More Than a Feeling: Personality, Polarization, and the Transformation of the US Congress 9780226456034

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More Than a Feeling

More Than a Feeling Personality, Polarization, and the Transformation of the US Congress

adam j. ramey, jonathan d. klingler, gary e. hollibaugh jr. the university of chicago press

chicago and london

The University of Chicago Press, Chicago 60637 The University of Chicago Press, Ltd., London c 2017 by The University of Chicago  All rights reserved. No part of this book may be used or reproduced in any manner whatsoever without written permission, except in the case of brief quotations in critical articles and reviews. For more information, contact the University of Chicago Press, 1427 E. 60th St., Chicago, IL 60637. Published 2017. Printed in the United States of America 26 25 24 23 22 21 20 19 18 17 1 2 3 4 5 ISBN-13: 978-0-226-45584-6 (cloth) ISBN-13: 978-0-226-45598-3 (paper) ISBN-13: 978-0-226-45603-4 (e-book) DOI: 10.7208/chicago/9780226456034.001.0001 LCCN: 2016048672

∞ This paper meets the requirements of ANSI/NISO Z39.48-1992 (Permanence of Paper).

for the many personalities of the united states congress

Contents List of Figures xi List of Tables xiii Acknowledgments xv PART I .

Foundations

chapter 1. Introduction 3 1.1

A Tale of Two Senators: Chuck and Roy Disagree on the Shutdown

1.2

4

Traits and Elite Behavior in Institutions

8

1.2.1 Translating Individual Differences into the Language of Institutions 1.3

9

The Elite Behavior in Institutions Agenda and Plan of the Book

16

chapter 2. Modeling Individual Differences: Translating Personality Traits into Mathematical Parameters 2.1

The Five-Factor Model

21

21

2.1.1 The Lexical and Questionnaire Schools of Thought

22

2.1.2 Causal Foundations and Stability in the Big Five 2.2

24

Challenges to the Five-Factor Model

2.3

Personality and Political Science

2.4

Modeling Personality

25

29

31

2.4.1 Defending Models of Personality

32

2.4.2 Parameterizing Core Cognitive Constraints

35

contents

viii

2.4.3 Measuring Personality-Based Cognitive Constraints 2.5

36

The Big Five Traits

36

2.5.1 Openness (to Experience) 2.5.2 Conscientiousness 2.5.3 Extraversion

37

40

41

2.5.4 Agreeableness 2.5.5 Neuroticism

43 44

2.6

A Framework for Political Choice

2.7

Considerations for Strategic Interactions

46

2.8

Modeling Individual Differences: Conclusion

51 54

chapter 3. Read My Lips: Measuring Personality Through Legislative Speech 56 3.1

Limitations of Existing Approaches for Elected Officials

56

3.2

Using Text to Measure Personality Traits

3.3

Measuring Personality: From Speeches to Scores

57

3.4

Validity of the Estimates

65

3.4.1 Strategic Misrepresentation and Authorship Concerns

65

3.4.2 Face Validity

PART II .

67

3.5

Read My Lips: Conclusion

3.6

Appendix

69

70

Revisiting the Textbook Congress

chapter 4. Securing Reelection: Deterrence and Disbursements 75 4.1

Who Attracts Quality Challengers?

4.2

Who Spends?

4.3

Individual Differences and Seeking Reelection: Conclusion

76

87 95

chapter 5. Committee Assignments 97 5.1

Congressional Committees and Core Cognitive

5.2

Plum Assignments

5.3

Becoming Chair

5.4

Committee Assignments: Conclusion

Constraints

99 104 111 114

60

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chapter 6. Proposing and Passing Legislation

116

6.1

Personality, Proposals, and Passage

117

6.2

Putting Bills on the Agenda

6.3

Workhorses and Show Horses

125

6.4

Predicting Legislative Success

128

6.5

Proposing and Passing Legislation: Conclusion

121

131

chapter 7. Cooperation, Obstruction, and Party Discipline: Shifting Norms in the US Congress 132 7.1

Rebellion, Obstruction, and Polarization

7.2

Party Brands, Loyalty, and the Big Five

7.3

Bucking the Party: Working Across Party Lines

7.4

Holding the Floor: Filibustering and Obstruction

7.5

Norms and the Shattering Thereof: Conclusion

chapter 8. Media Presence and Home Style 8.1

Who Tweets?

PART III .

140 145 149

151

152

8.2

Press Releases

8.3

Media Usage: Conclusion

chapter 9. Moving On

134 135

161 165

166

9.1

Moving On or Moving Out?

9.2

Lame Ducks and the Shadow of Irrelevance

170

9.3

Moving On: Conclusion

9.4

Appendix: A Model of Legislative Voting

175

183 184

Bringing It All Together

chapter 10. More than a Conclusion: Personality, Politics, and Polarization 191 10.1 Personality and the Congressional Life Cycle 10.2 Personality and Congress as an Institution

10.3 Personality and the Future Study of Elites and Institutions

Bibliography Index

233

203

201

192

196

List of Figures 1.1

Estimates of Congressional Ideology 4

1.2

Senator Grassley (R-IA) Tweet Example

3.1

LIWC Comparison of Bon Jovi and Nirvana Songs 59

3.2

Comparing LIWC (2001) Usage between the Pennebaker Corpus and Floor Speeches 61

3.3

Senate Scores over Time (Selected Members) 63

3.4

Word Count and Precision

4.1

Predicted Probabilities of Quality Challenger Entry

4.2

Predicted Campaign Disbursements 93

5.1

Personality and Plum Committee Assignments

110

5.2

Personality and Committee Chair Assignments

112

6.1

Personality and Bill Proposals

6.2

Personality and Ceremonial Bill Proposals

6.3

Conscientiousness and Legislative Effectiveness

7.1

Personality and Bipartisan Cosponsorship

7.2

Predicted Rates of Supporting Cloture 148

8.1

Twitter Adoption by House Members (2007–2010) 153

8.2

Predicting the Number of Days to Adoption of Twitter 160

8.3

Press Release Issue Focus and Agreeableness 164

9.1

Personality and Running for Election 173

9.2

Personality and Other Electoral Decisions 174

6

64 85

124 128 130

144

figures

xii

9.3

Personality and Lame Duck Absenteeism

183

10.1 Personality and Polarization in the House and Senate over Time 197 10.2 Personality and Tenure in the House and Senate over Time 200

List of Tables 2.1

Defining Terms for the Big Five 38

2.2

Core Cognitive Constraints

3.1

OLS Models of Personality and House ADA Score (1996–2008) 68

3.2

LIWC (2001) Categories 70

3.3

MRCPD Categories 72

4.1

Strategic Probit Model of Candidate Competition in US House Races, 1996–2012 84

4.2

Transfers from Campaign Committees to Other Candidates and Party Organizations, 1996–2012 90

4.3

Linear Regression of Logged Campaign Disbursements 92

5.1

Logistic Regression Models of Personality and Plum Committee Assignments 108

5.2

Logistic Regression Models of Personality and Chair Assignments 113

6.1

Negative Binomial Models of Personality and Bill Proposals 123

6.2

Binomial Regression Models of Personality and Ceremonial Bill Proposals 127

6.3

Tobit Models of Legislative Effectiveness 129

7.1

Predicting Loyalty—Party Votes, 104th–109th Congresses 138

48

tables

xiv

7.2

Binomial Regression Models of Bipartisan Cosponsorship 143

7.3

Predicting Minority Party Support for Cloture (104th–112th Congresses) 147

8.1

Who Tweets and How Often (2007–2010)? 158

8.2

Predicting Substantive vs. Credit-Claiming Behavior (2005–2007) 163

9.1

Multinomial Logit Regression Model of Personality and Electoral Decisions 172

9.2

Balance Statistics

9.3

Binomial Regression Models of Personality and Lame Duck Absenteeism 181

179

Acknowledgments It is often the case in science that the best and most novel avenues of inquiry arise by accident. This project counts itself among the works (of all qualities) that have emerged in this venerable tradition. While completing another project, two of us (Ramey and Klingler) were sipping Turkish coffee in Toulouse, France, on a fine autumn day. Klingler was conveying with great excitement his recent forays into the voluminous and fascinating literature on personality and decision-making. Ramey was equally excited about his own recent explorations in the area of quantitative text analysis. In the midst of this exchange, an idea was born from a simple question: could we measure theoretically relevant personality traits of individuals using text? We gambled that the answer was yes. Ramey and Klingler then set up a Skype call with Hollibaugh to see if (a) he thought they were crazy, and (b) if he was willing to forgo another joint project and instead pursue this idea. While he was not (and still is not) fully willing to give Ramey and Klingler a pass on the insanity question, Hollibaugh nevertheless rolled the dice on the project. In the years since, we have worked tirelessly toward a monumental goal—bridging the diverse literatures on personality, political institutions, and quantitative text analysis. Though much of that time was spent penning this manuscript, a nontrivial time was spent evangelizing our new set of ideas. Both tasks have been challenging, and without the support of those closest to us and this enterprise the final product would not have been possible. It is thus fitting that we begin by acknowledging those who have played significant roles in the development, growth, and maturation of this project.

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acknowledgments

Much of this book was written in unlikely locations. When together, we rarely sat in an office, typing away. Rather, much of our time fleshing out and executing the ideas expressed in this project was done in cafés, restaurants, cigar rooms, and the like. While it is impossible to give a full account of all such places, we give special thanks to the staff and management at those establishments where we took up significant table space over the course of our endeavor. In Toulouse, we thank the staff of La Rose de Sables, Ras La Tasse, and Al Diwan for a nearly endless supply of caffeine, sandwiches, raisin et menthe, and impeccable service. We offer particular recognition to Ras La Tasse for providing great air-conditioning during the canicule of summer 2015, and Al Diwan for providing us with a comfortable and productive spot to work during the night hours of the same visit. The final push on this book (as well as two articles written almost entirely at those locations) would have been stymied without them. In Abu Dhabi, the location of the largest number of joint visits, we thank the staffs of Tarbouche, the Marina al Bateen Lebanese restaurant (where many of our original ideas were formed), and Almaz by Momo. The constant supply of Turkish coffee and delicious food cannot be overlooked. In Chicago, we thank the staff at Iwan Ries & Co. for providing us with fine cigars and a comfortable spot to work mere steps away from the Palmer House Hilton. Last, we thank the entire staff at Nat Sherman’s in New York. The last push of this project was facilitated by their excellent Wi-Fi, impeccable ambiance, and unrivaled cigars. On the academic front, we thank those who have taken the time to read the manuscript (or patiently listen to us ramble on about it) and provide us with constructive feedback. These individuals include David Austen-Smith, Ken Benoit, Matt Blackwell, Richard Bonneau, Sean Bottomley, Sam Bowles, Dave Campbell, Charlotte Cavaille, Chris Chabris, Tyson Chatagnier, Heidi Colleran, Carsten de Dreu, Erik Dickson, Drew Dimmery, Conor Dowling, Ray Duch, Dominik Duell, Andy Eggers, Armin Falk, David Gelman, Michael Gibilisco, Michael Gill, Justin Grimmer, Mike Gurven, Matt Hall, Andy Harris, Pablo Hernandez-Lagos, Marc Hetherington, Astrid Hopfensitz, John Jost, Bethany Lacina, Geoff Layman, Daniel Magleby, Cesar Mantilla, Michael McDonald, Slava Mihkaylov, Jeff Mondak, Rebecca Morton, Jonathan Nagler, Tommaso Nannicini, David Nickerson, Bruce Oppenheimer, Elena Panova, John Patty, Michael Peress, Dave Primo, Ben Radcliff, Jason Reifler,

acknowledgments

xvii

Molly Roberts, Larry Rothenberg, Paul Seabright, Maya Sen, Jo Silvester, James Snyder, Arthur Spirling, Jonathan Stieglitz, Jean Tirole, Josh Tucker, Karine van der Straeten, Yannis Vassiliadis, Erik Voeten, Christina Wolbrecht, Elisabeth Wood, Jon Woon, Hye Young You, and many others. This list is long, mostly because of the many visiting scholars at the Institute for Advanced Study in Toulouse, whose early and continuing feedback allowed this project to take root and develop. Additionally, we received helpful critiques on different parts of the manuscript from seminar participants at Binghamton University, Columbia University, New York University, Northwestern University, the University of Notre Dame, the University of Rochester, and Vanderbilt University as well as from participants at the 5th Annual New Directions in Analyzing Text as Data Conference, the 2015 and 2016 Pyrenean Interdisciplinary Research Events, and various American, European, Midwest, and Southern Political Science Association meetings. Additionally, our home institutions have each contributed in their own ways. Ramey thanks Muataz Al Barwani, Benoit Marchand, and the entire team of staff working behind the scenes of NYU Abu Dhabi’s highperformance computing environment, BuTinah. Klingler acknowledges support received through ANR-Labex at the Institute for Advanced Study in Toulouse. Hollibaugh is grateful for support received through the University of Notre Dame’s Institute for Scholarship in the Liberal Arts in the College of Arts and Letters. We were extremely fortunate to work with the University of Chicago Press (with particular thanks to Rodney Powell and Holly Smith) and to have John Tryneski guiding us every step of the way. His guidance was invaluable, and we are particularly grateful that someone with his wisdom and expertise was willing to invest so much time, effort, and trust into three young—and relatively green—scholars. Not only has the book grown (both in scope and clarity) from the original prospectus and sample chapters that Ramey and Hollibaugh peddled to him several years ago, but the three of us have also grown as scholars. For this and other reasons too numerous to list here, we are very grateful. We also acknowledge that portions of Chapter 3 are drawn from our forthcoming (and more technically oriented) article in Political Science Research and Methods, “Measuring Elite Personality Using Speech.” Most of all, we would like to thank our families, who served as sources of support during the highs and lows of writing as well as sounding boards

xviii

acknowledgments

for complaints about the other coauthors. This work would not have been possible without the personal support of Adam and Lisa Guebert, Gary Hollibaugh, Sr., Katie Hollibaugh, Steven and Marijo Klingler, Dominic and Stacy Perry, Bruce and Valerie Quinnell, Youssef and Corinne Ramey, Tania Ramey, Lara Ramey, Mariah Ramey, and the other countless colorful personalities that populate each of our lives. This is for you.

chapter one

Introduction

O

n October 16, 2013, the United States Senate voted 81–18 in favor of a bipartisan agreement ending a 16-day partial government shutdown. This resulted from a partisan impasse over the continued implementation of the Affordable Care Act, alternatively referred to as Obamacare. A few months later, Speaker John Boehner (R-OH) was interviewed on The Tonight Show with Jay Leno. When asked about the aftermath of the shutdown, Speaker Boehner had this to say: Listen, I told my colleagues in July, I didn’t think shutting down the government over Obamacare [would] work because the President said I’m not going to negotiate. And so I told them in August. Probably not a good idea. Told them in early September. So I said, do you want to fight this fight? I’ll go fight the fight with you. But it was a very predictable disaster. And so the sooner we got it over with the better. But remember the issue. The issue was, we wanted to delay Obamacare for a year because it wasn’t ready. Then we asked them to delay the individual mandate for a year. So we were fighting for the right things; I just thought tactically it was not the right way to do it.1

Republicans agree on policy. We know this. Figure 1.1 presents a graphical depiction of the ideologies of all members of Congress during the

1. Jamie Weinstein, “Boehner Blames GOP for Government Shutdown on Leno: ‘It Was a Very Predictable Disaster,”’ Daily Caller, January 24, 2014, http://dailycaller.com /2014/01/24/boehner-blames-gop-for-government-shutdown-on-leno-it-was-a-very-predictab le-disaster/.

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4

figure 1.1. Estimates of Congressional Ideology

104th through the 112th Congresses, as measured by Poole and Rosenthal’s (1997) DW-NOMINATE scores, which are estimates of the latent ideology of legislators generated via examination of their recorded (or roll call) votes. As the graphic shows, Republicans are clustered on the right side of the ideological scale, and Democrats are clustered on the left side, indicating significant degrees of preference similarity within parties but little to none across parties. As the Speaker said on Leno, the caucus disagreed on tactics, even though the underlying policy preferences—to stop the implementation of the Affordable Care Act—were the same.

1.1

A Tale of Two Senators: Chuck and Roy Disagree on the Shutdown

We managed to divide ourselves on something we were unified on, over a goal that wasn’t achievable. —Senator Roy Blunt (R-MO)2

2. Jeremy W. Peters, “Losing a Lot to Get Little,” New York Times, October 17, 2013, A1.

introduction

5

There’s been a lot of talk about the negative impact of not raising the debt limit, but there’s too little focus on the negative consequences of ignoring the $17 trillion debt. Government spending has exploded since 2008, increasing the national debt by $6 trillion. Obamacare is a drag on the economy and hurting workers’ ability to find full-time jobs. Yet the President refuses to lead for fiscal responsibility, both short and long term, even with a government shutdown. This agreement raises the debt limit with no action on the debt. It’s a missed opportunity for forcing action to limit government and increase economic opportunities. —Senator Chuck Grassley (R-IA)3

On the day of the vote to end the shutdown, Republican senators Chuck Grassley (R-IA) and Roy Blunt (R-MO) entered the chamber with identical policy goals—to defund or delay implementation of the Affordable Care Act. Yet Senator Blunt voted for the agreement to end the shutdown and Senator Grassley voted against it. Additionally, several weeks prior, Senator Blunt had voted to end debate on H.J. Res. 59, a House-passed continuing resolution to fund the government—a vote many conservative activists decried as being essentially a vote against delaying implementation of the Affordable Care Act. Senator Grassley had voted to sustain the filibuster. In a press release issued the day of the vote to end debate on H.J. Res. 59, Senator Ted Cruz (R-TX) claimed that “far too many Republicans joined Harry Reid in giving the Democrats the ability to fund Obamacare.”4 These votes are particularly interesting because the spatial model of voting assumes legislators vote purely on the basis of the proximity of their ideal points to the positions of the alternatives under consideration. This approach carries the assumption that the tactics and procedures on which legislators vote achieve a particular policy position with certainty, that legislators are indifferent between tactics, and that the eventual policy outcome is known a priori to all legislators. This simplification—while useful for modeling purposes and tractability—is inherently flawed, as individuals who share common policy objectives frequently debate among

3. See http://www.grassley.senate.gov/news/news-releases/grassley-votes-against-continu ing-appropriations-act-2014 4. See http://www.cruz.senate.gov/?p=press_release&id=521. While Senator Blunt voted to invoke cloture, it should be noted that he—along with all other Senate Republicans—voted against final passage.

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chapter one

figure 1.2. Senator Grassley (R-IA) Tweet Example

themselves over which tactics and procedures are preferred, and which are most likely actually to achieve mutually agreed-upon ends. Given the context of the vote to end the shutdown as well as the votes that preceded it, the spatial model seems lacking. Indeed, while most of the support for continuing the shutdown came from those on the more conservative end of the spectrum, Senator Blunt is actually more conservative than Senator Grassley, as measured by their Common Space scores (Poole and Rosenthal 1997).5 However, if we look beyond their ideologies and instead at their preferences for how they conduct business, the votes might make more sense. For starters, Senator Grassley is reasonably well-known for his prolific use of Twitter (having adopted it relatively early, in November 2007, only a few months after President Barack Obama) and is notable for handling his account himself as opposed to appointing a designated staffer.6 He maintains this practice despite the occasional unclear or arguably strange tweet, an example of which is presented as Figure 1.2.7 This willingness not only to be an early adopter of a new technology (at least relative to his colleagues) but to manage his account himself— despite the occasional misstep—speaks volumes about Senator Grassley’s willingness to take risks. Senator Blunt, on the other hand, joined Twitter

5. On this scale, Senator Grassley scores a 0.343, indicating a moderately conservative ideology, whereas Senator Blunt scores a 0.453, putting him to the right of Senator Grassley. 6. Humberto Sanchez, “Chuck Grassley Talks Twitter Secrets,” Roll Call, January 27, 2015, http://hoh.rollcall.com/chuck-grassley-keeps-killing-it-140-characters-at-a-time/. 7. Hunter Schwarz, “Chuck Grassley Achieved Peak Chuck Grassley on Twitter Today,” Washington Post, April 2, 2015, http://www.washingtonpost.com/news/the-fix/wp/2015/04/02 /chuck-grassley-achieved-peak-chuck-grassley-on-twitter-today/; https://twitter.com/chuckgr assley/status/529356795924725760

introduction

7

in 2009—over a year after Senator Grassley joined—and a “new media” director manages his more official (and less personal) Twitter account.8 In contrast to Senator Grassley, Senator Blunt’s Twitter adoption timing and his more conventional social media management style paints the picture of someone less willing to partake in risky behavior. Considered in this light, their decisions to vote differently on the agreement to end the shutdown—as well as their different cloture votes prior to the shutdown— make more sense. Senator Grassley, being less risk averse, was willing to take the riskier option of letting the shutdown occur and the debt ceiling be reached in order to force a deal on the Affordable Care Act, whereas the more risk-averse Senator Blunt—despite having the same policy goals and being more conservative than Senator Grassley—was not. If this is even remotely true, then scholars of legislative behavior—and elite behavior more generally—need to look beyond the spatial model and account for varying preferences and beliefs over tactics. As such, we argue that legislators do not act solely upon policy positions per se but also on intermediary tactical actions that bridge individual policy preferences and legislative outcomes.9 Individuals have heterogeneous beliefs and uncertainty regarding which tactics are most likely to achieve their desired ends, and may also derive utility from how policy is produced. Accordingly, legislators use decision-theoretic methods to evaluate competing tactics. In the Blunt/Grassley example, both senators wanted to prevent implementation of the Affordable Care Act. However, they disagreed over whether the tactical mechanism—the shutdown—was the best and most appropriate means by which they could achieve their shared goal. Each senator drew upon his own preferences/beliefs pertaining to the translation from tactics to policy and came to a different decision, with one voting to continue the shutdown and the other voting to end it. Thus, the key to understanding legislative behavior is to characterize and estimate the beliefs and preferences of legislators over the tactics available to them. But how?

8. Robert Koenig, “Lawmakers Are Tweeting Up a Storm on Capitol Hill,” St. Louis Beacon, January 23, 2013, https://www.stlbeacon.org/#!/content/28998/twitter_capitol _hill. 9. That is, the process by which a particular tactical action transforms policy preferences into legislative outcomes is noisy, and legislators can prefer different tactics in pursuing the same outcome.

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1.2

Traits and Elite Behavior in Institutions

This question of how to understand legislators’ behavior as a function of beliefs and preferences over tactics is ripe for exploration, as scholars of institutions have, implicitly and explicitly, acknowledged the roles played by nonpolicy traits within institutional structures. For example, congressional scholars already acknowledge that nonpolicy individual differences are often important to legislative behavior and elections. Indeed, we frequently incorporate office motivation into our models of legislative behavior, and often incorporate terms for the valence characteristics of candidates into models of elections. These terms are often discussed in terms of personal character but also in terms of leadership ability. Within the institution of Congress itself, some theoretical models have incorporated nonpolicy qualities of individuals, including character, ability, and alternative motivations. However, there has been no systematic attempt to incorporate more broadly influential individual differences into these models. This lack of theoretical development is strange, since the classic and foundational works of American politics focused on describing individual differences in elite behavior. These works generally predate the new institutionalism of the 1980s. Indeed, a focus on personal style permeated early studies of elite behavior in Congress. Fenno’s classics Home Style: House Members in Their Districts (1978), and Congressmen in Committees (1973) are excellent examples of works that examine the individual differences that lead politicians to approach their institutional roles differently. Additionally, the study of the presidency was, until only fairly recently, dominated by studies of leadership traits and typologies of personal leadership styles. While Barber (1972) is a prime example of this style of work, more recent works (Rubenzer and Faschingbauer 2004) across political science, psychiatry, and psychology have since continued in this research tradition. The same is true for studies of the judiciary, as the structure of judicial decision making—either one person making decisions or a small group making decisions via deliberation—has lent itself to personalitybased analysis (Gibson 1981). However, in contrast to the history of model-based analysis of legislative behavior, and the more recent history for executive and bureaucratic behavior, studies of the judiciary have only very recently begun transitioning from a qualitative focus on individual differences and legalism to an institutional approach centered on

introduction

9

modeling and policy preferences, possibly in part due to the reluctance of some to treat the courts in ideological terms. These classics have inspired several important research agendas, but to a large extent they cannot meaningfully communicate with contemporary students of political institutions. While the classics were often qualitative studies that created interesting typologies of leadership, the dominant paradigm is quantitative and theorizes on the basis of rational choice-based models, both formal and informal. Nonetheless, a tremendous amount of accumulated qualitative knowledge exists on the influence of individual differences on elite behavior within American political institutions. Models have allowed scholars of American political institutions to clarify theories of institutional behavior, but they would gain from developing a language allowing individual differences to be comprehensively modeled. 1.2.1 Translating Individual Differences into the Language of Institutions In this book, we seek to identify a Rosetta stone that will allow students of political institutions to begin a dialogue with the rich literature on individual differences. Ideally, this approach would comprehensively and tractably include the most important persistent individual differences into models of institutional politics. The field most dedicated to studying these individual differences is personality psychology (Borghans, Duckworth, Heckman, and Weel 2008). There are many definitions of personality, but scholars generally argue that personality consists of several interrelated components, including differences in motivations, reputations, expectations, values, interests, and attitudes, rather than a single monolithic difference (Almagor, Tellegen, and Waller 1995; Benet-Martínez and Waller 1997; McAdams 2006; McAdams and Pals 2006; Roberts and Webb 2006). Caprara and Cervone (2000) broadly describe personality as a dynamic system of structures and processes organized by individuals in order to develop a sense of personal identity. Personality, as a dynamic system, is influenced by biological and environmental factors, though personality psychologists differ greatly over the extent to which personality is biologically determined and the degree of change that is possible over time (Costa and McCrae 1997; Roberts, Robins, Trzesniewski, and Caspi 2003; Rutter 2006). Nonetheless, personality psychologists strive to develop and refine theories of personality that account for the diverse

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chapter one

components of personality and how they influence one another (Caprara 1996). However, according to Costa and McCrae (1988) and others, only differences that stably persist can be considered elements of personality. Persistent elements of personality are referred to as personality traits. Individual differences within this subset possess properties making them particularly well suited for incorporation into models of institutional politics. By definition, traits do not shift much in the time frame used in our models, fitting many standard assumptions made in the revealed preference paradigm about policy preferences. That said, though personality traits are distinguished through their stability, and there is evidence of consistency in trait estimates over time, there is evidence that traits shift with age (Roberts and DelVecchio 2000; Roberts, Wood, and Caspi 2008). A prominent school of thought argues that personality traits never change after adolescence (Costa and McCrae 1994; McCrae 1994; Costa and McCrae 1997; McCrae and Costa 2005). On the other hand, there is evidence that some change occurs in individuals’ personality traits at various points in the lifespan (Costa, McCrae, and Siegler 1999; Roberts et al. 2003; Roberts, Walton, and Viechtbauer 2006; Roberts, Wood, and Caspi 2008). However, even those scholars who advocate for personality shifts claim this change requires consistent environmental pressure strong enough to overcome forces for stability (Borghans et al. 2008). Within most politicians’ careers, even if traits do shift, these changes should be small in magnitude, allowing scholars to assume they are fixed in the vast majority of contexts in which political scientists are interested. Personality trait psychologists have accumulated copious information about how personality traits capture myriad human behaviors. Moreover, they have identified trait measures with varying applications. The personality types and traits of ego control and ego resiliency identified by the Myers-Briggs Type Indicator (MBTI) questionnaire have been applied to several workplace and career applications (Block and Block 1980; Myers, McCaulley, and Most 1985).10 Personality psychologists have also developed lists of personality traits for the purposes of diagnosing

10. Ego control and ego resiliency capture persistent differences in individuals’ expression of their impulses and their ability to adapt to novel or stressful situations (Block and Block 1980). Myers-Briggs types are based on four dichotomies that characterize personality over time: extraversion/introversion; the information-gathering and perceiving function of sensing/intuition; the decision-making and judging function of thinking/feeling; and a preference for judging/perceiving overall (Myers, McCaulley, and Most 1985).

introduction

11

personality disorders, such as the Minnesota Multiphasic Personality Inventory (MMPI) and the California Personality Inventory (Gough 1985; Tellegen 2003). Yet other personality traits with negative implications for society or the polity—such as authoritarian personality, or the “dark triad” of narcissism, Machiavellianism, and psychopathy (Adorno, Frenkel-Brunswik, Levinson, and Sanford 1950; Altemeyer 1988; Paulhus and Williams 2002)—have been identified.11 So many traits have been proposed, however, that the task of identifying particularly important traits for elite behavior and understanding how these varied constructs relate to one another is daunting. personality to trait taxonomies. As mentioned, personality psychologists have proposed several trait taxonomies in their attempts to structure the numerous traits proposed by scholars in the field. Personality psychologist Gordon Allport notes that “each assessor has his own pet units and uses a pet battery of diagnostic devices” (Allport 1958, 258), and it is notable that this was stated in 1958, when the field was relatively young. With the passage of time and accumulation of measures and trait concepts from new scholars, personality psychology faced something of a crisis in the second half of the twentieth century. Hans Eysenck, one of the major players in the development of trait taxonomies, commented upon the problem by saying that “where we have literally hundreds of inventories incorporating thousands of traits, largely overlapping but also containing specific variance, each empirical finding is strictly speaking only relevant to a specific trait. . .this is not the way to build a unified scientific discipline” (Eysenck 1991, 786). Many attempts were made to unify the field by investigating the overarching structure of personality traits. The number of taxonomies receiving significant amounts of support and converts cannot be exhaustively counted using both hands. In the next chapter, we discuss the

11. The concept of authoritarian personality, which tends toward right-wing opinions, submission to authority, aggression, and adherence to existing social norms, has also been studied by personality psychologists (Adorno et al. 1950; Altemeyer 1988). More recently, the “dark triad” have earned attention in characterizing socially malevolent behavior (Paulhus and Williams 2002). This research offers exciting opportunities to identify new explanatory variables for elite behavior in institutions, though the traits listed above are only a sample of a large and lively literature; See Judge, Erez, Bono, and Thoresen (2002) for a review of traits viewed to be important to leadership.

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chapter one

leading taxonomies in greater detail. All of them attempt to uncover hierarchical structure in the traits developed in the discipline into broad factors, with as few as one factor to as many as sixteen factors at the highest level (Cattell 1943; Musek 2007). These taxonomies allow for the identification of the most fundamental dimensions of individual difference. Importantly for our purposes, trait taxonomies offer promise of identifying a large enough number of fundamental individual differences to provide a systematic characterization of an individual’s persistent traits, while reducing their number sufficiently to be incorporated into models and retain tractability. Therefore, as we work to link models of institutions with the literature on individual differences, it is important to find a taxonomy that is both defensible as a characterization of the most important dimensions of individual difference and whose dimensions can be associated with as much of the existing research in personality trait psychology as possible. trait taxonomies to the five-factor model. A five-factor structure for personality best fits these needs. These factors—Openness (to Experience), Conscientiousness, Extraversion, Agreeableness, and Neuroticism (often reverse-coded as Emotional Stability)—are commonly referred to as the Big Five. This structure is widely—though not universally— accepted. Citations of this typology surged in the 1990s, surpassing citation counts of earlier typologies such as Eysenck’s Big Three, leading it to be dominant among personality psychologists (Eysenck 1991; Eysenck 1992; John, Naumann, and Soto 2008). This structure was derived through two different empirical schools of thought, with the lexical school using clustering of words encoded into language to identify larger personality factors, while the questionnaire school eventually identified five dimensions of personality through factor analysis of personality questionnaires (Goldberg 1981; McCrae and Costa 1985b; Norman 1963). The two schools differ somewhat on the nature of the five factors as well as their theoretical underpinnings. While these differences cannot be dismissed, the similarity in both approaches’ findings—drawing from very different data sources—provide support for the Big Five construct. The framework has received criticism, but there is significant evidence that this taxonomy captures many of the most important individual differences (Costa and McCrae 1992a; Costa and McCrae 1992b; Eysenck 1992). The five-factor structure benefits from extensive research connecting it with lower-level traits, other taxonomies, and myriad phenomena

introduction

13

including leadership ability, academic and job performance, and health outcomes (John 1990; Judge et al. 2002; McCrae and John 1992; Ozer and Benet-Martínez 2006). Many of the traits discussed earlier in this chapter, including the components of the MBTI, the “dark triad,” authoritarian personality, and ego resiliency and control, have been linked to the Big Five conceptually and as a result of empirical investigation (Furnham 1996; Hodson, Hogg, and MacInnis 2009; Jakobwitz and Egan 2006; John 1990; Zuckerman, Kuhlman, Joireman, Teta, and Kraft 1993). As such, the five-factor model has enjoyed greater research coordination than its competitors, making it a useful taxonomy for service as an empirical intermediary between scholars of institutional politics and the diverse approaches to individual differences. Indeed, focused research on the Big Five has produced a great deal of knowledge about the empirical associations between the dimensions of the five-factor taxonomy and many consequential differences in individual behavior (Ozer and Benet-Martínez 2006). Theoretical models that are able to incorporate the Big Five as influences on elite behavior would gain from systematic consideration of the five dimensions of individual difference (and associated lower-level traits) that manifest across many contexts and domains. The works cited thus far demonstrate the potential that incorporating the Big Five into models of institutions holds. However, constructing such a framework is challenging, and the way in which the five-factor taxonomy is translated into modelable language is extremely important. from the five-factor model to parameterized personality. The dominant theoretical approach in the study of American political institutions is through the use of formal and informal theoretical models. Any approach to incorporating individual differences into the study of elite behavior is hampered in its ability to influence the academic conversation if those differences cannot be modeled. Readers familiar with models of institutions may react negatively to the idea of stepping away from the rational choice foundation of the models that have brought so much clarity to theories of institutional politics, but over the past decade there have been calls for more behavioral models, and our approach is relatively mild and generalizable (Bendor, Diermeier, Siegel, and Ting 2011; Moe 2009). Readers familiar with personality psychology may react negatively to the idea of characterizing broad factors of personality in (potentially) mathematical terms, though our approach is meant to be applicable both to formal and

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informal models. Importantly, the natures of the latent variables that each trait comprises suggest that a single constraint is at least somewhat associated with the many observable behaviors linked with personality factors, and there is a growing literature seeking to identify relationships between the Big Five traits and readily modelable qualities (Almlund, Duckworth, Heckman, and Kautz 2011; Becker, Deckers, Dohmen, Falk, and Kosse 2012; Borghans et al. 2008; Dohmen, Falk, Huffman, and Sunde 2008; Dohmen, Falk, Huffman, and Sunde 2010). Much of this literature has been written by economists and personality psychologists working together to develop a language for characterizing personality traits in economic terms, and several approaches have been suggested. Behavioral economics alters conventional preference specifications to (in part) be a function of personality traits. That is, personality traits lead individuals to gain or lose utility from doing specific classes of actions, so Introverts prefer silence, Conscientious individuals gain from dutiful actions, and Open individuals obtain utility from novelty. A bolder version of this approach could utilize constrained choice sets and random choices to model personality-driven behavior (Borghans et al. 2008). Another approach considers personality traits as constraints imposed on agents as they make choices, and traditional economic preference parameters emerge from these constrained choices (Almlund et al. 2011; Borghans et al. 2008). This approach is built on modeling utility functions for actions available to an agent in which the costs of actions are dependent in part on the agent’s personality traits. These include static constraints influencing the resources available for consumption, dynamic constraints influencing investment, and the information available for decision-making (Borghans et al. 2008). This model can be expanded to include learning and uncertainty as well as preferences over different actions (Almlund et al. 2011). A third approach is to link each of the Big Five traits to a general modelable economic parameter, as many of the economic parameters held as most central to economists, such as risk preferences, time preferences, social preferences, preference for leisure, and cost of effort, appear to be linked to personality traits (for a review, see Almlund et al. 2011). However, while there has been progress in characterizing the dimensions of the five-factor taxonomy in economic terms, some scholars are reluctant to pursue this line of inquiry, in part because a tight one-to-one mapping of key economic parameters onto traits has not been discovered (Becker et al. 2012; Borghans et al. 2008). Though these limitations must be taken

introduction

15

into account, many findings are available for consideration (Almlund et al. 2011; Becker et al. 2012). These connections will be discussed in more detail later. We take a slightly different approach than any of these three by instead utilizing findings from neuropsychology to conceptualize each personality trait measure in terms of a single core cognitive constraint. We then draw upon these core cognitive constraints and the experimental economics literature to propose and defend a translation of the core cognitive constraint into one or more parameters useful for modeling. While the parameters we suggest are undoubtedly simplified approximations of the core cognitive constraints captured by each personality trait’s measure, and all models fall short of the real world to some degree, we gain theoretical usefulness and focus in exchange. This approach has several benefits. First, it does not require a oneto-one relationship between personality trait measures and particular modeling parameters, but only that the parameter chosen is a reasonable approximation of the trait. This is not unusual in the study of Congress. NOMINATE produces scores on multiple dimensions of ideological preferences, but we typically incorporate them into formal models as a onedimensional parameter, which approximates what is captured in the data. Second, under certain and reasonable conditions, the Borghans et al. (2008) model of personality as a choice constraint leads personality traits to manifest themselves in terms of parameters already used by modelers in the study of institutional politics. So long as we make these assumptions clear and connect them soundly to theoretical models and empirical support, personality trait scores may be translated into modelable parameters and vice versa. This approach allows for the development of theoretical models that integrate nonpolicy traits into political institutions for the first time in a comprehensive manner, taking into account the most important individual differences that define agents. To sum up our approach, we have shown—and will continue to show—that the study of personality traits within the field of personality psychology offers many insights into how persistent individual differences influence behavior. Though the personality trait literature is so large that typologies are necessary to identify the highest-level individual differences that would be of interest to modelers, the five-factor taxonomy is particularly useful in creating a framework for modeling elite behavior in institutions because of its robustness across contexts and because researchers coordinating around that typology have taken special effort

chapter one

16

to identify the relationships between a wide variety of traits and those of the Big Five. We propose a framework for political choice characterizing the Big Five personality traits in terms of core cognitive constraints, backed with substantial support from neuropsychology. In many contexts, these core cognitive constraints imply modeling parameters that are very useful for institutionalists. As a result, in many models, the most important individual differences may be tractably included, providing a much richer model of elite behavior in institutions based on the findings of personality psychology. Using this as a baseline, further complications may be made and assumptions relaxed to better model more specific individual differences and better capture the complex nature of personality in the language of institutional models.

1.3

The Elite Behavior in Institutions Agenda and Plan of the Book

This book begins a long-term research agenda that will translate some of the accumulated knowledge on individual differences into the language of institutionalists. There is a need to develop a framework for theoretical models that can bridge the two worlds, a need for measures of political actors’ personality traits as well as empirical puzzles for theorists to use as jumping-off points for future theoretical development. We address all three of these needs in this volume, and advancement of this research agenda is important for several reasons. First, institutionalists can reconnect with their origins in leadership trait studies. Works like The Presidential Character and Home Style paint bold pictures of how individual differences between elites interact with institutional constraints to produce policy (Barber 1972; Fenno 1978). This is in contrast to the fact that some of the greatest advances in the study of American political institutions have come from rational choice– based models (Moe 2009), which themselves typically portray differences between agents in terms of differing policy preferences. This project aims to enrich the models that have served political scientists so well by translating the individual differences focused on in the classics of the field into modelable parameters. Second, institutionalists can use personality psychology to develop new insights into the characteristics that influence elite behavior in institutions. Indeed, personality psychologists have uncovered many persistent

introduction

17

individual differences associated with leadership ability as well as important life outcomes (Judge et al. 2002; Ozer and Benet-Martínez 2006). The work accumulated by personality psychology on the relationship between differences and outcomes offers a source of evidence for future theorybuilding and testing by political scientists. This project will facilitate the incorporation of these traits into our theoretical models of institutions by first translating the top-level Big Five traits into modelable terms that can then be complicated to mimic the properties of traits associated with factors of the Big Five. Finally, we can add to the insights provided by the spatial model by treating members as more than just walking DW-NOMINATE scores, but as individuals with recognizable and modelable individual differences. Senator Marco Rubio of Florida and Senator Jim Risch of Idaho had similar DW-NOMINATE scores during the 112th Congress, but both have different patterns of action in Congress, with Risch described as “quiet” and a “workhorse” while Rubio gave his party’s response to the State of the Union in 2013 and had been interested in running for president since before his election.12 The personality differences between Senators Rubio and Risch likely influence their behavior. While policy preferences are important, personality traits can add a great deal of explanatory power to empirical and theoretical models of political institutions. The effort to begin a dialogue between personality-oriented scholars and modelers is a challenging one. The differences in approach are great, and we recognize that the language of personality scholars is very different from the language spoken by modelers. There are societies around the world that hold their languages to be particularly beautiful to the point where officials purge foreign words from the language and some books are not permitted to be translated. These behaviors may help maintain the distinctive purity of each language’s beauty. However, they also lead to isolation among the speakers of those languages and mischaracterization of those speakers among outsiders. That said, while every translation

12. Michael Catalini, “The Most Conservative Member of the Senate Isn’t Who You’d Think,” National Journal, February 5, 2014, http://www.nationaljournal. com/hotline/2014/02 /05/most-conservative-member-senate-isnt-who-youd-think; Karen, Tumulty and Manuel Roig-Franzia, “Marco Rubio Emerges as GOP’s star. But Is He the Answer for Republicans?,” Washington Post, February 10, 2013, https://www.washingtonpost.com/politics/marco -rubio-emerges-as-gops-star-but-is-he-the-answer-for-republicans/2013/02/10/3710c464-7207 -11e2- a050-b83a7b35c4b5_story.html.

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results in information loss, and this can be particularly costly with first efforts, it is arguably better to begin a dialogue, however embarrassing and awkward, than to insist on purity and isolation. In any context, learning to be bilingual is awkward and can lead to some unfortunate misunderstandings at first, but through a process of experimentation and communication, the complexity and beauty of the languages involved increases as they borrow from one another. This book represents the first step in this agenda. We begin in Chapter 2 by reviewing the literature on the Big Five and introducing a theoretical approach that considers each of the Big Five personality trait measures in terms of a single root latent variable. This approach is grounded in the five-factor taxonomy’s roots in factor analysis, and allows us, with some reasonable modeling assumptions, to treat each of the Big Five’s root latent variables as a single parameter, depending on the application. In order to assess empirically the relationship between personality and legislative behavior, in Chapter 3 we introduce a method for measuring legislator personality. We are not the first scholars to attempt to measure the personalities of political elites, and past attempts have relied on expert ratings or administering personality inventories (Dietrich, Lasley, Mondak, Remmel, and Turner 2012; Rubenzer and Faschingbauer 2004). Both of these approaches have strengths and weaknesses. While expert ratings of politicians by psychologists are able to incorporate large amounts of data and make particularly clear statements about personality, the ratings of twenty-nine presidents in past work required 120 experts to contribute no fewer than two hours each (Rubenzer and Faschingbauer 2004). This process would make it very costly or impossible to create scores for important political actors such as House members or federal appointees who may not be known to experts. On the other hand, administering personality inventories to political elites creates measures comparable with the general population. Unfortunately, there is a great amount of nonresponse even for state legislators, and for high-profile or deceased political actors it would be difficult or impossible to obtain completed personality inventories (Dietrich et al. 2012). In this volume, we present Big Five personality trait estimates for political elites derived from the words they use. Our Elite LingUistiC Individual Difference EstimATION (or ELUCIDATION) scores are obtained using a support vector machine algorithm to classify individuals’ personality traits on the basis of the types of words they use. The benefits

introduction

19

of this approach will be discussed in Chapter 3. Notably, it allows for quick estimation of politicians’ personality traits once appropriate corpora are found, and can be conducted without the active participation of the political actors in question, which allows for study of otherwise unavailable individuals. As prior studies of political elites’ personalities have focused on presidents or state legislators, we choose to focus on the behavior of legislators in the United States Congress in order to demonstrate the usefulness of the measure for an otherwise difficult-to-study group. We also choose to focus on the United States Congress in order to demonstrate the applicability of personality to congressional study, where the New Institutionalism has been best established and where prior innovations in theoretical development first took root. As personality psychology suggests that traits should influence most areas of individual behavior, we take interest in several important stages in the congressional lifespan, including campaign disbursements and challenger deterrence (Chapter 4), committee assignments (Chapter 5), proposing and passing legislation (Chapter 6), obstructive and rebellious behavior (Chapter 7), media usage (Chapter 8), and “moving on” from Congress (Chapter 9). For each of these stages, we address one or two interesting puzzles in the literature that can be explained in part by personality differences.13 While we always use the theoretical framework presented in Chapter 2 to address these puzzles, some chapters (4–7) do so informally while others (8–9) do so with the aid of explicit formal models.14 The last chapter of the book (Chapter 10) synthesizes our findings and provides some insight as to how scholars going forward might think

13. We choose this approach to demonstrate that personality measures and models are useful throughout the subfield, while limiting our analysis to questions theoretically motivated primarily by the study of Congress, and not personality more generally. First and foremost, our aim is to contribute to the study of American political institutions. 14. This is so we can provide a general survey of the relationships between traits and a wide array of congressional behaviors in one volume. Future work on more specific questions can and should formalize the framework we advance, so that the clarity of formal modeling may be brought to bear and the promise of this agenda may be fully realized. The parameters that result from the translation process should become increasingly nuanced characterizations of the factors in the five-factor taxonomy. Though our framework is an admittedly limited attempt to characterize the underlying factors, what follows is but the first step in what will be a process of significant enrichment of our own theoretical language.

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about the intersection of ideology and personality. To this effect, Chapter 10 explores the linkages between personality and political polarization. We show that the trends in the Big Five correlate with polarization, providing evidence as to why Congress is growing more ideologically polarized.

chapter two

Modeling Individual Differences: Translating Personality Traits into Mathematical Parameters

I

n this chapter, we sketch our framework that illustrates how personality traits may be incorporated into theoretical models of legislative behavior. Drawing from economics, psychology, and neurology, we link each of the Big Five personality traits—Openness (to Experience), Conscientiousness, Extraversion, Agreeableness, and Neuroticism—to a core cognitive constraint or ability. We then translate each cognitive constraint into theoretical parameters, which are themselves incorporated into a general framework of legislator utility. This framework will facilitate the development of more specific models (both informal and formal) of legislative behavior that incorporate top-level individual differences beyond ideology.

2.1 The Five-Factor Model The five-factor structure has been influential over the last twenty-five years in personality psychology and has been used to organize many of the existing trait measures used by personality psychologists (Almlund et al. 2011; Furnham 1996; Hodson, Hogg, and MacInnis 2009; Jakobwitz and Egan 2006; Zuckerman et al. 1993). This typology, with empirical support in the lexical and questionnaire schools of personality psychology, has been used to bring order to the multitude of traits and trait typologies in the field (Eysenck 1991; John 1990). In their own right, the Big

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Five have been used to predict significant life outcomes in realms ranging from romantic fulfillment to mortality, with predictive power comparable to socioeconomic status and cognitive ability (Roberts, Kuncel, Shiner, Caspi, and Goldberg 2007). Additionally, for 2005–2009, keywords linked to the Big Five appeared in nearly eight times as many psychology publications as terms related to the formerly dominant sixteen personality factor (16PF) and Big Three typologies combined (John, Naumann, and Soto 2008).1 One of the major reasons that the five-factor typology has won so many converts is because it is empirically derived from both natural language and personality questionnaires. This broad base of support has produced diversity among proponents of this structure regarding theoretical approaches, the nature of the five factors, measurement techniques, and validity across contexts. While the Big Five framework is arguably dominant, it is not universally accepted, and there is no monolithic Big Five that has brought unity to those who endorse the five-factor typology. The differences between schools of thought among supporters of the fivefactor typology are significant. Nonetheless, the five-factor structure is a very useful tool for systematically translating individual differences into the language of modeling. As such, it is important—particularly because we introduce the five-factor structure to a new group of scholars—to convey clearly the diversity of perspectives within this literature as well as some noteworthy objections. 2.1.1 The Lexical and Questionnaire Schools of Thought The five-factor model’s roots within the lexical school reach back over a century, to when philosophers and psychologists focused on identifying the nature of character (Klages 1932; Webb 1915). Research in this tradition assumes the lexical hypothesis, that “those individual differences that are most salient and socially relevant in people’s lives will eventually become encoded into their language; the more important such a difference, the more likely is it to become expressed as a single word” (John, Angleitner, and Ostendorf 1988, 174). Researchers working with German

1. These two typologies were arguably the two most influential before the Big Five became prominent, in the lexical and questionnaire school, respectively (Cattell, Eber, and Tatsuoka 1970; Eysenck and Eysenck 1976).

modeling individual differences

23

and English dictionaries identified long lists of personality-relevant terms in both languages as well as subsets of English terms focusing on persistent personality traits (Allport and Odbert 1936; Klages 1932; Norman 1967). In the 1940s, scholars working in the lexical school applied factor analysis to these trait terms in an attempt to identify an underlying structure in personality traits in natural language, and these early efforts revealed twelve factors of personality traits (Cattell 1943; Cattell 1945a; Cattell 1945b). These factors formed the basis of the 16PF typology, one of the earliest lexical personality typologies (Cattell, Eber, and Tatsuoka 1970). However, over the next thirty-five years several attempts to replicate this structure failed (Digman and Takemoto-Chock 1981; Fiske 1949; Tupes and Christal 1961). Instead of the twelve-factor structure, researchers found a five-factor structure in trait-descriptive terms in natural language (Borgatta 1964; Norman 1963; Smith 1967; Tupes and Christal 1961). Additional data and refined sets of trait terms led the lexical school to coalesce around a five-factor structure in the 1980s (Goldberg 1981; Goldberg 1990; Goldberg 1993; Peabody and Goldberg 1989). The factors were called the Big Five to emphasize their breadth rather than to claim that there were only five personality traits (Goldberg 1981; John 1990). Researchers working in this tradition later developed bipolar marker words and inventories based on trait-descriptive adjectives to characterize and measure each factor (Goldberg 1990; Goldberg 1992; Saucier 1994). While the lexical tradition appeared to reach a consensus—first around Cattell’s 16PF typology and then around the five-factor typology in the early 1980s—researchers focusing on questionnaires and other observational measures had developed multiple and differing typologies (Myers, McCaulley, and Most 1985; Webb 1915; Wiggins 1968). A particularly influential typology in this tradition that arose in the 1940s used the two factors of Extraversion and Neuroticism, deriving this structure from experiments and questionnaires (Eysenck 1947). From its early days, the questionnaire school argued that personality had biological roots (Eysenck and Eysenck 1967). In the 1970s, questionnaire evidence suggested a third factor, Psychoticism, which joined Extraversion and Neuroticism to compose the Big Three typology (Eysenck and Eysenck 1976). While the trait of Psychoticism remained controversial, there was some consensus in the questionnaire school on the importance of Extraversion and Neuroticism (Eysenck 1986; Myers, McCaulley, and Most 1985; Wiggins 1968).

chapter two

24

In the 1970s, two researchers working with the 16PF questionnaire identified the familiar dimensions of Extraversion and Neuroticism but also a third dimension interpreted as Openness to Experience (Costa and McCrae 1976). As a result, they created a new personality inventory based on these three factors in the early 1980s, called the NeuroticismExtraversion-Openness Inventory, or NEO (Costa and McCrae 1985; Goldberg 1993; John, Naumann, and Soto 2008). After exchanges with the lexical school, the NEO was expanded to include items measuring the Big Five traits of Agreeableness and Conscientiousness, and this new questionnaire uncovered the same five-factor structure in individual responses that had appeared in linguistic analysis (Goldberg 1993; McCrae and Costa 1985b; McCrae and Costa 1985a; McCrae and Costa 1987). Scholars in this tradition developed inventories using nuanced questions about trait-descriptive behaviors to characterize and measure personality, including the 240-item Revised NEO Personality Inventory (NEO-PI-R) and the sixty-item NEO Five-Factor Inventory (NEO-FFI) (Costa and McCrae 1989; Costa and McCrae 1992a; Goldberg 1992; John, Hampson, and Goldberg 1991). In the 1990s and 2000s, researchers working in the questionnaire school worked to argue that the Big Five construct was biologically motivated, resulting in a Five-Factor Theory of personality (McCrae 1996).

