Measuring Change in Immigration policy [1 ed.] 9781593327453, 9781593326982

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The New Americans Recent Immigration and American Society

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Edited by Steven J. Gold and Rubén G. Rumbaut

A Series from LFB Scholarly Challen, Suzanna. Measuring Change in Immigration policy, LFB Scholarly Publishing LLC, 2013. ProQuest Ebook

Copyright © 2013. LFB Scholarly Publishing LLC. All rights reserved. Challen, Suzanna. Measuring Change in Immigration policy, LFB Scholarly Publishing LLC, 2013. ProQuest Ebook

Measuring Change in Immigration Policy

Copyright © 2013. LFB Scholarly Publishing LLC. All rights reserved.

Suzanna Challen

LFB Scholarly Publishing LLC El Paso 2014 Challen, Suzanna. Measuring Change in Immigration policy, LFB Scholarly Publishing LLC, 2013. ProQuest Ebook

Copyright © 2014 by LFB Scholarly Publishing LLC All rights reserved.

Copyright © 2013. LFB Scholarly Publishing LLC. All rights reserved.

Library of Congress Cataloging-in-Publication Data Challen, Suzanna. Measuring change in immigration policy / Suzanna Challen. pages cm. -- (The new Americans : recent immigration and American society) Includes bibliographical references and index. ISBN 978-1-59332-698-2 (hardcover : alk. paper) 1. United States--Emigration and immigration--Government policy. 2. United States--Emigration and immigration--History. I. Title. JV6483.C44 2014 325.73--dc23 2013034575

ISBN 978-1-59332-698-2 Printed on acid-free 250-year-life paper. Manufactured in the United States of America.

Challen, Suzanna. Measuring Change in Immigration policy, LFB Scholarly Publishing LLC, 2013. ProQuest Ebook

Copyright © 2013. LFB Scholarly Publishing LLC. All rights reserved.

To Geoffrey, with whom I am happy to share my life, and to all the immigrants who come to this country looking for a better life, whether or not they find it.

Challen, Suzanna. Measuring Change in Immigration policy, LFB Scholarly Publishing LLC, 2013. ProQuest Ebook

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Table of Contents

Acknowledgements ............................................................................... ix Chapter One: Introduction ..................................................................... 1 Chapter Two: Brain Gain? Skill Bias in U.S. Migrant Admissions Policy ................................................................................ 7 Chapter Three: Stalled in the Senate: Explaining change in U.S. migrant admissions policy since 1965 ............................ 35

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Chapter Four: Post-coding Aggregation: A methodological principle for independent data collection ..................... 103 Appendix A: Measuring Skill Bias: Important issues encountered in creating the skill bias measure .................................. 131 Appendix B: Constructing a ratio of bills passed to bills proposed ... 153 Appendix C: Measuring policymaker preferences ............................. 159 Appendix D: Aggregation in Practice: A methodological survey of policy datasets in political economy ........................ 187 Notes

...................................................................................... 207

References

...................................................................................... 227

Index

...................................................................................... 235

vii

Challen, Suzanna. Measuring Change in Immigration policy, LFB Scholarly Publishing LLC, 2013. ProQuest Ebook

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Acknowledgements

I want to thank Geoffrey Werner Challen for countless conversations and for your encouragement and love. I’d also like to thank Vessela Hristova for your friendship, and Corina Graif for our conversations about creative process. I was able to accomplish this because of the support of my parents, Howard Add Chapman and Dorothy Whitten Chapman, who supported my education, even when it took me out of Texas, and the friendship of Jessica Richter, who brought me to NYU where my academic interests began working with Bruce Bueno de Mesquita, Rebecca Morton, and Christopher Mitchell. Thanks to Beth Simmons, Peter Hall, Torben Iversen, and Michael Hiscox at Harvard University for your help refining my ideas and improving the quality of the work presented in this book. I extend special thanks to the patient librarians, John Collins and Terri Saint-Amour, who taught me how to systematically collect the information I needed for my research. Thank you to my hardworking assistants Elisha Heaps, Emily Naphtal, Cecilia Venegas, Irina Vaynerman, Jazmine Ramirez, Noah Nathan, Jitka Hiscox and Eric Portales, who have all gone on to other wonderful things. Thank you to Clare Putman and Thom Wall for your assistance many times over. Thank you to my International Migration Policy and Law Analysis (IMPALA) Database Project collaborators, the Weatherhead Center for International Affairs, the Institute for Quantitative Social Science, the Harvard University Committee on Australian Studies, the Center for International Development, the Mellon-MIT Inter-University Committee on NGOs and Forced Migration, and Leverett House and Eliot House, all of which supported me at various stages of the ten years of my life that were tied more or less to the work that went into this book. Thank you to my friend Lisa ix

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Acknowledgements

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Lutz for helping me to prepare this manuscript for publication. Thanks to Chuchu for sitting at my feet, or at least nearby, as I wrote.

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CHAPTER ONE

Introduction

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INTENT AND SCOPE The purpose of this book is to identify, in a comprehensive and complete manner, the policies that the United States has enacted to shape migrant admissions, and to understand why the U.S. has these policies. The research presented in the following chapters covers the time period from 1965 - 2008, covering all policy changes from the 89th Congress to the 110th Congress in U.S. migrant admissions. At the end of this introduction, I discuss the relevance of this research to understanding the Senate passage of the Border Security, Economic Opportunity and Immigration Modernization Act in June 2013. At the time this book went to press in July 2013, this bill had yet to be debated in the House of Representatives. STRUCTURE AND CONTRIBUTIONS The second chapter entitled, “Brain Gain? Skill bias in U.S. migrant admissions policy,” develops a conceptual and operational definition of skill bias, the level of emphasis placed on an applicant’s skills in an admissions decision relative to alternative types of selection criteria. I apply the measure to new data revealing the level of skill bias in U.S. migrant admissions policy between 1965 and 2008. Skill bias is both a critical determinant of the skill composition of the migrant population and a response to economic and public demand for highly skilled migrants. However, despite its central role, this is the first direct, comprehensive, annual measure of skill bias. 1

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The third chapter entitled, “Stalled in the Senate: Explaining change in U.S. migrant admissions policy since 1965,” presents new data characterizing change in U.S. migrant admissions policy as both expansive and infrequent over recent decades. I present a new theory of policy change that incorporates the role of supermajoritarian decision making procedures and organized anti-immigration groups to better account for both the expansive nature and the infrequency of policy change. The theory highlights the importance of a coalition of immigrant advocacy groups, employers and unions in achieving policy change and identifies the conditions under which this coalition is most likely to form and least likely to be blocked by an opposition of antiimmigration groups. The fourth chapter entitled, “Post-coding aggregation: A methodological principle for independent data collection,” presents a new technique developed to enable independent collection of flexible, high quality data: post-coding aggregation. Post-coding aggregation is a methodological principle that minimizes data loss, increases transparency, and grants data analysts the ability to decide how best to aggregate information to produce measures. I demonstrate how it increases the flexibility of data use by expanding the utility of data collections for a wider range of research objectives and improves the reliability and the content validity of measures in data analysis. RELEVANCE It may be argued that U.S. immigration policies are irrelevant because they are not enforced. Why collect information about policies when they do not actually reflect the reality of immigration? However, U.S. migrant admissions policy, I argue, is one aspect of U.S. immigration policy that is widely enforced. The U.S. Citizenship and Immigration Service issues or denies residence and work visas and green cards to individuals every day in accordance with admissions policies established by Congress. Of course, others also enter the U.S. without legal status. Our border security policies are not sufficiently enforced to prevent undocumented migration. However, the fact that undocumented migration occurs does not render legal migration irrelevant. Understanding changes in U.S. migrant admissions policy is relevant to understanding the actual state of affairs. It is not the entire state of affairs, of course, but an important part of it. Admissions policies state

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Introduction

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in black and white the criteria used to select our newest neighbors, and they reveal what U.S. policymakers are prioritizing or valuing in the process of selection. Immigration polarizes populations in receiving countries. Immigrants become scapegoats, portrayed as unwanted threats to language, religion and culture, and they serve as a physical reminder of change, whether in the economy or the society. Simultaneously, immigrants are portrayed as the courageous, hard-working everyman or woman, carving out a better life for their families through their labor. Everyone has an idea of whether they have something to lose or something to gain from immigration, and we make arguments accordingly. I hope this book provides you with some good information about U.S. migrant admissions policy that will enhance your understanding of, and allow you to assess your beliefs about, U.S. immigration.

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UPDATE: DISCUSSION OF THE BORDER SECURITY, ECONOMIC OPPORTUNITY, AND IMMIGRATION MODERNIZATION ACT In June 2013, just before this book went to press, the Senate passed the Border Security, Economic Opportunity, and Immigration Modernization Act, which has yet to be debated in the House of Representatives. At this point in time, the fate of this legislation appears far from secure. However, its successful passage by a supermajority of U.S. Senators is a considerable feat, and one that should be predicted by my theory of policy change in U.S. migrant admissions policy, written before these events unfolded. My theory highlights the importance of a coalition of immigrant advocacy groups, employers and unions in achieving policy change. A pro-immigration coalition did form around the Senate bill. The National Immigration Forum, the U.S. Chamber of Commerce, and the American Federation of Labor and Congress of Industrial Organizations (AFL-CIO), among others, all lobbied for its passage. With this empirical evidence satisfied, it is appropriate to consider the four main hypotheses generated by the theory in light of recent events. First, I hypothesize that policy stasis is more likely than policy change in U.S. migrant admissions policy. The passage of the Border

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Security, Economic Opportunity, and Immigration Modernization Act is the first successful Senate passage of comprehensive reform legislation adjusting the U.S. migrant admissions program since 1990, despite multiple attempts. In this book I present a comprehensive summary of attempts at policy change in the area of U.S. migrant admissions, demonstrating that proposed bills are far more likely to stall in the Senate than pass. However, there are conditions that make policy change more likely, and the theory identifies these. A pro-immigration coalition is hypothesized to form around expansive legislation, and the bill that passed the Senate is widely expansive. Although there are restrictions placed on visas for siblings and married adult children of U.S. citizens, these restrictions are minor in comparison with the widely expansive shift to unlimited visas for spouses, children and parents of permanent legal residents. Likewise, a new merit-based visa category replacing the existing diversity lottery provides an overall increase in skill-based permanent immigration. In the area of temporary migration, additional increases in work visas for the H-1B and H-2A programs, in addition to a new W-visa guest worker program, result in increased access for both high and generalskill temporary migrant workers. Policy change in the area of U.S. migrant admissions is hypothesized to occur following a period of declining unemployment. The unemployment rate as defined by the Bureau of Labor Statistics shows a steady decline in unemployment from a high of 10 percent in October 2009 to a rate of 7.6 percent in June 2013, when the Senate bill passed. The National Bureau of Economic Research indicates that the U.S. economy has been in an expansionary cycle since June of 2009. Finally, policy change is hypothesized to be more likely when the rights and benefits granted to admitted migrants are sufficiency high to maintain union support but sufficiently low to prevent substantial antiimmigration group opposition to the bill. Specifically, I predict that an expansionary increase in the admission of permanent immigrants with a high level of rights and benefits will reduce the likelihood of passage because of the strength of targeted anti-immigration group opposition. In contrast, I predict that an expansionary increase in the admission of temporary migrants with a high level or rights and benefits will improve the likelihood of passage, because union support is retained. The empirical support for this hypothesis is mixed. The Border Security, Economic Opportunity, and Immigration Modernization Act

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Introduction

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generally provides expansionary measures for both permanent and temporary migrants with a high level of rights and benefits, but it also includes a legalization program that provides a low level of rights and benefits during a long probationary period for undocumented migrants seeking to obtain permanent residency. Where the theory is least empirically supported is in the successful passage of widely expansive measures allowing unrestricted visas for spouses, children and parents of permanent residents. The theory predicts that anti-immigration groups will target opposition to any increase in the number of immigrants that will be admitted each year with access to welfare and other benefits. However, anti-immigration groups did not successfully oppose the expansionary changes to family reunification for permanent legal residents, and it did not appear to be a major point of contention in the debates preceding the bill’s passage. Instead, anti-immigration groups targeted opposition toward the legalization program contained in the bill, and successfully limited the expansion of rights and benefits to undocumented migrants. Under the Senate bill, undocumented migrants would be provided with a path to permanent residency and citizenship extending over a thirteen-year period. During the ten-year time period leading up to permanent residency status, undocumented migrants on a provisional status would not have access to health insurance or welfare benefits. In addition, the achievement of permanent residency status and eventually citizenship would require undocumented migrants to pay a fine and back taxes, to obtain English proficiency, to maintain consistent employment, and to pass background checks, making the access to rights and benefits granted with permanent residency and citizenship extremely difficult to obtain. Republican Senators made it clear that amendments expanding access to health insurance or benefits for undocumented migrants would kill the bill, suggesting the importance of the low level of rights and benefits provided in allowing for the successful passage of the bill. Also aligned with the hypothesis, a major turning point in achieving supermajority support for the Senate bill occurred in March 2013 when the U.S. Chamber of Commerce and the AFL-CIO reached an agreement on guest worker provisions, which had been the issue most likely to fracture the pro-immigration coalition. Employer support was obtained by providing generously expanded quotas for temporary

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migrant workers, while union support was retained with sufficiently high worker protection. In summary, the first successful Senate passage of comprehensive immigration reform since 1990 was made more likely by the successful formation of a pro-immigration coalition of interest groups, the expansive nature of the legislation, and improved labor market conditions. What is least explained by my theory is why, within the context of the U.S. Senate, the anti-immigration group opposition was only capable of influencing Republican Senators to limit the rights and benefits provided to undocumented migrants, but was not strong enough to block passage of the otherwise widely expansive bill. This stands in contrast to 2007 when anti-immigration group opposition played a major role in garnering sufficient Senate votes to block the passage of comprehensive immigration reform.

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CHAPTER TWO

Brain Gain? Skill Bias in U.S. Migrant Admissions Policy

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INTRODUCTION A measure of skill bias in U.S. migrant admissions policy is currently needed. Leading scholarship reveals that the skill composition of the migrant population is an important—if not the most important— determinant of how immigration affects the U.S. economy. As a persistent shortage of highly skilled workers remains inadequately addressed by U.S. education policy, immigration offers a short-term solution. While the U.S. population has grown increasingly wary of immigration, native workers of all skill levels prefer the admission of highly skilled migrants who are perceived as less likely to drain fiscal resources. Skill bias in U.S. migrant admissions policy is both a critical determinant of the skill composition of the migrant population and a response to economic and public demand for highly skilled migrants. However, despite its central role, we have no way to measure skill bias in U.S. migrant admissions policy. This chapter makes the following contributions. First, I develop a conceptual definition of skill bias. Second, I operationalize this definition in the context of U.S. migrant admissions policy to construct a direct, comprehensive and annual measure. Third, I apply the measure to new data revealing the level of skill bias in U.S. migrant admissions policy between 1965 and 2008. Collecting data of this quality required identifying all categories of entry for both permanent immigrants and temporary migrants, and historically tracing all changes made to the 7

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selection criteria and quotas using primary text legislation. This new data will enable us to address empirically whether policymakers respond to economic and public demands, and to determine to what extent policy affects the skill composition of the U.S. migrant population. I discuss surprising findings that emerge from the new data that challenge theoretical expectations. The remainder of this chapter is structured as follows. First, I present a conceptual definition skill bias. Second, I discuss how research on both the determinants and the outcomes of policy requires a new measure. Third, I develop an operational definition of skill bias in U.S. migrant admissions policy and discuss challenges that arise in applying it to U.S. data. Fourth, I acknowledge related work and identify how the new data improves upon existing scholarship. Finally, I present new data measuring the level of skill bias in U.S. migrant admissions policy, and I discuss findings that emerge from the data.

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A CONCEPTUAL DEFINITION OF SKILL BIAS There is inherent bias in the selection of permanent immigrants and temporary migrants, which policymakers determine by defining criteria for entry. Bias is the level of emphasis placed on one type of selection criteria in an admissions decision, relative to others. In the U.S., as well as across immigrant-receiving democratic countries, the most commonly observed selection criteria in migrant admissions relate to kinship, skill, ethnicity, and student status.1 Skill-based selection criteria have become increasingly relevant in recent decades as the U.S. both vies to attract highly skilled immigrants in order to boost competitiveness in the global economy, and attempts to integrate immigrants from diverse backgrounds into the U.S. economy, polity and society. Migrant admissions policy incorporates both permanent immigrant and temporary migrant admissions.2 For the remainder of this chapter, I define a permanent immigrant as an individual granted legal permanent residence in the United States, with the exception of permanent residence on the basis of refugee or asylee status. I define a temporary migrant as an individual admitted temporarily to the U.S. for the purpose of residence or work. Throughout the chapter, I use the terms permanent immigrant and temporary migrant to refer to these specific subsets of the U.S. migrant population, and I use the terms immigrant

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or migrant interchangeably when speaking more generally. At no point in this chapter does the term migrant refer to emigrants or internal, native migrants. Skill bias overlaps conceptually with the broader notion of the selectivity of national immigration policies. However, the concept of selection may encompass the outcomes of policy, which are a result of both the “self-selection” of immigrants and the selection instruments of national policy. Skill bias differs from selectivity in referring only to intentional skill selectivity in policy. Skill bias is the level of emphasis placed on an applicant’s skills in an admissions decision, relative to alternative types of selection criteria.

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RESEARCH ON THE OUTCOMES AND THE DETERMINANTS OF POLICY The lack of data measuring the level of skill bias in U.S. migrant admissions policy is currently hindering research. Leading economists argue that the skill composition of the U.S. migrant population is the most important factor determining the economic impact of immigration on the U.S. economy, and empirical evidence suggests that skill bias in U.S. migrant admissions policy plays an important role in determining the average skill level of recently admitted U.S. permanent immigrants. There is a persistent shortage of highly skilled workers in the U.S. economy that has produced increasing, and at times accelerating, demand for highly skilled workers over recent decades. In addition, the U.S. population prefers the immigration of highly skilled migrants, who are expected to be a lesser burden on fiscal resources. Despite the central role of policy in determining important outcomes and in responding to economic and public demand, we do not have a measure of skill bias in U.S. migrant admissions policy. As Figure 2.1 demonstrates, while there are measures for both the determinants and the outcomes of policy, there are currently no adequate measures of skill bias in U.S. migrant admissions policy. As a result, assumptions about the relationship between various phenomena cannot be empirically validated.

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Figure 2.1 Research on the outcomes and determinants of U.S. migrant admissions policy requires new measures.

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Outcomes of an Increase in Skill Bias Without historical measures of skill bias in U.S. migrant admissions policy, it is difficult to interpret how changes in U.S. migrant admissions policy have influenced the skill composition of the U.S. immigrant population in recent decades. Empirical research comparing immigrant cohorts arriving in the United States from 1970 to 1990 shows evidence of a decline in the skill level of immigrants relative to the native population. Borjas (1992) attributed the decline in the relative skills of U.S. immigrants to a shift in the national origins of immigrants, which he argued had come about as a result of changes in sending countries and as a result of a shift away from national origins quotas in U.S. migrant admissions policy toward a policy emphasizing kinship-based admissions. This empirical finding was supported by “a close to universal consensus” among economists and sociologists for most of the 1990s.3 However, there has been significant policy change in U.S. migrant admissions policy since the shift away from ethnicity bias in U.S. migrant admissions policy. Batalova (2006) identifies the expansionary changes made to U.S. migrant admissions policy brought about by the

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Immigration Act of 1990 and adjustments to the numerical restrictions placed on H-1B visas as important determinants of changes in the skill composition of more recent highly skilled migrants. Focusing on foreign-born workers in high-skill occupations, Batalova presents evidence from the 2000 census that highly skilled migrants arriving after 1990 are more educated on average than both highly skilled migrants arriving prior to 1990 and highly skilled native workers.4 Empirical research on the U.S. permanent immigrant population shows evidence of a disparity in the earnings and educational attainment of skill-based versus kinship-based permanent immigrants. Jasso, Rosenzweig and Smith (2000) provide evidence that principal applicants admitted through employment-based preference categories had 22 percent higher average earnings from 1972 - 1995 than permanent immigrants admitted on the basis of being husbands of U.S. citizens.5 More recently, Jasso and Rosenzweig (2009) use data from the New Immigrant Survey (2003-Round 1) to show that U.S. employment-based immigrants have higher educational attainment than marital-based immigrants. While over 35 percent of U.S. employmentbased immigrant respondents attained a post-graduate degree, less than 15 percent of marital immigrants achieved the same. Similarly, over 30 percent of U.S. employment-based immigrant respondents attained a college degree, in contrast to less than 20 percent of marital immigrants. These stark differences in the educational attainment of skill versus kinship-based permanent immigrants suggest that skill-based selection criteria significantly influence the skill composition of U.S. permanent immigrants. Jasso, Rosenzweig and Smith (2000) caution that, “an indirect substitution effect reduces the overall effect of increasing the number of high-skill visas to the extent that those who would have become immigrants via marriage now do so via an employment visa” (p. 19). However, even accounting for potential substitution effects, these large disparities suggest that an increase in skill bias in U.S. migrant admissions policy would increase the average skill level of new cohorts of legal U.S. migrants, as long as there are a sufficient number of applicants meeting the skill-based criteria. New data measuring the level of skill bias in U.S. migrant admissions policy will facilitate research exploring how policy effects the skill composition of the U.S. immigrant population. As will be

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discussed in the related work section of this chapter, the skill composition of the immigrant population is currently the best available proxy for the level of skill bias in U.S. migrant admissions policy. Without the ability to distinguish between these two important concepts, the relationship between them cannot be tested. A direct, comprehensive, annual measure of skill bias in U.S. migrant admissions policy provides the necessary first step to improving our understanding of the outcomes of U.S. migrant admissions policy.

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Determinants of an Increase in Skill Bias New data will also enable empirical research on what factors determine U.S. migrant admissions policy. Scholars commonly assume that policymakers want to maximize the economic gains from immigration, while also maximizing their own chances of re-election. According to these reasonable assumptions, policy change is predicted when a new policy will better achieve economic gains than the status quo policy, or when a new policy will better meet public demand. A review of the leading scholarship on the economic impact of immigration and public attitudes toward immigration shows that an increase in the level of skill bias in U.S. migrant admissions policy would do both. However, without data measuring the level of skill bias in U.S. migrant admissions policy, it is impossible to verify whether policymakers respond to economic and public demand. Maximizing economic gains from international migration The U.S. economy has experienced a persistent, and at times accelerating, demand for highly skilled workers over the past three decades. Goldin and Katz (2008) show that wages of highly skilled native workers have increased at a much higher rate relative to less skilled workers. The authors argue that this unmet demand is caused not by accelerating skill-biased technological change, as has been argued by many scholars, but by a slowdown in the educational attainment rates of the U.S. domestic population alongside a consistent rate of skill-biased technological change. Batalova (2006) presents evidence from the U.S. Bureau of Labor Statistics (BLS) stating that “according to BLS projections, six out of ten of the fastest growing

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occupations projected for the period between 2002 and 2010 rely on workers with at least an associate or Bachelor’s degree.” Empirical evidence suggests that highly skilled immigrants have little adverse effect on the opportunities for employment and wages of most native workers, including most native highly skilled workers. Batalova demonstrates that highly skilled migrants have a positive effect on the wages of highly skilled native workers in occupations with less than 35 percent of highly skilled migrants. In the majority of highly skilled occupations, she argues that:

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[H]ighly skilled immigrants have not been found to produce negative impacts on the earnings of all native workers. To the contrary, there is a positive (although non-linear) relationship between the presence of foreigners in a skilled job and the earnings of the overwhelming majority of native-born men and women. (93) Adverse effects on native worker wages have only been empirically demonstrated in fields with an unusually high concentration of foreign-born highly skilled migrants. Batalova finds evidence of an adverse effect of highly skilled migrants on native highly skilled worker wages only in occupations where immigrants make up more than thirty-five percent of the workforce. In a similar vein, Borjas (2005) provides evidence of an adverse effect of foreign-born doctoral students on native-born doctoral student wages across 22 fields of science and engineering. Borjas shows that the immigrant share of doctoral degrees awarded in U.S. has been increasing over recent decades, rising to 34.8 percent by the year 2000 on average across these fields. General-skill immigrants,6 in contrast, appear to have an adverse impact on native worker wages.7 Declining returns to general-skill relative to high-skill labor in the U.S. over recent decades yields predictions of deterioration in the prospects for the economic assimilation of more recent immigrants. Such predictions are supported by longitudinal studies of U.S. immigrant cohorts suggesting that more recent immigrant cohorts face a greater wage disparity relative to native workers than did earlier cohorts. Looking to the next generation, the educational attainment and earnings of the children of immigrants with

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higher levels of education outpaced that of the children of immigrants with lower education levels, suggesting that the skill composition of immigrants at the time of admission has long-term economic consequences.8 As immigrants increasingly form a disproportionate part of the U.S. labor force with less than a high school education, and as the prospects for economic assimilation of immigrants have, on average, diminished, research on the effect of immigration on the cost of the welfare state has increased in importance. Although far from conclusive, several major reports released in the 1990s suggested that the fiscal impact of immigration was considerable in regions of the country with a high immigrant population.9 Admitting highly skilled migrants makes all the more sense from a fiscal perspective. Immigrants with a higher level of education, opportunities for employment, and fluency in the English language are far less likely to rely on public services,10 and these immigrants contribute higher tax revenues, producing the potential for fiscal gains. In summary, research suggests an increase in the admission of highly skilled immigrants would meet an important labor shortage in the U.S. economy. Highly skilled immigrants have been shown to have few adverse effects on the wages and job opportunities of most native workers, including most highly skilled native workers. In addition, highly skilled immigrants are expected to bear a lower fiscal cost in terms of the use of services, while generating more tax revenue. The case is strong. An increase the level of skill bias in U.S. migrant admissions policy would better maximize the economic gains from immigration.11 However, we currently do not have a historical measure of the level of skill bias in U.S. migrant admissions policy to empirically test whether policymakers respond to economic demand. Satisfying voters Policymakers also want to satisfy voters, in order to maximize the chance of re-election. If voter attitudes toward immigration are shaped by the perceived impact of immigration on their own wages and employment opportunities, then similar predictions regarding policy change in U.S. migrant admissions would be generated to those just described under the policy objective of maximizing economic gains. When voters perceive that their own wages or employment

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opportunities are threatened by current migrant admissions, they will vote accordingly, forcing policymakers to respond to economy-wide fluctuations in wages and employment. However, researchers in political science and sociology argue that public support for immigration is not driven solely, or even primarily, by economic considerations. Recent findings by Hainmueller and Hiscox (2010) and Hainmueller and Hiscox (2007) suggest that education level appears a better predictor of individual attitudes toward immigration than factors relating to labor market competition or fiscal concerns. In addition, Hainmueller and Hiscox (2010) show that the level of public support for immigration also depends on the skill composition of immigrants admitted. The results of a randomized survey experiment conducted 2007 and 2008 on a nation-wide sample of U.S. respondents found that:

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…both highly skilled and low-skilled respondents strongly prefer highly skilled immigrants over low-skilled immigrants, and this preference is not decreasing in respondents’ skill levels. (p. 62, italics original) These results suggest that an increase in the level of high-skill bias in U.S. migrant admissions policy would better satisfy voters. Summary The skill composition of the U.S. migrant population, the economic impact of U.S. immigration, and the level of public support for immigration are interconnected phenomena of considerable importance to scholars and policymakers. At the center of these phenomena lies U.S. migrant admissions policy, which is both a determinant of and a response to these factors. The importance of policy in shaping these larger trends remains unknown. Although new data is not a panacea, certainly isolating policy effects remains difficult; it is a necessary first step to foster research on both the determinants and the outcomes of U.S. migrant admissions policy.

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SKILL BIAS IN U.S. MIGRANT ADMISSIONS POLICY Three steps are necessary to translate a conceptual definition of skill bias into an actual measure. First, I develop an operational definition of skill bias in U.S. migrant admissions policy. Second, I identify 1965 as a historical baseline for observing change in the level of skill bias. Third, I highlight key challenges that arose in the implementation of the operational definition over the time period of 1965 - 2008. I provide a more complete discussion of the methodology used to construct the skill bias measure, including a more extensive discussion of challenges addressed, in Appendix A.

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An operational definition of skill bias A measure of skill bias in U.S. migrant admissions policy should be a direct measure of policy, such that changes in the level of skill bias can be explained by changes in policy and not by changes in the variety of other factors that determine the stocks and flows of international migrants. The measure should ideally provide annual estimates of skill bias, rather than focusing only on years of major policy change or the yearly intervals during which major surveys have been conducted. Finally, a measure of skill bias should be comprehensive. The measure should consider both permanent immigrant admissions and temporary migrant admissions. In addition, because skill bias is a relative term (the level of emphasis placed on skills relative to alternative selection criteria), the measure must encompass all relevant visa categories of entry, not just a subset of visas that relate to employment. To date there is no direct, annual, comprehensive measure of the level of skill bias in U.S. migrant admissions policy. I operationalize the concept of skill bias in U.S. migrant admissions policy as the proportion of migrants that may be admitted under the existing law based on selection criteria that primarily relate to the skills of the applicant. This requires measurement of the number of available admissions slots with skill-based selection criteria, which I refer to as the skill-based quota space, and the number of available admission slots across all categories of entry, or the total quota space. The level of skill bias is simply the ratio of the skill-based quota space to the total quota space. Using this operational definition, skill bias is measured on a scale of 0 to 1, with zero indicating a selection system

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that gives no priority to the skills of an applicant and one indicating a selection system prioritizing only skill-based criteria.

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A baseline for observing policy change At the start of the 1960s, U.S. permanent immigrant admissions policy had a strong ethnicity bias. This was achieved through the national origins quota system, established in 1924, which placed numerical limits on the admission of migrants from a given sending country in proportion to the number of persons from that country residing in the U.S. in 1890.12 These quotas were maintained in large part by the Immigration and Nationality Act of 1952 (INA).13 The ethnicity bias in U.S. permanent immigrant admissions policy favoring Western European applicants was only substantially altered by the 1965 amendments to the INA.14 With the 1965 amendments, the national origins quota system was largely replaced by new “universal” selection criteria.15 Kinship became the most important priority shaping U.S. permanent immigrant admissions policy. It has been argued that the new criteria were initially implemented to duplicate the same demographic outcomes achieved by ethnicity-based criteria, so that they were simply the same policies operating under new labels.16 There is evidence that the extent of the diversity brought by policy changes made in the late 1960s was vastly underestimated at the time these changes were enacted. 17 However, the 1970s brought increasing diversity in immigrant inflows, proving that these new categories would not produce the same demographic outcomes as national origins quotas had in prior decades.18 At the very least, it can be argued that policy decisions made since the 1980s could no longer be made under the banner of maintaining a population of primarily Western European decent.19 Explaining the causes of the historical shift away from ethnicity bias in U.S. migrant admissions policy is not the focus of this chapter, and there is ample literature discussing the reasons why explicit racial preference in immigrant selection criteria was eliminated.20 Instead, this historical shift will be treated as a baseline for understanding change over time in the level of skill bias in U.S. permanent immigrant admissions. Specifically, this chapter will explore the extent to which policy change since 1965 affected the level of skill bias in U.S.

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permanent immigrant admissions policy and whether the changes in the level of skill bias have endured over time. An additional focus of this chapter will be observing changes in the level of skill bias in U.S. temporary migrant admissions policy. Temporary migrant admissions policy was not considered an integral part of U.S. migrant admissions policy in 1965 in the way that it is today.21 In recent decades, temporary migration has come to the forefront of scholarly discussions of U.S. migrant admissions policy largely due to three empirical trends. First, admissions levels have increased dramatically over recent decades. Second, there has been a notable increase in the number of U.S. nonimmigrant visa categories for admission.22 Third, there has been an increase in the number of individuals adjusting from nonimmigrant to permanent immigrant status, leading some scholars to argue that temporary migrant visas are increasingly serving as a channel to permanent residence. Although recent scholarship has explored changes over time in U.S. admissions policy toward specific types of temporary migrants, such as students23 or highly skilled workers,24 there is no scholarship to date that comprehensively considers how these changes influence the overall level of skill bias in U.S. temporary migrant admissions policy. Challenges to measuring skill bias Several challenges had to be addressed in order to implement the operational definition of skill bias, which requires constructing annual indicators of the skill-based quota space and total quota space in U.S. migrant admissions policy. First, I identified sources of information. Second, I determined the scope of the categories of entry included in the total quota space for each year. Although there is general consensus on what categories of entry are relevant to the admission of permanent immigrants, there is not yet a consensus on which categories of entry are relevant to the admission of temporary migrants. Third, in order to create an indicator for the skill-based quota space, I identified which categories of entry in each year utilize primarily skill-based selection criteria. Some categories of entry use multiple types of selection criteria, and for these cases I had to determine which type of selection criteria was primary. Fourth, in order to construct the skill-based and total quota space indicators, I recorded annual observations of the quotas placed on each category of entry. However, some categories of

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entry do not have any numerical limitation and other categories of entry have such significant exemptions to the established quota as to render the official quota meaningless. In these cases, quotas had to be estimated rather than observed. I will discuss how this was accomplished.

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Sources of information Only primary text legislation provides the information required to construct direct, comprehensive, annual measures of skill bias. Definitions of categories of entry that accompany administrative flow data do not include the dates of enactment or repeal for each category of entry, and discrepancies arise between these definitions and primary text legislation for some categories of entry. In addition, administrative definitions lack sufficient detail about the selection criteria and numerical limitations defined for each category of entry. Scholarly research on U.S. migrant admissions policy tends to provide information on selection criteria at a particular point in time, such as the date that major legislation is enacted, or for particular categories of entry. In general, these sources emphasize permanent immigrant admissions policy, and provide less comprehensive coverage of changes to categories of entry for temporary migrants. I historically traced changes in U.S. statutes and regulations from 1965 to 2008 to obtain three types of information.25 First, I recorded all dates of enactment, repeal or changes to existing categories of entry. This method ensures that I have a comprehensive list of each category of entry existing in U.S. law for each year during the time period. Second, I traced changes in legislation and regulations defining selection criteria for each category of entry. Finally, I historically traced legislation establishing or altering quotas placed on any category of entry throughout the time period. My method of historical tracing required identifying the relevant sections of U.S. law defining categories of entry, selection criteria and quotas. Once these sections were identified, I used annotated citations available through West Law in order to identify all amendments affecting relevant U.S. statutes, and I used the U.S. Statutes at Large to obtain the text of each amendment. I used the annually compiled Code of Federal Regulations: List of Sections Affected to identify all changes to relevant

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U.S. regulations, and I used the U.S. Federal Register to obtain the text of new or amending regulations.

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The scope of U.S. migrant admissions policy Categories of entry are defined in national legislation according to the purpose of entry and the type of permit issued. Generally speaking, the U.S. has a large number of categories of entry for family reunification, work, study and other purposes, and these categories of entry provide permits for temporary or permanent residence. Specifically, I identified 34 categories of entry relevant to my definition of temporary migrants and 20 categories of entry relevant to my definition for permanent immigrants. All 54 tracks of entry are listed in Table A.1 of Appendix A with brief descriptions. For permanent immigrant admissions, identifying the relevant categories of entry was straightforward, as there is a considerable level of consensus about what categories of entry are relevant to the concept of permanent immigrant admissions policy. I include all preference categories defined in U.S. law for numerically limited immigrant admissions, in addition to the numerically unlimited category of entry for the immediate relatives of U.S. Citizens. I exclude refugee-based immigrants and “special immigrant” categories of entry.26 with the important exception of the tracks of entry for “Western Hemispheric” immigrants prior to 1976. I also exclude legalization programs, and special provisions for Cubans to convert to immigrant status after one year of residence in the country. These decisions represent the mainstream definitions used to identify the permanent immigrant population in scholarly research.27 In contrast, there is not a general consensus about what categories of entry are relevant to temporary migrant admissions policy. This is in part because this subset of individuals is not well-defined in U.S. law, which subsumes temporary migrants into a broader category of “nonimmigrants.”28 The term nonimmigrant encompasses all noncitizens admitted to the U.S. except permanent immigrants, including visitors for the purpose of business and pleasure (tourists) that make up a much larger share of annual nonimmigrant admissions than temporary migrants.29 Tourist visas are not of conceptual importance for this chapter given the short duration of stay allowed (less than 90 days) and the limitations on these visas (which cannot be

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extended or transferred to an alternative status). I also exclude categories of entry for diplomats, employees of NATO and other international organizations, and foreign media representatives because these visas are established to foster international diplomacy and freedom of the press based on international norms of reciprocity. I include all other nonimmigrant visas for primary applicants that have an initial duration of at least six months and up to six years existing at any point during the time period. A specific list of the U.S. nonimmigrant visa categories that I include in my definition of temporary migrants appears in Appendix A.30

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Categories of entry with multiple types of selection criteria I determined whether admissions decisions for each category of entry were made primarily using skill-based selection criteria for each year. Selection criteria that I consider skill-based include those relating to the applicant’s language ability, work experience (including but not exclusive to work experience in sectors identified as facing domestic labor shortages), educational degrees, professional achievements, and employment contracts with domestic employers. In addition, I noted which categories of entry used primarily high-skill-based criteria. I identify selection criteria for highly skilled migrants to be those that require educational attainment at or above the 4-year college level or evidence of an unusually high level of talent in a given field. Selection criteria that are not considered skill-based include those relating to an applicant’s relationship to a citizen or permanent resident of the host country, an applicant’s country of origin, student status, or participation in criminal law enforcement proceedings. Table A.1 in Appendix A provides a short explanation of how I determined the primary selection criteria for each category of entry. Numerically unlimited categories of entry and quota exemptions The total quota space does not reflect the actual number of annual migrant admissions. Quota space is a measure of what the national migrant admissions policy could allow in a given year, given adequate applicant supply. Actual migrant admissions, as reported in national flow statistics, record the number of applicants the country did admit in

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a year, reflecting both the policy-driven quota space and the applicant pool. In the simplest case, the total quota space for a country in a given year can be obtained by summing together the quota allotted for each category of entry. However, some categories of entry do not have observable quotas, either because they are numerically unlimited, or because there are exemptions to the quota that may allow admissions at a significantly higher level than the established quota. For these categories of entry, it is necessary to estimate the expectation that policymakers have about how many migrants are likely to be admitted through that category of entry. This rests on the reasonable assumption that policymakers would cap a given category of entry if it was anticipated that there would be more eligible individuals seeking to obtain entry through that category of entry than there is a political consensus to support. Estimation was required for only 1 out of 20 of the categories of entry within the area of permanent immigrant admissions. In the area of U.S. temporary admissions prior to 1990 all categories of entry were numerically unlimited; therefore, the skill bias measure is only calculated from 1990 - 2008. Prior to 1990, a measure of skill bias for U.S. temporary admissions according to this methodology would rely entirely on estimates from flow data. Because this would not achieve an important property of the data—that of direct measurement—the skill bias measure has not been calculated for years prior to 1990. After 1990, 25 out of 34 of the categories of entry require estimated quotas. Appendix A describes the way in which estimated quotas were calculated. Later in this chapter, I provide evidence that the changes observed in the level of skill bias in U.S. temporary migrant admissions policy are primarily caused by changes in policy and not by changes in the estimated quotas for numerically limited categories of entry. RELATED WORK The lack of quality data measuring skill bias has hindered research, and there are only a few empirical studies that have attempted to measure skill bias in a comprehensive way, whether over time or across countries, or both. In an analysis of immigrant labor market performance in Europe, Kogan (2007) constructs a measure of the “relative selectivity of third-country immigrants” for fifteen EU countries from 1992-2000, which she operationalizes as the difference

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between the proportion of third-country immigrants with tertiary education and the tertiary educated native born. In a recent study of the role of diasporas in shaping international migration flows, Beine, Docquier and Özden (2009) consider the percentage of refugees among the migrant population as a measure of the “degree of selective immigration policy.” Both of these measures rely on data measuring the outcomes of policy, which are also the result of a variety of other factors shaping trends in international migration. As a result, variation in the indicators cannot be attributed to differences in policy with certainty. Hollifield, Hunt and Tichenor (2006) and Jasso, Rosenzweig and Smith (2000) use dummy variables that indicate the timing of major policy changes in U.S. migrant admissions policies. However, dummy variables cannot distinguish the effect of policy from the effect of other events occurring during the same year. Two large-scale data collection efforts provide information that is related to, but importantly distinct from, the skill bias measure presented in this chapter. The Migrant Integration Policy Index (MIPEX) is the first large-scale attempt to directly measure national policies relating to migrant integration.31 MIPEX includes labor market integration measures such as whether third country national workers (TCNs) are able to accept employment on terms equal to that of EU nationals, for example, and whether the state facilitates the recognition of skills and qualifications earned outside of the EU. However, these integration policies are importantly distinct from migrant admissions policies. The Mexican Migration Project (MMP) is a U.S. focused, large-scale data collection project that includes historical measures for U.S. border enforcement, including budget and apprehensions data, in addition to measures of visa accessibility and admissions according to immigration type of Mexican immigrants. However, consideration of U.S. immigration policy in this dataset does not claim to be comprehensive, given the specific focus on Mexican immigration. There has been an increasing amount of scholarship focusing on highly skilled migration policies. This work has provided several useful qualitative reviews of skill-based categories of entry in a variety of leading immigrant receiving countries.32 However, these studies do not attempt to provide quantitative measures of policy, and they are not comprehensive in focus. These studies tend to focus on comparing distinct features of the skill-based admissions policies across countries,

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rather than cross-national differences in the overall level of skill bias in national migrant admissions policies.

