different stages, subjects in the control group participated only in the fourth stage. All five experimental treatments contained the group assignment stage. Two different group assignment methods were tried. In the Original Treatment, subjects were shown five pairs of paintings by two artists, Paul Klee and Wassily Kandinsky, without being told the artist of each painting. They were then divided into two distinct groups depending on their preferences over these paintings. To measure the difference between such a group assignment based on painting preference and random assignment, two treatments with random group assignments were also implemented – a Random Within treatment and a Random Between treatment. In this assignment, each participant randomly drew one envelope from a stack, where each envelope contained a Maize or Blue slip. The color of the slip in their draw determined their group assignment. The second stage designed to enhance group identity involved communication within the members of the group via an online chat program. This was implemented in all three of the Original, Random Within, and Random Between treatments. Specifically, subjects were provided with two additional paintings and had to answer two questions about the artists who painted them. They were given 10 min to communicate with other members from their group via the online chat program to help them answer the question correctly. These answers were incentivized with correct answers being rewarded. To assess the impact of this task, a No Chat treatment was added that did not include this stage. In the third stage of the Original, Random Within, Random Between, and No Chat treatments, each subject was asked to allocate a given amount of experimental currency between two anonymous participants under three situations: (i) both of them were from her group, (ii) both were from the other group, and (iii) one was from her group and the other from the other group. Together, the second and the third stage were employed to enhance the group identity induced in the first stage. To see their impact, a No Help treatment was added, which did not include these two stages. Further, a comparison of the No Chat and No Help treatments allowed the authors to identify the effects of the other-other allocation task in the third stage. A key question that interests researchers in such studies where lab-induced identities are assigned is about what aspects of the design make these identities salient. In this context, it is worth paying particular attention to the different treatments that Chen and Li (2009) introduce to get a handle on this question. It is something we will come back to below.
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Fig. 1 Positive Reciprocity
E
N
B 725, 0 R
L
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Fig. 2 Negative Reciprocity
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Finally, in the fourth stage, subjects made decisions in 24 two person sequential games drawn from Charness and Rabin (2002) to analyze the impact of group identity on social preferences and economic outcomes. Of these 24, there were 5 versions of discrete dictator games that required the dictator to choose between: (400, 400) vs. (750, 400); (400, 400) vs. (750, 375); (300, 600) vs. (700, 500); (200, 700) vs. (600, 600); and (0, 800) vs. (400, 400). In these pairs, the first payoff is that of the recipient (Player A) and the second of the dictator (Player B). In addition, there were sequential games of the form shown in Figs. 1 and 2. In these games, player A has to first choose between enter (E) and not enter (N ). If she chooses N, the game ends. On the other hand, if she chooses E, the game comes to player B, who has to then choose between L and R. Observe the difference between the game in Figs. 1 and 2. In Fig. 1, A’s choice to enter is associated with
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good intentions, for if she were to choose N, then B’s payoff would have been 0, whereas following E, it is at least 375. On the other hand, in Fig. 2, A’s choice to enter reflects bad intentions since by entering she reduces B’s payoff from 750 to no more than 200. Hence, the first game may induce positive reciprocity on the part of B who may be willing to choose R and a lower payoff for herself in order to reward A for her good intentions. On the other hand, the second game may induce negative reciprocity on the part of B as she may be willing to choose R and a lower payoff for herself in order to punish A for her bad intentions. In each game, subjects were randomly matched with another participant and were randomly assigned the role of player A or B. In all treatments except Random Between, each subject made decisions under two situations: (i) the match is from the ingroup and (ii) the match is from the outgroup. In order to interpret the results, Chen and Li (2009) draw on the social preferences literature and consider a preference representation for player B in the spirit of the representations in Charness and Rabin (2002) and Fehr and Schmidt (1999). In the context of social preferences, the following type of preference representation has been extensively considered: uB ðxA , xB Þ ¼ xB ðρr þ σsÞðxB xA Þ where r ¼ 1, s ¼ 0 if xB xA, and r ¼ 0, s ¼ 1, if xB < xA. The interpretation of xB xA is that it represents a situation of advantageous inequality for B. In this situation, a socially minded B may feel a sense of guilt and she may be inclined to be charitable towards A. The parameter ρ measures B’s sensitivity towards advantageous inequality, i.e., her sense of charity. On the other hand, xB < xA captures a situation of disadvantageous inequality for B and, in this case, she may feel a sense of envy. The parameter σ measures B’s sensitivity towards disadvantageous inequality, i.e., her sense of envy. A priori, we may expect that ρ > 0 and σ < 0. Chen and Li (2009) adapt these class of preferences to accommodate concerns about group identity. Specifically, they model B’s preferences by: uB ðxA , xB Þ ¼ xB ðρð1 þ IaÞr þ σ ð1 þ IbÞsÞðxB xA Þ where I ¼ 1 if A is from B’s ingroup and I ¼ 0 if A is from B’s outgroup. In other words, the parameters, a and b, capture the additional ingroup effect for charity and envy, respectively. For example, when B receives a higher payoff than A, the parameter ρ measures the charity effect for an outgroup match, while ρ(1 þ a) measures the charity effect for an ingroup match. With that background, we can use the choices of subjects in player B’s role in the experimental games to estimate the preference parameters. The table below from Chen and Li (2009) provides the maximum likelihood estimates of these parameters (Table 1). The top Panel A provides the estimates of the charity and envy parameters for the control group. The charity parameter, ρ, is 0.427 for the control group, while in the Original treatment, it is 0.323 for outgroup matches and 0.474 for ingroup matches.
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Table 1 Estimates of charity and envy parameters Panel A Control (N ¼ 536) Panel B
Treatment (N ¼ 1896)
Charity ρ 0.427 (0.022)a Outgroup charity ρ0 0.323 (0.021)a
Envy σ 0.049 (0.025)b Outgroup envy σ0 0.112 (0.019)a
Ingroup charity ρ0 (1 þ a) 0.474 (0.018)a
Ingroup envy σ 0 (1 þ b) 0.008 (0.021)
Identity parameters a b 0.467 0.931 (0.112)a (0.192)a
Notes: Panel A reports estimates for the control sessions without identity, while panel B reports estimates for treatment sessions with identity a Significant at the 1% level b Significant at the 5% level c Significant at the 10% level
Similarly, the envy parameter, σ, is 0.049 for the control group, while in the Original treatment, it is 0.112 for outgroup matches and 0.008 for ingroup matches. The effect of group identity on charity is measured by the parameter a ¼ 0.467 and that on envy is given by parameter b ¼ 0.931. This indicates that when participants have a higher payoff, they show a 47% increase in charity concerns towards an ingroup match, compared with an outgroup match. Similarly, when participants have a lower payoff, they show a 93% decrease in envy for an ingroup match. Hence, these results allow us to reject the null hypothesis that group identity has no influence on distribution preferences. Specifically, participants’ charity (envy) towards an ingroup match is significantly greater (less) than that towards an outgroup match. To investigate group sensitive attitudes towards reciprocity, the games of positive and negative reciprocity outlined above were separately considered. In positive reciprocity games, it was found that participants were significantly more likely on average to reward ingroup than outgroup members for good behavior. In the logit estimates, player B’s likelihood to reward A in such games was found to increase by 18.6% if A happened to be from B’s ingroup. At the same time, in some of these games rewarding A necessitated that B’s payoff falls below A, i.e., B faces disadvantageous inequality. In those cases, it was found that the negative effect of envy on positive reciprocity is, in fact, stronger towards an ingroup match. In negative reciprocity games, subjects were also found to be significantly more forgiving towards misbehavior from an ingroup than an outgroup member. In such games, player B is 12.8% less likely to punish A for misbehavior if A happened to be from her ingroup. Further, B’s likelihood of punishing decreases with advantageous inequality, and this effect is stronger for ingroup members. Finally, we consider the important question of what generates group effects. The authors exploit the different treatments and games in their design as well as a postexperiment survey, where subjects were asked to report the strength of their ingroup
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attachment on a scale of 1–10, to answer this question. The first question of interest in this regard is whether the group assignment method matters for the results. To answer it, they compared results in the Original treatment, where group assignments were made based on painting preferences, and the Random Within treatment, where groups were formed based on random assignments. No significant difference in the proportion of participants who differentiated between ingroup and outgroup matches were found between these two treatments. Further, the two methods of group assignment did not have any significant impact on self-reported group attachments. Therefore, at least for the class of games analyzed here, random assignments seem to do just as well to generate the group effects and, given its simplicity, can be preferred. The next question that deserves attention is whether categorization into groups by itself is sufficient to produce group effects, or is it necessary for group members to interact and build solidarity by, say, helping each other like in the online chat task. To analyze the effect of the online chat stage, the No Chat and Original treatments can be compared. It was seen that there was no significant difference in the proportion of participants showing ingroup favoritism or outgroup discrimination in these two treatments. At the game level, there was evidence in only one of the 24 games that the online chat stage leads to significantly stronger ingroup favoritism. The chat stage, however, was found to generate significantly higher self-reported group attachment, which can be considered as the affective aspect of the group identification process. The other-other allocation task was also found to have no significant effect on participant behavior, nor the self-reported attachment to groups. Finally, on comparing the No Help and Original treatments, it was found that a (weakly) significantly larger proportion of subjects from the No Help treatment made group contingent decisions, compared to the Original treatment. The authors suggest that this may be because group effects induced by categorization have a tendency to deteriorate over time. So this larger proportion might be due to the fact that the game stage followed right after the group assignment stage in the No Help treatment. Next, we focus a bit on work that has looked into the connection between patterns of discrimination induced via lab-generated identities and their relationship with natural identities. Kranton and Sanders (2017) perform such an exercise. They conduct MGP style experiments with simple dictator game like allocation tasks as in Chen and Li (2009). However, unlike the latter, they do not just estimate average behavior. Rather, using a within subject design, they study individual behavior and are able to tightly classify individuals as “groupy” and “non-groupy.” Individuals are deemed to be “groupy” (“non-groupy”) when their estimated utility-type is different (not different) when allocating income to someone in their group than when allocating income to someone out of their group.1 They then use subject-specific data to investigate psychometric, demographic, and economic correlates of this groupy versus non-groupy distinction. Interestingly, they find that non-groupy participants are not distinguished by any basic demographic, nor by any of the Big Five
1
For instance, suppose they are willing to sacrifice their income to reduce advantageous inequality when interacting with an ingroup partner but not when they are interacting with an outgroup partner.