2.1.2

Causal Foundations and Stability in the Big Five

There is some controversy over the stability of the Big Five, raising questions about the causal roots of these traits. The lexical school tends to treat the five-factor taxonomy as a descriptive tool without a causal mechanism and provides a theory of how traits lead to the creation of trait terms in language but explicitly does not advance a theory to explain where traits originate (Goldberg 1993; Klages 1932; Saucier 2009b).2 The questionnaire school tends to hold that traits are biologically derived. This approach has deep roots in Eysenck’s early theories and has carried through to more modern proponents of five-factor typologies who have argued that these traits are differences that are essential to human survival and reproduction and have thus been selected upon in mating

2. As such, the lexical school is open to the perspective that trait dimensions may vary across languages and dialects, as cultural and historical forces are expected to influence the process by which traits are encoded into language (Allport and Odbert 1936).

modeling individual differences

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decisions (Buss 1996; Eysenck and Eysenck 1967; Eysenck 1986; Zuckerman 1994). Proponents of biologically derived personality have seized on the nearly unanimous agreement that personality traits are consistent over time (Fraley and Roberts 2005; McCrae 1994; Roberts and DelVecchio 2000). In other words, the rank-ordering of individuals’ traits do not change.3 Current controversy over trait stability is over mean-level change in personality, which in the context of rank-order consistency means all individuals change in the same way over time. The accepted position in the field for some time was that there is no change in personality traits after the age of thirty (McCrae 1994; Costa and McCrae 1997). However, in the past decade, longitudinal studies have detected significant mean-level change in adulthood, particularly declines in Neuroticism and increases in Agreeableness and Conscientiousness with age (McCrae et al. 1999; Roberts et al. 2003; Roberts, Walton, and Viechtbauer 2006; Roberts and Caspi 2008; Srivastava, John, Gosling, and Potter 2003). Genetic personality studies suggest that the overwhelmingly stable nature of most traits, emerging from childhood, is largely a function of genetics, while any fluctuations in adulthood are due to environmental pressures (Blonigen, Hicks, Krueger, Patrick, and Iacono 2006; McGue, Bacon, and Lykken 1993; Plomin and Nesselroade 1990). Additional genetic and neurological studies have found that the Big Five traits are 40–60% inherited, and that the Big Five are associated with activity in certain parts of the brain (Bouchard and Loehlin 2001; DeYoung et al. 2010). Furthermore, the political influence of personality traits also appears to be stable over time (Bloeser, Canache, Mitchell, Mondak, and Poore 2015). Though there appear to be several ways that traits can change in adulthood, personality rank-order consistency is strongly supported, allowing researchers to give relative personality trait measures causal prominence.

2.2 Challenges to the Five-Factor Model The five-factor typology of personality traits has attracted critics, and its dominant position in personality psychology is not unchallenged. Its detractors have questioned its applicability outside Anglophone

3. But see Magidson, Roberts, Collado-Rodriguez, and Lejuez (2014).

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populations, the validity of its measures, its appropriateness as a top-level trait structure, and the number of traits it incorporates; supporters have put forth a vigorous defense. The lexical school of thought has accumulated significant evidence for the five-factor taxonomy in Germanic languages by identifying the same five-factor structure in English, Dutch, and German (Goldberg 1990; Hofstee, Kiers, Raad, Goldberg, and Ostendorf 1997; Ostendorf 1990; Saucier and Goldberg 1996a).4 Other northern European languages—including Polish, Czech, Croatian, and Turkish—have all produced the five-factor structure in lexical analysis (see Saucier 2009a for a review), but Italian, Hungarian, and Greek have not exhibited a strong Openness/Intellect factor, and a Chinese lexical study found very poor replication of the five-factor structure (Caprara and Perguni 1994; Di Blas and Forzi 1998; Saucier 2009b; Szirmák and de Read 1994). Debates over the importance of cross-linguistic validity have more to do with the local application of the Big Five in cultures in which the taxonomy may not be linguistically supported rather than the usefulness of using the Big Five in northern European or Germanic languages (such as English). Despite these concerns, a massive examination of the Big Five Inventory (BFI) administered over fifty-six societies found that the BFI’s five-factor structure replicated well over diverse cultural regions, and that prior issues with the cross-cultural examination of the Big Five may be a result of “problems with individual instrument translations, the unrepresentativeness of samples, response biases and scale variations, and slightly different definitions of the Big Five across the BFI, the NEO-PI-R, and the EPQ [Eysenck Personality Questionnaire]” (Schmitt, McCrae, and Benet-Martínez 2007, 204). A second validity concern lies in the personality inventories on which most studies of the Big Five rely. Some might expect respondents to change their responses on personality inventories in order to maintain delusions about their own nature or project an image they believe is socially desirable. These concerns have been taken seriously, and selfreported scores have been compared with ratings provided by third parties (both intimates and detached experts). The NEO-PI inventory

4. Interestingly, the only incongruity found was in Dutch. The fifth factor represented itself in that language as unconventionality and rebelliousness rather than as intellect and imagination (De Raad, Mulder, Kloosterman and, Hofstee 1988; Hofstee et al. 1997).

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has been tested extensively along these lines. Scores provided by respondents and the scores provided by their romantic partners correlated at .56, while the scores between a subject’s spouse and peers correlated at .41 (McCrae and Costa 1989). Self-assessments and observer ratings of the Big Five across thirty-six studies were found to have a correlation of .46 across all five factors, and for individual factors both sources of ratings were correlated from .46 for Agreeableness to .62 for Extraversion (Connolly, Kavanagh, and Viswesvaran 2007). This is similar to the correlation of .45 across all five factors found by McCrae and Costa (2005). Though the ratings obtained by individuals and those provided by others are not identical, it is clear they correspond to observable behavior patterns. Apart from technical or broader theoretical challenges to the Big Five, many alternative taxonomies have been proposed as superior models of personality traits. Since the 1940s, a large number of trait structures other than the Big Five have achieved some degree of attention within the field (Almagor, Tellegen, and Waller 1995; Ashton et al. 2004; Bales 1970; Benet-Martínez and Waller 1997; Block and Block 1980; Buss and Plomin 1975; Cattell 1943; Clark and Watson 1999; Cloninger 1987; Comrey 1970; DeYoung 2007; DeYoung, Quilty, and Peterson 2007; Digman 1997; Eysenck 1947; Eysenck and Eysenck 1976; Gough 1985; Guilford 1975; Hogan and Hogan 2007; Jackson 1984; Freedman, Leary, Ossorio, and Goffey 1951; Musek 2007; Myers, McCaulley, and Most 1985; Saucier 2009b; Tellegen 1985; Wiggins 1979; Zuckerman et al. 1993). In the interests of brevity, only the most prominent challengers will be addressed, as many of the defenses of the Big Five vis-à-vis the bestsupported challengers hold for less well-regarded brethren. A convenient way to consider these various challenger trait structures is to separate them into systems that include much larger numbers of traits than the Big Five, and those that have fewer or a similar number of traits. The first category of challenger models argues that more than five traits characterize the structure of personality. Notable examples include Cattell’s 16PF, the Minnesota Multiphasic Personality Inventory, and the Myers-Briggs Type Indicator (Cattell 1943; Myers, McCaulley, and Most 1985; Tellegen 1985). This also includes various schemas of the Big Five that break up the five factors into anywhere between ten and thirty-six facets (Costa and McCrae 1992a; DeYoung, Quilty, and Peterson 2007; Hofstee, de Raad, and Goldberg 1992; Saucier and Ostendorf 1999; Soto and John 2009). For this project, and at this stage in our research agenda, it is more useful for us to adopt a common approach in the political

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psychology and trait psychology literature and first identify general relationships between higher-level trait measures and institutional phenomena based on general and simplified theoretical translations. Once these relationships are established, we advocate following up with more specific trait facet measures and more complex translations of personality traits. As such, we seek to use a defensible top-level typology first before following up with lower-level traits. By definition, the various models of Big Five facets all fit into the Big Five construct, and the MMPI, MBTI, and 16PF have all been fit into the five-factor model (Costa, Busch, Zonderman, and McCrae 1986; Furnham 1996; Mondak, Hibbing, Canache, Seligson, and Anderson 2010). The second category of challenger models argues that five traits or fewer characterize the structure of personality. Notable examples in this category are Eysenck’s Big Three, the Big Six, and the Big Seven (which adds negative and positive valence dimensions to the Big Five) (Almagor, Tellegen, and Waller 1995; Eysenck and Eysenck 1976; Saucier 2009b). Eysenck was an unyielding critic of the Big Five and provided many arguments against the model, but the primary argument that he used to advocate for his Big Three (Extraversion, Neuroticism, and Psychoticism) over the Big Five was that Agreeableness and Conscientiousness were merely facets of a higher-level factor of Psychoticism (Costa and McCrae 1992a; Costa and McCrae 1992b; Eysenck 1992). In response, some Big Five theorists argued that “any variable, A, that is formed out of two others, B and C, will inevitably relate to all variables associated with B, with C, or with both” and that Agreeableness and Conscientiousness were two orthogonal factors (Goldberg 1993, 31). Additionally, Eysenck took the position, as one can when discussing dimensions generated by factor analysis, that the fifth factor of Openness was not strong enough to be considered an independent factor equal to the others (Eysenck 1992). Though personality psychologists continue to debate the strength of each factor as well as the number of appropriate factors, the five-factor structure tends to carry the day due to its substantial, long-standing, and broad empirical support (Mondak et al. 2010). Notably, the Big Six and the Big Seven were developed because of concerns their proponents had with the empirical derivation of the fivefactor structure. In the case of the former, the concern is that the Big Five do not hold up outside of European languages, while in the case of the latter, the concern is that evaluative terms were omitted from the trait-descriptive terms used by the lexical school to derive the five-factor

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structure (Almagor, Tellegen, and Waller 1995; Saucier 2009b).5 As our approach is tailored to American political institutions and valence selfevaluations are not intuitively associated with elite behavior, we do not share this concern and deem the Big Five adequate for our needs until a better alternative arises.

2.3 Personality and Political Science Over the past ten years, political scientists have become increasingly sophisticated in their application of the five-factor typology to the study of political behavior. Initial examinations of the Big Five’s association with various political phenomena in the mass public found the traits to be broadly influential (Mondak and Halperin 2008; Mondak et al. 2010; Gerber, Huber, Doherty, Dowling, and Ha 2010; Gerber, Huber, Doherty, and Dowling 2011a). Initial examinations of the relationship between political ideology and the Big Five found support for a connection between Conscientiousness and conservatism as well as between Openness and liberalism (Carney and Potter 2008; Gerber et al. 2010; Riemann, Grubich, Hempel, Mergl, and Richter 1993; Stenner 2005). Further examination found support for associations between Agreeableness and both economic conservatism and social liberalism, and a weaker connection between both Extraversion and Emotional Stability and social conservatism (Gerber et al. 2010; Gerber et al. 2011a). Building on this, other work has found that more Conscientious and Emotionally Stable individuals tend to support conservative candidates, while more Agreeable and Open individuals tend to support left-leaning candidates (Caprara, Barbaranelli and Zimbardo 1999; Barbaranelli, Caprara, Vecchione, and Fraley 2007; Mondak and Halperin 2008; Mondak 2010). Additionally, as a valence trait for leaders, we may expect voters to prefer more sociable individuals; indeed, political psychologists have found evidence that voters evaluate leaders at least partially on the basis of their Extraversion (Caprara, Schwartz, Capanna, Vecchione, and Barbaranelli 2006).6

5. The Big Six taxonomy can be replicated across seven languages (including three nonEuropean tongues) and was developed in response to this concern (Ashton et al. 2004; Saucier 2009a). 6. Also see Klingler, Hollibaugh, and Ramey (2016).

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Moving into more detailed attitudes, more Open individuals are less likely to stigmatize people with AIDS, hold racial prejudice, hold authoritartian beliefs, or have homophobic attitudes (Alford and Hibbing 2007; Barbaranelli et al. 2007; Butler 2000; Cullen, Wright, and Alessandri 2002; Duriez and Soenens 2006; Flynn 2005; McCrae 1996; McCrae et al. 2007; Sibley and Duckitt 2008; Stenner 2005). Agreeableness is associated with a stronger psychological sense of community, which can serve as a basis for political trust (Almlund et al. 2011; Lounsbury, Loveland, and Gibson 2003). Furthermore, individuals who are less well-adjusted (as Neurotics tend to be) are more easily drawn into fanatical and toughminded positions (Soldz and Vaillant 1999); to this effect, Neuroticism has been found to be associated with dogmatic thinking and stronger support for the death penalty (Francis 1997; Robbers 2006). Research on the Big Five has also revealed connections to political information. One line of inquiry stems around the idea that Neurotics may have less stable political attitudes and more uncertainty about the attitudes they do have (Mondak et al. 2010). Psychologists have argued that Neurotics experience variability and “mental noise” in their cognitive operations, and this may cause additional uncertainty and instability in Neurotics’ responses (Flehmig, Steinborn, Langner, and Westhoff 2007; Robinson and Tamir 2005). Additional investigation suggests Neurotics are less likely to respond to survey questions more generally (Klingler, Hollibaugh, and Ramey [Forthcoming]; Ramey, Klingler, and Hollibaugh, [Forthcoming]). Openness, Conscientiousness, and Extraversion are all associated with higher levels of political interest, but only Openness seems to be linked to possession of political knowledge (Gerber, Huber, Doherty, and Dowling 2011b; Mondak and Halperin 2008; Mondak 2010). Openness is also associated with an increased willingness to discuss politics with others to persuade them to support other candidates, as well as vulnerability to being persuaded by others (Hibbing, Ritchie, and Anderson 2011; Mondak et al. 2010). In line with Agreeable individuals’ inclinations to be concerned about others, the trait is associated with distaste for political discourse and political competitiveness (Hibbing and Theiss-Morse 2002). Finally, the Big Five have been found to be associated with the types of media individuals consume to inform themselves about politics, as Conscientious individuals watch TV news frequently and Extraverts consume information from television and newspapers (Gerber et al. 2011b; Mondak and Halperin 2008).

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In general, research into the connections between the Big Five and political behavior has revealed inconsistent findings, though Extraversion appears to display the most consistent patterns (Gerber et al. 2011a). Extraversion is associated with contributing to voluntary associations, attending campaign rallies and events, speaking at meetings, signing petitions, and contacting elected officials (Bekkers 2005; Mondak and Halperin 2008; Mondak et al. 2010). Openness is also associated with political participation, including voting, contacting elected officials, protesting, and contributing to political campaigns (Brandstätter and Opp 2014; Mondak et al. 2010; Vecchione and Caprara 2009). There is evidence that Agreeableness is associated with diminished political participation, namely voter turnout, when it is associated with political participation at all (Mondak et al. 2010). Finally, the traditional association between Conscientiousness and duty suggests the trait might be involved with civic engagement, but some studies find a positive association while others find the opposite (Bekkers 2005; Gerber et al. 2011a; Weinschenk 2014).

2.4 Modeling Personality This chapter has only scratched the surface of the extensive literature on trait psychology and the five-factor model of personality traits. There are important differences in perspective among the supporters of this model of trait personality regarding the theoretical origins of the five-factor structure and the nature of Openness/Intellect, and many personality psychologists oppose the Big Five. However, the five-factor model has robust empirical support, valid measures, predictive power over many outcomes, and demonstrated consistency that makes it a useful model of the most important persistent differences in human behavior. One would expect that these strengths of the five-factor model would attract the interests of social scientists in other disciplines and fields. This is in fact the case. Political psychologists have embraced the Big Five and have connected a wide variety of political behaviors and variations in political information to traits within the five-factor moel (Gerber et al. 2011a; Klingler, Hollibaugh, and Ramey [Forthcoming]; Mondak, and Halperin 2008; Mondak et al. 2010). Over the past decade, scholars have worked to integrate the Big Five into theories of voter mobilization,

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the generation of political attitudes, and the processing and gathering of political information. During this same period, personality psychologists and economists have worked to incorporate the Big Five and trait psychology more generally into economic models, with a focus on the determinants of academic and work outcomes (Almlund et al. 2011; Borghans et al. 2008; Ferguson, Heckman, and Corr 2011); these studies have drawn from—and complemented—efforts to identify relationships between the Big Five and key economic parameters (Becker et al. 2012; Daly, Harmon, and Delaney 2009; Dohmen et al. 2008; Dohmen et al. 2010). These investigations of the Big Five outside of personality psychology have served to enrich political behavior and economics with a powerful explanatory theory of human behavior. Psychology in turn has been enriched with knowledge of new phenomena associated with personality traits and additional perspectives on crafting theories of personality and measuring the effects of personality differences (Almlund et al. 2011; Mondak et al. 2010). To the detriment of both psychology and political science, no such dialogue exists today between personality psychology and the study of political institutions. Noteworthy applications of the Big Five to political elites have not presented the Big Five in a manner that can be used to develop innovative models combining existing theory and personality (Dietrich et al. 2012; Hall 2015; Moe 2009; Rubenzer and Faschingbauer 2004). This is an opportunity, and personality trait psychology can contribute a great deal to the study of political institutions. What is holding us back is that scholars of institutions have not yet had usable methods for modeling personality traits. Without such a method, personality psychology cannot meaningfully influence the mainstream study of political institutions. 2.4.1

Defending Models of Personality

Incorporating personality traits into models of political institutions requires that each trait be translated into one or more modelable parameters. We are using the Big Five, and we must make several simplifying assumptions about the nature of each factor in order to restate top-level personality factors in modelable terms. We will not be arguing that the parameter(s) we choose is the factor itself; rather, we argue that these modeling parameters are our best approximations of the five factors in

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terms of the variables used in models of political institutions. Additionally, we begin with each factor and use it to derive parameters. It is neither our objective nor our practice to begin with convenient parameters and force them onto personality trait measures. The evidence from personality psychology leads us away from a strict rational choice paradigm and toward behavioral models of political institutions. Utilizing models with personality traits offers great benefits, but there are also some costs as well, and the gains are worth the losses. The clearest immediate benefit is that it makes personality psychology accessible to a new audience, and gives institutions scholars powerful new explanatory variables for institutional behavior and a strong incentive to engage with a literature with which they rarely interact. The evidence presented in this chapter illustrates the untapped potential of personality psychology for studying political institutions. A second benefit comes from the clarity offered by modeling. The mathematical basis of formal modeling forces each variable and assumption to be clearly defined. As a result, it is often possible to obtain unambiguous predictions of behavior on the basis of variation in personality. “Informal” models allow greater flexibility but at the potential cost of lower precision. Personality traits are broad and complex, but by focusing on the best representation of a trait, we can understand how that mechanism influences behavior within a clearly defined context. Creating hypotheses based on existing personality trait theory requires the selection of particular elements associated with a trait, and verbal elaboration about the influences of these elements within a particular process. In many circumstances, it is quite possible to derive completely opposing hypotheses regarding the association of a factor with a given phenomenon by selecting different characteristics associated with that factor and constructing a narrative with loosely defined or unstated assumptions around those different characteristics. A model-based approach forces these starting interpretations of traits and assumptions about processes to be clearly stated and open to criticism before hypotheses are generated and tested. The primary cost to modeling the Big Five personality traits is that it involves two forms of information loss. First, only one or a few elements associated with each trait may be selected to represent a top-level factor of personality. Second, approximating an element in either mathematical or precise verbal terms results in a loss of information as well.

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It is understandable that some may initially object to characterization of a complex factor of personality as a value in an equation (in the case of formal models), but these objections are largely manageable. Any information loss resulting from selecting one or a few elements associated with each trait must be experienced by traditional theorists as well as modelers. Whenever any scholar chooses to discuss the Big Five, some aspects are chosen for focus and others are left out.7 Furthermore, the Big Five were derived through factor analysis in both the questionnaire and lexical schools. Factor analysis is designed to capture latent variables that are commonly associated with a variety of phenomena. If we interpret these five factors to be independent, biologically derived entities worth discussing in their own right rather than simple descriptive indications of correlated phenomena, as is argued by many personality psychologists, then it is reasonable to claim that a single mechanism, cognitive limitation, or preference is the common link among the observable phenomena associated with each trait (Gerber et al. 2011a; Mondak and Halperin 2008; Mondak et al. 2010). In any case, the information loss from characterizing a trait in terms of a selected concept or concepts is experienced by both existing theoretical approaches to personality and modeling. Information loss from parameterizing a factor is a second cost to modeling. Though we consider traits in terms of cognitive constraints suggested by personality neuroscience and experimental economics (Becker et al. 2012; Borghans et al. 2008; DeYoung et al. 2010), no model of a cognitive constraint can fully capture its richness as it exists in the “real world.” Indeed, no model, neither verbal nor formal, is the true model except for the real world, and the real world lacks focus required to be useful (Clarke and Primo 2012). Accordingly, every model, whether stated verbally or in terms of equations, makes a tradeoff between information and precision.8 The information loss that inevitably results from parameterizing cognitive constraints allows us to make clearer and more context-based predictions, forces us to defend all of our assumptions, and allows these cognitive constraints to be considered by other modelers.

7. This is evident in efforts to identify prototypical trait adjectives, shorter personality inventories, and smaller sets of marker words, which are no small part of personality research (Costa and McCrae 1989; Saucier 1994; John, Naumann, and Soto 2008). 8. As mentioned, not even the Big Five model is immune to this loss of information.

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Parameterizing Core Cognitive Constraints

As previously mentioned, incorporating the Big Five into models of political institutions first requires us to characterize each trait as a single modelable concept. We are not the first to approach this task, and personality psychologists and economists alike have developed frameworks for choice models that incorporate personality traits and offer an attractive starting point for our approach (Almlund et al. 2011; Borghans et al. 2008). The economists note that personality traits impose constraints on agent choice behavior. More fundamentally, conventional economic preference parameters can be interpreted as consequences of these constraints. For example, high rates of measured time preference may be produced by the inability of agents to delay gratification, interpreted as a constraint, or by the inability of agents to imagine the future. (Borghans et al. 2008, 977)

The significant evidence in favor of a biological basis for personality traits suggests that biological phenomena lie behind a trait’s observed behaviors. Neuropsychology has characterized differences in neurochemistry, brain region volume, and resting state brain activity associated with the Big Five (Adelstein et al. 2011; DeYoung et al. 2010; Wacker, Chavanon, and Stemmler 2006). If there is a biological basis for personality, we should expect it to manifest itself in the brain. Accordingly, in the next section, we identify the core cognitive constraints captured through the Big Five factors by focusing on the biological differences in brain functioning that neuropsychology has associated with each trait. Once a core cognitive constraint is identified for each trait, we then express it in terms of modeling parameters. The core cognitive constraints for each trait influence behavior in systematic and modelable ways, but are dependent on the context included in the model. As we are interested in modeling personality in a manner useful for the study of the United States Congress, we construct a simple framework of legislative utility later in this chapter and identify a useful approximation of each core cognitive constraint’s influence on legislative utility. The resulting framework will direct the incorporation of each trait’s core cognitive constraint into models of political institutions used throughout this book.

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2.4.3

Measuring Personality-Based Cognitive Constraints

Creating models using each trait’s core cognitive constraint(s) only gets us halfway to our goal, as testing the hypotheses generated by theory requires that we obtain measures of each core cognitive constraint for political elites. Through the stable presence of cognitive constraints in myriad decisions over the course of a lifetime, cognitive constraints and contextual variables produce patterns of behavior we recognize as trait-dependent. These trait-dependent patterns of behavior lead to the descriptive terms and statements captured by the various personality inventories measuring the Big Five. Variables (such as experiences, abilities, or goals) that shape an individual’s decision to engage in traitdependent behavior could potentially reduce the ability of personality trait inventories to capture the core cognitive constraints in which we are interested.9 In the following chapter, we discuss our method for obtaining personality trait estimates of political elites. These estimates are derived from personality inventories and—based on the logic outlined here— provide reasonable, though perhaps noisy, measures of the core cognitive constraints.

2.5

The Big Five Traits

We now discuss each of the Big Five traits in detail and identify the core cognitive constraint for each trait, as suggested by neuropsychology. It should be noted that proponents of the five-factor typology have somewhat diverse characterizations of what exactly is captured by each factor, though the charactareziations are comparable. In acknowledgment of this

9. We assume, as do those who create personality inventories, that no variable is so correlated with enough of these personality trait inventories as to systematically bias the resulting estimates of each trait’s core cognitive constraint and latent factor. If a trait-dependent behavior is influenced by more than one core cognitive constraint, measures of that behavior should be correlated with two or more of the Big Five traits. If that trait-dependent behavior is a measure of a modeling parameter, we would find this measure to be correlated with multiple traits. Some of the parameters we select have measures, and these measures have been found to be correlated with multiple trait measures. To clarify, our model of personality does not claim that any parameter we select to represent a trait is a product of the trait. We are claiming that the parameter is the most useful translation of the trait itself into modeling language for a particular context.

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diversity of viewpoints and opinions, we include in Table 2.1 descriptions of each trait from different sources. We present the “mini-markers” for each trait developed by Saucier (1994) of the lexical school, the six facet names that compose each of the five factors developed by Costa, McCrae, and Dye (1991) of the questionnaire school, and six “prototypical” positive adjectives for each trait selected by experts familiar with the literature on personality (John, Naumann, and Soto 2008).10 2.5.1 Openness (to Experience) Openness is associated with a desire to explore and imagine new situations and ideas. The trait is linked with both an appreciation for aesthetic beauty and intellectual pursuits and a tendency for creativity and imagination (Borgatta 1964; Tupes and Christal 1961). However, as the name suggests, those with the trait have a taste for novel experiences. Openness is likely the most controversial of all of the Big Five personality traits. The questionnaire school tends to describe this factor as Openness, while the lexical school tends to describe it as Intellect (Saucier and Goldberg 1996a). The lexical school first identified the trait, with tupes and Christal’s (1961) first investigations characterizing it as Culture, but this gradually shifted toward the label of Intellect (Borgatta 1964; Goldberg 1990; Norman 1963). However, the trait was later independently identified by Costa and McCrae (1976) as Openness. This trait has been associated with a variety of career-related outcomes. In particular, it has been linked with adaptability and creativity in work tasks (George and Zhou 2001; Lepine, Colquit, and Erez 2000), and has also been associated with increased earnings in multiple societies (Heineck and Anger 2010; Heineck 2011; Mueller and Plug 2006). Openness is positively associated as well with leadership ability (Judge et al.

10. Prototype terms were obtained from expert assignment of three hundred traitdescriptive adjectives to the Big Five. Ten experts were used who were trained expert raters of personality. The prototypes listed here were the adjectives with the largest positive factor loadings resulting from a factor analysis of the categorized words (John, Naumann, and Soto 2008; McCrae and Costa 2005). Facet terms were assigned to lower level factors found by Costa, McCrae, and Dye (1991) in questionnaire data. The mini-marker terms were obtained from factor analysis of self-descriptions using words from an earlier set of one hundred marker terms (Goldberg 1990; Saucier 1994).

table 2.1 Defining Terms for the Big Five

Openness

Conscientiousness

Extraversion

Agreeableness

Neuroticism

Prototypes (Experts) (John, Naumann, and Soto 2008)

Facets (Questionnaire) (McCrae and Costa 2005)

Mini-Markers (Lexical) (Saucier 1994)

Artistic Curious Imaginative Insightful Original Wide interests

Fantasy Aesthetics Feelings Actions Ideas Values

Efficient Organized Planful Reliable Responsible Thorough

Competence Order Dutifulness Achievement Striving Self-Discipline Deliberation

Efficient Systematic Practical Disorganized (−) Sloppy (−) Inefficient (−) Careless (−) Talkative

Active Assertive Energetic Enthusiastic Outgoing Talkative

Warmth Gregariousness Assertiveness Activity Excitement Seeking Positive Emotions

Extraverted Bold Energetic Shy (−) Quiet (−) Bashful (−) Withdrawn (−) Sympathetic

Appreciative Forgiving Generous Kind Sympathetic Trusting

Trust Straightforwardness Altruism Compliance Modesty Tender-Mindedness

Warm Kind Cooperative Cold (−) Unsympathetic (−) Rude (−) Harsh (−) Moody

Anxious Self-pitying Tense Touchy Unstable Worrying

Anxiety Hostility Depression Self-Consciousness Impulsiveness Vulnerability

Jealous Temperamental Envious Touchy Fretful Unenvious (−) Relaxed (−)

Creative Imaginative Philosophical Intellectual Complex Deep Uncreative (−) Unintellectual (−) Organized

Mini-markers labeled with a minus sign convey a meaning opposite that of the marked factor.

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2002). Interestingly, but perhaps not surprisingly, a major non-career outcome connected with Openness is use of alcohol and tobacco, though there is little association between the trait and substance abuse or usage of harder drugs (Booth-Kewley and Vickers 1994; Soldz and Vaillant 1999). This trait has been associated with a variety of related cognitive functions, such as reduced latent inhibition—that is, blocking irrelevant stimuli from consciousness (Peterson and Carson 2000; Peterson, Smith, and Carson 2002)—as well as resting state functional connectivity (RSFC) activity within areas of the prefrontal cortex (PFC) associated with working memory (DeYoung, Peterson, and Higgins 2005; DeYoung, Shamosh, Green, Braver, and Gray 2009; Kaufman, DeYoung, Gray, Jiménez, Brown, and Mackintosh 2010; Sutin, Beason-Held, Resnick, and Costa 2009). It has also been linked with increased dopaminergic activity, but this connection appears to operate by altering activity within the PFC, rather than across the brain (Harris et al. 2005). Additionally, Openness has been linked to cognitive functions for imagination and creativity. More Open individuals have increased RSFC in the default mode network and other parts of the brain associated with cognitive flexibility and imagination as well as pattern recognition and apophenia, the detection of patterns in meaningless data (Adelstein et al. 2011). The default mode network has itself been linked to a capacity to imagine hypothetical scenarios (Spreng, Mar, and Kim 2009). While a study of the volume of different parts of the brain did not reveal any significant association between Openness and local brain volume, the increased presence of dopaminergic activity in some parts of the brains of Open individuals suggest the trait is associated with neurochemically induced—rather than simply volume-based—differences in cognitive function. Dopamine is used by the brain to drive responses to stimuli, and studies have shown that the brain’s reward system is triggered when one identifies an answer to a trivia question in a manner similar to the way it is triggered by monetary, social, or food rewards (Kang et al. 2009). Increased dopaminergic activity in Open individuals in the prefrontal cortex reduces latent inhibition and maintains working memory, and further increases motivation for intellectual exploration (DeYoung et al. 2011). Increased intellectual exploration along with greater perception and working memory all serve to combine in a powerful way both

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to drive and equip Open individuals for cognitive exploration. Openness’s core cognitive constraint is a compulsion to gather and process information.11 2.5.2 Conscientiousness People who are more Conscientious have been described as having a tendency toward hard work, responsibility, and planning (VandenBos 2007). Conscientious individuals tend to be more driven, goal-oriented, and uptight, and have been described as being better organized and having more willpower (Ozer and Benet-Martínez 2006). There is also a part of Conscientiousness that is associated with adherence to social norms. The trait terms in Table 2.1 include many words with connotations relating to hard work, competency, and drive. Conscientiousness has strong roots in the lexical tradition, but it also has deeper conceptual roots, being similar in many ways to the early personality parameter g—alternatively referred to as Character—which was used to predict academic success (Webb 1915). While Conscientiousness was left out of the early trait typologies of the questionnaire tradition, it was identified early on as an important trait by the lexical school and remained identified as such in both schools’ models of the Big Five (Borgatta 1964; Goldberg 1990; Norman 1963; Tupes and Christal 1961). Conscientiousness has been linked to several important life outcomes, such as decreased rates of mortality in children and the elderly (Roberts et al. 2007) as well as healthy habits and good health outcomes (Danner, Snowdon, and Friesen 2001; Friedman, Hawley, and Tucker 1994; Weiss and Costa 2005). In the same vein, when illness does strike, more Conscientious individuals stick with their treatment regimens more diligently (Kenford et al. 2002). Conscientious individuals also are less likely to divorce their marital partners (Roberts et al. 2007). Conscientious children tend to have higher levels of academic performance (Noftle and Robins 2007; Paunonen 2003), and this distinctive work ethic continues later in life in the workplace, where Conscientiousness is a strong predictor of job performance and training motivation (Colquitt, LePine, and Noe 2000; Mount, Barrick, and Stewart 1998; Salgado 1997) as well as leadership (Judge et al. 2002). 11. A clear secondary effect of a compulsion to collect and process information is possession of better knowledge and less uncertainty about the world.

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Physiologically, Conscientiousness has a positive correlation with the volume of the middle frontal gyrus in the left lateral PFC, a portion of the brain “crucially involved in maintaining working memory and the execution of planned action” (DeYoung et al. 2010, 826). The PFC is linked with the ability to plan and follow complex rules (Bunge and Zelazo 2006; Miller and Cohen 2001). The lateral PFC in particular is associated with impulsivity control (Brown, Manuck, Flory, and Hariri 2006). Examination of RSFC revealed an association between measures of Conscientiousness and the parts of the brain associated with planning for the future (Adelstein et al. 2011). The ability to maintain self-control also appears to be connected with Conscientiousness in neurological data. Activity in the dorsolateral PFC, which controls executive function and attention, has been linked with Conscientiousness (Forbes et al. 2014). It has been suggested that Conscientiousness may be associated with glucose metabolism, as glucose depletion is associated with self-control and the PFC consumes relatively large amounts of glucose (DeYoung and Gray 2009; Gailliot and Baumeister 2007). Responsiveness in the somatosensory cortex, a part of the brain associated with distractability and self-control, has also been associated with Conscientiousness (Schaefer, Rotte, Heinze, and Denke 2013). Overall, in terms of volume and brain activity, Conscientiousness has strong links with parts of the brain associated with self-control, planning, and execution of planned action. This evidence suggests that the core cognitive constraint of Conscientiousness is an increased capacity to realize planned future outcomes.12 2.5.3 Extraversion The Big Five personality trait of Extraversion is strongly associated with activity of many kinds. Extraverts are more sociable and talkative, and they also have a tendency to be more assertive and energetic, as indicated in the trait-descriptive terms in Table 2.1. Furthermore, and somewhat in

12. This core cognitive constraint is consistent with the descriptor words in Table 2.1 that focus on allowing people to lay groundwork for future actions (Organized, Planful, Thorough, Order, Systematic), pursue large goals (Achievement Striving), prepare for future liabilities (Efficient, Reliable, Competence, Deliberation, Practical), and recognize the consequences of their actions (Reliable, Responsible, Dutifulness, Self-Discipline).

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line with the sociability aspect of Extraversion, the trait is associated with a positive, sunny outlook on life. Extraversion is one of the least controversial traits, and both the lexical and questionnaire schools agree on its prominence, as it had a central position in the early Big Two typology of Eysenck (1947; 1976), which carried over to his three-factor model, and the early three-factor NEO as well as the lexical Big Five of Goldberg (1990; 1993) and the Saucier (2009a) Big Six typology. This trait has been found to have important relationships with longevity and career attainment. Even after controlling for age, education, and aspects of cognitive ability, Extraversion is positively linked with longevity (Roberts et al. 2007). This likely is in part due to the fact that more Extraverted individuals are more likely to engage in positive wellness behaviors and proactive measures that prevent accidents (Booth-Kewley and Vickers 1994). Extraverts display more social support and close relationships for coping with illness as well, which likely boosts their longevity (Berkman, Glass, Brissette, and Seeman 2000). Extraversion measured in adolescence has predicted occupational status nearly half a century later, and it also has the strongest positive correlation of all of the Big Five traits with leadership ability (Judge et al. 2002; Roberts et al. 2007). It is also positively associated with the probability of one’s being listed in Who’s Who in America and with an individual’s income (Soldz and Vaillant 1999). The drive and ability to lead manifests itself in some interesting ways, one example being that more Extraverted individuals are more likely to be chosen as jury forepersons (Clark, Boccaccini, Caillouet, and Chaplin 2007). Extraversion as well as Neuroticism have been studied for decades by scholars looking for neurological bases for personality, as both traits were used by Hans Eysenck in his work to ground personality in biology. Eysenck and Eysenck (1967) argued that Extraverts experienced lower degrees of cortical arousal to stimuli than introverts, and as a result preferred higher levels of stimulation overall. This theory spurred a great deal of interest, but it was not consistently supported in future studies (Matthews and Gilliland 1999; Zuckerman 2005). As a result, more attention was given to the connection between Extraversion and dopamine, and at least one study suggested that cortical arousal is greater in Extraverts when greater dopaminergic activity is present (Wacker, Chavanon, and Stemmler 2006).

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One major theory argues that Extraversion is associated with brain systems that lead to approaching behavior due to the dopamine system (Gray 1982), though it was modified with later findings to argue that Extraversion is associated with fixation on positive incentives and ignoring of negative incentives (Derryberry and Reed 1994). Extraverts have more dopamine terminals than introverts, and a prevailing theory today is that these dopamine terminals support coding stimuli in terms of reward, which drives behavior to approach stimuli as sources of potential reward (Depue and Collins 1999; Fischer, Wik, and Fredrikson 1997). It is important to note that dopamine’s role in Extraversion would facilitate a drive to achieve and fixation upon potential reward rather than enjoyment of the reward once it is achieved (Wacker, Chavanon, and Stemmler 2006). Despite dopamine’s popular image as a “pleasure” chemical, it is actually more accurately described as a prospective “wanting” chemical (Berridge, Robinson, and Aldridge 2009). Two prominent researchers, Luke D. Smillie and Jan Wacker, recently wrote that “variation in the reward-processing functions of the dopamine system” is the “dominant neurobiological perspective on this trait” (2014). Apart from the important association between dopaminergic activity and Extraversion, the trait has been associated with the size of the medial orbitofrontal cortex, which codes the reward value of stimuli and sensitivity to reward and the extinction of fear responses (Adelstein et al. 2011; DeYoung et al. 2010; Omura, Constable, and Canli 2005; Rauch et al. 2005). A recent study of Extraverts’ reward systems argues that they have a lower threshhold of reward needed to take a given action, a broader ability to find rewards for stimuli, and that they are more easily conditioned to associate stimuli with reward (Depue and Fu 2013). Through dopaminergic activity and the orbitofrontal cortex, Extraversion’s core cognitive constraint is sensitivity to and fixation on prospective reward (Corr, DeYoung, and McNaughton 2013). 2.5.4 Agreeableness Agreeableness has been linked with being likable, pleasant and harmonious in relationships with others (Graziano and Tobin 1997), altruism (Almlund et al. 2011; DeYoung et al. 2010), and a tendency to trust others (Almlund et al. 2011; John, Robins, and Pervin 2008). Agreeable individuals tend to be cooperative and to act unselfishly (VandenBos 2007). Overall, Agreeableness is associated with other-mindedness in many

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forms, both in terms of active generosity, and willingness to forgive and trust, as is visible in Table 2.1. Agreeableness emerged first from the lexical school, appearing in the typologies created by Peabody and Goldberg (1989), where it was sometimes referred to as Love. Along with Conscientiousness, the questionnaire school was hesitant to adopt Agreeableness into many typologies until Costa and McCrae (1985) incorporated it into the NEO and discovered empirical support for a five-factor structure containing the trait. While not strongly linked to mortality, Agreeableness has been found to be associated with divorce avoidance (Roberts et al. 2007) as well as higher marital quality (Soldz and Vaillant 1999). Agreeable individuals are also less likely to take risky actions in traffic that might endanger others, which is likely an offshoot of their generally other-minded nature (Booth-Kewley and Vickers 1994). The volume of the posterior cingulate cortex has been found to be positively associated with Agreeableness measures (DeYoung et al. 2010). This area of the brain is involved in the process of understanding other individuals’ beliefs (Saxe and Powell 2006). Additional social processing capacities have been associated with Agreeableness (Adelstein et al. 2011). For example, Agreeable individuals are less likely to interpret fearful faces negatively or with anger (Haas, Omura, Constable, and Canli 2007), potentially due to a larger fusiform gyri, which is responsible for perceiving faces (DeYoung et al. 2010). Additionally, the ability to understand others’ beliefs is part of the theory of mind capability thought to be essential in the ability to act altruistically (de Waal 2008). The ability to interpret and perceive the actions of others has also been linked to differences in empathy (Kaplan and Iacoboni 2006; Tankersley, Stowe, and Huettel 2007). The larger posterior singulate cortex has been associated with empathy, and Agreeable individuals have higher measured resting state activity in the posterior singulate cortex as well (Adelstein et al. 2011; Völlm et al. 2006). As Agreeable individuals have increased capacities to interpret the beliefs and motivations of others and experience empathy, the core cognitive constraint of Agreeableness solidly appears to be a capacity for altruism. 2.5.5

Neuroticism

Neuroticism, in line with the indications given by the trait-descriptive words included in Table 2.1, is associated with high levels of anxiety,

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depression, impulsiveness, and vulnerability to stress (Almlund et al. 2011). Furthermore, related traits include external locus of control, high irritability, and a sense of vulnerability to external conditions (John, Robins, and Pervin 2008). Neurotics tend to have low self-esteem and to be unstable, withdrawn, easily angered, and difficult to motivate. Neuroticism has a long history in both divisions of personality trait psychology. Neuroticism was one of Eysenck’s (1947; 1976) original Big Two, as well as the Big Three. The lexical school included it as well in the early typology of Tupes and Christal, labeling its inverse as Emotional Stability (Tupes and Christal 1961). This name has stuck with the lexical school, though in contrast with the dispute over Openness/Intellect, the label is not over substantive content, but rather the direction of the trait (Goldberg 1990; Saucier 1994). Neuroticism is associated with a variety of important life outcomes, including a shortened lifespan, increased probability of experiencing divorce, and relationship dissatisfaction (including conflict and domestic abuse) (Roberts et al. 2007). Additionally, Neurotic individuals’ tendency to ruminate over events may be a contributing factor to their generally poorer reactions to illness (David and Suis 1999; Scheier and Carver 1993), though the trait is also correlated with unhealthy behaviors, lack of preventative maintenance, and risky behavior in traffic, which all serve to undermine general well-being (Booth-Kewley and Vickers 1994). Furthemore, Neurotics are more likely to be depressed, engage in mood-altering drug use, use tobacco, and engage in alcohol abuse (Soldz and Vaillant 1999). Neurotics generally have weaker social support, lower-quality marriages, and difficulty adjusting to adult life (Soldz and Vaillant 1999). The low ability of Neurotics to adjust spills over into the workplace, where Neurotics exhibit noticeably lower levels of leadership (Judge et al. 2002). Additionally, Neuroticism is a strong negative predictor of job performance as well as income (Hogan and Holland 2003; Nyhus and Pons 2005; Salgado 1997), and Neurotic individuals are more likely to burn out from their jobs and quit (Thoresen, Kaplan, Barsky, Warren, and de Chermont 2003). More Neurotic individuals have been found to have larger midcingulate gyri (DeYoung et al. 2010), which is associated with the detection of error and pain, and a larger mid-cingulate gyrus may be associated with higher sensitivity to the possibility of error and negative outcomes (Carter et al. 1998, Eisenberger and Lieberman 2004). Building on this,

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Neuroticism has been associated with resting-state activity in areas of the brain associated with fear and self-evaluation (Adelstein et al. 2011). Neurotics have higher mean levels of cortisol and weaker responsiveness in cortisol levels to specific stressors, suggesting chronic stress and reduced ability to deal with specific stressful situations (Netter 2004). Neuroticism has also been found to be associated with reduced volume in the dorsomedial prefrontal cortex and posterior hippocampus, which are associated with negative self-evaluation and uncertainty detection, respectively (DeYoung et al. 2010; Gray and McNaughton 2003). The medial prefrontal cortex (MPFC) has been found to be associated with emotional regulation and evaluation of the self (Heatherton, Macrae, and Kelley 2004). Through lab experiments, activity in the MPFC was associated with the formation of self-referential memories, so a smaller MPFC may be associated with more uncertainty in selfreferential opinions (Macrae, Moran, Heatherton, Banfield, and Kelley 2004). The association between Neuroticism and sensitivity to error has been linked to the role of serotonin in the brain (Gray and McNaughton 2003). A broader theory of Extraversion and Neuroticism has argued that Neuroticism is a biochemically induced counterpoint to Extraversion, and as Extraversion is associated with a fixation on reward, Neuroticism is focused on a fixation on negative outcomes (DeYoung and Gray 2009; Gray and McNaughton 2003). In the lab, Neurotics are prone to behavioral inhibition through passive avoidance and “freezing,” presumably due to their fixation on threat and negative outcomes (DeYoung and Gray 2009). If Neurotics are preoccupied with error and threat, absent some shock, the best way to avoid negative outcomes and stress would be to withdraw and maintain the status quo. Whether it is through sensitivity to error, stress avoidance, or a tendency to negative self-evaluation and rumination, Neuroticism’s core cognitive constraint is a sensitivity to and fixation on prospective negative outcomes.

2.6

A Framework for Political Choice

We have now identified a core cognitive constraint for each of the Big Five that is based in personality neuroscience and arguably underlies trait-descriptive behavior. We now turn to the final step of outlining a

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modeling framework that can incorporate personality into models of political institutions, and we seek to make modeling assumptions that have empirical support and are useful to scholars in the field. Since our goal is to incorporate the most important individual differences into models of institutional behavior, and we have decided to begin by focusing on the United States Congress, the most useful way to consider the choices made by members of that body must emerge from the literature. The past half century has provided us with a basic model of legislative utility based on that derived from policy and that from holding office, and it has been modified in various ways (Calvert 1985; Fenno 1973; Mayhew 1974; Persson and Tabellini 2002). A framework for incorporating personality traits into models of institutional behavior may operate in two distinct ways. A specific approach would consider the presence of a trait to be an action-specific “bonus” that would increase the utility of a single action selected a priori for a given individual. For example, when considering the choice between voting to increase funding levels for the National Science Foundation or to maintain the status quo, the individual’s heterogeneous degree of Openness could be modeled as an additional source of utility for choosing to increase NSF funding. A general approach would consider the presence of a trait to be a utility multiplier or bonus based on general and fundamental properties of the outcome, such as risk, delay, and benefit to others. It is quite possible that some traits do incentivize specific actions. However, we find the general approach to be more attractive for several reasons. Our goal in this project is to provide researchers with a tool kit that they can use to model all institutional phenomena they encounter while taking personality into account, and a specific incentivization approach is inherently incapable of doing this. Additionally, the expression of personality traits through observable action is contextdependent, and a general utility approach can identify interesting situations in which traits and actions are related in counterintuitive ways that would otherwise go unnoticed. The general utility approach requires fresh assessment of decision-making with every set of institutions, but it offers the richest potential for theory-building. Having made the choice to pursue a general modeling approach, we now complicate the aforementioned basic model of legislative utility by incorporating the cognitive constraints. The utility gained from winning office in the future clearly includes policy utility that may be obtained by

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48 table 2.2 Core Cognitive Constraints Trait

Cognitive Constraint

Openness Conscientiousness Extraversion Agreeableness Neuroticism

Compulsion to gather and process information Capacity to realize planned future outcomes Sensitivity to and fixation on prospective reward Capacity for altruism Sensitivity to and fixation on prospective negative outcomes

holding office in the future and influencing future policymaking (Mayhew 1974). Importantly, Conscientiousness’s core cognitive constraint is a capacity to realize planned future outcomes. As an example, individuals who lack this ability will be less able to take advantage of possible policy returns from holding future office. Therefore, less Conscientious individuals will derive lower levels of current utility from future policy gains that they can neither imagine nor likely obtain through planned actions. This manifests itself as a smaller discount factor for all future utility, be it from office, policy, or any other source. This modeling decision is supported by attempts to link personality traits with measures of time preference. Experimental results from an economic discounting study found that higher scores on Conscientiousness were associated with lower discounting of future payoffs (Daly, Harmon, and Delaney 2009), and that “conscientiousness is particularly implicated in the ability to make sacrifices now for rewards later” (3).13 A second, related component is the utility gained from the wellbeing of others. Agreeableness is strongly associated with the capacity for empathy (that is, desiring the well-being of others) and development of a theory of mind that enables individuals to understand the incentives of other individuals (Adelstein et al. 2011). It stands to reason, and is supported by evidence in biology, that an individual who cannot understand the incentives of another cannot act altruistically even if he or she desires to do so (de Waal 2008). More Agreeable individuals are more capable of deriving utility from others’ utility. Given that Agreeableness is a top-level trait expected to have general applicability to human action,

13. However, in another study Conscientiousness was found to have a weaker relationship with time preferences (Dohmen et al. 2010).

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our framework must incorporate the impact each action makes on the well-being of others. This raises an important issue. Agreeableness might be modeled as utility derived from the well-being of others, but which others? Modeling Agreeableness requires the identification of a reference group for the altruistic utility the trait magnifies. Research into the biological roots of altruism suggest that concern for the welfare of immediate family or extended family through kin groups or tribes may be supported through natural selection (Dawkins 2006; Lehmann 2007). However, altruism directed to groups of individuals who are not closely related but interact frequently, such as nations, may be biologically supported in addition to kin-based altruism (Wilson and Sober 1994; Nowak, Tarnita, and Wilson 2010). In most of this research, researchers dismiss the idea of natural selection driving altruism toward the species as a whole (Dawkins 2006). This suggests that biologically derived altruistic motivations may possibly apply to members of any salient social group, including the nation, party, committees, chambers, states, or other important geographic constituencies. Within the context of institutional politics, it is difficult to make a strong claim from existing evidence as to what the reference group should be, but as a first step we choose to select the nation, which is particularly salient to decision-making at the federal level. This has some support in models of legislative utility that put weight on selfless statesmanship as well as some idea of the “welfare of the nation” apart from policy and office goals (Cavanagh 1982; Uslaner 1996). Therefore, we expand our framework to include motivation to hold office, enact policy, and act in accordance with the welfare of the nation, which are of course discounted if expected in the future. Extraversion and Neuroticism have respective core cognitive constraints of sensitivity to and fixation on prospective rewards and on negative outcomes (DeYoung and Gray 2009; Derryberry and Reed 1994; DeYoung 2014). Rewards and negative outcomes in this context refer to gains and losses relative to a neutral reference point, and modeling utility in such a manner is similar in many ways to the approach taken by proponents of prospect theory (Depue and Collins 1999; Derryberry and Reed 1994; Kahneman and Tversky 1979; Tversky and Kahneman 1992). Extraversion and Neuroticism may thus be modeled through weight parameters on relative gains and losses, respectively, and the assignment of a neutral reference point for comparison. This is straightforward to

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incorporate into contexts involving reservation values and/or status quo values, and we focus on these throughout the book.14 The core cognitive constraint of Openness, which is a compulsion to gather and process information, has both direct and indirect effects on legislative behavior. First, any action that provides significantly more information, experience, or learning will likely provide additional utility to more Open individuals. More generally, however, situations with multiple possible outcomes require individuals to devote costly cognitive resources to the imagination (and retention) of alternative scenarios, such as legislative outcomes, and Open individuals pay a lower net cost for the collection and retention of this information. Thus, Openness is associated with relatively higher utilities for convex combinations of outcomes, and reduced risk aversion by implication (Borghans et al. 2008; Pratt 1964). Modeling this as a form of risk preference is supported by experimental evidence; for example, a previous experiment in which respondents were repeatedly asked to choose between a varying safe amount of money and participation in a lottery found that individuals scoring higher on Openness chose the lottery at higher values of the safe amount of money (Dohmen et al. 2010). Therefore, decisions that lead to more uncertain utilities should receive a lower penalty for more Open individuals. We explicitly consider Neuroticism and Extraversion as forms of loss and gain sensitivity, which in line with prospect theory would suggest that both traits could be associated with risk preferences as well (Kahneman and Tversky 1979). Neuroticism and Extraversion, in various amounts, may imply concave utility functions, and thus risk aversion through net

14. Extraversion and Neuroticism parameterizations as weights on prospective reward and negative outcomes may suggest that the two are simply two poles on a continuum of reactions to payoffs. Under this characterization, Extraverts and Emotionally Stable people will be more “active” while introverted and Neurotic individuals will be more inhibited. While this characterization may initially appear to be consistent with common understandings of shy introverts and depressed Neurotics, it fails to utilize properties built into the weightbased parameterization. All actions fall into one of four categories with respect to the gains and negative outcomes they may lead to with respect to the status quo. They may lead to (1) potential gains and losses, (2) only potential gains, (3) only potential losses, or (4) the status quo. Given this fourfold categorization, while more Extraverted and Emotionally Stable people will both prefer (1) to a greater degree, the former will prefer (2) while the latter will prefer (3). Thus, the degree to which Extraversion and Emotional Stability influence choice will vary to different degrees based on how institutional contexts utilize carrots and sticks to incentivize behavior.

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overweighting of negative outcomes, through what may be referred to as common risk, while low degrees of Openness may imply steeper concave utility functions by imposing higher cognitive uncertainty costs. All three traits would then have some relationship to risk, but through two different mechanisms. We hope to resolve this empirically, as the association between risk and Openness has been shown to be considerably stronger than the link between risk preferences and Neuroticism or Extraversion, when such relationships have been found (Becker et al. 2012; Borghans, Heckman, Golsteyn, and Meijers 2009; Dohmen et al. 2010). Based on these empirical findings, as well as the opportunity to model two traits in a more foundational manner than as risk preferences alone, we choose to focus on the underlying weights on reward and negative outcomes for the latter traits rather than the implications of these weights for common risk. We accordingly capture the stronger relationship between Openness’s uncertainty costs and risk by modeling Openness as risk preference. In modeling decisions, Neuroticism and Extraversion are respectively best parameterized as weights placed on relative rewards or negative outcomes, and Openness as risk aversion. Therefore, after accounting for these traits, legislator utilities in our framework are derived from policy, office, and national welfare motivations, and these utility sources may be altered by weights placed on the reward components and negative components of outcomes as well as preferences over risk and time.