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NEW DATA I present new data measuring the level of skill bias in U.S. migrant admissions policy. The new data allows, for the first time, a clear observation of whether the level of skill bias in U.S. migrant admissions policy has increased over time. I present new data measuring the level of skill bias in U.S. permanent immigrant admissions policy since 1965 and in U.S. temporary migrant admissions policy since 1990. The measure of skill bias provides a comprehensive, annual measure of the emphasis placed on skill-based selection criteria, relative to alternative selection criteria, in U.S. migrant admissions policy. The measure is primarily, but not purely, a direct measure of policy. As already discussed, its construction requires some estimation using flow data for categories of entry that are not numerically limited by policymakers. However, these measures improve upon all preexisting measures of skill bias. In addition, I am able to provide evidence that the changes observed in the level of skill bias in U.S. temporary migrant admissions, which required the most estimation, are primarily determined by changes in policy, rather than by changes in the estimated quotas generated for numerically unlimited categories of entry. It should be noted that a general measure of skill bias treats equally categories of entry targeting high-skill and general-skill applicants, as selection criteria for all groups are still skill-based. However, simply excluding general-skill categories of entry from the skill-based quotient produces an indicator of bias toward highly skilled migrants. I calculate both measures. Skill bias in U.S. permanent immigrant admissions policy Figure 2.2 presents the level of skill bias in U.S. permanent immigrant admissions since 1965, which has never exceeded .22 on a scale of 0 to 1, with zero indicating a selection system that gives no priority to the skills of an applicant and one indicating a selection system prioritizing only skill-based criteria. Skill-based selection of permanent immigrants

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for the purpose of employment in the U.S. economy has remained the second most important priority in the admissions program, although a distant second to the dominant priority of family reunification.33 Since the 1965 amendments, U.S. admissions policy for permanent immigrants has only been substantially altered twice. Each policy change resulted in a modest increase in the level of skill bias in U.S. immigrant admissions policy, but maintained a predominantly kinship-biased selection regime.34 Figure 2.2 identifies these two policy changes, which are visible in the abrupt increases to the level of skill bias.

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Figure 2.2 Additional information on the methodology used to measure skill bias in U.S. permanent immigrant admissions policy can be found in Appendix A.

Under the 1976 amendments, Western Hemispheric immigrants were for the first time made subject to the preference system that had been applied to Eastern Hemispheric immigrants since 1965.35 This preference system maintained two channels for skill-based entry, the third and sixth preference categories, representing up to twenty percent of numerically limited immigrant admissions. Ten percent of visas were to be reserved for immigrant admissions through the third preference category, which maintained the following selection criteria:

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[Q]ualified immigrants who are members of the professions, or who because of their exceptional ability in the sciences or the arts will substantially benefit prospectively the national economy, cultural interests or welfare of the United States, and whose services in the professions, sciences or arts are sought by an employer in the United States.36 Another ten percent of visas were reserved for admissions through the sixth preference category for “qualified immigrants who are capable of performing specified skilled or unskilled labor, not of a temporary or seasonal nature, for which a shortage of willing and employable persons exists in the United States.”37 The remaining eighty percent of immigrant admissions subject to numerical limitations were reserved for kinshipbased immigration, in addition to the unlimited entry of immediate relatives of U.S. citizens, including parents, spouses and minor children.38 The 1976 amendments effectively adjusted the selection criteria used for the admission of permanent immigrants from the Western Hemisphere. The selection criteria shifted from entirely ethnicity-based to a new division of eighty percent family-based and twenty percent skill-based selection.39 While it was possible under previous law for any percentage of immigrants from the Western Hemisphere to be admitted for employment-based purposes, so long as labor certification could be achieved, there was no level of admissions slots that were specifically reserved for skill-based selection. Therefore, the increase in the level of skill bias caused by the 1976 amendments to the INA is the result of the final large-scale shift away from ethnicity-based selection criteria. In contrast, the Immigration Act of 1990 was a deliberate attempt on the part of some legislators and social actors to increase skill-based immigrant selection, and it remains the only attempt successfully enacted.40 The new legislation expanded admissions for all types of permanent immigrants, but particularly for skill-based permanent immigrants. The immediate impact of the Immigration Act of 1990, as clearly displayed in Figure 2.2, was a doubling of the level of skill bias in U.S. migrant admissions policy. Skill-based quota space jumped from 58,000 admission slots under previous legislation to 130,000 visa slots available under the new legislation.41 A comparison of the two lines displayed in Figure 2.2 also reveals that since 1990 high-skill bias makes up a much larger share of the general level of skill bias. Under the new legislation, skill-based visas

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are distributed across five preference categories, which place a greater emphasis on admitting highly skilled migrants. A first preference category of “priority” workers includes individuals of extraordinary ability in the sciences, arts, education, business or athletics; outstanding professors and researchers; and multinational executives and managers. The second preference category admits individuals of exceptional ability and members of the professions with an advanced degree or its equivalent, while the third preference category admits members of a profession with a bachelor’s degree or with shortage skills. Up to 10,000 of the 40,000 visas allotted for the third preference category may be used to admit applicants for unskilled labor where no American worker is available. The fourth preference category includes special immigrants, including religious workers, and a fifth category initiated an employment-creation investor program.42 The most surprising observation provided by the new data is that the changes in the level of skill bias brought about by the 1976 amendments and by the Immigration Act of 1990 have not endured over time. This is evident from the gentle negative slope observed during the years between and following these two policy enactments. This decline in skill bias is caused by growth in the kinship-based admission of immediate relatives of U.S. citizens, which remains a track of entry that is unlimited by U.S. law. Although the Immigration Act of 1990 established a “Worldwide Limit” on legal immigration,43 the act also included a mechanism establishing a floor of 226,000 as the minimum number of visas that are to be reserved for numerically-limited family-based immigrants. Therefore, the worldwide limit established in U.S. law is not an absolute limit, as it may be legally exceeded if immediate relatives of U.S. citizens exceed 254,000 in a given year. The new data reveals that the 1990 increase in the level of skill bias in U.S. migrant admissions policy has been largely undone by this steady increase in the admission of immediate relatives of U.S. citizens. Skill bias in U.S. temporary migrant admissions policy since 1990 Figure 2.3 provides a comparison of the levels of skill bias in U.S. admissions policy for temporary migrants and permanent immigrants over the time period from 1990 to the present.44 What is immediately visible in looking at this graph is that the level of skill bias in U.S.

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temporary migrant admissions is considerably higher than that of U.S. permanent admissions, reaching a high of 0.43 in 2000 and never falling below 0.35, a low reached only four years after in 2004. The data shows that the U.S. is placing a greater emphasis on obtaining skill-based migrants in its temporary admissions program. The skillbased quota space for U.S. temporary migrant admissions is considerably larger than that available for skill-based permanent immigrants. In 2008, U.S. temporary migrant admissions policy had a skill-based quota space over three times the size of the skill-based quota space for U.S. permanent immigrant admissions. In other words, U.S. temporary migrant admissions policy would allow more than three times as many skill-based temporary migrant workers to be admitted than would be able to adjust to permanent immigrant status on the basis of their skills under U.S. permanent immigrant admissions policy.

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Figure 2.3 Additional information on the methodology used to measure skill bias in U.S. permanent immigrant and temporary migrant admissions policy can be found in Appendix A.

While kinship-based admissions dominate U.S. permanent immigrant admissions, student status-based admissions dominate U.S.

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temporary migrant admissions. Throughout this time period, two uncapped channels of temporary entry primarily utilized by foreign students, the F-1 and J-1 visas, make up over half of the annual total quota space. These student categories of entry are visible in Figure 2.4, which demonstrates the amount of annual total quota space taken up by student visa categories in comparison to the major skill-based visa categories of entry. Appendix A provides a further discussion of why I do not classify categories of entry admitting migrants on the basis of student status as primarily skill-based. Students who wish to remain in the U.S. to work must convert their status to a skill-based temporary visa, unless they establish a relationship with a U.S. citizen or permanent resident or qualify for the limited “diversity” green card lottery.

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Figure 2.4 Additional information on the methodology used to estimate quotas for categories of entry in U.S. temporary migrant admissions policy can be found in Appendix A.

There is a considerably higher level of variance in the level of skill bias for temporary admissions. Figure 2.3 demonstrates that the level of skill bias in U.S. temporary migrant admissions undergoes considerable change between 1997 and 2005, in contrast to the level of skill bias in U.S. permanent immigrant admissions, which remains unchanged

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except for the minor downward sloping trend caused by the steady increase of immediate relatives of U.S. citizens. While the preference categories for admitting U.S. permanent immigrants have remained unchanged since 1990, there have been sixteen new visa categories of entry enacted for principal applicant temporary migrants. Table 2.1 reveals that only six of sixteen new visa categories enacted since 1990 have been skill-based. Of the new skill-based visas enacted since 1990, all except for two visas admit highly skilled applicants: The H-1C visa for nurses and the H-2R visa for returning non-agricultural seasonal workers. It could further be argued that although the H-1C visa for nurses does not require a four year college degree, it should also be considered a highskill visa. Batalova (2006) argued as much in her analysis of the highly skilled immigrant population, which included registered nurses in her definition of highly skilled for the following reason: Although the occupation ‘registered nurse (RN)’ does not technically fit the above definition of highly skilled occupation, as only 52 percent of nurses have a college education, I include it in the analysis because of its theoretical importance. RNs comprise the most educated group among health care personnel besides doctors. These changes reveal a trend similar to that observed in U.S. permanent immigrant admissions, which is that where new skill-based categories of entry have been enacted they have been more likely to be for the purpose of attracting highly skilled migrants. However, changes in visa categories alone do not explain the changes observed in the level of skill bias in U.S. temporary migrant admissions policy. This data does not account for the numerical limitations placed on new visa categories, which vary considerably in size. As explained in Appendix A, the skill bias measure for temporary migrant admissions policy relies more heavily on estimated quotas than was the case in constructing the measure for the level of skill bias in permanent immigrant admissions. However, a closer analysis of the data shows that the level of skill bias in U.S. temporary migrant admissions policy is shaped primarily by policy change and not by increasing inflows through numerically unlimited channels.

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Table 2.1: New visa categories in U.S. temporary migrant admissions policy since 1990 Visa category

Year Highly Skilled? enacted skilled?

TN

NAFTA professional

1993

Yes

Yes

S-5

Criminal informant

1994

No

No

S-6

Terrorist informant

1994

No

No

E-2

Treaty investor

1997

Yes

Yes

1997

Yes

Yes

1998

No

No

1999

Yes

No

2000

No

No

2000

No

No

2000

No

No

2000

No

No

2001

No

No

2002

No

No

2002

No

No

2005

Yes

Yes

Returning H-2B non-agricultural 2005 seasonal worker

Yes

No

H-1C

Temporary worker – Australian national in a specialty occupation Irish Peace Process Program Participant Temporary worker—nurse

T-1

Victim of human trafficking

E-3 Q-2

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Description

V-1 V-2 K-3 F-3 M-3 E-3R H-2R

Victim or informant of criminal activity Spouse of Legal Permanent Resident (LPR) with petition pending for three years or longer Child of LPR who has petition pending for three years or longer Spouse of U.S. citizen awaiting LPR visa Border commuter student Border commuter vocational or nonacademic student Returning E-3 worker

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Figure 2.5 presents the actual and estimated quotas for the five largest skill-based tracks of entry that make up U.S. temporary migrant admissions, which are designated by visa type. Policy changes to the annual cap on the H-1B visa for temporary workers in specialty occupations is primarily responsible for the upside down U shape visible in Figure 2.3 from the years 1997 to 2004 in both the general level of skill bias and the level of high-skill bias. However, as is also apparent in Figure 2.3, the level of skill bias increases in 2005, while the level of high-skill bias does not. This is a result of a policy change in 2005 that adjusted the numerical limit to H-2B workers, by making returning H-2B workers exempt from the annual cap. Instead, H-2R visas were created for these returning non-agricultural seasonal workers, and this significantly increased the potential annual inflow of such workers made possible under law for the years in which the exemption was in effect.

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Figure 2.5 Additional information on the methodology used to estimate quotas for categories of entry in U.S. temporary migrant admissions policy can be found in Appendix A.

What is most notable about the changes to the level of skill bias in U.S. temporary migrant admissions is that sunset provisions have largely undone the increases in the level of skill bias that are observed during the

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time period under consideration. Sunset provisions written into the legislation adjusting quotas to the H-1B and the H-2R visa categories largely reversed the changes. Combining these changes with overall changes across all categories of entry, the level of skill bias observed in U.S. migrant admissions in 2008 is lower than that observed in 1990. Likewise, the level of high-skill bias is at the same level as that observed in 1990, showing a slight increase in the level of emphasis placed on high-skill-based admissions within total skill-based admissions.

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SUMMARY OF FINDINGS The new data reveals surprising findings. The current level of skill bias in U.S. permanent immigrant admissions policy is similar to the level of skill bias observed in1965. Likewise, the current level of skill bias in U.S. temporary migrant admissions policy is slightly lower than the level observed in 1990. A larger share of skill-based admissions slots is being devoted to admitting highly skilled applicants, but not a larger share of overall admissions slots. There is little evidence to support the claim that policymakers are accelerating their effort to admit highly skilled migrants in response to economic and public demand for highly skilled workers. Instead, it appears that policymakers have responded to the demand for an increase in highly skilled migrant admissions in two ways. First, significant policy changes expanded opportunities for skillbased admissions in both permanent immigrant and temporary migrant admissions policy. However, all of these changes have largely been undone by the increase in numerically unlimited admissions of non skill-based immigrants and sunset provisions. Second, what has proved a more enduring change in U.S. permanent immigrant admissions policy is a notable increase in the share of skill-based admissions policy that is devoted to admitting highly skilled migrants. This trend is most visible in U.S. permanent immigrant admissions since 1990, but it is also visible in the new visa categories that have been enacted since 1990 in U.S. temporary migrant admissions. CONCLUSION This chapter made several important contributions. It presented a conceptual definition of skill bias, and operationalized the measure in

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the context of U.S. migrant admissions policy. It presented new data revealing the level of skill bias in U.S. migrant admissions policy between 1965 and 2008. The results of the new data are surprising in light of theoretical expectations. There is little evidence that policymakers have responded to economic and public demand to increase the level of emphasis on applicant skills in U.S. migrant admissions decisions. Policy responses increasing the level of skill bias have proved short-lived. The only enduring change has been an increase in the emphasis on selecting highly skilled applicants within skill-based admissions. However, the share of high-skill-based admissions slots is not increasing overall. This new data challenges theoretical expectations that policymakers respond to economic and public demand. Amid a turbulent economy and public concern regarding the fiscal costs of general-skill immigrants, policymakers have not responded with a notable increase in skill bias or high-skill bias in U.S. migrant admissions policy. The new data provides a first step to understanding the determinants of change in U.S. migrant admissions policy. The next chapter develops a new theory of the politics of U.S. migrant admissions policy that better explains these surprising empirical trends.

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CHAPTER THREE

Stalled in the Senate: Explaining change in U.S. migrant admissions policy since 1965

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INTRODUCTION Change in U.S. migrant admissions policy since 1965 has been both expansive and infrequent, but leading theories of the determinants of U.S. migrant admissions policy do not account for both characteristics. On the one hand, scholars have demonstrated that nativism is on the rise among the U.S. population, leading to expectations of restriction in U.S. migrant admissions policy. Yet, expansive policies persist, and no examples of restrictive legislation can be observed since 1965. On the other hand, scholars have argued that U.S. migrant admissions policy is an area dominated by special interest groups. Yet, even bills backed by both employers and ethnic lobby groups are more likely to stall in the Senate than pass. This despite the fact that organized labor, typically identified by economists as the foil to employer interests, has declined in membership over recent decades and has repositioned itself to appeal to immigrant workers. If nativism among the U.S. population is influential, then why have we observed no policy restriction? If policymakers are sheltered from the public on this issue, what hinders special interest groups from achieving policy change? This chapter presents a new theory that accounts for both the expansive nature and the infrequence of policy change in U.S. migrant 35

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admissions policy. The theory does two things that existing theories do not. First, I consider how supermajoritarian decision making procedures in the U.S. Senate create incentives for interest groups to form broad-based coalitions, while at the same time providing political opportunities for interest groups to block policy change with only a minority of Senate support. While scholars of U.S. policymaking have written extensively on these institutional features of the U.S. Senate, supermajoritarian decision making procedures have been overlooked in leading theories of the determinants of U.S. migrant admissions policy. Second, I incorporate organized anti-immigration groups as important special interest group actors, which have also been overlooked in most leading theories. These groups that may initially be viewed as extremist, fringe organizations form the basis of a blocking coalition opposing expansive policy change. With these two modifications to the interest group story, I present a theory that highlights the conditions explaining policy change. The theory highlights the importance of a coalition of immigrant advocacy groups, employers and unions in achieving policy change and identifies the conditions under which this coalition is most likely to form and least likely to be blocked by an anti-immigration group opposition. Four predictions result from the theory. Policy stasis is predicted to be more likely than policy change. When it occurs, policy change is predicted to be expansive and not restrictive. Policy change is predicted to be unlikely following a prolonged period of increasing unemployment. And finally, the theory predicts that it is not always easier to expand admission to migrants with a lower level of rights and benefits than to migrants with a higher level of rights and benefits. Empirical support for the theoretical claims and predictions are assessed through four case studies of U.S. policymaking on migrant admissions in recent decades. THE “NEW NATIVISM” AND POLICYMAKER PREFERENCES Recent scholarship provides ample evidence that nativism is alive and well in contemporary U.S. society. The cause of increasing nativist sentiments among the U.S. population is discussed elsewhere, and will not be the focus of this chapter.45 Instead, I focus on claims made in the

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literature regarding how nativist public sentiment influences policymaker preferences, and I assess these claims with new evidence. In the introduction of his edited volume entitled Immigrants Out! The New Nativism and the Anti-immigrant Impulse in the United States, Juan Perea describes restrictive policy change as the result of public pressure:

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During nativist times in the United States, democratic processes are turned against internal minorities deemed foreign or ‘un-American,’ resulting in discriminatory legislation and immigration restrictions. Nativist movements, and the legislation they spawn, seek to rid the nation of perceived enemies of the American way of life. The social sanction given to majoritarian legislative action is crucial in the creation of such enemies. Through legislative rejection of some perceived enemy in our midst, nativism seeks the ritual purification of American society, the separation of those who belong from those who do not. The majority enhances its status as the ‘real’ Americans, those who belong, and rejects those currently deemed threatening to American values. (p. 1) According to this portrayal, the politics of migrant admissions policy are majoritarian, and it is the voting public that influences policymakers to assert majority preferences. In contrast, some scholars reverse the causal arrow, claiming that policymakers seeking re-election act as political entrepreneurs, stirring up nativist sentiments using scapegoating tactics as an electoral campaign technique to divert voter attention away from pressing issues.46 Still others argue or imply that policymakers themselves share the nativist sentiments of the population, enacting legislation in accordance with these values.47 If an increasingly nativist public holds policymakers accountable through elections, or if politicians act strategically to garner electoral support or on the basis of personally held principles to limit immigration, then we should observe restrictive policy change to bring existing policy closer to the ideal point of the median voter or legislator. Some scholars have argued that we do observe such restriction, but their evidence is the passage of new policies relating to law enforcement and migrant access to public benefits, rather than

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policies directly regulating annual migrant admissions. Specifically, scholars have compiled evidence of restrictive policies over recent decades enhancing border security,48 implementing employer sanctions for the employment of undocumented migrants,49 cutting the level of migrant access to public welfare programs,50 and a recent surge of efforts to restrict immigrant rights and protections provided at the state and local level.51 These restrictive policies are clearly important. Restrictive measures of this type are anticipated to influence trends in international migration by changing the incentives for individuals to immigrate and the likelihood of admission outside of the framework for legal entry. However, in recent accounts of the new nativism there is remarkably little discussion of the policies directly regulating the type and level of international migrants that are legally admitted to the United States annually.

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Expansive policy change in U.S. migrant admissions policy since 1965 A focus on U.S. migrant admissions policy, in comparison with the other policy areas just discussed, reveals a remarkable finding.52 I provide empirical evidence that policy change in U.S. migrant admissions since 1965 has been expansive. I show that there is no evidence of restrictive policy change during recent decades. Before presenting the evidence, I first define the terms that form the basis of this claim. Migrant admissions policy consists of the positive criteria used to select international migrants for legal residence and work. In this chapter, I particularly define migrant admissions policy to include the legally defined categories of entry that determine what type of individuals are eligible for admission, and the numerical limitations, or quotas, that determine how many individuals may be admitted in a given year. The theory developed in this chapter focuses on policies relating to the admission of kinship and employment-based permanent immigrants53 and temporary migrant workers.54 I define expansive policy change in the area of U.S. migrant admissions policy as the passage of new legislation that increases the number of categories of entry for migrant admissions or the quotas that determine the maximum level of annual admissions through each category of entry. Restrictive policy change is defined as the passage of new legislation that reduces the number of categories of entry for

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migrant admissions or reduces the quotas placed on annual admissions through each category of entry. Two additional notes help to augment this definition. If quotas are placed for the first time on a pre-existing category of entry, I consider the policy change as restrictive if the quota is at or below the level of admissions observed over the preceding five years through that category of entry. Policy change that results from sunset provisions contained in legislation is not included in this definition of expansive and restrictive policy change, which only refers to the passage of new legislation. Sunset provisions go into effect without any action on the part of policymakers. While it reveals that policymakers lack sufficient consensus to reenact the policy, it is not indicative of a winning coalition of policymakers supporting the policy change. In order to observe whether there has been any restrictive policy change, I present newly assembled evidence comprehensively reviewing all policy change to U.S. migrant admissions policy since 1965, drawing from the historical record of changes to U.S. statutes and regulations.55 Although policies regulating permanent immigration are widely-discussed elsewhere in the scholarly literature,56 policies relating to the admission of temporary migrants since 1965 have not been comprehensively assembled.57 Policy change in permanent immigrant admissions In the area of permanent immigrant admissions, there have only been two policy changes since 1965 that have adjusted the categories of entry for admission or quotas placed on each category of entry. Table 3.1 presents a list of all changes to the categories of entry for permanent kinship and employment-based immigrant admissions and the numerical limitations placed on each category of entry following each policy change. The 1976 amendments to the Immigration and Nationality Act (INA) adjusted the admissions program for immigrants from the region defined in U.S. law as the Western Hemisphere. These immigrants were placed for the first time under the same admissions program that had since 1965 regulated admissions for Eastern Hemispheric immigrants.58 Under both the 1965 and the 1976 amendments, 170,000 was the overall cap placed on Eastern Hemispheric immigration and

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Employment creation Total quota for kinship and employment-based immigrants (excluding the numerically unlimited category for immediate relative of U.S. citizens) Immigrant admissions from the Western Hemisphere

Unskilled workers

Total kinship-based admissions (excluding the numerically unlimited category for immediate relative of U.S. citizens) Immediate relative of U.S. citizens Unmarried sons and daughters of U.S. citizens Spouses, children, and unmarried sons and daughters of permanent residents Married sons and daughters of U.S. citizens Siblings of U.S. citizens age 21 and older Total employment-based admissions Priority workers- extraordinary talent, outstanding professors and researchers, and executives Persons with advanced degrees or exceptional abilities Professionals Skilled workers

Categories of entry

No longer separated out as special immigrants

No longer separated out as special immigrants 120,000

40,000, up to 10,000 of which can be for unskilled workers 10,000 356,000

29,000

17,000

40,000

272,600

29,000

17,000

40,000

159,800

Not yet in existence

Not yet in existence

No quota 23,400 114,200 23,400 65,000 130,000

Not yet in existence

No quota 58,000 58,000 29,000 69,600 58,000

No quota 34,000 34,000 17,000 40,800 34,000

226,000

Quota since the Immigration Act of 1990

Not yet in existence

214,600

Quota following the 1976 amendments

125,800

Quota following the 1965 amendments

TABLE 3.1: Policy change to kinship and employment-based permanent immigrant admission policy since 1965

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120,000 was the overall cap placed on Western Hemispheric immigration. What is not shown on the table is that 6 percent of the total Eastern Hemispheric cap is allotted to the admission of humanitarian immigrants. Therefore, when Western Hemispheric immigrants were placed under the new admissions criteria in 1976, 6 percent of the 120,000 quota (7200 admissions slots) became reserved for humanitarian applicants. The intent of the 1976 amendments was to maintain, rather than to expand or restrict, the overall level of immigrant admissions under newly harmonized criteria. 59 The Immigration Act of 1990, in contrast, brought about expansive policy change. All categories of entry that existed prior to 1990 were maintained, while additional categories of entry were added in the area of employment-based admissions. Quota space under the newly expanded employment-based categories of entry more than doubled. Although there were reductions in the admissions slots reserved for distant relatives of U.S. citizens, these reductions were exceeded by an increase in the number of admissions slots reserved for the immediate family members of U.S. permanent residents. As a result, kinship-based admissions also increased with the 1990 policy change. These are the most recent changes to permanent immigrant admissions policy, despite several legislative attempts at policy change since 1990.60 Policy change in temporary migrant admissions In the area of U.S. temporary migrant admissions, there has been more frequent policy change than in the area of U.S. permanent immigrant admissions. This has primarily occurred through the creation of new visa categories. Since 1965, 27 new categories of entry have been adopted for primary applicants.61 Table 3.2 lists all visa categories for primary applicant temporary migrants in effect at any time since 1965, which includes but is not limited to all visa categories for temporary migrant workers. What is notable is that most of these categories of entry remain in effect through 2009. Only the Q-2 visa for Irish Peace Program Participants is no longer in effect as a result of sunset provisions.

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TABLE 3.2: Categories of entry for temporary migrant admission in effect since 1965 Year Enacted

Still in effect in 2009?

Explanatory note regarding changes in visa categories

Treaty traders

1952

YES

E converts to E-1 in 1997 when E-2 is created

F-1

Foreign student (academic or language training program)

1952

YES

H-1/ H-1B

Temporary worker-highly skilled

1952

YES

H-2/ H-2B

Temporary worker-seasonal

1952

YES

Temporary worker-trainee Cultural exchange visitor Fiancé(e) of U.S. citizen Intracompany transferee Vocational student Temporary worker-agricultural worker Professional under the CanadaU.S. Free Trade Agreement or NAFTA

1952 1961 1970 1970 1981

YES YES YES YES YES

1986

YES

1988

YES

Temporary worker-nurse

1989

YES

Person with extraordinary ability Person accompanying and assisting in the artistic or athletic performance by O-1 Internationally recognized athlete or member of an internationally recognized entertainment group and essential support Artist or entertainer in a reciprocal exchange program and essential support Artist or entertainer in a culturally unique program and essential support Participant in an international cultural exchange program Religious worker Criminal informant Terrorist informant Treaty investors

1990

YES

1990

YES

1990

YES

1990

YES

1990

YES

1990

YES

1990 1994 1994 1997

YES YES YES YES

Visa

Description of the Visa

E/E1

H-3 J-1 K-1 L-1 M-1 H-2A

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TC/ TN H-1A/ H-1C O-1 O-2

P-1

P-2

P-3 Q-1 R-1 S-5 S-6 E-2

H-1 converts to H-1B when H-1A is created in 1989 H-2 converts to H-2B when H-2A is separated out as a distinct category of entry in 1986

TC is replaced by TN with the 1993 adoption of NAFTA provisions Program ends in 1995, but it is replaced by H-1C in 1999

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TABLE 3.2: Categories of entry for temporary migrant admission in effect since 1965 (Continued) Visa

Description of the Visa

Irish Peace Process program participant T-1 Victim of human trafficking Victim or informant of criminal U-1 activity Spouse of Legal Permant V-1 Resident (LPR) who has petition pending for three years or longer Child of LPR who has petition V-2 pending for three years or longer Spouse of U.S. citizen awaiting K-3 LPR visa F-3 Border commuter student Border commuter vocational or M-3 nonacademic student Temporary worker in a specialty occupation arriving under the H-1B1 U.S. Free Trade Agreement with Chile or Singapore Temporary worker - Australian E-3 in a specialty occupation Copyright © 2013. LFB Scholarly Publishing LLC. All rights reserved.

Q2

Year Enacted

Still in effect in 2009?

2000

YES

2000

YES

2000

YES

200

YES

2000

YES

2001

YES

2002

YES

2002

YES

2003

YES

2005

YES

Explanatory note regarding changes in visa categories

Note that these are a subset of the H-1B quota

Quotas were not placed on any categories of entry for temporary migrants until 1990, and most that were added were introduced upon the enactment of new categories of entry. Two categories of entry that received quotas were pre-existing—the H-2B and the H-1B visa categories for temporary migrant workers. H-2B admissions did not exceed 30,000 in the five years preceding 1990,62 and the quota was set well above this level at 66,000. In the case of the H-1B program, admissions levels in years preceding 1990 reached almost 100,000, and the quota placed on the category of entry in 1990 was set at 65,000.63 However, the Immigration Act of 1990 created several new visa categories for different types of skilled temporary migrants that previously all were admitted through the H-1B category of entry. Specifically, the O and P visas for migrants of extraordinary ability, artists, athletes and entertainers were added. Therefore, although the quota that was placed on H-1B visas was low in comparison with the levels of previous years, the overall effect of the policy change was

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expansive with regard to the number of categories of entry for highly skilled temporary migrants. The O visas have no quota, and the P visa was given a quota of 12,500 in 1990, which was removed the following year. As a result of these accompanying changes, I do not consider the initial placement of quotas on either the H-2B or the H-1B categories of entry to be incidents of restrictive policy change. Table 3.3 shows all categories of entry for which quotas have been established since 1990. Of all twelve categories of entry, only one category of entry – the H-1B visa for professional workers in specialty occupations – experiences a reduction in a quota. This occurs in 2003 as a result of sunset provisions contained in the American Competitiveness in the 21st Century Act. The quota reduction reveals that there was not political consensus to renew the higher quota that had been established in 2000, but it does not reveal that there was political consensus to restrict admissions. This reduction follows two observed increases in the same quota in 1998 and 2000,64 both of which were the result of expansive policy change. All other categories of entry for temporary migrants remain numerically unlimited.

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TABLE 3.3: Quotas on categories of entry for temporary migrant admission since 1990 1990

E-3 -

H-1B 65000

H-1B1 H-1C H-2B 66000

1991

-

65000

-

-

66000

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

10500 10500 10500 10500 10500

65000 65000 65000 65000 65000 65000 65000 115000 115000 195000 195000 195000 65000 65000 65000 65000 65000 65000

6800 6800 6800 6800 6800 6800 6800

500 500 500 500 500 500 500 500 500 500 500

66000 66000 66000 66000 66000 66000 66000 66000 66000 66000 66000 66000 66000 66000 66000 66000 66000 66000

P-1 P-3 12500 Quota removed

Q-2 -

S-5 S-6 -

T-1 -

U-1 -

-

-

-

-

-

4000 4000 4000 4000 4000 4000 4000 4000 4000 4000 4000 4000

100 100 200 200 200 200 200 200 200 200 200 200 200 200 200 200

25 25 50 50 50 50 50 50 50 50 50 50 50 50 50 50

5000 5000 5000 5000 5000 5000 5000 5000 5000 5000

10000 10000 10000 10000 10000 10000 10000 10000 10000 10000

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It is notable that there has been no restriction since 1965 in the policies that determine the number and types of permanent immigrants and temporary migrants that may legally enter the country annually. This is surprising given that permanent immigrants and temporary migrant workers are the legal migrants that can be most accurately perceived as competing with native workers for jobs and affecting wages. Kinship and employment-based permanent immigrants are also the legal migrants that can be most accurately perceived as contributing to the change in the racial demography of the U.S. population. If restrictive policy change were to occur, we would expect policy change restricting the admission of these types of migrants. Yet, there are no observations of restrictive policy change since 1965.

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SPECIAL INTEREST GROUP PRESSURE AND CLIENT POLITICS An alternative explanation of U.S. migrant admissions policy is offered by political scientists who emphasize the importance of special interest groups in shaping policymaker preferences over U.S. migrant admissions policy, particularly the role of employers and ethnic lobby groups. These scholars argue that policymakers are largely sheltered from public opinion on the issue of U.S. migrant admissions policy, a view that contrasts starkly with predictions of restriction on the basis of a rise in nativism among the U.S. public.65 However, while theories emphasizing special interest groups better explain the expansive policy change observed in the U.S. since 1965, these scholars have difficulty explaining why many attempts to achieve expansive policy change never pass. Using a cost/benefit logic that builds on prior work by Lowi (1964) and Wilson (1973, 1980), Freeman (2006) identifies four policy types within the broader area of migration policy. The two policy types that are the focus of this chapter include permanent residence visas and nonimmigrant (temporary) visas for work, which are distinguished from non-immigrant visas for purposes other than work and from asylum claims (which are not considered in this analysis). According to Freeman’s typology, permanent residence visas are described as:

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[A] pure instance of distributive policy…Visa recipients…receive a concrete benefit. Moreover, because visas in the family category go, by definition, to persons who have relatives living in the country of destination, the kin of family immigrants constitute a direct beneficiary group as well. Employers seeking to hire foreign workers also profit from their admission in the employment-based category….These parties constitute relatively narrow groups that should have the resources to organize and the standing to influence policymakers. (232) In contrast with the concentrated benefit of an immigrant visa, Freeman argues that the cost of distributing permanent residence visas are diffuse across society. Based on this assessment of concentrated benefits and diffuse costs, Freeman predicts a mode of client politics in the area of permanent immigrant admissions, with legislators responding to a coalition of employers and immigrant-advocacy groups. Temporary work visas form a policy area in Freeman’s typology that exhibits both concentrated costs and benefits. Like in permanent immigrant admissions, the benefits are primarily for the visa recipient and the employers who contract them for labor: [Q]uota-based temporary worker programmes are directed at specific occupations or skill groups, opening the way to redistributive/interest group politics fueled by native workers in the targeted groups. Both beneficiary and loser groups, in this scenario, are readily identified. (237) However, Freeman acknowledges that this dynamic may apply more to general-skill than to high-skill temporary workers.66 “The recruitment of the highly-skilled has been successfully sold as a cost-free policy that produces substantial, if diffuse, benefits for the society in a global economy privileging technology and creativity” (238). Freeman therefore suggests that policymaking in the area of high-skill-based temporary migrant worker admissions may, at least at times, exhibit the dynamics of client politics, as legislators respond to employer preferences, without substantial opposition from domestic groups.