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personality factors, but are distinguished by lack of political affiliation. Independents are least likely to be groupy, and this difference is significant in their nationwide US sample. On the other hand, participants who are groupy are more likely to identify as Republicans or Democrats, more so the former. Another interesting observation that emerges from their experiment is that participants in the US counties with large drops in the share of employment in manufacturing also are more likely to be groupy. These findings are reaffirmed in the experimental findings reported in Kranton et al. (2020). In this experiment, along with the MGP treatment, there was also a political treatment which divided subjects into groups based on their political leanings. They find that political party members showed more ingroup bias than independents who professed the same political opinions. At the same time, the greater bias of these individuals was also present in an MGP treatment with lab-induced identities. In other words, groupys (non-groupys) remain so irrespective of whether group classifications are made based on natural affiliations or lab-induced ones. Another interesting contribution using the MGP paradigm is Currarini and Mengel (2016), who attempt to uncover the relationship and interplay between homophily and ingroup bias. They set up an experiment that records subjects’ behaviors towards ingroup and outgroup members in social preference games of the type in Charness and Rabin (2002) under two different matching institutions. Subjects were first randomly assigned a group (RED or BLUE), after which two different matching treatments were employed. Under the first matching protocol (EXO), one’s partner (RED or BLUE) was determined randomly. Under the second (ENDO), each subject’s partner preference and their willingness to pay (WTP) for the same were elicited and were then paired with either a RED or BLUE member (with probabilities based on their WTP). The authors find that even under their MGP set-up, in the ENDO treatment, 45% of the subjects were homophilous with a positive WTP. Further, they find that whereas in the EXO treatment there is significant evidence of ingroup bias, with subjects 34% more likely to act positively reciprocal and 39% less likely to act negatively reciprocal in ingroup matches compared to outgroup matches, aggregate ingroup bias either diminishes or totally vanishes (statistically) in ENDO. Hence, to the extent that participants’ expectations are correct, the strong preference for homophily cannot be entirely explained by the anticipation of ingroup biases. The authors, therefore, contend that the nature of the matching institution, specifically the ability to self-select into groups, may produce a shift in behavior and a reduction in ingroup bias. In other words, and perhaps counterintuitively, their evidence suggests that homophily may not be brought about by the anticipation of ingroup bias. Rather, the possibility to express homophilous preferences may end up reducing such biases.
Natural Identities The chapter will now look at ways in which natural identities have been used in lab or field experiments to determine group-based attitudes in the dictator, ultimatum, and trust games. The key question that confronts experimenters in this regard is how
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to make natural identities salient in an experiment. This is a nontrivial task for several reasons. First, there is no guarantee that such identity-based attitudes are cognitively salient to subjects at the time they participate in the experiment: how does the experimenter make them salient? Second, the strength of identity-based attitudes may vary across people, even if they are all marked by that dimension of identity. For instance, we are all marked by race, but our race sensitive attitudes are not of the same magnitude. If these differences matter to the question that the experimenter is interested in, then how does she elicit this information? Third, people’s natural identities are multidimensional. If the experimenter’s goal is to activate only one or two dimensions of that identity, how does she go about doing that? We will now look at a few design choices that have been employed by experimenters to get at these questions in the context of the games under consideration. One prominent way in which natural identities can be made salient in experiments is by using the family names of subjects. In the context of many identities, family names reveal the specific identity category to which an individual belongs. For instance, in the context of the caste system in India, an individual’s name very often reveals the caste to which she belongs. As it turns out, names not only reveal identities but given how central names are in the context of social cognition, also produce nontrivial identity based effects. To illustrate this, consider the following experiment reported in Fershtman and Gneezy (2001) that sought to examine ethnic discrimination in Israeli Jewish society by simultaneously using the trust, ultimatum, and dictator games. The two major Jewish ethnic groups are Ashkenazic Jews (European and American immigrants and their Israeli-born offspring) and Eastern Jews (Asian and African immigrants and their Israeli-born offspring), with the former better placed on multiple dimensions compared to the latter. To check for discrimination, the authors formed two groups by enlisting students with typical ethnic names from two different sets of universities. A student from the first group was then randomly matched with a student from the second group to form pairs. Each subject in a matched pair was told the name of their partner, which in the Israeli context, provides an indication of ethnic affiliation. The other thing that names reveal is the gender of the subjects, a fact that the experimenters also utilize. The authors find that in the trust game, out of an endowment of NIS 20, the average transfer to an Ashkenazic male partner was 15.15 whereas the average amount transferred to an Eastern male partner was 8.06. When attention was restricted to male senders, the transfer amounts were 17.16 and 5.62, respectively. Further, the mistrust of Eastern origin men was found to be common among men of all ethnic origins, including Eastern origin ones. At the same time, no evidence was found that for any given amount received, Eastern male partners sent back an amount that differed significantly from that received by an Ashkenazic male partner. This, therefore, rules out statistical discrimination as an explanation for the difference in amounts transferred to Eastern male partners. Could the explanation be in terms of a taste for discrimination? To test this the authors implemented a dictator game with the same matching protocol as in the trust game. The results of the dictator game showed that although there is some differential treatment of groups by ethnicity,
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there is no clear systematic taste for discrimination. The distribution of transfers to the Ashkenazic players was only marginally different from the distribution of transfers to Eastern players. The average transfers were similar. Therefore, the conclusion from simultaneously interpreting the results of the trust and dictator games is that the discrimination seen in the former is because of mistaken ethnic stereotypes associated with Eastern origin subjects and neither attributable to a taste for discrimination nor statistical discrimination. Interestingly, another dimension of ethnic stereotyping came out from the analysis of the ultimatum game that was administered using a similar matching protocol. Evidence of ethnic discrimination was found in this game, with Eastern players receiving larger transfers than Ashkenazic players. This discrimination, the authors contend, was the outcome of a common ethnic stereotype in Israeli society according to which men of Eastern origin are believed to react more harshly if treated unfairly. Finally, another key finding of the paper is the gender differences that were observed. First, no evidence of ethnic discrimination towards women was found in the trust game when they played the role of trustees. Second, Ashkenazic women are less trusted than Ashkenazic men, whereas Eastern women are more trusted than Eastern men. Further, women’s trust in their game partners was not found to be based on ethnic affiliation or on gender. Accordingly, women trust Ashkenazic male players less than men do and trust Eastern male players more than men do. A technique that experimenters often use to make natural identities salient in experiments is priming whereby some aspect of identity that is relevant for the study is made cognitively salient for subjects in a subtle way. One way of doing this is by exposing them to textual or audiovisual representations associated with that identity, or by making them perform a task that makes aspects of that identity salient. For instance, consider the following dictator game experiment conducted by Fong and Luttmer (2009) that was designed to examine the role of racial group loyalty on generosity in the United States. Specifically, the study wanted to see how race affected giving to Hurricane Katrina victims. In the study, to experimentally manipulate subjects’ beliefs about race, income, and worthiness of hurricane victims, they were first exposed to an audiovisual presentation with photographs of people after the hurricane along with an audio story about the residents of two cities (Slidell, LA and Biloxi, MS) that were hit by Katrina. The presentation consisted of a slide show with eight photographs of victims that were mainly White in one treatment condition, and mostly Black in the other. The photos showed devastation caused by Katrina such as flooding and demolished houses as well as relief aid received by victims. The audio information that accompanied the pictures varied along the lines of economic status, political leaning, religiosity, crime rates, and other demographic information for the city and was chosen so as to be potentially correlated with subjects’ perceptions about the racial composition of the city. Another aspect of design that comes out in this experiment is the need that experimenters feel in many situations to elicit subjective measures of identitybased attitudes to check whether variations in these attitudes have explanatory power. For instance, in the context of this study, it may be the case that subjective racial identity may matter more than objective race. There are different ways in