2.7 Considerations for Strategic Interactions The modeling framework advanced thus far satisfies two major objectives laid out previously. The parameterizations are well-grounded with empirical support, and the general utility approach provides a broad tool kit to scholars who seek to apply the Big Five personality traits to richly model decision-making in institutions. We apply the decision-theoretic parameterizations advanced above throughout the chapters to come, both through formal modeling and informal theorizing. However, in order for our framework to be maximally useful to institutionalists, it must also be applicable to strategic interactions and game theory. While the parameterizations of Openness, Conscientiousness, and Agreeableness pose no serious problem for application to game theory, the parameterizations for Neuroticism and Extraversion rely on using payoffs relative to a neutral reference point that have not yet

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been tractably incorporated into game theory and are thus problematic. Fortunately, there are several possible ways to parameterize these traits for use in strategic interactions based on the core cognitive constraints. Extraversion may be parameterized as expected success in contests, while Neuroticism may be parameterized as utility from inhibited actions, uncertainty, or a heterogeneous skill level within the quantal response equilibrium concept. As discussed in the prior section, Extraverts fixate on the potential for gain that actions bring, which leads to relatively less weight being placed on potential losses when calculating expected utilities. This is in part due to the relationship Extraversion appears to have with receptors for dopamine, which controls prospective reward-seeking behavior (Fischer, Wik, and Fredrikson 1997). The undue weight Extraverts place on potential gain (or overconfidence) should lead these individuals to persistently overestimate the gains or successes they expect to receive from contests. For example, with a contest success function, Extraverts’ reward fixation results in relative disregard of the possibility of loss and expectation of additional likelihood of success beyond what their objective resources would obtain. We can therefore potentially model Extraversion as a subjective resource that contributes to a contest success function.15 This has a useful application in modeling legislative utility in the form of modifications to election contest success functions, and potential applications for other contested outcomes. However, though we have framed this discussion in terms of contest success functions, we need not limit the application thereto, as they are just one example of a type of modeling framework in which “success” is defined; indeed, we could apply this cognitive constraint to nearly any expectation of legislator behavior, such as a fixation on the policy benefits that could potentially accrue from the passage of a bill (as opposed to the possibility for policy loss due to, say, flawed implementation). On the other hand, the sensitivity to negative outcomes underlying Neuroticism implies that when choosing actions, Neurotics fixate on the potential costs of being wrong relative to the neutral reference point of inaction, and this is supported in Neuroticism’s association with sensitivity to error (DeYoung et al. 2010). As a result, more Neurotic individuals

15. Extraversion increases expectations of success but does not necessarily change the objective probability thereof.

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have a greater fear of choosing poorly and will be more indecisive to avoid making error; this is supported by the finding that Neurotic individuals experience indecision and “freezing” behavior when forced to decide between conflicting goals (DeYoung and Gray 2009; Gray and McNaughton 2003).16 Neuroticism’s “freezing” effect on behavior can be modeled with an inhibition parameter in the utility function for legislative actions that represent indecision, such as voting present, or inaction, such as missing votes or not introducing legislation. The same process that leads Neuroticism’s fixation on negative outcomes to produce indecision should also drive more Neurotic individuals to hold more uncertain beliefs. Neurotic individuals, with a fear of making errors, have an incentive to self-deceive and avoid holding potentially inaccurate beliefs. Neuropsychologists have identified that Neurotics have more uncertain memories, particularly self-referential memories (Heatherton, Macrae, and Kelley 2004; Macrae et al. 2004).17 Neurotics’ fear of error, with some assumptions, drives them to hold uncertain beliefs, and these noisy beliefs are consistent with arguments that the trait is associated with “mental noise” (Flehmig et al. 2007; Robinson and Tamir 2005). There is an additional relevant implication of Neuroticism’s relationship with uncertain beliefs. Neurotics are more likely to hold uncertain and uninformative self-referential beliefs, which would include beliefs about their own utility from outcomes.18 In a choice between actions

16. This implication assumes that there is no exogenous negative outcome expected by Neurotics from indecision, which would drive them to take action in order to avoid negative outcomes. So long as such an outcome may be avoided, more Neurotic individuals would prefer to be indecisive and avoid any negative outcome rather than take action and face potential negative outcomes from an exaggerated probability of being wrong. 17. Again, this assumes that there is no potential negative outcome for not updating and reducing uncertainty, which would drive Neurotics to adopt a potentially inaccurate belief to avoid negative outcomes. On matters not salient, negative outcomes for uncertain or even uninformative beliefs might be quite low. If negative outcomes for uncertainty do not vary with Neuroticism, we should expect that more Neurotic individuals would decide to adopt more uninformative beliefs. 18. This uncertainty about the utility of outcomes serves to mitigate or remove the influence of negative outcome fixation. If Neurotics had certain expected utility beliefs, they should be negatively biased for the same reasons Extraverts hold positively biased beliefs. However, incentives to adopt uncertain beliefs make these biases less informative and influential.

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with unknown utilities, utility is maximized by assigning equal probability to each action. Thus, as Neurotic individuals hold a larger number of uninformative beliefs, they should be more likely to act in this payoffunresponsive fashion.19 This behavior is equivalent to “skill” in quantal response equilibrium (QRE) (McKelvey and Palfrey 1995; Rogers, Palfrey, and Camerer 2009). Individuals who are more Neurotic would be more uncertain in their payoffs and thus less payoff-responsive and skillful than less Neurotic individuals. QRE assumes homogeneous skill levels, but using the heterogenous quantal response equilibrium (HQRE) developed by Rogers, Palfrey, and Camerer (2009), game theoretic approaches can be applied to model players with varying responsiveness to payoffs. Though HQRE is not widely applied at present, it is a potentially quite useful application of Neuroticism to the modeling of legislative behavior. The modeling framework for strategic interactions is largely similar to the framework for modeling decisions, with a few exceptions. Extraversion is best parameterized as a subjective resource in success functions that biases expectations of success. On the other hand, Neuroticism is most usefully parameterized as inhibition utility assigned to actions allowing for inaction or indecision, though, depending on the purpose of the model, it may be useful to model Neuroticism as payoff-unresponsiveness through HQRE.

2.8

Modeling Individual Differences: Conclusion

When considering the overall framework for personality-influenced legislative utility in decisions, we consider policy, office, and national welfare motivations and allow payoffs from these goals to be transformed through time and risk preferences as well as weights placed on relative reward and negative components of outcomes. When we extend the framework to strategic interactions, we consider a fourth motivation, inhibition, and replace weights on outcomes relative to a neutral reference point with a biased expectation of success. Though inclusion of all of these

19. This is consistent with the fact that individuals with low Emotional Stability have a strong external locus of control, which distinguishes between the belief that external factors (such as luck, fate, or powerful others) control outcomes in one’s life in relation to internal factors (Judge et al. 2002, Rotter 1990).

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components in decision- or game-theoretic models may frequently be intractable, examination of their interaction will help us to understand how the top-level individual differences interact with ideological preferences to produce elite behavior in instutions.20 At this point, we have outlined a basic framework for modeling the core cognitive constraints underlying each of the Big Five personality traits in a manner useful for consideration of legislative institutions as well as political institutions more generally. Importantly, this framework can be used to construct appropriate models of legislative decision-making that incorporate some of the most important dimensions of individual difference.

20. However, it should be noted that models need not include every relevant parameter to be useful. See Clarke and Primo (2012) for a discussion.

chapter three

Read My Lips: Measuring Personality Through Legislative Speech

T

he key to studying how personality affects legislator behavior lies in measuring legislator personality in a consistent, reliable manner. While psychologists have developed several questionnaire inventories to assess Big Five traits for respondents in both surveys and the laboratory, there are, as we first noted in Chapter 1 and will discuss in more detail here, several reasons why such techniques would be inappropriate for studying the personalities of elected officials. Thus, the task at hand is to find an appropriate way to measure personality traits and, to the extent possible with elected officials, to generate estimates for all sitting members of the US Congress. In this chapter, we present a method that links traditional psychometric approaches with advances in machine learning in order to assess personality traits based on speeches and text. First, we review the traditional psychometric methods for assessing personality traits. We argue that these on their own are inappropriate for the study of elected officials. We then show how personality traits may be culled from text. Thereafter, we apply this method to a body of legislative speeches in the US Congress over the past two decades. Last, in light of political science research that extracts ideology from legislative speech, we show that this method is not simply regurgitating a measure of legislator preferences.

3.1

Limitations of Existing Approaches for Elected Officials

Despite advances in the measurement of the Big Five, most political science applications using these metrics involve surveys of voters (e.g.,

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Caprara, Barbaranelli, and Zimbardo 2002; Gerber et al. 2011a). While enlightening, these studies tell us little about the personality traits of elected officials. An obvious solution would be to survey legislators ourselves. Unfortunately, such an approach would be impractical for several reasons. First, survey or lab-based instruments are only implementable in the present, thus precluding us from being able to look at the dynamics of personality over time. Second, even if we restrict ourselves to contemporary Congresses, there is no reason to believe that legislators would be willing to take such inventories. Even if responses were possible to obtain, such estimates would be subject to selection bias and, possibly, strategic responses to questions. We are certainly not the first scholars to recognize this limitation, which is almost certainly to blame for the lack of research into elite personality. To our knowledge, only one study has attempted to apply traditional survey-based inventories with (state) legislators (Dietrich et al. 2012). In line with our concerns above, response rates were low—ranging from 17% to 26% of legislators—and the responses themselves displayed significant desirability bias. On each of the dimensions, in excess of 77% (and in many cases, more than 90%) of legislators responded in a way to convey the “positive” side of each dimension. Indeed, in the cases of Agreeableness, Openness, and Emotional Stability, the percentage of legislators identifying as non-agreeable, closed, and Neurotic are in the low single-digits. While these results may be “true,” they instead suggest that surveys, even when possible to conduct, will lead to low response rates and uninformative personality profile estimates. Instead, other methods are needed.1

3.2 Using Text to Measure Personality Traits To transcend the limitations of survey-based personality measures, we draw on a recent literature in machine learning that seeks to connect personality traits with both written and spoken words. (Golbeck, Robles, Edmondson, and Turner 2011; Li and Chignell 2010; Mairesse, Walker, Mehl, and Moore 2007; Mairesse and Walker 2010; Schuller et al. 2013). This literature uses traditional psychometric personality inventories in

1. Also see Ramey, Klingler, and Hollibaugh (forthcoming).

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conjunction with written texts, tweets, and auditory transcriptions to train predictive models for personality. Once the known personalities of a subset of authors are calibrated with their linguistic usage, “virgin texts” can be assessed for personality content, even in the absence of the true personalities as measured by traditional inventories. In a foundational piece in this literature, Mairesse et al. (2007) develop a method for generating personality estimates from speech and text. Using Pennebaker, and King’s (1999) corpus of nearly 1.9 million words from laboratory experiments, and Mehl, Gosling, and Pennebaker’s (2006) corpus of approximately 100,000 words from recorded conversations, Mairesse et al. (2007) train several machine learning models to best predict personality traits. Machine learning methods are a class of models that seek to predict an observed output with optimal combinations of features. The models are “trained” on a subset of data, and the estimates from this process are used to predict the rest of the data using only right-hand-side variables. A simple example would be to perform a linear regression of some known dependent variable y on a collection of independent variables x1 , x2 , . . . , xn for, say, half a sample of data. Then, using the estimated regression coefficients, we would generate predictions of y using only the xj ’s for the remaining sample. In this case, Mairesse et al. (2007) use the language used in both written and spoken language to predict personality traits. Crucial for our purposes, words are categorized according to Pennebaker, Francis, and Booth’s Linguistic Inquiry and Word Count (LIWC) dictionary as well as Coltheart’s (1981) MRC Psycholinguistic Database (MRCPD).2 Doing so allows scholars to generalize to different domains. Both the LIWC and MRCPD search for linguistic features in a collection of texts, such as the number of second person pronouns, punctuation, six-letter words, and more. After preprocessing the data using these dictionaries, Mairesse et al. (2007) train several machine learning algorithms on a random subsample of the data. They find that Support Vector Machines for Regression (SMOreg) best recovers personality measures of respondents in written trials. Therefore, we opt for using SMOreg in what follows. That said, for our purposes, which model we employ seems to make little substantive difference.

2. The categories are found in the appendix to this chapter.

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(a) “You Give Love a Bad Name”

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(b) “Smells Like Teen Spirit”

figure 3.1. LIWC Comparison of Bon Jovi and Nirvana Songs

Again, it is crucial to note that the Mairesse et al. (2007) approach does not rely on specific words, but rather the psycholinguistic properties of the words as measured through the LIWC and MRCPD. To illustrate this more concretely, consider an example from popular culture. It is wellknown that the transition from the rock music of the 1980s to that of the 1990s was a stark and difficult one. While the mainstream rock of the late 1980s was focused on living life and having fun, the 1990s world of grunge featured a “darker, heavier” type of sound (Witmer 2010, 24). One might even say that the personalities conveyed by the singers and authors of the songs of the eras were quite different. Though the subjects and content of songs will differ, the use of LIWC allows us to abstract away from those specifics and hone in on the relative frequencies of word categories. These categories are broad and known to be correlated with personality traits (Pennebaker, Francis, and Booth 2001). To that end, Figure 3.1 presents bar graphs of the top fifteen LIWC categories for an archetypal song from each era: Bon Jovi’s “You Give Love a Bad Name” and Nirvana’s “Smells Like Teen Spirit.” We notice readily that the top word categories are quite different across

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the songs. Bon Jovi tends to sing a lot about the present tense and emotions, with references to others, sex, and “doing things.” Nirvana uses a lot of cognitive mechanisms, relativistic language, and—when emotions are discussed at all—negative emotion words. Notably, Bon Jovi focuses more on the second person whereas Nirvana focuses on the first person. This example illustrates how two distinct texts about two different subjects may be processed using the LIWC to gain insight on the personalities and cognition of the people behind the words. It is this principle that we rely on as we seek to measure the manifest personalities of politicians.

3.3

Measuring Personality: From Speeches to Scores

Given the generality of the Mairesse et al. (2007) approach, we apply their method to legislative speech. To do this, we need legislator speeches to feed into the pretrained models. Since we desire to measure personality traits for legislators over as wide a time period as possible, the Congressional Record is perhaps the best option available, though there are issues with this. Since speeches in the Congressional Record are public, they might be written and delivered strategically. Specifically, legislators’ speeches may seek to convey ideological preferences, constituency preferences, or to mimic some sort of generic leadership profile. The first two concerns are easily dismissed; we demonstrate below that our measures of personality, while correlating with standard measures of legislator ideal points, explain small proportions of the overall variance. This suggests that (a) personality traits are not simply a recitation of ideology in different terms, and (b) they are to explain additional facets of behavior. The last concern is only an issue if legislators try to portray insincere personality profiles in their speeches. Again, this is likely not an issue. We are using the entire corpus of speeches delivered by every legislator, and it would be considerably difficult to maintain a generic profile over thousands of words of speech. Indeed, one-shot surveys are likely more susceptible to such short-term strategic manipulation. Even if these problems exist, they would attenuate our results. Additionally, if some legislators were being sincere while others were being strategic, the attenuation would be even worse and our estimated personality traits would explain little.

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figure 3.2. Comparing LIWC (2001) Usage between the Pennebaker Corpus and Floor Speeches

These concerns aside, we might still worry that the psycholinguistic content of legislator floor speeches might differ substantially from the Pennebaker essay corpus. To address this, Figure 3.2 plots the hyperbolic arcsine–transformed mean LIWC category usage from the Pennebaker data against the hyperbolic arcsine–transformed mean usage of members of the 114th House of Representatives. The hyperbolic arcsine can be thought of as a logarithmic transformation for data with zeros; we use it because some categories are much larger than others and, as such, visualization of the relationship is greatly improved. Each variable label is a LIWC 2001 category. As we see, the correlation on the untransformed scale is approximately 0.99; the rank correlation coefficient across the two is 0.72 (p < 0.001). Consequently, though the substantive content is surely different, legislators’ speeches do not differ psycholinguistically from those of the laboratory participants. All of these concerns having been addressed, we apply Mairesse et al.’s (2007) SMOreg model to the entire corpus of legislative floor speech by every sitting member of the House of Representatives and Senate from the 104th–113th Congresses. The estimation procedure is straightforward.

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First, we process legislators’ speeches in a given Congress (or year, as appropriate) through both the LIWC 2001 and MRCPD to get counts and proportions of word usage across all LIWC and MRCPD categories. We process the speeches by time period so as to account for any timedependent language (e.g., references to terrorism in the aftermath of September 11, 2001). Second, we standardize the LIWC and MRCPD results from the previous step and plug these into the Mairesse et al. (2007) corpus-level models. This process allows for better within-domain comparability. For example, all legislators might use more six-letter words than any lab respondent. Standardizing allows us to compare usage relative to the mean legislator. Third, we repeat this process for every Congress (or year). Fourth, and most critically, we “jackknife” legislator i s personality on dimension d in Congress c (c = 1, 2, . . . , Ci where Ci is the number of Congresses in which legislator i served). Specifically, legislator i’s jackknifed score  on dimension d in Congress c is θ˜icd = Ci1−1 c =c θˆicd . To put the equation into words, a legislator’s personality during a particular Congress is the average of his or her estimated personality during all other time periods. This correction addresses potential endogeneity between language and desired behavior within a specific time frame. For example, a legislator might use “agreeable language” to acquire cosponsors in a given Congress, whether or not he or she is actually Agreeable. We have tried other ways of getting around this issue, including computing a single lifetime arithmetic mean for all members. Doing this is not substantively different from our approach and unnecessarily complicates the empirical tests we run in subsequent chapters. Last, we generate measures of uncertainty using a sentence-level bootstrap (Lowe and Benoit 2011). Specifically, assume that legislator i uses Nic sentences during Congress c. For each legislator, we resample Nic sentences with replacement from their corpus of language during the given time frame (Efron and Tibshirani 1994). At the Congress level, we conduct hundred bootstraps per member and compute the empirical 95% confidence interval. To measure uncertainty in the jackknifed estimates, we take the legislator’s estimates by Congress across each of the hundred bootstraps, calculate the jackknife as described above, and then compute the empirical 95% confidence interval. The resulting jackknifed scores are thus obtained via the Elite LingUistiC Individual Difference EstimATION scores approach noted in Chapter 1. Crucially, we are agnostic as to whether ELUCIDATION

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figure 3.3. Senate Scores over Time (Selected Members)

scores are measures of “sincere” legislator personality. As with idealpoint estimates based on roll calls, we simply consider these estimates as revealed and potentially strategic preferences. Estimates of the traits and confidence intervals are presented for key Senate members (who served during the entire period of our data) in Figure 3.3. The scale for each trait ranges from 1 to 7. As we see, the estimates are stable and precisely estimated. Moreover, the results make intuitive sense both with our core cognitive constraint framework as well as with traditional understandings of the Big Five. For example, Senate Minority Leader Harry Reid (D-NV) is considerably more Extraverted than his Republican counterpart Mitch McConnell (R-KY), a finding consistent with the perspectives of even cursory observers of American politics. Critically, Reid’s and McConnell’s assuming the posts of Democratic and Republican leaders

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figure 3.4. Word Count and Precision Note: Points are individual members. The smoothed line is a LOESS-smoothed trend. The vertical dashed lines are the median member’s word count (approximately 11,000 words per year). Horizontal dashed lines are the average confidence interval width (approximately 0.3). Thus, most members’ personality estimates are their point estimates ±0.15.

in the 109th–110th Congress failed to produce any noticeable differences in their trait estimates. This suggests that our estimates are robust to concerns about potential strategic conveyance of artificial leadership profiles. Since some Congress members are certainly more talkative than others, we might be concerned that the precision of our estimates is strong only for the most vocal members. To assuage these concerns, Figure 3.4 presents a plot of the empirical confidence interval width for the House of Representatives against speech word counts by legislator; the associated figure for the Senate is qualitatively similar. On average, a typical

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member has around 11,000 words per year documented in the Congressional Record. As the graph shows, after around 5,000 words, the width of the confidence interval is about 0.3 or less; in other terms, the 95% interval is the legislator’s point estimate ±0.15 on a 7-point scale. Consequently, the estimates are extremely precise for almost all legislators during the time period analyzed.

3.4 Validity of the Estimates 3.4.1

Strategic Misrepresentation and Authorship Concerns

As we noted above, these estimates are based on revealed information. Since they are strategic actors, legislators have the opportunity to manipulate their language so as to convey fake personality profiles. While this sort of strategic misrepresentation could prove problematic, we ultimately do not think it is much of an issue. In this section, we explore why our approach is, at worst, not inferior to survey-based approaches and, at best, strictly preferred to them. The issue of misrepresentation is unique neither to political elites nor to our text-based procedure. The NEO-PI-R personality inventory (Costa and McCrae 1992a) discussed in Chapter 2 is often criticized for the fact that respondents are aware what the positive response is for each item; virtually any respondent would desire to be seen as Open, Conscientious, Extraverted, Agreeable, and Emotionally Stable. In the political science literature, this phenomenon manifested in recent work by Dietrich et al. (2012), who administered a Big Five questionnaire to state legislators in three states. Unsurprisingly, overwhelming majorities of legislators pool on the “positive” responses for each Big Five dimension. While any observer of American politics knows that legislators are nowhere near a random sample of the American population, such extreme skew in the distribution of Big Five traits suggests a major flaw in this approach. That said, our language-based approach is not necessarily free from the same concerns. If legislators actively respond to Big Five inventories knowing which side of the scale conveys positive valence, could they not do the same with their language? Surely, legislators are aware that using certain sorts of words could convey positive or negative information about themselves and screen their speech accordingly. That said, it is unlikely that legislators, or anyone, for that matter, knows of all the ties

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between their speech and their latent personality traits. For example, it seems unlikely that legislators screen their speeches for too many (or too few) six-letter words, commas, or first person pronouns. As a result, we should expect that all legislators would avoid (or emphasize) certain words commonly associated with positive valence but would differ in their usage of those where valence is unknown. Combining features for which legislators collectively pool on positive valence and those in which they unconsciously convey their “true” traits should produce estimates that skew toward valence pooling. Empirically, it turns out that legislators are largely unaware of the psycholinguistic properties of their speech. Our analysis of legislative speech using LIWC and MRCPD categories shows substantial variation across legislators. As a result, the concern that legislators might be able to strategically misrepresent their speech does not have much bite. Perhaps more worrisome is the issue of speechwriting. Even if the author is not gaming his or her language, he or she may not be the legislator in question. Since legislators almost surely farm out some of their speechwriting to staffers, how can we be certain that our estimates are even capturing legislator personality? This concern, unlike the strategic issue, is fairly easy to address (and refute) empirically. First, few members have full-time speechwriters as members of their professional staff; congressional disbursements show that only party leaders and a few of the more senior Congress members have paid speechwriters.3 Of course, a legislator could farm out speechwriting to any staffer, not just a dedicated speechwriter. At the same time, however, staff turnover is extremely high in Congress. For example, we examined both the Q3 2009 and Q3 2011 congressional disbursement reports to quantify these rates. The average House member lost more than one-third of his or her professional staff in this two-year window; the interquartile range for retention was [0.55, 0.74]. Given such high levels of turnover, we should expect that who is writing speeches for Congress members will change considerably over time. Since different speechwriters have different personalities and writing styles, we might see considerable variation in our estimates. Fortunately, we do not. As Figure 3.3 shows, for some of the longest-serving and highest-ranking members of the Senate during our time period, there is stability in the jackknifed personality scores. Indeed, as noted above,

3. See, e.g., http://disbursements.house.gov/2013q4/2013q4_singlevolume.pdf.

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neither Reid nor McConnell were party leaders at the start of our data, and yet their estimates are consistent over time. This suggests that whoever was writing their speeches maintained a clear, discernible linguistic pattern over time. This finding is consistent with the advice that the Congressional Research Service (CRS) provides to speechwriters: “Congressional speechwriters should make every effort to become familiar with the speaking style of the Member for whom they are writing, and adjust their drafts accordingly” (Neale 1998). 3.4.2

Face Validity

Having addressed the potential theoretical issues with our approach, it is important to examine the face validity of our estimates. Since we do not know the “true” personalities of Congress members, and because surveying them is problematic (or impossible, in the case of the deceased), we will need to validate our estimates indirectly. To that end, we follow a long tradition in the political psychology literature examining the linkages between personality and ideology at the mass level (Gerber et al. 2010; Gerber et al. 2011a; Mondak 2010). This literature has found strong and consistent links between Openness and liberalism and between both Conscientiousness and Emotional Stability and conservatism. Findings on the linkages between Extraversion and Agreeableness and ideology are more mixed. Mondak (2010) finds no links between Extraversion and ideology, and only a weak and model-dependent relationship between Agreeableness and liberalism. Gerber et al. (2011a) shed some light on this by showing that when ideology is separated into two dimensions—the economic and the social—Agreeableness is linked with social conservatism but not economic liberalism. Similarly, Gerber et al. (2010) find strong linkages between ideology and Openness, Conscientiousness, and Emotional Stability in the same directions as the rest of the literature, but the connections between left-right ideology and both Agreeableness and Extraversion are more nuanced. Given the findings of this literature, we can indirectly assess the face validity of our estimates by replicating their analyses at the elite level. To do this, we measure ideology using Groseclose, Levitt, and Snyder (1999) inflation-adjusted Americans for Democratic Action (ADA) scores. ADA scores are measures of legislator liberalism that come from the ADA’s annual scorecard, which is itself a compilation of around twenty votes that the organization views as being critical to assessing

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table 3.1 OLS Models of Personality and House ADA Score (1996–2008) Model 1

Model 2

Model 3

Model 4

22.606∗∗∗ (1.695)

22.455∗∗∗ (1.669)

21.315∗∗∗ (1.689)

21.339∗∗∗ (1.667)

−20.545∗∗∗ (1.448)

−17.932∗∗∗ (1.448)

−20.615∗∗∗ (1.437)

−18.161∗∗∗ (1.439)

Extraversion

15.436∗∗∗ (1.097)

13.958∗∗∗ (1.089)

15.468∗∗∗ (1.088)

14.081∗∗∗ (1.082)

Agreeableness

15.940∗∗∗ (2.923)

13.478∗∗∗ (2.889)

16.389∗∗∗ (2.900)

14.028∗∗∗ (2.872)

−21.465∗∗∗ (1.821)

−17.533∗∗∗ (1.831)

−22.022∗∗∗ (1.808)

−18.272∗∗∗ (1.823)

Openness Conscientiousness

Emotional Stability

−17.257∗∗∗ (1.630)

Male Age

−16.145∗∗∗ (1.628)

0.467∗∗∗ (0.060)

0.407∗∗∗ (0.060)

Constant

−14.588∗ (8.230)

−6.644 (8.140)

−34.908∗∗∗ (8.575)

−24.885∗∗∗ (8.521)

R2 Adj. R2 Num. obs.

0.104 0.103 3,602

0.131 0.130 3,602

0.119 0.117 3,602

0.142 0.141 3,602

Standard errors in parentheses. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

legislator liberalism in a given year. The Groseclose, Levitt, and Snyder (1999) adjustment makes the scores comparable over time by allowing for distortions in the scale over time. Since ADA scores measure liberalism, we should expect a positive relationship between Openness and the ADA score and negative relationships between Conscientiousness and Emotional Stability and the ADA score. Additionally, because ADA scores tap into the underlying economic conflict between the Democratic and Republican parties (approximate to the contemporary liberalconservative dimension), we should find a positive relationship between Agreeableness and the ADA score (Crespin and Rohde 2010; Poole and Rosenthal 1997).4 Finally, since the literature is mixed on the relationship between ideology and Extraversion, we remain agnostic regarding the expected sign (or significance). Table 3.1 presents several ordinary

4. No other trait has these divergent effects.

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least squares (OLS) models of ideology as a product of personality and demographic traits. All four traits (i.e., all the Big Five save Extraversion) with expected relationships have statistically significant coefficients in the expected directions in each model. Additionally, in line with previous literature, the coefficients on Extraversion are of smaller magnitudes than those for Openness, Conscientiousness, and Emotional Stability, and this holds for all four models (though the relationship between ideology and Extraversion is still a point of contention). Moreover, these results allay one natural concern with using potentially ideologically tinged legislative speeches to estimate personality, in that our personality estimates may be simply summaries of legislator ideology. However, the R2 s in Table 3.1 are not large, indicating that personality traits alone do not account for significant variation in ideology. This suggests that theoretical concerns about the dependence of the Big Five on ideology or the ideological content of the legislative record are not a problem.

3.5 Read My Lips: Conclusion In this chapter, we introduced a method to measure legislator personality using speech. While almost certainly imperfect, all the major issues with this approach—namely, strategic concerns and speechwriter effects—were shown to be essentially inconsequential. Moreover, given the strategic, temporal, and practical problems with the only main rival approach to measuring elite personality—surveys—we remain confident in the utility of our approach. Given the flexibility and generality of the linguistic method used herein, we expect that scholars of other legislative institutions (e.g., state legislatures, non-American legislatures) and, more generally, elite behavior, will be able to apply our technology to those other settings. That said, the key to understanding and analyzing elite behavior is not just the methodological approach described in this chapter. The theoretical framework from Chapter 2 is equally important, as it provides scholars of elite institutional behavior with a structure for forming precise predictions for elite behavior. To that end, in the rest of the book (Parts II and III) we show how the theoretical and empirical frameworks introduced thus far can be used to analyze legislator behavior across the congressional life cycle.

3.6

Appendix

table 3.2 LIWC (2001) Categories Category

Abbreviation

Examples

Word count Words/sentence Sentences ending in ? Unique words Dictionary words Words >6 letters Total pronouns 1st pers singular 1st pers plural Total 1st person Total 2nd person Total 3rd person Negations Assents Articles Prepositions Numbers

wc wps qmarks unique dic sixltr pronoun i we self you other negate assent article prep number

I, them, itself I, me, mine We, us, our I, we, me You, you’ll She, their, them No, not, never Yes, OK, mmhmm A, an, the To, with, above Second, thousand

Occupation School Job or work Achievement Leisure Home Sports Television and movies Music Money Metaphysical issues Religion Death and dying Physical states and functions Body states, symptoms Sex and sexuality Eating, drinking, dieting Sleeping, dreaming Grooming

occup school job achieve leisure home sports tv music money metaph relig death physcal body sexual eating sleep groom

Work, class, boss Class, student, college Employ, boss, career Earn, hero, win Cook, chat, movie Apartment, kitchen, family Football, game, play TV, sitcom, cinema Tunes, song, cd Cash, taxes, income God, heaven, coffin Altar, church, mosque Bury, coffin, kill ache, breast, sleep ache, heart, cough lust, penis, sex eat, swallow, taste asleep, bed, dreams wash, bath, clean

Standard Linguistic Dimensions

Personal Concerns

table 3.2 (Continued) Category

Abbreviation

Examples

Affective processes Positive emotion Positive feelings Optimism and energy Negative emotion Anxiety or fear Anger Sadness or depression Cognitive processes Causation Insight Discrepancy Inhibition Tentative Certainty Sensory and perceptual processes Seeing Hearing Feeling Social processes Communication Other references to people Family Friends Humans

affect posemo posfeel optim negemo anx anger sad cogmech cause insight discrep inhib tentat certain senses see hear feel social comm othref family friend human

Happy, cried, abandon Love, nice, sweet Happy, joy, love Certainty, pride, win Hurt, ugly, nasty Worried, fearful, nervous Hate, kill, annoyed Crying, grief, sad cause, know, ought because, effect, hence think, know, consider should, would, could block, constrain, stop maybe, perhaps, guess always, never Observing, heard, feeling View, saw, seen Listen, hearing Feels, touch Mate, talk, they, child Talk, share, converse 1st pl, 2nd, 3rd person pronouns Daughter, husband, aunt Buddy, friend, neighbor Adult, baby, boy

Time Past tense verb Present tense verb Future tense verb Space Up Down Inclusive Exclusive Motion

time past present future space up down incl excl motion

Hour, day, oclock Walked, were, had Walk, is, be Will, might, shall Around, over, up Up, above, over Down, below, under And, with, include But, without, exclude Arrive, car, go

Swear words Nonfluencies Fillers

swear nonflu fillers

Damn, piss, fuck Er, hm, umm Blah, Imean, youknow

Psychological Processes

Relativity

Experimental Dimensions

table 3.3 MRCPD Categories Category

Abbreviation

Age of Acquisition Brown Frequency Concreteness Familiarity Imagability K F Frequency K F Number of Categories K F Number of Samples Meaningfulness (Colorado) Meaningfulness (Paivo) Number of letters Number of phonemes Number of syllables T L Frequnecy

AOA BROWN_FREQ CONC FAM IMAG K_F_FREQ K_F_NCATS K_F_NSAMP MEANC MEANP NLET NPHON NSYL T_L_FREQ

chapter four

Securing Reelection: Deterrence and Disbursements Sometimes, guys like Eric Cantor are just radically out of touch with their district in ways that have nothing to do with politics. — Former Representative Charles “Joe” Scarborough (R-FL)1

M

ayhew argued decades ago that Congress members are “singleminded seekers of reelection,” and while this perspective has since been challenged, all incumbents seeking to return to Congress must keep reelection in mind (Mayhew 1974). The actions members take to pursue reelection thus hold an important place in the study of congressional behavior. Though the vast majority of members publicly plan to seek reelection, incumbent members vary substantially in how they manage their money and reputations among their constituents. Some incumbents save more of their war chests than others, even though the capacity of war chests to deter challengers is mixed, and fund-raising is costly (Baron 1989; Erikson and Palfrey 2000; Goodliffe 2005). Other incumbents prioritize policy or institutional power over maintaining trust through addressing constituency concerns, even though such actions may bring on short-term threats to their electoral chances (Fenno 1978; Fenno 1996). As we argued in Chapter 2, the five-factor model describes major and persistent dimensions on which individuals differ. The core cognitive constraint framework we advanced suggests the Big Five personality traits reflect underlying cognitive processes that may be modeled as risk,

1. Jonathan Topaz, “Scarborough: Cantor ‘Out of Touch,’ ” Politico, June 11, 2014, http://www.politico.com/story/2014/06/joe-scarborough-eric-cantor-out-of-touch-107708.

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time, and social preferences, as well as weights on potential gains and losses. Arguably, campaign tactics vary on these important dimensions. Thus, the Big Five personality traits should be associated with variation in legislators’ choices as they pursue reelection. In this chapter, we argue that incumbents’ Big Five personality traits should be associated with the decisions they make in allocating their time and money. We then argue that these differences are associated with variation in the probability of quality challenger emergence as well as campaign disbursements during reelection campaigns.

4.1

Who Attracts Quality Challengers?

The possible emergence of a quality challenger is an important factor in any congressional campaign. Theoretically and generally speaking, officemotivated legislators should never create openings for quality challengers to enter, but yet they do. Many approaches have been used to explain why experienced potential candidates decide to emerge or stay out of congressional races. Generally, quality challengers—as they hold (or have held) elected office—face higher opportunity costs for emergence than less qualified individuals (Banks and Kiewiet 1989). Thus, we see that quality challengers are generally more likely to emerge in a particular race when their chances of winning are greater (Jacobson 1989; Jacobson 2012). Some major factors affecting a potential challenger’s expectations of winning lie largely outside the immediate control of the incumbent. These factors include presidential approval, economic conditions, and the challenger’s ability to spend (Bianco 1984; Bond, Covington, and Fleisher 1985; Jacobson and Kernell 1983). However, several factors are under the control of incumbents. First, if the incumbent chooses to leave the seat open, a quality challenger is far more likely to enter the race (Banks and Kiewiet 1989; Bianco 1984; Gaddie and Bullock 2000; Jacobson 1989; Squire 1989; Wrighton and Squire 1997). If the incumbent is involved in a public scandal or another form of ethical violation, this lowers the probability of his or her reelection, and quality challengers are more likely to emerge (Groseclose and Krehbiel 1994; Jacobson and Dimock 1994; Peters and Welch 1980; Welch and Hibbing 1997). Though previous victories signal some minimal level of campaign ability and resources, several actions can signal vulnerabilities on this dimension to potential quality challengers. Incumbents

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who won their last election by a narrow margin are more likely to draw quality challengers (Bond, Covington, and Fleisher 1985; Jacobson 1989; Jacobson and Kernell 1983). Furthermore, the less money incumbents have in their campaign war chests the more likely they are to attract quality challengers, though the empirical evidence on this claim is mixed (Epstein and Zemsky 1995; Goldenberg, Traugott, and Baumgartner 1986; Goodliffe 2001; Goodliffe 2005; Goodliffe 2007a; Goodliffe 2007b; Hersch and McDougall 1994; Krasno and Green 1988). Finally, incumbent legislators may make themselves vulnerable by being “out of touch” with their constituents. When challengers find evidence of major votes that conflict with constituent preferences, incumbents suffer on Election Day (Arnold 1992; Fiorina 1974). Additionally, if incumbents possess voting records perceived to be too strongly partisan, and possibly inflexible to the concerns of their districts, they pay the price electorally (Ansolabehere, Synder, and Stewart 2001; Bovitz and Carson 2006; Canes-Wrone, Brady, and Cogan 2002). As incumbents with ideological positions distant from their constituents are more likely to draw challengers with positions that are more congruent with those of their constituents, we might expect ideological extremists to attract challengers as well (Hollibaugh, Rothenberg, and Rulison 2012). The literature on the electoral consequences of being out of touch reaches beyond the voting record in the form of a legislator’s home style and efforts to build trust among constituents while in office (Fenno 1978). If constituents do not identify with incumbents, members of Congress lose the trust of voters, and their reelection is threatened. Legislators maintain trust through many activities, including their voting record, but also via nonpolicymaking activities such as advertising and credit-claiming (Mayhew 1974). Legislators’ ability to credit-claim comes from both particularized benefits gained through legislation as well as casework, while advertising success is the cumulative result of many decisions, ranging from whether or not to maintain a primary residence in the district/state or in Washington, to whether or not to appear in local media or to be the subject matter of press releases (Mayhew 1974).2 These decisions can be obscure and hard to quantify, as they involve the allocation of time and effort resources. 2. We examine the content of press releases, and the degree to which legislators take positions or credit-claim, in Chapter 8. The results of that analysis are consistent with the hypothesized relationships between personality and constituent service.

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For example, in his 1996 book Senators on the Campaign Trail, Richard Fenno described driving US senator John Culver (D-IA) to an event in his home state. It struck me as odd, for example, that he would let himself be taken to the National Cattle Congress parade in my car with its New York license plates. As we left the Waterloo fairgrounds, having parked prominently near the entrance gate and having said goodbye to the parade organizers, he parodied their likely reaction. “‘He’s not from Iowa,’ they say. Where’d they ever get that idea? ‘He’s arrogant, insensitive, out of touch.’ What makes them think that? Huh? ‘He votes more with the East than the Midwest.’ Where’d that idea come from? Huh? All I’m doing is driving around with New York plates on my car!” He was sensitive to his weakness but was not treating it seriously. (Fenno 1996, 133)

Fenno continues to describe a pattern of decisions made by Senator Culver that prioritized becoming a respected Washington insider over maintaining trust at home, including a close relationship with the Washington press culminating in a glowing series on him in The New Yorker and arguably, his relationship with Fenno himself. As Fenno continues: In career terms, Senator Culver had made a choice in balancing a Washington career and a constituency career. He had chosen to pursue his institutional ambition—to become a respected policy player inside the Senate—at some cost to his elective office ambition. How great a cost, and how irretrievable the cost, was the question in 1979. He was hoping to catch up during his campaign. But there was little doubt that he had created a classic “out of touch” problem for himself at home, that he had not kept his constituency career in an optimal balance with his Washington career. (Fenno 1996, 132)

US Representative Charles Grassley emerged to challenge Culver in the 1980 election. Unsurprisingly, he attacked Culver as being out of touch, noting that Culver preferred to stay in Washington rather than spend time in Iowa (Fenno 1996). Culver lost to Grassley 45.5% to 53.4%. While the electoral costs of prioritizing institutional position and policy over constituency service are particularly well described in the case of former Senator Culver, they remain relevant today, as former House Majority Leader Eric Cantor lost his seat to an unknown primary challenger who successfully attacked Cantor for prioritizing his Washington

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leadership role over constituency service.3 Evidence for the linkage between constituency service and electoral support is mixed, but it may aid incumbents by discouraging the opposition (see Bond, Covington, and Fleisher 1985 for a review). Jacobson and Kernell (1983, 37) argue, Case work, trips back home to the district, newsletters, and all the other things members do to promote reelection are not aimed merely at winning votes in the next election. They are also meant to influence the perceptions politically active people form of the member’s hold on the district. The electoral value of incumbency lies not only in what it provides to the incumbent, but equally as well in how it affects the thinking of potential opponents and their supporters.

The constituency service activities that maintain voters’ trust in incumbents, and may deter challengers, require the limited time and effort of legislators and their staff. However, allocating time and effort to pursuing other objectives such as policy influence or power in Washington carries opportunity costs in terms of constituency service that are likely to attract experienced candidates to challenge congressional incumbents. The priorities legislators place on constituency service versus policy or institutional objectives can be examined through the utility framework put forth in Chapter 2, by which legislators derive utility from office benefits, policy and institutional benefits, and acting for the benefit of the nation. As described in Chapter 2, these utility components may have different qualities in terms of the time at which utility is allocated, the risk involved, and whether they offer rewards or punishments compared to the status quo.4 A legislator who spends all of his or her time and effort maintaining trust through constituency service and campaigning for

3. Ford O’Connell, “A Surprising Loss for Unsurprising Reasons.” US News and World Report, June 14, 2014, http://www.usnews.com/opinion/blogs/ford-oconnell/2014/06/14/eric -cantor-lost-touch-with-his-constituents-before-primary-loss; Patrick, O’Connor, Valerie Bauerlein, and Beth Reinhard, “Eric Cantor’s Focus on D.C. Led to His Virginia Defeat,” Wall Street Journal, June 11, 2014, http://www.wsj.com/articles/eric-cantors-focus-on-d-c-led -to-his-virginia-defeat-1402532326. 4. While it is true that future policy or institutional benefits depend on winning reelection, some resources must be diverted to pursuing policy or benefits in the institution for these benefits to be obtained. The issue is simply to what degree resources are diverted from reelection to pursue these goals.

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reelection is not going to be able to devote resources to fund-raising for party campaign committees, serving as a deputy whip, or other activities needed to build policy coalitions or institutional power. We expect that utility from pursuing reelection is likely different than utility gained from pursuing policy or power in the institution in terms of riskiness, delay, and the degree to which it represents a gain or loss relative to the status quo. As such, personality traits should influence preferences for pursuing reelection compared to pursuing policy or institutional power, or in other words, leadership style. Accordingly, incumbent personality traits should have an effect on potential quality challengers’ utility of entering a race through the effect of personality on leadership style and the electoral consequences of focusing on Washington objectives over constituent service. The Big Five personality traits should be expected to influence leadership style in several ways. First of all, actions that serve to maintain trust with constituents such as casework and advertising involve low levels of uncertainty and risk in comparison with the pursuit of policy and institutional objectives. Casework does not require legislation, is largely a turnkey process of communication, and “there can be no question about who should receive credit” (Bond, Covington, and Fleisher 1985). This logically extends to advertising, which also is a function of incumbent effort.5 In contrast, conflict within the chamber over policy or institutional power struggles is by its nature risky. Thus, we expect that prioritizing constituency service over pursuing policy or institutional power objectives involves less risk than the alternative. We parameterize Openness as a greater tolerance or preference for risk, so we would expect that more Open members should pursue constituency service to a lesser degree and thus be more likely to draw a quality challenger. Second, the pursuit of policy influence and institutional power in Congress often takes years to pay off, as it can take four or five terms for House members to advance their position in the chamber to exceed the mean (Hibbing 1991b). In contrast, the payoffs for successful constituency service are nearly immediate for US representatives facing biennial elections. The rewards of pursuing policy or institutional power goals thus

5. However, the pursuit of particularized benefits through pork requires legislation, which is inherently uncertain and thus risky (Mayhew 1974).

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pay off with greater delay than the rewards of building trust through constituency service.6 Our modeling framework considers Conscientiousness in terms of a reduced discounting of delayed rewards. We then expect that more Conscientious members should display a reduced preference for constituency service, and thus exhibit a higher probability of drawing quality challengers. Constituency service activities are fundamentally oriented toward maintaining trust and winning reelection. As such, the payoffs of these activities are different from the payoffs of policy or institutional power objectives in terms of the gains offered to incumbent legislators compared to the status quo. For incumbents, constituency service at best results in keeping what they already possess and maintaining the status quo.7 However, diverting time and effort to the pursuit of policy or institutional power objectives offers incumbents significant gains over the status quo in terms of policy achievements or election to a new leadership position. We model Extraversion as a sensitivity to gain relative to the status quo, so Extraverts should exhibit a relatively greater preference for pursuing policy or power within the chamber over constituency service. We should then expect incumbent legislators who are more Extraverted to be more likely to attract quality challengers in the general election. Finally, for Agreeableness and Neuroticism, the differences between constituency service and pursuing other goals are less clear. Agreeable legislators, who exhibit a higher preference for altruism and selfless behavior, may be drawn to sacrificing personal electoral interests in favor of advancing the interests of the nation as a whole. However, these legislators may also value constituency service intrinsically, and seek to serve the citizens they represent through providing access and casework. We model Neuroticism as sensitivity to loss and punishment, and it is

6. We do not deny that constituency service may have long-term benefits for senior members who eventually have schools, bridges, highways, and other public institutions named after them, but constituency service is typically viewed as an instrumental pursuit, and these rewards are better thought of as offshoots of institutional influence (Aldrich and Shepsle 2000; Fenno 1978; Mayhew 1974). 7. It is possible that legislators derive intrinsic utility from their vote share, but we do not believe it would substantially influence legislative decision-making should they do so.

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possible that incumbent legislators may lose their office by failing to maintain trust through adequate constituency service, but it is also possible that they may lose policy or institutional power utility by failing to defend their positions as well. Thus, we have no clear hypotheses with respect to Agreeableness and Neuroticism and the probability of drawing a quality challenger on the basis of constituency service. We expect, based on prior research, that quality challengers’ entry decisions should be influenced by challengers’ expectations about subsequent decisions by incumbents to retire or run for reelection. We argue in Chapter 9 that we should expect incumbents’ personality traits to influence their decisions to vacate their seats. However, we have already argued that quality challengers’ decisions to run are influenced by incumbent personality traits through the effect of those traits on the incumbent’s leadership style. There is therefore strategic interdependence between incumbent retirement and the emergence of quality challengers, and we expect incumbent personality to influence both of these phenomena. Given this strategic interdependence, we use the strategic probit model introduced by Signorino (1999) to make separate estimates of personality’s relationship with quality challenger emergence and with incumbent retirement (Signorino 2002). We do this by replicating the analysis of Carson (2005) for US House elections from 1996 through 2012, with the addition of ELUCIDATION scores. This method allows us to identify the relationship between each of the Big Five on the probability of challenger entry separate from an expected strategic mediating effect of personality on incumbent retirement. We also expect that certain traits may be prized by constituents as valence traits. We begin with the utility functions specified by Carson (2005) for four outcomes, Challenger Emerges, Incumbent Retires; Challenger Emerges, Incumbent Stays; Challenger Stays Out, Incumbent Retires; and Challenger Stays Out, Incumbent Stays. The quality challenger first makes his or her entry decision, and subsequently the incumbent chooses to retire or stay in the race. We specify a constant utility for the incumbent’s utility for seeking reelection and the challenger’s utility for staying out of the race, and allow each of the Big Five traits of the incumbent to influence the challenger’s utility for both outcomes in which he or she emerges and the incumbent’s utility for both outcomes in which he or she retires. We estimate this model with ELUCIDATION scores

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included as described.8 The estimates of the strategic probit for the challenger’s decision to emerge are reported in Table 4.1. The results of the strategic probit model largely comport with those of Carson (2005), with some interesting deviations. There is a negative and significant constant in the challenger’s utility of emerging, suggesting a positive utility for staying out of the race, as expected for quality challengers. We find the incumbent’s prior share of the vote, the vote share of the incumbent party presidential candidate, and midterm election years to be negatively and significantly associated with quality challenger utility, and open seats to have a very large positive impact on the utility of challengers emerging. However, we find two interesting contrasts with Carson (2005). From 1996 through 2012, incumbent party unity actually was associated with a decreased probability of quality candidate emergence, and prior incumbent spending was associated with increased chances of a quality challenger entering the race, in contrast with the earlier analysis. We suspect this is a result of increasing polarization in American politics over the last twenty years compared to the 1976–2000 period. Inspection of Table 4.1 reveals that each of the Big Five personality traits exhibits a significant relationship with the probability of attracting a quality general election challenger.9 The results of the strategic probit analysis suggest a strong positive relationship between Conscientiousness and the probability of drawing a quality challenger. As is visible in Figure 4.1, varying Conscientiousness from two standard deviations below its mean to two standard deviations above its mean results in 8. The data used for our analysis differ from the analysis of Carson (2005) in several minor ways. First, we investigate US House elections from 1996 through 2012 due to the constraints of our ELUCIDATION measures, while Carson (2005) covers the period from 1976 through 2000. We use party loyalty scores derived from Ramey and Asmussen (2013), while Carson (2005) includes incumbent support of the position taken by a majority of their party on CQ key votes. We count the Speaker and House Minority and Majority leaders as party leaders while Carson (2005) includes whips as well. Finally, we measure ideological extremism as the square of first dimension DW-NOMINATE scores (Poole and Rosenthal 1997) while Carson (2005) uses the absolute value. 9. Though one might be concerned about multicollinearity due to the multiplicity of covariates, the highest variance inflation factor (VIF) for any covariate was 2.81, suggesting that multicollinearity is not a significant concern in these analyses. No variable was used in all of the utility functions for either the incumbent or challenger, meeting the exclusion restriction set forth by Signorino (1999). We also estimated each model with Congress-level fixed effects, and results were substantively identical.

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84 table 4.1 Strategic Probit Model of Candidate Competition in US House Races, 1996–2012 Utility of Challenger Emergence Components Opennness Conscientiousness Extraversion Agreeableness Emotional Stability Party Unity Incumbent Spending Previous Incumbent Vote Presidential Vote Share Open Seat Midterm Election Cycle Challenger Constant

Wald Test BIC Log Likelihood Num. obs.

−0.947∗∗∗ (0.098) 1.726∗∗∗ (0.098) 0.277∗∗∗ (0.085) 2.582∗∗∗ (0.073) −0.885∗∗∗ (0.057) −0.132∗∗ (0.066) 2.427∗∗∗ (0.301) −0.283∗∗∗ (0.048) −0.248∗∗∗ (0.061) 28.755∗∗∗ 0.030 −0.672∗∗∗ (0.010) −0.995∗∗∗ (0.107)

49.774∗∗∗ 5,257.871 −2,472.72 3,722

Notes: Standard errors in parentheses. Observations are at the Congress-member level. Null hypotheses for the Wald tests are that all coefficients related to ELUCIDATION scores are zero. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

an increase in the predicted probability of quality challenger entry from 12% to 20%. This is equivalent to the increased probability of a quality challenger entering associated with a shift in the incumbent’s prior vote share from two standard deviations above the mean to the mean.10 This relationship between Conscientiousness and challenger quality is fully 10. In Figure 4.1, all continuous (categorical) independent variables are held at their means (modes), and 90% confidence intervals are shown. Values listed are the simulated probabilities that the challenger chooses to emerge, produced using the method described by King, Tomz, and Wittenberg (2000).