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This account of policy change in U.S. migrant admissions identifies only employers, immigrant advocates and unions as critical actors shaping policymaker preferences. It leads to predictions of a clientelistic relationship between policymakers and a pro-immigration coalition of employers and immigrant advocacy groups supporting expansive policy change. The only obstacles to policy passage highlighted are (1) the potential differences that may arise in the preferences of employers and immigrant advocacy groups and (2) opposition mounted by unions to particular policy proposals. Of course the interests of employers and immigrant advocacy groups are not always aligned. Although the political clout and funding provided by employers may be predicted to yield the most influence over policymakers, Wong (2006) argues that ethnic rights groups have played an important role in shaping the nature of U.S. migrant admissions policy since 1965. She argues that ethnic rights groups, which advocate for the rights and protections extended to migrants, have obtained influence over policymakers because policymakers are aware of the shifting racial demographic in the U.S. and the future importance of a growing segment of Latino voters, as well as because these groups have collaborated closely with human and civil rights activists.67 Expectations about union preferences are often derived from factors of production models, which suggest that immigrant workers compete with native workers of similar skill sets, while complementing the labor of native workers with differing skill sets. As a result, many scholars predict, as does Freeman, that native workers will oppose expansionary policies for migrant workers, particularly those that they perceive as competing with them for jobs or driving down wages. Support for these claims is found, for example, in union opposition to large-scale guestworker programs. However, there is reason to doubt that unions take the lead role in deterring expansive policy change advocated for by employers and immigrant advocates. First, the decline in union membership in the U.S. is well documented, indicating a decline in union influence over policymakers. In addition, there is evidence that, particularly since the 1990s, unions have often advocated alongside the pro-immigration special interest groups we might expect them to oppose. Both Watts (2002) and Briggs (2001b) document a strategic shift in union preferences, most notably observed in the policy positions of the

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American Federation of Labor and Congress of Industrial Organizations (AFL-CIO), as a result of an effort to appeal to immigrant workers in light of declining union membership and the threat of outsourcing. Rather than opposing all expansionary policy proposals, the AFL-CIO has publically end kinship-based immigrant admissions,68 legalization programs,69 and policies enhancing the rights and protections extended to migrant workers.70

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Infrequent policy change in U.S. migrant admissions policy since 1990 A depiction of the politics of U.S. migrant admissions policy as client politics leads to predictions of policy change when a new policy proposal is closer to the preferences of special interest group actors than the status quo. Policies that are supported by special interest groups should have a high probability of passage. As a result, we should expect to observe policy proposals ushered through Congress into successful policy change. In other words, we should observe a high conversion rate of policy proposals to law. To the extent that we observe a large number of policy proposals for change supported by special interest groups, we should observe frequent policy change. I assess the evidence of the level of difficulty achieving policy change by considering how many bills were considered by the Senate in the area of U.S. migrant admissions that affected categories of entry or quotas from the 101st Congress (1989-1990) through the 110th Congress (2007-2008). I contrast the number of bills that pass to the number of bills proposed. The ratio of bills passed to bills proposed is an indicator of the level of difficulty obtaining policy change, which cannot be assessed by looking only at policies that have passed. A ratio of one indicates that all policies proposed are passed, and a ratio of zero indicates that no policies passed. The lower the ratio of bills proposed to bills passed, the higher the level of difficulty achieving policy change. I acknowledge that this is an imperfect indicator for a number of reasons. During periods where migrant admissions policy is a focus of legislative action, there may be a larger number of bills proposed. Under these conditions, the number of bills does not necessarily indicate more difficulty achieving policy change. In order to fully determine that the bills are in disagreement, a close reading of each bill would be required, and this was beyond the scope of this study. A

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second problem is that bills may address the same or different sections of U.S. migrant admissions policy. It may be that bills proposing changes to U.S. professional migrant worker admissions are more easily passed than bills proposing changes to U.S. seasonal agricultural migrant worker admissions. A third problem is that bills might contain expansive or restrictive policies, or some combination of both. Unfortunately, this measure does not distinguish by each category of entry that is affected or by whether the policy proposals were expansive, restrictive, or some combination. Finally, some bills may contain additional provisions relating to border control or public benefits that are outside of the definition of U.S. migrant admissions policy. These limitations notwithstanding, this measure provides an indication of whether bills proposing changes to U.S. migrant admissions policy are frequently passed, or frequently hindered. A description of how these indicators were constructed is provided in Appendix B. I first demonstrate the total number of bills proposed to alter permanent immigrant admissions, temporary migrant admissions, or both during each Congress, calculating separate ratios for each Congress. I then calculate the ratio of bills proposed to bills passed in aggregate across each Congress, in addition to separately for each area of U.S. migrant admissions policy. The results of these calculations are presented in Table 3.4. The evidence casts doubt on claims that the relationship between policymakers and special interests is clientelistic. Overall, from 1989 to 2008, policy change in U.S. migrant admissions passed approximately 25 % of the time. It is interesting that there is a notable difference between the ratios for policy change relating to U.S. permanent immigrant admissions and U.S. temporary migrant admissions. Bills containing policy proposals altering only temporary migrant admissions policy were far more likely to pass than bills containing policy proposals altering permanent immigrant admissions. Only one policy change—The Immigration Act of 1990—has passed in the area of U.S. permanent immigrant admissions since 1990, but this was one of 20 bills considered by the Senate during the time period. Clearly, policy change has been infrequent since 1990 in the area of U.S. permanent immigrant admissions. The level of difficulty achieving policy passage varies across the different Congresses, with unusually high ratios of bill passage to bill proposals in the 106th and 107th

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Congresses (1999-2002). Since that time the ratio has been much lower, indicating an increased level of difficulty achieving policy change. TABLE 3.4: Ratio of bills passed to proposed bills considered by the U.S. Senate Congress

Bills proposed

Ratio

101 (1989-1990)

2

6

0.33

102

2

4

0.50

103

3

8

0.38

104

0

4

0.00

105

1

9

0.11

106

5

6

0.83

107

2

2

1.00

108

3

19

0.16

109

2

17

0.12

110 (2007-2008)

0

8

0.00

Total U.S. migrant admissions

20

83

0.24

Permanent immigrant admissions only

1

20

0.05

Temporary migrant admissions only

18

48

0.36

1

15

0.07

Both permanent immigrant and Copyright © 2013. LFB Scholarly Publishing LLC. All rights reserved.

Bills passed

temporary migrant admissions

A NEW THEORY OF THE POLITICS OF U.S. MIGRANT ADMISSIONS POLICY I present in this section a new theory explaining policy change in U.S. migrant admissions. I adopt the claim made by the theories just reviewed that special interest groups are the primary actors influencing policymaker preferences, which better accounts for the expansive nature of policy change since 1965. However, I build on existing theories in two important ways in order to better account for the infrequence of policy change. First, I incorporate the role of supermajoritarian decision making procedures, such as the Senate filibuster, which make it more difficult to pass than to block bills. By definition, the burden is on the supporters of a bill to achieve a larger than majority size winning coalition of Senate votes. Opponents of a bill must achieve a less than

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majority size coalition of Senate votes to block a bill from passing. Existing scholarship on U.S. policymaking, most notably the work of Krehbiel (1998) and Binder and Smith (1997), suggests the importance of supermajoritarian decision making procedures in predicting policy stasis and change. However, these institutional procedures have not been incorporated into leading theories of the determinants of U.S. immigration policy. I argue that supermajoritarian decision making procedures crucially shape the politics of U.S. migrant admissions by defining the size of the winning coalition of Senate votes that is required to achieve policy change. Second, I incorporate the role of an organized anti-immigration opposition. Anti-immigration groups are professional organizations that advocate restricting U.S. migrant admissions. I refer specifically to the leadership of research and membership organizations that advocate against immigration, and not to the larger subset of individuals in the voting public who hold anti-immigration preferences. Anti-immigration groups have been largely absent from scholarly explanations that highlight the role of special interest groups in influencing policymaker preferences, with the notable exception of Wong (2006). My definition of anti-immigration groups overlaps considerably with what Wong (2006) defines as “restrictionist environmental groups.” She describes the origin of these groups as emerging from: [O]ne segment of the environmentalist movement in the 1970s. The Federation for American Immigration Reform, an offshoot of Zero Population Growth (now Population Connection), is the most well known restrictionist organization. The goals of this membership organization in the early 1990s were (1) ‘to stop illegal immigration’; (2) ‘to reduce the amount of legal immigration’; (3) ‘to fix immigration quotas ‘in accordance with the demographic, natural resource, and economic goals of the United States’’; (4) ‘to promote population control and economic development’ in migrant sending countries; and (5) ‘to promote the assimilation of immigrants into U.S. culture. (p. 39)

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Although Wong (2006) identifies these groups as actors, she does not theorize about how these organizations achieve influence. Her emphasis is on explaining the role of ethnic rights groups in shaping policy outcomes. Anti-immigration groups are also discussed by scholars of nativism. However, because these scholars assume a larger role for the public in shaping policymaker preferences, these authors tend to focus on the role of anti-immigration groups in shaping public attitudes, rather than policymaker preferences. Schrag (2010) identifies the important role that these groups play in providing information to media sources, including an array of conservative talk radio and television personalities. Schrag identifies a retired doctor named John Tanton as the “godfather” of the organized anti-immigration movement in the United States since the mid 1970s: In 1977-79, he was president of Zero Population Growth. In 1979, after he couldn’t get support from the Sierra Club for immigration control, he started FAIR. In 2004, he was one of the drivers of the campaign to change the Sierra Club’s board and get it into the immigration fight…In addition to starting FAIR and Numbers-USA, the first with the support of some $1.5 million from the racialist Pioneer Fund…he also helped launch the Center for Immigration Studies (CIS), U.S. English, U.S. Inc., and Pro-English. (p. 178) Jacobson (2006) provides a more complete list of these groups operating at the national level who support limitations on access to citizenship at birth: Federation for Immigration Reform (FAIR), Vdare, Americans for Immigration Control, American Patrol, United to Secure America, Numbers USA, Project USA, Friends of Immigration Law Enforcers, Diversity Alliance for a Sustainable America, UNITED STATES Border Control, and Americans for Better Immigration…. There are a number of national environmental groups who also have immigration restriction as a central focus. (p. 645)

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However, leading accounts of the determinants of migrant admissions policy do not incorporate these actors. Freeman (2006) downplays the importance of anti-immigration advocacy groups, in part because a cost/benefit framework predicts that mobilization around the diffuse societal costs of immigration will be limited. Yet, Freeman (2001) further argues that there is an anti-populist norm in the United States, a claim that he supports empirically by showing that several restrictive policy proposals considered by U.S. legislators in the late 1990s met with little success. Freeman argues that despite an observable increase in populist efforts to restrict U.S. migrant admissions, client politics persists: At first glance this is what seems to be happening in the US. The immigration scene has been changed in important ways, new actors have appeared, and the cozy arrangements that assured pro-immigration spokespersons easy access to decision makers and more or less certain victory in key battles have partly broken down. Nevertheless, the evidence suggests that client politics is far from dead. Indeed, those groups committed to the maintenance and expansion of the American legal (and illegal) immigration system were able to defeat central elements of the restrictionist, populist agenda. And they did so during a moment when conservative immigration reform seemed more feasible than in at least the period since the 1965 liberal reforms ended the national origins quota system. (p. 68) This interpretation overlooks the role that organized antiimmigration advocacy groups play in blocking expansive policy change. Anti-immigration groups can have considerable influence over policy by pressuring senators to vote against expansive policy proposals that they oppose, even if they do not have the clout to generate a supermajority winning coalition of Senate votes to pass restrictive policy change. Supermajoritarian decision making procedures in the Senate grant considerable veto power to small, organized interests, revealing the potential of these actors to influence the likelihood of policy change.

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The main claims of the theory The theory relies upon four main claims about the politics of U.S. migrant admissions. These simplifying claims isolate the most important characteristics of policymaking on migrant admissions in the U.S. Senate. Claim 1: Senate votes on bills relating to U.S. migrant admissions are influenced by special interest groups, not by the public. There are four types of special interest groups relevant to the politics of U.S. migrant admissions policy: anti-immigration groups, immigrant advocacy groups, employers, and unions.

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Claim 2: Supermajoritarian decision making procedures in the U.S. Senate make it more difficult to pass than to block bills. By definition, the burden is on the supporters of a bill to achieve a larger than majority size winning coalition of Senate votes. Opponents of a bill must achieve a less than majority size coalition of Senate votes to block the bill from passing. Claim 3: A broad-based coalition of immigrant advocacy groups, employers and unions provides the highest probability that a sufficient number of Senate votes will join in support of a bill to achieve a winning coalition. I will refer to this broad-based coalition as the proimmigration coalition, which must include all three of these groups. Claim 4: Anti-immigration groups can be strong enough to block bills acting alone, even when the pro-immigration coalition forms. Therefore, while the formation of a pro-immigration coalition makes policy change more likely, it does not determine policy change. Interest group politics I assume that senator preferences toward U.S. migrant admissions policy are determined by consideration of which position maximizes the likelihood of re-election. In other words, I assume that policymaker preferences are derived from some consideration of the interests of their constituents and of the level of resources and funding needed to support their electoral campaigns. To the extent that a senator’s

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constituency is not voting on the issue of U.S. migrant admissions, the senator is likely to be influenced by special interest groups. Special interest groups provide funding and assistance with electoral mobilization to policymakers who vote in accordance with their preferences. Generally speaking, national political parties in the United States do not form clear platforms on the issue of immigration. The need to appeal to a majority of the population makes almost any position more likely to alienate rather than attract needed votes. Senators running state-wide campaigns tend not to campaign on the issue of immigration for this reason, and instead are elected on the basis of their positions on issues that are more clearly partisan. As a result, I adopt the view of scholars highlighting the importance of special interest groups, and not the public, in shaping policymaker preferences. I depict the politics of U.S. migrant admissions as a competitive arena consisting of four types of special interest groups vying to obtain Senate votes in line with their interests. This view contrasts with the work by Freeman (2006) previously discussed, which argues primarily for a client politics model in the areas of permanent immigrant admissions, as well as often in the area of highly skilled temporary migrant worker admissions. Departing from Freeman’s typology, I take more seriously the importance of an organized opposition to rising immigration levels, anticipating that there will be a sufficient perception of concentrated costs of immigration to mobilize antiimmigration advocates. Therefore, rather than distinguishing between client and interest group modes of politics across policy types, I expect interest group politics to prevail under most conditions in both debates regarding permanent immigrant and temporary migrant worker admissions. I consider a client politics model to be a subset of the interest group model, describing the politics that occur when interest groups that may be considered as pro-immigration groups are able to coordinate their efforts toward an agreed upon policy proposal, and at the same time when anti-immigration advocates are not strong enough to form a blocking coalition. Rather than considering this most likely in the area of permanent immigrant admissions (as does Freeman), for reasons to be discussed below, I predict client politics to be most likely in the area of highly skilled temporary migrant worker admissions.

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The pro-immigration coalition The formation of the pro-immigration coalition improves the probability of achieving policy change in the area of U.S. migrant admissions because it is the coalition that is most likely to obtain a supermajority of Senate votes. My emphasis on a coalition of employers, immigrant advocacy groups and unions contrasts with Freeman (2006)’s description of a coalition of immigrant-advocacy groups and employers with easy access to and considerable influence over policymakers. I contend that facing union opposition, expansive policies are far less likely to pass. Unions can join together with antiimmigration groups to generate a sufficient coalition of Senate votes to block a bill if a policy proposal is perceived to be against the interests of domestic workers. Therefore, in this chapter I define the pro-immigration coalition as a broad-based coalition of immigrant advocacy groups, employers, and unions. All three groups must support a bill in order to meet the definition of the pro-immigration coalition. To the extent that groups do not advocate in support of or opposition to a bill, I argue that these groups demonstrate tacit support for the policy change. Tacit support is observed because the groups are not pressuring senators to block the policy change. Of course, a pro-immigration coalition that is based on the full, rather than tacit, support of all members is most likely to achieve policy change. Yet, if at least the tacit support of all three members is achieved, then policy change is more likely than under any other conditions. The claim that bills supported by a pro-immigration coalition have the highest probability of passing rests on two additional sub-claims. The first sub-claim is that no group is powerful enough to obtain a sufficient number of Senate votes to achieve policy change acting alone. This claim contradicts existing scholarship emphasizing either the strength of ethnic lobbies or the strength of employers as the primary force determining U.S. migrant admissions policies.71 However, it better accounts for the challenge posed to special interest groups by the institutional requirement to achieve a supermajority size winning coalition of Senate votes. The second sub-claim is that other multi-member coalitions are less likely to achieve a supermajority of Senate votes. This claim is supported by two possible mechanisms developed in the literature. First, there is a higher level of distrust

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between traditionally pro-immigration actors and anti-immigration actors.72 Second, such coalitions may be politically costly. For example, union collaboration with anti-immigration groups may alienate immigrant membership.73

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Blocking by anti-immigration groups The formation of the pro-immigration coalition improves the probability of achieving policy change in the area of U.S. migrant admissions, but it does not determine policy change. Anti-immigration groups may be powerful enough to block the passage of a bill that is supported by the pro-immigration coalition. The strength of anti-immigration group opposition is determined by the type of policy proposal under consideration, and whether or not a legalization program is included within a policy proposal. Antiimmigration groups will put more effort into generating opposition to bills that expand permanent immigrant admissions than to bills that expand temporary migrant admissions. Likewise, anti-immigration groups will more strongly oppose any policy proposals that include a large-scale legalization program that would grant permanent residence status to undocumented migrants or that would provide an eventual pathway to permanent residency after a number of years of temporary residence. This claim is supported by the empirical observation that permanent immigrants in the United States have generally been granted a higher level of rights and benefits in comparison with temporary migrants. Most relevant to this discussion, permanent immigrants can reunite with immediate relatives through family reunification, a right which is expanded to include additional relatives once a permanent immigrant obtains citizenship. In this way permanent immigration contributes to the phenomenon of chain migration, which is a subject of concern for anti-immigration groups. In addition, permanent immigrants have been granted greater access to public welfare programs, which is also a subject of concern for fiscally motivated antiimmigration groups. Hollifield (2008) argues that the institutionalization of rights-based liberalism in social welfare and other policies of democratic immigrantreceiving states has reduced the state’s ability to limit immigration.

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The logic of this argument rests on the premise that rights, once extended, are difficult to retract.74 I claim that, holding other factors constant, anti-immigration groups will work harder to block expansionary policy proposals for permanent immigrant admissions than expansionary policy proposals for temporary migrant worker admissions. This is because anti-immigration groups are aware of the difficulty of retracting rights for permanent immigrants once they are extended. It is strategically difficult given the inability of antiimmigration groups to successfully achieve a supermajority of Senate votes aligned with their interests.

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Conditions that influence the formation of the pro-immigration coalition The formation of a pro-immigration coalition maximizes the likelihood of a winning coalition of Senate votes. Therefore, obstacles that arise in the formation of the pro-immigration coalition are as important as the strength of anti-immigration groups to an explanation of policy stasis and change in U.S. migrant admissions. Under what conditions is the pro-immigration coalition likely to form? In the following subsections I consider the conditions that shape the level of support held by special interest groups for specific policy proposals relating to migrant admissions. Specifically, I consider how three factors—the content of the policy proposals, the level of rights and benefits extended to migrants, and recent unemployment trends—influence the likelihood of a pro-immigration coalition. Following a list of these three claims, I provide definitions for each of the three causal factors as they are utilized in this theory. Claim 5: For all four types of special interest groups, the level of support for a bill is influenced by the policy proposals regarding admission that are contained in the bill. The policy proposals considered by the Senate are determined by exogenous factors. Claim 6: The level of support that immigrant advocacy groups, employers and unions have for a bill is also influenced by the level of rights and benefits extended to the type of migrants affected by the policy proposals for admission. The level of rights and benefits extended to each type of migrant is determined by exogenous factors.

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(The level of rights and benefits extended to migrants never exceeds the level of rights and benefits extended to citizens.) Claim 7: The level of support that employers and unions have for a bill is also influenced by the recent (lagged) rate of unemployment, which is determined by exogenous factors. Policy proposals for migrant admissions

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The level of support that interest groups have for a bill is determined, at least in part, by the content of the policy proposals that are contained in that bill. There are eight distinct hypothetical policy proposals for which special interest groups may have distinct preferences. Policy proposals can be either expansive or restrictive toward the admission of a particular type of migrant. Four types of migrants are considered in this theory, including kinship-based permanent immigrants, employment-based permanent immigrants, temporary migrant workers with high skills and temporary migrant workers with general skills. For example, one policy proposal is an expansive policy proposal for the admission of kinship-based permanent immigrants. An interest group may support, oppose or be indifferent toward a policy proposal. The level of rights and benefits extended to migrants The term “rights and benefits of migrants” as used in this theory may be defined as a catch-all, one-dimensional category for the various types of government protection for migrants that are provided in existing law. Such rights and benefits may include provisions ensuring the ability for migrants to reunite with family, to access the labor market, to obtain citizenship, to organize collectively, or to be protected from discrimination on the basis of various factors including nationality or race. In addition, standards for the sanitation of migrant workplaces and other conditions of employment, including minimum wage requirements, are also included in this general term. The level of rights and benefits extended to migrants may be thought of conceptually as ranging from low to high; however, for the purpose of identifying group preferences only the extreme values of “low” and “high” are considered. The level of rights and benefits extended to migrants is

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observed in combination with the eight policy proposals described in the previous section. For example, an interest group may have distinct preferences toward an expansive policy proposal for the admission of employment-based permanent immigrants, depending on whether such immigrants have a high or low level of rights and benefits.

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Recent unemployment trends Major expansive policy changes have occurred at the start of recessions in both permanent and temporary migrant admissions, with the Immigration Act of 1990 as the most prominent example. However, the expansionary policy change followed a pro-longed time period of declining unemployment. This finding calls into question the time horizon that should be considered when interpreting whether and how labor market conditions trigger policy change. Instead of assuming that actors respond immediately to labor market conditions, it may be more accurate to predict that the preferences of group actors are shaped by the recent experience of unemployment over the past year or several years. Therefore, I consider what is often referred to in quantitative social science as the lagged unemployment rate, rather than the current unemployment rate, as an important factor shaping the preferences of employers and unions. Scope limitations of the theory Before moving on to consider how each of these factors influences group preferences, it is important to acknowledge that this theory does not seek to explain the varied causes of the policy proposals, the level of rights and benefits extended to migrants, and recent unemployment trends. Rather, the theory considers only the effect of each of these factors on the likelihood of the formation of the pro-immigration coalition. Although this decision is largely a decision of scope, in the remainder of this section I outline my rationale for excluding consideration of the varied causal factors that determine which policy proposals relating to admission are considered by the Senate and the level of rights and benefits extended to migrants. It is the expectation of the author that it will be less controversial to the reader to consider unemployment trends as exogenous, and therefore this decision is not discussed further.

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Although special interest groups are at times involved in the drafting of legislation, the relationship between special interest groups and the content of policy proposals included in bills is considerably more complicated than can be considered within the scope of this theory. It would be simplistic and empirically inaccurate to suggest that senators only introduce bills with policy proposals that enjoy a winning coalition of Senate votes. Additional factors such as seniority in the Senate committee system and informational asymmetries may influence what policy proposals are included in introduced legislation. In addition, amendments can be added to a bill with the support of a simple majority, rather than a supermajority. Therefore it is possible for special interest groups to adjust the content of the policy proposals in a bill, even when they are unable to obtain a winning coalition to pass the bill as amended. It is beyond the scope of this theory to illustrate and explain the variety of ways that special interest groups act strategically to influence the content of policy proposals that are contained in Senate bills. Instead, this theory aims to highlight the conditions under which the pro-immigration coalition is most likely to form in support of a bill in order to reveal the conditions under which policy change is most likely to occur. Therefore, this theory does not seek to explain how a given policy proposal gets introduced or amended, but rather which policy proposals are most likely to generate the support of a pro-immigration coalition. In addition, this theory does not seek to explain how two policy proposals may become linked within a single bill, although group preferences for specific policy proposals regarding admissions do provide insight into the types of policy linkages that are more likely to be successful. It is similarly beyond the scope of this theory to explain the level of rights and benefits that has been extended to different types of migrants in the United States. This theory makes no claims about the set of relevant actors or the mode of politics that best depicts U.S. policymaking relating to migrant incorporation. Instead, this theory is solely concerned with explaining how the level of rights and benefits extended to various types of migrants influences group interests regarding migrant admissions policy. The level of rights and benefits extended to migrants is considered within the theory only as a status quo policy that is caused by exogenous factors. Predictions are

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generated for the hypothetical scenario of both high and low levels of rights and benefits for each type of migrant without reference to the empirical reality. In this way, predictions regarding the likelihood of a pro-immigration coalition in migrant admissions can be generated from a comparison of group interests under the hypothetical scenarios of each policy proposal for each type of migrant with differing levels of rights and benefits. These predictions speak to the conditions under which policy change in migrant admissions may be more or less likely, but they are not intended to provide an explanation for the level of rights and benefits extended to different types of migrants.

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Interest group preferences Having discussed the rationale for limiting the theoretical scope, the theory may be further developed. Group preferences are identified for anti-immigration groups, immigrant advocacy groups, employers and unions based on additional claims about the way in which interest group preferences are influenced by the content of policy proposals, the level of rights and benefits extended to migrants and recent unemployment trends. The specific claims generating preferences for each type of special interest group are discussed in the following subsections. These claims form the basis of predictions regarding the likelihood of the formation of a pro-immigration coalition and the likelihood of policy change in U.S. migrant admissions. The preferences of anti-immigration groups Anti-immigration group preferences are determined entirely by the content of the policy proposals for admission, with constant opposition to expansion and support for restriction. These preferences hold regardless of the level of rights and benefits extended to migrants or the recent rate of unemployment. There is an important distinction that should be noted between the preferences of anti-immigration groups and the strength of anti-immigration advocacy efforts. As already discussed in Claim 4, the level of rights and benefits that has historically been extended to permanent immigrants in the United States motivates anti-immigration groups to target their advocacy efforts to block the expansion of permanent immigration and legalization programs that grant permanent resident status to

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undocumented migrants. It is the strength of the anti-immigration group opposition, not group preferences, that varies with the level of rights and benefits extended to permanent versus temporary migrants. Claim 8: Anti-immigration groups always oppose policy proposals that will expand the admission of any type of migrant and always support policy proposals that will restrict the admission of any type of migrant.

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The preferences of immigrant advocacy groups In this chapter when I refer to immigrant advocacy groups, I refer to the set of all organizations that advocate to protect the rights of immigrants and to promote the interests of the immigrant population. Immigrant advocacy groups include ethnic lobby groups, human and civil rights organizations and some religious groups. This definition is in line with previous scholarship, particularly the variety of ethnic, religious and civil libertarian groups that Wong (2006) describes within her description of ethnic rights groups. Examples of immigrant advocacy groups that are important actors in this policy area include the National Council of La Raza, the Mexican American Legal Defense and Education Fund, the American Civil Liberties Union, the Immigration Policy Center, the Asian American Justice Center, and the National Immigration Forum, which is an umbrella organization of national and state level immigrant advocacy organizations. The United States Conference of Catholic Bishops also participates in immigrant advocacy.75 I consider immigrant advocacy groups as the set of all of these organizations, and I do not consider variation in the preferences across different types of immigrant advocates. Immigrant advocacy groups will always advocate for the protection of any benefit already extended to migrants, including that of admission. Therefore, immigrant advocacy groups always seek to protect the ability of immigrants to reunite with family members by supporting (or opposing) measures to expand (or restrict) the admission of kinship-based permanent immigrants. In addition, immigrant advocacy groups will always oppose restrictions on the admission of migrant workers. However, the level of support held by immigrant advocacy groups for a policy proposal to expand the admission of

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migrant workers will vary with the level of rights and benefits extended to migrant workers. Immigrant advocacy groups will throw their weight behind policy proposals that expand the admission of migrant workers with a high level of rights and benefits, but not behind policy proposals that they perceive as contributing to the exploitation of migrant workers.76 Claim 9: Immigrant advocacy groups always oppose policy proposals to restrict the admission of kinship-based immigrants, and always support policy proposals to expand the admission of kinship-based immigrants.

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Claim 10: Immigrant advocacy groups will always oppose restrictions on the admission of migrant workers regardless of the level of rights and benefits extended to migrant workers. Claim 11: Immigrant advocacy group support for a policy proposal to expand the admission of migrant workers increases as the level of rights and benefits for migrant workers increases. Immigrant advocacy groups will support policy proposals to expand the admission of migrant workers if the level of rights and benefits extended is high. Immigrant advocacy groups will be indifferent to policy proposals to expand the admission of migrant workers if the level of rights and benefits extended is low. The preferences of employers In this chapter, when I refer to employers I refer to the set of all organizations representing employer interests for the purpose of advocacy on U.S. migrant admissions policy. Examples of employer organizations that are important actors in this policy area include Compete America, the National Association of Manufacturers, the U.S. Chamber of Commerce, and the National Association of Independent Business. Throughout the chapter, I refer to employers in aggregate, and I do not distinguish among employers according to industry. It is likely that employers from industries that maintain a higher percentage of migrant workers in the workforce will be more active in advocacy on this issue. Likewise, it is likely that employers from industries relying on particular types of immigrants will be more active in advocacy on

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policy proposals relating to their admission. It would be ideal to account for the variation in preferences across different types of employers. However, in order to maintain sufficient parsimony in the theory to incorporate non-economic actors, I restrict my consideration of employers to an aggregate summary across all employer organizations. I expect that this simplification will not hinder the accuracy of the predictions of the theory. My empirical research revealed that it is extremely rare that bills propose cuts in one type of migrant worker alongside increases in the admission of another type of migrant worker. Proposals to adjust admissions for migrant workers are typically considered in isolation, in combination alongside cuts in another type of migrant such as kinship-based permanent immigrants, or in combination with other expansionary policies. As a result, it is usually the case that employers will have the same preferences for a given policy proposal, and those employers most impacted by the policy will take the lead in advocacy. Employers are only concerned with U.S. migrant admissions in so far as they are concerned with obtaining migrant workers.77 Employers always will oppose any restriction to the admission of migrant workers. However, the level of support that employers have for policy proposals to expand the admission of migrant workers varies with both recent unemployment trends and the level of rights and benefits that are extended to migrant workers. Following a loose labor market, employers have recent experience with labor surplus and will be less concerned with obtaining migrant workers generally, unless the migrant workers can be hired at more competitive rates than domestic workers. In contrast, following a tight labor market, employers have recent experience with labor shortage. Under this condition, the goal of obtaining additional workers will override other employer concerns. Claim 12: Employers are always indifferent to policy proposals to restrict or expand the admission of kinship-based permanent immigrants. Claim 13: Employers always oppose policy proposals to restrict the admission of migrant workers.

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Claim 14: Following a loose labor market, employer support increases for expansive policy proposals as the level of rights and benefits for migrant workers decreases. Employers will be indifferent to policy proposals to expand the admission of migrant workers if the level of rights and benefits extended to them is high. But, employers will actively support policy proposals to expand the admission of migrant workers if the level of rights and benefits extended to them is low. Claim 15: Following a tight labor market, employers always support policy proposals that are expansive to migrant workers, regardless of the level of rights and benefits extended to migrant workers.

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The preferences of unions In this chapter, when referring to unions I refer only to the leadership of organizations representing U.S. workers, and not to U.S. workers generally. The AFL-CIO is the most prominent labor organization, and it serves as an umbrella organization for 57 different unions. I primarily look to the AFL-CIO policy positions to speak for the broader organized labor movement. The United Farm Workers of America, United Food and Commercial Workers, the International Ladies Garment Workers Union, the United Brotherhood of Carpenters and Joiners of America, the Hotel Employees and Restaurant Employees and Bartenders International Union, and the Service Employees International Union are also important labor organizations, in addition to the AFL-CIO, with respect to advocacy in the area of U.S. migrant admissions policy. In this theory, I do not distinguish between the preferences of different unions based on the skills of its membership, whether the union is primarily an industrial or craft union, or the type of industry. It may be important to account for variation in preferences across these dimensions, particularly in light of predictions derived from factors of production models that unions will more likely oppose expansionary policies for the admission of migrant workers with similar skills to their own membership. Likewise, Wong (2006) provides a discussion of why craft unions, which are organized around restricting the labor supply, may be more opposed to immigration than industrial unions. My empirical research shows that union preferences often do not vary along these dimensions, and this observation corresponds with the

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portrayal of union preferences toward immigration policy in the work of other scholars. Wong (2006) highlights what varied U.S. labor unions have in common. First, unions facing declining membership have faced an incentive to appeal to immigrant workers by supporting expansionary policies and avoiding oppositional stances that alienate potential migrant worker membership.78 Second, unions have unanimously opposed traditional guestworker programs:

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The union movement as a whole objects to these programs because the foreign workers are bound to work for a single employer or else return to their native country—an arrangement that leaves them vulnerable to substandard wages and working conditions and other forms of exploitation. Moreover, in the past, employers have used large-scale temporary-worker programs to undermine unions among farmworkers. (p. 32) Where unions are divided in their policy positions, their advocacy efforts may have the effect of cancelling each other out. Therefore, there is an incentive for unions to collaborate. Unions also have in common a primary focus on protecting the interest of native workers, most keenly felt in times of economic recession. In this theory, union preferences vary with the recent rate of unemployment. Following a period of increasing unemployment, I argue that unions will oppose expanding the admission of any type of migrant worker and will support restricting the admission of any type of migrant worker. This is because the admission of migrant workers will be perceived as directly in opposition to the interests of domestic workers. Expansion of the admission of kinship-based permanent immigrants will also be opposed because these immigrants also have access to the labor market. However, following a period of declining unemployment, unions will support policies expanding kinship-based permanent immigrants.79 The level of support held by unions for policy proposals to expand the admission of migrant workers will vary with the level of rights and benefits extended to migrant workers. Unions will support expansionary policies for the admission of migrant workers with a high level of rights and benefits.

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Claim 16: Following a loose labor market, unions will oppose policy proposals that expand the admission of all types of migrants and will support policy proposals that restrict the admission of all types of migrants. Claim 17: Following a tight labor market, unions will oppose policy proposals that expand the admission of migrant workers with a low level of rights and benefits, but will support policy proposals that expand the admission of kinship-based permanent immigrants and migrant workers with a high level of rights and benefits.

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Summary of interest group preferences The claims just discussed identify group interests for 32 different scenarios. These scenarios are generated by considering group preferences for each of the 8 policy proposals relating to admission, including both expansive and restrictive proposals for each of four types of migrants: kinship-based permanent immigrants, employmentbased permanent immigrants, highly skilled temporary migrant workers, and temporary migrant workers with general skills. Sixteen scenarios are generated by varying whether a high or a low level of rights has been extended to each type of migrant considered in each of the eight policy proposals for admission. Finally, each of these sixteen possible scenarios is then considered both under the conditions of recent experience with a tight or loose labor market. The group preferences for each of these 32 scenarios are presented in Table 3.5. These claims about group interests, when viewed in combination, suggest the conditions under which a pro-immigration coalition is likely to form, which is crucial to an explanation of policy change in U.S. migrant admissions policy. Of the 32 scenarios considered, only three scenarios are likely to lead to the formation of a pro-immigration coalition because they maintain the support of immigrant advocacy groups, employers and unions. These scenarios include policy proposals to expand the admission of migrant workers (of each type) with a high level of rights following a tight labor market.

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Challen, Suzanna. Measuring Change in Immigration policy, LFB Scholarly Publishing LLC, 2013. ProQuest Ebook

High-skill temporary General-skill temporary Family-based permanent

Expansive

Expansive

General-skill temporary

Restrictive

Expansive

High-skill temporary

Restrictive Family-based permanent

Employment-based permanent

Employment-based permanent

Family-based permanent

Expansive

Expansive

Restrictive

Low

General-skill temporary

Expansive

Restrictive

Low

High-skill temporary

Expansive

High

High

High

Low

Low

Low

Low

Low

Low

High

High

Family-based permanent Employment-based permanent

Restrictive

High

High

Restrictive

General-skill temporary

Restrictive

High

Employment-based permanent High-skill temporary

Expansive

Restrictive

High

Family-based permanent

Expansive

High

General-skill temporary

Expansive

Loose

Loose

Loose

Tight

Tight

Tight

Tight

Tight

Tight

Tight

Tight

Tight

Tight

Tight

Tight

Tight

Tight

Tight

Tight

labor market

High

benefits

High-skill temporary

Type of migrant

Follows a tight or loose

Level of rights and

Expansive

Restrictive

Expansive /

Admissions policy proposal

Indifferent

Indifferent

Indifferent

Oppose

Indifferent

Oppose

Oppose

Support

Indifferent

Support

Support

Oppose

Indifferent

Oppose

Oppose

Support

Indifferent

Support

Support

Employers

Oppose

Oppose

Oppose

Oppose

Oppose

Oppose

Oppose

Oppose

Support

Oppose

Oppose

Oppose

Oppose

Oppose

Oppose

Support

Support

Support

Support

Unions

Support

Support

Support

Oppose

Oppose

Oppose

Oppose

Indifferent

Support

Indifferent

Indifferent

Oppose

Oppose

Oppose

Oppose

Support

Support

Support

Support

groups

advocacy

Immigrant

NO

NO

NO

NO

NO

NO

NO

NO

Incomplete

NO

NO

NO

NO

NO

NO

YES

Incomplete

YES

YES

coalition

immigration

Pro-

TABLE 3.5: Theoretical CLAIMS about group preferences toward policy proposals for migrant admissions

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Anti-

Strong Oppose

Oppose

Oppose

Support

Support

Support

Support

Strong Oppose

Strong Oppose

Oppose

Oppose

Support

Support

Support

Support

Strong Oppose

Strong Oppose

Oppose

Oppose

groups

immigration

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Family-based permanent Employment-based permanent High-skill temporary General-skill temporary Family-based permanent Employment-based permanent

Restrictive

Restrictive

Restrictive

Restrictive

General-skill temporary

Expansive

Expansive

High-skill temporary

Expansive

Expansive

Family-based permanent Employment-based permanent

Restrictive

Restrictive

Restrictive

High-skill temporary General-skill temporary

Restrictive

Employment-based permanent

Type of migrant

Expansive

Restrictive

Expansive /

Admissions policy proposal

High

Low

Low

Low

Low

Low

Low

Low

Low

High

High

High

Loose

Loose

Loose

Loose

Loose

Loose

Loose

Loose

Loose

Loose

Loose

Loose

Loose

labor market

benefits High

Follows a tight or loose

Level of rights and

Oppose

Indifferent

Oppose

Oppose

Support

Indifferent

Support

Support

Oppose

Indifferent

Oppose

Oppose

Indifferent

Employers

Support

Support

Support

Support

Oppose

Oppose

Oppose

Oppose

Support

Support

Support

Support

Oppose

Unions

Oppose

Oppose

Oppose

Oppose

Indifferent

Support

Indifferent

Indifferent

Oppose

Oppose

Oppose

Oppose

Support

groups

advocacy

Immigrant

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

coalition

immigration

Pro-

Support

Support

Support

Support

Strong Oppose

Strong Oppose

Oppose

Oppose

Support

Support

Support

Support

Strong Oppose

groups

immigration

Anti-

TABLE 3.5: Theoretical CLAIMS about group preferences toward policy proposals for migrant admissions (Continued)

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Two additional scenarios form an incomplete pro-immigration coalition, which means that at least one group that is necessary to the pro-immigration coalition remains indifferent to the policy proposal, but no group directly opposes the policy proposal. These scenarios indicate the potential of strengthened support through the linkage of policy proposals. For example, an expansive policy proposal for kinship-based permanent immigrants with a high level of rights and benefits following a tight labor market generates the support of unions and immigrant advocacy groups, while employers remain indifferent. The linkage of this policy proposal with, for example, a policy proposal to expand the admission of general-skill temporary migrant workers with a high level of rights and benefits following a tight labor market will produce a pro-immigration coalition. This is because the second proposal has employer support, in addition to the support of immigrant advocacy groups and unions. As long as a bill includes one policy proposal that each group supports and no policy proposals that any group outright opposes, there is room for compromise and collaboration between the members of the pro-immigration coalition. The remaining 27 scenarios generate opposing preferences between different groups that are necessary for the formation of the pro-immigration coalition. For example, an expansive policy for general-skill temporary workers with a high level of rights and benefits following a loose labor market places unions and immigrant advocacy groups in opposition. Where two groups are in opposition to each other, the coalition is unlikely to form, even through policy linkage. When any group that is necessary to the pro-immigration coalition opposes a policy proposal contained in a bill, the pro-immigration coalition is unlikely to form in support of the bill. These 27 scenarios include all restrictive policy proposals and all policy proposals following a loose labor market. Before generating predictions based on these claims regarding group preferences, three final claims are presented that relate to the way group preferences may combine to form the pro-immigration coalition. Claim 18: A pro-immigration coalition is most likely to form around a bill when all three groups necessary for the pro-immigration coalition (immigrant advocacy groups, employers and unions) support the policy proposals contained in the bill.

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Claim 19: A pro-immigration coalition may form around a bill that links two distinct policy proposals that each generate incomplete coalitions of support. Claim 20: When any group that is necessary to the pro-immigration coalition opposes a policy proposal contained in a bill, the proimmigration coalition is unlikely to form in support of the bill. Predicting the likelihood of policy stasis and change This theory generates four predictions about the likelihood of policy change in U.S. migrant admissions. These four predictions better account for the empirical observations of expansive, infrequent policy change. These predictions are derived from the claims just outlined, relating to the likelihood of the formation of a pro-immigration coalition and the likelihood of anti-immigration groups blocking policy change. In this section, I discuss each of these general predictions in light of the new data presented in the first half of the chapter.