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which experimenters have tried to elicit such attitudes. One way is to administer attitudinal surveys measuring explicit attitudes. However, the drawback with them is that respondents may censor themselves in such surveys so as not to appear bigoted. At the other extreme are implicit measures like the implicit association test (IAT). In this experiment, the authors used a different approach. They asked subjects a simple question to measure subjective racial identification: “How close do you feel to your ethnic or racial group? Very close, close, not very close, not close at all.” Sometimes using a simple measure like this can be useful as it is less demanding to administer than an IAT. At the same time, it presumably does not suffer from the problem of respondents giving biased answers. Subjects then played a dictator game in a laboratory setting in the following form. They were asked how they would like to split $100 between themselves and a charity (Habitat for Humanity) that benefits Katrina victims in the city they saw in the presentation. Interestingly, the authors found no effects of victims’ race on giving, on average. However – and this is why subjective racial attitudes matter – they did find that subjects who report feeling close to their racial/ethnic group give substantially more when the victims are of the same race than those who respond that they do not feel close to their group. Whites who reported being “close” or “very close” to their ethnic or racial group gave roughly $17 less when seeing pictures of Black victims rather than White ones. In contrast, Whites who said they were “not very close” or “not close at all” gave roughly $13 more in response to pictures showing Black victims. On the other hand, Blacks who reported feeling close to Blacks gave $16 more in response to pictures showing Black victims. Blacks who did not feel close to Blacks gave $72 less in response to pictures showing Black victims. These differences are all significant at the 1% level. Another way in which identities have been primed in this class of experiments is by making subjects perform a task or participate in an activity. McLeish and Oxoby (2011) provide an example of this in the context of an ultimatum game experiment which studied the effect of social interactions and salience in social identity on cooperativeness and expectations. They prime the subjects, all students of the same university, by asking them to write for 10 min about either a positive experience with a student from the same university (identity-priming treatment), or a positive experience with a student of another university (distinctiveness-priming treatment), or detailed directions from their dorm room or parking space to the laboratory (no-prime/control group). The first two treatments worked to bring attention to shared or distinct identities, while the control group was designed to not affect the salience of any identity. Subjects then played the ultimatum game. The authors find that subjects are most cooperative and have the highest demands in the identitypriming treatment, and are least cooperative and have the lowest demands in the distinctiveness-priming treatment. This shows that a salient social identity and greater social interactions increases willingness to cooperate, but also increases expectations. Yet another way in which group sensitive attitudes can be elicited in experiments is by drawing on people’s social networks in the real world. An example of such an approach is Glaeser et al. (2000), which attempts to study discrimination as it relates
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to social capital using a trust game. To that end, they conducted an experiment to measure trust and trustworthiness for individuals in different nodes in a friendship network. The subjects were recruited from an introductory economics course at Harvard University and participated in a trust game. At the experiment site, subjects were paired with another subject in the order of arrival (those who arrived together could play together, raising the likelihood that those who knew each other would play with one another). Observe that the design ensures that the subjects know the identity of the other subject in their pair. It is precisely this knowledge that creates variation in social connection with some subject pairs being well acquainted with one another while others being relative strangers. This allows the experimenters to study how different levels of social connection introduce discrimination in trusting behavior. After being paired, subjects jointly filled out a social connection survey which included nine questions about the social links between the partners (for example, mutual acquaintances). They were then separated and played a trust game in which the experimenter doubled the invested amount. Half of the recipients were given the opportunity to send a message (nonbinding promise) to the sender of their intended future action before the sender decided how much to give to the recipient. Their results support the hypothesis that social connection between the sender and the recipient increases both trust and trustworthiness. A one standard deviation increase in the amount of time that the pair knew each other raised the amount sent by 80 cents (approximately 19%) and return ratio by 5% (significant at the 90% and 95% level, respectively). Similarly, the number of common friends had a positive, though statistically insignificant, effect on the amount sent and the return ratio; ten extra friends were found to raise the return ratio by 2.6%. The authors also find a small but insignificant negative effect on the amount sent, but a large and very significant effect on the return ratio when testing whether individuals from different countries trust each other less. Race also was found to have a significant negative effect on the return ratio. 92% of the cases where the recipient sent back nothing occurred when the individuals were of different races. Finally, it was seen that an individual’s social capital – e.g., having better educated parents, working fewer hours for pay, having more friends, etc. – strongly predict the amount of money that as senders they receive back from recipients.
Dictator Games In this section, lessons from other experiments in the literature that have used the dictator game to study group-based discriminatory attitudes are highlighted. One important question that has interested researchers is about how young in life do parochial and discriminatory attitudes emerge. Fehr, Glätzle-Rützler, and Sutter (2013) study this question through a lab dictator game experiment. In their experiment, each subject was matched with an anonymous partner belonging to the same age cohort, and had to choose between two allocations in the three binary choice problems listed below:
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• Prosocial game (giver, partner): (1,1) or (1,0) • Envy game: (1,1) or (1,2) • Sharing game: (1,1) or (2,0) They classify subjects as egalitarian if they choose the allocation (1,1) in both the prosocial and envy games; altruistic if they choose (1,1) in the prosocial game and (1,2) in the envy game; and spiteful if they choose (1,0) in the prosocial game, (1,1) in the envy game, and (2,0) in the sharing game. In order to study the development of parochialism, they implemented an ingroup and an outgroup condition across subjects. While the recipient in the ingroup condition was known to be from the same class (his or her identity remained secret, of course), the recipient in the outgroup condition attended another school, but was in the same grade. Their experimental results show a strong decrease in spitefulness with age. Further, egalitarian considerations peak around the age of 8–11 years. As children move into their adolescent years, they are increasingly influenced by efficiency concerns and altruism becomes more important, with altruistic types being the modal type among 16–17 year olds. When it comes to parochialism, the noteworthy finding of the study is that it emerges in adolescence. What is more, it is particularly prominent among the altruistic types with significant ingroup favoritism evident in this group at the age of 14–15 years, and spitefulness is significantly stronger towards outgroup members from the age of 12–13 years onwards. On the other hand, for egalitarian types, there is no significant difference between the ingroup and outgroup conditions. In other words, there is joint development of altruism and parochialism starting with adolescence. The data from the envy game also reveals that the decline with age in the relative frequency of choosing (1,1) is much steeper for the ingroup than the outgroup condition. This indicates that as subjects get older, they are relatively more willing to accept disadvantageous inequality in the ingroup than in the outgroup condition. Finally, it was seen that females were more frequently classified as egalitarian than males. Continuing with the theme of gender differences in discriminatory behavior, Angerer et al. (2017) conducted an experiment with 824 children from Meran, a bilingual city in Italy, to examine how discrimination rates differ with gender for primary school children. Every subject is anonymously matched with two others from the same school grade but different schools, with the two recipients being from German-speaking and Italian-speaking schools, respectively. This is a variant of the dictator game, and the subject is given four tokens which they have to divide between the two students, and keep nothing for themselves. Although the majority of the students divide the tokens equally, 24.5% of the boys and 16.7% of the girls allocated three of four tokens to the receiver from their own language group. The authors conclude that gender differences in discriminatory behavior emerge early in life, with stronger discrimination by men found in studies with adults having roots in early childhood. In another experiment with children, Friesen et al. (2012) study ethnic discrimination in Canadian children. The children are made to play the dictator game three times, each time with 12 stickers that they have to divide among themselves, a White