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figure 4.1. Predicted Probabilities of Quality Challenger Entry

in line with our expectations that Conscientious members, who derive more utility from delayed payoffs, would choose to spend relatively less time pursuing the short-term rewards of constituency service compared to other objectives in Washington, and that this weakness would attract experienced challengers. Incumbent Extraversion is also shown in Table 4.1 to have a significant positive relationship with emergence of quality challengers, in line with our expectations. A shift in Extraversion from two standard deviations below the mean to two standard deviations above the mean is associated with a 3-point increase in the probability that an experience challenger emerges. This is a relatively weak relationship, equivalent to a shift in incumbent prior vote share from 94% to 81%. However, it still is consistent with our expectations, based on the argument that the reward orientation of Extraverted legislators leads them to divert time and effort from activities that help them maintain the office they already possess and instead toward other objectives which offer opportunities for gain, and that these choices create openings for quality challengers. In contrast with our expectations, Openness exhibits a negative and significant relationship with drawing a quality challenger. There is nearly a 4-point decrease in the probability of quality challenger emergence, as seen in Figure 4.1, when Openness varies across the simulated range. This contradicts our expectations for this trait, which were based on the

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assumption that the activities that make up constituency service (casework and advertising) were less risky than the pursuit of policy objectives or power within the chamber. This may be in part explainable by the risky nature of pursuing distributive benefits for members of both parties, which requires legislation. Further examination into the degree to which Openness is associated with procuring particularized benefits for legislators’ constituencies would be valuable. Another possibility is that more Open members who are more comfortable with uncertainty and risk may engage with their constituents in more thoughtful, innovative, and direct manners preferred by constituents. If Openness makes legislators more willing to deliberate with voters or try new communications technologies at the risk of confrontations, failures, or gaffes, and these approaches earn the incumbent trust of politically interested constituents, then more Open members should experience a reduced vulnerability to the emergence of quality challengers. Though we had no clear expectations for the traits of Agreeableness or Emotional Stability, the strategic probit suggests that both are associated with drawing a quality challenger. Agreeableness is very strongly positively associated with quality challenger emergence, with a nearly 6-point increase in the probability of quality challenger emergence over the empirical range of Agreeableness. This shift is about the same as that of a shift from an incumbent vote share of 68% in the prior election to 54%. This suggests that the altruism associated with the trait is primarily oriented toward the incumbent’s partisan “tribe” in the political context and serves to distract from maintaining trust among constituents. The orientation of Agreeableness’ altruistic tendencies toward the party good may lead legislators to forsake (arguably altruistic) constituency service efforts in favor of other activities that serve the needs of the party. Finally, Emotional Stability is weakly and negatively associated with the emergence of quality challengers. This is consistent with the proposition that legislators are relatively oriented toward preventing losses from institutional power and policy utility rather than toward office utility, leading Neurotic legislators to pursue objectives inside the Beltway out of fear of losing their position in the chamber or in policy fights. We have reason to believe citizens value politicians with nominally “good” personality traits such as Openness, Conscientiousness, Extraversion, Agreeableness, and Emotional Stability, and punish those who

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exhibit the opposite, opening up opportunities for quality challengers in the process (Klingler, Hollibaugh, and Ramey 2016). However, we find that three of the five “good” traits are associated with increasing the probability of a quality challenger. This suggests that the hypothesized relationships may be stronger than measured. It would be beneficial, if difficult, to follow up with a more direct examination of personality’s relationship with constituency service and access. However, our examination of press releases in Chapter 8 supports the overall argument. In Chapter 2, we argued that the Big Five traits should be related to incumbent politicians’ preferences for delay, risk, altruism, and the emphases they place on reward and punishment. In this section, we extend that logic to the relative weight incumbents place on constituency service in support of their reelection compared to other pursuits, and argue that personality traits should be associated with these priorities of incumbent members of Congress, in turn influencing their vulnerability to drawing a quality challenger. We find that incumbent Conscientiousness and Extraversion, respectively modeled as time preferences and relative weights placed on potential rewards, are associated with attracting quality challengers. Though these findings are not easily compatible with traditional characterizations of these traits, respectively focusing on duty/work orientation and sociability, they are implied by the core cognitive constraints framework. Furthermore, Openness was found to be associated with a diminished probability of drawing a quality challenger, suggesting that other factors, such as a valence preference for this trait, might advantage these legislators and ward off challengers.

4.2 Who Spends? After the competitive election environment has been set with the emergence of challengers (or lack thereof), incumbent legislators have to decide how much of their campaign funds to spend on their election campaigns. We should expect incumbents to attempt to minimize the amount of money they spend in campaigns given the substantial costs of fund-raising in terms of lost time and policy compromises that might be made (Baron 1989; Erikson and Palfrey 2000). Despite the costs of fundraising to incumbents, spending continues to rise in US congressional campaigns, and the cost of running for Congress has increased by 500%

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since 1984.11 In an effort to explain the high costs of legislative campaigns, scholars have proposed several goods obtained by this spending. First, candidates spend to inform voters about their views (Coleman and Manna 2000; Jacobson 1978; Jacobson 2012; Kim and LeVeck 2013). Incumbents’ roll call voting records provide citizens with strong signals about their ideological views (Box-Steffensmeier and Franklin 1995; Franklin 1991). Despite the presence of these signals, voters do not receive them without noise, as campaign spending appears to enhance voter knowledge of candidates, and incumbent spending increases voter knowledge of the incumbent’s record (Coleman and Manna 2000; Jacobson 2012; Kenny and McBurnett 1997). Depending on the ideological preferences of a district’s median voter, the incumbent, and the incumbent’s party, however, the benefits of incumbent spending are likely to vary. When a party has a clear reputation, incumbents in marginal districts must spend more to establish their distance from the party in the minds of voters, while this is not necessary in districts favorable to the incumbent’s party (Kim and LeVeck 2013). Additionally, campaign spending allows campaigns to mobilize citizens by lowering the costs of voting for citizens identified as favorable to the candidate. The information provision discussed above addresses one component of the costs of voting, but campaign expenditures also may be used to provide other services that lower the costs of voting as well (Caldeira, Patterson, and Markko 1985; Hogan 1999; Jacobson 2012). Furthermore, campaign spending may work to increase citizens’ perceived duty or consumption benefit from voting (Cox and Munger 1989). The campaign environment is competitive, and candidates’ efforts to communicate their views to the public and mobilize supporters exist alongside the actions of opponents. Candidates may also spend in order to define voters’ opinions of their opponents’ views (Geer 2008). As candidates in competitive elections spend to define the positions of both themselves and their opponents, voters are exposed to increasing volumes of considerations that, ceteris paribus, should benefit the candidates who spend the most and produce the most considerations to be sampled (Zaller 1992). Furthermore, candidates must compete with their 11. Michael Scherer, Pratheek Rebala, and Chris Wilson, “The Incredible Rise in Campaign Spending,” Time, October 23, 2014, http://time.com/3534117/the-incredible-rise-in -campaign-spending/.

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opponents to mobilize supporters on Election Day through spending on organizational capital (Holbrook and McClurg 2005; Masket 2009). Incumbents typically have greater access to campaign funds than challengers, and challengers who spend more experience greater probabilities of success, so we should expect incumbents to spend more when facing challengers who spend more themselves (Erikson and Palfrey 2000; Erikson and Wright 1993; Jacobson 2012). In such a competitive environment, incumbents who offer relatively low valence characteristics to voters (such as freshmen), or face quality challengers who present relatively high valence characteristics, must spend more on mobilization to compensate for this disadvantage (Jacobson and Kernell 1983; Mayhew 1974; Munger 1988). The decisions of incumbents to disburse war chests to aid in their own reelection rather than save for the future are thus influenced by a variety of considerations driven by the information environment as well as the ideological and valence characteristics of incumbents and their competition. However, incumbents also transfer funds to other candidates and party organizations in order to gain influence (Jacobson 2012; Larson 2004). Federal campaign finance regulations allow incumbents to transfer unlimited amounts of hard money to any national party committee, and making such transfers has been found to be associated with holding positions of influence in the House (Heberlig and Larson 2005; Larson 2004). As is visible in Table 4.2, transfers from reelection accounts soared from 1996 to 2012. During the last cycle in this period, the average amount transferred in Congress was $194,395. To put this in context, after the 1996 cycle, campaign transfers in total have ranked no lower than seventh as a joint source of campaign funds, out of more than eighty other industries as defined by the Federal Election Commission. Incumbents contribute substantial sums from their war chests to party and other candidates’ campaign organizations, adding an additional dimension to congressional campaign spending. Once funds have been gathered and the competitive environment is known, incumbents may choose to allocate their funds in three general categories. First, incumbents may decide to spend war chest funds on their own election to disseminate information and mobilize voters, which presumably increases the probability that the incumbent will obtain office in the next Congress. Second, incumbents may choose to save their war chest funds for future election cycles, which investment will help deter future quality challengers from entry and/or increase the probability of

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table 4.2 Transfers from Campaign Committees to Other Candidates and Party Organizations, 1996–2012

Year

Industry Rank

Total Contributions

Donations to Democrats

Donations to Republicans

Percentage Democrat

2012 2010 2008 2006 2004 2002 2000 1998 1996

6 3 5 3 5 5 6 7 33

$ 104,100,982 $ 114,425,699 $ 110,002,390 $ 95,936,271 $ 88,338,322 $ 49,145,950 $ 43,929,881 $ 23,200,170 $ 9,812,534

$ 49,719,082 $ 69,418,549 $ 76,441,240 $ 56,129,834 $ 58,151,850 $ 22,786,172 $ 19,911,959 $ 7,624,241 $ 3,616,704

$ 54,064,013 $ 44,800,677 $ 33,295,467 $ 39,624,642 $ 30,132,307 $ 26,295,780 $ 23,757,210 $ 15,569,259 $ 6,244,998

48 61 69 59 66 46 45 33 37

Percentage Republican 52 39 30 41 34 54 54 67 64

Notes: Data obtained from http://www.opensecrets.org/industries/totals.php?cycle=2014&ind=Q16. Rankings show how congressional campaigns rank in total campaign giving as compared to more than eighty industries, such as the automotive industry.

winning elections in the future (Goodliffe 2004). Finally, incumbents may choose to disburse their campaign funds by giving those funds to other political entities such as fellow candidates and party campaign organizations. As total campaign expenditures lump together spending on the incumbents’ campaign and funds provided to other entities, we may use the core cognitive constraints framework to consider the characteristics of spending versus saving and consider how the Big Five personality traits may influence the decision to disburse funds. Thinking about campaign expenditures in terms of spending versus saving draws attention to the fact that saving funds for the future delays the use of these funds for other objectives. There is some reason to believe that spending funds in a cycle to boost the margin of victory will have deterrent effects for the subsequent election (Krasno and Green 1988). Furthermore, as previously argued, transfers of campaign funds to party committees should have delayed payoffs in terms of position in the chamber and policy utility in future Congresses (Heberlig and Larson 2006). However, saving has long-term payoffs, while the delayed payoffs of investments in campaign blowouts and party committee transfers may taper off quickly as committee chairs make new demands for funds and subsequent elections lead potential challengers to update. We then expect that incumbent legislators sensitive to delay should still value the longer-term payoffs of saving more than the shorter-term payoffs from disbursements. Thus, as Conscientious incumbents would discount the future gains of building war chests to a lesser extent, we expect that

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Conscientiousness should be negatively associated with campaign disbursements among incumbent legislators. When we consider the degree to which each of these decisions can provide incumbent legislators with relative gains, we see that only giving money to others offers expectations of reward.12 For an incumbent legislator seeking reelection to the same office, winning the current election or winning future elections can only help preserve the utility of the office already possessed and offers little gain relative to already holding office. In contrast, giving to other legislators or the party may help the incumbent in efforts to rise in the chamber or build policy coalitions. Thus, as Extraversion leads legislators to place higher weights on gains over the status quo, we expect that Extraversion should be positively associated with campaign expenditures among incumbent legislators. Whether incumbents disburse campaign funds to improve the chances of winning their own campaigns, transfer those funds to build and maintain relationships with other politicians, or save funds to improve the chances of future victories, each of these three actions serves to prevent losses in comparison with the status quo. Given the assumption that individuals discount the future, we should expect legislators to prefer losses in the future to losses in the present, no matter how Conscientious they are. If the individual has any status within the chamber, there is much to lose from failing to contribute to others (Heberlig and Larson 2005; Heberlig, Hetherington, and Larson 2006; Larson 2004). Additionally, if campaign transfers help parties win marginal races, hold the majority, and prevent policy loss, there are additional loss-oriented reasons to give to the party (Herrnson 1986). Thus, loss-sensitive legislators should spend more and save less, generating the expectation that Neuroticism should be positively associated with campaign disbursements. As for Agreeable incumbents, who should have a greater capacity for altruism and acting for the benefit of the nation as a whole, there are no clear reasons to expect them to prefer to spend on donations to other candidates and party organizations. Finally, as both saving for the future and giving to others involve uncertainty and risk, there is no particular reason to expect Openness to have a relationship with campaign expenditures. We draw on the recent work of Kim and LeVeck (2013) to examine the relationship between the Big Five traits and the decision to disburse

12. Though it is possible that legislators gain intrinsic value from vote totals or shares.

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92 table 4.3 Linear Regression of Logged Campaign Disbursements Model 1

Model 2

Model 3

−0.084∗∗ (0.036) −0.073∗∗ (0.031) 0.106∗∗∗ (0.023) 0.093 (0.061) −0.171∗∗∗ (0.038) —

−0.048 (0.033) −0.032 (0.028) 0.081∗∗∗ (0.021) 0.068 (0.055) −0.167∗∗∗ (0.035) —

Incumbent Presidential Vote

—

—

Incumbent-Party Distance

—

—

Challenger’s Spending

—

—

Quality Challenger

—

—

Freshman

—

—

Uncertainty × Inc. Pres. Vote

—

—

Uncertainty × Party Distance

—

—

Inc. Pres. Vote × Party Distance

—

—

13.913∗∗∗ (0.175)

14.236∗∗∗ (0.161)

−0.030 (0.039) −0.089∗∗∗ (0.033) 0.102∗∗∗ (0.025) 0.112∗ (0.063) −0.197∗∗∗ (0.041) −1.798 (4.196) 0.003 (0.008) −7.528∗∗ (3.510) 0.000∗∗∗ (0.000) 0.080∗∗ (0.035) 0.160∗∗∗ (0.037) −0.023 (0.050) 48.831∗∗ (23.524) 0.004 (0.004) 13.991∗∗∗ (0.662)

0.001 (0.035) −0.054∗ (0.030) 0.082∗∗∗ (0.022) 0.094∗ (0.057) −0.177∗∗∗ (0.037) −27.375∗∗∗ (4.324) −0.048∗∗∗ (0.009) −5.496∗ (3.144) 0.000∗∗∗ (0.000) 0.109∗∗∗ (0.032) 0.230∗∗∗ (0.033) 0.298∗∗∗ (0.060) 34.989∗ (21.073) 0.002 (0.004) 18.248∗∗∗ (0.690)

No

Yes

No

Yes

9.6398∗∗ 0.01 0.01 3,327

7.8004∗∗ 0.20 0.20 3,327

7.2121∗∗ 0.11 0.10 2,712

5.9594∗∗ 0.29 0.28 2,712

Opennness Conscientiousness Extraversion Agreeableness Emotional Stability Uncertainty in Party Reputation

Constant

Congress FE? Wald Test R2 Adj. R2 Num. obs.

Model 4

Notes: Standard errors in parentheses. Observations are at the Congress-member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

campaign funds. Using the Database on Ideology, Money in Politics, and Elections (DIME) dataset introduced by Bonica (2013), we first regress campaign expenditures on ELUCIDATION scores. Then, we incorporate ELUCIDATION scores into the primary specifications used by Kim and LeVeck (2013).

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figure 4.2. Predicted Campaign Disbursements

The results in Table 4.3 indicate incumbent personality traits are associated with their campaign disbursements. A shift in Extraversion from two standard devations below the mean to two standard deviations above shifts the predicted campaign expenditures for a member of Congress from about $660,000 to just over $850,000. This finding is consistent with our expectation that the reward sensitivity of Extraverts would lead them to disburse more funds and seek rewards in the chamber through transfers. We also found Emotional Stability to be negatively associated with spending. As the trait shifts from two standard deviations below the mean to two standard deviations above, the predicted campaign disbursements of a House incumbent decreases from just over $890,000 to just under $630,000, which is what we expect to see given our understanding that the loss sensitivity of Neurotics would lead them to make additional disbursements to protect against the possibility of losing policy utility and/or political influence. These relationships are visible in more detail in Figure 4.2.13

13. Predicted values in Figure 4.2 are displayed in dollars and are on the unlogged scale. All continuous (categorical) independent variables are held at their means (modes), and 90% confidence intervals are used. Model 1 from Table 4.3 was used to produce the predicted probability plot.

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Perhaps unsurprisingly, we once again find that Conscientiousness is significantly associated with future-oriented activity. A shift in the trait from two standard deviations below the mean to two standard deviations above is associated with a $200,000 decrease in campaign disbursements by incumbent members of the US House. Conscientious members of Congress are thus spending less and leaving more of the resources they have for future use. Finally, Agreeableness is positively associated with campaign disbursements. We did not expect that Agreeable members of Congress should spend more to assist their parties and other members, but this may due to the party being the relative reference group for their altruism. We find that there is a relationship, though weak, and shifting Agreeableness from two standard deviations below its mean to two standard deviations above is associated with an increase in predicted campaign disbursements of just over $100,000. These findings suggest several interesting new directions in examining the relationships between legislators’ personality traits and campaign finance. The decision to spend involves decisions to save, decisions to give, and decisions to disburse funds for the candidates’ own campaign. Our framework for modeling personality traits is more closely related to some of these actions than others for each trait. In the future, it would be useful to separately examine transferred funds, rolled-over funds, and funds explicitly spent on the incumbents’ race and the role of personality traits on all of these variables. While we found support for all of our expected relationships, it would be worthwhile to examine these further with more precise data. Second, campaign finance data do break down disbursements by vendor, and in many cases it would be possible to separately tally expenditures by certain categories such as campaign consultants, technology infrastructure, staff salaries, and event space. Though it is beyond the scope of this chapter to examine more detailed campaign expenditures, there is much opportunity to model the characteristics of each of these elements in terms of overall campaign strategy and apply our framework to the broader decisions candidates make regarding how they seek to win their own races. Finally, it’s likely that both the institutional contexts that members operate in and district-level conditions influence many of these spending decisions. The expectations members have about their own reelection prospects likely influence the way personality traits are associated with campaign spending. Furthermore, the institutional environment ought

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to influence the relative benefits of transfers vis-à-vis spending. Modeling the interplay between our modeling parameters for personality traits and phenomena such as these offers exciting new possibilities for the examination of campaign spending.

4.3 Individual Differences and Seeking Reelection: Conclusion In this chapter, we examined quality challenger emergence as well as the degree to which incumbents disburse campaign funds, which are both key elements that characterize a congressional election campaign. We argued that these two aspects of the race are related to personality through the linking mechanisms of emphasis on constituent service and the attractiveness of saving or transferring campaign funds, or more broadly, how candidates generally choose to allocate their time, and how candidates choose to spend their money. When taken together, we see patterns in how incumbents’ Big Five traits influence campaign activity. First, Agreeableness is associated with both higher levels of spending and an increased probability of quality challenger emergence. We began with the expectation that this trait could lead Agreeable individuals to be less vulnerable to challengers as a result of an altruistic commitment to public service or moderation in the interests of national welfare, but the results instead suggest Agreeableness may orient these individuals toward the good of their party over their own electoral fortunes. Second, Extraverts are more likely both to have an experienced challenger and to spend their campaign war chests, in line with our expectations that the trait’s reward orientation leads these incumbents to be easily distracted by potential policy or institutional gains at the expense of constituency service, which they may perceive as unrewarding. On the other hand, Neurotics also are more likely to receive challengers and spend campaign funds. Our expectations for Neuroticism were somewhat unclear, but these findings suggest that Neurotics fear the loss of policy or institutional power more than losses in expected utility of winning reelection, and they adopt campaign tactics that put less priority on saving for future cycles or connecting with constituents. Conscientiousness exhibits relationships in line with our parameterization of the trait as discounting of the future. Conscientious individuals are more likely to be challenged and save more money, which reflects

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an orientation toward the long-term benefits of investing time in policy expertise and institutional power over the short-term benefits of using time to serve. As money, unlike time, may be hoarded, Conscientious individuals save these resources for future elections. There are several ways to further investigate the role the Big Five play in congressional campaigns. It would be beneficial to further investigate the direct associations between congressional leadership style and the Big Five and to peer deeper into the mechanism outlined here. Our examination of legislators’ press releases in Chapter 8 indicates relationships between personality traits and position-taking versus credit-claiming that support our argument in this chapter, but further examination would be fruitful. It would also be potentially enlightening to disentangle disbursements oriented toward a member’s campaign from transfers, and to examine the role personality plays in predicting both forms of disbursement. Additionally, there we identify a major opportunity to examine the influence of personality traits on campaign fund-raising. As we have reason to believe the Big Five are associated with the emergence of quality challengers, and quality challengers may be deterred by fund-raising, this would require a careful touch but would be a logical next step in applying the five-factor model to the study of congressional campaigns (Goodliffe 2007b). The analyses in this chapter demonstrated several ways in which personality is useful for the study of campaigns. First, personality traits inform us about qualities that might be difficult to observe otherwise, such as a legislator’s relative priorities or leadership style. Second, the differences that personality psychology argue are important, such as reward sensitivity and delay, can guide us to examine phenomena that may be overlooked otherwise, such as transfers. In both cases, incorporating elite personality into our analyses allows us to paint a more complete picture of why incumbents choose to allocate their time and their money in ways that may result in punishment on Election Day. Pursuing institutional power or policy over constituency service, or saving rather than spending money, provides incumbents with other benefits in terms of delay and partisan altruism, chances to avoid punishment, and/or opportunities to pursue glittering rewards.

chapter five

Committee Assignments Being a “team player” is necessary to gain a plush committee assignment. —Former Representative Ron Paul (R-TX)1

[Committee] chairmen are not autonomous. They owe their allegiance to the leadership. —Former Representative Thomas M. Davis III (R-VA)2

T

he importance of committees has waxed and waned over the course of congressional development. Wilson (1885) famously opined that “Congress in session is Congress on public exhibition, whilst Congress in its committee-rooms is Congress at work” (79). Fenno (1973) later reinforced the importance of committees, arguing that it was the structure of the committee system itself that enabled members of Congress to pursue their individual goals. However, the relative importance of the committee system has changed since then, and controversies have since emerged. Aldrich and Rohde’s (2001) conditional party government theory argued that, in response to recent trends of partisan sorting (Fiorina, Abrams, and Pope 2010) and polarization (Mccarty, Poole, and Rosenthal 2006)—and the more homogeneous and ideologically divergent parties that accompany them—there has been an increased centralization of congressional power within the partisan organizations, to the detriment of the committees themselves (Hall and Shepsle 2014). These changes have affected the relative importance of certain factors in the committee assignment process—with seniority becomimg less important and party

1. As quoted in Paul (2009). 2. Jim VandeHei and Juliet Eilperin, “GOP Leaders Tighten Hold in the House: Hastert, DeLay Reward Loyalty over Seniority,” Washington Post, January 14, 2003, A1.

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loyalty becoming more important (e.g., Aldrich and Rohde 2000; Cann 2008; Cox and McCubbins 2007; Deering and Wahlbeck 2006; Hall and Shepsle 2014; Heberlig, Hetherington, and Larson 2006; Heberlig and Larson 2012; Leighton and Lopez 2002; Moffett 2012).3 Nonetheless, as a testament to the continued (even if discounted) importance of committees to this day, Smith (2007) notes, “Committee assignments have great value for most legialators and probably are the most valuable institutional benefit that the average legislator receives” (58). The roles committees play are nearly as varied as their relative importance over time. Shepsle and Weingast (1987) note several—they are “‘gatekeepers’ in their relevant jurisdictions,” they are “repositories of policy expertise,” they are “policy incubators,” they “possess disproportionate control over the agenda in their policy domains,” and “they are deferred to, and that deference is reciprocated” (85)—and these roles have persisted over time, though to varying degrees. (e.g., Cox and McCubbins 2005; Crombez, Groseclose, and Krehbiel 2006; Denzau and Mackay 1983; Krehbiel 1990; Krehbiel 1991; Shepsle 1979). Moreover, while their agenda-setting power within their various policy domans has been co-opted by partisan leadership structures in recent years, their importance to partisan goals has only increased due to the aforementioned recent emphasis on party loyalty in the assignment process. However, regardless of the role and relative power of the committee system, the committee (and committee chair) assignment process is inherently a principal-agent problem, with parties (and the larger chambers) dependent on the “little legislatures” of committees using their agenda-setting power and informational advantages to advance partisan goals. Therefore, parties have every incentive to ensure that those they appoint will be willing to heed the call of party leaders, hence the reliance on observed measures of loyalty. Underneath these observed measures, however, lie important psychological processes that, we posit, are tied to personality traits and core cognitive constraints; we expand on this line of thought in Chapter 7 and investigate why certain members of Congress are more receptive to partisan pressures than others. Here, we take that heterogeneity for granted and examine its role in the committee assignment process, with a focus on how party leaders leverage observed differences in personality traits to ensure that 3. But see Krehbiel (1993) for an argument against the existence of an outsized role for parties in Congress.

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committees are filled with—and run by—those members who (party leaders believe) will most contribute to partisan goals. In doing so, we show that Agreeable legislators are more successful at obtaining higher-quality committee assignments, arguably due to a latent tendency to try to be “team players,” and are perhaps more responsive to leadership appeals to the welfare of the nation (interpreted through ideology). Additionally, because they tend to perceive more potential negative outcomes from the choices they make (and are thus more easily controlled by parties), less Emotionally Stable members receive plum postings; relatedly, since less Open members are less likely to take risks, they should be sought after by party leaders for influential committees, since they will be less likely to take risks that endanger the party agenda. We also show that more Extraverted members are more likely to obtain high-quality committee assignments, as they are particularly drawn by the potential rewards that may be gained from them. Finally, more Conscientious members are more likely to receive chairmanships, due to the managerial duties and long-term planning these positions require.

5.1 Congressional Committees and Core Cognitive Constraints As mentioned, the committee assignment process is a principal-agent problem. After each congressional election, party leaders are responsible for determining how many “slots” each party receives on each committee, which members are to be removed from committees due to a lack of slots, loyalty, seniority, or some other factor, and which members are to be added to committees.4 These leadership responsibilities are not ones to take lightly. As mentioned, committees have important gatekeeping roles, as 75% to 85% of all bills that are referred to committee never receive attention after referral, and yet two-thirds of those that do receive attention are passed by at least one chamber (Krutz 2005). Policy-specific informational advantages of committees—coupled with selective revelation of this information (Diermeier and Feddersen 2000)—ensure that committee members will become experts in the jurisdiction covered by their committees, thus ensuring greater deference is paid to them by other members of the legislature, as well as enabling them to extract 4. Informational advantages also explain why the majority party allows minority party members to serve on committees (Park 2012). Also see Ramey (2015a).

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additional policy gains in their areas of expertise (e.g., Adler and Lapinski 1997; Shepsle and Weingast 1987; Weingast and Marshall 1988); this same policy-specific expertise also leads to turf wars over legislation with ambiguous jurisdiction (King 1994).5 Collectively, these roles make committees highly influential in the legislative process, even if this influence has abated over time. To ensure that the actions taken by these (influential) groups comport reasonably well with their overall agendas, party leaders have—in recent decades—begun to weigh loyalty and partisan fealty more heavily in the committee assignment process (e.g., Hall and Shepsle 2014). However, despite these precautionary measures, individual committee members have their own goals—goals that often conflict with those of their parties. Because of this, party leaders often exert ex ante control of committees, appointing party loyalists and those who seem to be most susceptible to partisan pressure. However, ex post control is often more difficult. Removal of members after election losses (and the loss of committee slots that entails) is generally done by seniority (Grimmer and Powell 2013), though violations of this norm do occur; without the loss of slots, however, committee membership is generally treated as a property right. Moreover, the ability of parties to influence member behavior on votes in general remains somewhat contested (e.g., Chiou and Rothenberg 2009; Krehbiel 1993; Krehbiel 1998; McCarty, Poole, and Rosenthal 2001; Minozzi and Volden 2013; Snyder and Groseclose 2000). Thus, the actual ability of parties to influence committee behavior ex post is likely weaker than the ability to influence behavior ex ante (via the initial assignment process), a result common to principal-agent models. Therefore, if the structure of the committee system allows for minimal ex post influence, party leaders should instead focus ex ante on those individual members that are ex post susceptible to influence, either because of their particular political situations or because of innate personality traits that bias them toward seeing partisan organizations as stronger than they actually are. We therefore argue that legislators’ personality traits should matter during the committee assignment process. To test this assertion,

5. We are agnostic as to whether committee members are “preference outliers” in the policy areas under the purviews of the committees on which they sit (e.g., Gilligan and Krehbiel 1989; Groseclose 1994; Hall and Grofman 1990; Kiewiet and McCubbins 1991; Krehbiel 1990; Weingast and Marshall 1988). While observed instances of preference outliers may reflect genuine heterogeneity in preferences, they might also simply reflect a heightened, committee-specific ability to enact policy change.

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we examine assignments to “important” committees, with a focus on the House Ways and Means, Appropriations, and Rules Committees. By their nature, these committees exert outsized influence on the legislative process, other legislation, and the disbursement of federal funds; as such, the success or failure of party agendas depends in large part on the ability and/or willingness of members of these committees to push through important legislation.6 Therefore, party leadership has every incentive to stack these committees with those focused on the long-term goals of the party, those who will be willing to hew to the demands of the party (perhaps even at the expense of individual goals), those who will not take unnecessary risks that may potentially undermine the success of the party agenda, and those who will recognize that their continued service on these committees (and therefore their outsized influence in Congress) depends on not upsetting the leadership. These considerations suggest the importance of personality in the committee assignment processes. For starters, the desire of parties to have important committees stacked with those who will be team players—perhaps even at their own expenses—suggests the importance of Agreeableness, as more Agreeable members derive more of their utility from that of others under our core cognitive constraint framework. Indeed, with finite resources, party leaders will want to ensure that their decisions to whip will be as effective as possible and will, in theory, stack influential committees with those they believe to be the most receptive to these pressures (Burden and Frisby 2006); additionally, even though our parameterization makes no assumptions in particular about the relevant comparison group, party leaders should seek out those most likely to weigh the opinions of others in their own utility functions, as they will be more easily whipped.7 Openness should also play a part,

6. While the reason for the existence for committees may be informational in nature (e.g., Krehbiel 1991; Martorano 2006), we do not focus on the reasons for the development of the committee system here. Rather, we take the committee system as given and examine the partisan incentives within it. 7. Fenno (1973) noted, “When money-committee members and their selectors discuss the reasons for their appointments, they typically mention personal attributes such as ‘cooperative,’ ‘popular,’ ‘reasonable,’ ‘sober,’ [and] ‘easy to work with.’ Conversely, they were not (as their competitors sometimes were) ‘screwballs,’ ‘running around kicking everyone in the teeth,’ ‘shooting their mouths off,’ [or] ‘going off half-cocked’ ” (20–21). These qualities further suggest the importance of Agreeableness, though with the relevant group being the House as a whole, as opposed to one’s party.

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given its parameterization as a measure of risk preference. Indeed, party leadership should prefer that influential committees be composed of those who will be less likely to take unnecessary and unexpected risks that could hinder the implementation of the party’s agenda. On this note, Masters (1961) notes that one of the key criteria for assignment to one of these three committees is that the members do “not believe that the Congress is the proper place to initiate drastic and rapid changes in the direction of public policy,” further suggesting the desire for those less willing to take unnecessary risks (352). Therefore, we should expect a negative relationship between Openness and the likelihood of receiving an important committee assignment; indeed, elsewhere in this book, we have discussed the importance of Openness for party discipline, as less Open members are less likely to take risks that party leadership might find unpalatable. For related reasons, we should also expect Emotional Stability to matter. As Emotional Stability is negatively related to individuals’ sensitivity to potential negative outcomes, less Emotionally Stable members should place more weight on the possibility of leadership retaliation (potentially in the form of stripping them of committee assignments, support of a primary challenger, the refusal of fund-raising assistance, or some other action that hurts the member in terms of policy influence and/or electoral viability) if they do not toe the party line; therefore, Emotional Stability should be negatively related to the probability of being assigned to an important committee, as a way of maximizing parties’ abilities to control their members ex post and circumventing the underlying principal-agent problem, at least in part.8 Finally, as these committees offer the greatest potential for influence—both inside the House (via logrolling and agenda-setting) and outside the House (via the distribution of funds and policy influence)—we should expect more Extraverted members to find these committees more attractive, due to their increased sensitivity to the potential rewards that may be gained by sitting on them (e.g., Bullock 1973; Fenno 1973; Groseclose and Stewart 1998). Therefore, they may

8. Maltzman (1998) notes that “once a member is assigned to a ‘prestige’ committee such as Rules, Appropriations, or Ways and Means, the leverage that party leaders can use to shape their behavior is diminished” (50). While this might seem to run counter to our expectations, all we require is that members place some weight on the possibility of negative outcomes from failing to hew to party orthodoxy. Arguably, this is more likely to be true for less Emotionally Stable members—who are more likely to see potential negative outcomes where none may exist—which is entirely consistent with our expectations.

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exert additional effort in order to win placements on these committees, and this demand-side variation may result in more Extraverted members being more likely eventually to be placed on them.9 We are more circumspect in our expectations of the effects of Conscientiousness, as the concerns with Emotional Stability relate to medium- and long-term outcomes. Depending on how one weights these different longer-term preferences, Conscientiousness may increase or decrease the desirability of committee placements (both from the supply side and the demand side). However, given the different incentives at play, the effects of personality will likely be different for chair assignments than for committee assignments. In addition to the important gatekeeping roles inherent to committees, chairs are tasked with managing the calendar and ensuring that the scarce resources of time and agenda space are effectively utilized to the greatest benefit (Adler and Wilkerson 2012). Indeed, by strategically scheduling committee hearings and reports, and winnowing /gatekeeping appropriately, committee chairs influence the set of scheduling choices available to the Speaker (Cox and McCubbins 2005; Cox and McCubbins 2007; Krutz 2005). Therefore, we can say with some confidence that party leadership should prefer committee chairs who are aware of the long-term implications of committee action and/or inaction; that is, members of the leadership should prefer that committee chairs be more Conscientious. That said, we believe the roles that Emotional Stability and Openness play in the chair assignment process should be similar to those they play in the larger committee assignment process. As they should prefer with committee members themselves, party leadership should prefer that those in charge of committees be those least likely to take unnecessary and unexpected risks that could hinder the implementation of the party’s agenda; thus, they should prefer less Open individuals. Similarly, they should prefer less Emotionally Stable members as committee chairs, as these members will be more likely to see potential negative outcomes from the choices they make—with the relevant outcome here being loss of the chairmanship—and will take the necessary courses of action to avoid it. Additionally, though the incentives for Conscientious members to seek chairmanships should be similar to those that drive them to seek plum 9. Unfortunately, we cannot examine the relationship between Extraversion and variation in committee requests, as the data are not available for the time frame in question.

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assignments in general, the managerial aspects of chairmanships should incentivize leadership to specifically seek out Conscientious members for these roles, thus providing additional reasons for the pool of committee chairs to be particularly Conscientious. Finally, we should also note that we have similar expectations for the role of Agreeableness as it relates to the appointment of committee chairs. As with plum committee assignments, there should exist a positive relationship between Agreeableness and the likelihood of receiving a committee chairmanship, due to the desire to have committee chairs be responsive to partisan appeals to the national welfare, interpreted through ideology.10 However, we have no real expectations about the effects of Extraversion on the chair assignment process. While demand-side characteristics should make committee chairmanships more appealing to more Extraverted members, party leadership might want to avoid those who are too focused on policy, as they may be more likely to pursue policy that provides them with personal rewards that do not rank highly on the party’s list of priorities.11 Therefore, these countervailing effects serve to muddle our expectations regarding the likely effects of Extraversion on the chair assignment process.

5.2

Plum Assignments

We first examine the broader committee assignment process, focusing on assignment to three influential House committees—Ways and Means, Appropriations, and Rules. These three committees are notable because they are responsible for setting the rules of debate for all bills considered in the House (the Rules Committee) and also because all taxing and spending bills have to be referred to them (Ways and Means and Appropriations). Moreover, these three committees were denoted in the Legislative Reorganization Act of 1946 to be the three “exclusive” 10. Alternatively, committee chairs are endowed with leadership responsibilities, and will often have to set committee agendas and calendars in ways contrary to the preferences of the rest of the committee; in these cases, it may be beneficial to have less Agreeable chairs, as they will be less likely to succumb to the whims of committee majorities. That said, we are skeptical about the likelihood of this possibility, especially since we consistently find in other chapters that Agreeableness is empirically associated with being a team player and heeding the call of the party. 11. Since rank-and-file members do not set the committee agenda, this is less of an issue when plum committee assignments are doled out.

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committees of the House, though other committees have since joined their ranks (Fenno 1973). Additionally, as we have mentioned above, previous research has indicated that two of the most important determinants of committee assignments are seniority (e.g., Bullock 1985; Fenno 1973; Goodwin 1959; Grimmer and Powell 2013; Masters 1961) and party loyalty (e.g., Bullock 1985; Coker and Crain 1994; Cox and McCubbins 2007; Sinclair 1998). More senior members are more successful at acquiring important committee assignments, and party leaders reward and/or enforce loyalty via the use of committee assignments. To capture the former, we include Seniority, which denotes the number of terms a member has served. Accounting for loyalty is trickier. While many previous studies have used “party unity” scores, we should be sensitive to the critique that party unity scores capture “persuadability” as well as a “baseline” level of agreement with party priorities; that is, party unity scores may be high for a particular member because he or she is easily persuaded by party leaders (in spite of his or her own personal preferences), or because he or she already agrees with party leaders (regardless of the member’s innate persuadability).12 As it is the level of “persuadability” that is arguably more relevant to the process, a different measure is needed. Some previous studies have used campaign contributions to Hill committees (e.g., the Democratic Congressional Campaign Committee and the National Republican Congressional Committee) as a proxy for party loyalty (e.g., Frisch and Kelly 2006; Heberlig 2003; Heberlig and Larson 2012). Therefore, we include Campaign Contributions, which denotes the amount (in nominal thousands of dollars) that a member’s election committee donated to his or her party’s Hill committee in the previous election cycle (that is, the election cycle leading to the current Congress). Positive and significant values would suggest that more prolific donors are more successful at acquiring influential committee assignments. We also account for loyalty more directly as well, using data provided by Minozzi and Volden (2013). After categorizing votes into “party-free” votes and “party-influenced” votes, Minozzi and Volden (2013) estimate “party-free” ideal points—that is, what members’ ideal points would be in the absence of party influence—and Party Responsiveness, which captures the percentage of time on party-influenced votes in which a member voted with the party majority. We include the latter as an independent variable

12. See Krehbiel (1993).

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in our analyses, and we also include Distance from Floor Median, which is the absolute value of the distance between the “party-free” ideal points of the member in question and the floor median. Collectively, these variables should account for the role of loyalty in the committee assignment process. Next, to account for the possibility that electoral concerns are relevant in the committee assignment process, we include Electoral Security, which denotes the percentage of the vote (on a 0–100 scale) the member received in the election to the current Congress. However, we should note the conflicting findings on the role of electoral security in the committee assignment process. Masters (1961) and others suggest that more influential and important committee assignments go to those from safer districts.13 Conversely, Grimmer and Powell (2013) show that prestigious committee assignments are electoral subsidies, and that being involuntarily “exiled” from committees results in increased electoral vulnerability. Notably, Frisch and Kelly (2006) find that members use both pretexts (“safe districts” and “vulnerable districts”) as rationales for requesting prestigious committee assignments. However, it should be noted that other studies (e.g., Bullock 1973) find no relationship between electoral vulnerability and the prestige of committee assignments, and question the premise of whether they actually affect future election results (e.g., Bullock 1972). Regardless, because of the possibility for influence, we include Electoral Security as a control. Other covariates have also been found to be important to the assignment process. Regional balance has also been historically important, with southern identities being particularly salient, though this has diminished somewhat in recent years (e.g., Bullock and Sprague 1969; Smith and Ray 1983). We therefore include a variable (South) that equals one if the member represents a southern state and zero otherwise. We also control for potential desires for racial balance (or different representation styles) by including indicator variables for African-American and Latino that equal one if the member belongs to the group in question and zero otherwise. We also include an indicator variable equaling one if the member is retiring in the Congress in question, and zero otherwise (Retiree). Finally, due to property rights over committee seats (e.g., Polsby, Gallaher, and Rundquist 1969; Shepsle 1975; Weingast and Marshall 1988;

13. Also see Fenno (1966).

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Katz and Sala 1996), we include a lagged dependent variable (Previous Plum Committee) that equals one if the member in question had a plum committee assignment in the previous Congress and zero otherwise.14 To these previously investigated explanators of variance in committee assignments, we add our ELUCIDATION scores. We account for previously established correlations between personality and personal demographics by including variables for Age and whether or not the member is Female; the former denotes the member’s age at the end of the first session of each Congress, and the latter is an indicator variable equaling one if the member identifies as female and zero otherwise. Additionally, we include interactions between the ELUCIDATION scores and majority party status, since the relative institutional incentives will likely be different for majority versus minority parties. Using data from the 104th to 109th Congresses, Table 5.1 shows the results from a series of logistic regressions where the dependent variable is a binary variable that equals one if a member received an assignment to one of the committees in question during that Congress, and zero otherwise, controlling for ELUCIDATION scores and several control variables. Members of the party leadership teams are excluded. We also present graphical results in Figure 5.1, showing how the predicted probability of receiving a plum committee assignment varies with personality.15 We also include Congress-level fixed effects. As can be seen in Figure 5.1, our hypotheses regarding the effects of Openness, Extraversion, Agreeableness, and Emotional Stability are borne out, though Table 5.1 indicates that the results for Extraversion and Emotional Stability are less robust to model specification. Openness and Emotional Stability are associated with decreased probabilities of plum committee assignments, and the relative effects of the personality traits appear to be stronger for those in the majority party. Since Openness is modeled here as a form of risk preference, and party leaders want to avoid stacking these committees with members who “believe that the Congress is the proper place to initiate drastic and rapid changes in the

14. However, because of potential concerns about including lagged dependent variables on the right-hand side of regression models (e.g., Keele and Kelly 2006), we include this only as a robustness check. 15. In Figure 5.1, and the discussion that follows, the coefficient estimates from Model 4 were used, and all continuous (categorical) variables were held at their means (modes) unless denoted otherwise. We also present 90% confidence intervals.

table 5.1 Logistic Regression Models of Personality and Plum Committee Assignments

Openness Conscientiousness Extraversion Agreeableness Emotional Stability Majority Party Majority Party × Openness Majority Party × Conscientiousness Majority Party × Agreeableness Majority Party × Extraversion Majority Party × Emotional Stability Party Responsiveness

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

−0.808∗∗∗ (0.134) 0.094 (0.116) 0.254∗∗∗ (0.084) 1.445∗∗∗ (0.230) −0.501∗∗∗ (0.145) —

—

−1.105∗∗∗ (0.174) −0.386∗∗ (0.152) 0.496∗∗∗ (0.107) 2.091∗∗∗ (0.291) −0.973∗∗∗ (0.187) −3.446∗∗ (1.504) —

—

—

—

—

—

—

—

—

—

—

−0.447 (1.083) 0.516∗∗∗ (0.177) 0.086∗∗∗ (0.016) 0.006∗∗∗ (0.001) 5.473∗∗∗ (1.740) −1.233∗∗∗ (0.280) —

−0.890∗∗∗ (0.282) −0.465∗∗ (0.216) 0.396∗∗ (0.166) 2.291∗∗∗ (0.476) −0.553∗ (0.293) −0.095 (2.263) −0.295 (0.357) 0.176 (0.297) 0.134 (0.219) −0.336 (0.600) −0.723∗ (0.379) −0.529 (1.075) 0.451∗∗ (0.181) 0.082∗∗∗ (0.017) 0.006∗∗∗ (0.001) 5.604∗∗∗ (1.747) −1.180∗∗∗ (0.284) —

—

—

—

−0.811∗∗∗ (0.291) −0.613∗∗∗ (0.228) 0.447∗∗ (0.174) 2.393∗∗∗ (0.502) −0.513∗ (0.306) 2.244 (2.391) −0.473 (0.377) 0.434 (0.319) 0.123 (0.229) −0.392 (0.637) −0.656∗ (0.395) 0.723 (1.174) 0.572∗∗∗ (0.196) 0.101∗∗∗ (0.025) 0.002 (0.001) 3.007 (1.857) −1.483∗∗∗ (0.316) −0.084∗∗ (0.035) 0.008∗∗∗ (0.002) 0.440∗∗∗ (0.145) 0.018∗∗ (0.008) 0.180 (0.192) −0.915∗∗∗ (0.269) −0.202 (0.300) 0.005 (0.005)

−1.373∗∗ (0.623) −0.449 (0.513) 0.746∗∗ (0.366) 2.220∗∗ (0.969) −0.029 (0.642) −8.212 (5.647) 0.263 (0.787) 1.283∗ (0.702) −0.710 (0.490) −2.314∗ (1.281) 0.273 (0.845) −4.225∗ (2.282) 0.877∗ (0.458) −0.247∗∗∗ (0.089) 0.007∗ (0.004) 16.749∗∗∗ (4.973) −1.796∗∗ (0.712) −0.139 (0.110) −0.001 (0.006) 0.168 (0.316) −0.004 (0.017) 0.120 (0.406) −0.073 (0.560) −0.465 (0.779) 0.005 (0.011)

−1.351∗∗ (0.643) −0.276 (0.526) 0.587 (0.368) 2.127∗∗ (1.004) 0.076 (0.652) −9.748 (6.547) 0.128 (0.821) 1.713∗∗ (0.756) −0.677 (0.513) −2.805∗∗ (1.353) 0.332 (0.891) −4.783∗∗ (2.348) 0.958∗∗ (0.471) −0.296∗∗∗ (0.108) 0.008∗∗ (0.004) 20.126∗∗∗ (5.966) −1.871∗∗ (0.748) −0.560∗∗ (0.224) −0.001 (0.006) 0.226 (0.332) −0.010 (0.017) 0.226 (0.408) 0.061 (0.564) −0.574 (0.807) 0.006 (0.011)

−1.223∗ (0.639) −0.440 (0.533) 0.665∗ (0.369) 2.193∗∗ (1.040) −0.091 (0.678) −16.242∗∗ (7.515) −0.201 (0.838) 1.921∗∗ (0.765) −0.763 (0.523) −2.902∗∗ (1.374) 0.675 (0.923) −1.927 (2.814) 0.596 (0.522) −0.285∗∗∗ (0.106) 0.012∗∗∗ (0.004) 26.956∗∗∗ (7.098) −1.827∗∗ (0.819) −0.582∗∗ (0.231) −0.001 (0.006) 0.103 (0.339) −0.004 (0.018) 0.266 (0.410) 0.104 (0.562) −0.484 (0.804) 0.008 (0.012)

Distance from Floor Median

—

Seniority

—

Campaign Contributions

—

Majority Party × Party Responsiveness Majority Party × Distance from Floor Median Majority Party × Seniority Majority Party × Campaign Contributions South

—

—

—

—

Age

—

—

—

Female

—

—

—

African-American

—

—

—

Latino

—

—

—

Electoral Security

—

—

—

—

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table 5.1 (Continued) Retiree

—

—

—

Previous Plum Committee

—

—

—

−0.279 (0.299) —

−3.041∗∗∗ (0.643)

−3.069∗∗∗ (1.149)

−5.135∗∗∗ (1.500)

No

No

84.731∗∗∗ 2,661.980 −1,307.713 2,343

88.535∗∗∗ 1,989.349 −946.140 1,749

Constant

Congress FE? Wald Test BIC Log Likelihood Num. obs.

−8.198∗∗∗ (1.711)

−1.412 (1.071) 10.414∗∗∗ (0.990) −3.960 (3.303)

No

No

97.343∗∗∗ 2,015.360 −940.479 1,749

97.699∗∗∗ 2,001.416 −900.116 1,722

—

—

—

—

−3.303 (3.369)

−5.176 (3.560)

No

No

Yes

20.779∗∗ 668.128 −229.747 1,722

22.359∗∗ 589.419 −201.781 1,272

23.898∗∗ 607.683 −196.616 1,272

Notes: Standard errors in parentheses. Observations are at the Congress-member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Members of the party leadership omitted. The set of observations for Models 6 and 7 are those where the member did not have a plum committee assignment in the previous Congress; Retiree is omitted from these models due to perfect separation concerns. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

direction of public policy,” the negative relationship is consistent with both qualitative case studies and our modeling framework (Masters 1961, 352). Additionally, the negative relationship between Emotional Stability and committee assignments is consistent with Emotional Stability’s core cognitive constraint of sensitivity to negative outcomes; those who are most sensitive to the possibility of having their committee assignments removed are more easily (implicitly) controlled by their parties and are therefore more desirable for these committee spots. It is also notable that the effects seem to be greater for those in the majority party, since the potential risks are greater for those in the majority. However, the potential rewards are also greater for those in the majority party, and this is borne out with the slightly steeper slope for those in the majority party when the relationship between Extraversion and plum committee assignments is examined (though the confidence intervals overlap). As we mentioned before, the demand for plum committee spots should be higher for more Extraverted members, since they will be more sensitive to the possible rewards that come from spots on these influential committees. Therefore, we should expect higher rates of plum committee assignments among these members (in part due to the additional effort exerted by more Extraverted members to acquire the positions). Additionally, since the potential influence is greater for those in the majority, we should expect to find a stronger relationship for those in the majority, which is exactly what we do find.

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figure 5.1. Personality and Plum Committee Assignments

Finally, Agreeableness plays a strong role in determining whether one gets a plum committee assignment, and this is consistent regardless of whether one is in the minority or the majority. This suggests that those who are most effectively able to consider the wishes of their party apart from their own personal policy goals (that is, those who are seen as most Agreeable) are more likely to be on important and influential committees, as they will be less likely to vote against the wishes of the party. More generally, these results are consistent with the framing of the core cognitive constraint of Agreeableness as a capacity for altriusm and consideration of the well-being of others—so long as the “others” in question are the other members of one’s party, a dynamic we repeatedly find

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throughout the course of this book. However, it should also be noted that these results are consistent with qualitative case studies suggesting that party leaders want these committees to be staffed with those who are “cooperative, popular, reasonable, sober, [and] easy to work with,” which suggests Agreeableness is highly desired, though this also suggests a desire for committee comity in general, as opposed to simply partisan comity (Fenno 1973, 20–21); that said, these motivations are not necessarily mutually exclusive. Overall, our hypotheses regarding how personality relates to plum committee assignments are generally supported.