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H1: Policy stasis is more likely than policy change in U.S. migrant admissions policy. H2: When policy change occurs in the area of U.S. migrant admissions, it is likely to be expansionary and unlikely to be restrictive. H3: When policy change occurs in the area of U.S. migrant admissions, it will likely follow a period of declining unemployment. Policy change is unlikely following a period of increasing unemployment. H4: There is a non-linear relationship between the level of rights and benefits extended to migrants and the likelihood of expansionary policy change in migrant admissions. Predicting policy stasis The first hypothesis generated by the theory predicts policy stasis to be more likely than policy change in the arena of U.S. migrant admissions since 1965. The mechanism underlying this prediction is the

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requirement of a supermajority winning coalition of Senate votes for bill passage, which until now has not been incorporated into existing theories of the determinants of U.S. migrant admissions policy. As discussed earlier in this chapter, theories highlighting the importance of public opinion and special interest groups in determining U.S. migrant admissions policy generate predictions of policy change (whether restrictive or expansive), rather than policy stasis. However, new data presented in this chapter revealed that policy change is infrequent, and proposed bills are far more likely to stall in the Senate than be enacted.

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Predicting expansive policies The second hypothesis generated by the theory predicts expansionary policy change to be more likely in the arena of U.S. migrant admissions than restrictive policy change. In fact, restrictive policy change is predicted to be unlikely, even after periods of a high rate of unemployment. Two related mechanisms underlie this prediction. First, the preferences of employers, unions and immigrant advocacy groups are only likely to align around expansive policy proposals. Second, a pro-immigration coalition is most likely to obtain a supermajority winning coalition of Senate votes. This prediction directly conflicts with predictions highlighting the role of declining public support for immigration in shaping policymaker preferences, although it aligns with the predictions of existing theories emphasizing the role of special interest groups in shaping policymaker preferences. New data presented earlier in this chapter supports this prediction. There has not been any restrictive policy change since 1965 in either U.S. permanent or temporary migrant admissions. Predicting policy change following a tight labor market The third hypothesis generated by the theory predicts policy change to be more likely in the arena of U.S. migrant admissions following a period of declining or low unemployment. Two mechanisms underlie this prediction. First, following a tight labor market, employer, union and immigrant advocacy group interests are more likely to align. Following a loose labor market, in contrast, union preferences are more likely to be in opposition to employer and immigrant advocacy group

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preferences. Second, a pro-immigration coalition improves the probability that a supermajority winning coalition of Senate votes will form in support of a bill. What is most surprising about this prediction, relative to predictions generated by existing scholarship, is the emphasis on lagged rather than current unemployment rates. Expansive policy measures are predicted to be more likely following a period of declining unemployment, even if unemployment begins to rise and is expected to worsen. The lagged effect of unemployment has been unanticipated or underspecified in existing theories. The only expansive policy change observed in U.S. permanent immigrant admissions policy occurred in 1990, following almost a decade of declining unemployment. Additional expansive policy change in U.S. temporary migrant admissions to increase the H-1B quotas for professional workers in specialty occupations also occurred following years of declining unemployment in the late 1990s. Figure 3.1 displays the lagged rate of U.S. unemployment, calculated by taking the average of the rate of unemployment observed over the previous five years, and the level of difficulty achieving policy change, which is measured using the data already presented showing the ratio of bills passed to bills proposed in the U.S. Senate from 19892008. It is notable that following upon the lowest unemployment levels during the time period, there is a striking increase in the ratio of bills passed to bills proposed. During the 106th Congress, from 1999-2000, 5 out of 6 bills pass, and in the 107th Congress two policies are proposed and both pass. Likewise from 1995-1996, following the highest unemployment levels observed during this time period, none of the four bills proposed in the 104th Congress pass and only 1 of the 9 bills proposed in the 105th Congress pass. Predicting a non-linear effect of rights and benefits on the likelihood of policy change The fourth hypothesis generated by the theory predicts that the relationship between the level of rights and benefits extended to migrants and the likelihood of expansive policy change in the area of migrant admissions is non-linear. In other words, the likelihood of expansive policy change for a certain type of migrant is not decreasing or increasing at a constant rate according to the level of rights and benefits

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Figure 3 .1 Unemployment data was obtained from the International Labor Organization database LABORSTA

that have been extended to that type of migrant. In the area of permanent immigrant admissions, the high level of rights granted historically to permanent residents reduces the likelihood of expansionary policy change by increasing the strength of targeted antiimmigration group opposition. In contrast, in the area of temporary migrant admissions, it is the admission of migrant workers with a high level of rights and benefits that is more likely to be expanded because of the greater likelihood of forming the pro-immigration coalition. This prediction conflicts with predictions of a negative, linear relationship between the level of rights and benefits extended to migrants and the likelihood of expansionary policy change. The new data on proposed bills considered by the Senate since 1990 revealed that policy change was more easily and more frequently achieved in the area of U.S. temporary migrant admissions. During a period of declining unemployment in the late 1990s and early 2000s, two increases occur in the quota for H-1B workers as a result of expansive policy change. Likewise, of the 6 categories of entry added

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to U.S. temporary migrant worker admissions since 1990, 5 of the 6 are for highly skilled temporary migrant workers. These migrant workers are extended a higher level of rights and benefits than general-skill temporary migrant workers.80 Repeated attempts to pass legislation reforming the agricultural seasonal worker program, admitting generalskill temporary migrants with a low level of rights and benefits, have failed to pass throughout this time period. Only one policy change is observed since 1990 altering the admission of temporary migrant workers with a low level of rights and benefits. In 2005, an amendment is passed increasing the number of H-2B non-agricultural seasonal workers for three years by exempting returning H-2B workers from the annual quota.

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FOUR EMPIRICAL CASES OF U.S. MIGRANT ADMISSIONS POLICYMAKING The predictions of the theory account for the empirical evidence of expansive, infrequent policy change in U.S. migrant admissions policy. In the remainder of this chapter, I assess the empirical support for the claims made in the development of the theory. I consider four major attempts, both successful and unsuccessful, at policy change in U.S. migrant admissions since 1990. In the area of U.S. permanent immigrant admissions, I focus on the legislative efforts in the 101st Congress that led to the successful enactment of the Immigration Act of 1990, which brought about the only change to the categories of entry and numerical limitations regulating U.S. permanent immigrant admissions since 1976. I contrast this with the recent unsuccessful attempt at policy change in 2007 during the 110th Congress, which, had it passed, would have achieved the largest increase in employment-based immigrant admissions to date, while reducing kinship-based immigrant admissions. The 2007 attempt also included policy proposals for a large temporary guestworker program, in addition to a legalization program. In the area of U.S. temporary migrant admissions, I focus on the legislative efforts in the 106th Congress that led to the enactment of the American Competitiveness in the 21st Century Act of 2000, which dramatically increased the annual quota for H-1B visas for professionals in specialty occupations. I contrast this with an unsuccessful attempt to enact the Agricultural Job Opportunities, Benefits and Security Act (AgJOBS)

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legislation in the 109th Congress, which combined an expansive temporary agricultural worker program with expansive rights and protections, including an “earned-legalization” program. In this section, I first consider the variation across these four cases in the factors that I identify in the theory as increasing the likelihood of the formation of a pro-immigration coalition and the strength of the anti-immigration group opposition. Based on the variation in these factors, I identify which claims of the theory I am able to assess. Then, I present measures of senator preferences based on voting behavior during three of these cases, and I use these measures to estimate the distribution of senator preferences under four hypothetical scenarios of proposed legislation. Finally, I illustrate policymaker and special interest group behavior during each attempt at policy change through qualitative descriptions based on a review of secondary sources.

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Variation across the four cases The cases selected provide variation on two of the three conditions identified in the theory as shaping the likelihood of the formation of a pro-immigration coalition. Specifically, there is variation in both the types of policies considered and the level of rights and benefits that are extended to the types of migrants for which admissions policies are considered. There is also variation in the conditions determining the strength of the anti-immigration group opposition, specifically the presence or absence of expansionary proposals for the admission of permanent immigrants and legalization programs. However, the economic conditions preceding the attempts at policy change do not vary. Figure 3.2 shows both the general-and high-skill unemployment rates for the United States since 1969, and it indicates with dotted lines the time periods of focus in each of these case studies. All four attempts at policy change considered in this chapter follow a prolonged period of declining unemployment. In addition, economic cycle data provided by the National Bureau of Economic Research shows that each of the four case studies occurs during expansionary economic conditions.81 When the Senate voted on the Immigration Act of 1990 in July 1989, the country had been in an expansionary cycle for 7 years, since November of 1982. When the Senate voted on the American Competitiveness in the 21st Century Act in September of 2000, the country had been in an

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expansionary cycle for 9 years, since March of 1991. And, when the Senate voted on AgJobs legislation in April of 2005 and on comprehensive immigration reform legislation in June of 2007, the country had been in an expansionary for 4 and 6 year respectively, since November of 2001. Because the cases considered do not vary in the economic conditions, they do not provide evidence to support the claims about group preferences following a loose labor market.

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Figure 3.2 Unemployment data was obtained from the International Labor Organization database LABORSTA. The Unemployment rate for the highly skilled population was calculated for the economically active population in the occupational categories for professionals and managers. The definitions used for collecting data change twice during this time period, but these two general categories exist in a similar form in each of the three series.

These case studies provide empirical evidence to assess the claims of the theory regarding how the types of policies considered and the level of rights and benefits extended to migrants influence the likelihood of policy passage following a tight labor market. The immigration Act of 1990 and the comprehensive reform proposals considered in 2007 both contain expansionary policy proposals for the admission of employment-based permanentimmigrants. However, they differ in that the Immigration Act of 1990

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contained expansionary policy proposals for the admission of kinshipbased permanent immigrants, while the 2007 proposals contained restrictive policy proposals for the admission of kinship-based permanent immigrants. There is no variance in the level of rights and benefits extended to permanent immigrants, which I consider high for both kinship and employment-based immigrants throughout the time period. As a result, I do not test claims about group preferences for policy proposals expanding or restricting the admission of permanent immigrants with a low level of rights and benefits. The American Competitiveness in the 21st Century Act, the 2005 attempt to pass AgJOBS legislation, and the 2007 comprehensive immigration reform proposals all contain expansionary policy proposals for temporary migrant workers. These policies vary in the type of migrant considered and the level of rights and benefits that have been extended to them. The H-1B category of entry that was increased in 2000 admits highly-skilled professional migrant workers who generally have a high level of rights and benefits relative to other temporary migrants, although a lower level of rights and benefits than permanent residents. In contrast, the policy proposals considered in 2005 and 2007 relate to general-skilled migrant workers who have historically been extended a low level of rights and benefits. However, AgJOBS legislation differs from the 2007 guestworker program in that the bill contains provisions increasing the level of rights and protections extended to H-2A workers considerably, and it includes a legalization program for these workers. In contrast, the 2007 proposals did not enhance the rights and protections of general-skill temporary workers. Although the 2007 proposal did include a legalization program, it was not extended to include the guestworkers that would be admitted under the new program. Table 3.6 provides a summary of the type of variance observed across the four cases on each of the conditions identified in my theory as shaping the likelihood of a pro-immigration coalition forming and the strength of the anti-immigration group opposition. As a result of this variation, I can test the following claims. First, I can test whether, as predicted, a pro-immigration coalition forms in 1990, 2000, and 2005, but not in 2007. I can test whether the pro-immigration coalition does not form in 2007 because immigrant advocacy groups oppose restriction to kinship-based admissions and unions oppose expansion of

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HIGH level of rights and benefits for permanent immigrants, who have access to family reunification, the labor market, naturalization, public welfare programs, among other rights and protections Following a TIGHT labor market No Pro-immigration coalition will form, Strong antiimmigration group opposition

Level of rights and benefits extended to the types of migrants considered in the policy proposals

Legalization program Predictions

Economic conditions

2007 Expansive policy proposal for4 employment-based permanent immigrants and general-skill temporary migrant workers; Restrictive policy proposal for kinshipbased permanent immigrants HIGH level of rights for permanent immigrants, LOW level of rights and benefits would be extended to the general-skill temporary migrant workers under the new guestworker program Following a TIGHT labor market Yes No pro-immigration coalition, Strongest antiimmigration group opposition

1990 Expansive policy proposals for employment and kinshipbased permanent immigrants

Policy proposals considered by the U.S. Senate

Following a TIGHT labor market No Pro-immigration coalition will form, Mild antiimmigration group opposition

HIGH level of rights and benefits in comparison with other temporary migrant workers, but generally a lower level of rights and benefits in comparison with permanent immigrants

Expansive policy proposal for highly skilled temporary migrant workers

2000

HIGH(er) rights and benefits for general-skill temporary workers than provided in existing legislation or alternative bills. The bill included higher wages, and a legalization program, among other protections Following a TIGHT labor market Yes Pro-immigration coalition will form, Strong antiimmigration group opposition

Expansive policy proposal for general-skill temporary migrant workers

2005

TABLE 3.6: Variation in the factors shaping the likelihood of pro-immigration coalition and the strength of anti-immigration group opposition

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admissions to general-skill temporary migrant workers with a low level of rights and benefits. I can also test whether, as predicted, antiimmigration groups pose a strong opposition to the bills considered in 1990, 2005, and 2007, and a milder opposition to the bill considered in 2000. In addition, I can observe whether the strongest opposition from anti-immigration groups occurs in 2007, when the Senate considers legislation containing both a legalization program and an expansionary program for permanent immigrant admissions.

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Estimating the distribution of senator preferences I construct a classification of senator preferences based on whether a senator’s votes most closely align with the expressed preferences of employers, unions, immigrant advocacy groups or anti-immigration groups. If there is not a clear alignment between a senator’s voting record and an interest group, then the senator is classified as a swing voter.82 In combination these measures produce an estimate of the distribution of preferences across the senators in office during each of the three Congresses. I consider the distribution of Senate votes for each Congress under four hypothetical political scenarios of interest group coalitions. Appendix C provides a detailed description of how these measures were constructed. The first scenario is a bill that is proposed by a coalition of employers and immigrant advocacy groups, which is opposed by a coalition of unions and anti-immigration groups. The second scenario is bill proposed by a coalition of employers and immigrant advocacy groups, which is opposed by anti-immigration groups. In this scenario, unions do not articulate clear policy preferences, and instead exhibit tacit support for policy change. The third scenario is a bill actively supported by a coalition of employers, immigrant advocacy groups and unions, which is opposed by anti-immigration groups. A fourth scenario is a bill proposed by a coalition of anti-immigration groups and employers, which is countered by a coalition of immigrant advocacy groups and unions.83 Table 3.7 presents the estimated distribution of senator preferences for each Congress for each of these four hypothetical bill scenarios. These estimates provide considerable support for my theoretical claims. As expected under Claim 3, support for a bill is highest when unions join with immigrant advocacy groups and employers in support of the

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bill. The coalition of senators supporting the bill increases and the number of senators opposing the bill is reduced when unions are not opposed to the bill. As expected under Claim 4, under no scenario is the pro-immigration coalition guaranteed a supermajority of Senate support. Anti-immigration groups have the potential to block expansive legislation in each of the first three scenarios, although the number of swing voters that they must influence is highest when they are advocating in opposition of legislation that is supported by the proimmigration coalition. The final scenario reveals that a coalition of employers and anti-immigration groups could potentially obtain enough Senate votes to achieve policy change if these two groups, which are usually in opposition to one another, find policy proposals that they agree upon. However, as expected, this coalition has a lower likelihood of passage than a bill supported by the pro-immigration coalition. TABLE 3.7: Estimation of the distribution of senator preferences Senators supporting

Senators opposed

Swing voters

Scenario 1: Senator preferences for a bill supported by employers and immigrant

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advocacy groups, opposed by anti-immigration groups and unions 2000 (106th Congress)

22

31

47

2005 (109th Congress)

34

40

26

2007 (110th Congress)

39

44

17

Scenario 2: Senator preferences for a bill supported by employers and immigrant advocacy groups, tacit support by unions, opposed by anti-immigration groups 2000 (106th Congress)

49

15

36

2005 (109th Congress)

47

25

28

2007 (110th Congress)

50

28

22

Scenario 3: Senator preferences for a bill supported by employers and immigrant advocacy groups, and unions, opposed by anti-immigration groups 2000 (106th Congress)

53

15

32

2005 (109th Congress)

54

25

25

2007 (110th Congress)

56

28

16

Scenario 4: Senator preferences for a bill supported by employers and antiimmigration groups, opposed by immigrant advocacy groups and unions 2000 (106th Congress)

31

14

55

2005 (109th Congress)

44

14

42

2007 (110th Congress)

44

19

37

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101st Congress: The Immigration Act of 1990

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The Immigration Act of 1990 was supported by a coalition of immigrant-advocacy groups and employers who were able to fight to achieve a compromise bill expanding both kinship and employmentbased immigrant admissions. This was achieved with the tacit support of unions. Briggs (2001a) summarizes the AFL-CIO as largely absent from the political advocacy scene. [T]he AFL-CIO did not take a prominent role in the political posturing preceding the ultimate passage of the Immigration Act of 1990. While it did not clearly articulate what it favored, it did specify what it was against. At its 1989 Convention, a resolution was adopted that stated that ‘it opposes any reduction in the number of family-based visas or any erosion in the definition of family. Furthermore, it opposed increasing the number of employment-based immigrants because they represented a ‘brain drain’ of other nations and the AFL-CIO preferred to expand domestic policies to increase investment in education and job training in the country. The only specific provision the AFL-CIO sought to influence was a section of non-immigrant labor policy governing the temporary admission of foreign performing talent and their accompanying technical workers. Although unions did not strongly support the bill, they also did not advocate against it. As a result, it was primarily immigrant advocacy groups and employers seeking to influence Senate votes to support the bill. Debate surrounding two alternative pieces of proposed legislation considered early in the 101st Congress by the Senate Judiciary Committee reveals the obstacles that had to be overcome in order to form the pro-immigration coalition. Senators Kennedy and Simpson introduced S. 358, which contained key proposals including an overall cap on permanent immigrant admissions, a shift in priority toward the admission of immediate family members rather than siblings and adult children of U.S. citizens, and the introduction of an independent immigrant category of entry that would be allocated according to a point system,

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as part of an overall increase in employment-based admissions.84 An alternative bill, S. 448, was introduced by Senator Paul Simon (D-Ill.). Both the Kennedy-Simpson bill and Senator Simon’s proposals maintained a kinship-bias in overall U.S. immigrant selection; however, the proposals in S. 448 were less restrictive toward kinship-based admissions.85 S. 448 did not include a cap on overall immigration levels, as did the Kennedy-Simpson bill, and it also did not contain measures that would have reduced the number of admissions slots available to the siblings of U.S. citizens. Both of these measures were opposed by immigrant advocacy groups. The Congressional Quarterly Almanac reported in 1989:

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An immigration cap was opposed by Hispanic groups and by the National Immigration Forum, a broader coalition of groups concerned with immigration issues. Forum President Rick Swartz said a cap 'would pit groups against one another in competing for more limited slots.' He called it a 'sledgehammer approach' (p. 267). Likewise, both Asian-American and Hispanic groups were united in opposition to the restriction of the admission of siblings of U.S. citizens. The Congressional Quarterly Almanac reported: Asian-American and Hispanic groups…said it failed to account for cultural differences. 'We consider the nuclear family as also including brothers and sisters,' said Melinda Yee, Executive Director of the Organization of Chinese Americans, Inc. Her view was echoed by spokesmen for MALDEF, the Mexican-American Legal Defense and Education Fund, and for the National Council of La Raza, a coalition of community-based Hispanic organizations in 32 states (p. 267). These groups also opposed the inclusion of English proficiency criteria in the point system proposed by Senators Kennedy and Simpson for selecting independent immigrants, arguing that a language requirement would favor Western European immigrants.86 The differences visible in these two proposals were resolved by the full Senate Judiciary Committee through a compromise.87 In short, the

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compromise version of S. 358, as eventually passed by the Senate Judiciary Committee, maintained the Kennedy-Simpson proposal for an overall cap on permanent immigrant admissions,88 but it removed the restrictions on the admission of siblings of U.S. citizens and the English proficiency criteria in line with Senator Simon’s proposals. The Senate then spent three days debating S. 358, altering the legislation through numerous floor amendments prior to its passage.89 Two important amendments were added, revealing the increased momentum for expansive legislation as the pro-immigration coalition consolidated its support behind a widely-expansive bill. One amendment, known as the Hatch-DeConcini amendment, acting in favor of kinship-based admissions, established a floor, such that immediate relatives of U.S. citizens could not squeeze numericallylimited kinship-based admissions below the level of 216,000 annual admissions. This amendment had the effect of eliminating any overall cap placed on immigration. Congressional Quarterly reported on the amendment’s adoption and the reaction of Senator Alan Simpson: ‘I do not understand the purpose of this amendment, other than just tremendous group pressure,' Simpson complained. Groups representing Hispanics and Asian immigrants, the principal beneficiaries of existing law, had lobbied furiously against new curbs on family-preference visas. Simpson said members should face up to 'the hideous reality that there are many more people who want to come here than there is room or intent to accommodate them’ (p. 269). A floor amendment proposed by Senator Arlen Specter (R-Pa.) increased the number of employment-based admissions from 120,000 to 150,000, adding additional 30,000 admissions slots to the overall cap on immigration.90 Notably, two amendments proposed by Senator Jesse Helms (R-NC) that would have increased employment-based admissions at the cost of kinship-based admissions were rejected.91 By the time S. 358 was passed by the Senate, the pro-immigration coalition was secured. As predicted, anti-immigration groups were strongly opposed to the bill. Dillin (1989) reported that Assistant Director of FAIR Pat Burns stated:

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Congress has failed to address the root problems of America’s immigration policy...Kennedy-Simpson is a patched-together compromise that caved in to virtually every pressure group, from the League of United Latin American Citizens to the Irish. The House version of the bill would be even more expansive, and the threat of a filibuster by Senator Simpson played a key role in shaping the final conference agreement. Widely expansive measures passed by the House Judiciary Committee would have removed the overall cap on family reunification for immediate relatives of U.S. permanent residents. The House proposals received the threat of veto from Republican President George H. W. Bush.92 However, the threat of a presidential veto was never tested because Republican Senator Alan Simpson used the threat of a filibuster to prevent conferee appointments in the Senate in order to ensure that certain measures, including a numerical limit on admissions of immediate relatives of U.S. permanent residents, would be included in the final conference agreement.93 These measures had been included in the Senate version of the bill, but not the House version. Because Simpson’s co-author of the Senate legislation, Senator Edward Kennedy, preferred the more expansive House legislation, the threat of filibuster was crucial in shaping the version of the Immigration Act of 1990 that was finally enacted. The final version of the Immigration Act of 1990 included widely expansive policy proposals for the admission of permanent kinship and employment-based immigrants, in addition to highly skilled temporary migrants. All of the types of migrants considered in the bill, both permanent immigrants and highly skilled temporary migrant workers, had a generally high level of rights and benefits extended to them. In addition, the unemployment rate had been declining for almost a decade. According to the theory just presented, the conditions were favorable for the formation of a pro-immigration coalition. A review of the empirical evidence shows that, as predicted, immigrant advocacy groups and employers united in support of the bill. Unions remained on the sidelines, and I interpret the union position as tacit support for the bill. The theory also predicts strong opposition on the part of antiimmigration groups because of policy proposals to expand the admission of permanent immigrants. The empirical evidence suggests that these groups brought considerable pressure on Republican Senator

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Alan Simpson and President George Bush in an effort to limit the expansiveness of the final bill. FAIR released a statement following the Senate bill passage: In a triumph of special interest politics, the Senate yesterday gutted the major reforms of the so-called Kennedy-Simpson legal immigration reform bill, including the elimination of a cap on family chain migration and important modifications to family preferences…According to Dan Stein, the executive director of FAIR, the bill was destroyed by an anti-reform coalition of cheap labor mavens, professional ethnic advocates, and immigration lawyers…This bill, if enacted, will insure that America faces unmanageable immigration flows for the next 20 years, and will render insurmountable educational and economic challenges in California and related areas today.

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However, in this case, the anti-immigration groups were unable to obtain a sufficient number of Senate votes to block policy change, and the interests of a pro-immigration coalition prevailed with the enactment of the Immigration Act of 1990. 110th Congress: Unsuccessful attempt at comprehensive reform President George W. Bush was pushing for immigration reform as the end of his second term in office approached, and in part it was the Administration’s support that led Homeland Security Secretary Michael Chertoff and Commerce Secretary Carlos Gutierrez to organize regular meetings with a group of veteran senators in attempt to broker a piece of comprehensive reform legislation.94 The initial result of these meetings included, among other things, a drastic revision of the U.S. permanent immigrant admissions program.95 However, amendments adjusting other aspects of the legislation, including a legalization program for unauthorized migrants already residing in the United States and an enlarged guestworker program, brought about the bill’s downfall. On June8, 2007, three votes were held to invoke cloture and bring about a vote on the bill, but the Senate was unwilling. The bill was therefore pulled from the Senate floor.96

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On June 18, 2007 another revised bill, S. 1639, was introduced by Senators Kennedy and Specter which contained the same proposals regarding permanent immigrant admissions. Like S. 1348, S. 1639 proposed major changes to the permanent immigrant admissions program, restricting kinship-based admissions while drastically increasing the number of visas allocated to employment-based immigrants. In the area of kinship-based immigration, restrictive proposals included the elimination of all family preference categories for siblings and adult children of U.S. citizens, as well as for the adult children of permanent residents. Admissions for spouses and minor children of U.S. citizens remained uncapped in the legislation, although S. 1639 removed parents from the definition of immediate relatives of U.S. citizens and placed an annual numerical limit of 40,000 on their entry. The admission of spouses and minor children of permanent residents remained capped at 87,000 annual admissions.97 Employment-based immigrant admissions were to be drastically reformed under S. 1639, replacing the first three preference categories with a selection program for “merit-based immigrants” based on a point system that distributed up to 100 points according to four main criteria:98 employment; education; English language proficiency and civics; and having an extended family relationship to a U.S. citizen or legal permanent resident.99 The most striking changes proposed by S. 1639 with regard to permanent immigrant admissions related to the proportion of total visas to be distributed to the new employment-based (“merit-based”) immigrant selection program in comparison to kinshipbased admissions. Following some transitional provisions for reducing backlogs, the legislation would have established limits of 127,000 on kinship-based immigration, not including immediate relatives of U.S. citizens, which would now only include spouses and minor children. Meanwhile the merit-based point system would allocate 380,000 visas annually. Had this legislation passed, it would have been a historical leap in the level of skill bias in U.S. immigration policy. In fact, for better or worse, it would have generated a fundamental shift in the priorities underlying U.S. permanent immigrant admissions since 1965. However, the prospect of such major change in the priorities of the U.S. permanent immigrant admissions program ended when the Senate on June 28, 2007 failed to invoke cloture in a vote of 46-53.100 The defeat of the bill came after an unusual halt to the amendment process on the day before, which was in part a result of the decision making

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procedure assigned to the task by Majority Leader Harry Reid (DNev.). Congressional Quarterly Almanac reported on the proceedings:

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In an attempt to prevent opponents from dragging out the debate, Reid had used a rare procedural tactic to structure amendments, known as a “clay pigeon.” Instead of holding upor-down votes on each amendment, senators cast votes on tabling the individual amendments. If a component was tabled, which required at least 50 votes, it was killed. If a component was not tabled, senators could not dispense with it without unanimous consent….the process came to a halt when the chamber refused 45-52 to table a seventh amendment that would have dropped all references in the bill to requirements for issuing secure driver’s licenses and identification cards under a 2005 law. (p. 15-11) The 110th Congress ended without substantial change to immigration policy, leaving U.S. permanent immigrant admissions largely unchanged from the preference system established in 1990. Kinship-based immigration remained the primary focus of the program, which continues to suffer from lengthy backlogs in several categories. Congressional Quarterly Almanac quoted President Bush as stating that, “‘The American people understand the status quo is unacceptable when it comes to our immigration laws…A lot of us worked hard to see if we couldn’t find a common ground—it didn’t work.’”101 The bills under consideration in the 110th Congress contained policy proposals to restrict the admission of kinship-based permanent immigrants, while expanding the admission of employment-based permanent immigrants. Additional provisions of the bill included an expanded general-skill temporary worker program and an “earned legalization” program that would grant permanent resident status to some undocumented migrants. The theory predicts that a proimmigration coalition is unlikely to form around these proposals. Employers are predicted to support the bill; however, immigrant advocacy groups are expected to oppose any policy proposal that would restrict the admission of kinship-based permanent immigrants. Unions are predicted to oppose the expansion of admissions to general-skill temporary workers with a low level of rights and benefits.

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The empirical evidence supports the predictions regarding employers and unions, although in both cases the empirical evidence is weakened by factors unanticipated by the theory. Although employers were generally among the bill’s supporters, they were not as eager in their support as predicted. Pear (2007) reported that:

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The Republican architects of the Senate bill…thought they were doing a favor for high-tech companies when they proposed a ‘point system’ to evaluate immigrants seeking green cards….But the high-tech companies were upset because the bill would have stripped them of the ability to sponsor specific immigrants for particular jobs. The companies flooded Senate offices with letters, telephone calls and e-mail messages seeking changes to the bill. Mr. Ballmer, the blunt-spoken chief executive of Microsoft, Craig R. Barrett, the chairman of Intel, and other executives pressed their concerns in person. The disagreement over the selection of employment-based immigrants through a point system is unanticipated by the theory. A division among union preferences was also unanticipated by the theory. Three unions supported the bill because of the legalization program, while most other unions opposed the bill on the basis of the guestworker program. Greenhouse (2007) reported that the brunt of union advocacy fell out in opposition to the bill: [T]hree prominent unions—the service employees, the farm workers, and the hotel restaurant and apparel workers—have backed the legislation. But that support, advocates say, has been outweighed by opposition from the AFL-CIO and virtually all other unions, including auto workers, Teamsters, food and commercial workers, and construction unions. In contrast to the theoretical predictions, immigrant advocacy groups did support the bill, which was the first time in recent decades that immigrant advocacy groups have supported a bill that would restrict kinship-based admissions. It appears that these groups were willing to compromise their preference for maintaining kinship-based

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admissions in exchange for the inclusion of a legalization program. Archibold (2007) reported that: Frank Sharry, executive director of the National Immigration Forum, one of the groups in a broad coalition backing the bill, said many supporters had problems with aspects of the bill but supported the measure as a first step after years of failure to get any overhaul.

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The inclusion of a legalization program brought immigrant advocate support, but it also shifted the stakes for anti-immigration groups. The theory predicts that anti-immigration groups will more strongly oppose bills that expand permanent immigration and that include a legalization program that grants permanent resident status. The empirical evidence shows that a strong opposition was mounted by anti-immigration groups, primarily due to the legalization program. Schrag (2010) describes a well-organized opposition: In April 2007, in concert with FAIR, thirty four radio talkshow hosts, who had made immigration their prime issue, organized themselves into a quasi-lobby called Hold Their Feet to the Fire and brought their microphones and some of their listeners to Washington, broadcasting to their home audiences, urging anti-immigration emails and faxes, and working congressional offices to head off comprehensive immigration reform. Anything that might lead to legalization was ‘amnesty.’ Kiely (2007) reported: Opponents claimed a populist victory over what Roy Beck of NumbersUSA called ‘the cheap labor lobby.’ Beck said his organization, which focuses on immigration-related issues, generated more than 2 million faxes against the immigration bill since May. Hours before the Senate vote, the Capitol switchboard was swamped with phone calls.

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Overall, the theory predicts that a coalition of employers and immigrant advocates is much less likely to be successful when unions join with anti-immigration groups to oppose legislation. In this case the coalition of (most) unions and anti-immigration groups were successful in obtaining the support of enough Republican senators to block policy change.

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106th Congress: American Competitiveness in the 21st Century Act of 2000 The American Competitiveness in the 21st Century Act of 2000 was the response of the U.S. legislature to heavy lobbying by the tech industry for increases in the annual limit on highly skilled temporary migrant workers in the H-1B visa category. These employers argued that increases were required to maintain economic growth amid labor market conditions of full employment coupled with increasing unmet demand for skilled workers in a booming economy.102 However, attempts within the pro-immigration coalition to link increases to H-1B visas to other immigration issues stalled the passage of this widely popular measure. Supermajoritarian procedures were used to prevent policy linkages with proposals that may have increased opposition to the bill’s passage. Republican senators were able to block Democrats from introducing amendments from the “Latino Immigrant Fairness Act,” supported by Democratic President Bill Clinton, to the bill relating to H-1B temporary workers.103 Following a successful cloture vote in the Senate, limiting debate, and additional efforts by the Senate Majority Leader to block Democratic amendments, the changes to the H-1B visas were passed through the Senate without additional policy linkages. Congressional Quarterly Almanac (2000) reported: Democrats …decried the way Majority Leader Trent Lott, RMiss., brought up the H-1B bill. Once the Senate had invoked cloture, limiting debate on the bill, Lott blocked Democrats from offering germane amendments by putting forward a series of minor amendments of his own, thereby 'filling the amendment tree' and leaving no room for Democratic proposals...Democrats failed in an effort to waive germaneness rules and allow consideration of their amendment. The vote was 43-55. (15-11-15-12)

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With an election year looming, the Republican-majority House decided to quickly pass the Senate legislation on the same day, rather than face floor debates on the more controversial immigration issues.104 The final Senate vote of 96-1 shows that the pro-immigration coalition was not broken by these attempts at policy linkage.105 The adjustments to H-1B visas had been stalled, but not derailed. The American Competitiveness in the 21st Century Act of 2000, introduced after seven years of declining unemployment, included proposals to expand the admission of highly-skilled temporary migrants, a group of migrants with a high level of rights and benefits relative to other temporary migrants, but with a lower level of rights and benefits than permanent residents. This case is a critical test of the theory. Policy change is predicted to be most likely to occur under these conditions, and the empirical evidence is supportive of the theoretical predictions. Specifically, the theory predicts that a proimmigration coalition is likely to form around such a proposal, and that anti-immigration opposition will be mild. The empirical evidence shows that the bill was supported by immigrant advocacy groups and employers, and that the bill received the tacit support of unions. Although immigrant advocacy groups unsuccessfully attempted to link other policy issues to the bill, in the end they were willing to support the stand-alone bill. Unions released public statements opposing the legislation, citing common occurrences of fraud in the H-1B program and calling into question the policy of admitting foreign workers rather than addressing the labor shortage through education policy.106 However, beyond releasing such statements, unions did not participate in advocacy to oppose the policy change. Congressional Quarterly Almanac (2000) reported that: The technology industry also may benefit from organized labor’s relatively low-key response to the new push for H-1Bs. Organized labor is not well represented in most high technology workplaces. And the intense battle over granting permanent normal trading relations to China is likely to leave labor with little time or political capital to spend on the H-1B fight. Democrats ‘will feel less pressure on this’ from labor because of China, said Rep. Barney Frank, D-Mass, who said he believes the industry has shown it needs workers. (p. 15-4)

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The theory also predicts that anti-immigration groups will oppose this bill, but without the intensity of their opposition to expansive legislation in the area of permanent immigration. As anticipated, the opposition of anti-immigration groups toward the bill was primarily aimed at preventing policy linkage with a legalization program. U.S. Newswire (2000) reported a statement released by FAIR stating: Congress should resist White House demands for yet another amnesty by putting off all consideration of immigrationrelated bills until 2001," says Dan Stein, executive director of the Federation for American Immigration Reform (FAIR). Right now, Congress is squeezed between high tech firms that want more cheap foreign H-1B workers and the administration trying to push another amnesty—one that's ‘completely unjustified,’ says FAIR…Stein expressed dismay that Congress would consider far-reaching amnesty proposals without considering the staggering implications of the wholesale breakdown in American immigration law enforcement: ‘The continued discussions of amnesty is a signal to the entire world that the U.S. has literally lost control of its borders and that our laws have no meaning’ said Stein. ‘The alliance with big business interests seeking cheap labor shows a short-sighted willingness to compromise the essential rule of law,’ said Stein. As predicted by the theory, without the inclusion of the legalization program, anti-immigration group opposition to the bill was mild. The theory predicts that expansionary policies for the admission of temporary migrant workers with a high level of rights and benefits following a tight labor market are most likely to pass. This case is supportive of this prediction; the pro-immigration coalition achieved almost unanimous support in the Senate. 109th Congress: Unsuccessful attempt to pass AgJOBS The case of the failure to adopt AgJOBS legislation in the 109th Congress provides an example of a pro-immigration coalition that was united, but successfully blocked by opposition groups. AgJOBS advocates have declared it a hard-won compromise piece of legislation

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between immigrant-advocacy groups, unions and agricultural employers, the work of years of negotiations between the groups.107 The legislation would have expanded the admission of general-skill temporary migrant workers in agriculture while expanding their rights and protections, in part through an earned-legalization program providing access to permanent residency. Supermajoritarian decision making procedures played a critical role in the failure of AgJOBS to pass in the Senate, which was introduced as an amendment to a larger supplemental spending bill. Bee (2005) reported:

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‘We’re scrambling; we’re doing everything we can to get 60 votes,’ Sacramento-based United Farm Workers spokesman Marc Grossman said. But ‘no matter what happens, [the legislation] will gain valuable visibility. It is now on the front burner of the debate. However, debate on the proposed amendment containing the AgJOBS legislation was not limited by a cloture vote, and therefore it was removed from the Senate floor. What is notable is that the vote to limit debate lost by a small margin– 53-45. In fact, the amendment had a majority of support in the Senate, and it was only the supermajority requirement that kept the amendment from being included in the Senate version of the final bill. It can only be speculated whether the AgJOBS amendment, if passed by the Senate, would have been included in the final conference agreement. A comparison of votes on two additional amendments considered alongside the amendment containing the AgJOBS legislation provides important context. One amendment, successfully passed, expanded the H-2B program, the parallel temporary migrant admissions program for non-agricultural seasonal workers, by exempting returning H-2B workers from being counted against the annual quota for two fiscal years. This policy proposal did not alter the rights or protections of H2B temporary migrants, but rather provided a short-term increase in admissions. In contrast, a third amendment containing a competing proposal for an extended agricultural worker program was proposed, which did not include any legalization program and which would have reduced existing rights and protections extended to temporary migrant

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workers. This policy proposal, despite being more “employer-friendly,” faired far worse in a vote to invoke cloture than did the AgJOBS legislation. The vote was 21-77, falling far short of even a majority of support. As predicted by the theory, a policy expanding the admission of temporary migrant workers with a low level of rights and protections will be less likely to pass than a policy expanding the admission of temporary migrant workers with a high level of rights and protections, because the pro-immigration coalition will be more likely to form. The inclusion of a legalization program in AgJOBS legislation increased anti-immigration group opposition, hindering the passage of this expansive policy proposal that would have increased the rights and benefits of agricultural seasonal workers. The theory predicts that the conditions were conducive for the formation of a pro-immigration coalition. The empirical evidence supports this prediction, as the AgJOBS legislation was a compromise bill drafted initially by a combination of immigration advocacy groups, employers and unions through negotiations initiated in California. It may be that the union support for the bill had more to do with the goodwill fostered by long-standing negotiations among the proimmigration coalition actors than the recent improvement in the rate of unemployment, which had only been in decline for one year. The bill under consideration expanded the admission of temporary general-skill workers while increasing the level of rights and benefits extended to these workers. In addition, a legalization program was included that would grant permanent residence to some general-skill temporary migrants, which the theory predicts should generate stronger opposition by anti-immigration groups. Gerstein (2005) reported: ‘There is no question this is an amnesty. There’s no way to get around that,’ said a lobbyist who opposed the bill, Rosemary Jencks. ‘Not only does it give amnesty to illegal aliens, it invites former illegal aliens to come back and claim amnesty,’ said Ms. Jencks, who works for a group that favors lower immigration levels, NumbersUSA. In this case, the anti-immigration groups did oppose the bill, and the opposition prevailed by a thin margin. A cloture vote revealed that a majority of senators supported the bill, but the coalition of supporters was just short of supermajority requirements.