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recipient, an East Asian recipient, and a South Asian recipient. Identities are made salient by showing subjects photos of the recipients before the game. The authors find that only 16% of White children and 21% of South Asian children favored each of the other groups, while the percentage was 31% for East Asian children. They conclude that children from the dominant White identity tend to favor White recipients, while East Asian students do not favor recipients from their own ethnic identity and may, in fact, show outgroup favoritism. This reiterates a message we learned from Fong and Luttmer (2009) – it is not ethnicity per se but rather ethnic identification that may drive discriminatory behavior. An important question relates to how an individual’s different identities work to guide group-sensitive behavior. Ravetti et al. (2019) study discrimination in the presence of multiple identities – specifically, ethnicity, and union membership – with coal miners in Mpumalanga Province of South Africa. The experiment’s sample selection relied on three criteria: union membership, ethnolinguistic identity, and gender. Participants were randomly paired, and each subject played an anonymous dictator game posing both as a dictator (50 rounds) and as a recipient (50 rounds). To further study the effect of varying information sets, the authors had three treatments. In the first, dictators had no information about the recipient (first 10 rounds). In the second, the dictator received information regarding the recipient’s ethnolinguistic identity and union status (next 20 rounds). Finally, in the last 20 rounds, the dictators had the option of signaling their own union membership and ethnic status to the recipient after being informed of their counterpart’s characteristics. This treatment was meant to test whether the givers would try to convey their status to recipients (for example, when they were being more generous to a certain group due to their affiliation). The authors found that union and ethnic identity operate in contrasting fashions. Unionization acts as a factor of workers solidarity, with unionized dictators making relatively more generous offers not just when matched with other unionized workers but also with nonunionized workers. On the other hand, ethnicity drives discrimination among workers, with dictators from the ethnic majority group making relatively less generous offers to not just matches from the ethnic minority groups but their own group as well. An interesting question pertains to how group-based attitudes vary with economic circumstances. Aksoy and Palma (2019) conducted a two-period lab-in-the-field experiment with coffee farmers in a small, isolated village in Guatemala. The first period was before the harvest season (scarcity period) and the second period was during the harvest season (abundance period). In both periods, subjects played a sequence of games, one of which was the dictator game. They employed a 2 2 within-subjects design with subjects playing four rounds of the dictator game with: (1) an ingroup recipient and (2) an outgroup recipient; and during (1) abundance and (2) scarcity periods. The groups were formed based on natural village identities. They found that in the abundance period, subjects gave significantly higher amounts to ingroup members (about 33% of the endowment) than to outgroup members (about 22% of the endowment). In the scarcity period, however, this favoritism fades, with a statistically significant increase in giving towards the outgroup member (about 31% of the endowment) whereas giving towards the ingroup member does
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not change. They conclude that scarcity eliminates the ingroup bias in prosocial behavior, notably by an increase in giving towards the outgroup rather than a decrease in giving towards the ingroup. Another important question pertaining to discrimination is whether it is driven more by positive attitudes towards the ingroup or negative ones towards the outgroup. In a field experiment, Grosskopf and Pearce (2017) attempt to answer this question by studying discrimination based on ethnicity among the poorest people in England. Subjects are first asked to divide a sum of money between two strangers; they then play the standard dictator game with another stranger. In both rounds, the ethnicity of the stranger(s) was varied. The ethnic identity of the recipient was revealed to the subjects through the surname of the recipient, which was of either Western European or English origin (e.g., Smith), or of Muslim origin (e.g., Islam). They found that subjects give around half as much to recipients with Muslim surnames as compared to those with English surnames or those who are anonymous. This evidence allowed them to conclude that observed discriminatory attitudes are guided more by outgroup negativity rather than ingroup favoritism. Kolstad and Wiig (2013) attempted to study whether education reduces ingroup favoritism by conducting a field experiment in Luanda, Angola. The experiment consisted of two rounds of an anonymous, one-shot standard dictator game. In the first round, subjects had to divide a sum of money between themselves and someone from the same credit group, and in the second round, they did so with someone from an outside credit group. The results support ingroup favoritism among microcredit clients. Moreover, there is a positive causal effect of education on ingroup favoritism, which goes against the belief that education broadens an individual’s worldview. Ben-Ner et al. (2009) investigated favoritism for ingroup versus outgroup along multiple identity categories (body type, political views, nationality, religion, etc.) in four alternative contexts, one of which was the dictator game (The other contexts were working with another person on a critical project, commuting with another person, and sharing an office with another person). They found that ingroup members are treated more favorably than ones from the outgroup in all the four context and in nearly all identity categories, with family and kinship, political views, religion, sports-team loyalty, and music preference being the significant identity categories that produce discrimination. Surprisingly, the effect of gender was found to be insignificant. This section concludes by noting an important insight from Ockenfels and Werner (2014), who investigated the role of beliefs on ingroup favoritism using a dictator game. In keeping with existing literature, they found that if the identities of the dictator and recipient are publicly known, then dictators discriminate in the favor of ingroup members. However, there is substantially less ingroup favoritism if the dictator is informed that the recipient is unaware of the shared group membership. Moreover, in an environment where dictators are ex ante uninformed about the recipient’s identity, when given the choice, they prefer to avoid information about it if it guarantees that their identity is concealed from the recipient. These results, therefore, indicate that ingroup favoritism is not just a simple function of shared
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group identity but rather the belief of the dictator about the recipient’s belief (that is, the dictator’s second-order belief) is also an important driver of it.
Trust Games Some further lessons learnt from trust games are presented in this section. To understand the role of religiosity in trust, Chuah et al. (2016) conducted a binary trust game experiment with “low” and “high” stakes. To study discriminatory attitudes, they used different social identities, with religion and religiosity being their main variable of interest.2 They measured religiosity based on the denominationrobust eight-item instrument by Rohrbaugh and Jessor (1975) which takes into consideration different dimensions of religion (such as belief, ritual, etc.) and delivers an individual’s score between 0 and 32. The authors found that senders of all levels of religiosity believe that receivers of high religiosity are trustworthier and discriminate by being more likely to trust them more than receivers of no or lower religiosity. They also found that religiosity enhances the ingroup favoritism shown by senders towards receivers of their own religious affiliation, showing higher trust for ingroup members. Interestingly, they show that religious participants believe that those belonging to some faith are trustworthier, but only invest more trust on those of the same religion. Finally, they find that religiosity is positively associated with the general willingness of senders to discriminate across a range of nonreligious social identities. Overall, they conclude that interpersonal similarity in religiosity promotes trust. Burns (2012) conducted a standard trust game experiment in six public schools in Cape Town with Black, White, and mixed race students. The race of the partner was made salient through photographs. The experimental results show that Black proposers make significantly lower offers (25% of their endowment on average) than non-Blacks proposers (38%), and Black responders receive significantly lower offers (27% of the proposer’s endowment on average) compared to non-Black responders (37%). Lower offers are Black responders is evident in the behavior of all proposers, including Blacks proposers. These lower offers may be attributable to an expectation that Black responders would remit less than others on average. (The actual results show Black responders remitted 26% of the tripled offer compared with 37% for non-Blacks.) Notably, non-Black proposers with Black partners are significantly more likely to make a zero-offer, preferring to “opt out” of any interaction with a Black partner, or make significantly lower offers, even after taking expectations into account. Finally, they find that responders are significantly less likely to make any return offer to a Black proposer. This suggests a systematic pattern of distrust towards Black partners, surprisingly even by Black proposers, which the author partially attributes to mistaken expectations.
2
The subject pool consisted of students in China, Malaysia, and the UK.