5.3 Becoming Chair We next look at the ways in which personality influences the committee chairmanship assignment process. As with the assignment of members to influential committees, seniority has long been the most important determination of chair selection in the United States Congress (e.g., Frisch and Kelly 2006; Hall and Shepsle 2014; Polsby, Gallaher, and Rundquist 1969). However, loyalty is also important, and contributions to the Hill committees are associated with higher likelihoods of receiving chairmanships, especially in recent years (e.g., Cann 2008, Deering and Wahlbeck 2006; Heberlig, Hetherington, and Larson 2006; Heberlig and Larson 2012). Using data from the 104th to 109th Congresses, Table 5.2 shows the results from a series of logistic regressions where the dependent variable is a binary variable that equals one if a member received a chair assignment during that Congress, and zero otherwise, controlling for ELUCIDATION scores and several control variables. Only majority party members are included in the analysis, and members of the party leadership are excluded. As with Table 5.1, we include the control variables for Seniority, Party Responsiveness, Distance from Floor Median, and Campaign Contributions that we described in the previous section to account for the influences of the seniority system and loyalty. We again include the variables for South, Age, Electoral Security, and Retiree. Additionally, due to constraints on the number of committee memberships those on exclusive committees may hold, as well as property rights over committees, we again include a control variable for Previous Plum Committee that equals one if a member had a plum committee assignment in the previous Congress and zero otherwise. We do not include controls for

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figure 5.2. Personality and Committee Chair Assignments

Female, African-American, or Latino due to perfect separation concerns. Finally, we include a variable indicating the number of members in the majority party at the start of the Congress (Majority Party Size), since a larger pool from which to draw chairs may, at the margins, affect the probability of any single member being named to a chairmanship. Importantly, we also present graphical results in Figure 5.2, showing how the predicted probability of receiving a chair assignment varies with personality.16 Unlike our committee assignment results, our committee chair results are much more mixed. As Table 5.2 indicates, our hypotheses regarding the roles of Agreeableness and Openness are not as expected; rather, we find no evidence of any relationship between these traits and the probability of being named a committee chair. However, our results for Conscientiousness and Emotional Stability are as expected (though the latter is more dependent on model specification), with more Conscientious members being more likely to be named chair and Emotionally Stable members less likely, as illustrated in Figure 5.2. The results are substantively significant as well, with less Conscientious members (those two standard deviations below the mean on this trait) having predicted

16. In Figure 5.2, and the discussion that follows, the coefficient estimates from Model 10 were used, and all continuous (categorical) variables were held at their means (modes) unless denoted otherwise. We also present 90% confidence intervals.

table 5.2 Logistic Regression Models of Personality and Chair Assignments Model 8

Model 9

Model 10

Model 11

Model 12

Model 13

Model 14

0.218 (0.272) 1.682∗∗∗ (0.296) −0.354∗∗ (0.177) −0.714 (0.491) 0.265 (0.323) —

−0.380 (0.655) 1.499∗∗ (0.723) −0.120 (0.429) −0.932 (1.180) 0.262 (0.815) 6.369 (5.178) 0.863 (0.574) 0.356∗∗∗ (0.091) 0.006∗∗ (0.003) −1.137∗ (0.590) −0.059∗ (0.033) 0.022 (0.019) −0.063 (0.054) −0.127 (1.169) −2.012∗∗∗ (0.760) —

−0.362 (0.674) 1.431∗∗ (0.727) −0.020 (0.433) −0.692 (1.201) 0.074 (0.823) 10.012∗ (5.782) 0.941 (0.616) 0.392∗∗∗ (0.095) 0.006∗∗ (0.003) −1.292∗∗ (0.624) −0.065∗∗ (0.033) 0.035∗ (0.021) −0.368∗ (0.209) 0.037 (1.208) −2.253∗∗∗ (0.819) —

3.137 (12.889)

−0.668 (0.539) 1.204∗∗ (0.568) 0.155 (0.325) 0.186 (0.945) −0.605 (0.642) 6.857∗ (3.753) 0.722 (0.546) 0.325∗∗∗ (0.077) 0.006∗∗ (0.002) −1.492∗∗∗ (0.540) −0.054∗∗ (0.027) 0.039∗∗ (0.017) −0.334∗ (0.178) −0.828 (0.815) −1.715∗∗∗ (0.587) 4.208∗∗∗ (0.494) 64.183 (39.743)

Distance from Floor Median

—

Seniority

—

Campaign Contributions

—

South

—

−0.522 (0.395) 1.157∗∗∗ (0.402) 0.254 (0.241) 0.675 (0.658) −0.576 (0.423) 2.977 (2.452) 0.809∗∗ (0.381) 0.415∗∗∗ (0.042) 0.004∗∗∗ (0.002) —

Age

—

—

Electoral Security

—

—

Retiree

—

—

Majority Party Size

—

—

Previous Plum Committee

—

Previous Committee Chair

—

−1.906∗∗∗ (0.404) —

−0.461 (0.422) 1.184∗∗∗ (0.429) 0.226 (0.255) 0.967 (0.697) −0.878∗ (0.480) 5.474∗ (2.966) 0.893∗∗ (0.401) 0.498∗∗∗ (0.060) 0.003∗ (0.002) −1.298∗∗∗ (0.406) −0.053∗∗ (0.021) 0.031∗∗ (0.013) −0.001 (0.036) 0.267 (0.552) −1.920∗∗∗ (0.436) —

−6.606∗∗∗ (1.343)

−12.452∗∗∗ (3.221)

−14.160 (8.776)

−0.680 (0.529) 1.288∗∗ (0.561) 0.083 (0.317) 0.164 (0.920) −0.492 (0.622) 3.972 (3.371) 0.756 (0.516) 0.309∗∗∗ (0.076) 0.005∗∗ (0.002) −1.322∗∗ (0.517) −0.053∗∗ (0.027) 0.031∗ (0.016) −0.028 (0.046) −0.870 (0.791) −1.526∗∗∗ (0.560) 4.177∗∗∗ (0.485) −3.685 (11.031)

No

No

No

No

No

Yes

Yes

77.891∗∗∗ 707.437 −332.374 1,230

29.407∗∗∗ 449.737 −187.406 908

31.192∗∗∗ 451.897 −171.592 893

12.402∗∗ 351.627 −118.060 893

6.673 280.615 −86.682 815

12.009∗∗ 367.435 −115.772 893

7.204 293.250 −82.945 815

Openness Conscientiousness Extraversion Agreeableness Emotional Stability Party Responsiveness

Constant

Congress FE? Wald Test BIC Log Likelihood Num. obs.

69.183 (46.041)

Notes: Standard errors in parentheses. Observations are at the Congress-member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Members of the party leadership and minority party members omitted. The set of observations for Models 12 and 14 are those where the member did not have a chairmanship in the previous Congress. Two-tailed tests:∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

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probabilities of between 0.3% and 3.2% of receiving a committee chairmanship. More Conscientious members, on the other hand (that is, those two standard deviations above the mean), are much more likely to receive chairmanships, with predicted probabilities ranging between 9.5% and 40.9%. The results for Emotional Stability are more muted, however, with the least Emotionally Stable members having predicted probabilities of between 5.8% and 21.3% of receiving a committee chairmanship, and the most Emotionally Stable having only a 1.1% to 5.2% chance.17 Our failed hypothesis about the effects of Agreeableness might reflect countervailing pressures between the demands of seeking the benefit of multiple relevant social groups. While we consistently find relationships between Agreeableness and the likelihood of being a team player (in the context of being a good partisan) in the rest of this book, the much smaller and more intimate contexts of committees might provide additional social pressures on members to be loyal to those with whom they share committee assignments. Additionally, while we find no significant relationship between Openness and the likelihood of receiving a chair assignment, we consistently find negative point estimates, in line with our expectations of the party preferring less Open members, since they will be less likely to take unnecessary risks that put the party agenda at risk. More data—or a more fine-grained analysis of subcommittee appointments—are needed. Finally, once controls are included, we find no consistent (or significant) relationship between Extraversion and the likelihood of becoming a committee chair, which is in line with our a priori expectations of countervailing demand-side and supply-side effects.

5.4

Committee Assignments: Conclusion

In this chapter, we have illustrated the importance of legislator personality in the committee and committee chair assignment processes.

17. One might object that committee chairs have to be Conscientious, and therefore being chair leads members to speak in ways that cause them to appear more Conscientious, regardless of their true personalities. However, recall that a legislator’s estimated personality during a particular Congress is the average of his or her estimated personality during all other time periods. This correction addresses potential endogeneity between language and chair-related speech within a particular Congress. Therefore, we see no cause for concern.

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Our results support the previously uncovered empirical regularities that seniority and observed loyalty matter but also showcase the importance of innate psychological traits, with most of our hypotheses supported by empirical analysis. In particular, we show that Openness, Extraversion, Agreeableness, and Emotional Stability are important predictors of who receives influential committee assignments, and Conscientiousness is an important predictor of who gets named to committee chairmanships. Moreover, the ways in which these traits do so are consistent with our underlying modeling framework. The increased risk acceptance of Open individuals leads them to be appointed to prestigious committees at lower rates. The increased weight they place on the preferences of their parties and other colleagues causes Agreeable individuals to be looked to more often for these same positions. Less Emotionally Stable individuals are put on these same committees at higher rates, presumably because of their increased sensitivity to potential negative outcomes, which makes them easier to control ex post by their parties. Conversely, more Extraverted members are more likely to seek out influential committees (and therefore take the necessary steps to make themselves appear to be appealing candidates, presumably) because of their heightened sensitivity to potential rewards, coupled with the increased influence these committees bring. Finally, more Conscientious people are asked to be committee chairs more often, which we argue is in part due to their increased attention to long-term incentives; by scheduling committee hearings and reports strategically, and winnowing/gatekeeping appropriately, committee chairs influence the set of future scheduling choices available to the Speaker, so party leadership should prefer chairs who are more Conscientious and aware of the future implications of their actions. However, we should note that we have not yet examined the relationships between personality traits and actual behavior within Congress; instead, we have only discussed how personality traits affect pre-electoral decisions, as well as how Congress organizes itself after each election. In the next several chapters, we will examine variation in legislative tactics in an attempt to further contextualize our personality modeling framework as it applies to Congress.

chapter six

Proposing and Passing Legislation Each Member goes about the job differently, but I always thought legislators should be pushing and passing solutions, and introducing legislation is one of the best tools we are given. —Representative Carolyn Maloney (D-NY)1

Sometimes even bad policy has a way of making it to the top. You just rub and rub and rub until people are sore. It’s a wearing-down factor. —Former Senator Larry Craig (R-ID)2

I

n 1995, Senators John McCain (R-AZ) and Russ Feingold (D-WI) published an op-ed in Roll Call—the Washington, DC–based newspaper focused on legislative affairs—in which they called for campaign finance reform and, in particular, limits on soft money.3 That September, they introducted S. 1219, the Senate Campaign Finance Reform Act of 1995. While the bill did not become law that year, it was revised over the next half decade in order to secure the requisite number of votes, and passed Congress in March of 2002 (in a different form) as the Bipartisan Campaign Finance Reform Act.4 Congressional observers

1. Paul Singer, “Members Offered Many Bills but Passed Few,” Roll Call, December 1, 2008. 2. Karen Foerstel, “Campaign Finance Passage Ends a Political Odyssey,” CQ Weekly, March 23, 2002. 3. Seth Gitell, “Making Sense of McCain-Feingold and Campaign-Finance Reform,” The Atlantic, July–August 2003, http://www.theatlantic.com/magazine/archive/2003/07/making -sense-of-mccain-feingold-and-campaign-finance-reform/302758/. 4. William M Welch and Jim Drinkard, “Passage Ends Long Struggle for McCain, Feingold,” USA Today, March 20, 2002, http://usatoday30.usatoday.com/news/washdc/2002 /03/21/usat-mccain.htm.

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and members of Congress alike chalked up the bill’s eventual success to the dogged tenacity of its two cosponsors, with supporters and detractors alike acknowledging that their persistence was instrumental to its success. The decisions of Senators McCain and Feingold to expend significant energy and legislative capital over the better part of a decade can likely be explained, at least in part, by their underlying personality traits. In this spirit, this chapter looks at how personality explains the behavior of members of Congress during the policymaking process. In particular, we examine how many bills members propose, the types of bills they propose, and how successful they are at passing legislation. All five personality traits explain substantial variation in tactics and effectiveness at various stages in the process, and the ways in which they do so are generally in line with what we would expect given our core cognitive constraint framework.

6.1 Personality, Proposals, and Passage An essential part of a legislator’s job is proposing and passing legislation. This is inherently a multistage process and requires members of Congress to answer several questions—whether to propose bills, what kinds of bills to propose, and how much effort to expend shepherding bills through the legislative process. We begin with the decision to propose a bill. While ideology and whether or not a member is part of the majority will likely have an effect on how many bills a legislator will put on the docket, we know precious little else as to what intrinsic factors may motivate a legislator to propose bills. As we outlined in Chapter 2, our modeling framework posits that Extraversion reflects the sensitivity of individuals to potential rewards from choices, and Emotional Stability reflects their sensitivities to potential negative outcomes. Given that proposing more bills inherently provides more opportunities to reap legislative rewards (via credit-claiming in the Mayhewian [1974] tradition, the passage of policy that the member agrees with, or a number of other factors) as well as more opportunities to suffer negative legislative outcomes (since, for example, going on the record more often may provide more ammunition for opposing candidates and outside groups in the future), more Extraverted

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and more Emotionally Stable members of Congress should propose more bills. Openness should also matter at this stage, since proposing more bills inherently opens one up to more risk (as mentioned above), and our modeling framework posits that Openness reflects the underlying risk preferences of individuals. More Conscientious members should propose more bills on average, as higher rates of bill proposals allows members to cultivate reputations of being policy entrepreneurs, which inherently provide long-term (as opposed to short-term) gains; moreover, as Wawro (2000) notes, “the tasks and responsibilities of the primary sponsor typically involve entrepreneurial activities such as gathering and communicating information, coalition building, and shepherding legislation” (27). Finally, more Agreeable members should propose fewer bills, as there exists a limited amount of time per Congress, and the marginal effect of proposing additional bills means that bills proposed by other members will necessarily be “crowded out” of the agenda during the pre-floor “winnowing” process (Krutz 2005). Since Agreeable members derive more of their utility from the welfare of the nation, this crowdingout effect should serve as a disincentive to propose additional bills that might detract from the interests of other citizens, regardless of whether the relevant comparison group is the nation, fellow partisans—which is consistent with the appearance of a team player dynamic that we have uncovered in other chapters in this book—or the chamber as a whole. It should also be noted that since being in the minority hampers one’s ability to have one’s bills passed—conditional on them being proposed—those in the minority should propose fewer bills. However, since the insitutional contexts of being in the majority versus the minority also change the relative rewards and negative outcomes of activity as well as the risks associated with choices and the amount of delay required to achieve outcomes, we should expect to see some interactive effects between majority party status and the aforementioned personality traits. In terms of the factors influencing whether bills are ceremonial/ symbolic or substantive in nature, we believe Conscientiousness should play an important role. Lacking substantive importance (at least relatively speaking), ceremonial/symbolic bills should be easier to pass, as they will be less controversial; therefore, they should also take less time to pass. As Conscientiousness reflects an underlying time preference (or a discount factor) in our framework, more Conscientious members should place greater emphasis on long-term benefits and should therefore be

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more willing to expend the effort and legislative capital needed to ensure that more substantive bills pass, due to the higher potential for longterm policy significance. Therefore, we should expect more Conscientious members to propose fewer ceremonial/symbolic bills as a proportion of total bills proposed. These effects should, however, be conditional on the abilities of members of Congress to succeed in getting their bills enacted into law; those in the majority should find it easier to have their preferred policies passed by the chamber, all else equal. Thus, we should expect the effects of personality to interact with majority party status. Members of the minority party will have to work harder and expend more effort shaping policy-relevant bills to the liking of the majority party; in these cases, unless they are extremely forward-looking and value the future highly—that is, unless they are highly Conscientious—they should be more likely to propose proportionally more ceremonial/symbolic bills. More Conscientious members of the minority party, however, should be less deterred by the partisan obstacles in their paths—as they place more weight on future payoffs—and should therefore be relatively similar to highly Conscientious members of the majority in proposing fewer ceremonial/symbolic bills. However, we also believe Openness should play a role in the determination of what kind of bills to propose, though we are more circumspect about the relationship between Openness and majority party status. As substantive bills, by definition, have the potential for policy significance, they also entail greater risk, as policy implementation will typically be delegated to agencies, thereby inducing uncertainty about what the implemented policy will look like (e.g., Epstein and O’Halloran 1999). Since we model Openness as an underlying risk preference, we expect to see a relationship between Openness and the rate at which members propose ceremonial/symbolic bills. However, we have some a priori uncertainty about the possibility of interactive effects between majority party status and Openness (aside from the aforementioned expectation of different intercepts), largely due to the possibility of countervailing effects. Whereas more Open members of the minority party may propose more substantive bills due to the higher level of risk acceptance, they may also propose more ceremonial/symbolic bills due to the expectation that they will be stymied due to partisan concerns, thereby making substantive bills seem like the safe choice (since the majority party will hamper their ability to be enacted into law) and ceremonial/symbolic bills seem like the

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riskier choice (since they actually have the potential of being enacted into law). However, we expect a more straightforward relationship between Openness and majority party status for members of the majority party— since substantive bills will be more likely to be enacted in these cases, they will be perceived as the riskier choice, and we therefore expect more Open members of the majority party to propose ceremonial/symbolic bills at lower rates. Finally, we will look at how effective individual members are at shepherding their proposals through the legislative process, as measured by Volden and Wiseman’s (2014) legislative effectiveness scores (LES), which measure House member effectiveness by examining the importance of bills sponsored by each member as well as how far each bill makes it through the legislative process. These factors are then weighed and aggregated to create the LES index.5 Higher values indicate greater effectiveness and lower values the opposite. We have argued that more Conscientious members of Congress should be more likely to engage in behavior wherein the rewards may not materialize in the immediate term, and another example of this is the legislative process itself. As it generally takes a significant amount of time for bills to progress through the legislative process—if they do at all—members of Congress who work hard to ensure passage of their bills should be, to some extent, motivated by future payoffs, especially since policy implementation is rarely immediate. Moreover, political action committees give more money to those who have been effective legislators in the past, providing additional future payoffs to more effective legislators (BoxSteffensmeier and Grant 1999). Therefore, members of Congress more concerned with future payoffs should be more effective, at least according to the LES index measure. Conversely, those who place less emphasis on future payoffs—that is, those who are less Conscientious—should be less inclined to spend time shepherding their bills through the process and should be less effective. However, we also anticipate strong interactive effects between Conscientiousness and majority party status, with

5. For each Congress, the LES index is normalized to have a mean of one and minimum of zero. The standard deviation in each Congress is approximately one, though the index is heavily right-skewed. For a more thorough explantion of how the LES index is generated, see Volden and Wiseman (2014).

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a stronger effect for members of the majority party, simply because of the heightened abilities of the majority party to be effective, at least as measured by the LES index.

6.2 Putting Bills on the Agenda For nearly every law that becomes so through the normal course of action, the process begins with a member of Congress proposing a bill, and members choose to propose bills when they do in large part due to reelection and policy concerns; as such, many instances of bill proposals are simply instances of position-taking (e.g., Fenno 1973; Mayhew 1974; Rocca and Gordon 2010). Structural factors also matter as well, and Schiller (1995) shows that several structural factors influence the rate at which members of Congress propose bills, including ideology, seniority, whether or not they are members of powerful committees, and whether they are committee or subcommittee chairs; other research has also shown the importance of being in a party leadership position as well as the majority party (Garand and Burke 2006). In our empirical analysis of the explanators of variance in bill proposal rates in the House, we include the aforementioned factors. Ideology is parameterized as the member’s first-dimension DW-NOMINATE score (Pool and Rosenthal 1997), and Extremity is the squared first-dimension DW-NOMINATE score. Majority Party is an indicator variable equaling one if the member and Speaker are of the same party, and zero otherwise. Seniority denotes the number of terms a member has served. Electoral Security denotes the percentage of the vote (on a 0–100 scale) the member received in the election to the current Congress. Committee Chair and Subcommittee Chair are indicator variables equaling one if the member served in the relevant committee leadership role in the Congress under analysis, and zero otherwise. Power Committee is an indicator variable that captures whether the member in question sat on at least one of the three most powerful committes in the House—Appropriations, Rules, and Ways and Means; if the member sat on at least one of these committees, the Power Committee variable equals one, and it equals zero otherwise. Finally, we account for the importance of party leadership by including indicator variables for Speaker, Majority Leadership, and Minority Leadership, which are indicator variables equaling one if the

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member served in the relevant role in the Congress under analysis and zero otherwise. To these previously investigated explanators of variance in proposal rates, we add our ELUCIDATION scores. We account for previously established correlations between personality and personal demographics by including variables for Age and whether or not the member is Female; the former denotes the member’s age at the end of the first session of each Congress, and the latter is an indicator variable equaling one if the member identifies as female and zero otherwise. Additionally, we include interactions between the ELUCIDATION scores and majority party status, though, as mentioned, we are agnostic about how personality and majority party status interact (if they do at all). Table 6.1 shows the results from a series of negative binomial regressions of the number of bills introduced by each member during the 104th to 112th Congresses, controlling for ELUCIDATION scores and several control variables. Models with and without fixed effects for Congress are estimated. Additionally, we present in Figure 6.1 the predicted values of the number of bills proposed in each Congress, varying majority party status and the ELUCIDATION scores.6 The results presented in Table 6.1 and Figure 6.1 indicate that personality traits affect bill sponsorship rates in statistically and substantively significant ways. Moreover, the effects are generally in the directions consistent with our core cognitive constraint framework. We predicted higher rates of bill sponsorship for more Open members, due to the increased risk inherent in proposing more bills and the parameterization of Openness as a form of risk preference; this is supported by the higher rates of bill sponsorship for more Open members (regardless of majority party status) seen in Figure 6.1. We also expected higher rates of bill sponsorship for more Conscientious members, since we model Conscientiousness as a time preference (or discount factor) and higher rates of bill proposals should allow members to cultivate reputations for being policy entrepreneurs, which will provide long-term gains; the results for members of the minority party are consistent with these predictions, though a

6. In Figure 6.1, and the discussion that follows, the coefficient estimates from Model 4 were used, and all continuous (categorical) variables were held at their means (modes) unless denoted otherwise. We also present 90% confidence intervals.

table 6.1 Negative Binomial Models of Personality and Bill Proposals Model 1

Model 2

Model 3

Model 4

Model 5

0.213∗∗∗ (0.033) 0.177∗∗∗ (0.029) 0.058∗∗∗ (0.021) −0.475∗∗∗ (0.056) 0.182∗∗∗ (0.036) — —

0.192∗∗∗ (0.033) 0.151∗∗∗ (0.029) 0.051∗∗ (0.021) −0.424∗∗∗ (0.056) 0.166∗∗∗ (0.036) 0.324∗∗∗ (0.027) —

0.117∗∗∗ (0.033) 0.098∗∗∗ (0.029) 0.090∗∗∗ (0.021) −0.346∗∗∗ (0.055) 0.119∗∗∗ (0.036) 0.273∗∗∗ (0.032) —

—

—

—

—

—

—

—

—

—

0.181∗∗∗ (0.052) 0.144∗∗∗ (0.042) 0.158∗∗∗ (0.032) −0.503∗∗∗ (0.085) 0.186∗∗∗ (0.054) 0.687∗∗ (0.309) −0.079 (0.066) −0.016 (0.057) −0.137∗∗∗ (0.042) 0.177 (0.109) −0.071 (0.070) −0.197∗∗∗ (0.032) 0.260∗∗∗ (0.072) 0.000 (0.001) 0.169∗∗∗ (0.034) 0.020∗∗∗ (0.004) −0.001 (0.001) 0.334∗∗∗ (0.058) 0.088∗∗∗ (0.034) −0.081∗∗∗ (0.027) −0.903∗∗∗ (0.279) −0.235∗∗∗ (0.079) −0.306∗∗∗ (0.080) 1.423∗∗∗ (0.258)

Majority Party × Openness Majority Party × Conscientiousness Majority Party × Agreeableness Majority Party × Extraversion Majority Party × Emotional Stability Ideology

—

—

—

—

Extremism

—

Age

—

−0.257∗∗∗ (0.031) 0.410∗∗∗ (0.071) —

Female

—

—

Seniority

—

—

Electoral Security

—

—

Committee Chair

—

—

Subcommittee Chair

—

—

Power Committee

—

—

Speaker

—

—

Majority Leadership

—

—

Minority Leadership

—

—

2.111∗∗∗ (0.160)

1.901∗∗∗ (0.158)

−0.209∗∗∗ (0.032) 0.395∗∗∗ (0.071) 0.002 (0.001) 0.176∗∗∗ (0.035) 0.024∗∗∗ (0.004) −0.001 (0.001) 0.293∗∗∗ (0.059) 0.072∗∗ (0.034) −0.091∗∗∗ (0.027) −1.025∗∗∗ (0.282) −0.228∗∗∗ (0.081) −0.320∗∗∗ (0.081) 1.920∗∗∗ (0.179)

0.129∗∗ (0.053) 0.142∗∗∗ (0.042) 0.154∗∗∗ (0.032) −0.473∗∗∗ (0.086) 0.183∗∗∗ (0.054) 0.642∗∗ (0.313) −0.032 (0.067) −0.086 (0.057) −0.106∗∗ (0.042) 0.217∗∗ (0.111) −0.112 (0.070) −0.206∗∗∗ (0.032) 0.385∗∗∗ (0.071) 0.002∗ (0.001) 0.171∗∗∗ (0.035) 0.023∗∗∗ (0.004) −0.001 (0.001) 0.302∗∗∗ (0.060) 0.071∗∗ (0.035) −0.091∗∗∗ (0.027) −1.002∗∗∗ (0.281) −0.220∗∗∗ (0.080) −0.325∗∗∗ (0.081) 1.743∗∗∗ (0.259)

No

No

No

No

Yes

184.800∗∗∗ 26,489.226 −13,215.786 3,775

146.121∗∗∗ 26,319.012 −13,118.330 3,771

114.992∗∗∗ 25,572.275 −12,704.031 3,680

139.005∗∗∗ 25,589.632 −12,692.183 3,680

165.934∗∗∗ 25,516.218 −12,622.633 3,680

Openness Conscientiousness Extraversion Agreeableness Emotional Stability Majority Party

Constant

Congress FE? Wald Test BIC Log Likelihood Num. obs.

Notes: Standard errors in parentheses. Observations are at the Congress-member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

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figure 6.1. Personality and Bill Proposals

net effect of zero for members of the majority party cannot be ruled out at the 90% level. Additionally, since we view Extraversion as a sensitivity to potential rewards from choices, and Emotional Stability (or rather, its converse) as a sensitivity to potential negative outcomes, we expected both to be positively associated with higher rates of bill sponsorship, since proposing more bills inherently provides more opportunities to reap legislative rewards as well as more opportunities to suffer negative legislative outcomes. This is once again borne out by the data, regardless of majority party status. Finally, since Agreeable members derive more of their

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utility from that of others, we expected more Agreeable members to propose fewer bills on average, to avoid crowding out of the agenda bills proposed by other members to advance the interests of constituents around the nation as a whole. As shown in Figure 6.1, that is exactly the case, regardless of party status. Collectively, these results suggest that personality traits are important predictors of members’ floor activity. Moreover, the ways in which the traits affect floor activity are largely consistent with our framework of considering personality in terms of core cognitive constraints. Intriguingly, the effects of personality on floor activity are stronger for members of the minority party.

6.3 Workhorses and Show Horses Of course, analysis of the number of bills members proposed, while interesting in and of itself, only tells part of the story. Indeed, such an analysis by its very nature must omit nearly any discussion of the content of the bills proposed. Are these purely symbolic bills intended, for example, to rename local post offices after local figures, or are these more substantive bills geared toward effecting major policy changes? Different types of bills should reflect different underlying goals, and their successful shepherding through the legislative process should require varying degrees of effort. However, different types of bills also face different hurdles, and more substantive bills are at greater risk of being prevented from reaching the floor due to partisan factors (e.g., Aldrich 1995; Cox and McCubbins 2005; Rohde 1991). To estimate the relative importance of these variables, we analyze all bills proposed in the House during the 104th through 112th Congresses. Each bill was categorized using Volden and Wiseman’s (2014) coding criteria as being ceremonial/symbolic in nature or of substantive importance.7 Table 6.2 presents the results of a series of binomial logistic regressions; in each model, the proportion of proposed bills that are

7. Volden and Wiseman (2014) classify bills as ceremonial/symbolic if any of the following phrases occur in the title: “commemoration, commemorate, for the private relief of, for the relief of, medal, mint coins, posthumous, public holiday, to designate, to encourage, to express the sense of Congress, to provide for correction of, to name, to redesignate, to remove any doubt, to rename, and retention of the name” (21).

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ceremonial/symbolic in nature is the dependent variable.8 All models include the same covariates as those in Table 6.1. We also present 90% confidence intervals. The results in Table 6.2 support our hypothesis regarding Conscientiousness and fail to support our hypothesis regarding Openness. The point estimate on Conscientiousness is negative and statistically significant at all conventional levels in all models, and the interaction between Conscientiousness and Majority Party is significant in the two models where it exists (and the marginal effect of a one-unit increase in Conscientiousness is negative regardless of whether majority or minority members are examined). Openness, on the other hand, does not behave as expected. Contrary to expectations, the main effect is positive (where it is significant), and the interaction between Openness and Majority Party is never significant. To further investigate these relationships, we plot them graphically in Figure 6.2.9 As Figure 6.2 shows, more Conscientious members of the House typically propose proportionally fewer ceremonial/symbolic bills, instead dedicating their energies to more substantive arenas with arguably longerterm payoffs. The effects of Openness, however, are much more muddled. While the predicted proposal rates increase in Openness for all members, we cannot rule out the possibility of no change in the proposal rate for members in the minority (or even a slightly negative change) due to the width of the confidence intervals. Future work should more closely examine the relationship between the content of legislation (and legislative agenda-setting) and personality in order to pin down the exact relationship. Nonetheless, these results provide further evidence of the importance of personality traits, as well as for the modeling of Conscientiousness as a form of time preference or discount factor.

8. We drop all observations where zero bills were proposed. Because of concerns that this would substantively affect the results, we also estimated a series of negative binomial regressions where the dependent variable is the number of ceremonial/symbolic bills each member proposed during each Congress, with a logged offset of one plus the total number of bills proposed per member-Congress dyad. These models allow us to include more observations, and the results are substantively identical to those presented here. However, we present the logistic results—despite the slightly lower N—because the interpretation as a proportion is more intuitive. 9. In this figure, all continuous (categorical) variables were held at their means (modes). Estimates from Model 9 are used to generate the plots. We also present 90% confidence intervals.

table 6.2 Binomial Regression Models of Personality and Ceremonial Bill Proposals

Openness Conscientiousness Extraversion Agreeableness Emotional Stability Majority Party Majority Party × Openness Majority Party × Conscientiousness Majority Party × Extraversion Majority Party × Agreeableness Majority Party × Emotional Stability Ideology

Model 6

Model 7

Model 8

Model 9

Model 10

0.206∗∗∗ (0.063) −0.522∗∗∗ (0.052) 0.104∗∗∗ (0.039) 0.238∗∗ (0.100) 0.109∗ (0.065) — —

0.126∗ (0.065) −0.434∗∗∗ (0.055) 0.030 (0.041) 0.184∗ (0.105) 0.188∗∗∗ (0.068) −0.235∗∗∗ (0.050) —

0.179∗∗∗ (0.068) −0.389∗∗∗ (0.057) 0.015 (0.041) 0.131 (0.108) 0.195∗∗∗ (0.071) −0.180∗∗∗ (0.063) —

—

—

—

—

—

—

—

—

—

—

—

—

—

−0.269∗∗∗

0.198∗ (0.103) −0.527∗∗∗ (0.078) 0.189∗∗∗ (0.060) 0.204 (0.156) 0.244∗∗ (0.102) 1.346∗∗ (0.594) −0.074 (0.135) 0.271∗∗ (0.110) −0.326∗∗∗ (0.082) −0.183 (0.211) −0.088 (0.137) −0.270∗∗∗ (0.060) −0.121 (0.137) 0.007∗∗ (0.003) −0.233∗∗∗ (0.066) −0.028∗∗∗ (0.008) 0.008∗∗∗ (0.002) −0.306∗∗ (0.122) −0.157∗∗ (0.071) −0.076 (0.056) 0.622 (0.616) 0.125 (0.164) 0.304∗∗ (0.152) −4.833∗∗∗ (0.453)

0.170 (0.104) −0.575∗∗∗ (0.078) 0.219∗∗∗ (0.060) 0.284∗ (0.154) 0.210∗∗ (0.103) 1.398∗∗ (0.596) −0.073 (0.137) 0.293∗∗∗ (0.111) −0.348∗∗∗ (0.083) −0.227 (0.211) −0.051 (0.140) −0.282∗∗∗ (0.061) −0.000 (0.138) 0.010∗∗∗ (0.003) −0.246∗∗∗ (0.067) −0.026∗∗∗ (0.008) 0.005∗∗∗ (0.002) −0.317∗∗ (0.123) −0.160∗∗ (0.072) −0.089 (0.056) 0.526 (0.618) 0.156 (0.165) 0.332∗∗ (0.152) −5.082∗∗∗ (0.462)

Extremism

—

Age

—

(0.054) 0.021 (0.129) —

Female

—

—

Seniority

—

—

Electoral Security

—

—

Committee Chair

—

—

Subcommittee Chair

—

—

Power Committee

—

—

Speaker

—

—

Majority Leadership

—

—

Minority Leadership

—

—

−3.666∗∗∗ (0.282)

−3.338∗∗∗ (0.295)

−0.243∗∗∗ (0.060) −0.135 (0.136) 0.007∗∗ (0.003) −0.221∗∗∗ (0.066) −0.028∗∗∗ (0.008) 0.008∗∗∗ (0.002) −0.289∗∗ (0.120) −0.147∗∗ (0.071) −0.070 (0.056) 0.500 (0.615) 0.108 (0.164) 0.322∗∗ (0.150) −4.181∗∗∗ (0.348)

No

No

No

No

Yes

158.819∗∗∗ 7,953.042 −3,951.812 3,736

132.283∗∗∗ 7,888.172 −3,907.028 3,732

104.899∗∗∗ 7,784.173 −3,814.085 3,648

121.810∗∗∗ 7,799.813 −3,801.378 3,648

143.216∗∗∗ 7,770.897 −3,754.078 3,648

Constant

Congress FE? Wald Test BIC Log Likelihood Num. obs.

Notes: Standard errors in parentheses. Observations are at the Congress-member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

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figure 6.2. Personality and Ceremonial Bill Proposals

6.4

Predicting Legislative Success

Finally, we look at how effective individual members of Congress are at shepherding their policy proposals through the legislative process. Previous studies have found that majority party status, seniority, having a powerful leadership position, and electoral security are all associated with greater “effectiveness” in Congress (Volden and Wiseman 2014). Evidence on the role of gender is more mixed, though suggests female legislators tend to be more effective than males (e.g., Bratton and Haynie 1999; Jeydel and Taylor 2003; Volden, Wiseman, and Wittmer 2013). However, since the decision to shepherd bills through the legislative process—as well as the decision to expend time and energy crafting bills in such a way that they are passable in some form—requires expense of present effort for the possibility of future rewards, we anticipate that personality traits (in particular Conscientiousness) should also play a role. To test this possibility, we rely on Volden and Wiseman’s (2014) LES from the 104th through 112th Houses. Table 6.3 presents the results of a series of Tobit regressions. As noted, in these scores measure how effective each individual member of the House is by examining both the importance of bills sponsored by individual members of Congress as well as how far each bill makes it through the legislative process. These factors are

table 6.3 Tobit Models of Legislative Effectiveness

Openness

Model 11

Model 12

Model 13

Model 14

Model 15

0.329∗∗∗ (0.071) 0.533∗∗∗ (0.061) −0.241∗∗∗ (0.045) −0.638∗∗∗ (0.119) 0.526∗∗∗ (0.075) — —

0.438∗∗∗ (0.068) 0.369∗∗∗ (0.059) −0.153∗∗∗ (0.043) −0.525∗∗∗ (0.112) 0.420∗∗∗ (0.072) 1.105∗∗∗ (0.055) —

0.210∗∗∗ (0.057) 0.103∗∗ (0.050) −0.035 (0.036) −0.327∗∗∗ (0.094) 0.247∗∗∗ (0.061) 0.577∗∗∗ (0.055) —

—

—

—

—

—

—

—

—

—

0.086 (0.090) −0.044 (0.071) 0.048 (0.055) −0.127 (0.145) 0.015 (0.092) −0.471 (0.539) 0.174 (0.116) 0.266∗∗∗ (0.099) −0.136∗ (0.073) −0.353∗ (0.189) 0.402∗∗∗ (0.121) 0.114∗∗ (0.056) −0.310∗∗ (0.127) 0.001 (0.003) 0.056 (0.060) 0.061∗∗∗ (0.007) −0.002 (0.002) 3.430∗∗∗ (0.105) 0.527∗∗∗ (0.060) −0.157∗∗∗ (0.047) −0.540 (0.437) 0.309∗∗ (0.138) −0.105 (0.135) 0.301 (0.447) 0.164∗∗∗ (0.012)

Majority Party × Openness Majority Party × Conscientiousness Majority Party × Extraversion Majority Party × Agreeableness Majority Party × Emotional Stability Ideology

—

—

—

—

Extremism

—

Age

—

−0.008 (0.063) −0.429∗∗∗ (0.145) —

Female

—

—

Seniority

—

—

Electoral Security

—

—

Committee Chair

—

—

Subcommittee Chair

—

—

Power Committee

—

—

Speaker

—

—

Majority Leadership

—

—

Minority Leadership

—

—

−0.572∗ (0.339) 0.433∗∗∗ (0.012)

−1.285∗∗∗ (0.321) 0.368∗∗∗ (0.012)

0.135∗∗ (0.056) −0.396∗∗∗ (0.124) 0.001 (0.003) 0.062 (0.060) 0.057∗∗∗ (0.007) −0.001 (0.002) 3.507∗∗∗ (0.104) 0.558∗∗∗ (0.060) −0.158∗∗∗ (0.047) −0.570 (0.437) 0.296∗∗ (0.138) −0.046 (0.135) −0.346 (0.309) 0.168∗∗∗ (0.012)

0.092 (0.090) −0.042 (0.071) 0.048 (0.055) −0.130 (0.145) 0.013 (0.092) −0.472 (0.539) 0.168 (0.115) 0.279∗∗∗ (0.097) −0.144∗∗ (0.072) −0.362∗ (0.188) 0.413∗∗∗ (0.120) 0.115∗∗ (0.056) −0.347∗∗∗ (0.124) 0.000 (0.003) 0.054 (0.060) 0.060∗∗∗ (0.007) −0.002 (0.002) 3.433∗∗∗ (0.105) 0.529∗∗∗ (0.060) −0.154∗∗∗ (0.047) −0.516 (0.436) 0.306∗∗ (0.138) −0.108 (0.135) 0.320 (0.443) 0.165∗∗∗ (0.012)

No

No

No

No

Yes

182.023∗∗∗ 13,966.453 −6,954.400 3,775 (39)

160.277∗∗∗ 13,481.472 −6,699.560 3,771 (39)

33.722∗∗∗ 11,797.685 −5,816.736 3,680 (32)

57.451∗∗∗ 11,815.305 −5,805.019 3,680 (32)

53.636∗∗∗ 11,877.353 −5,803.201 3,680 (32)

Conscientiousness Extraversion Agreeableness Emotional Stability Majority Party

Constant Ln(scale)

Congress FE? Wald Test BIC Log Likelihood Num. Obs. (Cens. Obs.)

Notes: Standard errors in parentheses. Observations are at the Congress-member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

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figure 6.3. Conscientiousness and Legislative Effectiveness

then weighted and aggregated to create the LES index.10 Higher values indicate greater effectiveness and lower values indicate the opposite. Table 6.3 presents the results of a series of Tobit regressions; in all of these, the LES index is the dependent variable.11 The same covariates used in Tables 6.1 and 6.2 are used here. In the models without interaction terms, the coefficient on Conscientiousness is positive and significant, suggesting more Conscientious members have higher levels of legislative effectiveness. However, once the ELUCIDATION scores are interacted with Majority Party, the effects of Conscientiousness become more nuanced, as the magnitude of the main effect is reduced (and loses significance), while the interaction between Conscientiousness and Majority Party is positive and significant. Because these models include interaction terms, we plot the relationship between Conscientiousness and legislative effectiveness, conditional on majority party status, in Figure 6.3.12 While the effect of Conscientiousness on legislative effectiveness is positive and substantively strong when members 10. For each Congress, the LES index is normalized to have a mean of one and minimum of zero. The standard deviation in each Congress is approximately one, though the index is heavily right-skewed. For a more thorough explanation of how the LES index is generated, see Volden and Wiseman (2014). 11. Tobit models are used, since the LES index is constrainted to be nonnegative. 12. In this figure, all continuous (categorical) variables were held at their means (modes).

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of Congress are in the majority party, it is effectively zero for minority party members. Collectively, these results suggest that when other factors are in place that make it easier to be an effective legislator, Conscientiousness can play a major role; otherwise, however, it does not seem as if personality factors can overcome institutional or ideological obstacles.

6.5 Proposing and Passing Legislation: Conclusion The results presented in this chapter illustrate the importance of legislator personality in the policymaking process. While structural, ideological, and institutional factors all matter a great detail with respect to the influence they wield over the policymaking process, we have shown that personality does as well. Though structural factors—like majority party status—affect the number of bills proposed during each Congress, all five personality traits matter as well. More Conscientious members are more likely to propose more substantive bills as well as be effective members of Congress more generally, though the effects of this personality trait will be mitigated by the same structural factors. Regardless, most of the expectations derived from our core cognitive constraint framework have been borne out, though it should be noted that we have only looked at a small subset of the types of actions legislators can take. Moreover, we have yet to examine more unconventional and unorthodox measures of effecting (or preventing) policy change within Congress as well as within the larger media spheres. We cover these topics in the next two chapters.

chapter seven

Cooperation, Obstruction, and Party Discipline: Shifting Norms in the US Congress [O]ne of the most sacred rules of the Senate—the filibuster. . . . It is a unique privilege that serves to aid small states from being trampled by the desires of larger states. Indeed, I view the use of the filibuster as a shield, rather than a sword. Invoked to protect rights, not to suppress them. —Senator Harry Reid (D-NV) in 19951 It’s time to change the Senate before this institution becomes obsolete. . . . The American people believe Congress is broken. The American people believe the Senate is broken. And I agree. —Senator Harry Reid (D-NV) in 20132

I

n November 2013, Senate Majority Leader Harry Reid (D-NV) decided that “enough was enough.” The Democratic majority in the Senate was constantly stifled by the minority Republicans’ use of the parliamentary tactic known as the filibuster. A filibuster traditionally consisted of one or more senators preventing a vote on a bill by talking for hours on end (Binder and Smith 1997; Koger 2010). In the twentieth century, various reforms were enacted to curtail the role of the filibuster. These reforms allowed the endless debate to be cut off with a supermajority vote (known as cloture); the most recent supermajority threshold—three-fifths, or sixty votes in the current Senate—was implemented in 1975.

1. Congressional Record, S.434, January 5, 1995. 2. “Senate Guts Filibuster Power,” The Hill, November 21, 2013, http://thehill.com /homenews/senate/191042-dems-reid-may-go-nuclear-thursday.

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Despite this reform, the amount of filibustering has skyrocketed in recent years (Binder and Smith 1997). Past filibustering had been relatively rare and involved members literally holding the floor hostage. Filibustering in the current era involves no such endless debate. Rather, it means that every time a majority wants to get something through, they need a three-fifths vote to actually move things along. Majority Leader Reid viewed this state of affairs as deleterious to the Senate and to President Obama’s agenda. Thus, he took the move to significantly curtail the filibuster. By going “nuclear,” as predicted in the URL of the Webpage cited in the note to this chapter’s second epigraph—replacing the cloture requirement of three-fifths with a simple majority on votes on executive and judicial nominees (except the Supreme Court)—Reid sought to relieve some of the gridlock in the Senate. This decision seems to fly in the face of Reid’s earlier stance as quoted in the first chapter epigraph. How could he have gone from a vigorous defender of the filibuster to one of its greatest opponents? One big difference between 2013 and 1995 is that in 2013 the Democrats were in the majority and in 1995 they were in the minority. Indeed, the filibuster is a tactic that allows minorities to restrain majorities from pushing through their agenda. As such, it makes perfect sense that minority Reid would support the filibuster but that majority Reid would view it as a hindrance. Thus, as Binder and Smith (1997) note, the key to explaining why some senators filibuster and others do not is not preference for preserving (or eliminating) institutional norms. Rather, the driving force behind who uses the filibuster, when, and why is a desire to enhance the party brand (Cox and McCubbins 2005; Cox and McCubbins 2007). Understanding the filibuster in terms of its relationship to the party brand allows us to move beyond a peculiarity of Senate to a broader set of questions surrounding legislative norm-following, loyalty, and rebellion. Specifically, what leads legislators to toe the party line in their voting behavior? Why do some legislators eschew partisan interests and work across the aisle? And of course, what leads some senators to hold the floor hostage using the filibuster? This chapter examines all three of these phenomena through the lens of our core cognitive constraint framework. If legislators with different personality traits value the party brand differently, we should expect their tactical choices in all three cases to be influenced by the Big Five.

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In all three cases, we argue that both Agreeableness and Openness are key factors for explaining the behavioral heterogeneity of legislators. Agreeableness, driven by its core cognitive constraint of altruism, leads members to surrender their own self-interest and to advance the interests of others in a reference group. While we initially expected to find this would drive members to work together for the national welfare, we instead find this manifests through placing more weight on the wishes of party leadership. Openness, a trait linked with decreased risk aversion, leads members to take bigger risks and, thus, to buck the party line, even in high-profile situations.

7.1

Rebellion, Obstruction, and Polarization

One of our principal motivations for writing this book is to gain an understanding of the linkages between legislator personality and polarization. To this point in our presentation, most of our attention has focused on exploring the linkages between personality and legislative behavior through the lens of our core cognitive constraint framework. Though the examples considered in the previous chapters undoubtedly speak to polarization, we have yet to explicitly address how polarization links directly with our framework. To be sure, campaign finance (Chapter 4), legislative committee assignments (Chapter 5), and bill sponsorship (Chapter 6) are all affected by both personality traits and polarization. For example, we found that ideological extremists are more likely to raise campaign war chests, more likely to receive plum committee assignments, and less likely to cosponsor across party lines. That said, the rise of near unanimous party loyalty and the fall in bipartisan cosponsorship rates in the House, and the rise of obstruction in the Senate, are for scholar and pundit alike three of the most critical consequences of ideological polarization. While no one doubts that polarization is behind these three deleterious trends in legislative behavior, little is known as to the microfoundations behind this devolution of traditional legislative norms. Specifically, while we know that depletion of ideological moderates has led to increased party loyalty, we have only a cursory understanding of why recent cadres of ideological extremists should behave differently in terms of procedure and norms. In this chapter, we explore these microfoundations by asking three critical questions. First, why are some House members increasingly loyal to their

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parties even after accounting for preferences? Second, why do some House members buck their parties and cosponsor legislation across party lines? Third, why do Senators in the minority obstruct more today than in the past, controlling for their preferences? To answer these questions, we apply our core cognitive constraint framework to show that legislators’ personality traits are largely to blame for the rise of party loyalty, the decrease in legislators working across the aisle, and the skyrocketing rates of senatorial obstruction.3 Crucially, we demonstrate these effects holding ideology constant. As a result, we are able to demonstrate that factors more fundamental than either partisanship or policy preferences are the culprits behind contemporary Washington gridlock.

7.2 Party Brands, Loyalty, and the Big Five Party loyalty has always been a complex and controversial topic in the study of American legislative institutions. Throughout the last two and a half decades of research, scholars have tended to fall into one of two camps, those who argue that preferences trump party (Crombez, Groseclose, and Krehbiel 2006; Krehbiel 1993; Krehbiel 1998) and those who argue that party effects can and do exist independently of preferences (Aldrich and Rohde 2001; Cox and McCubbins 2005; Cox and McCubbins 2007; Rohde 1991; Smith 2007). The Krehbiel camp argues that legislators evaluate the relative policy merits of legislation and vote in accordance with their policy preferences. Any evidence of party effects is usually considered an artifact of the contemporary conflation of party and preference; or the understanding that most Republicans are conservatives and most Democrats are liberals. The second camp argues that party effects do exist, but that when and under what conditions they emerge is nonuniform. More recently, a hybrid literature has emerged that tries to carefully measure when exactly party effects (if they exist) might emerge. Snyder and Groseclose (2000) and Minozzi and Volden (2013) argue that when votes are sufficiently lopsided and the stakes are low, parties should not

3. In Chapter 10, we show that the traits driving dysfunction correlate highly with measures of ideological polarization.

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care how their members vote. In contrast, close votes are those in which the parties—in particular, the majority party—are more sensitive to outcomes, and thus these are ones in which party effects may emerge. Both Snyder and Groseclose (2000) and Minozzi and Volden (2013) use the non-lopsided votes to measure legislator “party-free” preferences and find evidence of party effects on close votes, even after controlling for these preference estimates. Minozzi and Volden (2013) explain this finding in connection with a rich literature that argues that the central goal of parties is to present voters with a cohesive brand (Cox and McCubbins 2005; Cox and McCubbins 2007; Woon and Pope 2008; Pope and Woon 2008; Hager and Talbert 2000). Since close votes usually surround contentious issues that are central to the party’s platform, parties should expect loyalty from their ranks, as divisions might signal a weak brand to their core constituencies. Minozzi and Volden (2013) take this a step further than the usual literature by arguing that, since ideological extremists are those for whom the brand matters the most, loyalty on close votes should be increasing in ideological extremism. We have good reason to believe that the Big Five in general and our core cognitive constraint framework in particular can explain legislator heterogeneity in terms of loyalty. Since personality traits are persistent individual differences, we argue that these factors should play a key role in explaining why some legislators buck the party line and others remain loyal. As we have argued, Open legislators have reduced risk aversion and, as such, should be less deterred by the uncertainty of confrontation from bucking the party line. Conscientious legislators— those who have the capacity to realize future rewards—should be more loyal, as doing so ensures the potential rewards of future plum committee assignments/chairmanships (see Chapter 5). Assuming that there exists a possibility of potential rewards for party loyalty (Cox and McCubbins 2007), Extraverted legislators should be loyal, as they are more sensitive to these inducements. Agreeable legislators are more likely to heed the call of others, as they surrender their personal goals to helping a broader collective, which is driven by their capacity for altruism. However, if the larger collective is the nation as a whole, then it is unclear what effect Agreeableness has on party loyalty (if it affects it at all). On the other hand, if the larger collective is one’s party, then Agreeableness could manifest as the placement of additional weight on the wishes of the party as a whole, which would be consistent with the Minozzi and Volden

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(2013) theory of party brand. Finally, Neurotic legislators—being sensitive to negative outcomes—should be more likely to toe the party line, and hence their Emotionally Stable colleagues should be more prone to rebellion. In Table 7.1, we examine the role of the Big Five in predicting loyalty on close votes. Our measures of loyalty, drawn from the Minozzi and Volden (2013) method, are the percentages of the time the legislator voted with the party majority on close votes. Minozzi and Volden (2013) identify close, party-influenced votes using an iterative algorithm that proceeds as follows: 1. Partition roll call votes into party-free and party-influenced votes using the Snyder and Groseclose (2000) criteria. Specifically, any votes in which more than 65% of votes are either yea or nay are classified as party-free votes. 2. Run the Clinton, Jackman, and Rivers (2004) Bayesian ideal point estimator to generate party-free ideal points using only party-free votes. 3. Run a series of logistic regressions of each party-free vote on the party-free ideal point and a dummy for party. 4. Reclassify as party-influenced any votes in which the coefficient on party in the Step 3 regressions is statistically significant at the p = 0.01 level. 5. Repeat steps 2–4 until no roll calls need to be reclassified according to the criteria set forth in Step 4.

Since the literature argues that party effects might be asymmetric (e.g., Smith 2007), we separate our models by majority or minority party membership. Further, we control for all of the key covariates that the literature suggests are important for the study of loyalty. Baseline Loyalty is Minozzi and Volden’s (2013) measure of loyalty on lopsided votes. This captures the simple idea that some legislators will be loyal on all votes, regardless of whether the party places much emphasis on them. Distance from Median is the ideological distance of a legislator from the floor median. Minozzi and Volden (2013) argue that ideological extremists will be more loyal than moderates. This measure is designed to capture that idea, measuring ideology using non-close votes so as to avoid endogeneity concerns. Presidential Vote Share is the normalized presidential two-party vote and is widely used as a proxy for constituency preferences. South is a dummy variable for legislators from the eleven states of the Confederacy. Legislator Vote Share is the legislator’s share of the two-party vote in the last election. Female, Latino, and African-American are dummy variables

table 7.1 Predicting Loyalty—Party Votes, 104th–109th Congresses Majority Party

Minority Party

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Distance from Median

0.342∗∗∗ (0.028) −2.369∗∗∗ (0.440) 0.175 (0.423) −0.540∗ (0.283) 1.921∗∗ (0.753) −0.142 (0.492) — —

South

—

Legislator Vote Share

—

Female

—

African-American

—

Latino

—

Seniority

—

Freshman

—

Lame Duck

—

Best Committee

—

Leader

—

Power Committee

—

Speaker

—

—

0.436∗∗∗ (0.037) 0.618 (0.759) 2.007∗∗∗ (0.585) 0.133 (0.460) −3.202∗∗∗ (1.232) 1.197 (0.770) 1.394∗∗ (0.543) 0.080∗∗ (0.034) −2.873∗∗∗ (0.639) 0.076∗∗∗ (0.023) 1.880∗∗∗ (0.641) 1.881∗∗ (0.858) 1.746∗ (1.010) −0.015 (0.068) 1.213 (0.832) 0.410 (1.497) 0.161∗∗∗ (0.052) 4.136∗∗ (1.742) 0.211 (0.697) —

0.556∗∗∗ (0.027) −0.311 (0.510) 1.011∗∗ (0.396) −0.041 (0.310) −0.796 (0.831) 0.533 (0.519) 8.345∗∗∗ (0.437) 0.014 (0.025) −0.305 (0.436) −0.015 (0.016) −0.044 (0.434) 0.316 (0.583) 0.286 (0.680) −0.097∗∗ (0.046) 0.244 (0.564) −0.206 (1.011) 0.118∗∗∗ (0.035) 2.107∗ (1.176) −0.316 (0.469) —

Committee Chair

—

—

—

—

64.844∗∗∗ (3.260)

0.521∗∗∗ (0.023) −0.832∗∗ (0.327) 0.349 (0.312) −0.317 (0.205) 0.393 (0.551) −0.137 (0.364) 3.413∗∗∗ (0.384) 0.162∗∗∗ (0.021) −0.003 (0.275) −0.027∗∗ (0.012) −2.205∗∗∗ (0.447) 1.053 (2.159) 1.129 (0.810) −0.092∗∗ (0.046) 1.371∗∗∗ (0.408) 1.014∗ (0.613) 0.124∗∗∗ (0.035) 2.054∗∗ (0.952) 0.606∗ (0.350) 2.080 (2.016) 0.595 (0.488) 28.775∗∗∗ (2.816)

0.570∗∗∗ (0.037) 1.581∗∗ (0.785) 1.502∗∗ (0.604) 0.214 (0.472) −2.350∗ (1.268) 2.187∗∗∗ (0.785) —

Presidential Vote Share

0.373∗∗∗ (0.026) −1.040∗∗∗ (0.396) −0.463 (0.376) −0.269 (0.248) 1.479∗∗ (0.664) −0.674 (0.440) 6.042∗∗∗ (0.426) 0.188∗∗∗ (0.022) −0.284 (0.332) −0.042∗∗∗ (0.014) −1.720∗∗∗ (0.542) 0.336 (2.619) 2.076∗∗ (0.981) 0.046 (0.055) 0.389 (0.488) 1.716∗∗ (0.741) 0.100∗∗ (0.042) 1.750 (1.154) 1.047∗∗ (0.423) 1.019 (2.442) 0.190 (0.591) 48.324∗∗∗ (3.157)

29.071∗∗∗ (4.795)

34.337∗∗∗ (4.800)

12.728∗∗∗ (3.350)

No

No

Yes

No

No

Yes

36.285∗∗∗ 0.143 0.139 1,278

18.287∗∗∗ 0.406 0.396 1,252

9.936∗∗∗ 0.599 0.590 1,252

32.439∗∗∗ 0.184 0.180 1,162

24.087∗∗∗ 0.312 0.300 1,146

9.521∗ 0.691 0.684 1,146

Baseline Loyalty Openness Conscientiousness Extraversion Agreeableness Emotional Stability

Constant

Congress FE? Wald Test R2 Adj. R2 Num. Obs.