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A SUMMARY OF THE EVIDENCE These four cases of successful and unsuccessful attempts at policy change in U.S. migrant admissions provide a considerable level of support for the main claims underlying the theory. Table 3.8 presents a summary of the evidence collected with respect to each of the four main claims. In all four of the cases, supermajoritarian decision making procedures played an important role in shaping the policy process, lowering the barrier to block bills and increasing the barrier to pass bills. Special interest groups were active in all four of the case studies, and were influential in shaping senate votes. Empirical evidence from the 106th Congress showed that senators may avoid debate on controversial issues prior to an election, revealing some influence of the public that is unaccounted for in the theory. In addition, empirical evidence from the 110th Congress showed that anti-immigration groups do mobilize public advocacy with respect to bills, encouraging the public to phone, send faxes and email senators. However, the overall influence of the public was minor in these four cases, relative to the influential role of special interest groups. Pro-immigration coalitions formed in both cases of successful policy enactment, although they were formed with the tacit support of unions. A pro-immigration coalition was also forged in the unsuccessful policy attempt in 2005 to pass AgJobs. The evidence supports the theoretical claims that although a pro-immigration coalition spanning immigrant advocacy groups, unions and employers improves the likelihood of policy change, the formation of a proimmigration coalition does not determine policy change. In the 2005 unsuccessful attempt at policy change, a bill supported by a proimmigration coalition was successfully blocked by opponents, primarily in response to opportunities for legalization that would have extended permanent resident status to undocumented migrants already residing in the United States. In the 2007 unsuccessful attempt at comprehensive reform, anti-immigration group opposition was especially strong in response to a bill that contained both an expansion of employment-based permanent immigrant admissions and a legalization program for undocumented migrants.

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Anti-immigration groups were not able to block policy passage, but they lobbied to influence Republican Senator Alan Simpson.

A veto threat by President George Bush and a filibuster threat by Senator Alan Simpson had the effect of making the legislation somewhat less expansive, although the bill remained widely expansive Union tacit support generated a pro-immigration coalition in support of the bill

Claim 2: Supermajoritarian decision making procedures make it more difficult to pass than to block bills

Claim 3: A pro-immigration coalition is most likely to achieve a sufficient number of Senate votes to form a winning coalition Claim 4: Anti-immigration groups can be strong enough to block bills acting alone, even when the proimmigration coalition forms.

Senator Alan Simpson describes "tremendous group pressure," as immigrant advocacy groups and employers play a dominant role in shaping the expansive legislation

Claim 1: Senate votes on bills relating to U.S. migrant admissions are influenced by special interest groups (and not by the public)

The Immigration Act of 1990 (101st Congress)

Anti-immigration groups were able to convince a sufficient number of Republican senators to block the bill from passing.

Unions split in their support for this bill, with most unions opposing the bill. A proimmigration coalition did not form.

Unsuccessful policy attempt in 2007 (110th Congress) Anti-immigration groups lobbied to block the bill, influencing Republican senators, despite the endorsement of (then unpopular) President George W. Bush and general public dissatisfaction with current policy The first bill failed in three cloture votes and the second bill failed through a clay pigeon decision making procedure for amendments that required a majority to table or unanimity to pass each amendment.

Permanent immigrant admissions

Unions voiced opposition to the bill, but did not advocate against the bill. Union tacit support generated a proimmigration coalition in support of the bill. Anti-immigration groups did not mount a strong opposition to this bill once the legalization program was detached, and they were not able to block the bill from passing

A successful cloture vote in the Senate limiting debate blocked Democratic amendments, and the bill was passed as a standalone bill without more controversial amendments.

The American Competitiveness in the 21st Century Act of 2000 (106th Congress) The advocacy of special interest groups, especially employers, was prominent in this case. The looming election did contribute to the speedy passage of the bill, as legislators were hesitant to debate more controversial issues.

Anti-immigration groups were able to block the amendment from passing, despite majority support for the amendment.

A pro-immigration coalition formed in support of the bill.

Debate on the proposed amendment containing the AgJOBS legislation was not limited by a cloture vote, and therefore it was removed from the Senate floor, despite a majority of Senate support.

Unsuccessful attempt to pass AgJOBS legislation in 2005 (109th Congress) The AgJOBS legislation was the result of a compromise between immigrant advocacy groups, employers and unions in California. Antiimmigration groups were active in opposing the legalization program.

Temporary migrant admissions

TABLE 3.8: Empirical evidence for the main claims of the theory from four empirical case studies

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Table 3.9 shows the theoretical predictions of special interest group preferences alongside the observed preferences of each group in each of the four case studies. In all but one case study the theory generated accurate predictions. The support by immigrant advocacy groups in 2007 for a bill that would have restricted kinship-based admissions remains unpredicted by the theory. It is possible that that the rise of undocumented migration has influenced the politics of U.S. migrant admissions policy by shifting the preferences of immigrant advocates to favor protection for undocumented migrants already in the country above the protection of kinship-based admissions. Although not accounted for in the theory, this is an important topic for future research. Unions remained relatively uninvolved in U.S. migrant admissions policy advocacy in the two successful attempts at policy change in 1990 and 2000. I have interpreted the low involvement of unions as tacit support for policy change. An alternative explanation of the low involvement of unions might be dissention among unions. Although this was clearly evident in the 2007 case, in the 1990 and 2000 cases I did not find direct evidence of union disagreement. Public statements from major unions on either bill were absent from the secondary source descriptions of each case, which implies a low level of advocacy. In the 2000 attempt at policy change, unions were occupied with advocacy against legislation in a different policy area, in this case legislation relating to the trade partner status of China. This theory does not account for the influence of alternative legislative proposals being considered simultaneously, which may dampen the advocacy efforts of some special interest groups. It cannot be known whether unions would have advocated against the H-1B quota increase if the legislation regarding China had not been under consideration. CONCLUSION The public may be ready for an overhaul in U.S. migrant admissions policy, but the theory presented in this chapter suggests that conditions favoring policy change are not present. The recent economic downturn, which has placed more American workers out of jobs, is predicted to hinder policy change. As the economy improves, the policy changes that are most likely to occur are those expanding admissions, rather than restricting admissions. Expansionary change in U.S. migrant admissions

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Predicted: Support Observed: Support Predicted: Support Observed: Support

Predicted: Support Observed: Support Predicted: Support Observed: Tacit Support

Predicted: STRONG oppose Observed: STRONG oppose Predicted: Support Observed: Support Predicted: Support Observed: Mild support Predicted: Oppose Observed: Oppose

Predicted: STRONG oppose Observed: STRONG oppose Predicted: Support Observed: Support Predicted: Support Observed: Support Predicted: Support Observed: Tacit Support

Anti-immigration groups

Immigrant advocacy groups

Employers

Unions

Predicted: Support Observed: Support

Predicted: Support Observed: Support

Unsuccessful policy attempt in 2007 (110th Congress)

Temporary migrant admissions The American Unsuccessful attempt Competitiveness in to pass AgJOBS st the 21 Century Act legislation in 2005 of 2000 (109th Congress) (106th Congress) Predicted: STRONG oppose Predicted: Oppose Observed: Observed: Oppose STRONG oppose

The Immigration Act of 1990 (101st Congress)

TABLE 3.9: Predicted and observed preferences of special interest groups Permanent immigrant admissions

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policy is predicted to be most likely for highly skilled temporary migrant workers. The case of the 110th Congress shows that immigrant advocacy groups may prefer a legalization program even at the cost of protecting kinship-based admissions. While this shift in preferences would open up the possibility of restriction in kinship-based admissions, the theory also suggests why expansive policy measures for employment-based permanent immigration may remain unlikely to pass. Legalization programs that grant permanent residence status strengthen the opposition of anti-immigration groups. Supermajoritarian decision making procedures create conditions in which anti-immigration groups may continue to be a formidable obstacle to policy change in U.S. migrant admissions.

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CHAPTER FOUR

Post-coding Aggregation: A methodological principle for independent data collection

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INTRODUCTION In the contemporary research environment, data sharing is easy and instantaneous. Data collection increasingly occurs outside of a research design for causal inference and with the intention of use in multiple research projects. I refer to this type of data collection as independent data collection. As a result, data analysts are increasingly using data that they did not collect. This chapter reveals a blind spot in scholarly discussions of measurement methodology, which have largely overlooked the implications of independent data collection. Data is often collected according to standards of measurement designed for data analysis. Data collectors commonly think of a particular use of data, which serves as a basis for decisions about how to aggregate information to achieve general measures of theoretical interest. As a result, data is collected often without transparency and in such a way that does not retain the more specific observations that form the basis of scoring decisions. These practices make it difficult for data analysts to verify the validity of data and to assess its suitability for specific measurement objectives. In addition, a considerable amount of rigidity is imposed upon data users, who must accept decisions made by the data collector as set in stone. As a result, as much data of potential use to the field is lost in the process of data collection as gained. The current approach to data collection is far from optimal. 103 Challen, Suzanna. Measuring Change in Immigration policy, LFB Scholarly Publishing LLC, 2013. ProQuest Ebook

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The primary contribution of this chapter is a new technique developed to enable independent collection of flexible, high quality data: post-coding aggregation. Post-coding aggregation is a methodological principle that minimizes data loss, increases transparency, and grants data analysts the ability to decide how best to aggregate information to produce measures for their own projects. First, I identify three critical aggregation decisions that have important implications for measurement. Next, I advance the principle of postcoding aggregation and demonstrate how it increases the flexibility of data use by expanding the utility of data collections for a wider range of research objectives and improves the reliability and the content validity of measures in data analysis. Throughout, I support my arguments using the results of a methodological survey of four leading datasets measuring government policy. To conclude, I address three issues raised by adherence to a methodology of post-coding aggregation, including resource demands, sub-national variation and the use of experts as sources.

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INDEPENDENT DATA COLLECTION Political scientists are actively engaged in the task of collecting new quantitative, macro-comparative data. This type of data is also commonly called time-series, cross sectional (TSCS) data, and Beck (2001) provides a helpful summary of its distinctive features. Throughout this chapter, I limit my consideration of data collection to this type of data. Independent data collection is a long-standing practice in political science, particularly among scholars of international relations who have engaged in pioneering large-scale data collection projects like the Correlates of War and Polity projects.108 Recent independent data collection efforts include the eight historically oriented and integrated replication databases (HIRDs) recently reviewed by Lieberman (2010). These projects emerge as part of a larger trend in political science of increasing accessibility of new data. Data sharing has become a mainstream practice as the field has adopted replication standards for the validation of published research.109 Replication data enables not only an assessment of the authors’ initial findings, but also original research.110 Data collection and sharing outside of academia is also relevant. In many substantive areas of research, academics rely upon the staff researchers of international

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organizations, government agencies, foundations and large policy think tanks to provide new data. Increasingly data collection in political science does not occur within the context of a single research project, yet political methodologists and empirical political scientists hesitate to consider data collection an independent research endeavor. This hesitancy arises from legitimate concerns that when data is collected without the guidance of a research design for conducting causal inference, it may be interpreted incorrectly. An additional concern is that independent data collection will be inefficient because the data collector will not have any basis for determining what data should and should not be gathered. I address each of these points in turn. Data collection should always be undertaken with close consultation to the existing body of theoretical work debating both the definitions of important concepts and the empirical relationships between distinct concepts. However, if done correctly, data collection need not occur within the bounds of a specific research design for causal inference that is meant to test a particular hypothesis or set of hypotheses about the relationship between specific concepts. It can be overly narrow to think of only one particular use of data, rather than the variety of likely uses of new data, at the stage of data collection. This is especially the case in substantive fields where data collection is costly, and where a paucity of data is currently hindering progress along multiple research trajectories. Data collection for use only in one research project is inefficient. The data collection efforts of various researchers working on the same subject area may overlap. In addition, data collected for each project will be limited to the needs of that project. Issues of coverage cannot be easily addressed by combining different datasets, because most likely different methodologies will be utilized. Efficiency in data collection is enhanced by independent data collection. A benefit of independent data collection is derived from the ability of the data collector to be systematic in the collection of a wider range of information, such that a larger array of data is collected with a consistent methodology. Where it might be inefficient from the perspective of a data analyst working on a particular research project to systematically collect additional information that will not be directly put to use, it is a desirable task from the perspective of the data collector who has developed a

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specialization in developing indicators and scoring cases based on certain types of sources. This is because the marginal cost of collecting the additional data is low and the potential gain is substantial, as the additional information may expand the utility of the dataset to new researchers and projects. Research in political science depends upon new data collection. A prime example is research on the determinants and the effects of government policies within the fields of international and comparative political economy. As in other areas, empirical research on government policy demands more data to promote systematic comparison and to provide quantitative evidence to test causal claims. However, crossnational data collection on government policies can be both costly and time-consuming, as foreign language expertise and a detailed knowledge of each national legal system can be required. Absent direct measures of policy, researchers often rely on outcomes of policy as a proxy, but these substitutions violate the content validity of the measure and hinder the interpretation of findings. As a result, scholars have begun to devote resources to the development of direct, comparable, quantitative measures of government policies that can be used by a wide variety of researchers. To support my argument for post-coding aggregation, I survey four policy datasets that are among the most ambitious attempts to systematically construct quantitative indicators of government policies across a large number of countries. The Alliance Treaty Obligations and Provisions (ATOP) dataset develops indicators of foreign policy established through military alliance treaties.111 The OECD Employment Protection Legislation (EPL) Database develops indicators of labor market regulation.112 The Migrant Integration Policy Index (MIPEX) develops indicators of migrant integration policies.113 Finally, the Social Citizenship Indicator Program (SCIP) develops quantitative measures of welfare policies.114 Each of these datasets represents an example of independent data collection. The data has been created intentionally for use in multiple research projects, and has been used widely for data analysis by researchers who were not involved in the process of collecting the data. Throughout this chapter, I draw upon examples from this survey to illustrate the concepts utilized and the claims made in advancing a new methodological principle for aggregation in independent data collection. The full dataset survey is presented in Appendix D.

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RE-DEFINING MEASUREMENT TO INCORPORATE INDEPENDENT DATA COLLECTION The independent task of data collection forms part of the increasing specialization of researchers within the field of political science. As with most types of specialization, there are benefits to independent data collection that should be acknowledged, as well as new challenges that must be addressed. One important challenge is posed by the task of aggregation. Decisions relating to the aggregation of information made at the time data is collected can either facilitate or hinder valid measurement in data analysis. Yet, researchers engaged in independent data collection currently have no methodological principles to inform their decision making. While the challenges posed to measurement at the stage of data analysis have been widely discussed, the challenges posed to measurement at the stage of data collection have been largely overlooked. Adcock and Collier (2001) provide the most thorough discussion of measurement methodology by political scientists recently published. The authors define measurement validity in the following way, “measurement is valid when the scores, derived from a given indicator, can meaningfully be interpreted in terms of the systematized concept that the indicator seeks to operationalize" (p. 531). The authors distinguish between the background concept and the systematized concept, and they argue that it is the validity of measurement of the systematized concept with which researchers should be concerned. To illustrate the distinction between the background concept and the systematized concept, consider the analysis of welfare state retrenchment by Korpi and Palme (2003) utilizing data from the SCIP. The authors first provide a discussion of competing conceptions of the welfare state: In Europe, since the late nineteenth century Verein für Sozialpolitik in Imperial Germany, and up to the present, scholarly texts on social policy have defined their subject matter in broad terms as including, in addition to social insurance and social services, also labor markets, employment policies, and parts of industrial relations. In marked contrast, comparative studies on welfare-state development came to use

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Measuring Change in Immigration Policy the size of social expenditures to the GDP, ‘the welfare state effort’ indicator, as their central dependent variable. While this indicator is relevant, as is widely recognized, it is connected with serious problems and nobody appears to have argued that it provides a full or the best definition of the welfare state. (p. 426 – 427)

The authors then define the background concept of the welfare state as, “policies to affect outcomes of, and conditions for, distributive processes in the sphere of markets so as to decrease inequality and/or poverty” (p. 428). For the purpose of conducting their analysis, the authors then more narrowly define the welfare state to form a systematized concept of welfare state retrenchment:

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We here introduce a new basis for the study of retrenchment by considering cuts in social citizenship rights in major social insurance programs… giving benefits during short-term absences from work, that is, sickness cash insurance, work accident insurance, and unemployment insurance. (p. 432-3) In this way the broader background concept of the welfare state is narrowed to focus on three specific social insurance programs. The distinction between the systematized and background concept is fundamental to Adcock and Collier’s definition of measurement, but this distinction is made with the role of the data analyst, and not the data collector, in mind. By linking measurement validity to the systematized concept, Adcock and Collier acknowledge that their definition of measurement relies upon limiting choices. “Because background concepts routinely include a variety of meanings, the formation of systematized concepts often involves choosing among them...scholars routinely emphasize different aspects of a background concept ...each of which is potentially plausible” (p. 532). It is on the basis of the research design that limiting choices in defining a systematized concept are justified. For example, the limiting choices encountered by Korpi and Palme (2003) in isolating a systematized concept of welfare state retrenchment are recounted and justified on the basis of the research design:

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[A]lthough the variables used here are central, they do not exhaust possible areas where retrenchment may appear. Thus, for example, in the above three programs we do not include changes in rules referring to waiting days, duration, conditions for benefits, and the like. Even more importantly, we cannot consider here areas such as social services, health-care services, family policy, and social assistance programs. While several program changes can be seen as a restructuring or recasting of welfare states, we argue that those observed here can be described as retrenchment. (p. 432-433)

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I argue that, in contrast to the data analyst, the data collector need not be limited by a systematized concept, but rather by the background concept. Adcock and Collier caution that valid measurement of the background concept is difficult to assess: To pose the question of validity we need a specific conceptual referent against which to assess the adequacy of a given measure. A systematized concept provides that referent. By contrast, if analysts seek to establish measurement validity in relation to a background concept with multiple competing meanings, they may find a different answer to the validity question for each meaning. (p. 533) However, an independent data collector can more feasibly collect information to measure a variety of different aspects of a phenomenon, all of which might be included in what Adcock and Collier describe as “the matrix of potential meanings (that are commonly associated) with the background concept” (p. 532). Measuring a background concept in a variety of ways is preferable from the perspective of the data collector. While the data analyst faces the task of obtaining a valid measure for a particular systematized concept, the independent data collector faces the task of defining indicators and scoring cases or units for each indicator in such a way as to provide multiple data analysts with the appropriate data to measure a variety of systematized concepts. By collecting information that enables a background concept to be measured in a variety of ways, the choice of which systematized concept to operationalize in a research project may be preserved for the

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data analyst because it is not, or at least not entirely, determined in advance by decisions of the data collector. To better highlight the distinct tasks of measurement in data collection and data analysis, I find it useful to modify Adcock and Collier's framework to distinguish between indicators and measures. Indicators, as conceived of by Adcock and Collier, operationalize a systematized concept, implying that the indicator provides information corresponding to the systematized concept that conveys its meaning at the same level of generality at which it is defined. I rename indicators that correspond with the meaning and the level of generality of a systematized concept as measures, because these indicators measure a specific systematized concept in accordance with the definition of measurement validity provided by Adcock and Collier. In contrast, I use the term indicator to refer to the conceptual dimensions defined by data collectors along which units are scored. A measure used by a data analyst may be identical to an indicator defined by a data collector, or a measure may combine multiple indicators. The MIPEX project provides an illustration of the distinction between measures and indicators. The authors are interested in benchmarking national policies on migrant integration for a variety of countries with respect to a normative framework of the highest standards identified in European law, protocols or objectives. The methodology of benchmarking results in a single index score for each country summarizing its national policies on migrant integration.115 A data analyst seeking a measure of the proximity of national migrant integration policies to the highest EU standards could utilize the MIPEX index as a measure of migrant integration policies. Or, a data analyst may utilize any combination of any subset of the 142 indicators that are scored individually to construct the MIPEX index. Figure 4.1 provides a visual summary of the distinct tasks of measurement for data analysis and data collection. An aggregate index that combines multiple indicators can be constructed by the data collector or by the data analyst. The decision of which measure to use should be made with reference to the systematized concept defined by the analyst. The validity of any indicator or index used as a measure of a systematized concept should be assessed by the analyst.

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Figure 4.1: The distinct tasks of measurement in data collection and data analysis

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THE METHODOLOGY OF AGGREGATION Aggregation is an unavoidable and ultimately subjective task that is inherent to the construction of macro-comparative quantitative data. The task of aggregation may be defined as the process of combining disparate pieces of observed information to create a single measure. Because a measure is used in data analysis, and may be constructed from multiple indicators, aggregation may be conceptualized as occurring in two stages. In data collection, aggregation will often be necessary when a researcher is engaged in the task of scoring a unit or case for a defined indicator. Aggregation occurs as the relevant information from available sources is summarized into a single score. I refer to this stage of aggregation in the remainder of this chapter as precoding aggregation. Aggregation also may be required if more specific indicators that have already been scored need to be combined to create a measure for use in data analysis. I refer to this second stage of aggregation as post-coding aggregation. I advance a methodological principle of post-coding aggregation. I will demonstrate that the stage at which aggregation occurs has important implications for both the flexibility of data use and data quality. Three decisions made at the stage of independent data collection crucially shape the stage at which the aggregation of information is required. The first decision is to determine the level of generality at which indicators will be defined. A methodological principle of postcoding aggregation requires that indicators be defined at a level of generality that is proximate to the level of generality at which information is provided by sources, in order to minimize the amount of information that must be combined prior to scoring a given indicator. In contrast, a methodology of pre-coding aggregation is reflected if indicators are defined at a level of generality that aligns with the background concept, without consideration of the type of information provided by sources. The second decision is to determine the scoring methodology for each indicator. The scoring methodology for an indicator may require only direct observation on the part of a coder, or it may require that the coder incorporate some type of ranking or scaling procedure when determining a score. A methodological principle of post-coding aggregation requires that indicators be defined in such a way that preserves the direct observations that form the basis of scores. Ideally,

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any ranking or scaling procedure should occur as a second step, because the process of ranking often requires that a coder combine observations. In contrast, a methodology of pre-coding aggregation is reflected if scores provide a ranking of units based on a variety of factors that are not separately coded. The third decision is to determine the methodology for constructing aggregate indices. A methodological principle of postcoding aggregation requires that aggregate indices be constructed as a second step following an initial scoring of all factors that will be incorporated into the construction of an index, in addition to providing publically accessible instructions on how the various indicators are combined, including any weighting procedures utilized. In other words, a data user should be able to reconstruct any aggregate index using the indicators and instructions provided by the data collector, without the need for any additional information. Aggregate indices that require information for scoring that is not made available to data users reflect a methodological principle of pre-coding aggregation. The decision of how to construct aggregate indices is relevant for many, but not all, data collection projects, as it is not always the case that data collectors seek to construct aggregate indices. THE ADVANTAGES OF POST-CODING AGGREGATION A methodological principle of post-coding aggregation for independent data collection increases the flexibility of data use and also promotes data quality. The mechanisms by which post-coding aggregation better achieves these dual objectives will be discussed in greater detail in the next two sections of this chapter. For quick reference, these mechanisms are also summarized in Table 4.1. I illustrate the way that adherence to a methodological principle of post-coding aggregation in practice influences the flexibility of data use and data quality using examples from the four policy datasets.

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The method of constructing aggregate indices

3

Aggregate indices provide the most flexible data use when they can be completely constructed or reconstructed using only the indicators provided in the dataset along with additional instructions for how these indicators are combined. In this way data analysts can choose whether or not to use the aggregate index as it is provided by the data collector, or whether to construct another aggregate index that better suits the particular research design.

The methodology Direct observations provide the most flexible data of scoring assigned because these observations can be used to rank or to indicators scale units as a second step after coding. However, depending on the way in which scoring options are defined, it is not always possible to retrieve direct observations from indicators that have been scored initially according to a ranking procedure.

Flexibility of data use Indicators that are defined at a low level of generality increase the flexibility of data use because they can be used individually or in varying combinations to produce measures for data analysis.

2

Aggregation decision 1 The level of generality at which indicators are defined

If the information required to construct an index is transparent and the process is easily reversible, data quality is enhanced. The transparency of the process enables data analysts to identify issues with content validity. Measurement is improved for a given project if the data analyst has the ability to reconstruct a measure to exclude any factors hindering content validity or to add additional factors that might improve content validity.

Direct observations can be most easily verified by referencing sources because they do not require subjective decisions on the part of the coder. Ranking procedures may hinder data quality when scoring options are not adequately defined for coders, such that they are defined in relative rather than objective terms. Such procedures require subjective judgement that may vary across coders.

Data quality Indicators that are defined at a low level of generality tend to be more reliably scored

TABLE 4.1: How aggregation decisions influence the flexibility of data use and data quality

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Flexibility of data use as an objective of independent data collection complicates the task of measurement. Rather than tailoring measures to a particular project, data must meet the needs of multiple researchers simultaneously. However, flexibility of data use is not necessarily at odds with the objectives of the data analyst, who is seeking quality measurement in a particular research project. An important contribution of this chapter is to show that better practices in data collection not only increase flexibility of data use, but also improve the quality of measures available for use in particular projects. It is important to acknowledge that the methodology of aggregation is not the only aspect of independent data collection that bears influence on the objectives of flexibility of data use and data quality. Decisions about the scope or coverage of a dataset, for example, also play a considerable role in determining the level of flexibility. However, for a dataset of a given size, aggregation decisions largely determine the level of flexibility that is preserved in new data. Data quality is also influenced, for example, by decisions relating to which sources will be referenced when scoring indicators.116 These and other decisions are also important, but they are not the focus of this chapter. Post-coding aggregation and flexibility of data use The flexibility of a dataset may be defined as the number of distinct ways that indicators can be used, whether individually or in aggregation, to construct measures for use in data analysis, without fundamentally altering the original set of indicators and scores. Aggregation influences the flexibility of data by influencing the amount of information that is retained or lost in the scoring of indicators. Empirical observations required for the scoring of indicators should be preserved in the scoring of data. Where empirical observations are required for the scoring of indicators but cannot be isolated in the final dataset, there is data loss that limits the flexibility of data use where it might otherwise have been preserved. Some data loss is generally required in constructing quantitative indicators, as a far more complex historical record is being interpreted into a single quantitative score for each unit. A principle of post-coding aggregation when applied to the three aggregation decisions minimizes data loss in data collection.

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Defining indicators Defining indicators at a lower level of generality tends to increase the flexibility of data use because indicators can be used individually or in varying combinations to produce measures for data analysis. In this way, researchers interested in concepts at different levels of generality may benefit from the same dataset. For example, a researcher may benefit from EPL indicators that score a variety of government policies relating to employment protection because these indicators can be aggregated into a more general measure of the strictness of employment protection legislation. Another researcher who is interested specifically in observing change over time in levels of severance pay for regular employees may find the same dataset useful because such policies have been individually scored. Indicators that are defined at a higher level of generality tend to result in greater data loss as the factors relevant to the scoring of the indicator are not each individually scored.

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Scoring methodology The methodology of scoring assigned to indicators can also influence the level of data loss. Scoring methodologies that require only direct observation on the part of coders provide the most flexible data because direct observations can be used as measures, or they can be combined to form ordinal rankings of units along conceptual dimensions of interest.117 Where ranking is required as part of the scoring methodology, direct observations are preserved only if each scoring option reflects unique information. For example, an EPL indicator of the length of the notice period required for the dismissal of an employee with 9 months of tenured regular employment is scored on a strictness scale of 0 to 6. A coder is instructed to score this indicator according to the length of the notice period, with a score of 0 if 0 months, 1 if less than or equal to .4 months, 2 if less than or equal to .8 months, 3 if less than or equal to 1.2 months, 4 if less than 1.6 months, 5 if less than 2 months and 6 if greater than or equal to 2 months.118 The exact number of months is not recorded, which causes some data loss that might otherwise be preserved in the data collection process. However, because there are six choices of narrow ranges of months, it is possible to identify the notice

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period to within a small range for any country year. Each possible score on the six point ranking is defined with enough precision that it can be converted into an objective binomial variable showing the presence or absence of a policy of that range of duration. This increases the flexibility of data use. In contrast, MIPEX indicators are also scored with a ranking procedure; however, the scoring options are defined with less precision. For example, an indicator for the policy strand of labor market access within the policy dimension of labor market integration is defined as, “State facilitation of recognition of skills and qualifications obtained outside the EU.” The definition of this indicator includes the following list of specific sub-policies that may exist in a country year: a) existence of state agencies/information centres that promote the recognition of skills and qualifications; b) national guidelines on fair procedures, timelines and fees for assessments by professional, governmental, and nongovernmental organisations; c) provision of information on conversion courses/profession-based language courses and on procedures for assessment of skills and qualifications (regardless of whether assessments are conducted by governmental or non-governmental organisations)119 A coder is asked to score this indicator on a 1 to 3 scale where a score of 3 indicates that national policy includes b and (a or c), a score of 2 indicates that national policy includes a or c, and a score of 1 indicates that none of these components are contained in national policy. A coder must first determine whether the sub-policies identified by a, b and c are present in the national policy prior to determining the score. However, this information is not retained. Instead, the data user can only determine whether a country had either “b and (a or c)”, “(a or c)” or none of these policies. A researcher who would prefer to know whether each country provides information on language courses (“c”) would be unable to obtain this information as it is aggregated with information relating to whether there exist state agencies that promote the recognition of skills or qualifications (“a”). If the scoring options had been defined with more precision, the same benchmarking

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indicator could have been made, but in such a way that the indicator could also be disaggregated into objective indicators showing the presence or absence of each sub-policy. Post-coding aggregation and data quality Data quality as an objective of data collection has been more widely discussed than flexibility of data use because it directly pertains to the reliability and validity of measures used in data analysis. What has been overlooked is how the method of aggregation utilized in independent data collection can either facilitate or hinder the construction of reliable and valid measures in data analysis. In this section I discuss each of these points in turn.

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Post-coding aggregation and reliability Pre-coding aggregation requires coders to make subjective decisions about how to combine disparate and potentially conflicting pieces of information from sources in order to determine a single score for an indicator for a particular case. Lieberman (2010) identifies four obstacles that can arise in the process of scoring an indicator that influence the level of certainty with which the coder is able to determine a single score. [T]he relative uncertainty of the historical record is a form of measurement error that may originate from a number of factors. First, there may simply be a lack of good sources, and for certain times and places, one may have no authoritative basis for characterizing a given variable. Second, there may be conflicting accounts of the same phenomenon related to a variable of interest (for an excellent discussion, see Davenport and Ball (2002)). For example, one source might describe a given election as “free and fair,” whereas another reports on “irregularities,” making it difficult to characterize the quality of a political regime. Third, the researcher may unearth multiple and contradictory phenomena, such as growing rates of intermarriage between ethnic groups but also new examples of interethnic violence, making it difficult to score a variable on the nature of interethnic relations. Finally, additional

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ambiguities may arise in attempting to classify observations using the rules set out in a classificatory scheme or codebook. (p. 44, citations in original) The level of certainty with which a coder is able to score an indicator directly relates to the reliability of the data. Adcock and Collier (2001) state that, “Random error, which occurs when repeated applications of a given measurement procedure yield inconsistent results, is conventionally labeled a problem of reliability.” Where a given coder is uncertain of how to interpret sources to arrive at a single score, it is more likely that the same sources and method would result in a different score if given to a second coder for verification.120 Lieberman therefore proposes that coders should include a subjective measure of the level of certainty with which they were able to determine a score. This would inform users of the data about the likelihood of error and allow users of the data to decide whether or not to include scores that are determined with a lower level of certainty. I argue that additional steps should be taken at the stage of data collection to improve the reliability of data. While it may be good practice for coders to subjectively report the level of certainty with which a score was determined, it is equally important for data collectors to develop indicators that can be coded with a high level of certainty. Creating indicators that require too much aggregation prior to coding makes it highly likely that the coder will encounter one or more of the problems listed by Lieberman, increasing the likelihood of measurement error. I argue that such error is not entirely random, because the likelihood of error may be systematically influenced by decisions made about the way that indicators are defined. More specific indicators will typically be more reliably scored. Contrasting the indicators from two of the policy datasets surveyed illustrates these points. ATOP indicators are defined in such a way that most scoring is achieved based on direct observation without precoding aggregation, and using sources that can be easily verified by data users. Indicators are scored in all but a few cases by a direct reading of the treaty document, and they have been defined to systematically record information about the institutional design of alliances as established in the text of treaty documents. The level of generality of the indicators is therefore intentionally closer to the level

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of generality of the information provided in the source documents than to the level of generality at which the background concept is defined. Likewise, indicators are worded in a highly specific manner to record the information contained in the treaties in a clear, systematic way. The coder need not make subjective judgments or assign ordinal ranking scores relative to an external framework. Likewise, the treaty documents are made publically available. In contrast, SCIP indicators of replacement levels provide an example of indicators that require pre-coding aggregation and produce scores that are difficult to assess. SCIP includes other indicators identifying the conditions of the insurance programs, but there is insufficient information regarding how these indicators combine to generate the replacement level scores and whether the other indicators are necessary or sufficient to calculate the replacement levels. Because replacement levels are among the most important indicators produced by the SCIP, the lack of information about how they are constructed for each unit is problematic from a standpoint of data quality. The data user cannot verify and must instead trust that the data collection team has accurately identified the proper equation for calculating the replacement levels and utilized valid information in each calculation to achieve accurate scores. To the extent that scoring options are not precisely defined, ranking procedures can also increase the likelihood of measurement error. Ranking and scaling devices may require that a scorer make a subjective decision about how a unit should be scored relative to the way a similar indicator was scored for other units, or relative to the scorer’s own idea of what the range of possible outcomes might be for a given indicator. Scores that provide rankings based on a variety of factors that are not individually coded can also be more difficult to verify, reducing the likelihood that errors made in scoring will be caught and remedied. For example, the EPL dataset includes an indicator of the possibility of reinstatement following an unfair dismissal. This indicator would be initially scored as zero if there is no right or practice of reinstatement, 1 if reinstatement rarely or sometimes is made available, 2 if reinstatement fairly often is made available, and 3 if reinstatement is (almost) always made available. This score is then multiplied by 2 to place the scores on the 0 to 6 strictness scale.121 There is no way to confirm that scorers were consistent in the interpretation of terms such as “rarely” and “fairly often” across units,

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as these terms are not defined in the publicly available information provided about how indicators were scored.122 The user of the data must look at more detailed country notes in order to interpret why, for example, a score of 2 for fairly often was chosen above a score of 1 for rarely or sometimes made available.

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Post-coding aggregation and content validity The ability to reconstruct aggregate measures to suit specific research projects is of crucial importance to promoting valid measurement. Adcock and Collier (2001) argue that systematic measurement error, or bias, is minimized to the degree that there is correspondence between the systematized concept and the measure. Content validation is an assessment of the degree of correspondence between the systematized concept and the measure. Adcock and Collier describe this validation test as assessing the "adequacy of content" of a measure, which should be "assessed through two further questions. First, are key elements omitted from the [measure]? Second, are inappropriate elements included in the [measure]?" (p. 538, in accordance with the distinction that I made previously in this chapter between measures and indicators, I have substituted the term ‘measure’ where the authors originally used the term ‘indicator.’) Pre-coding aggregation may prevent a data analyst from assessing whether or not there are any key elements omitted from a measure, or whether there are any inappropriate elements included in a measure. Where transparency is achieved in pre-coding aggregation, the omitted element or inappropriate element may be identified, but little can be done to adjust the measure.123 Only through post-coding aggregation of more specific indicators can an omitted element be easily incorporated or an inappropriate element removed from a measure. For example, consider a data analyst who is interested in testing whether antidiscrimination laws contribute to more progressive migrant integration policies. Because the MIPEX index incorporates anti-discrimination laws, this index cannot be used as a measure for migrant integration policy for this analysis. However, the MIPEX index is constructed in a transparent and reversible way, allowing the analyst to easily reconstruct an alternative measure excluding the indicators relating to anti-discrimination law.

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POST-CODING AGGREGATION IN PRACTICE The methodological survey of four policy datasets identifies whether each policy dataset has been assembled utilizing the methodological principle of post-coding aggregation. This requires an observation of the way that the three aggregation decisions outlined above have been resolved in each data collection project. The level of generality at which indicators are defined, the scoring method that is identified for each indicator, and the method of construction of any aggregate indices are considered. A dataset is identified as utilizing a post-coding methodology of aggregation if all three aggregation decisions are made in accordance with a principle of post-coding aggregation. Table 4.2 provides a summary of the way that aggregation decisions are resolved in practice and identifies the method of aggregation primarily utilized for each of the four data collection projects. All four of the datasets adhere to a principle of post-coding aggregation with regard to the level of generality at which indicators are defined. Each dataset disaggregated the background concept of interest into indicators that were defined at a much lower level of generality. However, the four datasets surveyed vary in the methods used in scoring indicators. The EPL and MIPEX datasets both incorporate a ranking process into the scoring methodology, while the ATOP and SCIP datasets require only direct observation on the part of coders. The scoring options for EPL indicators were defined with sufficient precision to retain direct observations, while the scoring options for MIPEX indicators were less precisely defined such that there was considerable data loss. Three of the four data collection projects constructed aggregate indices. The Strictness of Employment Protection Index provides the best example of an index that adheres to a methodological principle of post-coding aggregation. The index combines in a transparent and reversible way separately coded indicators that require little pre-coding aggregation. The MIPEX index also is constructed in a transparent and reversible way, but the indicators that make it up require substantial pre-coding aggregation due to the scoring methodology utilized for most indicators. In contrast, the SCIP indicators that calculate replacement levels provide an example of aggregate indices that are not constructed in a transparent way and that cannot be reconstructed from the other related indicators included in the dataset.

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The ATOP and EPL data collection projects primarily adhere to a methodology of post-coding aggregation, and the SCIP and MIPEX data collection projects primarily adhere to a methodology of precoding aggregation. Notwithstanding these classifications, each of the four datasets has at least some attributes that align with a principle of post-coding aggregation. In Appendix D I discuss in more detail these elements for each dataset to illustrate the decisions that influence the method of aggregation in the data collection process. SOME ISSUES RAISED BY POST-CODING AGGREGATION

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Three important sets of issues are raised by adherence to a methodological principle of post-coding aggregation. The first issue relates to whether a principle of post-coding aggregation places an unduly high demand on independent data collectors in terms of time and resources. The second issue relates to the level of analysis and how sub-national variation can hinder post-coding aggregation. A final issue relates to the ambiguity of aggregation when data collection projects utilize experts as sources. Each of these issues will be addressed in turn. The issue of limited resources An important issue relates to whether adherence to post-coding aggregation increases the cost and the time involved in collecting new data, which is already an endeavor strained by resource constraints. Nicoletti and Pryor (2006) describe the challenges to constructing comparative policy measures in the following way: On an aggregative level they are extremely expensive to make since they require assembling a huge database and assistance from a great number of governments and lawyers to clarify particular points of law. In most cases, only international organizations or large and generously financed teams have the resources and the clout to obtain such data and carry out such a task.124

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Methodology of Aggregation:

The method of Constructing an aggregate constructing aggregate index is not an objective of indices the project. Rather, the primary objective of the project is to disaggregate the concept of military alliances.