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Falk and Zehnder (2013) conducted a trust game field experiment with about 1000 residents, representing all of the 13 districts of the city of Zurich. In the experiment, proposers could condition their investment decision on the residential district of the responder. The authors find that trust levels measured in terms of the amount invested differ systematically across the residential districts of the responders, i.e., there is trust discrimination across districts. These differences are accounted for by differences in beliefs about district-specific levels of trustworthiness. In terms of significant correlates, the richer the district, higher is the level of investment, and the higher the fraction of foreigners and levels of religious fragmentation, the lower are investments. The results also reveal that different levels of investment into districts are significantly correlated with the respective willingness to repay on the part of the responders. This indicates that proposers correctly anticipate the relative trustworthiness of inhabitants of different districts and discriminate on the basis of this belief. Finally, it was found that there is ingroup favoritism with proposers investing significantly higher amounts into their own districts compared to other districts. Haile, Sadrieh, and Verbon (2008) employed the trust game to investigate the effect of heterogeneity in income and race on cooperation in South Africa. Each participant played the trust game, and decided how much to transfer as a sender (from an initial endowment of 20 Rand) and transfer back as a receiver (for each of 11 possible outcomes). Additionally, each subject also reported the amount they expected as a return on their own transfer as a sender as well as the amount expected as an investment in the role of a receiver. The authors varied the information that was given to the participants regarding their counterpart; in the “information treatment,” information on both race and income level were given, whereas no such information was given in the “no information” treatment. The authors find no significant differences in behavior when no information is provided. However, when the information is available, it significantly affects trust behavior. Specifically, the low-income subjects from both racial groups invest significantly less in partnerships with the highincome subjects of the other racial group than in any other partnership. The authors attribute this behavior to cross-racial envy. Etang, Fielding, and Knowles (2011) studied the impact of social distance on trust and trustworthiness using experimental data from Cameroon. Subjects played the standard trust game as senders with a partner, who could be from the same village as them or from a different village. They found that senders send significantly more money when the responder is from the same village (74% of the endowment on average) compared to when they are from a different village (63%). This effect was also seen with gender, education, and membership of rotating credit groups. As for reciprocity, there was no significant difference between the amounts sent back to ingroup versus outgroup members. They conclude that trust diminishes with social distance. Meier et al. (2016) conducted trust game experiments in high schools in two neighborhoods in the metropolitan area of Palermo, Italy, to study the effect that an environment of crime has on trust and trustworthiness. The subjects were similar in terms of ethnicity and religion. What distinguished them was whether they were
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from a high or low Mafia area. They authors found that in the trust game, students who were from a neighborhood with high Mafia involvement displayed lower generalized trust and trustworthiness. At the same time, they showed higher ingroup favoritism. These findings support the argument that the historical informal institution of organized crime can undermine current institutions. Tsutsui and Zizzo (2014) examined the effect of group status on trust levels. Subjects were randomly assigned to one of two groups – “Blue” or “Red”/“Not Blue.” The “Not Blue” group in the instructions was referred to as “not belonging to any group,” or “outsiders to the group,” reflecting lower status as compared to the “Blue” group. The groups were of different sizes (four or eight individuals), reflecting minority or majority status. In stage 1, subjects played the standard trust game with no information about their matched partner’s group. In stages 2–4, they played three games as proposers and three as receivers, receiving information about the average rate of return and investment on a round-by-round basis, as well as with information on the anonymous partner’s group affiliation. Before stage 2, their WTP/WTA for trading group membership was elicited. The experimental findings show that subjects of low status (“Not Blue”) are less trusting of others of low status, and that minority subjects return more to majority subjects. Subjects like being in majorities and dislike being in a low status group. Further, subjects who value their group more return comparatively less to insiders relative to outsiders. Hargreaves Heap and Zizzo (2009) implement a similar design involving the trust game with artificial groups as the one above. In their case, the artificial groups are labelled “Blue” and “Red.” They too find that trust falls with groups because of discrimination against outgroup members. Burnham, McCabe, and Smith (2000) studied the effect of wording on trust to examine the hypothesis that human subjects have a preconscious friend-or-foe mental mechanism for evaluating the intentions of another person. They experimented with a variant of the trust game under conditions of both single play and repeat play. To implement the two treatments – friend and foe – subjects were primed by using the word “partner” or “opponent,” respectively. They found that in the single-play condition, partners and opponents are equally trusting, but partners are significantly more trustworthy than opponents. On the other hand in repeat play, partners are most trusting than opponents, and marginally significantly more trustworthy than opponents. However, they find that both trust and trustworthiness erode over time. Finally, note that one can also find examples in the literature where the presence of salient groups does not produce discriminatory trust attitudes. For instance, Bouckaert and Dhaene (2004) investigated interethnic trust and reciprocity with a sample of male small businessmen, who were either of Turkish or Belgian ethnic origin and owned small businesses in the city of Ghent. The participants played the standard trust game with each pair of matched subjects knowing each other’s first name (which signaled their ethnic origin). Their findings suggest that the average trust and the average reciprocity of Belgian and Turkish participants are independent of ethnic affiliation and, significantly, independent of the ethnic origin of the opposite party.
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Ultimatum Games In this section, the focus is on some additional insights from experiments involving the ultimatum game. Ferraro and Cummings (2007) conduct an ultimatum game experiment with Hispanic and Navajo subjects in Albuquerque, New Mexico, with matched pairs bargaining over $10. The innovation in the design was that before playing the game, the subjects in each session were placed together in a room to allow them to observe the ethnic composition of their session. So, even though they did not know the ethnicity of their partner in the game, they knew the composition of the group. There were four experimental sessions: all Hispanic, majority Hispanic, all Navajo, and majority Navajo. The authors find that Hispanic responders have higher minimum acceptable offers than Navajo responders. They also find that both groups tend to discriminate against the other group: their mean reservation prices increase with an increase in the proportion of subjects from the other ethnic group. As for the proposers, the authors find that while playing with their own ethnic group, Navajos make significantly lower offers than Hispanics ($3.83 on average as compared to $4.90 respectively). Finally, they note that the ethnic composition of the group affects the offers made: Hispanic offers decrease with an increase in the proportion of Navajo subjects, while Navajo offers increase with the proportion of Hispanic subjects. Based on subjects’ elicited beliefs about likelihood of proposals being accepted, it is also evident that statistical discrimination does not play a role in the behavior of Hispanic proposers and is rather taste based. Boarini, Laslier, and Robin (2009) look at a transcontinental ultimatum game to better understand bilateral bargaining when one of the parties is from a high-income country (France) and the other from a low-income one (India).3 The two transcontinental treatments are French proposer to Indian receiver (FtoI), and vice versa (ItoF), with two other within-country benchmark treatments. In each treatment, subjects played six one-shot ultimatum games with a $10 endowment. The results of the experiment indicate, first, that on average, Indian senders were more generous toward French receivers ($3.92) than French senders toward Indian receivers ($2.63). Indeed, both French and Indian senders send lower offers when the receiver is Indian ($2.63 and $3.53, respectively), and with French receivers, Indian senders offer more money than French senders ($3.48). Second, they find that when low offers (less than $3) are made, the rejection rate by Indian receivers (19% for French senders and 25% for Indian senders) is far less than French receivers (55% for Indian senders and 60% for French senders). It was also evident that offers decreased to Indian receivers over each round of the game. Overall, bargaining in the FtoI treatment mostly ended up with unequal splits of money in favor of French subjects, while nearly equal splits were the most frequent outcome in ItoF treatment interactions. The authors attribute the observed differences in the experiment to the fact that the value of the experimental currency (US Dollar) in terms of the goods it can buy is
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The experimental sessions were conducted in the years 2002 and 2003.
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more in India than in France, and, on average, the experimental participants in France would be wealthier than those in India – with both these observations reiterated as part of the experimental design. Finally, we conclude by referring to an ultimatum game experiment by Eckel and Grossman (2001) that tries to study how attitudes of and towards different sexes affects bargaining behavior. In this study, there are three possible pairings: either subjects know the sex of their partner and are paired with a subject of the same or the opposite sex, or the sex of their partner remains unknown. The experiment is carried out in eight repetitions of the ultimatum game, with a sum of $5 to be divided. Consistent with known insights from the literature, they find that women’s proposals are on average more generous than men’s, regardless of the sex of the partner, and women respondents are more likely to accept an offer of a given amount. What is noteworthy is that offers from female opponents were significantly more likely to be accepted, a result they refer to as chivalry. At the same time, women paired with women almost never fail to reach an agreement, a result they term solidarity.
Cross-References ▶ Field Experiments: Correspondence Studies ▶ Gender-Based Discrimination in Health: Evidence from Cross-Country ▶ Lab-in-the-Field Experiments ▶ Taste-Based Discrimination Acknowledgments I thank Ananya Sen (Pomona College), Reem Qamar (Ashoka University), and Saujanya Bharadwaj (Ashoka University) for excellent research assistance.