— — — — — — — — — — — —

Notes: Standard errors in parentheses. Observations are at the Congress-member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

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for gender, ethnicity, and race, respectively. Seniority is the legislator’s length of service in the House. Freshman and Lame Duck are dummy variables for whether a legislator is in his or her first or final term in office. Best Committee is Groseclose and Stewart’s (1998) GroseWart score for a legislator’s best committee assignment. Last, Leader, Power Committee, Speaker, and Committee Chair are dummy variables for whether the member is on one of the so-called power committees (see Chapter 5), a Speaker, or a committee chair, respectively. We see that the effects of the Big Five are generally in the expected direction. For simplicity, we focus on the most fully specified models (Models 3 and 6). On party-influenced votes, Open members are more likely to rebel. Conscientious majority members show no statistically significant difference from their less Conscientious colleagues with respect to loyalty on party votes. However, for the minority, the effect is both strong in magnitude and statistically significant. Since the minority’s future reward is one day to attain majority status, loyalty and strengthening the brand are all the more important. Indeed, minority members are focused on long-term effects of their loyalty; unified messaging is the only tool for helping the minority get back into the majority. In turn, this is a way to get access to plum committee/chair assignments (see Chapter 5). Additionally, Agreeableness always has a positive effect for the majority party (which is consistent with the party as opposed to the nation being the larger reference group), but its significance does vary according to specification—it is significant for Models 1, 2, 4, and 5 but not for Models 3 and 6. Emotional Stability has its predicted negative effect as well, but it too varies in significance according to specification. Importantly, the coefficients on the Big Five traits are also substantively significant. The loyalty rate on party-influenced votes is, on average, 89.87%, with an interquartile range of [87.42,94.93]. Recall that the coefficient on Openness is nearly 1%. Thus, increasing Openness by one point results in a decrease in loyalty equivalent to around 13% of the interquartile range. Similarly, increasing Conscientiousness by one point leads to an increase of loyalty of about 14% of the interquartile range. Thus, even after accounting for ideology, baseline party support, and the host of other variables identified by Minozzi and Volden (2013), the Big Five traits have huge substantive effects on party loyalty. Indeed, the Wald tests across all six specifications indicate that the Big Five always improve the fit of the various models.

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7.3

Bucking the Party: Working Across Party Lines

In Chapter 6, we explored how legislator personality influenced the number of and types of bills proposed by legislators. While bills are introduced and sponsored by individual members of Congress, and the decision to propose a bill (as well as what type of bill to propose) is ultimately the decision of the individual member, a bill’s path through the legislative process often requires the assistance of other legislators. Legislators can, of course, seek copartisans to cosponsor their bills. However, legislators can and often do choose to cosponsor across party lines. Doing so can improve public image or electoral performance, but it also may draw the ire of one’s party leadership. Given these tradeoffs, one can see this section of the current chapter as the flip side of the previous section. Whereas there we examined the reasons why legislators remain loyal on party votes, here we focus our attention on why some legislators eschew their parties and work across the aisle. Harbridge and Malhotra (2011) show that bipartisan cosponsorship rates are negatively correlated with district-level partisan strength, largely because representatives from safer districts (see Chapter 5) feel less partisan pressure to moderate and thus engage in bipartisan cosponsorship less often. Conversely, representatives from more centrist—or even hostile— districts engage in bipartisan cosponsorship at higher rates. However, while bipartisan cosponsorship has its electoral purposes and may serve to make one seem less out of step with district preferences (e.g., CanesWrone, Brady, and Cogan 2002; Hollibaugh, Rothenberg, and Rulison 2012; Jessee 2012), it may also reflect an underlying preference on the part of legislators to engage in behavior that benefits the national welfare by building broader consensus on policy. If so, such actions likely indicate high levels of legislator Agreeableness; one way this could manifest is by more Agreeable legislators engaging in bipartisan cosponsorship at higher rates. However, it is also possible that the “others” in question are not fellow citizens or other legislators but their parties and/or constituents. In this case, we should expect to see the rate of bipartisan cosponsorship be more sensitive to partisan/constituent preferences when Agreeableness is high. Additionally, we should be cognizant of the electoral incentives at play. To wit, we posit that Emotional Stability should have strong associations with how responsive legislators are to district partisanship. As Emotional Stability is modeled as a fixation on negative outcomes

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in our framework, less Emotionally Stable legislators should be more likely to use cosponsorship activities to ensure electoral success and avoid potential negative outcomes (i.e., losing reelection). Indeed, singleminded seekers of reelection should view losing reelection as the ultimate negative outcome, as the office is a necessary condition for any sort of formal policy influence. Because of this, less Emotionally Stable legislators should hew more stringently to the electoral incentives in their districts than more stable ones; that is, they should engage in bipartisan cosponsorship at higher rates in more competitive districts (because their margin of victory is likely more dependent on cross-partisan votes) and at lower rates in less competitive districts (as the primary election is the larger hurdle to reelection in these cases). Legislators who are more Emotionally Stable should exhibit the opposite behavior. Of course, cosponsoring across party lines is not always a positive thing. Legislators from safe districts who nonetheless cosponsor across the aisle risk being primaried and potentially losing their seats. Thus, we expect that legislators more willing to undertake this risk—that is, Open legislators—should be more likely to engage in bipartisan cosponsorship. Extraversion, on the other hand, should exhibit different patterns visà-vis electoral security. Because of its posited relationship with a fixation on reward, higher levels of Extraversion should result in legislators acting more in line with the electoral incentives in their districts. More Extraverted legislators in competitive districts should therefore engage in bipartisan cosponsorship at higher (lower) rates, and less Extraverted legislators should do the opposite. This is because the relative policy gains for legislators in secure seats are higher for partisan cosponsorship as opposed to bipartisan cosponsorship, as they can make gains in terms of both policy and higher vote totals. Conversely, for those in more competitive seats, the relative policy gains from more partisan cosponsorship may not outweigh the potential electoral losses that would result from being seen as more extreme. To examine these dynamics, we estimate a series of binomial logistic regressions predicting bipartisan cosponsorship, using the same covariates as were employed in Tables 6.1 and 6.2. However, instead of interacting the ELUCIDATION scores with majority party status, we interact them with Electoral Security, in order to capture the dynamics uncovered in Harbridge and Malhotra (2011)—that bipartisan cosponsorship rates are conditional on electoral security—as well as to test our contention that different personality profiles will react to different levels of electoral

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security in different ways. Data from the 104th through 109th Houses are used. Like Harbridge and Malhotra (2011), we code as members engaging in bipartisan cosponsorship those who cosponsor bills for which at least 20% of the cosponsors are from the party opposite that of the bill’s original sponsor. We also estimate models with Congress-level fixed effects.4 All of these results are presented in Table 7.2, and the predicted rates of bipartisan cosponsorship are graphically presented in Figure 7.1 for ease of interpretation.5 Figure 7.1 indicates that our hypotheses regarding three of the traits (Openness, Extraversion, and Emotional Stability) are supported to varying extents, while the results for Agreeableness require some additional interpretation, which we do below. As expected, due to the association of Openness with risk preferences, the actions of less Open (and therefore more risk-averse) members is strongly affected by their electoral security, with those in the most competitive districts engaging in bipartisan cosponsorship at high rates (presumably to attract the necessary support of moderates and voters of other political parties), and those in the safest districts engaging in it at comparatively lower rates (presumably to consolidate support among partisan voters due to the relative importance of the primary in these districts); conversely, the most Open members are unaffected by changes in their electoral prospects. Given its relationship to negative outcomes sensitivity, with those scoring the highest on this trait the least sensitive to negative outcomes, Emotional Stability should display similar patterns, which is exactly what we find. The least stable (or, equivalently, the most Neurotic) members act like the most risk-averse (or least Open) members, engaging in bipartisan cosponsorship more often when they represent unfriendly districts, and less often when the district is friendlier. The results for the most stable members are similar to those for the most Open members, and perhaps

4. As before, we also estimate negative binomial models where the dependent variable is the number of times a member engaged in bipartisan cosponsorship during each Congress; these models included offsets of one plus the logged total number of cosponsored bills. Results are substantively identical to those presented here. 5. In this figure, all continuous (categorical) independent variables are held at their means (modes). Estimates for “Below Average” (“Above Average”) trait levels are estimated by holding the ELUCIDATION score under analysis at two standard deviations below [above]. Estimates from Model 10 are used to generate the plots. We also present 90% confidence intervals.

table 7.2 Binomial Regression Models of Bipartisan Cosponsorship

Openness Conscientiousness Extraversion Agreeableness Emotional Stability Electoral Security Electoral Security × Openness Electoral Security × Conscientiousness Electoral Security × Extraversion Electoral Security × Agreeableness Electoral Security × Emotional Stability Majority Party

Model 7

Model 8

Model 9

Model 10

Model 11

−0.073∗∗∗ (0.008) 0.068∗∗∗ (0.007) −0.139∗∗∗ (0.005) −0.069∗∗∗ (0.013) 0.004 (0.009) —

0.052∗∗∗ (0.008) 0.030∗∗∗ (0.007) −0.045∗∗∗ (0.005) −0.128∗∗∗ (0.014) 0.035∗∗∗ (0.009) —

—

—

0.043∗∗∗ (0.009) 0.006 (0.007) −0.035∗∗∗ (0.005) −0.086∗∗∗ (0.014) −0.011 (0.010) −0.001∗∗∗ (0.000) —

—

—

—

—

—

—

—

—

—

—

—

—

—

−0.129∗∗∗ (0.044) −0.016 (0.037) 0.084∗∗∗ (0.028) 0.073 (0.072) −0.267∗∗∗ (0.047) −0.010∗∗∗ (0.003) 0.003∗∗∗ (0.001) 0.000 (0.001) −0.002∗∗∗ (0.000) −0.002∗∗ (0.001) 0.004∗∗∗ (0.001) 0.089∗∗∗ (0.017) 0.391∗∗∗ (0.017) −1.939∗∗∗ (0.020) −0.001∗∗∗ (0.000) −0.119∗∗∗ (0.008) 0.006∗∗∗ (0.001) −0.043∗∗ (0.019) 0.049∗∗∗ (0.010) 0.059∗∗∗ (0.007) −0.036 (0.153) −0.232∗∗∗ (0.027) −0.073∗∗∗ (0.018) 1.556∗∗∗ (0.210)

−0.144∗∗∗ (0.044) −0.047 (0.037) 0.084∗∗∗ (0.028) 0.083 (0.072) −0.231∗∗∗ (0.047) −0.010∗∗∗ (0.003) 0.003∗∗∗ (0.001) 0.001 (0.001) −0.002∗∗∗ (0.000) −0.002∗∗ (0.001) 0.003∗∗∗ (0.001) 0.086∗∗∗ (0.017) 0.406∗∗∗ (0.018) −1.891∗∗∗ (0.020) −0.000 (0.000) −0.125∗∗∗ (0.008) 0.008∗∗∗ (0.001) −0.061∗∗∗ (0.019) 0.045∗∗∗ (0.010) 0.053∗∗∗ (0.007) −0.054 (0.154) −0.242∗∗∗ (0.027) −0.072∗∗∗ (0.018) 1.469∗∗∗ (0.210)

Ideology

—

Extremism

—

Age

—

0.086∗∗∗ (0.016) 0.424∗∗∗ (0.017) −1.999∗∗∗ (0.019) —

Female

—

—

Seniority

—

—

Committee Chair

—

—

Subcommittee Chair

—

—

Power Committee

—

—

Speaker

—

—

Majority Leadership

—

—

Minority Leadership

—

—

0.942∗∗∗ (0.038)

0.833∗∗∗ (0.039)

0.086∗∗∗ (0.017) 0.394∗∗∗ (0.017) −1.942∗∗∗ (0.020) −0.001∗∗∗ (0.000) −0.120∗∗∗ (0.008) 0.007∗∗∗ (0.001) −0.042∗∗ (0.019) 0.049∗∗∗ (0.010) 0.057∗∗∗ (0.007) −0.040 (0.153) −0.239∗∗∗ (0.027) −0.079∗∗∗ (0.018) 1.031∗∗∗ (0.046)

No

No

No

No

Yes

1,032.774∗∗∗ 43,848.019 −21,900.539 2,499

161.585∗∗∗ 27,465.207 −13,697.401 2,497

148.872∗∗∗ 26,313.983 −13,082.945 2,427

210.570∗∗∗ 26,291.038 −13,051.986 2,427

209.947∗∗∗ 24,977.758 −12,375.860 2,427

Constant

Congress FE? Wald Test BIC Log Likelihood Num. obs.

Notes: Standard errors in parentheses. Observations are at the Congress-member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

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figure 7.1. Personality and Bipartisan Cosponsorship

even stronger, as there appears to be a slight preference to buck district preferences. Extraversion displays a pattern opposite to that of Openness and Emotional Stability, in line with its role as a proxy for sensitivity to potential reward; thus, more Extraverted members should be more likely to make choices that they believe will enable them to achieve institutional rewards, namely advancement within the party leadership (just as less Emotionally Stable members should be more likely to make choices they believe will enable them to avoid the negative outcome of losing reelection). Specifically, legislators who cosponsor across party lines potentially harm their own opportunities for advancement within the party leadership or for plum committee assignments (see Chapter 5). In line with these theoretical expectations, we see that Extraverted members are less likely to engage in bipartisan cosponsorship when they are in electorally safe

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environments. This makes intuitive sense; when electoral concerns are set aside, members are more concerned with pursuing the rewards associated with institutional advancement. Since bipartisan cosponsorship can have a deleterious effect on a member’s intraparty advancement, members should engage in this activity less. That is precisely what we observe. The effect of Agreeableness on bipartisan cosponsorship requires some more in-depth interpretation, as we discussed two possible phenomena that could have arisen (neither of which were mutually exclusive). Across all levels of Electoral Security, the point estimates for the cosponsorship rates for less Agreeable members are higher than they are for more Agreeable members (though the confidence intervals overlap for the most competitive districts), and the difference increases with Electoral Security, suggesting that more Agreeable legislators are more sensitive to the wishes of their constituents and parties, and indifferent to the wishes of the other party. Overall, our hypotheses regarding how personality relates to bipartisan cosponsorship are generally supported by the data, suggesting that personality is very important for understanding how elites respond to risk and uncertainty over outcomes.

7.4 Holding the Floor: Filibustering and Obstruction Having considered loyalty and bipartisan cosponsorship in the House, we now pivot to discuss the rise of obstruction in the Senate. As we noted at the start of the chapter, the filibuster is a senatorial tactic that almost exclusively benefits the minority party. Since the minority cannot normally muster enough majority party defectors to defeat a measure, the filibuster gives minority members a powerful tactic to prevent majority legislation from passing. In this section, we examine the extent to which personality traits might affect filibustering behavior. Since the filibuster is primarily a minority tactic, we focus our attention both theoretically and empirically on minority party members. It turns out that measuring filibusters is not as straightforward a task as one might surmise. Filibusters that are ended by unanimous consent or those for which cloture is never attempted are, by necessity, excluded from our analysis. For the remaining cases, we must choose whether to restrict our attention to votes in which invoking the term cloture is explicit or to also include motions to proceed. Since the Senate itself includes both

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types of votes in its cloture records, we pool them together and hereafter refer to them collectively as cloture votes.6 To examine support for cloture, we aggregate the decisions to support cloture at the Congress-member level. Thus, our dependent variable is binomial in nature—i.e., the number of times the legislator supported cloture out of the total number of cloture votes. For covariates, we include the Big Five personality traits as well as other variables common in the filibuster literature. Namely, we follow Binder and Smith (1997) in controlling for ideological extremism, region, majority party status, state size, and seniority. Additionally, we control for gender and lame duck status. Extremism is measured as the absolute value of a senator’s DWNOMINATE score. We expect that ideological extremity is negatively associated with filibustering, as ideological extremists are less likely to compromise with the majority when they are minority members and, occasionally, some majority party extremists team up with the minority to stifle the majority. Senator Rand Paul’s (R-KY) filibuster of the PATRIOT Act renewal in 2015 is a key example of the latter. Female, South, Lame Duck, and Small State are all dummy variables equal to one if the legislator is female, from one of the eleven states of the Confederacy, in his or her final term in the Senate, or from a state with fewer than three House members, respectively (Binder and Smith 1997). Chamber Seniority is the number of years the member has served in the Senate. Binder and Smith (1997) note that, for historical reasons (usually pertaining to civil rights debates in the 1960s), senators from the South are usually less supportive of cloture than their northern compatriots. Similarly, as the first of this chapter’s two epigraph quotations from Harry Reid suggests, small-state senators should also support cloture at lower rates than those from large states.7 In line with our argument in the previous section, we believe that Openness (and possibly Agreeableness) will provide added explanatory power to the variables identified in the literature. Specifically, we expect that more Open legislators will be more likely to rebel against the interests of their party; Open minority members will be more likely to do so. Similarly, as Agreeable members were previously shown in this chapter

6. See http://www.senate.gov/pagelayout/reference/cloture_motions/clotureCounts.htm. If we work with only the narrower pool of votes, the results hold. 7. As for the effects of gender and lame duck status, we remain agnostic about their effects, but control for them for sake of consistency with the literature.

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table 7.3 Predicting Minority Party Support for Cloture (104th–112th Congresses)

Openness Conscientiousness Extraversion Agreeableness Emotional Stability Extremism

Model 12

Model 13

Model 14

0.266∗∗∗ (0.033) −0.101∗∗∗ (0.027) 0.117∗∗∗ (0.022) −0.357∗∗∗ (0.066) 0.189∗∗∗ (0.038) —

−0.368∗∗ (0.186)

0.202∗∗∗ (0.036) −0.105∗∗∗ (0.030) 0.164∗∗∗ (0.023) −0.255∗∗∗ (0.072) −0.044 (0.043) −2.610∗∗∗ (0.114) 0.048 (0.047) −0.105∗∗∗ (0.035) 0.054 (0.047) 0.001 (0.002) 0.166∗∗∗ (0.036) 1.031∗∗∗ (0.217)

0.133∗∗∗ (0.038) 0.018 (0.032) 0.052∗∗ (0.026) −0.296∗∗∗ (0.075) 0.049 (0.045) −2.401∗∗∗ (0.122) 0.032 (0.049) −0.046 (0.038) 0.124∗∗ (0.050) −0.003 (0.002) 0.193∗∗∗ (0.038) −0.721∗∗∗ (0.252)

No

No

Yes

163.534∗∗∗ 4,889.858 −2,426.801 421

110.951∗∗∗ 3,969.705 −1,948.800 407

30.034∗∗∗ 2,370.996 −1,125.410 407

Female

—

South

—

Lame Duck

—

Chamber Seniority

—

Small State

—

Intercept

Congress FE? Wald Test BIC Log Likelihood Num. Obs.

Notes: Standard errors in parentheses. Observations are at the Congressmember level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

to be more likely to sacrifice their policy inclinations and to support the party brand, we expect that Agreeableness will lead minority members to support cloture less often. Table 7.3 shows the results from a series of binomial regressions for minority party members; the dependent variable is the number of times a legislator supported cloture across the number of votes in which the legislator voted. The results are generally in line with extant literature; extremists are generally less supportive of cloture across the board. Note, however, that the intercept is generally negative, indicating that baseline

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figure 7.2. Predicted Rates of Supporting Cloture

levels of cloture support are low for the minority. This is to be expected; the filibuster is, after all, primarily a minority tactic, and hence baseline supports for cloture should be low. Gender, region (i.e., being from the South), and seniority generally have null or modest effects. Lame duck legislators tend to buck their party in the fully specified model (Model 14); lame duck minority legislators are more inclined to support cloture. Somewhat paradoxically, minority members from small states tend to support cloture more often than their colleagues from large states. The results from the Big Five parameters are particularly enlightening. For the minority party, Openness and Agreeableness have the most consistent and, in terms of magnitude, strongest effects on cloture support. As predicted, Open minority members are more likely to support cloture, and Agreeable minority members are less so. Interestingly, the magnitude of the impact of Agreeableness is robust to the addition of covariates and actually strengthens with the addition of Congress fixed effects. In order to get a sense of the magnitude of the Agreeableness effects, Figure 7.2 shows the predicted rates of cloture support for minority party members (holding all other continuous [categorical] variables at their means [modes]). We see that the least Agreeable members, regardless of their degree of ideological extremity, support cloture at high rates

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(in excess of 65% across votes). As Agreeableness increases, the probability of supporting cloture reduces to around 25%. Indeed, when we focus on an area one standard deviation to the left and right of the mean of Agreeableness, we see that the rate of cloture support decreases by just under 10%. Substantively, this drop is enormous; a 10 percentage point shift is large relative to the minority baseline rate of cloture support (approximately 53%). This strong effect for Agreeableness is both interesting and seemingly at odds with conventional understanding of the Big Five. Specifically, while our core cognitive constraint approach explains the Agreeableness finding, traditional interpretations of Agreeableness suggest that more Agreeable senators should be more likely to oppose obstruction. Clearly, this is not the case. The divergence in both expectations and empirical outcomes highlights one of the central reasons for this book; namely, by grounding the Big Five through our framework, we are able to theorize about elite behavior in a clear, unambiguous fashion. If we had simply run our models with the Big Five in them but without this theoretical grounding, we would not be able to make any sense of these findings.

7.5 Norms and the Shattering Thereof: Conclusion Party loyalty, cooperation across party lines, and obstruction are three topics that have received a good deal of attention by political scientists in recent years. Traditionally, all three were thought to be driven by ideological and electoral factors. Guided by the spatial model, existing scholarship identified ideological extremity (both within-party and acrossparty) as a key predictor for when legislators choose to rebel and when they choose to work across the aisle as well as for when senators choose to obstruct. In this chapter, we augmented the existing wisdom on the subject by introducing our core cognitive constraint framework. We found that, even after accounting for ideology and other factors employed in the vast extant literature, our ELUCIDATION scores explain a great deal of variation in observed rebellion and obstruction. Specifically, Open members are more likely to bear the uncertainty costs of rebelling against the majority party. Agreeable members are more likely to support the party brand. We also found that personality has a significant role in whether or not legislators choose to work with members of the other party. For starters,

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the actions of less Open (and therefore more risk-averse) members were found to be strongly affected by their electoral security, with those in the most competitive districts engaging in bipartisan cosponsorship at high rates (presumably to attract the necessary support of moderates and voters of other political parties) and those in the safest districts engaging in it at comparatively lower rates (presumably to consolidate support among partisan voters due to the relative importance of the primary in these districts); conversely, the most Open members were found to be unaffected by changes in their electoral prospects. Given the modeling of Openness as risk preference, these findings are promising. Additionally, we found that Emotional Stability displayed patterns consistent with our hypothesized relationship between the trait and punishment sensitivity, with the least stable (or, equivalently, the most Neurotic) members acting like the most risk-averse (or least Open) members, engaging in bipartisan cosponsorship more often when they represent unfriendly districts and less often when the district is friendlier; conversely, more stable members are less responsive to district preferences, presumably because they focus less on the potential punishment. Extraversion displays a pattern opposite to that of Openness and Emotional Stability, in line with its role as a proxy for sensitivity to potential reward; we see that more Extraverted members act more in line with the electoral incentives of their districts, engaging in bipartisan cosponsorship more when their districts are more competitive, and doing so less often when their districts are ostensibly safer. Less Extraverted members are less sensitive to these incentives. Finally, our results suggested that more Agreeable legislators are more sensitive to the preferences of their constituents and parties when it comes to the decision of whether or not to work with the opposing party which is again, in contrast to our initial assumption that Agreeableness captured weight placed on the utility of the nation as a whole. While most of the political science literature attributes changes in these legislative and electoral behaviors to intra-party ideological homogeneity, ideological preferences, and the incentives of primaries versus general elections, we have presented concrete evidence that—even after controlling for ideology and other relevant factors—individuals’ personality traits have a strong, independent impact on legislative norm-following, loyalty, and rebellion, inside and outside of the chamber. Thus, while it is almost certainly the case that ideological polarization plays a role in Washington dysfunction, personality is an important contributor to growing tactical polarization.

chapter eight

Media Presence and Home Style I don’t e-mail. —Senator Lindsay Graham (R-SC) 1

Fred and I hit a deer on hiway [sic] 136 south of Dyersville. After I pulled fender rubbing on tire we continued to farm. Assume deer dead. —Senator Chuck Grassley (R-IA) 2

I

n anticipation of a forthcoming presidential bid in 2016, Senator Lindsay Graham (R-SC) dropped a bombshell on the political world: he does not use e-mail. This revelation was immediately found to be at odds with the age of mass technology in which we live. Upon further investigation, Politico found that Senator Graham was not alone. His older colleague from Alabama (and fellow Republican), Senator Richard Shelby, confessed that, in his view, “[t]he best thing is person-to-person like I’m talking to you. To my staff, talk to them on the phone but also notes. Hand notes. I write a lot. I’ve been here a while; I’m a little older than y’all.”3 Almost immediately, Politico began referring to this cadre of apparently technophobic legislators as the “Luddite Caucus.” In defense of his good friend, Senator John McCain (R-AZ) revealed that, while he does not use e-mail frequently, he does use another prominent technology—Twitter—to communicate with his constituents. Senator Cory Booker (D-NJ), one of the youngest members of the Senate,

1. “Sen. Lindsey Graham Admits He’s Never Sent an Email,” Time, March 8, 2015, http:// time.com/3736775/lindsey-graham-senate-politics-email/. 2. Twitter post, October 26, 2012, 01:13 UTC. 3. Quoted in http://www.politico.com/story/2015/03/lindsey-graham-email-senate-115923 .html.

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concurred and added that he also uses Instagram and Facebook. Octogenarian senator Chuck Grassley (R) of Iowa also uses social media quite actively. What is more, his tweets include anything from reports on constituent service to congratulating local sports teams and even, as the second chapter epigraph above demonstrates, chronicles of his daily exploits. The decision of which medium of communication to employ vis-àvis constituents is thus not simply explained by generational differences. Legislators’ choices of how best to reach out to their constituents and what issues to emphasize are neatly summarized by what Fenno (1978) calls home style. In this chapter, we analyze differing legislative home styles through the lens of personality and our core cognitive constraint framework. We focus our attention on two broad home style questions: when/why do legislators adopt new methods of reaching out to constituents, and why do some members emphasize policy while others emphasize particularistic credit-claiming in their media usage? To answer these questions, we look at two different sources of data. First, we examine the decision to use Twitter among legislators in the early days of its existence (2007–2010). Second, we examine patterns of topical usage among legislators using a traditional means of communication: press releases (Grimmer 2013). We argue and demonstrate that, even after controlling for a host of factors that scholars believe to explain the adoption of new technology and the patterns of issue emphasis, the Big Five framework has additional explanatory power for this important part of legislative behavior.

8.1

Who Tweets?

Twitter debuted in 2006 as a medium of communication whereby users can send tweets, short messages limited to 140 characters. By 2014, the number of active US Twitter users was estimated to be around 45 million.4 In its early days, few politicians in the United States viewed Twitter as a valuable means for connecting with their constituents. That all changed in late April of 2007, when then-Senator Barack Obama registered a public Twitter account and issued his first tweet. 4. See http://www.pcworld.com/article/2159420/twitter-users-to-grow-244-percent-in -2014-us-still-largest-market.html.

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figure 8.1. Twitter Adoption by House Members (2007–2010)

While Obama became quite popular and would go on to lead a successful campaign for the Democratic presidential nomination, few of his fellow legislators followed suit. Using data from Chi and Yang (2010), Figure 8.1 shows the percentage of members of the House of Representatives with active Twitter accounts during the early days of Twitter— 2007–2010. While the percentage of members with Twitter accounts grew steadily over this three-year period, it was not until Senator Obama became President Obama in 2009 that the number of House members on Twitter rose precipitously. Though the change was dramatic, we can see that as late as the summer of 2010 only about 40% of House members were on Twitter. Why did some legislators adopt Twitter during the early days while others did not? Among those who did, what explains when they adopted it, how often they tweet, and how many people follow them? To answer these questions, we must consider the decision calculus of rational legislators with respect to adopting a new technology. As with investing in a start-up company, making a move to a new medium of communication requires a significant investment of time and energy. Moreover, as the technology is new, there are substantial risks. On the upside, if the medium proves to be adopted by the public at large, the legislator will be rewarded for being ahead of the curve. These rewards could be

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electoral—e.g., young and tech-savvy voters might jump on his or her electoral bandwagon—or they could come in the form of prestige from the legislator’s perceived foresight. For example, in the 2008 presidential campaign, then-Senator Obama assembled a team of technological “wizards” to develop sophisticated models of voter turnout. While the class of traditional consultants scoffed at this decision initially, Obama’s success vindicated the risks, and countless politicians tried desperately to copy his approach or gain access to his data. Of course, there is a downside as well. If the new medium proves to be a dud, the legislator will suffer a loss in prestige. Furthermore, the amount of time and effort invested in the medium will be seen as wasted in retrospect. Indeed, it is possible that constituents, preferring older, more traditional forms of political communication, may retaliate against the incumbent electorally. Thus, at its very core, the question of who adopted Twitter is really about legislators’ relative emphasis on the prospective rewards and negative outcomes for doing so. To formalize this, we introduce a simple model. There is a set of legislators i = 1, 2, . . . , N who must decide whether or not to adopt Twitter at various time points, t = 1, 2, . . . , ∞. Let  ai,t denote whether or not i has adopted as of time t and, thus, nt = i ai,t is the number of legislators who have adopted Twitter as of time t. Assuming that he or she discounts the future at a rate of δ ∈ (0, 1), a generic legislator’s expected utility for adoption at time t0 is

(8.1)

Eui (ai,t0 = 1) =

⎧ ∞ ⎨ ⎩

t=t0

δ t pαB(nt0 )

⎫ ⎬ ⎭

− δ t0 (1 − p)βC(t0 ),

where p is the probability that Twitter turns out to be a good thing in the long run, B(·) and C(·) are non-negative benefits and costs from adoption that are assumed to be monotonically decreasing, and α and β are parameters that capture the legislator’s sensitivity to the rewards and negative outcomess from adoption (both assumed to be larger than unity), respectively. We normalize the utility for not adopting to 0. Obviously, p is not known a priori. In the empirical application that follows, we operationalize p based on the number of favorable signals that legislators receive about Twitter to date. In this formulation, benefits are accrued only if Twitter is good, and costs are incurred only if it is bad. The benefits and costs depend on the

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number of adopters to date and time, respectively. Benefits are accrued infinitely and, since they decrease with the number of current adopters, heavily favor early adopters. Costs are paid at the time of incursion only and, since they decrease over time (as Twitter’s viability becomes evident), encourage legislators sensitive to them to delay adoption. A legislator will adopt today over tomorrow if his or her expected utility for doing so is higher: that is, (8.2) (8.3)

Eui (ai,t0 = 1) ≥ Eui (ai,t0 +1 = 1) ⎧ ⎫ ∞ ⎨ ⎬ δ t pαB(nt0 ) − δ t0 (1 − p)βC(t0 ) ⎩ ⎭ t=t0

≥

⎧ ∞ ⎨  ⎩

⎫ ⎬

δ t pαB(nt0 +1 )

⎭

t=t0 +1

− δ t0 +1 (1 − p)βC(t0 + 1)

Define the net benefit of adopting today by NB(ai,t0 ) = Eui (ai,t0 = 1) − Eui (ai,t0 +1 = 1). Rearranging and collecting terms, the net benefit to adoption today is ⎧ ⎨

(8.4)

t0

NB(ai,t0 ) = pα δ B(nt0 ) + ⎩

∞  t=t0 +1

⎫ ⎬ δ (B(nt0 ) − B(nt0 +1 )) ⎭ t



− (1 − p)β δ t0 C(t0 ) − δ t0 +1 C(t0 + 1) and the legislator will adopt if this equation is greater than 0. It is useful to see how this net benefit changes according to variations in the model parameters. We first consider changes in the sensitivity to the benefits, α. Differentiating Equation 8.4 with respect to α,

(8.5)

⎫ ⎧ ∞ ⎬ ⎨  ∂NB(ai,t0 ) δ t (B(nt0 ) − B(nt0 +1 )) . = p δ t0 B(nt0 ) + ⎭ ⎩ ∂α t=t0 +1

This equation is always positive and is increasing in the probability of Twitter being a good thing, p, and is enhanced when nt0 +1 > nt0 This makes sense intuitively; when the number of adopters in the next period is higher than today, the benefits of jumping on the bandwagon earlier are apparent.

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We can similarly check how varying the sensitivity to the costs of adoption affect the net benefits: (8.6)



∂NB(ai,t0 ) = −(1 − p) δ t0 C(t0 ) − δ t0 +1 C(t0 + 1) . ∂β

Since today’s costs are always higher than tomorrow’s, regardless of how many people adopt in the interim, the term in brackets is always positive. Thus, the partial effects of sensitivity to negative outcomes are always non-positive. When the probability that Twitter is a good thing is 1, sensitivity to negative outcomes never matters. However, for all other cases, sensitivity to negative outcomes has a negative effect on the benefits of adopting early. It is also useful to see how the net benefit is affected by altering the legislator’s beliefs that Twitter will turn out to be a good thing. Differentiating with respect to p, we obtain

(8.7)

⎫ ⎧ ∞ ⎬ ⎨  ∂NB(ai,t0 ) δ t (B(nt0 ) − B(nt0 +1 )) = α δ t0 B(nt0 ) + ⎭ ⎩ ∂p t=t0 +1



 + β δ t0 C(t0 ) − δ t0 +1 C(t0 + 1) . Since all the terms in brackets are positive, the net benefit is strictly increasing in p. This is not surprising, as an increase in the probability that Twitter is a good investment means that the benefits will outweigh any potential costs of early adoption. Last, it is important for us to consider how a change in legislator’s weight on the future affects the decision to adopt today versus tomorrow. Differentiating the net benefit with respect to δ yields ⎫ ⎧ ∞ ⎬ ⎨  ∂NB(ai,t0 ) (8.8) tδ t−1 (B(nt0 ) − B(nt0 +1 )) − = pα t0 δ t0 −1 B(nt0 ) + ⎭ ⎩ ∂δ

(8.9)

t=t0 +1

 (1 − p)β t0 δ t0 −1 C(t0 ) − (t0 + 1)δ t0 C(t0 + 1) .

Though this equation is a seemingly massive expression, we can nevertheless see that it is always positive. This is partly driven by the infinite accumulation of benefits inside the first bracketed term. That said, even

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if the number of Twitter adoptees is constant from t0 to t0 + 1, the equation will still be positive as long as the instantaneous benefit outweighs the instantaneous costs of adoption. To see why, set nt0 = nt0 +1 and substitute into the above: (8.10)



∂NB(ai,t0 ) = pα t0 δ t0 −1 B(nt0 ) ∂δ 

− (1 − p)β t0 δ t0 −1 C(t0 ) − (t0 + 1)δ t0 C(t0 + 1) .

Note that the second bracketed term (around the costs) is always negative, because t0 < t0 + 1. Since this term is multiplied by −(1 − p)β, the overall cost term is always positive and, hence, the overall expression is positive as well. The intuitions from this model line up cleanly with the cognitive constraint framework we proposed in Chapter 2. Recall that Extraversion and Emotionally Stability are related to sensitivity to reward and negative outcomes, respectively. Specifically, Extraverts are more sensitive to reward than Introverts, and Emotionally Stable legislators are less sensitive to negative outcomes than Neurotics. In our model above, the parameters α and β are defined as sensitivity to reward and negative outcomes, respectively. Linking the comparative statics for these parameters with the Big Five, we should expect legislators who are Extraverted (high α) and Emotionally Stable (low β) should be more willing to undertake the risks associated with adopting the new platform sooner. Going further, it seems natural to think that, as time goes on and information is revealed, the risks associated with adopting the new platform will dissipate. Recall that p is the long-run probability that Twitter turns out to be a “good” thing to adopt. We capture this idea empirically by controlling for the average seniority of legislators who adopted prior to legislator i as a proxy for evolving uncertainty. When the average seniority of Twitter users among Congress is low, prospective users would have to rely on relatively weak signals. When the average seniority is high, legislators receive more credible signals about Twitter’s value and should be more likely to adopt. To test these predictions, we run a series of regressions, the results of which are found in Table 8.1. We examine three different phenomena with respect to adoption: whether or not a legislator adopted Twitter at all during the time period in question (estimated via logit), how long it took

table 8.1 Who Tweets and How Often (2007–2010)?

Openness Conscientiousness Extraversion Agreeableness Emotional Stability Average Seniority of Adopters (before i) Seniority of Adopters × Openness Seniority of Adopters × Conscientiousness Seniority of Adopters × Extraversion Seniority of Adopters × Agreeableness Seniority of Adopters × Emotional Stability Age Incumbent Democrat Facebook Ideology Extremism Constant Log(scale)

Who? Model Wald Test BIC Log Likelihood Num. Obs.

Twitter Adopted?

Days to Adopt

Number of Tweets

−0.347 (0.411) −0.264 (0.369) 0.555∗∗ (0.245) 1.273∗ (0.718) −0.824∗ (0.428) —

0.522∗∗ (0.210) −0.743∗∗∗ (0.124) −0.281∗∗∗ (0.103) 0.277 (0.297) 0.347∗∗ (0.160) −0.044 (0.050) −0.035∗∗ (0.013) 0.053∗∗∗ (0.007) 0.017∗∗∗ (0.006) −0.027 (0.017) −0.022∗∗ (0.009) 0.004∗ (0.002) 0.162∗∗∗ (0.047) 0.185∗ (0.107) −0.207∗∗∗ (0.043) 0.125 (0.111) −0.277∗∗ (0.113) 8.700∗∗∗ (2.052) −1.645∗∗∗ (0.066)

−0.027∗∗ (0.013) 0.147∗∗∗ (0.015) 0.078∗∗∗ (0.009) 0.300∗∗∗ (0.024) 0.336∗∗∗ (0.014) —

— — — — — −0.025∗ (0.015) −1.236∗∗∗ (0.466) −2.009∗∗∗ (0.758) 1.352∗∗∗ (0.281) −1.263 (0.783) 2.126∗∗ (0.839) −21.783 (23.822) —

— — — — — −0.015∗∗∗ (0.001) 0.093∗∗∗ (0.013) −0.066∗∗ (0.033) −0.471∗∗∗ (0.010) 0.951∗∗∗ (0.035) −0.841∗∗∗ (0.032) −7.330∗∗∗ (0.469) —

All Legislators Logit

All Legislators Censored Weibull

Adoptees Poisson

8.247 570.696 −210.176 409

282.056∗∗∗ 2,690.144 −1,248.892 408

5,020.27∗∗∗ 41,065.778 −20,468.401 174

Notes: Standard errors in parentheses. Observations are at the member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Additionally, out of the interest of space, the vast majority of control variables are ommited from this table. Full regression tables are available in the online appendix. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

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legislators to adopt (estimated using a censored Weibull distribution model), and the frequency of Twitter usage among adopters (estimated with a Poisson point process). Our principal covariates of interest are the ELUCIDATION scores. To capture the evolving risks associated with adoption, we control for the number of followers per tweet prior to the current observation i. Since we believe that individuals with differing personality traits will react to these risks heterogeneously, we interact the ELUCIDATION scores with followers per tweet in the days to adopt regression.5 Following Chi and Yang (2010), we also control for a number of variables that may impact legislators social media behavior. For demographics, we control for whether or not the legislator is Black, their gender (Female), and Age. We add controls for the population (Log Population) and median income (Log Income) of their districts, as we might believe that highly populated and wealthy areas might lead legislators to adopt Twitter and use it more frequently. A number of political and institutional controls are also controlled for. We control for party (Democrat), Seniority, whether or not they are an Incumbent, and both their Ideology and its square (Extremity). The reason for these controls is quite intuitive; since the Democrats were in the majority during this time period, Republicans, non-incumbents, and more junior members might have greater incentives to use new and non-traditional outlets for their platforms. Similarly, conservatives and ideologues of either tinge (measured by the squared term) might also be more likely to use Twitter. Last, it seems obvious that legislators already using other social media platforms might view adopting and using Twitter as less costly than the members of the “Luddite Caucus.” As such, we control for whether members have active Facebook, MySpace, RSS, Flickr, and YouTube accounts. As we see in Table 8.1,6 the results are generally in line with our theoretical expectations. While Conscientiousness does not have a statistically significant effect on adoption and time to adopt (measured in days relative to Obama’s first tweet), it is positively associated with the number of tweets sent by a legislator. Extraversion is positively associated with Twitter adoption, negatively associated with time to adopt (i.e.,

5. We cannot do this in the simple logistic regression due to perfect prediction. Specifically, the number of followers per tweet before observation i is the same number for all legislators who did not adopt Twitter. 6. Out of the interest of space, many of the demographic covariates are suppressed. All are available in the online appendix.

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i

figure 8.2. Predicting the Number of Days to Adoption of Twitter

Extraverts adopt Twitter sooner), and positively associated with the number of tweets sent. Agreeableness’s effects are in the same direction as Extraversion, though its effect on time to adopt is imprecisely estimated. Last, Emotional Stability’s results are somewhat of a mixed bag. Contrary to our expectations, it is negatively associated with the probability of adoption (i.e., Neurotics are more likely to adopt Twitter); however, in line with our predictions, Emotionally Stable legislators tweet more than their Neurotic counterparts. To make sense of this, Figure 8.2 presents predicted values for the number of days to adopt using the censored Weibull model. We hold everything except Emotional Stability and seniority of adopters fixed. As the figure demonstrates, Neurotic (i.e., not Emotionally Stable) legislators condition their time to adoption on the signals received. When the average seniority of previous adopters is relatively low (the dark line), Neurotic legislators delay adoption considerably. However, when it is high, Neurotics are much faster to adopt. This finding is in line with both our framework and more specific claims about Neuroticism made by Klingler, Hollibaugh, and Ramey (forthcoming). Specifically, Neurotics are more prone to indecision than their Emotionally Stable counterparts. As such, it is not surprising that Neurotics without sufficiently favorable signals would delay adoption considerably.

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8.2 Press Releases The use of media to communicate with constituents is just half the battle. Once legislators decide to reach out to their constituents using the tools of mass communication, they must choose which issues and policies to emphasize. In a monumental study, Grimmer (2013) applies textanalytic methods to study all press releases sent by sitting US senators for the years 2005 to 2007. His work comes to two conclusions. First, most press releases tend to cluster as either issue-taking (e.g., “I support the War in Iraq”) or credit-claiming (e.g., “I have raised $200 million for the new school in Tuscaloosa”). Second, and more critically, he finds that which of these home styles senators adopt is linked with their ideological extremism. Specifically, the most liberal and most conservative senators tend to emphasize policy, whereas legislators in the middle tend to emphasize credit-claiming and pork. Grimmer’s findings notwithstanding, we believe that our core cognitive constraint framework can offer additional insight in this arena. Conscientious and Extraverted legislators, driven by their capacity to reap the benefits of and their fixation on prospective rewards, respectively, should credit-claim less. The reasoning is quite straightforward. For Conscientious legislators, credit-claiming is a shortsighted tactic that might serve electoral interests but will not at all help legislators with respect to longterm policy benefits. For Extraverted legislators, credit-claiming produces smaller (in magnitude) rewards than those obtained from taking policy stands. Indeed, though the rewards from credit-claiming are smaller, they are reaped with greater certainty. As a result, Open legislators, having a reduced degree of risk aversion, are less deterred by the risks associated with position-taking than their less Open colleagues. Agreeable legislators, who place greater weight on the utility of others (and therefore less weight on their own personal utility), will often act against their own best interests. As mentioned above, the personal rewards from credit-claiming are smaller than those from position-taking, so we should expect Agreeable members to credit-claim more often. Last, Neurotic legislators, being sensitive to prospective electoral punishment, should credit-claim more (hence, Emotionally Stable legislators should do so less). We know that credit-claiming is a relatively safer activity than position-taking, and, as such, any punishments from doing it would be minimal when compared to those pursuant to position-taking.

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Obviously, a number of other senator and electoral characteristics should matter as well. For members, we control for their party (Democrat), whether they are in the Majority, their Age, whether or not they are a Former House Member, whether they are a Freshman, whether they are members of the Appropriations Committee, and both their DWNOMINATE Ideology and its square. For most of these controls, the connections with either credit-claiming or position-taking are clear. Former House members, Appropriations Committee members, and majority party members might be more likely to credit-claim. Similarly, ideologues (the squared ideology term) and minority party members might be more policy-motivated. On the electoral side, we control for the percentage of the vote the senator’s party received in his or her state in 2004 (Vote Share), whether he or she is up for reelection (On Cycle), and for his or her State Population. We expect that more vulnerable members and those up for reelection might have greater incentives to credit-claim. To analyze these hypotheses empirically, we run a series of OLS regressions using Grimmer’s (2013) data; the results are found in Table 8.2. The dependent variable in the first three columns is the difference in the proportion of credit-claiming and proportion of position-taking press releases by a given legislator. Positive values indicate more creditclaiming and negative values indicate more position-taking. The last two columns are just the raw credit-claiming and position-taking proportions. We see that the effects of the ELUCIDATION scores line up neatly with our theoretical expectations. Across all models, Conscientious, Extraverted, and Emotionally Stable legislators position-take more than they credit-claim, even after controlling for Grimmer’s covariates. Agreeable members credit-claim quite a bit, again in line with our theoretical expectations; moreover, it should be noted that if the larger reference group is the party as opposed to the nation as a whole, then the desire of Agreeable legislators to assist the party brand—and help the party win reelection—will lead them to be more likely to credit-claim. This is because position-taking carries much greater risks of exposing intra-party division, and hence focusing on this would be detrimental to the party brand. Since Agreeableness has the largest effect in terms of magnitude, it is useful to dig a little deeper into its implications for legislator behavior. Figure 8.3 plots the standardized Agreeableness of legislators for two of Grimmer’s (2013) forty-four press release topics: Iraq and the

table 8.2 Predicting Substantive vs. Credit-Claiming Behavior (2005–2007) Difference in Rates of Credit-Claiming and Position-Taking

Rate of Credit-Claiming

Rate of Position-Taking

−0.055∗ (0.031) −0.053∗∗ (0.025) −0.062∗∗∗ (0.020) 0.121∗∗ (0.058) −0.069∗∗ (0.034) −0.474∗∗∗ (0.164) −0.055∗ (0.030) 0.030 (0.025) 0.002 (0.025) −0.139 (0.155) 0.095∗∗∗ (0.024) 0.011 (0.035) 0.014 (0.018) —

−0.031 (0.019) −0.033∗∗ (0.016) −0.033∗∗∗ (0.013) 0.074∗∗ (0.037) −0.023 (0.022) −0.218∗∗ (0.104) −0.016 (0.019) 0.027∗ (0.016) −0.004 (0.016) 0.068 (0.094) 0.062∗∗∗ (0.015) −0.006 (0.022) 0.017 (0.011) —

0.040∗∗∗ (0.015) 0.028∗∗ (0.012) 0.024∗∗ (0.010) −0.053∗ (0.028) 0.038∗∗ (0.016) 0.291∗∗∗ (0.079) 0.048∗∗∗ (0.014) −0.004 (0.012) 0.004 (0.012) −0.003 (0.071) −0.043∗∗∗ (0.012) −0.004 (0.017) −0.004 (0.009) —

—

—

—

—

—

0.348∗∗∗ (0.107)

0.031 (0.081)

18.337∗∗∗ 0.136 0.096 293

28.779∗∗∗ 0.216 0.180 293

Ideology

−0.071∗∗ (0.032) −0.061∗∗ (0.026) −0.057∗∗∗ (0.021) 0.127∗∗ (0.060) −0.060∗ (0.035) −0.510∗∗∗ (0.169) −0.063∗∗ (0.031) 0.031 (0.025) −0.008 (0.026) 0.071 (0.153) 0.105∗∗∗ (0.025) −0.002 (0.036) 0.021 (0.019) —

Extremity

—

Appropriations Member

—

−0.094∗∗∗ (0.032) −0.060∗∗ (0.025) −0.056∗∗∗ (0.021) 0.151∗∗ (0.060) −0.070∗∗ (0.035) −0.258 (0.195) −0.152∗∗ (0.070) 0.050∗ (0.026) −0.014 (0.026) 0.061 (0.151) 0.127∗∗∗ (0.026) 0.007 (0.036) 0.023 (0.019) −0.060∗∗ (0.028) −0.035∗∗ (0.014) —

0.316∗ (0.174)

0.404∗∗ (0.176)

0.130∗∗∗ (0.029) 0.291∗ (0.169)

24.904∗∗∗ 0.181 0.143 293

28.952∗∗∗ 0.208 0.166 293

25.016∗∗∗ 0.236 0.198 293

Openness Conscientiousness Extraversion Agreeableness Emotional Stability Vote Share (2004) Democrat Majority On Cycle Age(100) Fmr. House Mem. Freshman State Pop. (millions)

Intercept

Wald Test R2 Adj. R2 Num. Obs.

Notes: Standard errors in parentheses. Observations are at the Year-member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

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figure 8.3. Press Release Issue Focus and Agreeableness

defense budget. We focus on these topics, both related to national defense, because press releases in the Iraq category are position-taking whereas the defense budget releases deal with appropriations and other credit-claiming subcategories. We see that legislator press releases on the defense budget topic have a modal Agreeableness close to one standard deviation above the Senate mean. Among press releases on the Iraq War, we have the exact opposite; the modal Agreeableness of legislators focusing on this topic is approximately one standard deviation below the chamber mean. These distributions make sense given the regressions above, as well as our theoretical framework, if the reference category in the present context is the party (as suggested by Table 8.2) and not the nation as a whole. Agreeable legislators prefer to focus on topics that further the party brand and avoid contentious policy-related issues. To that end, we see that most of the mass of the empirical density for the Iraq topic is below the chamber mean for Agreeableness. The relationship is not perfect (there are local maxima on the other side of the x-axis), but it generally comports with our theoretical framework.

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8.3 Media Usage: Conclusion Communicating with constituents is a key part of legislators’ jobs. Studying how they communicate and what information they communicate— their home style—is key to understanding the process of representation. In this chapter, we showed that personality matters in significant ways for understanding how legislators craft their home styles. Specifically, we showed that legislators’ decisions to adopt new tools for communicating with constituents depend on both the quality of information they have about the new tools and their personality types. Similarly, whether legislators choose to emphasize policy or to credit-claim is also highly dependent on personality. Additionally, our results once again suggest that the altruism of Agreeableness may orient these individuals toward the good of their party (as opposed to that of the nation as a whole). Going beyond empirics, in this chapter we have showed how our framework connects with formal modeling of elite behavior. One of our defenses of the core cognitive constraint framework outlined in Chapter 2 is its ability to link up more cleanly with the formal and game-theoretic modeling that has become a core part of the study of American political institutions. By showing how parameters in a simple formal model of technology adoption connect with personality traits, we have paved the way for future theoretical and methodological work that directly addresses the personality traits of decision makers, whoever they might be.

chapter nine

Moving On I’m 61 years old. I’ve been in Congress 20 years. If I have to fight to become chairman of a committee, given the job I’ve done, I need to move on. —Former Representative Chris Shays (R-CT)1 Right now, [my wife] and I are back at the ranch, we’re working our cattle and we’re loving every minute of it. —Former Representative John Salazar (D-CO)2

O

n November 2, 2010, the first midterm elections of Barack Obama’s presidency took place, and Democrats lost six seats in the Senate and sixty-four (as well as control of the chamber) in the House. Among the Democrats losing reelection was John Salazar (D-CO). Prior to the loss, Representative Salazar had missed only thirty-five votes of over fifteen hundred cast that session. Afterward, he missed twenty-eight of the ninety-nine votes cast during the lame duck session, instead choosing to spend time at his ranch. Contrast his behavior after the election to that of Phil Hare (D-IL), also voted out in 2010; unlike his Coloradan colleague, Representative Hare missed only two votes during the lame duck session. In this chapter we show that legislators’ decisions at the ends of their careers are influenced to a large extent by their personality traits. We look at decisions to run for reelection, aspirations for higher office, retirement, and behavior in the lame duck session (the first of which brings

1. Lauren W Whittington, “More GOP Moderates Looking for Exit,” Roll Call, September 20, 2007, http://www.rollcall.com/issues/53_30/-20087-1.html. 2. John Ingold, “Salazar Ducking Out: After Losing Re-election, the Manassa Dem Skips 28 of 99 Votes Since Nov. 2,” Denver Post, December 25, 2010, B1.