3

Post-coding Aggregation

Indicators are scored based on direct observations

The methodology of scoring assigned to indicators

2

ATOP

The level of generality Over 100 indicators are at which indicators are defined to collect defined information on five subcategories relating to the background concept of military alliances.

1

Aggregation decision

Post-coding Aggregation

The Strictness of Employment Protection Index is constructed in a transparent way using only indicators available in the dataset and publically available instructions.

Indicators are scored with a ranking procedure. The direct observations that form the basis of the rankings are largely preserved in the data because of the precision with which scoring options are defined.

25 indicators are defined to collect information on three sub-policy areas relating to the background concept of employment protection regulation.

EPL

Pre-coding Aggregation

The Migrant Integration Policy Index is constructed in a transparent way using only indicators available in the dataset and publically available instructions.

Indicators are scored with a ranking procedure. The direct observations that form the basis of the rankings are not preserved in the data because of the scoring options are not defined with sufficient precision.

142 indicators are defined to collect information on four policy dimensions that are defined for each of six policy areas that relate to the background concept of migrant integration policy.

MIPEX

TABLE 4.2: Classification of the methodology of aggregation for four policy datasets

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SCIP

Pre-coding Aggregation

Replacement levels are constructed in a way that is not transparent, and that cannot be replicated using the indicators provided in the dataset.

Indicators are scored based on direct observations

Two sets of indicators, totaling almost 300 indicators, collect information on four public insurance programs that relate to the background concept of welfare policy or social citizenship.

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Although there are examples included in this chapter of academics that have carried out independent data collection, it is undeniable that the resources required to complete a cross-national, comparative dataset are difficult to obtain. Graduate students and young faculty can be deterred from working on data collection projects because they may take years to complete and require lengthy and highly competitive application processes for funding. There are considerable obstacles to obtaining external funding for large-scale data collection projects. A researcher needs to make a convincing case that interesting findings will come from the data before it is even collected. This has become even more difficult as the obstacles to conducting valid causal inference using time-series, cross-sectional data have become well known. I argue that a methodology of post-coding aggregation does not require substantially more resources than what is already demanded by the size of a data collection project. A methodology of post-coding aggregation seeks to reduce data loss, but it does not demand that additional data be gathered. What is demanded is that data that would be aggregated to determine scores for more general indicators be individually scored as more specific indicators. Therefore, post-coding aggregation may increase the initial time investment required by researchers to define the indicators and scoring methodology for a data collection project. It will often be useful for researchers to conduct a pilot phase of data collection in order to refine indicator definitions in light of difficulties encountered when attempting to score indicators using available sources. However, additional upfront costs may be surpassed by efficiency gains in the process of scoring indicators. Scoring can be completed more quickly when coders are not required to aggregate information or make subjective decisions when determining the score for each unit. A consultation process may also be initiated with researchers currently working in the substantive area that will benefit from new data. The consultations ensure that data collectors are made aware of what information researchers require in their research projects, and what existing data is currently used or being collected. However, the needs of researchers should not determine the level of generality at which indicators are defined. The needs of researchers at present are only part of what data collectors should consider in the collection of new data. Future researchers will continue to use high quality data for years, and the needs of research will change during that time. Data

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collectors should seek to maximize the flexibility of data use in order to best meet future as well as current needs of the research community. A consultation process may be costly to the extent that it requires longdistance collaboration or workshops to bring scholars together. However, these costs are associated with independent data collection generally, and they are not brought about by adherence to post-coding aggregation. A methodology of post-coding aggregation may increase the technological demands on data collectors who must be able to store and provide access to a much larger amount of data. However, academic institutions increasingly provide technological support to academic researchers, and younger scholars often bring additional tech savvy to collaborative data collection projects. Adherence to post-coding aggregation may also require that data collectors provide more comprehensive documentation of the research design underlying new data than is commonly presented in codebooks and methodological appendices. Because the indicators will be more specific than many concepts of theoretical interest, data collectors will want to suggest methods for aggregating more specific indicators to construct general measures of importance to researchers. Suggested methods of aggregation demonstrate the potential uses of new data in a way that makes the benefits of the new data apparent to data users. However, post-coding aggregation enables data collectors to convey that these are merely suggestions. Data users retain the ability to combine more specific indicators in whatever way best suits their own research design. I argue that any additional costs brought about by adherence to a methodological principle of post-coding aggregation are outweighed by the added value received in both data quality and flexibility of data use. It may be that researchers working under strict resource constraints may feel that a trade-off must be made between the desired size of a project and the methodology of aggregation that will be utilized. Where this is the case, the methodology of aggregation rather than the breadth of coverage should be privileged. For example, it is preferable to collect data on a smaller range of policy areas in such a way that achieves the objectives of flexibility of data use and data quality, rather than to construct data on more policy areas that is appropriate for only a limited number of uses or that requires so much pre-coding aggregation that the validity of the data cannot be verified. Data collection projects

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can be expanded as additional resources become available. However, if data is collected in such a way that imposes rigid assumptions upon data analysts, then the data will need to be re-collected to suit most future research needs. The issue of sub-national variation

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Post-coding aggregation can be hindered when a background concept relates to phenomena occurring at distinct levels of analysis. This arises as an obstacle, for example, in coding policies that vary across states or provinces in federal countries. This obstacle is in some ways unavoidable, but it can be addressed in one of three ways. The first method is to construct paired datasets that provide sub-national and national level data. The second method is to carefully define indicators to ensure that only policies that do not have sub-national variation across the universe of cases are included. This basically requires limiting the scope of the dataset to include only those indicators for which post-coding aggregation can be achieved. A final option is to include additional indicators by utilizing pre-coding aggregation, but with additional publically available information describing the method of aggregation utilized in order to construct the relevant indicators. Experts as sources The use of expert consultants as sources of information poses interesting challenges to the methodology of post-coding aggregation. If faced with a choice between utilizing a variety of textual sources and asking an expert, it might appear that the choice of an expert is a more expedient way of achieving indicator scores that require little precoding aggregation. However, best practices should require that experts provide documentation of the sources referenced, or that the data collection team identify sources that can verify all, or at least most, of the empirical observations provided by experts. The expert questionnaire or records of the interview should become part of the sources made publically available to data users, especially when the information cannot be confirmed by alternative sources. While such strict protocols for expert sources are rarely adhered to in practice, obtaining such information should be part of the due diligence required

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of data collectors. Data collection projects that provide only the names of experts, or only the names of the organizations or government agencies where experts have been consulted, do not provide sufficient information for researchers working in years ahead to assess the validity of the scores for indicators contained in a dataset. An additional question may be raised about the level of generality at which experts should be asked to provide information. Is there a risk that adherence to a methodology of post-coding aggregation will result in less accurate measurement? This question stems from the possibility that experts may provide more accurate answers to general questions than might be constructed from the aggregation of their answers to more specific questions. Experts may have an accurate sense of general features of their field, even when they are uncertain about specific variation. Alternatively, experts answering more general questions may draw upon types of information unanticipated by researchers, and that would otherwise be excluded from measures constructed using more specific indicators defined by the data collector. These hypothetical scenarios suggest that there may be a trade-off between maximizing the flexibility of data use and achieving data quality when using expert sources. I argue that data quality is far more likely to be enhanced than hindered by adherence to a methodology of post-coding aggregation when relying on expert sources. It is important to consider that experts speaking on the basis of their own experience or recent research may have biased impressions of the general features of their field. An expert may over-emphasize the importance of some factors that have been unusually present in their own experience, while underemphasizing factors that have featured less prominently. A data collector should design a questionnaire or interview that can help to reveal what biases exist in the information provided by experts. This can best be achieved by asking experts to provide answers to a well-developed set of specific questions that cover both the variety of features relevant to the phenomenon and the experience and sources that the expert is drawing upon to answer these questions. To account for factors overlooked by the data collector, experts can be directly asked whether there are any additional topics that are not addressed in the questions. Most importantly, general questions should be asked in addition to, not in place of, specific questions. Inconsistencies between scores obtained from the aggregation of more specific answers and scores

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obtained from answers to more general questions can ideally be resolved through follow-up questionnaires or interviews. Where this is infeasible, inconsistencies can be acknowledged, and both sets of answers can be used to produce two distinct measures of the same concept. CONCLUSION

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In this chapter, I identified a blind spot in methodological discussions of measurement, which have failed to acknowledge the growing importance of independent data collection. I have demonstrated the importance of developing methodological principles to guide data collectors by considering the way that aggregation decisions made at the stage of data collection can influence measurement in data analysis. A methodological principle of post-coding aggregation has been advanced that increases the flexibility of data use and improves the quality of data. Measurement methodology will be improved by the continued development of methodological principles for independent data collection. Already identified in this discussion are important issues relating to sources and scope that also influence the dual objectives of flexibility of data use and data quality.

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APPENDIX A

Measuring Skill Bias: Important issues encountered in creating the skill bias measure

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INTRODUCTION In this Appendix, I discuss important issues encountered in creating the skill bias measure utilized in Chapter 1. I discuss the following issues. First, I discuss how I resolved the issue of a time gap between the date of policy enactment and the date that a policy went into effect. Second, I discuss un-capped categories of entry and significant legal exemptions to quotas. Third, I discuss how direct policy measurement of temporary migrant admissions is hindered prior to 1990. Fourth, I discuss the scope of the categories of entry included in the measure and justifications for categories of entry excluded. Fifth, I discuss categories of entry that have overlapping selection criteria. Finally, I discuss why I do not consider foreign students as skill-based migrants. Time gap between the date of enactment and the date in effect I chose to code policies according to the date of enactment, rather than the date the law went into effect. In other words, a category of entry is first coded in the calendar year in which it was initially enacted (or in the first year in the dataset if it was enacted in a year prior to that year), through the last calendar year in which it was in effect (or through 2008 if it remained in effect in that year, which is the last year contained in the dataset). In the case that a policy was replaced by a new policy, I 131 Challen, Suzanna. Measuring Change in Immigration policy, LFB Scholarly Publishing LLC, 2013. ProQuest Ebook

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Appendix A

coded it only until the year in which the new policy was enacted, even if in actuality the old policy was in effect in that year prior to the new policy going into effect. This decision was made in order to allow the comparison of policy decisions with the economic conditions at the time the policy decision was made, rather than at the time the policy came into effect, which may be somewhat later. As a result of this decision, however, some programs may appear to be in effect in the data for more years than they actually were in effect. For example, the Immigration Reform and Control Act of 1986 established the N-5 Program for admitting immigrants disadvantaged by the 1965 amendments to the Immigration and Nationality Act. In law, it was established to be in effect for the fiscal years 1987 and 1988. This program was extended in 1988, to be in effect for fiscal year 1989 and 1990, and then again in 1990 to be in effect for fiscal year 1991. Because I am coding date of enactment according to calendar years, I show this category of entry as existing from 1986-1991, rather than from 1987-1991. Likewise, I show the change in quota that was applied by the 1988 extension as occurring in 1988, the date it was enacted, rather than in 1989, which was the date it went into effect. I made two adjustments when implementing this method, based on what seemed to be most reasonable and to best approximate the intention of the method, given unusual circumstances. First, the Immigration Act of 1990 created a new diversity lottery to begin in U.S. fiscal year 1995. It also created a transitional program for fiscal years 1992-1995. Rather than double code the transitional program and the permanent program from 1990 -1995, which would have made it appear that the quota was twice as large (summing the quota for the transitional category of entry and the permanent track), I instead combined the transitional and permanent categories of entry into one track, enacted in 1990, with the quota for the transitional program in place from 1990-1994, and the quota for the permanent program in place from 1995 onwards. Second, Public Law 109-13, enacted on May 11, 2005, included a provision that enabled returning H-2B seasonal workers for nonagricultural labor admitted in the previous three fiscal years to be exempted from the annual numerical limit on H-2B workers. The legislation was to be enforced as if it had been enacted prior to fiscal year 2004 through 2006. Then on October 17, 2006, Public Law 109-

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364 extended the exemption through fiscal year 2007. In my dataset I have estimated the quota for 2005, the year in which the program was enacted, despite the language that indicated it should be applied as if it had been enacted in time for fiscal year 2004. Likewise, because the exemption expired in fiscal year 2007, I show the program as no longer in effect in 2008 in my dataset, despite the few months of the fiscal year 2007 that do overlap into calendar year 2008. I made this coding decision so that it would be visible that the program ended, given that 2008 is the last year in the dataset, and also because DOS flow data shows only a small number of H-2R visas were issued in 2008.

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Un-capped categories of entry and significant legal exemptions to quotas For a given country year, the size of a category of entry is determined by the quota size allotted to that category of entry. However, some categories of entry do not have nationally defined quotas, either because they are numerically unlimited, or because there are exemptions to the quota that may allow actual admissions to be at a significantly higher level than the established quota. For these categories of entry, it is necessary to estimate the expectation that policymakers have about how many migrants are likely to be admitted through that category of entry. This rests on the reasonable assumption that policymakers would cap a given category of entry if it was anticipated that there would be more eligible individuals seeking to obtain entry through that category of entry than there is a political consensus to support. When possible, estimated quotas were calculated by taking the maximum of actual migrant admissions from that category of entry over the previous five years. The data used for the estimates of quotas for temporary migrant categories of entry is from the U.S. Department of State Visa Office. It was selected based on the methodology of collection which counts visas issued, rather than admissions (which can double count individuals who come and go across borders).125 However, for the immediate relatives category of entry within permanent immigrant admissions, annual data including both first-entry and within country adjustment of status for legal permanent residents from the Department of Homeland Security, Office of Immigration

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Statistics is used.126 Admissions data is preferable for assessing immigration in-flows because they combine visas issued abroad with the number of individuals adjusting their status within the United States. For newly enacted, uncapped categories of entry, I take the maximum flow observed within the first five years following enactment of the category of entry as the estimated quota for the year of enactment through the fifth year. Following that year, the original methodology is used. This was unnecessary in measuring skill bias for the U.S. permanent immigrant admissions program, because the only category of entry that required an estimated quota, that of immediate relatives of U.S. Citizens, was established more than five years before the time period under consideration. This method was used on 16 temporary categories of entry enacted in the years 1990 and after, out of a total of 25 temporary categories of entry that required estimation. In estimating quotas for uncapped categories of entry and categories of entry with exemptions, there are three cases of importance to note. First, for the H-1B visa, since 2000, exemptions to the legally defined quota have been estimated due to exemptions granted in legislation alongside stated quotas. Specifically, for years 2004-2008, I have increased the H-1B cap of 65,000 by 20,000 for exemptions for U.S. earned masters or higher degrees, as stated in the law. In addition, I have added an additional 15,000 for exemptions for employees of higher education or nonprofit entities. For years 2000 – 2003, I have increased the H-1B cap of 195,000 by 15,000 for exemptions for employees of higher education or non-profit entities. Using published data in a DHS CIS report127 on H-1B petitions, I used data of approved petitions for initial employment by major occupations groups (excluding petitions for continuing employment) for 2004 and 2005, including the occupational groups identified in the report as satisfying the requirements of exemption: education; museum, library and archival sciences; and religion and theology. The number of petitions approved for initial employment in each of these groups summed in both years to be approximately 15,000. Therefore, from 2000-20003, I list a quota for the H-1B category of entry of 210,000 rather than 195,000, and from 2004-2008, I list a quota of 100,000 rather than 65,000. Second, the E-3 visa also maintains since its enactment in 2005 a quota exemption for returning E-3 visa holders. A similar measure was

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added in the same year to the H-2B category of entry, allowing returning H-2B visa holders to not be counted against the annual quota. Therefore in each case I created two separate categories of entry. For E3 visas, I created one category of entry for annually capped E-3 visas with the quota established in law, and the other for returning E-3 visa holders, which is uncapped. I similarly divided H-2B and returning H2R categories of entry, and applied the quota established in law to the H-2B category of entry only. For the uncapped channels of returning E3 and returning H-2R visa holders, I estimated the quota using DOS flow data according to the methodology just described. Third, the TN visa for NAFTA professionals is likely underestimated because Canadians are not issued visas by the Department of State, which is the source of the permits-issued flow data used to estimate the quota for this category of entry.

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Direct policy measurement of temporary admissions hindered prior to 1990 The purpose of creating the skill bias measure is to have a direct measure of policy, that is distinct from outcomes or flow data. However, where categories of entry remain uncapped, quotas are estimated using flow data. In the area of U.S. permanent admissions, only one out of twenty total categories of entry required an estimated quota. However, in the area of U.S. temporary admissions, prior to 1990 there are no categories of entry that are given numerical limits. A measure of skill bias for U.S. temporary admissions according to this methodology would therefore rely entirely on estimates from flow data prior to 1990. Because this would not achieve the purpose of the measure, the skill bias measure has not been calculated for years prior to 1990. In the case of U.S. temporary migrant admissions, the skill bias measure is only calculated from 1990 - 2008. In 1990, quotas were established for several major categories of entry, including temporary workers for specialty occupations (the H-1B visa) and seasonal workers in non-agricultural labor (the H-2B visa), and in addition in that year a quota was established (to be repealed a year later) for internationally recognized athletes and entertainers (the P-1 and P-3 visas). The quotas for specialty workers (H-1B) have been

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adjusted several times since 1990, and exemptions have been added to this category of entry and also to the H-2B track. Most of the categories of entry added since 1990 have been given numerical limits, including that for Australian nationals in specialty occupations (the E-3 visa), nurses (the H-1C visa), Irish Peace Process Program participants (the Q-2 visa), and law enforcement related visas (the S-5, S-6, T-1 and U-1 visas). Despite these policy changes, since 1990 many categories of entry considered remain numerically unlimited. Therefore, although the measure of skill bias for U.S. temporary admissions since 1990 is a more direct measure of policy than would be the case using flow data alone, it nevertheless relies heavily on outcome data. Figure 1.5 showed however that the major changes observed in this data are due to policy change to numerically limited categories of entry, rather than through change in unlimited categories of entry for which quotas are estimated using flow data.

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The scope of the categories of entry included in the measure and justifications for categories of entry excluded In my analysis, I consider 20 categories of entry for permanent immigrant admissions, and 34 categories of entry for temporary migrant admissions. (All 54 categories of entry are listed in Table A.1 with brief descriptions.) For both permanent and temporary admissions programs, in developing my measure of skill bias I chose to consider only entry categories of entry for principal applicants, and to exclude additional categories of entry for accompanying family members of applicants. There are only a few cases where this was impossible because the law does not distinguish either the quota or the visa type for accompanying family from that of the principal applicant. This was the case for temporary E visas for treaty traders and investors, and also for the quota for Q-2 visas for participants in the Irish Peace Process Program. In developing my measure of skill bias for U.S. permanent admissions, I excluded all categories of entry for refugees, asylum seekers and other humanitarian migrants. The quota for refugee admissions is set annually by the President, and therefore occurs through a distinct decision making process. I also excluded all “special immigrant” categories of entry, with the important exception of the

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categories of entry for “Western Hemispheric” immigrants prior to 1976. I did not include legalization programs as categories of entry. In developing my measure of skill bias for U.S. temporary migrant admissions, I excluded all categories of entry for visitors, people in transit, and crew members because these are not intended for short-term residence or work as temporary migrants, but rather for short-term stays of 90 days or less. An individual in the United States on a visitor visa cannot adjust to another temporary migrant status from within the country. Likewise, also excluded are all categories of entry for media correspondents, diplomatic personnel and employees of NATO and international organizations. There was no permits-issued data available from the DOS for the category of entry for professionals arriving under the Canada-U.S. Trade Agreement was in effect until the passage of NAFTA. The reason for this is that Canadian citizens are not issued visas for this track, and are allowed to enter without visas so long as they qualify. Therefore, this category of entry is excluded from the analysis, but this exclusion only affects the years 1988-1992.

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Categories of entry that have overlapping selection criteria For categories of entry that have overlapping selection criteria, incorporating both skill and non skill-based factors, it must be determined which type of selection criteria is primary. While I have attempted to make these determinations as systematically as possible, the results will be open to debate. Table A.1 displays each category of entry included in the measures of skill bias for both temporary and permanent admissions and indicates whether the category of entry is identified as a skill-based category of entry and/or a high-skill-based category of entry, and provides a short justification for these important coding decisions. Should foreign students be considered as skill-based migrants? I have not included selection criteria relating to enrollment in an educational institution as skill-based criteria, thus I do not include foreign students in the subset of migrants who are selected primarily based on their skills. This is generally because the U.S. temporary

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admissions program tends to operate by providing visas to students who have already been admitted by U.S. educational institutions, which must meet some standards but which are not necessarily required to select students based on demonstrated skills. Likewise, employment of foreign students is generally restricted, and therefore it can be assumed that the benefit of skills obtained by a foreign student during the education is not intended to directly benefit the U.S. economy. (Table A.1 presents further justification for these coding decisions. See specifically the F-1, F-3, M-1, M-3, H-3 and J-1 categories of entry.)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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Table A.1: Senate votes on U.S. migrant admissions and special interest group preferences (Continued)

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APPENDIX B

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Constructing a ratio of bills passed to bills proposed

In this appendix, I describe the method by which I constructed indicators reflecting the ratio between the number of bills passed to bills proposed in the U.S. Senate. The level of conversion from bills to law indicates the level of difficulty achieving policy change in the area of U.S. migrant admissions. A ratio of 1 suggests that all policy proposals are passed into law, or ease of policy passage. The lower the ratio, the more difficult policy passage appears. Likewise, to the extent that there is a high number of proposals for change, but few or no policy proposals pass (a low ratio of bills proposed to bills passed), policy change may be described as infrequently successful. If no policies pass, the ratio is zero. In order to construct this indicator, I had to first identify all bills considered in the U.S. Senate from each Congress that relate to U.S. migrant admissions policy. Specifically, I had to identify the subset of bills affecting the number of categories of entry or the numerical limitations placed on those categories of entry. Unfortunately there is not already a classification of bills that provides a sufficient level of detail to identify the subset of bills relating to U.S. migrant admissions policy. Changes to migrant admissions policy have occurred as amendments to appropriations bills or other bills enacting treaties, for example, which are not primarily related to immigration. Even limiting the search to bills considered by the House and Senate Judiciary Committees left out a number of important bills. 153

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To overcome these obstacles, I obtained the full text of all bills in electronic format and conducted a key word search that successfully identified all events of successful policy change, in addition to what I expect to be entire subset of bills considered by the Senate on the subject of migrant admissions policy during each Congress. My search is limited to the 101st – 110th Congresses because the full-text of bills is only available in electronic form through the Library of Congress THOMAS database and LexisNexis Congressional since the 101st Congress. Unfortunately, the query options provided by THOMAS and LexisNexis also did not enable me to isolate the universe of bills that I needed to search. Instead, I consulted data from the Congressional Bills Project, in order to identify the bill numbers of all public bills from each session that either passed the House or were introduced in the Senate. This excludes all bills that were introduced in the House but never considered by the Senate. I then retrieved the full text of each of these bills from THOMAS. The total number of bills searched was 29,483. By creating a database to hold the full text of each bill, I was then able to conduct a key word search. Fortunately, U.S. law is consistent in the vocabulary used when drafting legislation regarding U.S. migrant admissions. I searched for all bills including “immigrant” (which also picked up on the word nonimmigrant) and “admission” (which also included admissions) or “visa status.” In total 204 bills were identified as containing these key words. The addition of visa status to the search retrieved 6 of the 204 bills, which included one bill that was relevant and that passed. All other bills contained the words immigrant or nonimmigrant and admission. It was then necessary for me to read the text of each of the 204 bills identified to identify which bills were relevant in accordance with my definition of U.S. migrant admissions policy. Specifically, I looked to see if categories of entry or numerical limitations for the categories of entry were affected by the policy proposals contained in the bill. Many of the bills identified were not relevant because they dealt with immigrant admissions for asylum, special immigrants, or border security procedures for immigrant admissions, for example. Also irrelevant were bills that adjusted procedures relating to migrant admissions that left unchanged the categories of entry or numerical limitations. In total, I identified 83 bills as meeting my definition.

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For each of the 83 bills, I recorded the Congress in which the bill was considered, and whether the changes proposed in the bill related to permanent immigrant admissions, temporary migrant admissions or both. I also recorded whether or not the bill became law. This information enables me to calculate the ratio of policy proposals to policies passed. I was not able to separately record which policies passed the Senate but did not pass in the House. As a result, I do not account for those bills passed by the Senate but not by the House in my count of bills passed, only those bills that passed in both chambers.

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Table B.1: Bills proposing changes to U.S. migrant admissions policy since 1989

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Table B.1: Bills proposing changes to U.S. migrant admissions policy since 1989 (Continued)

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APPENDIX C

Measuring policymaker preferences

THE MEASURE

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I construct a classification of senator preferences based on whether a senator’s votes most closely align with the expressed preferences of employers, unions, immigrant advocacy groups or anti-immigration groups. If there is not a clear alignment between a senator’s voting record and an interest group, then the senator is classified as a swing voter. In combination these measures produce an estimate of the distribution of preferences in the Senate during each Congress. Assumptions The measurement methodology requires two important assumptions. First, I make the reasonable assumption that senator preferences are stable over time. Second, I assume that senator preferences are revealed through their voting behavior. Strategic voting is an important challenge to this second assumption, and I discuss the steps that I have taken to minimize measurement error as a result of strategic voting at the end of this appendix. Scope I consider Senate votes relating to U.S. migrant admissions during the 106th, 109th and 110th Congresses, which constitute three of the four case studies considered in this chapter. I exclude voting records from the 101st Congress because of the level of turnover in Senate seats. Between 1990 and 2000, 49 Senate seats change. In contrast, between 159

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the 106th Congress and the 109th Congress (2000 to 2005) there is a change in only 28 Senate seats, and between the 109th and 110th Congress (2005 to 2007) there is a change in only 12 Senate seats. In total, I classify the preferences of 140 senators in office during the 106th, 109th or 110th Congress. Of these 140 senators, 96 of the senators were in office for at least two sessions and 64 senators were in office during all three sessions. Each senator’s preference is classified only once, on the basis of all votes selected for consideration in which he or she participated across the three sessions. MEASUREMENT METHODOLOGY

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Constructing a measure of senator preferences based on roll-call voting data requires several steps. First, Senate votes must be identified on which special interest groups have clear preferences. Second, classifiers must be defined in such a way that outputs a score for each senator with respect to the alignment of the senator’s vote with each of the four interest group positions. Third, all scores produced by each of the 21 classifiers must be aggregated, and the maximum and minimum score for each senator observed. Each of these steps is described in more detail. Senate votes and interest group positions For each Congressional session, I identified the roll-call votes addressing U.S. migrant admissions policy on which special interest groups have observable preferences. I identify the positions of each special interest group for each vote, indicating whether these preferences are clearly observable, likely, or unclear. In the 106th Congress I consider Senate votes 257 and 260. In the 109th Congress, I consider Senate votes 97, 98 and 102. In the 110th Congress I consider Senate votes 174, 175, 177, 180, 203, 204, 206, 229, 230, 231, 232, 233, 234, and 235. Table C.1 presents a description of each Senate vote considered, important aspects of the political context in which voting occurs, and the special interest group positions identified with respect to each vote.

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Defining classifiers I construct 21 classifiers that assign scores for each senator based on their votes. Classifiers consider Senate votes both in isolation and in combination with one another. Each classifier produces four scores for a given senator, one score with respect to alignment with each special interest group position. A score of +1 indicates that the Senate vote aligned with a special interest group’s preference, and a score of -1 indicates that the Senate vote opposed the preference of a special interest group. A score of 0 indicates that the vote does not clearly indicate either alignment with or opposition to an interest group. Table C.2 presents each of the 21 classifiers and the outputs associated with each classifier.

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Constructing measures of senator preferences By summing across the scores from each classifier, each senator receives an aggregate score estimating alignment with each type of special interest group. A senator is classified as having preferences aligning with the special interest group for which he or she has the highest aggregate score. Senators who cannot be classified based on their maximum score are identified as swing voters, meaning that they are not clearly aligned with any particular group. I also record whether there is a clear minimum score, indicating the group with which a senator is most opposed. A special condition occurs when senators have identical scores for two groups, such that they have a tie for which interest group they are most closely aligned with. When generating distributions of senators, these senators are classified as aligning with those two groups if these groups are in a coalition but as swing voters if the groups are in opposition to one another. Table C.3 lists each of the 140 senators and the preference classifications assigned to each. To estimate the level of confidence with which measures should be interpreted, I include additional information including the total number of nonzero scores contributed by all classifiers that operate on this senator’s score and the difference between the number of classifiers that produced nonzero scores and the absolute value of the sum of the scores for each classifier, which will be zero when there are no conflicting scores across the classifiers for a

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given senator. Confidence in the measures is improved to the extent that measures are based on a larger number of nonzero scores and a smaller number of conflicting scores across each of the classifiers. The median number of positive or negative scores attributed to a given senator using this classification scheme was 7, within a range of 0 to 15. 32 of the 141 senators are positively or negatively scored for only one classifier, and 2 senators have no positive or negative scores. Either these senators did not vote or they voted in ways that did not reveal clear preferences. These 34 senators do not have a clear maximum score, and are therefore among those senators who are classified as swing voters. Excluding these 34 senators, 7 was also the most frequent number of nonzero scores contributing to a senator’s classification.

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THE DISTRIBUTION OF SENATOR PREFERENCES In combination these measures also produce an estimate of the distribution of preferences across the senators in office during each of the three Congresses. I consider the distribution of Senate votes for each Congress under four hypothetical political scenarios of interest group coalitions. The first scenario is a bill that is proposed by a coalition of employers and immigrant advocacy groups, which is countered by a coalition of unions and anti-immigration groups. The second scenario is bill proposed by a coalition of employers and immigrant advocacy groups, which is opposed by anti-immigration groups. In this scenario, unions do not articulate clear policy preferences. The third scenario is a bill proposed by a coalition of employers, immigrant advocacy groups and unions (the proimmigration coalition, as defined in my theory), which is countered by anti-immigration groups. A fourth scenario is a bill proposed by a coalition of anti-immigration groups and employers, which is countered by a coalition of immigrant advocacy groups and unions. These are the most likely coalitions that may arise in the politics of migrant admissions policy. Other coalitions, such as between anti-immigration groups and immigrant advocacy groups or between unions and employers, are highly unlikely and therefore have not been considered.

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Scenario 1: A bill is proposed that is supported by a coalition between employers and immigrant advocacy groups and opposed by a coalition between unions and anti-immigration groups

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.

The number of senators supporting the bill is determined by taking the sum of the senators who are classified as most closely aligning with employers or immigrant advocacy groups, senators classified as equally most aligned with both groups, and senators classified as swing voters that are most opposed to the preferences of anti-immigration groups. The number of senators opposing the bill is determined by taking the sum of senators who are classified as most closely aligning with antiimmigration groups or unions or as equally most aligned with both groups. The swing voters are all of those senators who are not clearly aligned with any group, in addition to those senators equally most aligned with two groups that are not in coalition, such as employers and anti-immigration groups. Although some Swing voters can be observed as being most opposed to either anti-immigration group preferences or immigrant advocacy group preferences, these votes cannot be assigned to either coalition with certainty because of the possibility that a senator most opposed, for example, to immigration advocacy group preferences may still vote in favor of a bill supported by a coalition of immigration advocacy groups and employers.

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Scenario 2: A bill is proposed that is supported by a coalition of employers and immigrant advocacy groups and opposed by antiimmigration groups. Unions are neither openly supportive nor openly opposed to the bill.

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The number of senators supporting the bill is determined by taking the sum of the senators who are classified as most closely aligning with employers or immigrant advocacy groups, senators classified as equally most aligned with both groups, and senators classified as swing voters that are most opposed to the preferences of anti-immigration groups. The number of senators opposing the bill is the number of senators who are classified as most closely aligning with anti-immigration groups. The swing voters are all of those senators who are not clearly aligned with any group, in addition to those senators aligned with unions and senators equally most aligned with two groups that are not in coalition, such as employers and anti-immigration groups. Scenario 3: A bill is proposed that is supported by a coalition between employer, immigrant advocacy groups and unions and opposed by anti-immigration groups

The number of senators supporting the bill is determined by taking the sum of the senators who are classified as most closely aligning with employers, immigrant advocacy groups or unions, senators classified as equally most aligned with any combination of these groups, and senators classified as swing voters that are most opposed to the preferences of anti-immigration groups. The number of senators opposing the bill is the number of senators who are classified as most closely aligning with anti-immigration groups. The swing voters are all

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of those senators who are not clearly aligned with any group, in addition to senators equally most aligned with two groups that are not in coalition, such as employers and anti-immigration groups.

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Scenario 4: A bill is proposed that is supported by a coalition of anti-immigration groups and employers, but opposed by immigration advocacy groups and unions

The number of senators supporting the bill is determined by taking the sum of the senators who are classified as most closely aligning with employers or anti-immigration groups and senators classified as equally most aligned with both groups. The number of senators opposing the bill is determined by taking the sum of senators who are classified as most closely aligning with immigration advocacy groups or unions and senators who are classified as equally most aligned with both. The swing voters are all of those senators who are not clearly aligned with any group, in addition to those senators equally most aligned with two groups that are not in coalition, such as unions and anti-immigration groups. Although some Swing voters can be observed as being most opposed to either anti-immigration group preferences or immigrant advocacy group preferences, these votes cannot be assigned to either coalition with certainty because of the possibility that a senator most opposed, for example, to immigration advocacy group preferences may still vote in favor of a bill supported by a coalition of immigration advocacy groups and unions. THE ISSUE OF STRATEGIC VOTING Senate votes provide an imperfect opportunity for the observation of senator preferences because senators may vote strategically. I cannot eliminate or fully account for the possibility of strategic voting, but I take two steps to reduce the likelihood that a senator will be misclassified due to strategic voting.

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First, I classify senator preferences on the basis of multiple votes across up to three sessions of Congress, depending on their participation in roll-call votes and how long they remain in office. I construct one measure for each senators’ preferences, based on all votes in which they participate. As previously stated, this requires the reasonable assumption that senator preferences are stable over time. I do not construct a different score for each senator based on their votes for each session of Congress because these scores would be based on a small number of votes and more likely to be biased by incidents of strategic voting. It is expected that those scores that are based on a larger number of classifiers are more likely to reveal true preferences of senators than those generated by a small number of classifiers. Second, because this is a small-N sample of votes, I am able to consider the political context in which votes occur when defining the classifiers. For example, in the110th Congress there is a special voting procedure used for votes on six amendments (Senate votes 229 – 234). The clay pigeon voting procedure, described earlier in this chapter, requires a Senate majority to table an amendment, or unanimity to pass the amendment if it cannot be tabled. A vote of in support of tabling an amendment may indicate lack of support for the particular amendment at hand, or it may indicate that the senator supports the bill that is being amended. Senator preferences therefore cannot be clearly identified from any of these votes in isolation, and as a result I do not make classifiers that assign scores based on these votes individually. Instead, I define classifiers that capture the most obvious cases of support or opposition for the bill, those senators that vote to table all or none of the six amendments. Because these amendments vary considerably in content, a vote of yes or no to all six of the amendments reveals that a senator is not voting on the content of the amendments but rather strategically in support or opposition for the bill. In summary, confidence in even those scores that are based on a smaller number of classifiers is improved because classifiers are defined in such a way that accounts for the political context in which votes occur.

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Table C.1: Bills proposing changes to U.S. migrant admissions policy since 1989

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Table C.1: Bills proposing changes to U.S. migrant admissions policy since 1989 (Continued)

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Table C.1: Bills proposing changes to U.S. migrant admissions policy since 1989 (Continued)

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Table C.1: Bills proposing changes to U.S. migrant admissions policy since 1989 (Continued)

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Table C.1: Bills proposing changes to U.S. migrant admissions policy since 1989 (Continued)

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Table C.1: Bills proposing changes to U.S. migrant admissions policy since 1989 (Continued)

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Table C.1: Bills proposing changes to U.S. migrant admissions policy since 1989 (Continued)

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Table C.1: Bills proposing changes to U.S. migrant admissions policy since 1989 (Continued)

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Table C.1: Bills proposing changes to U.S. migrant admissions policy since 1989 (Continued)

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Table C.1: Bills proposing changes to U.S. migrant admissions policy since 1989 (Continued)

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Table C.2: Classifiers and outputs for scoring Senator votes according to interest group positions

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Table C.2: Classifiers and outputs for scoring Senator votes according to interest group positions (Continued)

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Table C.3: Senator preferences measured according to alignment with special interest group positions

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Table C.3: Senator preferences measured according to alignment with special interest group positions (Continued)

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Table C.3: Senator preferences measured according to alignment with special interest group positions (Continued)

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Table C.3: Senator preferences measured according to alignment with special interest group positions (Continued)

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Table C.3: Senator preferences measured according to alignment with special interest group positions (Continued)

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Table C.3: Senator preferences measured according to alignment with special interest group positions (Continued)

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Table C.3: Senator preferences measured according to alignment with special interest group positions (Continued)

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APPENDIX D

Aggregation in Practice: A methodological survey of policy datasets in political economy

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INTRODUCTION In this paper, I have advocated for post-coding aggregation as a methodological principle for independent data collection based on claims that, when practiced, post-coding aggregation better achieves the dual goals of flexibility of data use and data quality. To better illustrate the mechanisms that underlie these claims, I have conducted a methodological survey of leading policy datasets currently used by researchers in the field of political economy. The four datasets included in this survey have a primary objective of measuring policy. While the principle of post-coding aggregation extends to other types of macrocomparative, quantitative data, it is useful to narrow the scope of the survey. By comparing policy datasets, which share many common features, the aggregation decisions made by data collectors and the implications of these decisions for data quality and flexibility of data use become more apparent. The first objective of the survey is to determine whether a dataset has been assembled utilizing the methodological principle of postcoding aggregation. This requires an observation of the way that the three aggregation decisions outlined in this chapter have been resolved in each data collection project. The level of generality at which 187 Challen, Suzanna. Measuring Change in Immigration policy, LFB Scholarly Publishing LLC, 2013. ProQuest Ebook

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indicators are defined, the scoring method that is identified for each indicator, and the method of construction of any aggregate indices is considered. A dataset is identified as utilizing a post-coding methodology of aggregation if all three aggregation decisions are made in accordance with a principle of post-coding aggregation. The second objective of the dataset survey is to assess how well each dataset achieves the dual objectives of flexibility of data use and data quality as a result of the methodology of aggregation. I have suggested that aggregation decisions are not the only decisions made in independent data collection that influence flexibility of data use and data quality. Decisions relating to scope and sources, for example, are also important. Therefore, in seeking to isolate the role that methods of aggregation play in preserving the flexibility of data use and improving data quality, it is most useful to illustrate how these objectives are met or hindered as a result of actual decisions relating to aggregation that have been made in practice. It is not as useful, especially with a small sample, to attempt to make comparisons across datasets as to which is the most flexible or of the best quality in a general sense, and this will not be attempted here. What is facilitated by the small sample size is the ability to consider aggregation decisions in detail, in order to illustrate how a principle of post-coding aggregation can be applied in practice. These illustrations pertain especially to data collection for policy measures, but may also be useful in suggesting ways of approaching aggregation decisions in the collection of other types of data. Lieberman (2010) provides a rare exception of a recent scholarly review that has also focused on the methods of data collection. This dataset survey includes one of the datasets also surveyed by Lieberman (2010)—the Alliance Treaty Obligations and Provisions (ATOP) dataset129—but does not overlap in its focus. Lieberman (2010) focuses on decisions relating to sources, citations, the interpretation of the historical record and issues of comparability and their implications for data quality. In contrast, this survey focuses on decisions relating to the aggregation of information in the process of defining and scoring indicators and indices, and the implications of these decisions for both flexibility of data use and data quality.