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Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Economic Stratification and Stereotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Allport and Blumer on Prejudice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Norms and Stereotypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stereotypes and Group Position . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stereotypes, Discrimination, and Policing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Abstract
Economists studying discrimination often assume rational conscious decisionmaking, ignoring the role of stereotypes – generalizations about groups – that can influence decision-making in ways that lead to racial inequality. Stereotype formation is not accidental but rather is shaped by a society’s prevailing stratification system. In stratified societies, negative stereotypes about subordinated groups are internalized within individuals in their daily actions and decisionmaking in the form of implicit or unconscious bias. This mechanism serves to reproduce and reinforce racial hierarchies, based on stereotype-induced discrimination and its intergenerational transmission of racial bias that leads to discrimination. This chapter explores these topics and applies them to our understanding of the sources of racial disparities in policing. Keywords
Stereotypes · Implicit bias · Racial discrimination · Racial profiling · Stratification · Policing S. Seguino (*) Department of Economics Fellow, Gund Institute for the Environment, University of Vermont, Burlington, VT, USA e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2023 A. Deshpande (ed.), Handbook on Economics of Discrimination and Affirmative Action, https://doi.org/10.1007/978-981-19-4166-5_47
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Introduction The research conducted by economists that explores race and gender discrimination in labor markets relies primarily on regression analysis techniques that control for individuals’ productivity-related characteristics. Although a race “residual” continues to be observed in wage regressions, some economists dispute the conclusion that racial bias factors into hiring and wage decisions. Instead, they argue that inequality is due to the existence of “unobserved productivity differentials.” Still others note, however, that race is “more than a dummy variable” – that the race variable, in other words, is insufficient to identify the causal mechanisms, sometimes unquantifiable, by which race matters and that racial inequality is produced (Figart 1997). The debate has been rendered lifeless and adrift, in part due to a lack of a welldeveloped theoretical framework for understanding racial bias beyond Becker’s “taste for discrimination.” Research based on field experiments – in particular, job audits – provides a welcome addition to the toolkit in the study of racial discrimination in labor markets as well as in other domains such as housing and bank lending. In one of the earliest studies of this kind, Devah Pager and Bruce Western (2009) matched teams of testers who then applied for entry-level jobs in New York City. Their results reveal how employers respond to applicants who are equally qualified but vary by race, ethnicity, and criminal record. Applicants with a criminal record were less likely to be called back for an interview than those without a record. But black applicants without a criminal record received a smaller percentage of callbacks than white applicants with a criminal record. Given racial disparities in the criminal justice system, negative stereotypes about the criminal background of all black applicants could well have dampened the employment opportunities of even those without a criminal record. The Pager and Western study highlights the influence of negative racial stereotypes on economic opportunities, especially for African Americans. “Ban the box” efforts that had begun years earlier as a result gained momentum. “Ban the box” is a slightly misleading term; the policy does not deny employers access to applicants’ criminal background information. It simply defers criminal history until later in the process, to give all applicants a fairer chance to be judged on their qualifications. From a social psychology perspective, this is a good thing. Research shows that what we learn first about another person colors how we see the information we learn next – the primacy effect (Willis and Todorov 2016). By moving the criminal history to the end of the application, those reviewing applications can avoid reacting to negative stereotypes they might hold about the formerly incarcerated long enough to also absorb information on the applicant’s actual job qualifications. Some studies evaluating the impact of “ban the box” on racial bias, however, have revealed the deep reach of negative (antiblack) racial stereotypes. A disturbing finding from one study is that with the adoption of “ban the box,” racial discrimination worsened for black applicants without a criminal record although callback rates for formerly incarcerated black applicants increased (Doleac and Hansen 2016). Put differently, in the absence of information on the criminal
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background of the applicant, employers use race as a proxy for criminal history. Employers are, in a word, racially profiling. The benefit of the job audit methodology used in these studies is that it allows us to isolate the effect of racial stereotypes on employer decision-making. The results suggest that the underlying determinants of discrimination derive at least in part from antiblack and anti-brown racial norms and stereotypes, a subject matter that has been understudied by economists who instead typically assume rational and thus conscious decision-making on the part of employers. Although audit studies are a step forward in providing evidence of racial discrimination, that is not sufficient. Also needed is a theoretical framework for understanding the origin of and motivation for negative racial norms and stereotypes. Statistical discrimination has been the go-to explanation, based on the notion that generalizations about the characteristics of a group are used in place of an examination of an individual’s actual characteristics because information is costly to obtain. This approach, too, begs the question of why at least some portion of statistical discrimination is based on faulty stereotypes that remain unchanged in the face of evidence to the contrary. In this chapter, I seek to shed light on this important lacuna in discrimination research, first by positing stratification theory as a framework for understanding the origins and perpetuation of negative and inaccurate racial norms and stereotypes. Secondly, I apply this to an analysis of traffic policing data in the United States. Finally, I discuss research from social psychology experiments that serve as an alternative methodology to shed light on racial stereotypes and discrimination.
Economic Stratification and Stereotypes Stratification economics is the study of the processes and institutions that result in a hierarchical economic and social ordering based on ascriptive characteristics. Unlike mainstream economics, whose unit of analysis is the individual, stratification economics emphasizes the role of groups and the structural forces that shape intergroup inequality. Stratification economics explores the intentionality of a dominant group to maintain its privileged economic position, a distinctly different approach than the mainstream’s lens which typically lays the causes of intergroup inequality at the feet of the “deficits” of groups lower on the social and economic hierarchy. The mechanisms by which intergroup inequality is transmitted over time rely on a stratification infrastructure, the latter comprised of racial norms and stereotypes that are internalized at the individual level and act as a “stealth” factor in reproducing hierarchy (Jost and Banaji 1994; Augoustinos and Walker 1998; Seguino 2021). These norms and stereotypes are constructed to advantage dominant group(s) and disparage subordinate groups in ways that justify the latter’s exclusion from access to and control over material resources. Before turning to an elaboration of the concepts of norms and stereotypes from social psychology, I discuss the underpinnings of economic stratification based on intergroup rivalry.
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Allport and Blumer on Prejudice Gordon Allport published The Nature of Prejudice (1954) at a time when the Civil Rights movement in the United States was gaining momentum. Social psychologists define prejudice as a negative attitude toward people in a distinguishable group, based solely on their membership in that group. The characteristics assigned to members of that group are negative and applied to the group as a whole. Allport’s research represents a second wave of prejudice theory in which he argued that it is rooted in normal processes of categorization and stereotyping. Allport and subsequent social psychologists have argued that the human mind cannot avoid creating categories, putting some people into one group based on certain characteristics and others into another group based on their different characteristics (Dovidio and Gaertner 2010). We can consciously manage only a small amount of the information we are exposed to. Categorization is thus an adaptive mechanism to conserve on cognitive resources required to process information, it is argued. The output of categorization is stereotypes – generalizations about groups, based on membership in that group. (The concept of statistical discrimination is related to this aspect of information processing and will be discussed later in this chapter.) Although it is normal for humans to categorize, Allport argued that racial animus and prejudice are due to faulty stereotypes. The antidote, according to Allport, is intergroup contact under specific conditions. That contact should take place such that the various groups possess equal status and common goals. This structured contact is hypothesized to give those holding prejudicial views the opportunity to confront and revise their faulty stereotypes. His work led to the emergence of contact theory. Allport did not explore the source of “faulty” stereotypes. Herbert Blumer, however, did. In his 1958 article, “Race Prejudice as a Sense of Group Position,” Blumer argued that prejudice is a tool of stratification (Blumer 1958). Central to Blumer’s theory is the idea that prejudice is not an individual feeling but rather is an outcome of how racial groups form mental constructs of themselves and of other groups, resulting in a sense of relative group position. To characterize the outgroup at the same time also defines one’s own group. Prejudice and the resulting mental constructs serve as constituent components of the infrastructure that facilitates the exploitation and exclusion of subordinate groups. Blumer identifies four “feelings” present in the racially dominant group: (1) a feeling that the dominant group is superior, (2) a feeling that the subordinate group is intrinsically different and alien, (3) a feeling that the dominant group has a proprietary right to prized economic assets and privileges, and (4) fear and suspicion that the subordinate harbors designs on the material assets and privileges of the dominant group. Blumer’s work is a prescient contribution to economic stratification theory. His work spawned the emergence of threat theory, whereby it is theorized that the larger the size of the subordinate group, the more the dominant group feels a threat to its own interests, exacerbating the negative attitudes toward the subordinate group.