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us full circle back to Chapter 4’s discussion of pre-electoral behavior). We find some of the Big Five traits explain significant variation in the rates at which members run for higher office and retire, and under which circumstances they do so. Finally, we show that Conscientiousness is an important predictor of absenteeism rates, especially among lame ducks. We first investigate the role of personality in the decisions to run for reelection, run for higher office, or retire. Our analysis begins with the proposition that the decision to run for reelection is actually a decision to run for reelection, to run for another office, or to resign/retire, as suggested by Kiewiet and Zeng (1993).3 Each of these decisions carries with it some amount of risk and uncertainty as well as potential rewards (and— presumably—costs, if for no other reason than that of the costs of running for reelection and/or the downstream effects on post-congressional private sector income, as suggested by Diermeier, Keane, and Merlo [2005]). Each of these choices also involves a different time horizon (due to different term lengths as well as the difficulty of reentering Congress after resigning/retiring), and each choice affects the others in different ways. We anticipate that these factors interact with members’ personality traits to influence how they make electoral decisions. To start, we anticipate Conscientiousness will have the most direct interpretation, given its parameterization as a measure of time preference. More Conscientious members should be more forward-looking, and should therefore place more weight on the long-term effects of their actions. In the present context, this means that more Conscientious members should base their decisions regarding upcoming elections more on what they expect the election result to be, and should therefore appear as if they are behaving more strategically. If we think of the expected value of the choice to run for reelection as a weighted average of the outcome in the case of a win and of that in the case of a loss, then the expected value of the choice to run for reelection is higher (lower) when electoral security is higher (lower). Given sufficiently high (low) levels of electoral security, the expected value of the choice to run for reelection will be higher (lower) than that of running for a different office, retiring, or accepting an executive appointment if offered. Moreover, since the payoffs from the choices will not accrue to the legislator until the time of the election and are therefore

3. We omit those involuntarily removed from service.

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discounted, the expected utility advantages from running for reelection in safe districts will be higher for Conscientious members, and the expected utility disadvantages of running for reelection in unsafe seats will also be exacerbated. We also anticipate Agreeableness will play a role, as more Agreeable members should be more willing to act against their own interests and in the interest of the nation as a whole, since they place greater weight on the utility of others (and correspondingly less weight on their own personal utility) and exhibit preferences for selfless behavior. In the context of the decision to run for reelection, run for another office, or resign/retire, this means that more Agreeable members in safe districts should be less likely to run for office (and therefore more likely to retire or run for another office); here, more Agreeable candidates will give up near-certain reelections either to run for another office or to step aside to let another individual have a shot at taking the seat. Conversely, more Agreeable members in unsafe districts should be more likely to run for office. The role of Openness is more difficult to pin down, as it requires us to make judgments about the relative riskiness of each decision. Running for reelection is risky due to the inherent uncertainty, but it is—in some sense—the status quo, and the prospect of having to secure postcongressional employment might itself seem risky. For similar reasons, the relative riskiness of running for another office versus retiring and/or resigning is difficult to ascertain ex ante. Therefore, while we anticipate Openness having some sort of effect, we do not hypothesize about it ex ante; instead, we will use our empirical analysis to try to estimate the relative riskiness of the different courses of action. For similar reasons, the effects of Extraversion and Emotional Stability are difficult to specify ex ante, as we cannot be certain which courses of action have the highest and lowest relative potential costs and benefits. Following the analyses of the decision to run for reelection (or do something else), we will look at the aftermath of failed reelection bids (as well as decisions not to run for reelection) and examine how members behave in lame duck sessions. Indeed, in Federalist Paper No. 72, Alexander Hamilton argued that without the potential for future (officeholding and/or policy-related) rewards, or the inherent constraints imposed by running for reelection, elected officials would fail to discharge their duties in ways that maximize the public good:

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There are few men who would not feel much less zeal in the discharge of a duty when they were conscious that the advantages of the station with which it was connected must be relinquished at a determinate period, than when they were permitted to entertain a hope of obtaining, by meriting, a continuance of them.

Significantly, many scholars have discovered that severing (or merely weakening) the electoral connection results in representational changes. Rothenberg and Sanders (2000b) show that when members of Congress no longer have to face reelection in their current districts, they miss more votes and their voting patterns change.4 Similarly, lame ducks place less weight on constituency preferences than do other legislators in lame duck sessions (Rothenberg and Sanders 2000a).5 Relatedly, Jones (2003) finds that retiring members take fewer public positions on votes.6 Other research has shown that term limits result in decreased responsiveness to district and partisan interests and increased absenteeism (e.g., Carey, Niemi, and Powell 1998; Wright 2007). Finally, safer members tend to be less responsive to their constituencies (Griffin 2006), to propose fewer bills, and to be absent more often (Konisky and Ueda 2011). An explanation for this pattern of decreased responsiveness might be found within Fenno’s (1973) characterization of legislators as having four main goals—reelection, influence within Congress, public policy, and private gain outside of Congress (sometimes called leisure). This framework suggests that when two are no longer attainable (reelection and influence within Congress), effort should be redirected toward the others (public policy and leisure). However, due to the limited time they have left in which to legislatively engage, lame ducks necessarily find it difficult to influence policy. Therefore, lame ducks—as well as those with weaker electoral connections more generally—should allocate more of their time and effort to ventures outside of Congress. However, considerable heterogeneity exists within this group, with some, as we’ve noted, continuing to serve their districts (such as former Representative Hare), while others show up to work less often, instead turning their attention elsewhere

4. But see Carson, Crespin, Jenkins, and Vander Wielen (2004). 5. But see Lawrence (2007). 6. Jones (2003) codes a failure to take a public position as “the number of roll-call votes during a given Congress on which a member does not vote, pair, or otherwise announce a position,” which is somewhat different than simply missing votes (855).

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(such as former Representative Salazar). Indeed, though Rothenberg and Sanders (2000b) note that “exiting legislators are far more prone to find something else to do,” there exists little explanation of heterogeneity in absence rates (or other forms of shirking behavior) among lame ducks (322). Our analysis suggests that personality is one source of this underlying heterogeneity.

9.1

Moving On or Moving Out?

We first examine the relationship between personality traits and congressional continuation decisions among representatives. Our primary covariates of interest are our ELUCIDATION scores, and we include all of them in our model. We add to these other covariates that have been shown to influence whether members run for reelection, run for another office, or resign/retire. Among these are measures of influence in the chamber, as members with influential positions are less likely to give them up to run for higher office or retire (Groseclose and Krehbiel 1994; Hall and Van Houweling 1995). Therefore, we include indicator variables for Committee Chair and Subcommittee Chair that equal one if the member holds the position in question and zero otherwise. We also include Power Committee, which equals one if the member sits on the Ways and Means, Appropriations, or Rules Committee, and zero otherwise. For similar reasons, we also include an indicator variable for Majority Party. Finally, Party Leadership equals one if the member serves in an official capacity in the party leadership (including the Speaker) and zero otherwise. Other research (Johnson, Oppenheimer, and Selin 2012; Kiewiet and Zeng 1993) has also noted the importance of potential competition, with an emphasis on the number of districts (and therefore members of Congress) in the state. The possibility of having to compete against other seasoned members of Congress for higher office acts as a deterrent to progressive ambition. Therefore, we also include Number of Districts in State. Ideology is also important, not just in terms of extremity (e.g., Carson 2003, Hibbing 1991a), but also in terms of left-right orientation, with more conservative members more likely to retire early (Murakami 2009). Thus, we include Ideology, which is the first-dimension DW-NOMINATE score (Poole and Rosenthal 1997), as well as the squared first-dimension score, which we deem Ideological Extremity.

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Finally, we need to account for the underlying risk of running, or the underlying likelihood of winning. We do this by including Electoral Security, which is the percentage of the vote the member received in the most recent election. Additionally, since we anticipate that personality should influence how individuals respond to risk, we interact Electoral Security with our ELUCIDATION scores. Table 9.1 shows the results from a series of multinomial logistic regressions of the choices (either to Run for Reelection, Run for Other Office, Resign or Retire, or Accept an Appointment) made by each member during the 104th–112th Congresses, with Run for Reelection as the reference category. Models with and without fixed effects for Congress are estimated. To facilitate substantive interpretation, we also present the results graphically in Figures 9.1 and 9.2.7 Our findings regarding the importance of personality in reelection decisions broadly comport with the hypotheses we laid out to begin this chapter. All personality traits are significant at conventional levels in at least one equation, and their effects are conditioned by the electoral environment. For starters, more Conscientious members act more in line with the electoral incentives at play, which suggests they pay more attention to long-term goals, a finding in line with the framing of Conscientiousness as a time preference. Indeed, in unsafe districts (those where the incumbent’s share of the vote was two standard deviations below the average), an increase in Conscientiousness from two standard deviations below the mean to two standard deviations above decreases the likelihood of running for reelection from about 96% to about 88%; in safe districts (those where the incumbent’s share of the vote was two standard deviations above the average), the same change in Conscientiousness increases the likelihood of running, from 88% to about 98%. Additionally, the effects of Conscientiousness are observed in how incumbents choose which action to take in lieu of running again. More Conscientious members in unsafe districts are more likely to retire and/or resign, whereas the choices in safe districts are about equally distributed between retiring/resigning and running for another office. In no case is the probability of accepting an appointment (which is, of course, conditional on being offered one) materially affected.

7. Both Figures 9.1 and 9.2 were generated by setting all categorical variables to their modes, and all others at their mean values. We also present 90% confidence intervals.

table 9.1 Multinomial Logit Regression Model of Personality and Electoral Decisions

Openness Conscientiousness Extraversion Agreeableness Emotional Stability Electoral Security Electoral Security × Openness Electoral Security × Conscientiousness Electoral Security × Extraversion Electoral Security × Agreeableness Electoral Security × Emotional Stability Age Female Seniority Seniority2 Committee Chair Subcommittee Chair Power Committee Ideology Ideological Extremity Majority Party Party Leadership Number of Districts in State Constant

Run for Other Office

Resign or Retire

Accept Appointment

Run for Other Office

Resign or Retire

Accept Appointment

0.074 (0.365) 0.005 (0.788) 0.438 (0.747) −2.370∗∗∗ (0.388) −0.337 (0.784) −0.081∗∗∗ (0.021) −0.009 (0.007) −0.005 (0.012) 0.009 (0.011) 0.036∗∗∗ (0.009) −0.009 (0.012) −0.032∗∗ (0.013) −0.279 (0.373) 0.819∗∗∗ (0.157) −0.054∗∗∗ (0.013) −1.090 (0.784) −0.774∗∗ (0.343) −0.736∗∗∗ (0.268) 0.687∗∗ (0.315) −0.246 (0.676) 0.056 (0.265) −1.361∗ (0.737) −0.052∗∗∗ (0.012) 4.573∗∗∗ (0.080)

2.239∗∗∗ (0.176) 2.038∗∗∗ (0.612) 0.053 (0.627) −3.849∗∗∗ (0.264) 0.128 (0.516) 0.036∗ (0.020) −0.032∗∗∗ (0.005) −0.033∗∗∗ (0.009) −0.001 (0.009) 0.055∗∗∗ (0.007) −0.004 (0.008) 0.027∗∗ (0.011) −0.021 (0.276) 0.437∗∗∗ (0.077) −0.016∗∗∗ (0.004) −0.096 (0.371) 0.010 (0.270) −0.123 (0.185) 0.969∗∗∗ (0.242) −1.536∗∗∗ (0.560) −0.229 (0.261) −0.151 (0.417) −0.003 (0.006) −6.861∗∗∗ (0.051)

−1.640∗∗∗ (0.211) −0.358∗ (0.210) −3.737∗∗∗ (0.169) 1.451∗∗∗ (0.201) −3.936∗∗∗ (0.150) −0.335∗∗∗ (0.066) 0.035∗∗∗ (0.014) 0.005 (0.012) 0.043∗∗∗ (0.008) −0.019 (0.022) 0.045∗∗∗ (0.015) −0.003 (0.045) −0.141 (1.150) 1.276∗∗ (0.545) −0.072∗∗ (0.034) 0.362 (0.601) −28.261∗∗∗ (0.000) 0.574 (0.661) 1.247 (0.760) −1.967∗∗∗ (0.264) −0.005 (0.644) 0.493 (0.948) 0.009 (0.022) 14.828∗∗∗ (0.053)

−0.037 (0.364) 0.170 (0.784) 0.421 (0.754) −2.728∗∗∗ (0.400) −0.133 (0.798) −0.086∗∗∗ (0.021) −0.007 (0.007) −0.006 (0.012) 0.009 (0.011) 0.040∗∗∗ (0.009) −0.012 (0.013) −0.033∗∗ (0.013) −0.270 (0.374) 0.820∗∗∗ (0.158) −0.054∗∗∗ (0.013) −1.158 (0.788) −0.793∗∗ (0.345) −0.731∗∗∗ (0.269) 0.784∗∗ (0.337) −0.502 (0.718) 0.002 (0.276) −1.376∗ (0.738) −0.052∗∗∗ (0.012) 4.861∗∗∗ (0.080)

2.095∗∗∗ (0.181) 2.116∗∗∗ (0.606) 0.015 (0.630) −3.748∗∗∗ (0.265) 0.150 (0.518) 0.040∗ (0.020) −0.030∗∗∗ (0.005) −0.033∗∗∗ (0.009) −0.001 (0.009) 0.053∗∗∗ (0.007) −0.004 (0.008) 0.026∗∗ (0.011) −0.031 (0.278) 0.445∗∗∗ (0.077) −0.016∗∗∗ (0.004) −0.143 (0.374) −0.007 (0.272) −0.127 (0.186) 1.010∗∗∗ (0.244) −1.709∗∗∗ (0.582) −0.186 (0.262) −0.147 (0.418) −0.003 (0.006) −7.087∗∗∗ (0.050)

−2.487∗∗∗ (0.221) 0.280 (0.209) −4.057∗∗∗ (0.194) −0.401∗ (0.205) −2.937∗∗∗ (0.170) −0.410∗∗∗ (0.065) 0.047∗∗∗ (0.013) −0.002 (0.013) 0.046∗∗∗ (0.008) 0.009 (0.022) 0.029∗ (0.015) −0.004 (0.045) −0.097 (0.528) 1.282∗∗ (0.547) −0.072∗∗ (0.034) 0.080 (0.467) −38.915∗∗∗ (0.000) 0.389 (0.685) 1.417∗ (0.740) −2.810∗∗∗ (0.264) 0.125 (0.664) 0.646 (1.001) 0.007 (0.023) −7.416∗∗∗ (0.059)

Congress FE?

No

Yes

Wald test BIC Log likelihood Num. obs.

1,254,795.076∗∗∗ 2,660.549 −1,038.386 3,321

286,699.961∗∗∗ 2,804.394 −1,025.174 3,321

Notes: Standard errors in parentheses. Observations are at the Congress-member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Reference category is Run for Reelection. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

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figure 9.1. Personality and Running for Election

We also find that Agreeableness acts as predicted, with more Agreeable members acting against their own interests; our results are broadly consistent with selflessness more generally, but they are also consistent with a team player dynamic where the reference group is one’s party. In unsafe districts, more Agreeable members are less likely to retire/resign or run for other offices, and are more likely to run for reelection, presumably in part due to the inherent difficulties in recruiting quality candidates for difficult-to-win seats. Conversely, more Agreeable members in safe districts are less likely to run for reelection, are presumably more likely to succumb to recruitment efforts on behalf of the party and to run for higher office, and are also more willing to step aside for other candidates and to retire/resign. Additionally, there is no substantive effect of Agreeableness on the probability of accepting an appointment.

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figure 9.2. Personality and Other Electoral Decisions

Extraversion also has effects consistent with our framework, as more Extraverted members are more likely to pursue other offices, presumably because they feel the potential rewards from these (generally) higherranked offices are greater than those they might be able to achieve within the House. Interestingly, these effects are stronger for those in safer districts, with the least Extraverted members in both safe and unsafe districts running for a different office less than 1% of the time but the most Extraverted members in unsafe districts running about 5% of the time and those in safe districts running about 12% of the time. This dynamic suggests that members in these districts may have stronger beliefs in their own abilities to win office (potentially due to higher previous vote totals), which is consistent with the parameterization of Extraversion as a sensitivity to potential rewards. However, the effects of Openness and Emotional Stability are murkier. In unsafe districts, higher levels of Openness are associated with

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lower rates of running for reelection and higher rates of retiring/resigning. In safe districts, higher levels of Openness are associated with higher rates of running for reelection and lower rates of retiring/resigning and running for higher office. As we posit that Openness is a proxy for risk preference, this is contrary to our expectations as previously laid out, as running for reelection should theoretically be riskier in unsafe districts. However, we should also consider that the risks in question might not be electoral in nature but instead partisan/institutional. In these cases, more Open legislators might be more willing to flout the wishes of the party—which are likely to manifest as a desire for members in safe seats to run for higher office (since they will be quality candidates, and the party will likely continue to hold safe seats with new candidates) and for those in unsafe seats to continue to run for reelection given their incumbency advantages. While we cannot determine this with certainty, it is a promising avenue for future research. Finally, the effects of Emotional Stability, while statistically significant in the Accept Appointment equation in both models, are substantively negligible and unclear in every case. Nonetheless, regardless of the ambiguous results for Openness and Emotional Stability, the results for Agreeableness and Conscientiousness are as predicted as well as (along with Extraversion) consistent with our core cognitive constraint framework.

9.2 Lame Ducks and the Shadow of Irrelevance Finally, we examine what happens in the final lame duck sessions of members’ careers, once it is apparent they will no longer be serving in elected office in the near future. We also take this opportunity to further showcase the ability of our framework to be parameterized into more formal language, as the core cognitive constraint of Conscientiousness is readily parameterized into mathematical parameters as a discount rate. Consider a simple model of a legislator’s decision to vote/abstain.8 Legislators vote up or down on a bill, effectively choosing between a “bad” policy that provides them with instantaneous policy utility B ∈ R

8. Substantively, this can be considered as a decision to show up physically to vote, and the policy bargaining that takes place beforehand. Therefore, the probabilities of being pivotal or of being whipped are only learned after one has made the decision to show up.

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and a “good” policy that provides them with instantaneous policy utility G ∈ R, where G > B. Regardless of the policy, we assume implementation begins in the following period and the policy utilities are delayed by one period.9 Legislators are pivotal with probability p ∈ (0, 1). If they are pivotal, then the choice to vote/abstain determines the outcome. If they are not pivotal, they do not affect the outcome. In these cases, they receive G with probability q ∈ (0, 1) and otherwise. Pivotal legislators may B be whipped with probability w ∈ 0, 12 .10 If a legislator is whipped, then we assume that if the legislator votes, he or she will vote in accordance with party wishes. Moreover, we assume the party will only whip a legislator if the legislator would have voted contrary to party wishes otherwise. Therefore, if a legislator votes after being whipped, then that individual will vote for the less preferred policy outcome (B). However, if the legislator votes after being whipped, then starting in the following period, he or she will receive some benefit to his or her officeholding benefits ε > 0.11 If the legislator abstains after being whipped, and ensures that his or her favored policy G passes, then he or she will receive the same reduction in officeholding benefits starting in the following period. We further assume that ε < G − B, since party resources are finite and the party can choose to influence other people—presumably those who are most indifferent between outcomes. Legislators come in two types, lame ducks who will no longer be in the legislature in the following periods, and those who will serve for the forseeable future. Legislators earn instantaneous officeholding benefits ω > 0 each period they are in office. We assume that if legislators shirk their responsibilities to vote, they engage in extralegislative activities (which can include personal activities, leisure activities, or even planning for the future) that provide them with an instantaneous extralegislative benefit β > 0. Legislators earn these benefits each period they are not performing legislative duties. Finally, all

9. We assume legislators of all types receive the same policy benefit in period t = 0, so we do not concern ourselves with determining the status quo. 10. This upper constraint reflects the assumption that party leadership will only pressure those legislators that would otherwise vote against party wishes. Arguably, the majority of legislators vote with their parties more than half the time. Moreover, as Minozzi and Volden (2013) note, fewer than half of all votes are whipped. 11. Therefore, lame ducks are not whipped in equilibrium.

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instantaneous utilities are discounted by a factor δ t , where δ ∈ (0, 1) and t denotes the time period under consideration. As we show in the appendix to this chapter, this model lends itself to three testable hypotheses. First, legislators with higher discount factors have higher utilities of voting relative to not voting, largely due to the long-term effects of policy. As Conscientiousness represents time preferences within our framework, it is a useful proxy for the discount factor. Therefore, we should expect that more Conscientious members of Congress should miss votes at lower rates. While this expectation is consistent with several theories of Conscientiousness, specifically examining lame ducks versus non–lame ducks allows us to test our particular parameterization. To wit, lame ducks should be more responsive to changes in Conscientiousness, as they only have two classical Fennoian (1973) pursuits (policymaking and nonelected) to consider, whereas those expecting to serve in elected office in the upcoming year have to consider these two pursuits as well as reelection and the maintenance of influence that will enable them to further their policy goals. Therefore, given fewer constraints, lame ducks should be more willing and able to act according to their own personal preferences and not necessarily those imposed upon them by their parties and/or constituencies; their behavior should therefore be more responsive to changes in their own personal characteristics. Second, our model indicates that the relative utility of voting versus not voting is more affected by changes in the discount factor for lame ducks than non–lame ducks, largely due to the long-term effects on officeholding benefits, to which lame ducks are not privy. Therefore, continuing with our use of Conscientiousness as a proxy for the discount factor, we expect that the relative effect of Conscientiousness should be stronger for lame ducks. Finally, non–lame ducks should always see a larger relative benefit to voting than lame ducks, again due to the long-term effects of officeholding benefits, leading the former to miss votes at lower rates. Before proceeding to the empirical analysis, it should be noted that while lame duck status allows us to compare those members of Congress who have weakened electoral connections to those for whom running for reelection is (potentially) a real concern, this research design poses its own problems. Chief among them is the nonrandom assignment of lame duck status, itself likely correlated with actions taken (or not taken) immediately before the lame duck period. Indeed, candidates who are

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more hardworking may be more likely to win their elections (though we can only speculate as to whether this hypothetical outcome might be due to valence effects or more effective campaigning) and may also be less likely to shirk their legislative duties (therefore providing voters fewer reasons to vote against them). This makes it difficult to separate the effect of lame duck status from the the factors that caused it in the first place. In this context, selection models and instrumental variables models would likely lead to incorrect inferences. The canonical Heckman (1979) selection model assumes incidental truncation and only addresses how that mechanism affects the outcome. However, when the outcome is observed for units that did not select into the sample (and lame duck legislators will still vote), this can lead to inappropriate inferences. Additionally, legislators may be lame ducks due to self-selection or having lost an election, and therefore self-selection only applies to a subset. However, this also suggests an instrumental variables approach will be problematic, as it will be somewhat difficult to find a good instrument or exclusion restriction, since decisions to retire (or pursue a different elected office) and abstain in the lame duck session are both made by the legislator and hence likely influenced by the same or similar factors. Moreover, as mentioned above, there likely exist factors that make legislators more likely to retire (or more likely to lose an election) that also affect their propensity for absenteeism, thus making it difficult to estimate the true effect of lame duck status. Matching can help solve this problem. Though we cannot randomly assign lame duck status, we can estimate the probabilities of each member receiving the lame duck “treatment.” After doing so, we can match members into a sample that consists of sets that have and have not received the treatment in question, with both sets having approximately equal probabilities of receiving the treatment. Here, to calculate the treatment probabilities based on observed pretreatment characteristics theorized to affect treatment assignment (Rosenbuam and Rubin 1983, Rosenbuam and Rubin 1984), we perform nearest-neighbor matching on observable covariates and follow up this matching-based preprocessing with a series of parametric regression models, per Ho, Imai, King, and Stuart (2007). To ensure our results are not dependent on the matching procedure used, we also present results using unmatched data. After matching on observed covariates for every member of the House from the 105th through the 112th Congresses (the 104th Congress had no

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table 9.2 Balance Statistics Nearest-Neighbor Matching Statistic

Full Sample

Matched Sample

Sample Size Treatment Units (Lame Ducks) Control Units (Non–Lame Ducks) Mean Pr(Lame Duck)—Treatment Group Mean Pr(Lame Duck)—Control Group Percentage Improvement in Balance

3,277 328 2,949 0.153 0.098

656 328 328 0.153 0.152 98.872

lame duck session), we present the results in Table 9.2. 12 As can be seen, the probability of being a lame duck is about 6 percentage points higher for those in the treatment groups. In both matched samples, however, the probabilities are more well-balanced. Covariates used in the matching procedure include Ideology and Ideological Extremity, respectively measured by a member’s DWNOMINATE score and its squared term. All Big Five traits are included (as proxies for core cognitive constraints), as are Age and an indicator variable for Female members; these two terms are included because of relationships between these demographic characteristics and personality traits and because members are more likely to retire (and therefore become lame ducks by choice) as they get older. Additionally, Seniority (measured by years of congressional service) as well as dichotomous variables for Majority Party, Committee Chair, Subcommittee Chair, and Power Committee (defined as the Appropriations, Rules, and Ways and

12. We focus on the House—and not the Senate—because of possible concerns about post-congressional employment. Indeed, as Diermeier, Keane, and Merlo (2005) note, the increase in post-congressional wages after service in the Senate is significantly larger than that for former representatives. Moreover, the marginal effect of each additional year in the Senate on expected wages is no less than that of an additional year in the House. Additionally, the non-pecuniary awards accruing to former Senators are significantly higher than those for representatives. Collectively, this implies greater long-term rewards accruing to former senators, for whom increased absenteeism might reflect a desire to pursue long-term gains arising from post-congressional employment; here Conscientiousness might increase absenteeism. To minimize these concerns, we focus on the House. Though there may exist outside options for policy utility leading Conscientious members to spend the lame duck session searching for policy-relevant jobs, this would only attenuate our findings, and our controls for influential legislator types implicitly address this possibility.

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Means Committees) are included. Additionally, Speaker, Majority Party Leadership, and Minority Party Leadership are included to account for the possibility that these more influential legislators may be less likely to retire voluntarily (at least without seeking higher office) and are more able to wield their influence in order to deliver benefits to their districts (though Speakers will likely be less likely to vote overall, due to their service as presiding officers); alternatively, they may be less likely to participate due to their heightened abilities to represent their districts through alternate means. Finally, we include Electoral Security—measured as a member’s vote share in the previous Congress’s election (as retiring members will not have this measure for the current session, and also because vote share in the current Congress’s election is all but equivalent to a posttreatment variable due to strategic expectations)—to capture safety. To uncover the effect of Conscientiousness, we estimate a series of binomial logistic regression models on the matched and unmatched data. In all models, we include a dichotomous variable denoting Lame Ducks who were not going to be serving in any sort of elected office in the next session of Congress (e.g., members who left the House due to successful elections to the Senate or statehouses were not coded as such). We also include independent variables for the Big Five personality traits and interact them with Lame Duck in order to test our hypothesis that the effect of Conscientiousness is contingent on the strength of the electoral connection, which should be minimized for those who will not be holding elected office in the upcoming year. We also include the covariates used in the matching procedures. As Figure 9.3 shows, for all levels of Conscientiousness, lame ducks miss votes at higher rates, consistent with our predictions.13 Also consistent with our expectations is the finding that, for both lame ducks and non–lame ducks, higher levels of Conscientiousness are associated with lower rates of abstention. However, the effects of Conscientiousness vary across legislator type. As Conscientiousness increases from two standard deviations below the sample mean to two above, the rate of absenteeism for lame ducks decreases from about 25–30% to about 10–15%. For other members of Congress, increases in Conscientiousness are associated

13. Figure 9.3 is based off Models 2 and 5 in Table 9.3, and was created by setting the continuous (categorical) variables to their means (modes); 90% confidence intervals are also presented.

table 9.3 Binomial Regression Models of Personality and Lame Duck Absenteeism Unmatched Original Data

Lame Duck Openness Conscientiousness Extraversion Agreeableness Emotional Stability Lame Duck × Openness Lame Duck × Conscientiousness Lame Duck × Extraversion Lame Duck × Agreeableness Lame Duck × Emotional Stability Majority Party

Nearest-Neighbor Matched Data

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

2.550∗∗∗ (0.349) 0.154∗∗∗ (0.043) −0.095∗∗∗ (0.037) −0.113∗∗∗ (0.027) −0.059 (0.072) 0.062 (0.044) 0.415∗∗∗ (0.075) −0.180∗∗∗ (0.065) 0.029 (0.047) −0.673∗∗∗ (0.121) 0.107 (0.078) —

2.275∗∗∗ (0.364) 0.130∗∗∗ (0.045) −0.165∗∗∗ (0.039) −0.083∗∗∗ (0.028) −0.070 (0.075) −0.001 (0.047) 0.406∗∗∗ (0.078) −0.163∗∗ (0.067) 0.090∗ (0.049) −0.527∗∗∗ (0.126) −0.033 (0.082) −0.203∗∗∗ (0.033) 0.281∗∗∗ (0.032) 0.228∗∗ (0.093) 0.017∗∗∗ (0.001) 0.252∗∗∗ (0.035) 0.004∗∗ (0.002) 0.030∗∗∗ (0.004) −0.444∗∗∗ (0.069) −0.048 (0.038) −0.012 (0.029) 0.412∗∗ (0.209) −0.031 (0.078) −0.033 (0.087) —

2.090∗∗∗ (0.381) 0.080∗ (0.048) −0.231∗∗∗ (0.041) −0.055∗ (0.030) 0.035 (0.079) −0.050 (0.051) 0.466∗∗∗ (0.082) −0.200∗∗∗ (0.071) 0.142∗∗∗ (0.051) −0.591∗∗∗ (0.132) 0.031 (0.086) −0.195∗∗∗ (0.034) −0.079 (0.103) 0.183∗ (0.106) 0.013∗∗∗ (0.001) 0.204∗∗∗ (0.039) 0.003∗ (0.002) 0.047∗∗∗ (0.004) −0.496∗∗∗ (0.072) −0.053 (0.039) −0.027 (0.031) 0.306 (0.209) −0.091 (0.080) −0.044 (0.088) −0.343∗∗∗ (0.087)

1.160 (0.708) 0.079 (0.136) 0.033 (0.110) −0.100 (0.082) −0.204 (0.226) −0.328∗∗ (0.128) 0.480∗∗∗ (0.149) −0.307∗∗ (0.123) 0.023 (0.091) −0.514∗∗ (0.246) 0.487∗∗∗ (0.143) —

1.658∗∗ (0.677) 0.057 (0.137) −0.213∗ (0.111) 0.074 (0.084) 0.272 (0.218) −0.749∗∗∗ (0.131) 0.557∗∗∗ (0.150) −0.215∗ (0.124) −0.064 (0.092) −0.865∗∗∗ (0.241) 0.671∗∗∗ (0.147) −0.330∗∗∗ (0.057) 0.513∗∗∗ (0.060) 0.994∗∗∗ (0.180) 0.025∗∗∗ (0.002) −0.037 (0.070) −0.011∗∗∗ (0.002) 0.030∗∗∗ (0.006) −0.581∗∗∗ (0.119) 0.017 (0.063) 0.141∗∗∗ (0.046) −13.138 (224.605) −0.439∗∗ (0.193) −0.725∗ (0.403) —

1.775∗∗ (0.704) 0.045 (0.138) −0.238∗∗ (0.115) 0.135 (0.091) 0.316 (0.230) −0.922∗∗∗ (0.143) 0.726∗∗∗ (0.154) −0.302∗∗ (0.129) −0.029 (0.100) −1.131∗∗∗ (0.259) 0.829∗∗∗ (0.161) −0.377∗∗∗ (0.064) 0.152 (0.198) 0.238 (0.213) 0.022∗∗∗ (0.002) 0.009 (0.080) −0.019∗∗∗ (0.003) 0.057∗∗∗ (0.008) −0.660∗∗∗ (0.132) −0.012 (0.070) 0.106∗ (0.055) −13.590 (370.310) −0.481∗∗ (0.211) −0.492 (0.428) −0.461∗∗∗ (0.156)

Ideology

—

Ideological Extremity

—

Electoral Security

—

Female

—

Age

—

Seniority

—

Committee Chair

—

Subcommittee Chair

—

Power Committee

—

Speaker

—

Majority Party Leadership

—

Minority Party Leadership

—

Democrat

—

— — — — — — — — — — — — —

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182 table 9.3 (Continued) Unmatched Original Data

Constant

State FE? Congress FE? Wald Test BIC Log Likelihood Num. obs.

Nearest-Neighbor Matched Data

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

−2.540∗∗∗ (0.208)

−3.631∗∗∗ (0.236)

−6.307∗∗∗ (0.535)

−1.164∗ (0.649)

−2.545∗∗∗ (0.625)

−6.123∗∗∗ (1.223)

No No

No No

Yes Yes

No No

No No

Yes Yes

248.331∗∗∗ 20,103.956 −10,003.281 3,348

237.218∗∗∗ 19,260.341 −9,528.987 3,277

251.533∗∗∗ 18,516.733 −8,926.484 3,277

206.179∗∗∗ 5,730.253 −2,826.210 656

248.765∗∗∗ 5,251.891 −2,544.868 656

309.044∗∗∗ 4,950.844 −2,212.732 656

Notes: Standard errors in parentheses. Observations are at the Congress-member level. Null hypotheses for the Wald tests are that all coefficients related to the personality traits are zero. Two-tailed tests: ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗ p < 0.1

with comparatively small decreases in the abstention rate (from about 4–7% to about 3–4%, depending on whether matching is used). Relatedly, the expected difference in absentee rates between lame ducks and non–lame ducks clearly decreases as Conscientiousness increases, falling from about 20–25 percentage points at the low end of Conscientiousness to about 5–10 percentage points at the high end. Collectively, these results support our predictions of stronger effects of Conscientiousness on lame ducks. Moreover, these results support the proposition that not only does Conscientiousness decrease absenteeism but that it also represents a preference between long-term (policy) benefits and short-term (officeholding) benefits. When the prospect of short-term officeholding benefits is severely curtailed (as it is for lame ducks), and electoral and partisan pressures are at least somewhat reduced, then legislators are freer to place greater weight on their own preferences, not just in terms of policy (Ramey 2015b), but also in terms of tactics. Free from longterm partisan consequences of missing votes, less Conscientious members miss votes at higher rates, whereas more Conscientious members (who care more about long-term policy and partisan implications) only see a mild dropoff in abstention rates, in part due to the maintenance of these external incentives. Overall, all of our hypotheses are supported.

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figure 9.3. Personality and Lame Duck Absenteeism

9.3 Moving On: Conclusion In this final substantive chapter, we have explored the role of personality in the decision to run for reelection, retire, run for higher office, or (if offered) to accept an executive appointment. We have also investigated how personality affects the behavior of members after elections, in lame duck sessions. More Extraverted members are more likely to run for higher office, regardless of the electoral security of their seats. More Agreeable members are more likely to behave contrary to their electoral interests, as they are more likely to run for reelection in unsafe districts (instead of running for a different office or retiring and spending more time preparing for their likely post-congressional careers) and more likely to step aside in safe districts and either run for other office and/or retire/resign. Finally, more Conscientious members are more likely to “think down the game tree” and act more in line with strategic incentives, given their electoral environment; those in safer districts are more likely to run for reelection, and those in more competitive districts are more likely to retire and/or resign. Additionally, we have shown that Conscientiousness has strong and expected effects on member behavior after elections. More Conscientious members are less likely to abstain from votes, but the effects are conditional on whether the member is expecting to serve in elected office after

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the session ends. The behavior of lame duck members is affected more by changes in Conscientiousness, whereas those that plan on representing some electorate in the new year are also affected by their underlying personality traits but to a much lesser extent. Collectively, these results show that the decisions that lead to the end of one’s congressional career—as well as those that take place after it is clear the end is nigh—are influenced by personality. Moreover, as with the other dynamics we have examined in this book, they can be derived from our larger core cognitive constraint framework.

9.4

Appendix: A Model of Legislative Voting

Given the model as described in the main text, the utility functions for lame duck legislators are as follows: EU (Vote|LD) = EU (¬Vote|LD) =

∞ 

δ i (pG + (1 − p) (qG + (1 − q)B)) + ω +

∞ 

i=1

i=1

∞ 

∞ 

δ i (pB + (1 − p) (qG + (1 − q)B)) + ω +

i=1

δi β δi β

i=0

And those for non–lame duck legislators are EU (Vote|NLD) =

∞ 

δ i (p(wB + (1 − w)G) + (1 − p) (qG + (1 − q)B))

i=1

+ω

∞ 

δ i + pwε

i=0

EU (¬Vote|NLD) =

∞ 

∞ 

δi

i=1

δ i (p(wG + (1 − w)B) + (1 − p) (qG + (1 − q)B))

i=1

+ω

∞  i=0

δ i − pwε

∞ 

δi + β

i=1

First examine the decision of lame duck legislators. By assumption, they will vote if EU (Vote|LD) − EU (¬Vote|LD) > 0:

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EU(Vote|LD) − EU(¬Vote|LD) =

∞ 

δ i (pG + (1 − p)(qG + (1 − q)B))

i=1

+ω+

∞ 

δi β −

i=1

∞ 

δ i (pB + (1 − p)

i=1

× (qG + (1 − q)B)) − ω −

∞ 

δi β

i=0

=

δp(G − B) −β 1−δ

> β, or if the long-term difference Therefore, they will vote if δp(G−B) 1−δ in policy outcomes is greater than the instantaneous extralegislative benefits. We can examine the effect of the discount factor on the decision of lame ducks by taking the derivative of this value with respect to δ: ∂ ∂δ



δp(G − B) −β 1−δ

 =

p(G − B) (1 − δ)2

By the assumption of G > B, this is always positive. Therefore, increases in the discount factor always make voting more attractive than not voting. Proposition 1 is therefore derived: Proposition 1 For lame duck legislators, increases in the discount factor strictly increase the utility of voting relative to not voting. Now examine the decision of non–lame duck legislators to vote. By assumption, they will vote if EU (Vote|NLD) − EU (¬Vote|NLD) > 0:

EU (Vote|NLD) − EU (¬Vote|NLD) =

∞ 

δ i (p(wB + (1 − w)G)

i=1

+ (1 − p) (qG + (1 − q)B)) −

∞ 

δ i (p(wG + (1 − w)B)

i=1

+ (1 − p) (qG + (1 − q)B))

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+ pwε

∞ 

δi + ω

i=1

+ pwε

∞ 

∞  i=0

δi − ω

∞ 

δi

i=0

δi − β

i=1

δ(p(G − B)(1 − 2w) + 2pwε) = −β 1−δ > β, or if the longTherefore, they will vote if δ(p(G−B)(1−2w)+2pwε) 1−δ term difference in policy outcomes and officeholding benefits is greater than the instantaneous extralegislative benefits. We can examine the effect of the discount factor on the decision of non–lame ducks by taking the derivative of this value with respect to δ: ∂ ∂δ



δ (p(G − B)(1 − 2w) + 2pwε) −β 1−δ

 =

p(G − B)(1 − 2w) + 2pwε (1 − δ)2

By the assumptions of G > B, w ∈ 0, 12 , and ε > 0, this quantity is always positive. Therefore, increases in the discount factor always make voting relatively more attractive than not voting. Proposition 2 is therefore derived: Proposition 2 For non–lame duck legislators, increases in the discount factor strictly increase the utility of voting relative to not voting. Propositions 1 and 2 lead to Corollary 1: Corollary 1 For all legislators, increases in the discount factor strictly increase the utility of voting relative to not voting. We can also look at the first derivatives of the differences to examine the relative effects of changes in the discount factor on the likelihood of voting. This simply involves looking at the differences in the first derivatives of the differences: ∂ ∂δ



=

   ∂ δ (p(G − B)(1 − 2w) + 2pwε) δp(G − B) −β − −β 1−δ ∂δ 1−δ (G − B − ε)2pw (1 − δ)2

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If this value is positive, then the relative utility of voting versus not voting is affected more for lame ducks than non–lame ducks by changes in the discount factor. Since ε < G − B by assumption, this is always true. Therefore, Proposition 3 is derived: Proposition 3 Lame duck legislators are more sensitive than non–lame duck legislators to changes in the discount factor. Finally, look at the relative benefits of voting across types by comparing the differences in differences in utility. Recall that the relative benefit of voting for lame ducks is δp(G − B) −β 1−δ and the same for non–lame ducks is δ (p(G − B)(1 − 2w) + 2pwε) −β 1−δ Therefore, if the difference between the two is less then zero, than non–lame ducks always see a larger relative benefit to voting than do lame ducks. It is trivial to show that δp(G − B) −β − 1−δ



reduces to −

δ (p(G − B)(1 − 2w) + 2pwε) −β 1−δ



2δpw(G − B − ε) 1−δ

and this quantity is always negative, since G−B > ε. Therefore, non–lame ducks always see larger relative benefits to voting than do lame ducks, and Proposition 4 is derived: Proposition 4 The benefit of voting relative to abstaining is always smaller for lame duck legislators relative to non–lame duck legislators. From these results, we can develop empirical hypotheses. Corollary 1 suggests that legislators with higher discount factors have higher utilities of voting. Using our core cognitive constraint framework, it is apparent

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that Conscientiousness is a ready proxy for the discount factor. Therefore, Hypothesis 1 is derived. Hypothesis 1 More Conscientious members of Congress should miss votes at lower rates. Next, Proposition 3 indicates that the relative utility of voting versus not voting is more affected by changes in the discount factor for lame ducks than are non–lame ducks. Continuing with the parameterization of Conscientiousness as a proxy for the discount factor, Hypothesis 2 is readily derived: Hypothesis 2 The relative effect of Conscientiousness should be stronger for lame ducks. Finally, Proposition 4 suggests that non–lame ducks always see a larger relative benefit to voting than do lame ducks. Therefore, Hypothesis 3 is readily derived: Hypothesis 3

Lame duck legislators should miss votes at higher rates.

chapter ten

More than a Conclusion: Personality, Politics, and Polarization The congressmen and senators used to go have a drink in D.C. They would disagree all day long, but they would find that time to sit down and learn about each other personally. I think that’s totally wiped out; I don’t think it really exists anymore. That’s how politics should work: You disagree with someone, but then you have a drink with them and try to see their side, and they try to see your side. I don’t know if that’s in the climate at all anymore. —Zach Galifianakis1

T

hroughout the course of this book, we have made the argument that the actions taken by legislators in pursuit of policy outcomes have qualities of their own that are evaluated differently between individuals, and that these qualities can be captured by the Big Five personality taxonomy. Importantly, we have linked each of the Big Five traits to a particular modelable parameter, and this approach has enabled us to generate empirically testable hypotheses. These hypotheses have taken on varying degrees of formalism, from the “informal” theories presented in Chapters 4 through 7, to the more rigid formal models outlined in Chapters 8 and 9. We believe these varied examples showcase the value of our framework, as the underlying concepts can be mathematically formalized in important ways, but they by no means need to be. We hope these examples and the underlying theory will be of use to scholars of Congress in particular as well as to the study of elites and institutions in general.

1. Josh Modell, “Campaign Stars Zach Galifianakis and Will Ferrell Talk Politics and Pee,” A.V. Club, August 8, 2012, http://www.avclub.com/article/icampaign-istars-zach -galifianakis-and-will-ferrel-83493.

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In this final chapter, we review our major theoretical arguments, methodological approach, and empirical findings. We proceed to discuss the implications for these findings on the study of elite behavior in institutions beyond the US Congress. Last, we propose ways of extending the study of elite behavior in institutions going forward.

10.1

Personality and the Congressional Life Cycle

In Chapter 2 we laid out a theoretical framework for incorporating the Big Five into formal and informal rational choice-based models of political institutions. We examined the historical development of the five-factor approach to trait personality as well as much of the evidence for this approach and the properties of the five-factor structure. Substantial support has been found for a biological basis of personality traits, and we leveraged this grounding of personality by using findings from neuropsychology to identify core cognitive constraints for each of the Big Five rooted in brain functioning. We proceeded to model the identified core cognitive constraints as parameters that may be easily used in existing institutional models, which we applied throughout the book. In Chapter 3, we presented a technique to measure the personality traits of legislators using their speech. Crucially, this method, unlike methods for ideologically scaling text, is agnostic as to the substance of speech. The method’s use of dictionaries (LIWC and MRCPD) that measure the psycholinguistic properties of words as opposed to the meaning of words themselves is generally applicable to other settings. We showed that precisely estimated personality traits of American elites are indeed possible to obtain, as long as the appropriate amount of text is available for analysis. We believe this paves the way for a rich agenda exploring the personality traits of other elites, including presidents, judges, bureaucrats, state legislators, and even those in non-American institutional settings. We began our examination of the congressional lifespan in Chapter 4, by opening with a focus on the environment incumbent members face during reelection. Specifically, we addressed the role of incumbent personality in deterring quality challengers from entering the race and in driving the decision to save or transfer campaign funds. We found that House members who are more Conscientious and Extraverted—that is, those who favor the long-term benefits of institutional power and policy which come with an increased focus on Washington over constituency

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service—are more likely to attract quality challengers. Agreeable members of Congress (who may be more sensitive to the benefits of their copartisans and neglect constituency service) are also more likely to draw quality challengers. We find that incumbent personality traits are significantly associated with campaign disbursements as well, as Extraverts and Neurotics disburse significantly more funds than their less Extraverted and Neurotic peers, due to their sensitivity to gains and negative outcomes from institutional power and policy. Finally, more Conscientious members, who place a higher value on having campaign funds available in the future, are less likely to disburse from their campaign committees. Moving beyond reelection, in Chapter 5 we illustrated the importance of personality in determining the internal organization of Congress. Members who are more Open and Emotionally Stable are less likely to receive appointments to powerful committees, presumably because these people are less easily controlled by the larger partisan organizations. More Open members will be more willing to take risks, thus creating uncertainty in the types of bills that will be voted out of committee. Because of this, the party organizations will be very hesitant to place members of these types on committees. Similarly, more Emotionally Stable members will be less sensitive to the potential punishments (e.g., being removed from plum committees) accorded to those deviating from party orthodoxy, and will therefore be less easily controlled by the party. As such, they will be less likely to be placed on powerful committees. Conversely, more Agreeable members, placing more weight on the preferences of others (e.g., their parties), will be more likely to be placed on committees. And more Extraverted members will be more likely to be placed on powerful committees, simply because they place greater weight on the possible rewards from these seats, resulting in demand-side dynamics; these members will also be more willing to pursue big policy changes desired by their parties. Finally, committee chairmanships are more likely to be given to more Conscientious members, due to the long-term planning necessitated by these positions. In Chapter 6, we discussed the legislative process itself, focusing on bill proposals and legislative effectiveness. Here, we found Conscientiousness to be particularly important, as more Conscientious members of Congress are more likely to propose substantive—as opposed to symbolic—legislation and also more likely to be more effective legislators overall. Both of these dynamics can be traced back to the role of Conscientiousness as a measure of time preference. As more substantive

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bills should require more effort in the drafting process and will also be more contentious than symbolic bills empty of substance, they will require greater amounts of effort and time to propose and shepherd through the legislative process, and more Conscientious members will be willing to expend the necessary time and effort. For similar reasons, more Conscientious members will be more effective overall. In Chapter 7, we used the Big Five to understand the microfoundations of contemporary Washington dysfunction. We found that more Open legislators are more likely to rebel in the House and more Agreeable legislators are more likely to filibuster in the Senate. Additionally, we showed that personality has a significant role in whether or not legislators choose to work with members of the other party. The cosponsorship patterns of those who are less Open (and therefore more risk-averse) and/or less Emotionally Stable (and therefore more focused on potential negative outcomes) were found to be strongly affected by their electoral security, and the most Open members were found to be less affected by changes in their electoral prospects, due to a decreased risk sensitivity and lesser focus on negative outcomes; Extraversion had the opposite effect, with more Extraverted members acting more in line with the electoral incentives of their districts. Our results suggested that more Agreeable legislators are more sensitive to the preferences of their constituents and parties when it comes to the decision of whether or not to work with the opposing party. In Chapter 8, we examined how personality traits affect the home style of legislators. First, we presented a formal model of technology adoption to explain the linkages between the Big Five and the decision of Congress members to adopt Twitter as a communication tool. Empirically, we found that Emotionally Stable and Neurotic legislators diverge considerably when it comes to their dependence on favorable signals in making their decision to adopt Twitter. Second, we reexamined Grimmer’s (2013) study of senatorial press releases and showed that all Big Five traits impact whether legislators choose to credit-claim or position-take. One of the strongest effects found was for Agreeableness, with increased Agreeableness associated with considerably more credit-claiming among senators. Finally, in Chapter 9 we came full-circle and reexamined the decision to run for reelection or retire, as well as behavior within the chamber after the election. We found that more Agreeable members are more

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willing to put aside their own interests in favor of those of the party. They are more likely to retire when they are in safe seats—presumably due to pressure from party leadership to step aside so another candidate may be groomed—and less likely to do so when the seat is competitive— presumably because the incumbent will be the party’s best chance to keep the seat. More Extraverted incumbents will be more likely to run for higher office, regardless of the safety of their seats, due to the larger potential rewards these offices offer. In the lame duck periods occuring immediately after elections, Conscientiousness plays a major role in explaining legislators’ decisions to participate in the legislative process. Across all types of legislators, more Conscientious members have lower absence rates in lame duck sessions. However, the effects of Conscientiousness are heterogenous—the personality trait has a much stronger relationship with absence rates for lame duck members than non–lame ducks. For those members who will not be holding any elected office in the next Congress, the effect of Conscientiousness is strong, with the most Conscientious lame ducks much more likely than less Conscientious lame ducks to show up and vote (and only slightly less likely to do so than non–lame ducks). Our investigation of the roles that the Big Five personality traits play throughout the congressional life cycle began with the creation of an ambitious framework for modeling personality and generating predictions for congressional behavior. When we consider our findings as a whole, we identify several major takeaways that should help to further develop this tentative common framework. We found broad support for most of the hypotheses derived through decision-theoretic modeling of Conscientiousness, Openness to Experience, Extraversion, and Emotional Stability as laid out in Chapter 2. Our data suggested that Conscientious legislators took actions that were linked to delayed payoffs, and more Open legislators were willing to take greater risks in rebelling against party leadership and adopting new technologies, and this predilection toward unpredictability leads them to be less likely to be assigned to powerful committees. In addition, we found that Extraverts disburse more campaign funds and are more likely to run for higher office, due to their focus on potential rewards of action over other considerations, and we found that less Emotionally Stable members are more inhibited in adopting new technology, though they also disburse more funds to avoid negative outcomes. These larger patterns are encouraging in that

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we believe they provide a great deal of support for the framework we use to identify what could be considered unusual predictions for the influence of personality on elite political behavior. On the other hand, in the framework we use we began by parameterizing Agreeableness as a politician’s altruistic utility for the welfare of the nation. Personality neuroscience suggested Agreeableness was associated with deriving utility from the well-being of others, but many different “others” were plausible, and we assumed that the nation was best justified as the reference group for this model, given the salience of the nation in federal policymaking. However, repeatedly through this book, we found Agreeableness to be associated with taking actions in support of party rather than the nation as a whole or norms of cooperative statesmanship. This suggests that, going forward, an individual’s party is the most appropriate reference group to use for modeling the altruistic utility of Agreeableness in federal legislators, though few other broad changes to our model appear necessary.