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THE FOUR POLICY DATASETS I survey the methodology of aggregation of four leading quantitative, macro- comparative datasets measuring government policy. Government policy is an area of data collection that can be both costly and time-consuming, as foreign language expertise and a detailed knowledge of each national legal system can be required. Therefore, in many areas of policy there are no direct measures of policy, and instead outcomes of policies must be used as measures of policy. These four policy datasets are among the most ambitious attempts to systematically construct quantitative indicators of government policies across a large number of countries. The ATOP database, already introduced, develops quantitative measures of foreign policy. The OECD Employment Protection Legislation (EPL) Database develops indicators of labor market regulation.130 The Migrant Integration Policy Index (MIPEX) produces indicators of migrant integration policies.131 Finally, the Social Citizenship Indicator Program (SCIP) has developed quantitative measures of welfare policies.132 In each case, I consulted the publically available codebooks and methodological information relevant to the most recent version of the data available at the time of writing this chapter. The third edition of the ATOP database, the 2008 edition of the EPL data, and the second edition of MIPEX data were those consulted The SCIP data, available up to the year 2000, was most recently updated in June 2008, according to the date posted on the website.133 As is reflective of the field at large, these data collection projects are the work of academics and staff researchers at both governmental and non-governmental organizations. The ATOP and SCIP datasets were led entirely by academic researchers at institutions of higher education. In contrast, the EPL dataset is the work of staff researchers at the OECD, while MIPEX is represents collaboration between governmental and non-governmental organizations and academic researchers. Most importantly, each of these datasets represents an example of independent data collection. The data has been created intentionally for use in multiple research projects, and has been used widely for data analysis by researchers who were not involved in the process of collecting the data.

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IDENTIFYING THE METHODOLOGY OF AGGREGATION The ATOP and EPL data collection projects primarily adhere to a methodology of post-coding aggregation, and the SCIP and MIPEX data collection projects primarily adhere to a methodology of precoding aggregation. In this section, I summarize the rationale behind these classifications. Notwithstanding these classifications, each of the four datasets has at least some attributes that align with a principle of post-coding aggregation and in this section I highlight these elements for each dataset in order to illustrate the decisions that influence the method of aggregation in the data collection process. Each data collection project is discussed in turn. I identify the background concept, discuss the way in which aggregation decisions are resolved, and provide a classification of the methodology of aggregation. Alliance Treaty Obligations and Provisions (ATOP)

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Background concept The background concept motivating the ATOP data collection project is military alliances. The ATOP codebook provides the operational definition134 of military alliances utilized in the project: written agreements, signed by official representatives of at least two independent states, that include promises to aid a partner in the event of military conflict, to remain neutral in the event of conflict, to refrain from military conflict with one another, or to consult/cooperate in the event of international crises that create a potential for military conflict.135 Aggregation decisions A major contribution of ATOP is to allow for data analysts to distinguish between types of military alliances according to their institutional design. For example, in an initial publication utilizing the data Leeds (2003) contributes to theoretical debates over the relationship between alliances and conflict by showing that distinct types of military alliances have different implications for the initiation of conflict. Therefore, unlike other data collection projects considered

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in this survey, the construction of a single indicator or index that is defined at the same level of generality as the background concept, in this case military alliances, is not a key objective of the project. Instead, the key objective is to disaggregate the concept of military alliances. This objective is achieved in part through the coding of indicators relating to the obligations of members that are defined in treaty documents. Each treaty must contain at least one of five obligations that are included in the ATOP project’s operational definition of a military alliance. For each alliance, the information about obligations of members is scored on a descriptive coding sheet as well as using quantitative scoring of indicators. In addition to defining indicators relating to the obligations contained in treaty documents, there are also indicators relating to the formation, members and duration of the alliance; the institutionalization of the alliance; and, the context in which the treaty was signed. There are in total over 100 quantitative indicators scored for each alliance, and 64 independently coded qualitative coding questions covering the same subject matter. The indicators specifically ask about the text of the treaty documents themselves, and do not require, for the most part, that the coder look beyond the text of the treaty for information. In this way there is little aggregation of information required across source documents. There are two exceptions to this general rule that are mentioned in the ATOP Codebook. First, the duration of an alliance was not always possible to determine based on the treaty document itself, and at times required secondary sources including newspaper articles for coding.136 Second, there were some alliances that were known to exist from secondary sources, but for which primary text versions of the treaty could not be located. For these alliances, which constitute only 33 of the 648 treaties included in the more recent edition of the data, secondary sources were used as sources for coding.137 Where multiple sources of information were consulted for the scoring of indicators, such as in the examples just provided, precoding aggregation of information may have been required. However, these exceptions represent only a small portion of the total data collected. The definition of indicators is highly specific, such that coders are not required in the scoring process to resolve disparate pieces of information within the treaty, to make subjective assessments, or to provide scores relative to an ordinal ranking system.

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Methodology of aggregation Given that a primary goal of the ATOP data collection project is to disaggregate the commonly used concept of military alliances, it is perhaps unsurprising that this dataset adheres primarily to a principle of post-coding aggregation. The indicators are scored in all but a few cases by a direct reading of the treaty document, and they have been defined to systematically record information about the institutional design of alliances as established in the text of treaty documents. The level of generality of the indicators is therefore intentionally closer to the level of generality of the information provided in the source documents than to the level of generality at which the background concept is defined. Likewise, indicators are worded in a highly specific manner to record the information contained in the treaties in a clear, systematic way. The coder need not make subjective judgments or assign ordinal ranking scores relative to an external framework. Employment Protection Legislation (EPL)

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Background concept The background concept motivating the EPL data collection project is employment protection regulation, for which the authors provide the following definition: Employment protection refers both to regulations concerning hiring (e.g. rules favouring disadvantaged groups, conditions for using temporary or fixed-term contracts, training requirements) and firing (e.g. redundancy procedures, mandated prenotification periods and severance payments, special requirements for collective dismissals and short-time work schemes). Various institutional arrangements can provide employment protection: the private market, labour legislation, collective bargaining agreements and, not the least, court interpretations of legislative and contractual provisions. Some forms of de facto regulations are likely to be adopted even in the absence of legislation, simply because both workers and firms derive advantages from long-term employment relations. Accordingly, the collection and use of

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available data in this chapter go beyond a narrow concept of employment protection legislation and follows a broader definition of regulation which aims to incorporate prevailing protective standards whatever their origin. 138

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Aggregation decisions The EPL data collection project is built largely around the objective of creating a summary indicator of the strictness of employment protection regulation, which is used as a policy measure in various OECD data analyses seeking to understand the influence of variations in the strictness of employment protection on employment trends and other economic outcomes of interest.139 The EPL database has 25 indicators to measure employment protection regulation,140 which measure three distinct subtypes of employment protection policies: the protection of employees with regular contracts, the protection of employees with temporary contracts, and the standards of protection relating to collective dismissals. Each indicator is scored on a scale of 0 to 6, with 0 representing the lowest level of strictness and 6 representing the highest level of strictness. The indicators are for the most part defined in a way that is highly specific, such that little aggregation of information is required prior to coding. For example, the indicators relating to protection of employees with regular contracts include the length of delay involved before notice can start, the length of the notice period, the length of severance pay, the length of the trial period, the length of compensation following dismissal and the maximum time to make a claim of unfair dismissal. Because these policies are either contained in national legislative or administrative law or form part of a collective bargaining agreement in the absence of legislation, it can be assumed that there is not subnational variation in policies that requires resolution through aggregation prior to assigning a single score to each value. Each possible score from 0 to 6 is defined in terms of a specific quantity of months, so that it is not a subjective determination of strictness but rather the scorer selects the score based on objective observation. For example, the length of the notice period required for an employee with 9 months of tenured regular employment is scored as 0 if 0 months, 1 if less than or equal to .4 months, 2 if less than or equal

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to .8 months, 3 if less than or equal to 1.2 months, 4 if less than 1.6 months, 5 if less than 2 months and 6 if greater than or equal to 2 months.141 In this way, the exact number of months is not recorded, which causes some data loss that might otherwise be preserved in the data collection process. However, because there are six choices of narrow ranges of months, it is possible to identify the notice period to within a small range for any country year. In other words, each possible score on the six point ranking is defined with enough precision that it can be converted into an objective binomial variable showing the presence or absence of a policy of that range of duration. This increases the flexibility of data use. There are a small number of indicators that require more precoding aggregation. For example, the possibility of reinstatement following an unfair dismissal would be initially scored as zero if there is no right or practice of reinstatement, 1 if reinstatement rarely or sometimes is made available, 2 if reinstatement fairly often is made available, and 3 if reinstatement is (almost) always made available. This score is then multiplied by 2 to place the scores on the 0 to 6 strictness scale.142 Here the coder is required to make a judgment about the level of frequency that reinstatement is made available, which may require aggregating information across a variety of cases. The user of the data must look at more detailed country notes in order to interpret why, for example, a score of 2 for fairly often was chosen above a score of 1 for rarely or sometimes made available. The indicators requiring pre-coding aggregation both reveal missed opportunities for more flexible data use as well as potential issues with data quality. Because the information used to determine these scores is not itself scored, but rather is briefly described in a qualitative country note, it cannot be as useful to other data analysts who might be interested in such information for separate analyses. Likewise we must assume that scorers were consistent in the interpretation of terms such as “rarely” and “fairly often” across units, as these terms are not defined in the publicly available information provided about how indicators were scored.143 Methodology of aggregation In contrast to the ATOP database, the EPL database did not set out to disaggregate the background concept of strictness of employment

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protection regulation, but rather to construct an index directly measuring the background concept. The aggregate index of strictness of employment protection is created only as a second step through the assignment of weights in combining 25 separately coded indicators. The process of aggregation to form the employment protection strictness index is both transparent and reversible, such that researchers who disagree with the method of aggregation could design another indicator by the use of different weights or by choosing to include only a subset of indicators. The 25 indicators are defined at a level of generality that is closer to the more detailed knowledge of the experts who were consulted as sources. The coding of each indicator on the 0 to 6 strictness scale can for almost all of the indicators be easily converted into binary scores because each possible score is uniquely and specifically defined. In this way, binary measures of duration or for the presence of specific types of regulations can be created out of the existing indicators without recoding the data. Only 2 of 21 indicators required substantial pre-coding aggregation. Based on these observations, I categorize the EPL dataset as adhering more closely to a methodology of post-coding aggregation.

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Migrant Integration Policy Index (MIPEX) Background concept The background concept motivating the MIPEX data collection project is migrant integration policy.144 The authors define the background concept in the following way: MIPEX measures policies that promote integration in European societies. Integration in both social and civic terms rests on the concept of equal opportunities for all. In socioeconomic terms, migrants must have equal opportunities to lead just as dignified, independent and active lives as the rest of the population. In civic terms, all residents can commit themselves to mutual rights and responsibilities on the basis of equality. When migrants feel secure, confident and welcome, they are able to invest in their new country of residence and make valued contributions to society. Over time, migrants can

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Appendix D take up more opportunities to participate, more rights, more responsibilities, and, if they wish, full national citizenship. 145

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Aggregation decisions MIPEX produces an aggregate index of migrant integration policies that incorporates six policy areas, referred to in the project as policy “strands,” including labor market access, family reunion, long-term residence, political participation, access to nationality and antidiscrimination. Each policy strand is divided into four policy dimensions, for example within the policy strand of labor market access there four policy dimensions including eligibility, labor market integration measures, security of employment and the rights associated with labor market access. A total of 142 indicators are defined at the level of policy dimensions, which is a level of generality that is much lower than the overarching background concept. In addition, to form an index of migrant integration policies, the indicator scores are combined in a transparent and easily reversible way.146 Despite the considerable work done by the authors of the MIPEX dataset to disaggregate the background concept into specific indicators for coding, there is a surprising level of pre-coding aggregation that is required when scoring the indicators. This pre-coding aggregation is primarily the result of two key features of the dataset. First, the MIPEX dataset does not seek to provide objective measures of policies, but rather to benchmark national policies with respect to one another and to a normative standard. The authors describe their approach in the following way: For each policy area, MIPEX identifies the highest European standards on the most relevant policies. Some of these standards are contained in EC Directives, which EU Members States are obliged to transpose into their national laws. Others come from Council of Europe Conventions that ratifying countries have committed to implement. When Directives and Conventions only provide minimum standards or allow numerous derogations, MIPEX turns to higher standards of best practice: EC Presidency Conclusions, proposals for EC Directives put forward by European-wide stakeholders, or the policy recommendations of comprehensive comparable

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European research projects.....The combined set of the highest European standards serve as MIPEX’s normative framework.147 Second, the normative standard is brought to bear on how the indicators are scored, such that there is not a set of objective data that is first collected and then compared to a normative standard after coding as a second stage, but rather the standard is being applied already during the process of scoring. By inserting a normative standard to which policies must be compared prior to coding, already a form of pre-coding aggregation is required. The authors describe the general approach to defining MIPEX indicators as:

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For each, the normative framework is translated into three answer options. The maximum of 3 points is awarded when real policies meet best practice. A score of 2 is given when policies lie halfway to best practice, and a score of 1 when they are furthest from best practice and thus unfavorable.148 Two specific examples may better demonstrate how this approach was applied in practice for the majority of indicators. As a first example, an indicator for the policy strand of labor market access within the policy dimension of labor market integration measures is specifically defined as, “State facilitation of recognition of skills and qualifications obtained outside the EU.” The definition of this indicator includes a list of specific sub-policies that may exist in a country year, which includes: a) existence of state agencies/information centres that promote the recognition of skills and qualifications; b) national guidelines on fair procedures, timelines and fees for assessments by professional, governmental, and nongovernmental organisations; c) provision of information on conversion courses/profession-based language courses and on procedures for assessment of skills and qualifications (regardless of whether assessments are conducted by governmental or non-governmental organisations)149

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A coder is asked to score this indicator on a 1 to 3 scale where a score of 3 indicates that national policy includes b and (a or c), a score of 2 indicates that national policy includes a or c, and a score of 1 indicates that none of these components are contained in national policy. An expert who is asked to code this indicator must first determine whether a, b and c are present in the national policy prior to determining the score according to the criteria given for each score. However, this information is not collected directly. Instead, information is lost as the data user can only determine whether a country had either “b and (a or c)”, “(a or c)” or none of these policies. A researcher who would prefer to know whether each country had specifically provision of information conversion courses (“c”) would be unable to obtain this information as it is aggregated with information relating to whether there exist state agencies/information centres that promote the recognition of skills or qualifications (“a”). If these had been coded separately first, the same benchmarking indicator could have been made, but in such a way that the indicator could also be disaggregated into objective indicators showing the presence or absence of each subpolicy. Another example in the same policy strand and dimension is an indicator that is defined as, “Equality of access to vocational training/study grants.” The coder is asked to score this indicator on a 1 to 3 scale where the score of 3 indicates “No distinction made between EU and non-EU nationals in terms of education and vocational training, including study grants,” a score of 2 indicates, “Equal treatment in terms of education and vocational training only after more than one but less than three years of legal employment,” and a score of 1 indicates, “Third country nationals do not have equal access to education and vocational training, only after three years, or other limiting conditions exist.”150 Here the coder is required to consider the requirements for EU and non-EU nationals for a variety of vocational programs and grants. In addition, there is not an indicator recording the information gathered about what these requirements are so that the differences can be objectively observed by data users. Instead only the fact that there is or is not a difference between the requirements is preserved in the score. Therefore there is considerable data lost, and it is unclear how requirements for various programs are resolved if some are alike for EU and non-EU nationals and others differ. A final problem with the

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data arises when seeking to use the data for cross-national comparison. Because objective measures of the requirements are not recorded in the data it is impossible to rank non-EU nationals’ access in one country against non-EU nationals’ access in another country. The data records whether or not non-EU national access is equal to EU national access in a single country. However, without knowing what the level of access is for a given country, it is impossible to know objectively whether nonEU nationals have a more expansive set of rights in one country in comparison to another.

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Methodology of aggregation In line with a principle of post-coding aggregation, MIPEX disaggregates the background concept of migrant integration policy into 142 indicators, and provides a transparent and reversible process for aggregating the indicators to form a migrant integration policy index. Unfortunately, despite the low level of generality at which the indicators are defined relative to the background concept, the way in which the indicators are scored results in substantial pre-coding aggregation of information that results in both a loss of flexibility of data use while raising questions about data quality. Because the majority of the indicators utilized in the MIPEX index are affected by this methodological approach, I categorize the MIPEX dataset as primarily adhering to a methodology of pre-coding aggregation. A similar objective of benchmarking could have been achieved as a second step following an initial round of objective coding of each of the various components to be considered in the benchmarking assessment. This two-step approach would have provided a more transparent process of benchmarking, while preserving the flexibility of the data for alternative uses. Likewise, in contrast to the EPL dataset which also utilized a ranking system in scoring, the imprecise way in which three scoring options were defined for each indicator prevents the data user from converting the ranked scores for indicators into objective binary variables. This results in considerable loss in the flexibility of data use.

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Social Citizenship Indicator Program (SCIP) Background concept The background concepts motivating the SCIP are social citizenship and the welfare state, which the authors treat as two sides of the same coin.151 The authors define social citizenship and the welfare state in the following way: The Social Citizenship Indicator Program was initiated in the early 1980’s to establish a theoretically relevant and empirically reliable set of institutional data for comparative research on welfare state developments. It is focused on citizens’ rights and duties legislated in programs to alleviate economic needs characterising the human condition: old age, illness, unemployment, work accidents and family change. Rights and duties legislated in such social insurance programs form a key part of modern welfare states and of what Thomas H. Marshall termed social citizenship.”152

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Aggregation decisions The SCIP developed two sets of indicators, including 194 indicators collecting information on old-age pension programs and 131 indicators collecting information separately on unemployment, sickness and accident insurance programs.153 The common feature of both sets of indicators is an emphasis on calculating replacement levels for the standard worker. With this purpose in mind, the authors choose to code only insurance programs that apply to a standard worker with attributes that they have defined. The authors justify this methodological approach as a method for dealing with comparative issues of equivalence. In comparative research covering several countries and long time periods it is fruitful, in fact necessary, to have at least one standard point of reference in relation to which comparison can be made. In view of the purpose of this program and availability of data, as such a reference point we have chosen a model-typical average production worker case we labeled

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Standard Worker, here defined as a worker in manufacturing or the metal industry. In the following we have made assumptions about family composition, the age of family members, work- and contribution-careers etc. We have assumed that the Standard Worker has had earnings equal to what has been labeled the Average Production Worker’s Wage (APWW).154

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The replacement level is calculated in a variety of ways based on varying assumptions. The typical benefit levels chosen for comparison are: standard, minimum, full and maximum...The standard benefit refers to benefit received by the typical case of the Standard Worker...The full benefit levels reflect the extent of fulfillment of all conditions affecting benefit levels, such as contribution time...Minimum benefits reflect conditions in the major schemes where average production workers are included. Maximum benefit levels reflect stratification in terms of income-related benefits above those of the APWW. Correspondingly the minimum benefit reflects the floor to which a person can fall in case of lost earnings within the major scheme that includes the Standard Worker. Not only social insurance benefits but also means tested benefits may be included here. (italics in the original)155 The formula used to determine the gross replacement levels for a given insurance program in a specific country year is not provided to the data user. Therefore, it cannot be determined the extent to which pre-coding aggregation is required, because it cannot be known whether additional information that is not captured by the other indicators in the dataset was utilized to obtain the scores for these indicators. The codebook states that, “Where replacement rates differ (e.g. between states or rural and urban areas) medians or averages have been estimated, or, if these are not available, the region with the largest population or the region with the largest city has been coded.”156 This quote gives some indication that in countries where there is subnational variation, the coding of these indicators may require a

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considerable amount of pre-coding aggregation. Likewise, net replacement levels incorporate tax levels into the equation without a separate coding of the relevant tax policies. Because tax-related information is required to calculate net replacement levels that is not coded elsewhere in the dataset, considerable pre-coding aggregation is required in the scoring of these indicators. While the exact method of calculation of replacement levels remains obscure, a variety of factors are scored as separate indicators that are important to the calculation. For example, the Average Production Worker’s Wage is scored as a separate indicator for each year. In addition, indicators relating to the conditions for the insurance program, including the reference period, the contribution period, and whether there is an income ceiling or means test are coded as separate indicators. These factors are related to the calculation of replacement levels; however, these indicators alone cannot be aggregated postcoding to generate replacement level scores.

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Methodology of aggregation The SCIP does not aim to create an aggregate index of welfare policy, but rather to calculate replacement levels for each of four insurance programs for a standard worker than can be used, either individually or in some combination, as indicators of the background concept. The decision to generate specific assumptions about the standard worker and to code only insurance programs that serve the standard worker limit the flexibility of data use; however, this is more of an issue of the scope of the dataset, rather than aggregation. With regard to aggregation, coding a replacement level requires a calculation that incorporates a variety of factors, and the precise formula for a specific country year remains obscure. Likewise, in some countries there is subnational variation in how replacement levels are determined that must be reconciled prior to coding. Without knowing the precise formula, it is impossible to know the extent to which pre-coding aggregation of information is required, although it appears likely that a substantial amount of pre-coding is required. In addition, it is not possible for the data user to combine the indicators that are included in the dataset to regenerate the replacement levels.

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THE IMPACT OF AGGREGATION DECISIONS ON FLEXIBILITY OF DATA USE AND DATA QUALITY The survey of four policy datasets provides examples of how a principle of post-coding aggregation may be fruitfully applied to the three aggregation decisions to promote both the flexibility of data use and the quality of data. It also provides examples of how obtaining these dual objectives may be hindered when independent data collection does not adhere to a principle of post-coding aggregation. In this section, each aggregation decision and its impact on the objectives of flexibility of data use and data quality will be discussed in turn, drawing upon examples from the four datasets included in the survey.

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Defining Indicators All four of the datasets adhere to a principle of post-coding aggregation with regard to the level of generality at which indicators are defined. Each dataset disaggregated the background concept of interest into indicators that were defined at a much lower level of generality, expanding the number of indicators available to researchers. Approximately 100 ATOP indicators are defined to collect information on a variety of features of military alliances, including the formation, members and duration of the alliance; treaty obligations; the institutionalization of the alliance; and, the context in which the treaty was signed.157 25 EPL indicators are defined to collection information about three sub-policy areas of employment protection for regular employees, temporary employees, and collective dismissals.158 Over 140 MIPEX indicators cover 6 policy areas, each with four subdimensions of policy identified, including labor market access, family reunion, long-term residence, political participation, access to nationality and anti-discrimination.159 Almost 300 SCIP indicators are defined at the level of one of four major insurance programs, including work accident, sickness and unemployment insurance, in addition to old age pensions.160 The reliability of the scores for each indicator improves as the disparity between the between the level of generality at which indicators are defined and the level of generality of the information that

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is provided by sources is reduced. The sources utilized across these projects vary, and it was not possible to observe this level of disparity directly for all four datasets. The ATOP and SCIP data collection projects use textual sources, which make this comparison possible to observe. The ATOP project uses primary text treaty documents as sources for scoring almost all indicators, and there is little disparity between the level of generality at which indicators are defined and the level of generality of the information in the treaties.161 This is unsurprising given that a key objective of the ATOP project is to disaggregate the background concept of military alliances to account for variation in institutional design. SCIP lists among its publically available citations primarily English-language secondary sources published by international organizations that summarize national welfare policies.162 These sources tend to discuss insurance programs separately, which is reflected in the definition of SCIP indicators. In conducting this dataset survey, I was unable to determine in some instances how the coders identified the insurance program of interest using the sources cited, and how they were certain that the information provided in the secondary source related to the particular insurance program they intended to code. In other words, the information in the sources is at too high of a level of generality and generally is insufficient for an accurate scoring of some indicators. This is the case, for example, for the coding of unemployment insurance in Denmark in 2000.163 It is probable that additional sources were used that are not included in the list of citations that has been made publicly available. In contrast, the EPL and MIPEX data collection projects rely on country experts as sources,164 and therefore it is more difficult to compare the level of generality at which indicators are defined with the level of generality of information provided by sources. It might be sufficient to say that the work to disaggregate such broad concepts as employment protection regulation and migrant integration policy improves the reliability of data, given how high the level of generality of the background concept of interest. Also it can be assumed that more detailed indicators are more proximate to the type of detailed information available from experts in these fields. However, there is a larger issue relating to aggregation that arises when experts are consulted as sources that is worthy of discussion. In short, it is more difficult to observe when aggregation is occurring. A single response

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from an expert may appear to lack any aggregation because it is derived from one source, but it may in fact represent an expert opinion formed based on a variety of potentially conflicting sources or experiences. This issue is addressed at greater length in the concluding section of this chapter where limitations to a methodological principle of postcoding aggregation are discussed.

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Scoring methodology The four datasets included in the dataset survey vary in the methods used in scoring indicators. The EPL and MIPEX datasets both incorporate a ranking process into the scoring methodology, which in both cases might have been better achieved as a second stage process following an initial scoring of objective indicators. However, the way in which the EPL indicators are defined is more precise, such that the ranked scores can be easily converted into binary objective measures. The MIPEX indicators are defined with less precision, such that the objective information used to generate the ranking is not preserved in the data. For this reason the EPL dataset is observed as adhering to a methodological principle of post-coding aggregation, while the MIPEX dataset is observed as adhering to a methodological principle of precoding aggregation. Although an ordinal ranking device can be a useful way of measuring a concept of interest, there are a variety of ways that ranking while scoring may limit the flexibility of data use. Some users may disagree with the framework that forms the basis of the ranking, and these researchers may wish to rank national policies according to a different framework. As important, many data analysts do not want an ordinal ranking of countries at all, but rather prefer objective measures of whether certain policies exist in certain country years and the conditions and procedures that are related to these policies. Because of the lack of precision with which MIPEX indicators are defined, the objective observations of national policy that were used to determine the MIPEX rankings are not preserved in the indicator scores. Therefore a data analyst cannot retrieve these objective observations without reading the qualitative country notes and contacting the experts.

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This data loss is an inefficient outcome that could have been avoided if the objective observations that form the basis of the MIPEX rankings were individually coded first as indicators and then combined into rankings as a second step. If the objective observations were preserved in the MIPEX data, the data would be useful for a much wider array of research projects. In addition, the validity of the indicator scores could be more easily verified if the specific observations that form the basis of MIPEX rankings were made clear.

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Aggregate indices The EPL provides the best example of an index that adheres to a methodological principle of post-coding aggregation. The Strictness of Employment Protection Index combines in a transparent and reversible way separately coded indicators that require little pre-coding aggregation. The MIPEX index also is constructed in a transparent and reversible way, but the indicators that make it up require substantial pre-coding aggregation. Data users of the EPL and MIPEX data have the flexibility to re-construct aggregate indices by removing specific indicators or adjusting the weights applied to various indicators, in such a way that may improve the content validity of the measure with respect to a specific research design. In contrast, the SCIP indicators that calculate replacement levels provide an example of indices that are not constructed in a transparent way and that cannot be reconstructed from the other related indicators included in the dataset. Although many indicators relating to conditions of the insurance programs are coded individually, there is insufficient information regarding how these indicators combine to generate the replacement levels and whether they are necessary or sufficient to calculate the replacement levels. Because replacement rates are among the most important indicators produced by the SCIP, the lack of information about how they are constructed for each unit is problematic from a stand point of data quality. The data user cannot verify and must instead trust that the data collection team has accurately identified the proper equation for calculating the replacement levels and utilized valid information in the calculation to achieve accurate scores. The lack of transparency may also prevent the assessment of the content validity of these indicators when used as measures for data analysis.

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Notes

CHAPTER 2

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1

Humanitarian-based selection criteria are also important, but these will not be considered in this chapter. 2 Skill bias is a characteristic of migrant admissions policy, which is one feature of immigration control policy. Immigration control policy has been defined as "the role of the nation state in establishing rules of entry and exit,” Hollifield (2008) p. 185 and may be distinguished from two other important themes often encompassed by the term immigration policy— policies relating to national security and immigrant incorporation. Migrant admissions policy consists of the criteria used to select international migrants for legal residence and work. Selection criteria may be further distinguished from general eligibility criteria, which, although often contested, define what types of individuals must be refused entry, generally for purposes of protecting public health and national security. Instead, selection criteria are additional criteria beyond minimal eligibility criteria, specifying what types of individuals, and how many, should be admitted under each legal category of entry. 3 Jasso, Rosenzweig, and Smith (2000) 4 Evidence of a decline in skills of the U.S. foreign-born population is not necessarily inconsistent with evidence of an increase in the skills of U.S. permanent immigrants, as demonstrated by Jasso, Rosenzweig, and Smith (2000). Some combination of the rate of growth of the share of nonimmigrants and unauthorized migrants among the U.S. foreign-born population and changes in the skill composition of each of these groups may overshadow changes in skill composition of the permanent immigrant population. 5 This evidence is based on a fixed price index derived from INS records of immigrant self-reported occupations at the time of admission to the U.S.

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from 1972 – 1995, which are assigned income levels according to the average earnings of 1980 Census respondents working in each occupation. 6 I use the term general-skill in place of unskilled or low-skilled to describe workers without specialization obtained through higher education or work experience that qualifies as highly skilled. 7 Borjas, Freeman, and Katz (1992) p. 213-244 produced evidence that immigration, in combination with trade, was a significant factor leading to a decline in the relative weekly wage of native workers with less than a high school education. 8 Card, DiNardo, and Estes (1998) 9 Smith and Edmonston (1997); Borjas and Trejo (1991) p. 195-211 10 See, for example, Table 3 in Camarota (2003) 11 See for example, “Guidelines for the Reform of Immigration Policy” by Barry R. Chiswick published in the University of Miami Law Review, Vol. 36, Issue 5 (September 1982), pp. 893-930, also republished in Chiswick (2005) p. 400 12 Note that the national origins quota system did not apply to immigrants from the area defined in U.S. law as the Western Hemisphere—generally North and South America. Western hemispheric migration at this time was not numerically limited. Thus the Western-European bias in the US policy was with regard to Eastern Hemispheric migration, primarily toward the region defined as the “Asia-Pacific Triangle.” 13 For a summary of the changes made in 1952 see, "Developments in the Law Immigration and Nationality." (1953) p. 643-745; see also Cornelius, et al. (2004) p. 63. Hing (2004) p. 77 describes the introduction of a fourcategory selection system within country-of-origin quotas giving preference to highly educated or exceptionally talented migrants, followed by family members of US citizens and residents. However, because the preference system was subsumed within county-of-origin quotas, he argues, “under the 1952 law national origins remained the determining factor in immigrant admissions, and northern and western Europe were heavily favored.” 14 See also Hing (2004), p. 93-100 for a summary of the changes made in 1965. 15 Joppke (2005) p. 18-21 16 Hing (2004) p. 111 states in the conclusion of Ch. 6 covering 1965 to 1990, “Whatever reform came about, it was always with an eye toward what color or ethnic background qualified immigrants would bring, rather than simply what skills they could offer. The looks and ethnic background

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of prospective immigrants remained a test for which direction policymakers would take.” 17 ibid. p. 95 see also Borjas (1990) p. 29-33, as cited by Castles and Miller (2003) p. 75 18 Castles and Miller (2003) p. 75-76; see also Cornelius, et al. (2004) p. 63 19 See also Joppke (2005) p. 7 20 Castles and Miller (2009); Joppke (2005); Cornelius, et al. (2004) 21 Keely (1999) p. 95-96 states that, “It is easy to forget that current views about nonimmigrant visa policy and practice were not always the conventional wisdom. For example, during the discussions of the 1965 amendments to the Immigration and Nationality Act of 1952 (the McCarran-Walter Act), the connections between educational and laborrelated nonimmigrant visas on the one hand and permanent immigration on the other was not a major issue. The concept that nonimmigrant and immigrant visas for occupational skills should be seen as a whole, rather than two separate spheres of activity, was not part of the discussion 30 years ago. This is not such a surprise. The level of globalization of the economy and of firms and the extent of international labor markets were in their infancy compared to today. In addition, temporary labor in the mid1960s still meant unskilled, primarily agricultural labor. The United States ended the bracero program in 1964. In that context, temporary laborers hardly conjured up images of managers developing overseas markets for new products or researchers for communication technology...” 22 Because there is no consensus over which nonimmigrant visas are relevant to research on the phenomenon of temporary migration, it is not necessarily the case that an increase of nonimmigrant categories of admission is indicative of expansion in temporary migrant admissions policy. 23 Bhagwati and Rao (1999) p. 239-258, Chiswick (1999) p. 211-222 24 Salt (1997); Papademetriou (1999) p. 29-56; Lowell (2005) 25 I benefited from the assistance of four undergraduate research assistants in completing the historical tracing of all relevant U.S. statutes and regulations: Elisha Rivera, Emily Naphtal, Eric Portales and Cecilia Venegas. 26 8 USC 1101 (a)(27) 27 For example, Jasso, Rosenzweig, and Smith (2000) exclude both “IRCA immigrants,” those who received permanent residence through the legalization program established by the 1986 Immigration Reform and Control Act, in addition to refugee/asylee-based immigrants from their

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analysis of the changing skill composition of permanent immigrants from 1972 – 1995. 28 8 USC 1101 (a)(15); Lowell (1999) p. 1 describes this legal category in the following way, “There have always been those who come to the United States for short stays—to work and conduct business, to study, or to visit and sightsee. No single definition best describes this type of international migrant; they have come to be known for what they are not. With a few notable exceptions (e.g. holders of H-1B and L visas), they are not intending, or permitted under the terms of their visas, to reside permanently in the United States with the full range of rights granted to ‘immigrants.’ Legally they are, therefore, not immigrants, they are ‘nonimmigrants’ whose legal rights and responsibilities are limited to certain activities and whose stay is of limited duration.” 29 Lowell (1999) p. 1 30 See subsection four of the discussion of “Issues that arose in creating the skill bias measure” provided in Appendix A. Subsection 4 is subtitled, “The scope of the tracks included in the measure and justifications for tracks excluded.” See also Table A.1 for a comprehensive list of all categories of entry included in the definition of permanent immigrant admissions policy and temporary migrant admissions policy. 31 Niessen et al. (2007) 32 See the UK report published by the Home Office, entitled, “Migration policies towards highly skilled foreign workers” by G. McLaughlan and J. Salt. See also the Australian report published by the Dept. of Immigration and Multicultural Affairs entitled, “Evaluation of the general skilled migration categories” by R. Birrell, L. Hawthorne and S. Richardson. 33 I exclude from this analysis consideration of admissions for refugees and other humanitarian migrants, which are a distant third in priority to kinship and skill-based criteria. 34 It should be noted that an increase in the level of skill bias can be caused either by an increased emphasis on skill-based selection relative to alternative factors, or by a reduction in the emphasis on some alternative factor relative to skill. Therefore, at least in theory, it is not always the case that an increase or decrease in skill bias is a result of a deliberate attempt to adjust the level of emphasis placed on skill in migrant admissions. However, with the exception of the 1976 amendments, the trends visible in the level of skill bias in Figures 2.2 and 2.3 are the result of increases and decreases to skill-based tracks of entry, rather than the unintended consequence of reductions in other categories of entry. The category of

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entry for immediate relatives of US citizens is the only exception to this, as discussed in this section. 35 79 Stat 912-913, amending section 203 of the Immigration and Nationality Act 36 90 Stat 2704, section 3, amending section 202 (e) (3) of the Immigration and Nationality Act. Note that a comparison of the 1976 and 1965 language of the 3rd preference category reveals that the addition of the final sentence relating to the services being requested by a US employer was only added in 1976 and was absent from the initial preference system in 1965, see 79 Stat 913, section 3, amending section 203 (a) (2) of the Immigration and Nationality Act. 37 90 Stat 2704, section 3, amending section 202 (e) (6) of the Immigration and Nationality Act. 38 90 Stat 2704, section 3, amending section 202 (e) (1), (2), (4), and (5) of the Immigration and Nationality Act. The numerically limited categories of entry admitted unmarried (adult) sons and daughters of US citizens, the spouses and unmarried, minor children of legal permanent residents, married sons and daughters of US citizens, and siblings of US citizens. 39 Country-based quotas limiting the number of arrivals from any given sending country also were introduced for Western Hemispheric countries, similar to the quotas already in effect since 1965 for Eastern Hemispheric countries. However, the country-based quotas served only as an additional numerical limitation within those criteria, such that applicants could only be considered for admission through a family or skill-based category of entry until their country of origin’s numerical limit was reached for that year. 40 P.L. 101-649; 104 Stat 4978 41 I exclude consideration of special immigrants, and thus the fourth preference category for certain special immigrants. As a result I calculate a skill based quota space of 130,000 rather than 140,000. 42 H. Rpt. 101-95 (October 26, 1990), p. 121 43 H. Rpt. 101-95 (October 26, 1990), p. 120 (Note that the level excludes humanitarian migrants.) 44 Because no numerical limitations were placed on any temporary track of entry until 1990, a direct measure of policy cannot be made for the earlier time period of 1965 – 1990. More information regarding the selection of this time period and the measurement methodology can be found in Appendix A.