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Norms and Stereotypes Stereotypes are oversimplified generalizations about groups of people. They may be positive or negative. In either case, the stereotype is a generalization that does not take individual differences into account. I focus on racial stereotypes and outcomes in this chapter, but a similar application of stereotypes to gender inequality has been made (Seguino 2013). What do we mean by racial stereotypes? Racial stereotypes in stratified societies describe the ways in which racial groups presumably differ, usually in ways that justify the racial division of labor and unequal access to and control over material resources. Stratification-inducing stereotypes typically describe members of the dominant group as harder working, more intelligent, and less inclined than other groups to criminal behavior. Stereotypes thus legitimize the “superiority” or greater deservingness of white – often male – control, undervaluing men and women of subaltern groups, and white women. This is not to suggest that women of the dominant group (white women) do not benefit from their interfamilial relationships with white men, thus participating in and benefiting from the racial hierarchy. Another way to think about this is that white women, though part of the subordinate group in a gender stratified society, possess the protective factor of whiteness in a racially stratified society. A fuller development of stratification theory is needed, however, to understand how racial and gender stratification systems interact, complement, or substitute for each other. Negative racial stereotypes of the subordinate group contribute to and reinforce hierarchical racial norms. Norms are rules, often informal, that specify acceptable behavioral, social, and spatial boundaries for members of racial groups. The effect of such norms is strong because the consequence of violating norms is social approbation. One of the strongest drivers of human behavior is our need for belonging. Norms thus exert a powerful effect on behavior, related to our desire to avoid the risk of rejection and social exclusion. Norms and stereotypes are mutually causative. On the one hand, stereotypes contribute to racial discrimination, for example, in access to good jobs, based on the cultivation of beliefs that subordinate groups are less intelligent and less hardworking and other negative attributes. This contributes to job segregation, with subordinated racial groups in the lowest wage, least secure jobs and dominant racial groups holding the highest quality jobs with greater managerial and supervisory responsibility. A norm – a behavioral expectation – emerges whereby the unwritten rule is that members of the subordinated group are tasked to “stay in their place” with members of the dominant group expected to be the “rightful” sole occupiers of positions of high decision-making responsibility and power. Norms also shape the perceptions and expectations of subordinate group members regarding what jobs and positions they might feasibly aspire to. To the extent norms restrict the subordinate group’s access to important positions and resources, negative stereotypes are reinforced. This observation is linked to social role theory which, although focused on gender roles, is instructive in highlighting the impact of everyday observations of the differences in dominant and subordinate group roles on a group’s assessments of
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what its proper role is in society. Movement of subordinate groups into roles they had previously been excluded from or for which they are underrepresented can help to dispel negative stereotypes. There is mounting evidence, for example, that samerace teachers are beneficial to students from underrepresented racial groups in the United States. Gershenson et al. (2021) find that black students who had just one black teacher by third grade were 13% more likely to enroll in college and those who had had two were 32% more likely.
Stereotypes and Group Position If, as Blumer argues, a dominant group works to intentionally maintain its superior position, concrete mechanisms are required to achieve that goal. Brute force is of course one means. But “turf maintenance” (Darity et al. 2010) may be more subtly and extensively accomplished through the creation and perpetuation of negative stereotypes of the subordinated group(s). Stereotypes inform everyday decisionmaking that produces and reproduces the power and privilege of the dominant group, influencing who gets bank loans, who is elected to political office, and what jobs are filled and by whom. And, because stereotypes influence individual decision-making, often at an unconscious level, they act as a “stealth” factor in enforcing a hierarchical system. A deeper understanding of the role of stereotypes as a mechanism to preserve group position can help to inform theoretical and empirical research on discrimination. Social psychologists have developed a series of theories of the determinants of stereotype construction. Earlier theories related the construction of stereotypes to ego-justification, that is, as a mechanism to preserve self-esteem. This individualistic view (whereby stereotypes emerge at the individual level) has been disputed, with social psychologists subsequently arguing instead that stereotypes serve the function of group and system justification (Jost and Banaji 1994; Augoustinos and Walker 1998). Group justification theories posit that stereotypes emerge to protect the status of a social group as a whole. System justification theory posits that stereotypes emerge to justify existing systemic social arrangements at the expense of (out)group interests. Stereotypes, in other words, serve the ideological function of reinforcing the dominance of one group over other(s) by “explaining the poverty or powerlessness of some groups and the success of others in ways that make these differences seem legitimate and even natural” (Jost and Banaji 1994: 10). This theoretical stance stands in contrast to the view that stereotyping is merely a means to economize on cognitive resources. Further supporting the notion that the function of stereotypes is to legitimize unequal systems, evidence shows that stereotypes are not evenly distributed across groups. Rather, it is the subordinate groups that are more likely to be stereotyped (Banaji and Greenwald 2016). Further, those with more power (ability to control) show a greater tendency to stereotype than those with low power (Glaser 2015). In one study, for example, mangers and subordinates in the hotel industry assessed the
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suitability of job applicants. They were primed by being shown stereotyped information about the applicants. Subordinates paid more attention to the individual applicants’ attributes than did managers, whose judgments of the outgroup were influenced by the consistency of the association of the stereotypes with the applicants (Guinote and Phillips 2010). Recent research has demonstrated the unconscious nature of stereotyping (commonly referred to as implicit bias), which may explain the sometimes unwitting participation of broad swaths of society in perpetuating negative racial stereotypes without the dominant group visibly wielding its power (Banaji and Greenwald 2016; Eberhardt 2019). The dissemination of stereotypes occurs not only at the level of the family and friends but also in our broader culture – via media, religious organizations, and political institutions, to name a few – the preponderance of which are headed by members of the dominant group. The now famous “doll test,” which revealed that even very young children hold negative racial stereotypes, speaks to the depth and breadth of our exposure to stereotypes that delegitimize and pathologize members of subordinate groups (Clark and Clark 1939). Social role theory argues that simple observation on a daily basis of social hierarchies (whites as managers, blacks as janitors; women as caretakers, men as breadwinners), facilitated by job and spatial segregation, normalizes stereotypes and indeed provides evidence of their veracity. Stratification theory then posits a very different approach to understanding discrimination than mainstream economists who instead emphasize statistical discrimination. The latter is assumed to be based on rational decision-making by actors who assess the credentials or qualities of, for example, job seekers, based on group averages rather than the individual’s attributes. The underlying theory is that information is imperfect and incomplete and is costly to acquire for profit-maximizing agents. Imperfect information means that relevant qualifications or attributes cannot be easily observed. Even in cases where assessments are based on some kind of assessment (SATs, personality tests, etc.), some aspects of the job may require skills or traits not easily discerned in tests (e.g., honesty, integrity, drive), with the result that employers revert to stereotypes that may lead test scores to be discounted. Not all adherents to statistical discrimination theory hold that stereotypes are an accurate reflection of group averages. Loury (2002), among others, posits that people start with stereotypes – correct or incorrect – for historical reasons. In this case, stereotypes become self-fulfilling and self-reproducing. In sum, from a stratification theory perspective, stereotypes and the resulting implicit bias are situated in individuals who transmit bias and discrimination in their everyday behaviors. Collective action is not needed. All that is required is for the dominant group to have sufficient ability to influence the cultural production of stereotypes. With members of dominant groups overpopulating the culturally and politically important roles of movie producers, media moguls, newspaper editors, politicians, and CEOs, everyday social interactions result in a diffuse but extensive dissemination of negative stereotypes about the outgroup. Discrimination metastasizes throughout the social body, long after evidence of any explicit bias has diminished.