10.2

Personality and Congress as an Institution

At this point, we shift our focus from the effects of personality on the behavior of individual members of Congress to the effects of personality on Congress as an institution, and in particular on the oft-discussed phenomenon of polarization. Until now, we have distinguished between ideological polarization and tactical polarization, where the latter has been linked with personality traits. While these two can be considered distinct, the correlations between the Big Five and ideology found in Chapter 3 suggest there is at least some overlap in the two phenomena. While we would like to explore this in great detail, we have much less data (since we only have personality estimates for 1996–2014), so we can only speculate. Nonetheless, the data we do have suggest that all five Big Five traits are correlated with congressional polarization (as measured as the absolute distance between the mean DW-NOMINATE scores for the Democratic and Republican parties) in ways consistent with our theory. Figure 10.1 shows the trends in both ideological polarization and the Big Five over time for both the House and the Senate. In both chambers, Openness has tended to decrease over time, whereas polarization has been on a secular increase.

figure 10.1. Personality and Polarization in the House and Senate over Time

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Similarly, Conscientiousness has been on the decline in both chambers, though the pattern is much more prominent in the House. These patterns are consistent with a scenario wherein less Conscientious members focus more on the short(er)-term gains accrued from scoring political points against the opposing party and winning reelection, and neglecting the longer-term gains that may be earned by fostering collegial relationships with members of the opposing party or engaging in the legislative process. This pattern would only be exacerbated in times of high polarization, where the perceived short-term opportunities seem greater and the possibility of long-term working relationships (and successful passage of legislation) seem more remote; in this scenario, high polarization would lead to less Conscientious members cooperating less, which would in turn result in higher levels of polarization. However, this pattern is also consistent with a scenario wherein less Conscientious members are more likely to win primaries because they will be rewarded on Election Day for scoring political points as opposed to fostering long-term relationships and working on substantive legislation. Once again, we detect a distinct relationship but lack the data to pin down the causal mechanism. Extraversion, with its hypothesized role as a weight on potential rewards, also works as expected. Alone among the Big Five in our illustration is the indication of an overall increase in the mean level of Extraversion in both chambers, suggesting that the average member of Congress has become more sensitive to the potential rewards resulting from his or her actions. Coupled with the decrease in Conscientiousness— and the corresponding decrease in long-term preferences in favor of short-term preferences—this suggests that members in both chambers have become more intensely focused on short-term rewards, which by their very nature do not include constituency service or fostering longterm working relationships with members of the other party. Instead, their focus on reward leads to a focus on advancement within the chamber, gained through ideologically polarizing behavior and party service. However, as with Openness and Conscientiousness, this is but one scenario, and it may be that polarization has been the driving force behind the increase in Extraversion—or that the two traits reinforce each other— due to changing preferences at the candidate selection level, though, again, at the present time we lack the data to know for certain. Likewise, despite our findings, our data at present do not allow us to distinguish between a lack of Agreeableness as a cause of polarization, a

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result of it, or related (or unrelated) to it in some different way. Indeed, while the most obvious conclusion might be that polarization is a direct cause of disagreeable legislators failing to see the benefits of compromise, it may be that in times of high ideological polarization voters intentionally choose more disagreeable candidates who will be less willing and/or able to compromise. We cannot be certain which direction the causal arrow points, but this is clearly an area where future research would be highly valued. Though we are not in a position to tease out the causal arrows in the relation between personality and congressional polarization, we can dig a little deeper into the data and attempt to peel back at least one additional layer of the onion. We know that members of Congress are coming and going—specifically, some members are entering (particularly in the large freshman classes of 1994, 2006, 2008, and 2010) and others are retiring. These incoming and outgoing cohorts of members may themselves display differing levels of the Big Five traits, which may in turn help to explain variation in the trends discussed above. A simple way to examine these ideas is to look at the relationship between each of the Big Five traits and tenure across chambers. Figure 10.2 presents regression lines and confidence intervals derived from simple linear regressions of each trait on tenure (as measured in years served). While clear variation is evident across the traits and chambers, some fairly stark patterns hold across contexts. In both the House and the Senate, younger generations of Congress members (e.g., those with low tenure) are significantly less Open, Conscientious, Agreeable, and Emotionally Stable than members who have been around for a while. This finding, given the aggregate temporal trends shown if Figure 10.1, seems to suggest that newer cohorts of Congress members are likely behind the secular decrease over time in these four traits. If these strong relationships were to hold for successive cohorts, the resulting effects on congressional dysfunction would be debilitating. Putting all of these results together, we find patterns suggesting that personality and ideological polarization are related in expected ways. While more data are needed, and over a longer period of time, to tease out the causal direction of the relationship, these very preliminary results suggest that scholars of polarization—as well as the media writ large— should note how the distributions of personality traits within Congress have changed in recent years. Members of Congress have become, in

figure 10.2. Personality and Tenure in the House and Senate over Time

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the aggregate, less Conscientious, less Agreeable, less Open to Experience and, by our theoretical framework, less future-oriented, less concerned about the welfare of others, and more risk-averse. In other words, the Congress of 2014 compared to that of 1996 is populated with relatively selfish, short-sighted cowards, and this undoubtedly contributes to the tactical polarization we see in the contemporary institution.

10.3 Personality and the Future Study of Elites and Institutions One of our principal motivations in writing this book was to establish theoretical and methodological approaches for microfounding elite behavior in American institutions. While this book has focused on the United States Congress, we hope others will apply our framework to the study of other political institutions. Countless elite bodies within the American context and beyond have rich textual sources that may be fed into algorithms similar to that presented in Chapter 3 to estimate the personality traits of members of those bodies. For example, one could use Supreme Court opinions or presidential speeches to measure the personality traits of justices or presidents, respectively. In turn, the theoretical framework from Chapter 2 can be applied to develop precise predictions for behavior in those different contexts. Indeed, our theoretical framework will greatly aid the study of elite behavior in institutions going forward. Scholars of the courts, presidency, bureaucracy, or other legislative environments now have a solid foundation for incorporating the personality of decision makers into both theoretical and empirical applications in those diverse environments. While our approach represents a great advance in the study of elite behavior, there are still yet further areas for theoretical and empirical refinement. On the theoretical side, our focus was on parameterizing the Big Five. This was driven by both the evidence for the Big Five’s importance in the psychology literature and the overwhelming influence of this framework in the existing political science literature. However, as we noted in Chapter 2, recent research in personality psychology has sought to break the Big Five into thirty finer-grained subunits, or facets. The HEXACO model of personality traits, also called the Big Six, incorporates a sixth dimension of Honesty-Humility, and it is gathering adherents in the psychology community (Ashton and Lee 2009). Parameterizing the Big Six, and the thirty Big Five facets are theoretical goals going forward.

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Empirically, we would like not only to develop novel recognizers for these facets but also to develop new macro-level recognizers for moral foundations, authoritarianism, and other traits of emerging scholarly interest. Improving our measurement of Big Five text recognizers using new corpora such as social media text (e.g., Twitter) and languages beyond English would allow us to generate estimates for nonofficeholding elites and politicians outside the Anglosphere. We also seek to refine the existing recognizer through a new round of laboratory experiments (akin to the original Pennebaker and King [1999] study). In this endeavor, we are keenly interested in using recent advances in machine learning to measure elite personality in as fine grained a manner as is possible. The revolutionary field of neural networks presents a rich avenue for such further explorations. The passive measurement of traits for previously unreachable political elites appears to be full of potential. Last, but certainly not least, while our theory generally qualifies and explains congressional behavior in predictable and expected ways, several of our findings were somewhat unexpected. The altruistic motivations captured by Agreeableness within different political contexts; the precise relationships between Emotional Stability, Openness to Experience, and Extraversion on one hand and risk and uncertainty on the other; and the way elites perceive actions and payoffs to be risky, delayed, negative, rewards, or beneficial to key groups all require further articulation through analysis of survey data and experiments. These puzzles are fertile grounds for future research, as they will allow us to further explore and refine the framework. Much work remains, and this book represents several first steps. That said, the basic argument of this work is unquestionable—personality matters both theoretically and empirically in the study of elite behavior. The theory and methods presented in Chapters 2 and 3, coupled with the myriad examples in Chapters 4–9, equip scholars of American institutions with an unrivaled tool kit for deepening our knowledge of why, how, and when political elites behave the way they do.

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Index Page numbers followed by f and t indicate figures and tables. 16PF typology, 22–24, 27–28 absenteeism, 167, 169, 178–182, 183f academic performance, 13, 32, 40 Affordable Care Act, 3–5, 7 Agreeableness: altruism and, 38t, 43–44, 48–49, 81, 86–87, 91, 94–96, 134, 136, 165, 196, 202; campaign disbursements and, 92t, 94; challengers and, 86; cognitive constraint for, 44, 48t, 91, 136; committee assignments and, 99, 101–102, 104, 107, 108t, 110–115, 123t, 127t, 129t; credit-claiming and, 163t, 194; defining terms for, 38t; divorce and, 44; face validity and, 67; fusiform gyri and, 44; future studies of, 202; lame ducks and, 183; legislation issues and, 118; legislative speech and, 57, 67–68, 193; lexical approach and, 44; mathematical modeling of, 21, 24–25, 27–31, 38t, 43–44, 48–49, 51, 195; media and, 158t, 160, 162–165, 194; mortality and, 44; moving on and, 168, 172t, 173, 175, 181t, 194–195; national welfare and, 196; other-mindedness and, 43–44; party discipline and, 134, 136–140, 142, 143t, 145–150, 174, 196; perception of others and, 44; polarization and, 198–199, 201; political science and, 29; posterior cingulate cortex and, 44; press releases and, 1, 162–164; risk and, 198; securing reelection and, 81–82, 84t, 86, 92t, 94–95;

social relationships and, 43–44; strategic interactions and, 51; trait taxonomy of, 12; trust and, 43–44; Twitter and, 158t; well-being of others and, 48–49, 110–111, 196 AIDS, 30 Aldrich, John H., 97 algorithms, 18, 58, 137, 201 Allport, Gordon, 11 altruism: Agreeableness and, 38t, 43–44, 48–49, 81, 86–87, 91, 94–96, 134, 136, 165, 196, 202; capacity for, 44, 48t, 91, 136; mathematical models and, 38t, 43–44, 48–49; securing reelection and, 81, 86–87, 91, 94–96; understanding others’ beliefs and, 44, 48; well-being of others and, 49 Americans for Democratic Action (ADA) scores, 67–68 anxiety, 38t, 44–45, 71t Appropriations Committee, 101, 102n8, 104, 121, 162–164, 170, 179 authoritarian personality, 11, 13, 202 Barber, James David, 8 Bayesian methods, 137 Big Five. See five-factor model; specific trait Big Five Inventory (BFI), 26 Big Seven, 28–29 Big Six, 28–29, 42 Big Three, 12, 22–23, 28, 45

234 Big Two, 42, 45 Binder, Sarah A., 133, 146 Bipartisan Campaign Finance Reform Act, 116–117 bipartisanship, 3, 116, 134, 140–145, 150 Blunt, Roy, 4–7 Boehner, John, 3 Bon Jovi, 59–60 Booker, Cory, 151–152 Booth, Roger J., 58 Borghans model, 15 California Personality Inventory, 11 campaign disbursements, 19; Agreeableness and, 92t, 94; Conscientiousness and, 91, 92t, 94; Emotional Stability and, 92t; Extraversion and, 92t, 93, 193; Neuroticism and, 93, 193; Openness and, 92t; regression analysis of, 92t; securing reelection and, 76, 87–95; war chests and, 75, 77, 89–90, 95, 134 campaign finance reform, 116–117 Cantor, Eric, 75, 78–79 capacity for altruism, 44, 48t, 91, 136 Caprara, Gian Vittorio, 9 Carson, Jamie L., 82–83 Cattell, Raymond B., 23 Cervone, Daniel, 9 chairmanships: becoming chair and, 111–114; committee assignments and, 90, 97–99, 103–104, 111–115, 121, 123t, 127t, 129t, 136, 138t, 139, 143t, 166, 170, 172t, 179t, 181t, 193; legislation issues and, 121, 123t, 127t, 129t; moving on and, 166, 170, 172t, 179–180, 181t; party discipline and, 136, 138t, 139, 143t; subcommittee, 121, 123t, 127t, 129t, 143t, 170, 172t, 179, 181t challenger models, 27–28 challengers: Agreeableness and, 86; campaign disbursements and, 87–90, 95–96; committee assignments and, 102; Conscientiousness and, 84–85; Emotional Stability and, 86; Openness and, 85–86; polarization and, 192–193; securing reelection and, 19, 75–90, 95–96, 192–193 Chi, Feng, 153, 159 Christal, Raymond E., 45 cloture, 5n4, 7, 133, 145–149

index cognitive constraints, 15–16; Agreeableness and, 44, 48t, 91, 136; altruism and, 38t, 43–44, 48–49, 81, 86–87, 91, 94–96, 134, 136, 165, 196, 202; committee assignments and, 98–104, 109–110; Conscientiousness and, 41, 48; dopaminergic activity and, 39, 42–43, 52; Extraversion and, 43, 48t, 49–51; legislation issues and, 117, 122, 125, 131; legislative speech and, 63; mathematical models and, 21, 34–36, 40–41, 43–44, 46–52, 55, 192; measuring, 36; media and, 152, 157, 161, 165; moving on and, 175, 179, 184, 187–188; Neuroticism and, 46, 48t, 49–51; Openness and, 40, 48t, 50; parameterizing, 35; party discipline and, 133–136, 149; prefrontal cortex (PFC) and, 39, 41, 46; resting state functional connectivity (RSFC) activity and, 39, 41; securing reelection and, 75, 87, 90; strategic interactions and, 51–54 committee assignments: agenda power and, 98; Agreeableness and, 99, 101–102, 104, 107, 108t, 110–115, 123t, 127t, 129t; ante control and, 100; Appropriations Committee and, 101, 102n8, 104, 121, 162, 170, 179; campaign disbursements and, 94; centralization of power and, 97; chairmanships and, 90, 97–99, 103–104, 111–115, 121, 123t, 127t, 129t, 136, 138t, 139, 143t, 166, 170, 172t, 179t, 181t, 193; challengers and, 102; cognitive constraints and, 98–104, 109–110; Conscientiousness and, 103, 108t, 112, 113t, 115; Democrats and, 105; electoral security and, 106, 108t, 111, 113t; Emotional Stability and, 102–103, 107–109, 112–115; ex post control and, 100, 102, 115; Extraversion and, 103–104, 107–109, 113t, 114–115; as gatekeepers, 98; House of Representatives and, 101–105; ideologies and, 97, 99, 104; importance of committees and, 97–98; leadership and, 97–104, 107, 111, 115; legislation issues and, 193–194; Legislative Reorganization Act and, 104–105; Openness and, 101–103, 107, 108t, 112–115; party-free preferences and, 105–106, 136; party-influenced

index preferences and, 105, 137, 139; party loyalty and, 98–100, 105–106, 111, 115; persuadability and, 105; plum assignments and, 104–111; principal-agent problem and, 98–100, 102; property rights and, 100, 106, 111; psychology and, 98, 115; regression analysis and, 107, 108t, 111, 113t; repositories of policy expertise and, 98; Rules Committee and, 101, 102n8, 104, 121, 170, 179; seniority and, 97–100, 105, 108t, 111, 113t, 115; strategic interactions and, 103, 115; utility functions and, 101; Ways and Means Committee and, 101, 102n8, 104, 121, 170 Common Space scores, 6 Congressional Record, 60, 65 Congressional Research Service (CSR), 67 Congressmen in Committees (Fenno), 8 Conscientiousness: ability to handle complexity, 41; academic performance and, 40; campaign disbursements and, 91, 92t; challenger quality and, 84–85; cognitive constraint of, 41, 48; committee assignments and, 103, 108t, 112, 113t, 115; constituents and, 192–193; credit-claiming and, 163t; decline of, 198; defining terms for, 38t; divorce and, 40; dutiful actions and, 14; face validity and, 68–69, 162; goals and, 40; health and, 40; incumbents and, 90–91, 192–193, 195; job performance and, 40; lame ducks and, 167, 177, 180, 182–184, 195; leadership and, 40; legislation issues and, 118, 120–122, 123t, 126–131, 195; legislative speech and, 67–69; lexical approach and, 40; mathematical modeling of, 21, 24–25, 28–31, 38t, 40–41, 44, 48, 51, 195; media and, 158–159, 163t; mortality rates and, 40; moving on and, 167–168, 171, 172t, 175, 177–184, 188, 195; party discipline and, 14t, 138t, 139, 143t; party loyalty and, 138t, 139; polarization and, 198–199, 201; political science and, 30; prefrontal cortex (PFC) and, 41; press releases and, 161–162; securing reelection and, 81, 83–87, 91, 92t, 94–95; self-control and, 41; time preference

235 and, 193; trait taxonomy of, 12; Twitter and, 158t; willpower and, 40; working memory and, 41 constituents: Conscientiousness and, 192–193; Extraversion and, 192–193; legislation issues and, 125; media and, 151–154, 161, 165; party discipline and, 140, 145, 150; polarization and, 194; securing reelection and, 75, 77–82, 85–87, 95; Twitter and, 152–160 Costa, Paul T., Jr., 10, 27, 37 credit-claiming, 77, 96, 117, 152, 161–166, 194 Cruz, Ted, 5 Culber, John, 78 dark triad, 11, 13 Davis, Thomas M., III, 97 decision-making: constrained choice and, 14; ego and, 10n10; individual, 8, 54–55; mathematical models and, 47, 49, 51, 55; rational choice and, 9, 13, 16, 33, 192; securing reelection and, 81n7 Democratic Congressional Campaign Committee, 105 Democrats, 196; committee assignments and, 105; ideology of, 3–4; legislative speech and, 63, 67–68; loss of Senate seats by, 166; media and, 153, 158t, 159, 162, 163t; moving on and, 166, 181t; Obamacare and, 5; party discipline and, 132–133, 135; preference similarity and, 4; securing reelection and, 90t depression, 38t, 45, 71t Diermeier, Daniel, 167, 179n12 discount factors, 48, 118, 122, 126, 177, 185–188 divorce, 40, 44–45 dopaminergic activity, 39, 42–43, 52 DW-NOMINATE scores, 4, 15, 17, 83n8, 121, 146, 162, 170, 179, 196 Dye, David A., 37 ego: credit-claiming and, 77, 96, 117, 152, 161–166, 194; Minnesota Multiphasic Personality Inventory (MMPI) and, 11, 28; Myers-Brigg Type Indicator (MBTI) and, 10, 13, 28 electoral security: committee assignments and, 106, 108t, 111, 113t; lame ducks

236 electoral security (cont.) and, 167, 171, 172t, 180, 181t, 183; legislation issues and, 121, 123t, 127t, 128, 129t; moving on and, 167, 171, 172t, 180, 181t, 183; party discipline and, 141–145, 150, 194 elite behavior: authoritarian personality and, 11, 13, 202; ELUCIDATION scores and, 18, 62–63, 82, 83n8, 84t, 92, 107, 111, 122, 130, 141, 142n5, 149, 159, 162, 170–171; five-factor model and, 12–15, 18–19; formal modeling of, 165; future studies of, 201–202; incumbent resource allocation and, 96; individual differences and, 9–11; institutions and, 8–20, 191–192, 201–202; lack of research in, 57; leadership and, 8–9, 16–17; legislative speech and, 57, 62–63, 65, 67, 69; outcome uncertainty and, 145; personality and, 29, 32, 36, 55, 196, 201–202; psychology and, 9–19; risk and, 145; self-evaluations and, 29; spatial model and, 7; trait taxonomies and, 11–16 ELUCIDATION scores: elite behavior and, 18, 62–63, 82, 83n8, 84t, 92, 107, 111, 122, 130, 141, 142n5, 149, 159, 162, 170–171; legislative speech and, 62–63; rebellion and, 149; securing reelection and, 82, 83n8, 84t, 92 e-mail, 151 Emotional Stability: campaign disbursements and, 92t; challengers and, 86; committee assignments and, 102–103, 107–109, 112–115; credit-claiming and, 163t; face validity and, 67–69, 162; future studies of, 202; legislation issues and, 117, 123t, 124, 127t, 129t; legislative speech and, 57, 67–69, 193; mathematical modeling of, 29, 45, 50n14, 54n19, 195; media and, 157, 158t, 160, 163t, 168, 194; moving on and, 172t, 174–175, 181t; party discipline and, 138t, 139–144, 147t, 150, 174; polarization and, 199; political science and, 29; press releases and, 1, 162; rebellion and, 137, 195; risk and, 198, 202; securing reelection and, 84t, 86, 92t, 93; trait taxonomy of, 12; Twitter and, 157, 158t, 159–160

index ethics, 40, 76 ethnicity, 139, 159 ex ante control, 100, 168 Experience. See Openness ex post control, 100, 102, 115 Extraversion: activity and, 41–42; campaign disbursements and, 92t, 93, 193, 195; career attainment and, 42; cognitive constraint of, 43, 48t, 49–51; committee assignments and, 103–104, 107–109, 113t, 114–115; constituents and, 192–193; cortical arousal and, 42; credit-claiming and, 163t; defining terms for, 38t; dopamine and, 42–43, 52; energy and, 41–42; face validity and, 67–69, 162; fixation on reward and, 43; future studies of, 202; health and, 42; increase in, 198; incumbents and, 85, 192–193, 195; Introversion and, 10n10, 14, 42–43, 50n14, 157; lame ducks and, 183; leadership and, 42; legislation issues and, 117, 123t, 124, 127t, 129t; legislative speech and, 67–69; lexical approach and, 42; longevity and, 42; mathematical modeling of, 21, 23–24, 27–31, 38t, 41–43, 46, 48t, 49–52, 54, 195; media and, 157–160, 163t; moving on and, 168, 172t, 174–175, 181t, 195; Neuroticism and, 46; optimism and, 42; party discipline and, 136, 138t, 141–144, 147t, 150, 174; polarization and, 198; political science and, 29–31; press releases and, 1, 162; prospective rewards and, 50n14; questionnaire approach and, 23–24, 42; reward sensitivity and, 198; risk and, 194, 198, 202; securing reelection and, 81, 84t, 85–87, 91–93; strategic interactions and, 51–52; trait taxonomy of, 12; Twitter and, 157–160; utility functions and, 50–51 extremism, 77, 83n8, 123t, 127t, 129t, 134, 136–137, 143t, 146–147, 161 Eysenck, Hans, 11–12, 28, 42, 45 Facebook, 152, 158t, 159 face validity, 67–69 factor analysis, 12, 18, 23, 28, 34, 37n10 Feingold, Russ, 116–117 Fenno, Richard, 1, 8, 78, 97, 101n7, 152, 169, 177

index

237

filibusters: cloture and, 5n4, 7, 132–133, 145–149; curtailment of, 132; increase of, 133; obstruction and, 5, 132–133, 145–149, 194; party brand and, 133, 135–139, 147, 149, 162, 164 five-factor model: 16PF typology and, 22–24, 27–28; Big Five and, 12, 36–46 (see also specific trait); causal foundations of, 24–25; challenges to, 25–29; congressional campaign studies and, 96; dark triad and, 11, 13; economic parameters and, 14–15; elite behavior and, 12–15, 18–19; factor analysis and, 12, 18, 23, 28, 34, 37n10; future studies of, 201–202; higher-facet considerations and, 26; leadership and, 13; lexical approach and, 12, 21–24, 26, 28–29, 34, 37, 38t, 40, 42, 44–45; lower-level traits and, 12–13; mathematics of, 21–29; paramaterized personality and, 13–16; political science and, 29–31; predictive power of, 21–22; questionnaire approach and, 10, 12, 21–24, 26, 34, 37, 38t, 40, 42, 44, 56, 65; securing reelection and, 75; stability of, 24–25; structure of, 21–25, 192; trait taxonomies to, 12–13, 18, 19n14; twelve-factor structure and, 23 Flickr, 159 Francis, Leslie J., 58 Frisch, Scott A., 106 fusiform gyri, 44

heterogeneous quantal response equilibrium (HQRE), 54 HEXACO model, 201 hippocampus, 46 H. J. Res. 59, 5 Ho, Daniel E., 178 Hollibaugh, Gary E., Jr., 160 home style, 8, 152, 161, 165, 194 Home Style: House Members in Their Districts (Fenno), 8 homophobia, 30 Honesty-Humility, 201 House of Representatives, 18; Appropriations Committee and, 101, 102n8, 104, 121, 162–164, 170, 179; committee assignments and, 101–105; government shutdown and, 5; home style and, 8; legislation issues and, 120–121, 125–126, 128; Legislative Reorganization Act and, 104–105; legislative speech and, 61, 64–66, 68t; Majority Leader of, 78, 83n8, 121, 123t, 127t, 129t; media and, 153, 162, 163t; moving on and, 174, 178–180; party discipline and, 134–135, 139, 145–146; polarization and, 192, 194, 196–198; Rules Committee and, 101, 102n8, 104, 121, 170, 179; securing reelection and, 78–80, 82, 83n8, 84t, 89, 93–94; Twitter and, 153; Ways and Means Committee and, 101, 102n8, 104, 121, 170

Galifianakis, Zach, 191 game theory, 51–52 gender, 128, 139, 146, 148, 159 Gerber, Alan S., 67 glucose, 41 Goldberg, Lewis R., 42 Gosling, Samuel D., 58 government shutdown, 3–7 Graham, Lindsay, 151 Grassley, Charles, 4–7, 78, 151–152 Grimmer, Justin, 106, 161–162, 194 Groseclose, Tim, 67–68, 135–136 GroseWart score, 139

ideologies: committee assignments and, 97, 99, 104; court system and, 9; Database on Ideology, Money in Politics, and Elections (DIME) and, 92; Democrats and, 3–4; DW-NOMINATE scores and, 4, 15, 17, 83n8, 121, 146, 162, 170, 179, 196; extremism and, 77, 83n8, 123t, 127t, 129t, 134, 136–137, 143t, 146–147, 161; lame ducks and, 179, 181t; legislation issues and, 117, 121, 123t, 127t, 129t, 131; legislative speech and, 56, 60, 67–69, 192, 196; mathematical models and, 21, 29, 55; media and, 158t, 159, 161–162, 163t; moving on and, 170, 172t, 179, 181t; party discipline and, 134–137, 139, 143t, 146, 148–150; party loyalty and, 149; polarization and, 20, 83, 97, 134, 135n3, 150, 192, 196–201; Republicans and, 3–4;

Hamilton, Alexander, 168 Harbridge, Laurel, 140–142 health issues, 13, 40, 42, 45 Heckman, James J., 178

index

238 ideologies (cont.) securing reelection and, 77, 83n8, 88–89, 92; vote analysis and, 6 imagination, 26n4, 39, 50 Imai, Kosuke, 178 incumbents: being out of touch and, 75, 77–78; campaign disbursements and, 87–96; Conscientiousness and, 90–91, 192–193, 195; ethical violations and, 76; Extraversion and, 85, 192–193, 195; factors under control of, 76–77; ideologies and, 77; media and, 154, 158t, 159; moving on and, 171; public scandal and, 76; securing reelection and, 75–96, 192–193, 195; vulnerability and, 77 inflation, 67, 83n9 informal models, 14, 33 Instagram, 152 institutionalism: committee assignments and, 107, 111; elite behavior and, 8–20; ELUCIDATION scores and, 18, 62–63, 82, 83n8, 84t, 92, 107, 111, 122, 130, 141, 142n5, 149, 159, 162, 170–171; legislative speech and, 62–63; securing reel and, 82, 83n8, 84t, 92 institutions: Congressional effects and, 196–201; elite behavior and, 8–20, 191–192, 201–202; future study of, 201–202; home style and, 8, 152, 161, 165, 194; individual differences in language of, 9–11; leadership and, 16; parameterized personality and, 13–16; polarization and, 196–201; political choice and, 46–51; psychology and, 16–17; securing reelection and, 80–81 Intellect, 26, 31, 37–39, 45 Introversion, 10n10, 14, 42–43, 50n14, 157 jackknifed scores, 62, 66 Jacobson, Gary C., 79 job performance, 13, 40, 45 Jones, David R., 169 Keane, Michael, 167, 179n12 Kelly, Sean Q., 105–106 Kernell, Samuel, 79 Kiewiet, D. Roderick, 167 Kim, Henry A., 91–92 King, Gary, 178 King, Laura A., 202

Klingler, Jonathan D., 160 Krehbiel, Keith, 135 lame ducks: absenteeism and, 167, 169, 178–182, 183f; Agreeableness and, 183; Conscientiousness and, 167, 177, 180, 182–184, 195; constituency preferences and, 169; discount factors and, 177, 185–188; electoral security and, 167, 171, 172t, 180, 181t, 183; Extraversion and, 183; ideologies and, 179, 181t; irrelevance and, 175–182; lack of influence of, 169–170, 175–182; legislative voting model and, 184–188; moving on and, 166–170, 175–188; party discipline and, 138t, 139, 146–148; rebellion and, 148 leadership: authoritarian personality and, 11n11; committee assignments and, 97–104, 107, 111, 115; Conscientiousness and, 40; elite behavior and, 8–9, 16–17; Extraversion and, 42; five-factor model and, 13; legislation issues and, 121, 123t, 127t, 128, 129t; legislative speech and, 60, 64; moving on and, 170, 172t, 176n10, 180, 181t; Neuroticism and, 45; Openness and, 37–38; party discipline and, 101–104, 107, 111, 115, 121, 134, 140, 143t, 144, 170, 172t, 176n10, 180, 181t, 195; securing reelection and, 79–82, 96 legislation issues: Agreeableness and, 118; bill passage and, 116–121, 128, 131; campaign finance reform and, 116–117; chairmanships and, 121, 123t, 127t, 129t; cognitive constraints and, 117, 122, 125, 131; Conscientiousness and, 118, 120–122, 123t, 126–131, 193–195; constituents and, 125; discount factors and, 48, 118, 122, 126, 177, 185–188; electoral security and, 121, 123t, 127t, 128, 129t; Emotional Stability and, 117, 123t, 124, 127t, 129t; Extraversion and, 117, 123t, 124, 127t, 129t; House of Representatives and, 120–121, 125–126, 128; ideologies and, 117, 121, 123t, 127t, 129t, 131; leadership and, 121, 123t, 127t, 128, 129t; LES index and, 120–121, 128–130; Openness and, 118–122, 123t, 126, 127t, 129t; predicting success and,

index 128–131; proposals and, 117–128, 193; regression analysis and, 122, 123t, 125–128, 130; Senate and, 116; seniority and, 121, 123t, 127t, 128, 129t, 157; show horses and, 125–127; workhorses and, 17, 125–127 legislative effectiveness scores (LES), 120–121, 128–130 Legislative Reorganization Act, 104–105 legislative speech: ADA scores and, 67–68; Agreeableness and, 57, 67–68, 193; authorship concerns and, 65–67; cognitive constraints and, 63; Congressional Record and, 60, 65; Conscientiousness and, 67–69; Democrats and, 63, 67–68; elite behavior and, 57, 62–63, 65, 67, 69; Emotional Stability and, 57, 67–69, 193; Extraversion and, 67–69; face validity and, 67–69; House of Representatives and, 61, 64–66, 68t; ideologies and, 56, 60, 67–69, 192, 196; jackknifed scores and, 62, 66; leadership and, 60, 64; limits of existing approaches for analyzing, 56–57; linguistic analysis and, 58–63, 66–67, 69, 70t; LIWC categories and, 58–62, 66, 70t–71t, 192; machine learning and, 56–58, 202; MRCPD and, 58–59, 62, 66, 72t, 192; Neuroticism and, 57; Openness and, 57, 67–69, 193; political science and, 56–57, 65; psychology and, 56, 67, 71t; regression analysis and, 58; Republicans and, 68; scores and, 60–65; Senate and, 61–64, 66; strategic interactions and, 57, 60, 63–67, 69; talkative members and, 64–65; textual measurement and, 57–60; validity of estimates and, 65–69; voter surveys and, 56–57 legislative voting model, 184–188 leisure, 14, 70t, 169, 176 Leno, Jay, 3–4 LeVeck, Brad L., 91–92 Levitt, Steven D., 67–68 lexical approach, 21, 29, 34, 37, 38t, 45; 16PF typology and, 22–24, 27–28; Agreeableness and, 44; causal foundations and, 24–25; challenges to, 26; Conscientiousness and, 40; English and, 22–23; Extraversion and, 42; factor

239 analysis and, 23; flexibility of, 69; German and, 22–23, 26; Gosling and, 58; hypothesis of, 22–23; legislative speech and, 57–60; LIWC dictionary and, 58–62, 66, 70t–71t, 192; machine learning and, 56–58, 202; Mairesse and, 57–62; Mehl and, 58; MRCPD and, 58–59, 62, 66, 72t, 192; northern European languages and, 26; Openness and, 26; Pennebaker and, 58; roots of, 22–24; stability and, 24–25; virgin texts and, 58; word clustering and, 12 Linguistic Inquiry and World Count (LIWC), 58–62, 66, 70t–71t, 192 linguistics: ELUCIDATION scores and, 18 (see also ELUCIDATION scores); legislative speech and, 58–63, 66–67, 69, 70t; mathematical models and, 24, 26; psycholinguistics and, 58–61, 66, 192 Luddite Caucus, 151, 159 Machiavellianism, 11 machine learning, 56–58, 202 Mairesse, François, 57–62 Malhotra, Neil, 140–142 Maltzman, Forrest, 102n8 Masters, Nicholas A., 102 mathematical models: accessibility and, 33; Agreeableness and, 21, 24–25, 27–31, 38t, 43–44, 48–49, 51, 195; algorithms and, 18, 58, 137, 201; challenger models and, 27–28; clarity and, 33; cognitive constraints and, 21, 34–36, 40–41, 43–44, 46–52, 55, 192; confidence intervals and, 62–65, 84n10, 93n13, 107n15, 109, 112n16, 122n6, 126, 142n5, 145, 171n7, 180n13, 199; Conscientiousness and, 21, 24–25, 28–31, 38t, 40–41, 44, 48, 51, 195; decision-making and, 47, 49, 51, 55; Emotional Stability and, 29, 45, 50n14, 54n19, 195; Extraversion and, 21, 23–24, 27–31, 38t, 41–43, 46, 48t, 49–52, 54, 195; ideologies and, 21, 29, 55; informal, 14, 33; information loss and, 33–34; linguistics and, 24, 26; Neuroticism and, 21, 23–25, 28, 30, 38t, 42, 44–46, 48t, 49–54; Openness and, 21, 24, 26, 28–31, 37–40, 45, 47, 50–51, 195; parameterized personality and, 13–16; political choice and, 46–51; psychology and, 21–22, 25,

240 mathematical models (cont.) 28–36, 45, 53; regression analysis and, 199 (see also regression analysis); strategic interactions and, 51–54; utility functions and, 14, 50–51 Mayhew, David R., 117 McCain, John, 116–117, 151 McConnell, Mitch, 63, 67 McCrae, Robert R., 10, 27, 37 media: Agreeableness and, 158t, 160, 162–165, 194; cognitive constraints and, 152, 157, 161, 165; Conscientiousness and, 158–159, 163t; constituents and, 151–154, 161, 165; credit-claiming and, 77, 96, 117, 152, 161–166, 194; Democrats and, 153, 158t, 159, 162, 163t; e-mail and, 151; Emotional Stability and, 157, 158t, 160, 163t, 168, 194; Extraversion and, 157–160, 163t; Facebook and, 152, 158t, 159; Flickr and, 159; home style and, 8, 152, 161, 165, 194; House of Representatives and, 153, 162, 163t; ideologies and, 158t, 159, 161–162, 163t; incumbents and, 154, 158t, 159; Instagram and, 152; Luddite Caucus and, 151, 159; MySpace and, 159; Neuroticism and, 157, 160–161, 194; Obama and, 152–154; press releases and, 5, 77, 87, 95, 152, 161–164, 194; regression analysis and, 157–159, 162, 164; Republicans and, 151, 159; rewards of, 153–154, 157, 161; RSS and, 159; Senate and, 164; seniority and, 157–160; Twitter and, 6–7, 58, 151–160, 194, 202; YouTube and, 159 medial prefrontal cortex (MPFC), 46 Mehl, Matthias R., 58 Merlo, Antonio, 167, 179n12 mid-cingulate gyri, 45–46 Minnesota Multiphasic Personality Inventory (MMPI), 11, 28 Minozzi, William, 105, 136–137 Mondak, Jeffrey J., 67 mortality, 22, 40, 44 moving on: absenteeism and, 167, 169, 178–182, 183f; Agreeableness and, 168, 172t, 173, 175, 181t, 194–195; chairmanships and, 166, 170, 172t, 179–180, 181t; cognitive constraints and, 175, 179, 184, 187–188;

index Conscientiousness and, 167–168, 171, 172t, 175, 177–184, 188, 195; Democrats and, 166, 181t; discount factors and, 177, 185–188; electoral security and, 167, 171, 172t, 180, 181t, 183; Emotional Stability and, 172t, 174–175, 181t; Extraversion and, 168, 172t, 174–175, 181t, 195; House of Representatives and, 174, 178–180; ideologies and, 170, 172t, 179, 181t; incumbents and, 171; lame ducks and, 166–170, 175–188; leadership and, 170, 172t, 176n10, 180, 181t; legislative voting model and, 184–188; leisure and, 169, 176; Openness and, 168, 172t, 174–175, 181t; regression analysis and, 171, 172t, 178, 180, 181t; Senate and, 166, 179n12, 180; seniority and, 172t, 179, 181t; strategic interactions and, 167, 180, 183; utility functions and, 184 MRC Psycholinguistic Database (MRCPD), 58–59, 62, 66, 72t, 192 music, 59–60 Myers-Brigg Type Indicator (MBTI), 10, 13, 28 MySpace, 159 narcissism, 11 National Republican Congressional Committee, 105 NEO Five-Factor Inventory, 24 NEO (Neuroticism-Extraversion-Openness) Inventory, 24, 26, 65 neuropsychology, 15–16, 35–36, 53, 192 Neuroticism: anger and, 45; anxiety and, 38t, 44–45, 71t; campaign disbursements and, 93, 193; cognitive constraint of, 46, 48t, 49–51; defining terms for, 38t; depression and, 45; divorce and, 45; drug use and, 45; Extraversion and, 46; fixation on negativity and, 46; freezing effect of, 53; fund distribution and, 193; health issues and, 45; leadership and, 45; legislative speech and, 57; life outcomes and, 45; marriage quality and, 45; mathematical modeling of, 21, 23–25, 28, 30, 38t, 42, 44–46, 48t, 49–54; media and, 157, 160–161, 194; mid-cingulate gyri and, 45–46; party discipline and, 137, 142, 150; political science and, 30; posterior hippocampus and, 46;

index prefrontal cortex (PFC) and, 46; press releases and, 1; prospective rewards and, 50n14; quantal response equilibrium (QRE) and, 54; questionnaire approach and, 23–24; rebellion and, 137; resting-state activity and, 46; rumination tendencies and, 45; securing reelection and, 81–82, 86, 91, 93, 95; self-esteem and, 45–46; sensitivity to error and, 52–53; social issues of, 45; strategic interactions and, 51–54; stress and, 45–46; Twitter and, 158t, 159–160; uncertain memories and, 53; utility functions and, 50–51, 53; vulnerability to external conditions and, 45 Obama, Barack, 6, 133, 152–154, 159, 166 Obamacare, 3–5, 7 obstruction: cloture and, 5n4, 7, 133, 145–149; filibustering and, 5, 132–133, 145–149, 194; Openness and, 158t, 163t; party brand and, 133, 135–139, 147, 149, 162, 164; party discipline and, 19, 134–135, 145–149; polarization and, 134–135; rebellion and, 19, 133–135, 137, 149–150, 195; working across party lines and, 140–145 Openness: campaign disbursements and, 92t; career-related outcomes and, 37, 39; cognitive constraint of, 40, 48t, 50; committee assignments and, 101–103, 107, 108t, 112–115; compulsion to gather and process information and, 40; credit-claiming and, 163t; decline of, 196; defining terms for, 38t; dopamine and, 39; face validity and, 67–69, 162; flexibility and, 39; future studies of, 202; imagination and, 39, 50; Intellect and, 26, 31, 37–39, 45; leadership and, 37–38; legislation issues and, 118–122, 123t, 126, 127t, 129t; legislative speech and, 57, 67–69, 193; lexical approach and, 26; mathematical modeling of, 21, 24, 26, 28–31, 37–40, 45, 47, 50–51, 195; media and, 158t, 163t; moving on and, 168, 172t, 174–175, 181t; novelty and, 14; party discipline and, 136, 138t, 139, 174; party loyalty and, 138t, 139; polarization and, 198–199, 201; political science and, 29–30; prefrontal cortex and, 39; press

241 releases and, 1; quality challengers and, 85–86; questionnaire approach and, 24; reduced latent inhibition and, 39; resting state functional connectivity (RSFC) activity and, 39; risk and, 136, 194, 198; securing reelection and, 80, 85–87, 91; shifting norms and, 134, 138t, 139, 142–148, 150; trait taxonomies and, 12; Twitter and, 158t; utility functions and, 51; working memory and, 39 ordinary least squares (OLS), 68–69, 162 outliers, 100n5 partisan pressures, 98, 100, 140, 182 party brand, 133, 135–139, 147, 149, 162, 164 party discipline: Agreeableness and, 134, 136–140, 142, 143t, 145–150, 174, 196; chairmanships and, 136, 138t, 139, 143t; close votes and, 136–137; cloture and, 5n4, 7, 133, 145–149; cognitive constraints and, 133–136, 149; Conscientiousness and, 14t, 138t, 139, 143t; constituents and, 140, 145, 150; Democrats and, 132–133, 135; electoral security and, 141–145, 150, 194; Emotional Stability and, 138t, 139–144, 147t, 150, 174; Extraversion and, 136, 138t, 141–144, 147t, 150, 174; House of Representatives and, 134–135, 139, 145–146; ideologies and, 134–137, 139, 143t, 146, 148–150; leadership and, 101–104, 107, 111, 115, 121, 134, 140, 143t, 144, 170, 172t, 176n10, 180, 181t, 195; Minozzi-Volden algorithm and, 137; Neuroticism and, 137, 142, 150; obstruction and, 19, 134–135, 145–149; Openness and, 136, 138t, 139, 174; partisan pressures and, 98, 100, 140, 182; polarization and, 134–135; political science and, 150; rebellion and, 19, 133–135, 137, 149–150, 195; regression analysis and, 137, 141, 143t, 147–148; Republicans and, 132, 135; securing reelection and, 90t, 105; Senate and, 132–134, 145–146; seniority and, 138t, 139, 143t, 146–148; shifting norms and, 149–150; war chests and, 134; working across party lines and, 140–145 party loyalty: bipartisanship and, 3, 116, 134, 140–145, 150; committee assignments

242 party loyalty (cont.) and, 98–100, 105–106, 111, 115; ideologies and, 134, 149; increased, 134–135; Minozzi-Volden algorithm and, 137; party brands and, 135–139; rebellion and, 133–135, 137, 149–150, 195; rewards for, 136–137; securing reelection and, 83; working across party lines and, 140–145 Paul, Rand, 146 Paul, Ron, 97 Pennebaker, James W., 58, 61, 202 personality: biologically derived, 25; California Personality Inventory and, 11; cognitive constraints and, 15 (see also cognitive constraints); Congress as institution and, 196–201; Congressional life cycle and, 192–196; constrained choice and, 14; dark triad and, 11, 13; defending models of, 32–34; diverse components of, 9–10; economic language for, 14; elite behavior and, 29, 32, 36, 55, 196, 201–202; factor analysis and, 12, 18, 23, 28, 34, 37n10; five-factor model and, 13–16 (see also five-factor model); future study of, 201–202; genetic studies and, 25; home style and, 8, 152, 161, 165, 194; ideologies and, 20 (see also ideologies); lexical approach and, 57–58 (see also lexical approach); limits of existing approaches for analyzing, 56–57; machine learning and, 56–58, 202; many definitions for, 9; mathematical models and, 31–36; measuring through legislative speech, 56–72; Minnesota Multiphasic Personality Inventory (MMPI) and, 11, 28; Myers-Brigg Type Indicator (MBTI) and, 10, 13, 28; parameterized, 13–16; persistent elements of, 10; political science and, 29–32, 201; questionnaire approach and, 10 (see also questionnaire approach); refining theories of, 9–10; trait taxonomies and, 10–13; using text to measure, 57–60. See also specific trait polarization: Agreeableness and, 198–199, 201; challengers and, 192–193; Congress as institution and, 196–201; Conscientiousness and, 198–199, 201; constituents and, 194; Emotional

index Stability and, 199; Extraversion and, 198; House of Representatives and, 192, 194, 196–198; ideologies and, 20, 83, 97, 134, 135n3, 150, 192, 196–201; institutions and, 196–201; obstruction and, 134–135; Openness and, 198–199, 201; party discipline and, 134–135; Senate and, 194, 196, 197t, 199 policy preferences, 4, 7–10, 16, 17, 135 political choice, 46–51 political science, 8; Agreeableness and, 29; Conscientiousness and, 30; Emotional Stability and, 29; Extraversion and, 29–31; legislative speech and, 56–57, 65; Neuroticism and, 30; Openness and, 29–30; party discipline and, 150; personality and, 29–32, 201 posterior cingulate cortex, 44 Powell, Eleanor Neff, 106 prefrontal cortex (PFC), 39, 41, 46 prejudice, 30 Presidential Character, The (Barber), 16 press releases, 5, 77, 87, 95, 152, 161–164, 194 principal-agent problem, 98–100, 102 psychiatry, 8 psycholinguistics, 58–61, 66, 192 psychology: committee assignments and, 98, 115; elite behavior and, 9–19; five-factor model and, 192, 201 (see also five-factor model); legislative speech and, 56, 67, 71t; mathematical models and, 21–22, 25, 28–36, 45, 53; securing reelection and, 96 psychopathy, 11 Psychoticism, 23, 28 public good, 168 quantal response equilibrium (QRE), 54 questionnaire approach: Extraversion and, 23–24, 42; factor analysis and, 10, 12, 21–24, 26, 34, 37, 38t, 40, 42, 44, 56, 65; Myers-Brigg Type Indicator (MBTI) and, 10, 13, 28; Neuroticism and, 23–24; nuanced questions and, 24; Openness and, 24; roots of, 22–24 Ramey, Adam J., 83n8, 160 rational-choice models, 9, 13, 16, 33, 192 rebellion, 26n4; ELUCIDATION scores and, 149; Emotional Stability and, 137,

index 195; lame ducks and, 148; Neuroticism and, 137; party discipline and, 19, 133–135, 137, 149–150, 195; polarization and, 134–135; working across party lines and, 140–145 regression analysis: binomial, 122, 123t, 125–126, 127t, 141, 142n4, 143t, 146–148, 180, 181t; campaign disbursements and, 92t; committee assignments and, 107, 108t, 111, 113t; confidence intervals and, 62–65, 84n10, 93n13, 107n15, 109, 112n16, 122n6, 126, 142n5, 145, 171n7, 180n13, 199; legislation issues and, 122, 123t, 125–128, 130; legislative speech and, 58; media and, 157–159, 162, 164; moving on and, 171, 172t, 178, 180, 181t; ordinary least squares (OLS), 68–69, 162; party discipline and, 137, 141, 143t, 147–148; securing reelection and, 92t; SMOreg and, 18, 58; Tobit, 128–130 Reid, Harry, 5, 63–64, 67, 132–133, 146 Republicans, 196; ideology of, 3–4; legislative speech and, 63, 68; media and, 151, 159; National Republican Congressional Committee and, 105; party discipline and, 132, 135; policy agreement and, 3–5; securing reelection and, 90t, 105 resting state functional connectivity (RSFC) activity, 39, 41 Revised NEO Personality Inventory (NEO-PI-R), 24, 26, 65 Risch, Jim, 17 Rohde, David W., 97 Roll Call newspaper, 116 roll-call votes, 4, 137, 169 Rothenberg, Lawrence S., 169–170 RSS, 159 Rubio, Marco, 17 Rules Committee, 101, 102n8, 104, 121, 170, 179 Salazar, John, 166, 170 Sanders, Mitchell S., 169–170 Saucier, Gerard, 37, 42 scandals, 76 Scarborough, Charles "Joe," 75 securing reelection: Agreeableness and, 81–82, 84t, 86, 92t, 94–95; being out of touch and, 75, 77–78; campaign disbursements and, 76, 87–95;

243 challengers and, 19, 75–90, 95–96, 192–193; cognitive constraints and, 75, 87, 90; competitive environment and, 88–89; Conscientiousness and, 81, 83–87, 91, 92t, 94–95; constituents and, 75, 77–82, 85–87, 95; decision-making and, 81n7; Democrats and, 90t; Emotional Stability and, 84t, 86, 92t, 93; ethical violations and, 76; Extraversion and, 81, 84t, 85–87, 91–93, 192–193; five-factor model and, 75; House of Representatives and, 78–80, 82, 83n8, 84t, 89, 93–94; ideologies and, 77, 83n8, 88–89, 92; incumbents and, 75–96, 192–193, 195; individual differences in, 95–96; leadership and, 79–82, 96; moving on and, 166 (see also moving on); Neuroticism and, 81–82, 86, 91, 93, 95; Openness and, 80, 85–87, 91; party loyalty and, 83; psychology and, 96; public plans for, 75; public scandal and, 76; regression analysis and, 92t; Republicans and, 90t, 105; Senate and, 78; strategic interactions and, 82–86; trust and, 75, 77–82, 86; utility functions and, 82, 83n9; variance inflation factor (VIF) and, 83n9; war chests and, 75, 77, 89–90, 95 self control, 41 self-esteem, 45 Senate: filibusters and, 5, 132–133, 145–149, 194; government shutdown and, 3; legislation issues and, 116; legislative speech and, 61–64, 66; loss of seats by Democrats in, 166; Majority Leader of, 5, 63, 132–133, 146; media and, 164; moving on and, 166, 179n12, 180; party discipline and, 132–134, 145–146; polarization and, 194, 196, 197t, 199; securing reelection and, 78; three-fifths majority and, 132–133 Senators on the Campaign Trail (Fenno), 78 seniority: committee assignments and, 97–100, 105, 108t, 111, 113t, 115; legislation issues and, 121, 123t, 127t, 128, 129t, 157; media and, 157–160; moving on and, 172t, 179, 181t; party discipline and, 138t, 139, 143t, 146–148 Shays, Chris, 166 Shelby, Richard, 151 Shepsle, Kenneth A., 98

index

244 show horses, 125–127 Signorino, Curtis S., 83n9 Smith, Steven S., 98, 133, 146 Snyder, James M., Jr., 67–68, 135–136 spatial model, 5–7, 17, 149 Stewart, Charles, III, 139 strategic interactions: Agreeableness and, 51; cognitive constraints and, 51–54; committee assignments and, 103, 115; considerations for, 51–54; Extraversion and, 51–52; game theory and, 51–52; legislative speech and, 57, 60, 63–67, 69; mathematical models and, 51–54; moving on and, 167, 180, 183; Neuroticism and, 51–54; quantal response equilibrium (QRE) and, 54; securing reelection and, 82–86 stress, 10n10, 45–46 Stuart, Elizabeth A., 178 subcommittee chairs, 121, 123t, 127t, 129t, 143t, 170, 172t, 179, 181t supermajority, 132 Support Vector Machines for Regression (SMOreg), 18, 58 Supreme Court, 133, 201 tactical actions, 3, 7, 133, 150, 196, 201 three-fifths majority, 132–133 Tobit regression, 128–130 Tonight Show with Jay Leno, The (TV show), 3 trust: Agreeableness and, 43–44; maintaining, 81; securing reelection and, 75, 77–82, 86 Tupes, Ernest C., 45 twelve-factor structure, 23 Twitter, 58, 194, 202; Blunt and, 6–7; constituents and, 152–160; cost-benefit analysis of, 154–158; debut of, 152; Emotional Stability and, 157–160; Extraversion and, 157–160; Grassley and, 6; growth of, 152–153; House of

Representatives and, 153; McCain and, 151; Neuroticism and, 158t, 159–160; Obama and, 6, 152–154, 159; risk and, 153–154 US Congress: DW-NOMINATE scores and, 4, 15, 17, 83n8, 121, 146, 162, 170, 179, 196; ELUCIDATION scores and, 18, 62–63, 82, 83n8, 84t, 92, 107, 111, 122, 130, 141, 142n5, 149, 159, 162, 170–171; legislation issues and, 116–131; legislative speech and, 56–69. See also House of Representatives; Senate utility functions: committee assignments and, 101; Extraversion and, 50–51; mathematical models and, 14, 50–51; moving on and, 184; Neuroticism and, 50–51, 53; Openness and, 51; securing reelection and, 82, 83n9 variance inflation factor (VIF), 83n9 Volden, Craig, 105, 125, 128, 135–137 voter mobilization, 31, 89 voter surveys, 56–57 war chests, 75, 77, 89–90, 95, 134 Wawro, Gregory, 118 Ways and Means Committee, 101, 102n8, 104, 121, 170 Weibull distribution model, 158t, 159–160 Weingast, Barry R., 98 well-being of others, 48–49, 110–111, 196 Who’s Who in America, 42 Wilson, Woodrow, 97 Wiseman, Alan E., 125, 128 workhorses, 17, 125–127 working memory, 39, 41 Yang, Nathan, 153, 159 YouTube, 159 Zeng, Langche, 167