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CHAPTER 3

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45

For a review of nativism in the United States through contemporary times see, for example, Schrag (2010); Navarro (2009) p. 488; and Perea (1997). Additional research focuses on the determinants of public attitudes toward immigration, see for example recent research by Hiscox and Hainmueller (2010) and Hiscox and Hainmueller (2007) p. 399 46 There is an extensive literature on the rise of far-right parties in Europe in the 1990s advocating on anti-immigration platforms and the role that these parties have played in shifting more center-right parties to advocate for more restrictive policies to reclaim far-right voters. See for example, Norris (2005), Givens (2005) and Kitschelt and McGann (1995). Although Freeman (2001) p. 65-95 argues that an anti-populist norm restricts the level of anti-immigrant rhetoric in US national elections, as will be discussed later in this chapter, such rhetoric is not altogether absent. Perea (1997) p. 3 makes a similar argument relating to state politics in California, “Governor Wilson of California enjoyed great political success blaming the economic atrophy in his state on undocumented persons. Conservative Republican politicians make use of immigration reform as one of their most prominent political issues. The undocumented make easy targets: they lack the power to vote and so they lack political legitimacy, clout and voice. Beyond their inability to vote, they are extraordinarily vulnerable to exploitation. They dare not assert any rights they may have publicly out of fear that identification will result in their deportation from the country.” 47 Navarro (2009) p. 280-1 argues in a section of his book that is entitled, “Politicians enter the Ant-Immigrant Movement” that “September 11 fostered a resurgence of nativism. Supported by both Republican and Democratic politicians, the Bush administration seized the moment to create a ‘policy nexus’ between immigration reform and the war on terrorism. For most in Congress, the two issues became intertwined if not inseparable.” 48 Newton (2005) p. 139-167 describes the expansion of border control operations brought about by the Immigration Reform and Immigrant Responsibility Act, “In 1996, Congress passed a bill designed to crack down on illegal immigration as well as those immigrants relying on public assistance. The bill called for stricter enforcement of employer sanctions and document verification provisions of the weak Immigration Control and Reform Act of 1986. Additional stipulations for securing the southern border included the extension of deterrence programs already operating

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along the border in San Diego, California (Operation Gatekeeper) and El Paso, Texas (Operation Hold the Line), which included repairing and reinforcing fences, as well as adding several thousand INS agents per year through fiscal year 2001. The law’s expansion of Operation Gatekeeper represented the most comprehensive effort the U.S. government has undertaken to enforce its southern border since the establishment of the U.S. border control in 1924.” For a more extensive look at the effects of enhanced border control beginning in the mid 1990s on U.S. immigration see Nevins (2002) and Massey, Durand, and Malone (2002). 49 Employer sanctions were first implemented by the Immigration Reform and Control Act of 1986 and strengthened by the IIRAIRA in 1996, as described in the previous footnote. Wong (2006) p. 70-94 provides an interesting description of the evolution of union preferences with regard to employer sanctions from supportive in the 1980s to opposed in the late 1990s. Despite the shift in union preferences, employer sanctions remain in effect. Although enforcement of these provisions is weak, Schrag (2010) p. 188-191 provides evidence of employer raids occurring from 2006-2008. 50 Borjas (2001) provides review of the restrictive changes to the level of immigrant access to welfare programs brought about in 1996 under the Personal Responsibility and Work Opportunity Act and the Immigration Reform and Immigrant Responsibility Act.’ 51 Schrag (2010) p. 180 states that, “Even before the defeat of comprehensive federal immigration reform, state and local governments had been rushing to fill the vacuum...In 2007, forty-six state legislatures passed 240 new laws on immigration—some 1,560 were introduced, nearly triple the number for the same period in 2006. In the first six months of 2008, another 1,267 bills were considered in forty-five state legislatures, and at least 175 laws and resolutions were enacted in thirty-nine.” See also, Navarro (2009) p. 284-295 for a description of some of these state and local level legislative efforts. 52 This finding aligns with the review of policy change in U.S. migrant admissions policy for permanent immigrants provided by Tichenor (2002), Freeman (2001), and Wong (2006). However, these authors do not provide comprehensive coverage of changes to U.S. temporary migrant admissions policy. Also, this finding resonates with what Hollifield (2008) and Cornelius and Tsuda (2004) refer to a “gap hypothesis,” which is an empirical observation of dissonance between public opinion and policy outputs, in addition to policy outputs and enforcement. However, these

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authors do not provide a systematic listing of policy changes to U.S. migrant admissions policy. 53 I define a permanent immigrant as an individual granted legal permanent residence in the United States, with the exception of permanent residence on the basis of refugee or asylee status. A kinship-based permanent immigrant is a permanent immigrant that is admitted as a primary applicant on the basis of their relationship with a U.S. citizen or permanent resident. An employment-based permanent immigrant is a permanent immigrant that is admitted as a primary applicant on the basis of their individual skills or an employment contract with a U.S. employer. Kinship and employmentbased admissions programs are clearly defined in U.S. law. The theory developed in this chapter speaks only to the admission of kinship and employment-based permanent immigrants, and does not extend to permanent immigrants that obtain their status through the diversity lottery, or through the special immigrant categories of entry. 54 I define a temporary migrant as an individual admitted temporarily to the U.S. for the purpose of residence or work. This definition excludes tourists. A temporary migrant worker is a temporary migrant that is admitted as a primary applicant on a visa that is clearly defined in U.S. law as intended for work. Specifically, I include migrants holding E, H, L, O, P, Q-1, R and TN visas as temporary migrant workers. The theory developed in this chapter does not extend to the admission other temporary migrants admitted for the purpose of study, cultural exchange, and participation in law enforcement proceedings, for example. I also exclude diplomats, employees of NATO and other international organizations, and foreign media representatives because these visas are established to foster international diplomacy and freedom of the press based on international norms of reciprocity. When I refer to migrant workers without specifying a particular type of migrant worker, then the term is meant to refer to all types of migrant workers, including all temporary migrant workers and employment-based permanent immigrants. 55 I historically traced legislation establishing or altering quotas placed on any category of entry throughout the time period. My method of historical tracing required identifying the relevant sections of U.S. law defining categories of entry and quotas. Once these sections were identified, I used annotated citations available through West Law in order to identify all amendments affecting relevant U.S. statutes, and I used the U.S. Statutes at Large to obtain the text of each amendment. I used the annually compiled Code of Federal Regulations: List of Sections Affected to identify all

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changes and additions to relevant U.S. regulations, and I used the U.S. Federal Register to obtain the text of new or amending regulations. 56 See for example, Zolberg (2006), Tichenor (2002), and Wong (2006) 57 Although recent scholarship has explored changes over time in U.S. admissions policy toward specific types of temporary migrants, such as students or highly skilled workers (see for example Lowell (1999)), there is no scholarship to date that comprehensively considers all policy changes to the categories of entry and numerical limitations for U.S. temporary migrant admissions since 1965. 58 The Western Hemispheric region as defined in U.S. law included North and South America. The geographical region defined as the Eastern Hemisphere included all of Western Europe, Africa, Asia and Oceania. 59 The 1976 Amendments to the INA also extended per country limitations on immigrant admissions from the Western Hemisphere. This was particularly important for immigration from Mexico. Along with Canada, Mexico’s annual quota was effectively reduced from 40,000 to 20,000 with this Act. This reduction did not remove any category of entry nor did it influence the overall level of immigrants that were eligible to be admitted under each category of entry. Rather it reduced the proportion of admissions slots that may be taken up annually by nationals from a given country. Therefore, I do not consider this as a restrictive policy change in U.S. migrant admissions policy as defined in this paper. However, it should be acknowledged as an important policy change that restricted the accessibility of legal immigration to the United States particularly for Mexican nationals. Under the 1976 law, annual immigrant admissions from each country world-wide was limited to 20,000, which previously had applied only to Eastern Hemispheric immigration. 60 The American Competitiveness in the 21st Century Act of 2000 removed the country of origin quotas for employment-based permanent immigrant admissions. However, this policy change did not adjust the categories of entry or numerical limitations placed on each category of entry. Rather, the policy change was meant to benefit large sending countries, such as China and India, which quickly used up all of their quota space of 20,000. As a result, this policy change increased accessibility of employment-based immigration to individuals residing in these large sending countries, but it did not adjust the overall immigrant admissions program. 61 There are also separate visas categories allowing the admission of immediate family members of temporary migrants for many visa classes.

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The holders of such visas are secondary applicants whose admission is determined on the basis of the primary applicant, and therefore I do not consider these as distinct categories of entry for the purpose of this chapter. 62 Table 41, p. 128 of the U.S. Immigration and Naturalization Service (INS) 1990 Statistical Yearbook 63 Table 4, p. 54 of the U.S. Immigration and Naturalization Service (INS) 1990 Statistical Yearbook 64 1998 and 2000 are the year policy changes were enacted, which become visible in 1999 and 2001 on Table 3. 65 For example, Tichenor (2002) p. 246 argues that, “From the Refugee Act of 1980 to the Immigration Act of 1990, the policy choices of national officials were molded by several forces well insulated from the preferences of ordinary citizens: an ideological convergence of liberal and conservative politicians and interest groups in favor of immigration; the dominance of pro-immigration expertise in the findings of national commissions and other governmental bodies; and international pressures such as global trade competition and foreign policy commitments. “ 66 The terms “unskilled” or “low skill” are often to contrast against highly skilled migrants; however, the author prefers the term “general-skill” to refer to the skill set of migrant workers that have not obtained a specialization that qualifies them as highly skilled, whether through higher education or work experience. 67 Wong (2006) p. 3 states that, “Latinos and Asian Americans found ways to influence policy outcomes even though they commanded far fewer economic resources than those available to most business and union lobbyists. These ethnic organizations were most successful when they practiced an inclusive form of identity politics. Casting their demands for rights in universal terms, they formed alliances with other civil rights and humanitarian organizations. It was these coalitions that helped them sway the votes of moderate and undecided lawmakers. Ethnic minority organizations influenced political outcomes even before the populations they represented gained significant political clout in the voting booth. Latino electoral power was emergent in some states in the 1990s, but Latino organizations were already significant players in federal policymaking on immigration issues.” 68 ibid. p. 33 argues that “the interests of union members have shaped the liberal position of the labor movement on other aspects of immigration policy. Since the 1950s, the AFL-CIO has supported family-based admissions, giving them higher preference than employer-sponsored

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admissions. This stand not only serves the interests of union members who want family members to come to the United States; it also advances the movement’s interests in limiting admissions of permanent laborers.” 69 Briggs (2001) describes, “When the AFL-CIO Executive Committee met in New Orleans in February 2000, it consummated its break from the past. It was announced that the AFL-CIO would seek to have the employer sanctions provisions of IRCA repealed and that it favored a new amnesty to cover most of the six million illegal immigrants believed to be in the United States. After that meeting, the president of the Hotel and Restaurant Employees Union, John Wilhelm, said, ‘the labor movement is on the side of immigration in this country.’ Business lobbyists hailed the new policy stance.” 70 The AFL-CIO Council Report following the 26th Constitutional Convention in 2009 maintains a section entitled, “Fighting for Immigrant Rights,” (p. 42) which states, “Recognizing the labor movement needed to unite with a pro-worker reform agenda, we asked former Secretary of Labor Ray Marshall to head a special task force of officers of unions most affected by immigration policy. Working with the Economic Policy Institute (EPI), the taskforce developed a framework for immigration reform that was agreed to in April 2009 by all of the unions concerned. The framework, which is now guiding the debate over immigration reform, consists of five interconnected pieces: 1) a future flow system that is based on labor market shortages and managed by an independent commission; 2) rational operational control of borders; 3) a secure and effective mechanism to monitor work authorization and hold accountable employers who hire undocumented workers; 4) legalization; and 5) no expansion of temporary or ‘guestworker’ programs.” Available online: http://www.aflcio.org/aboutus/thisistheaflcio/convention/2009/ecreport_09. cfm 71 Wong (2006); Freeman (1995) p. 881-902 72 Freeman (2006) 73 Watts (2002) 74 Joppke (2005) p. 2 makes a similar argument, "Selecting according to family ties combines the principles of state interests and individual rights, with interesting variations between states that situate this right either in the family member abroad or in the family member already admitted...Interestingly, family ties (between children and parents) are the only ascriptive element that is universally recognized in contemporary

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immigrant selection, because it is couched as an individual right and carried by a consensus that the family is the fundamental building block of society…selection according to skills or economic need, which is currently having a revival in Europe, is the only domain in which state interests reign supreme: here no rights of the individual have to be respected (except those of basic fairness and due process.)" 75 Wong (2006) p. 33-39 provides a helpful description of many of these groups. 76 ibid. p. 34 makes a similar argument, “Policies that increase the admission of certain categories of immigrants can actually undermine the political goals of rights groups. For example, Hispanic and Asian American rights organizations have long opposed the admission of traditional guest workers, temporary workers who enjoy neither labor nor civil rights, and who do not have the opportunity to apply for permanent residency.” 77 The spouse and child of migrant workers are admitted under a derivative status as secondary applicants on the application of the migrant worker. 78 As discussed earlier in this paper, the public shift in union public policy positions ranging from policy issues ranging employer sanctions to legalization programs, in particular that of the AFL-CIO, has been well documented by scholars including Watts (2002) and Briggs (2001) p. 213. 79 Wong (2006) p. 33 (see quotation in footnote 22) 80 Since 1990, the TN, S-5, S-6, E-2, E-3, Q-2, H-1C, T-1, U-1, V-1, V-2, K3, F-3, M-3, E-3R and H-2R visas have been created. Of these, the TN, E2, E-3, H-1C, E-3R, and H-2R visas are for temporary migrant workers. Of these, only the H-2R is for low-skilled temporary migrant workers. The H1C is for temporary migrant nurses, who are generally extended a lower level of rights and protections than H-1B and L visa holders, but a higher level of rights and protections than H-2R visa holders. 81 Available online: http://www.nber.org/cycles/cyclesmain.html 82 Senator preferences that are aligned with an interest group do not necessarily indicate that the interest group has influence over the Senator. In each of my four case studies I present evidence in support of my claim that special interest groups are influential in determining Senator preferences, and more influential than voters. However, this measure alone does not provide such evidence. 83 These are the most likely coalitions that may arise in the politics of migrant admissions policy. Other coalitions, such as between antiimmigration groups and immigrant advocacy groups or between unions and employers, are highly unlikely and therefore have not been considered.

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CRS Report 89-190 EPW (March 21, 1989), p. 1. The Kennedy-Simpson bill was not strongly supported by either business groups or anti-immigration advocacy groups. Congressional Quarterly Almanac (1989) p. 269 reported that, “…a new coalition put together by the U.S. Chamber of Commerce wanted something much more ambitious. 'We would like to pull out a separate business-immigration visa system,' said Virginia Thomas, a Chamber lobbyist who specialized in immigration issues. Among other things, the coalition wanted to ease procedures for granting temporary visas, because companies often need to bring people into the country for short periods. The current system, Thomas said, 'is very inflexible and archaic and doesn't respond to business needs. Likewise, while anti-immigration groups, including the Federation of American Immigration Reform (FAIR) supported an overall cap on immigration, they preferred that it also include admissions of humanitarian migrants, which were excluded from the cap under the Kennedy-Simpson bill. ibid. p. 267 86 ibid. p. 267 87 ibid. p. 265 the compromise was introduced as a substitute that was adopted in the form of an amendment to S. 358 on June 8, 1989 88 Excluding humanitarian immigration 89 ibid. p. 265 states that S. 358 was passed by the Senate on July 13, 1989 in a vote of 81-17 90 PR Newswire (1989) reported that the “Among the groups supporting Specter’s proposal were the U.S. Chamber of Commerce, the International Ladies Garment Union, the American Council of International Personnel, the American Council for Nationalities Service, the Nationalities Service Center of Philadelphia…and the Chicago Committee for Immigrant and Refugee Protection.” 91 The amendments proposed by Senator Helms were rejected in votes of 2771 and 22-77, respectively. 92 Congressional Quarterly Almanac (1990) p. 477 93 ibid. p. 479 94 Congressional Quarterly Almanac (2007) p. 15-9 95 The proposals were introduced on May 21, 2007 as Senate Amendment 1150 to S. 1348. 96 ibid. p. 15-9 97 CRS Report RL32235 (February 29, 2008), p. 19. 98 In addition to the merit-based point system, an additional point system was proposed by S. 1639 for individuals seeking legalization through an 85

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alternative provision in the legislation that established a Z visa to facilitate what was called “Z to LPR adjustments” in legal status for unauthorized migrants already residing in the United States. For more information, see, CRS Report RL34030 (September 4, 2007), p. 17. 99 ibid. p. 34. 100 CRS Report RL32235 (February 29, 2008), p. 15-11. 101 Congressional Quarterly Almanac (2007) p. 5-3 102 Hyman (2000) reported that, “Technology companies have lobbied hard for the bill’s passage to combat what they believe is a massive work-force shortage. The information Technology Association of America contends that as many as 845,000 technology jobs have gone unfilled this year.” 103 Congressional Quarterly Almanac (2000) p. 15-9 reported, “One provision would make it easier for Guatemalans, Hondurans, and Salvadorans who have been in the United States since the 1980s to get permanent residency...the other issue is known as 'legal amnesty.' In the 1986 immigration overhaul, Congress granted amnesty to thousands of illegal immigrants if they had been in the United States since 1981. But Immigration and Naturalization Service (INS) regulations implementing the law led to years of litigation. Courts generally have held that the INS misapplied the law, and have provided some relief to aliens who missed the application deadline. The 1996 immigration law stripped the courts of jurisdiction, however, unless immigrants could show they had tried to apply for amnesty but had been rebuffed. The Democratic amendment seeks to make eligible for residency anyone who has been in the United States since 1986 and is 'of good moral character'." 104 ibid. p. 15-12 reported that, “the House leadership had watched the Senate GOP grapple with politically sensitive side issues surrounding the H-1B bill and wanted to avoid a similar battle in the House.” 105 ibid. p. 15-3 106 (Hyman (2000) reported, “The bill is not without dissenting voices. Jack Golodner, president of the AFL-CIO’s department for professional employees, said the legislation is overkill. ‘We don’t think that there is evidence of a worker shortage meriting this type of increase,’ he said. He said companies should train the existing potential workforce—including older, female and black people—rather than importing employees.” 107 The Farmworker Justice website advocating for AgJOBS legislation states, “The AgJOBS compromise was carefully negotiated by the United Farm Workers and major agribusiness employers after years of intense conflict. AgJOBS is endorsed by major labor and management

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representatives, as well as a broad spectrum of organizations, including Latino community leaders, civil rights organizations, religious groups and farmworkers themselves.” Available Online: http://www.fwjustice.org/Immigration_Labor/AgJOBS_Info.htm, last accessed 5/17/2010

CHAPTER 4

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For the Correlates of War project see Singer, Bremer, and Stuckey (1972) and Sarkees (2000); For the Polity IV project see Marshall and Jaggers (2002) 109 See for example, Herrnson (1995) p. 452 and King (1995). p. 444, also referenced by Lieberman (2010) p. 38. 110 Lieberman (2010) p. 39 also underscores the importance of replication data for producing original research. 111 Leeds et al. (2002),see also: http://atop.rice.edu/, last accessed 26 September 2010 112 OECD (1999) p. 50, see also: www.oecd.org/employment/protection, last accessed 26 September 2010 113 Niessen et al. (2007), see also: http://www.integrationindex.eu/, last accessed 26 September 2010 114 Korpi and Palme (2007), see also: https://dspace.it.su.se/dspace/, last accessed 26 September 2010 115 The index score can be used as a point of comparison with other countries regarding how closely national law approximates normative standards. The MIPEX index incorporates six policy areas, referred to in the project as policy “strands,” including labor market access, family reunion, long-term residence, political participation, access to nationality and antidiscrimination. Each policy strand is divided into four policy dimensions, for example within the policy strand of labor market access there are four policy dimensions including eligibility, labor market integration measures, security of employment and the rights associated with labor market access. A total of 142 indicators are defined at the level of policy dimensions. As an example, an indicator for the policy strand of labor market access within the policy dimension of labor market integration measures is specifically defined as, “State facilitation of recognition of skills and qualifications obtained outside the EU.” The indicator is much more narrowly defined than the background concept of migrant integration policy.

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For an excellent discussion of sources through the lens of the proximity of observations, see Lieberman (2010) p. 40-43. 117 For example, binary variables reflecting the presence or absence of various policies relating to asylum have been combined into an aggregate index of the “deterrent effect” of national asylum policy by Thielemann (2004) p. 43. The Asylum Policy/Deterrence Index is coded along a five point scale of “deterrent effect,” and the index incorporates dummy variables relating to access, determination procedures and integration measures for asylum seekers. 118 OECD (no date) 119 Niessen et al. (no date) 120 Lieberman acknowledges commonly used procedures to assess the reliability of data, including the use of multiple coders and tests for intercoder reliability, but finds these safe-guards alone to be inadequate. “Correspondence of scores among multiple coders may give the false impression of better measurement in cases where very little actual information has been obtained, whereas the unearthing of additional historical records and facts—i.e., more observations—could generate new ambiguities about classification. For example, if the only fact presented to coders was that elections in a given country-year were 'widely regarded as free and fair,' multiple coders might easily classify the case as a democracy; but if presented with greater details about the extent of voting irregularities, freedom of the press, restrictions on autonomy, etc., coders might be uncertain about how to aggregate such facts. The latter example generates more uncertainty about a precise coding but provides a more accurate representation of the case under investigation.” (p. 44-45) 121 OECD (no date) 122 ibid. 123 It is important to note a distinction between transparent scoring and scoring that preserves the flexibility of data use. Data can be collected in a transparent way that improves its reliability, but in such a way that does not retain its flexibility of use. For example, detailed narratives may accompany scores to provide more transparency into the sources consulted and the decisions made in process of aggregation. Narratives that accompany scoring may reduce the likelihood of error in coding because they force the coder to be explicit about decisions encountered in aggregation, and they improve the likelihood that what errors do occur will be caught and corrected. While these narratives improve data reliability, they do not preserve the flexibility of data use. The more specific factors

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that contributed to the scoring decision cannot be easily recombined in new ways because they are not individually scored. 124 Nicoletti and Pryor (2006) p. 435

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U.S. Department of State statistics on temporary migrant visas issued are available online: http://www.travel.state.gov/xls/NIVIssuedFYs1989-2009Detailed.xls, last accessed on May 16, 2010. 126 U.S. Department of Homeland Security immigrant admissions data was obtained directly from the Office of Immigration Statistics. Data from the Yearbook on Immigration Statistics since fiscal year 1997 is available online at: http://www.dhs.gov/files/statistics/publications/archive.shtm#1 127 A series of reports entitled “Characteristics of Specialty Occupation Workers (H-1B)” were published first by the Immigration and Naturalization Service of the Department of Justice, and following creation of the Department of Homeland Security, by the Citizenship and Immigration Services Division as mandated by Public Law 105-277. These reports exist for the years 2000-2006. These reports are difficult to find, and can be accessed online (by scrolling down the page on this website): http://www.colosseumbuilders.com/Guild/h1b/library/reports/index.htm, last accessed May 17, 2010

APPENDIX C 128

Vote descriptions provided in Table A.1 are either direct quotes or close summaries of the Congressional Quarterly Almanac summaries of each vote.

APPENDIX D 129

Leeds et al. (2002),see also: http://atop.rice.edu/, last accessed 26 September 2010 130 (1999) p. 50., see also: www.oecd.org/employment/protection, last accessed 26 September 2010 131 Niessen et al. (2007), see also: http://www.integrationindex.eu/, last accessed 26 September 2010

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Korpi and Palme (2007), see also: https://dspace.it.su.se/dspace/, last accessed 26 September 2010 133 See: https://dspace.it.su.se/dspace/handle/10102/7, last accessed 26 September 2010 134 The ATOP codebook provides both a conceptual and operational definition of military alliances, and the authors acknowledge that the operational definition is slightly broader because it includes non-aggression pacts, which would not fall under their conceptual definition of military alliances. The decision to include non-aggression pacts is a practical one, made to better align the ATOP data with the widely-used Correlates of War (COW) dataset. Therefore, because it is broader, I consider this operational definition of military alliances to serve as the clearest specification of the background concept motivating the ATOP data collection project 135 Leeds (2005) 136 ibid. 137 ibid. 138 (1999) p. 50-51. 139 See for example (2004) p. 61. 140 There are 21 indicators defined as “Items” in the document “Calculating Summary Indicators of Employment Protection Strictness.” However, Item 3 (length of the notice period) and Item 4 (Severance pay) are each subdivided into three separate indicators for employees with varying lengths of tenure (9 months, 4 years, 20 years). I count each of these as a separate indicator. 141 OECD (no date) 142 ibid. 143 ibid. 144 More specifically, the authors describe the way in which they operationalize the concept of integration policy as focusing on six policy areas, referred to in the project as policy “strands,” including labor market access, family reunion, long-term residence, political participation, access to nationality and anti-discrimination. Each of these policy areas might be considered a background concept, and indeed the project compiles separate index scores for each of these policy areas in addition to a more general inclusion index that compiles scores from all six of the policy areas. However, the project authors do not define conceptually each policy area, and instead rely on the definition of indicators within each policy area to demonstrate what the authors consider to be relevant to a definition of that

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policy area. Therefore, based on both the title and objectives of the project, as well as the conceptual definitions provided, I consider migrant integration policies or policies that promote migrant integration to be the motivating background concept for the MIPEX dataset. 145 Niessen et al. (2007) p. 4 146 ibid. p. 189-190 147 ibid. p. 5 148 ibid. p. 5 149 Niessen et al. (no date) 150 ibid 151 The authors describe the background concept in the introduction as, “the multidimensional concept of social rights and duties as defined in national legislation on social insurance.”(p.3, available online: https://dspace.it.su.se/dspace/bitstream/10102/1522/1/Codebook.pdf Likewise, the authors have also contrasted this data with alternative existing measures of the welfare state (see, for example. Korpi and Palme (2003), p. 426 - 427) For this reason, I identify the modern welfare state and social citizenship to be the background concepts motivating the data collection endeavor. I do not consider these as two distinct background concepts, because the authors seem to use the terms interchangeably. This is not to say that there could not be distinctions made between the concepts, but rather that the authors of the data collection do not themselves identify and define the concepts in such a way as to show that they are distinguishing between them and collecting data in distinct ways to measure two concepts. Rather, the indicators defined seem to be aimed at measuring the welfare state and social rights simultaneously, as two sides of the same coin. 152 Korpi and Palme (2008a) 153 Korpi and Palme (2008b) The number of indicators listed on this document is greater than the number described in the codebook. 154 Korpi and Palme (2008a) 155 ibid. 156 ibid. 157 Leeds (2005) 158 OECD (no date) 159 Niessen et al. (2007) p. 189-191 160 Korpi and Palme (2008b) 161 Leeds (2005)

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Citations for each indicator are listed in separate documents for each country year for each insurance program, see for example: https://dspace.it.su.se/dspace/handle/10102/1584. 163 For example, the coding comments for the country of Denmark in the year 2000 include a general remark describing the Danish system of unemployment insurance, “The system has been a state supported voluntary kind with the unions as main organisers of the insurance. As in other countries with similar systems we have here chosen the metal workers’ fund to characterise the situation of the average production worker (APW).” (p. 6, Coding Comments for Denmark, available online: https://dspace.it.su.se/dspace/bitstream/10102/1484/1/Coding+Comments+ Denmark.pdf) When looking at the coding of unemployment insurance for Denmark in 2000, the main source listed is the English-language secondary source “Social Protection in the Nordic Countries 2000,” published by the Nordic Council. The page numbers referenced for this document do point to a discussion of unemployment insurance programs in Denmark, as well as other Nordic countries, but the text does not include a discussion of what type of programs specifically exist in Denmark, and which of those pertains to metal workers. In addition, when coding the conditions of unemployment insurance (Variable 6 in the SCIP coding scheme), the authors score data according to indicators for the reference period, the contribution period, the income ceiling, and whether a means test is utilized. The source cited provides a description of entitlement to daily cash benefits in Denmark, which allows coding for the reference and contribution period, but there is not a discussion of information that would inform scoring of the remaining two indicators. 164 Niessen et al. (2007) p. 5-6; The EPL data available for the 1980s and 1990s utilized both expert sources and surveys conducted by the European Commission and Wyatt Watson. An email from Danielle Venn, Employment, Labour and Social Affairs Directorate, OECD on 17 September 2010 explains, “… the Wyatt Watson and European Commission reports were used in the initial coding of the indicator in the mid-1990s. Since then (and in our 2008 update), for all OECD countries we used information provided by member governments. For non-member countries, we primarily used labour regulation available on the internet, and this information was then verified by our colleagues at the ILO.”

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Leeds, Brett Ashley. 2003. "Do Alliances Deter Aggression? the Influence of Military Alliances on the Initiation of Militarized Interstate Disputes." The American Journal of Political Science 47 : 427-39. Leeds, Brett Ashley. 2005. "ATOP Codebook." Available online: http://atop.rice.edu/. (26 September 2010). Leeds, Brett Ashley, Jeffrey M. Ritter, Sara McLaughlin Mitchell, and Andrew G. Long. 2002. "Alliance Treaty Obligations and Provisions, 1815-1944." International Interactions 28 : 237-60. Lieberman, Evan S. 2010. "Bridging the Qualitative-Quantitative Divide: Best Practices in the Development of Historically Oriented Replication Databases." Annual Review of Political Science 13 (1): 37-59. Lowell, B. L. 1999. "Temporary Visas for Work, Study, and Cultural Exchange: Introduction and Summary." In Foreign Temporary Workers in America: Policies that Benefit the U.S. Economy, ed. B. L. Lowell. Westport, Connecticut: Quorum Books, 1-28. Lowell, B. L. 2005. "Policies and Regulations for Managing Skilled International Migration for Work." United Nations, Mortality and Migration Section of the Population Division/DESA http://www.un.org.ezpprod1.hul.harvard.edu/esa/population/publications/ittmigdev2005/P03LLowell.pdf. Marshall, Monty G., and Keith Jaggers. 2002. "Polity IV Project: Political Regime Characteristics and Transitions, 1800-2002." http://www.cidcm.umd.edu/inscr/polity/index.htm. Massey, Douglas S., Jorge Durand, and Nolan J. Malone. 2002. Beyond smoke and mirrors : Mexican immigration in an era of economic integration. New York: Russell Sage Foundation. Home Office London. Migration policies towards highly skilled foreign workers by G. McLaughlan and J. Salt. Text in: google; Accessed: December 20, 2010. Navarro, Armando. 2009. The immigration crisis: nativism, armed vigilantism, and the rise of a countervailing movement. Lanham, MD: AltaMira Press. Nevins, Joseph. 2002. Operation Gatekeeper: the rise of the "illegal alien" and the making of the U.S.-Mexico boundary. New York: Routledge. Newton, Lina. 2005. ""it's Not a Question of being Anti-Immigration": Categories of Deservedness in Immigration Policy making." In Deserving and entitled: social constructions and public policy, ed. Anne L. Schneider and Helen M. Ingram. Albany: State University of New York, 139-167.

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Nicoletti, G., and F. L. Pryor. 2006. "Subjective and Objective Measures of Governmental Regulations in OECD Nations." Journal of Economic Behavior & Organization 59 (3): 433-49. Niessen, Jan, Thomas Huddleston, Laura Citron, Andrew Geddes, and Dirk Jacobs. 2007. Migrant Integration Policy Index. Brussels: British Council and Migration Policy Group. Niessen, Jan, Thomas Huddleston, Laura Citron, Andrew Geddes, and Dirk Jacobs. no date. "Review of Indicators for the 2006 Migrant Integration Policy Index: Excel Version." available online: http://www.integrationindex.eu/multiattachments/2704.html. Norris, Pippa. 2005. Radical right: voters and parties in the electoral market. New York: Cambridge University Press. OECD. 1999. Employment outlook: annual report. Paris: Organisation for Economic Co-operation and Development. OECD. 2004. Employment outlook: annual report. Paris: Organisation for Economic Co-operation and Development. OECD. no date. "Calculating Summary Indicators of Employment Protection Strictness." available online: http://www.oecd.org/dataoecd/24/40/42740190.pdf. Papademetriou, Demetrios G. 1999. "Skilled Temporary Workers in the Global Economy: Creating a Balanced and Forward-Looking Selection Process." In Foreign Temporary Workers in America: Policies that Benefit the U.S. Economy, ed. B. L. Lowell. Westport, Connecticut: Quorum Books, 2956. Robert Pear, "High-Tech Titans Strike Out on Immigration Bill," The New York Times, June 25, 2007. sec. A; column 0; National Desk; p. 1. Perea, Juan F. 1997a. Immigrants out! : the new nativism and the antiimmigrant impulse in the United States. New York: New York University Press. Perea, Juan F. 1997b. "Introduction." In Immigrants out!: the new nativism and the anti-immigrant impulse in the United States, ed. Juan F. Perea. New York: New York University Press, 342. PR Newswire. "Sen. Specter's Amendment to the Immigration Bill that would Allow More Business-Sponsored Immigration." July 13, 1989. Salt, J. 1997. "International Movements of the Highly Skilled." OECD Social Employment and Migration Working Papers (No. 3). Sarkees, Meredith Reid. 2000. "The Correlates of War Data on War: An Update to 1997." Conflict Management and Peace Science 18 (1): 123-44.

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Index

29-30, 32-33, 38-39, 41, 4344, 48, 76 Client politics, 45-46, 48, 53, 55 Cloture, 88-89, 93, 96-97 Compete America, 64 Content validity, 104, 106, 121 Council of La Raza, 63, 85 Data quality, 112-113, 115, 118, 120, 126, 128-129 Educational attainment, 11-13, 21 Employers, 21, 35-36, 45-47, 5456, 58, 60, 62, 64-67, 71-73, 82-84, 87, 90, 92, 94-95, 9798 Employment, 11-14, 16, 21, 2324, 26-27, 37-39, 41, 45-46, 59, 68, 77, 79, 83-84, 86-92, 98, 102, 107, 116 Ethnicity bias, 10, 17 Expansive policy change, 36, 38, 41, 44-47, 53, 74-76 Family reunification, 20, 24, 57, 86 Federation for American Immigration Reform (FAIR), 51-52, 86-87, 92, 94 Federation for Immigration Reform, 52 Fiscal cost, 14

Admissions, 7-12, 14-39, 41, 43-62, 64-66, 71-77, 80, 8391, 96-98, 100, 102 Aggregation, 103, 112, 115 Agricultural Job Opportunities, Benefits and Security Act (AgJOBS), 77, 79, 95, 96 Alliance Treaty Obligations and Provisions (ATOP), 106, 119, 122-123 Amendment, 19, 76, 85-86, 89, 93, 95-96 American Civil Liberties Union, 63 American Competitiveness in the 21st Century Act of 2000, 77, 92-93 American Federation of Labor and Congress of Industrial Organizations (AFL-CIO), 47, 66, 83, 91 Anti-immigration groups, 51, 5354, 57, 62, 82 Asian American Justice Center, 63 Blocking coalition, 36, 55 Border control, 49 Categories of entry, 7, 16, 18-24,

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236 Flexibility of data use, 104, 112113, 115-118, 126, 128-129 General-skill, 13, 24 H-1B, 11, 32, 43-44, 74, 76-77, 79, 92-94, 100 H-2A, 79 Highly skilled, 7-9, 11-15, 18, 21, 24, 27, 30, 33-34, 68, 76, 87, 92, 102 Historically Oriented and Integrated Replication Databases (HIRDs), 104 Hotel Employees and Restaurant Employees and Bartenders International Union, 66 Immigrant advocacy groups, 36, 46-47, 54-56, 58, 62-63, 7173, 80, 82, 84, 87, 90-91, 94, 98, 100, 102 Immigration Act of 1990, 11, 2627, 41, 43, 50, 60, 76, 78-79, 83, 87-88, 132 Immigration and Nationality Act of 1952 (INA), 17 1965 amendments, 6-7, 1619, 24-25, 34-35, 38-39, 41, 44-45, 47, 50, 53, 73-74, 89 1976 amendments, 25-27, 41 Independent data collection, 103107, 112-113, 115, 118, 125126, 129 Indicator, 18, 24, 48, 109-110, 112, 116-122, 125, 127 Interest groups, 35, 45, 47-48, 5051, 53- 56, 58- 61, 73, 97, 100 International Ladies Garment Workers Union, 66

Index Kinship, 8, 10-11, 25-27, 29, 3839, 41, 47, 59, 63-65, 67-68, 71, 77, 79-80, 83-84, 85-91, 100, 102 kinship-based admissions, 10, 29, 41, 80, 84-86, 88, 89, 91, 100, 102 Labor market, 59, 65-67, 74 Labor shortage, 14, 65, 94 Language, 14, 21, 85, 89, 106, 117 Legal entry, 38 Legalization, 20, 47, 57, 62, 77, 79-80, 88, 91-98, 102 Legislation, 7, 19-20, 26, 32, 35, 37-39, 60, 76-77, 79-80, 82, 84-85, 87-89, 91-96, 100, 116 Measurement, 16, 22, 103-104, 107-110, 115-121, 128-129, 131 Media, 20, 52 Median voter, 37 Methodology, 16, 22, 25, 28-29, 32, 103-105, 107, 109, 112113, 115-116, 122-123, 125129 Mexican American Legal Defense and Education Fund (MALDEF), 63, 85 Migrant admissions policy, 7-12, 14- 22, 24, 26-31, 33-35, 3739, 45, 47-50, 52, 54, 61, 64, 66, 71, 72-73, 76, 100, 102 Migrant Integration Policy Index (MIPEX), 23, 106, 110, 117, 121, 122-123 Modes of politics, 55 National Association of Independent Business, 64

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Index National Association of Manufacturers, 64 National Immigration Forum, 63, 84, 91 National origins, 10, 17, 53 Native workers, 7, 11, 12-14, 4547, 67 Nativism, 35-38, 45, 51 Nonimmigrant, 18, 20-21 NumbersUSA, 92, 97 Nurses, 30, 135 OECD Employment Protection Legislation (EPL) Database, 106, 116, 120, 122-123 Permanent immigrant, 7-9, 11, 1620, 22, 24-26, 28-31, 33, 39, 41, 44-46, 49-50, 55, 57, 59, 62-63, 65, 67-68, 71, 74-77, 79-80, 84-85, 87-90, 98 Point system, 84-85, 89-91 Policy linkages, 61, 93 Policy stasis, 50, 58, 72-73 Policymaker preferences, 36, 4546, 50-51, 54, 73 Post-coding aggregation, 103, 106, 112-113, 115, 121-123, 125129 Pro-immigration coalition, 46, 5456, 58, 60-62, 71-74, 76-77, 80, 82, 84-88, 90, 93-96, 98 Public attitudes, 12, 51 Public benefits, 37, 49 Quota, 7, 10, 17-19, 22, 24, 29, 31-33, 38-39, 43-44, 48, 51, 74 Ratio of bills proposed to bills passed, 48-49 Regulations, 19, 39

237 Reliability, 104, 118-119 Replication, 104 Restrictive, 35-39, 44-45, 48, 5253, 59, 68, 72-73, 79, 84, 88 Rights, 36-37, 47, 51, 57-68, 71, 73, 75-77, 79-80, 87, 90, 93, 95-97, 108 Scope, 18, 20, 48, 60-62, 115, 127, 129 Scoring decisions, 103 Scoring methodology, 112, 116, 125 Seasonal workers, 30, 32, 76, 96 Senate Judiciary Committee, 8486 Senator preferences, 54, 77, 82 Service Employees International Union, 66 Skill bias, 7-12, 14-19, 22-28, 3034, 89, 131 skill bias measure, 16, 22, 30 Skill composition, 7, 9-11, 13, 15 Skill-based quota space, 16, 18, 28 Skill-based selection criteria, 8, 11,16, 18, 21, 24 Social Citizenship Indicator Program (SCIP), 106-107, 120, 122-123 Sources, 18-19, 52, 77, 112, 115, 118-119, 123, 125, 127-129 Students, 13, 29, 125, 131 Sub-national variation, 104, 123, 127 Sunset provisions, 32, 34, 39, 42, 44 Supermajoritarian decision making procedures, 35, 50, 97

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238

Union, 47, 55-56, 66-67, 74, 87, 91, 97, 100 United Brotherhood of Carpenters and Joiners of America, 66 United Farm Workers of America, 66 United Food and Commercial Workers, 66 United States Conference of Catholic Bishops, 63 Visas, 11, 16, 18, 20, 25-27, 2930, 32, 43, 45-46, 77, 84, 86, 88-89, 93 Voters, 14, 15, 47, 82 Wages, 12, 13-14, 45, 47, 67 Winning coalition, 39, 50, 53-54, 56, 58, 60, 73-74

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Supermajority, 53, 55-57, 61, 7374, 82, 96-97 Technological change, 12 Temporary migrant, 7-8, 18-21, 28, 30, 39, 41, 43-44, 57, 62, 76, 78, 87, 93, 96-97 The Alliance Treaty Obligations and Provisions (ATOP) dataset, 106 Third country national, 22-23 Time-Series, Cross-Sectional (TSCS) data, 104 Total quota space, 16, 18, 21, 29 Tourist visas, 20 U.S. Chamber of Commerce, 64 Unemployment, 36, 58, 60, 62, 65, 67, 73-74, 76, 78, 87, 93, 97, 108

Index

Challen, Suzanna. Measuring Change in Immigration policy, LFB Scholarly Publishing LLC, 2013. ProQuest Ebook