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Stereotypes, Discrimination, and Policing Stereotypes contribute to discrimination because they are the lens through which we evaluate others. There is thus a good deal to learn from the application of social psychology research on stereotypes as applied to racial disparities in the criminal justice system and, in particular, race data in traffic policing. Traffic policing is the most common interaction people have with law enforcement. Because traffic stops generate large data sets, they provide a useful window into racial discrimination in other aspects of policing that can help us to understand the impact of stereotypes on discrimination in other domains, such as labor markets, healthcare, and housing. Numerous studies document the higher stop, arrest, and search rates of black and Latino/a drivers as compared to white drivers in the United States (Baumgartner et al. 2018; Pierson et al. 2020). An analysis of a national sample of almost 100 million traffic stops finds that black drivers nationally are about 50% more likely to be stopped by the police than white drivers (Pierson et al. 2020), although in some jurisdictions the disparity is much higher. For example, Baumgartner et al. (2018) who analyzed data on 20 million North Carolina traffic stops found that black drivers were 63% more likely to be stopped even though, as a whole, they drive 16% less than white drivers. Taking into account less time on the road, police were 95% more likely to stop black as compared to white drivers. Stop rate disparities in and of themselves do not prove bias toward black drivers, in part because of the benchmarking problem, that is, the lack of precise knowledge of the racial composition of drivers on the road. Post-stop outcomes, such as search rates by race, offer a more reliable means to assess racial discrimination in policing. This is because researchers know with certainty both the number of searches by race and the number of traffic stops by race. There is overwhelmingly consistent evidence that black drivers in the United States are searched at a substantially higher rate than white drivers, and similar, although less consistent evidence of oversearching of Latinx drivers as compared to white drivers. While national data indicate that black drivers are twice as likely to be searched as white drivers, in some jurisdictions, the disparity is much higher. For example, in Vermont, black drivers are 3.5 times more likely to be searched than white drivers, and in the town of Brattleboro, Vermont, the black/white search rate ratio reaches 8.8 (Seguino et al. 2021). Here, too, it may be argued that this disparity is not necessarily evidence of bias if drivers of color are found to more frequently be transporting contraband. To address this, researchers have identified several tests for racial discrimination in traffic policing. The “veil of darkness” test is based on the hypothesis that police are less likely to know the race of a motorist before making a stop after dark than they are during daylight. The test for racial discrimination in the decision of whom to stop (i.e., racial profiling) is based on a comparison of the racial shares of traffic stops made during daylight to the racial shares made after dark. Pierson et al.’s (2020) national study found that black stop rates are higher during the day than at night,
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consistent with a hypothesis of racial bias in the decision to stop vehicles. (It should be noted that the “veil of darkness” test may not be valid for smaller towns in which the police may position themselves under streetlights or other lit areas at night, thus enabling them to discern the race of the driver.) Another approach to identifying the role of bias in search rate disparities is the “hit rate” test, derived from a game theoretic framework. The underlying model on which the hit rate test is based assumes that in the absence of racial bias, officers pursue a search strategy that maximizes the number of successful outcomes (hits), where a successful search outcome is defined as uncovering some contraband such as illegal drugs, weapons, or stolen goods. The hypothesis is that in equilibrium, absent bias on the part of officers, hit rates will be equal across racial groups. Hit rates that are lower for some racial groups than others are indicative of biased decision-making in whom to search. This is because a lower hit rate for some racial groups (e.g., blacks) than others means police officers are wrong more often in their decision to search black drivers than those of other racial groups. Police departments could improve the allocation of police resources by reducing searches of those groups with lower hit rates until hit rates equalize across racial groups. The failure to do so implies racially biased behavior. Numerous studies have found evidence of racial bias in the decision to search, based on the hit rate test (Baumgartner et al. 2018). An interpretation of those results is that the police rely on a lower threshold of evidence to initiate a search of a vehicle with a black driver than with a white driver. In those cases, the evidence suggests oversearching of black drivers and/or undersearching of white drivers. Finally, some studies rely on logit regressions to assess the probability of a search, controlling for other factors in addition to the race of the driver that might explain the decision to search. Studies control for the age and gender of the driver, the reason for the stop, and the day and time of the stop, and some studies include data on the race, age, gender, and tenure of officers conducting the search. If, even after controlling for other factors, race is a statistically significant predictor of a search, then that provides additional evidence that the race of the driver, independent of these other factors, influences traffic policing. Similar analyses have been conducted on the probability of finding contraband during a search (Seguino et al. 2021). Where these tests reveal racial discrimination in traffic policing, they do not necessarily identify the source of the bias. Is it located in the individual officer who may have a “taste for discrimination” – that is, who is explicitly or implicitly biased? Is it a function of leadership’s deployment decisions, with communities of color more heavily monitored than white neighborhoods? Are the disparities due to a few “bad apples” or to an organizational environment in which bias is condoned or even encouraged? It is possible that case studies can help us answer some of these questions. When the tests reveal racial bias, regardless of what level of an organization is responsible for that bias, an underlying factor is negative racial stereotypes – whether held by police leadership or by specific officers. Police make decisions with incomplete information, and stereotypes help fill that void. But the tests just described are not direct tests of stereotype-driven bias.
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We can, however, turn to social psychology experiments to help illuminate the role of racial bias in decision-making. Perhaps the most famous experimental design is the Implicit Association Test (IAT). In this computer-administered test, participants rapidly categorize two opposing concepts (e.g., bad/good, honest/dishonest) with a person’s perceived race (e.g., black/white, although tests exist for other racial groups as well). Pairings more aligned with our underlying stereotypes and thus implicit biases are easier and thus faster to make, while contradictory pairings (not aligned with our unconscious biases and stereotypes) are harder to make, leading us to respond more slowly. The faster one associates positive words with white and negative words with black, the stronger the automatic preference for whites is assumed to be. Almost 75% of those who have taken the IAT in the United States exhibit strong automatic white preference (Banaji and Greenwald 2016). The IAT assesses attitudes, but does that translate into discriminatory behavior? In a metaanalysis of 122 research reports, Greenwald et al. (2009) find that IAT scores are predictive of bias in consequential situations, such as medical treatments, hiring, and voting. With regard to policing, shooter bias tests similarly reveal underlying implicit (antiblack) racial biases (Glaser 2015). In these tests, participants are positioned in front of a video monitor with hands on a control stick and are shown a series of photographs of black and white men holding either a gun or a harmless object like a cell phone. Participants are instructed to shoot when they observe that any of the men is perceived to be holding a gun. The results indicate that participants tend to shoot black men more quickly and erroneously shoot more unarmed black men than white men. Correll, Park, Judd, and Wittenbink (2007) further investigated whether stereotypes cause shooter bias. The authors primed participants in the study, manipulating the strength of association between blacks and guns by having participants carry out the shooter bias experiment, but with different proportions of armed blacks and whites. (Priming is used in social psychology experiments to activate an association.) Priming, that is, exposure to a stimulus, is often so brief as to be inaccessible to the conscious mind, even while it registers in the unconscious mind. This was followed by repeats of the task, with results showing that those who had been exposed to more black shooters in the previous experiment were more likely to show shooter bias. Glaser and Knowles (2008) found that IAT scores that measure the strength of association with between blacks and whites and weapons was a good predictor of shooter bias. Still other experimental designs reveal the depth of negative racial stereotypes that inform policing and the racially disparate results observed in traffic stop studies. Wilson, Hugengbeg, and Rule (2017) asked subjects to evaluate people’s perceptions of physical stature. They found that when asked to rate the height, weight, and strength of young black and white men from photographs showing only their face, black men were consistently rated as taller, heaver, and stronger than white men. The authors then evaluated whether this racial bias was related to their perception of the
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capacity to do harm. They found that white participants rated black men as capable of doing more harm than white men of the same physical stature. The long history of scientific racism that both dehumanized people of African descent and implanted notions of racial inferiority persists and influences our perceptions today. A series of text messages exchanged by police officers in San Francisco in 2016, for example, described black and other minorities as “wild animals, cockroaches, savages, barbarians, and monkeys” (Eberhardt 2019: 145). To test the impact of such imagery on behavior, Goff et al. (2008) showed study participants a video of officers surrounding and beating a suspect whose racial identity could not be clearly determined. Some were led to believe that the suspect was white and others that the suspect was black. Participants who had been primed with animal imagery were more likely to believe the beating was justified but only when the suspect was black. In still another study of this kind, nonblack study participants were shown a series of faces and asked to imagine that the person shown had behaved aggressively toward a police officer but was not carrying a weapon. Study participants indicated that the police would be justified in using more force to subdue the black men than white men (Eberhardt 2019). Glaser (2015: 86) summarizes these research findings: “Whether we like it or not, the implicit stereotypes our culture feeds us, associating blacks with aggression, danger, weapons, put all of us at risk of committing discriminatory acts like racially biased, wrongful shooting.” The impact of stereotypes and biases is not limited to shooter bias but instead is likely representative of their much broader impact on discriminatory behavior in other domains.
Conclusion Economists studying discrimination often assume rational conscious decisionmaking. While stereotypes – generalizations about groups – can influence decisions, there has been little exploration – at least by economists – of the origin or content of stereotypes and their resulting impact on social norms. Social psychologists have explored these topics in great depth. Norms and stereotypes are the lens through which we evaluate others. Stratification theory further helps us to understand how intergroup inequality is produced and reproduced. In stratified societies, negative stereotypes about subordinated groups, and resulting norms, are internalized within individuals in their daily actions and decision-making. This mechanism serves to reproduce and reinforce racial hierarchies, based on stereotype-induced discrimination. As such, economists’ assumption of exogenous preferences is flawed. Rather, social psychology research provides a foundation for understanding the endogeneity of our perceptions and thus decisionmaking, determined in large part by the cultural environment in which we live. It
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offers a means to understand the intergenerational transmission of racial bias, leading to discrimination. The production of negative racial stereotypes and the resulting effect on norms create the invisible infrastructure of racial discrimination. Absent a centralized authority and racial “policing” to enforce the racial hierarchy, this is a critical mechanism by which dominant groups maintain their privileged position.
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