sign in sign up
<<
Home , Alex Jones, Breitbart News, Mark Levin, Movement conservatism, Social conservatism, Steven Crowder

WHITE AND IT’S CHALLENGE TO THE AMERICAN RIGHT

A Dissertation submitted to the Faculty of the Graduate School of Arts and Sciences of in partial fulfillment of the requirements for the degree of Doctor of Philosophy In Government

By

Christopher J. Schorr, M.A.

Washington, DC July 31, 2020

Copyright © 2020 by Christopher J. Schorr All Rights Reserved

ii

WHITE NATIONALISM AND IT’S CHALLENGE TO THE AMERICAN RIGHT

Christopher J. Schorr, M.A.

Dissertation Advisor: Michael A. Bailey, Ph.D.

ABSTRACT

The American Right is undergoing a profound transformation. President Trump’s nomination and subsequent election in 2016 appear to have solidified the Republican Party’s grasp on the loyalties of ’s white working-class. The president accomplished this feat, in part, by shifting the GOP towards populist and nationalist positions on key issues; however, he also made liberal use of racially charged rhetoric commonly referred to as “ politics.” This same period also saw the emergence of a new, explicitly white nationalist, alternative rightwing movement. The “Alt Right” is vehemently opposed not only to the conventional targets of white racists – e.g., people of color and – but also to and to the nation’s civic nationalist identity.

Though distinct, both events reflect the polarization of the American political system around white identity. The key driver of this trend appears to be demographic change – whites’ declining share of the population – however, important unanswered questions remain: What unites conservatism and on “the Right” and what is the function of conservatism in this context? During the 2016 Republican Primary Election, why was it that appeals to white identity found greater or lesser resonance in some communities and with some

iii voters? What besides demographic change – which is slow, on-going, and inevitable – accounts for the renewed salience of white identity?

The risks posed by white nationalism to American society and to the American political system are substantial. This dissertation provides researchers and citizens with a richer understanding of white nationalism and of conservatism in the hopes that the mainstreaming of the former (its victory over the latter) can be averted.

INDEX WORDS: White nationalism, Conservatism, Ideology, , Identity,

Social Institutions, Political Behavior

iv

DEDICATION

To my wife Aja,

I couldn’t have done it without you.

v

ACKNOWLEDGMENTS

This dissertation was unexpected. I was well underway writing on another subject when I found myself mesmerized by the circus-like 2016 Republican Party Primary and horrified by a strange and disturbing new movement calling itself the “Alternative Right.” The silver lining for me was that events had conspired to turn my attention towards a far more interesting and relevant subject – a fact for which I am grateful, though not nearly as grateful as I am to the many people in my life that have helped to make this dissertation possible.

I want to thank my dissertation committee for their support throughout this process. I especially want to thank my committee chair Mike Bailey for his mentorship and patience. I am grateful to Jon Ladd for his technical assistance, particularly regarding survey design. I am likewise grateful to Hans Noel for his insights that made this project stronger.

I want thank George Shambaugh, who I consider an honorary committee member for his generous feedback and encouragement. I thank Clyde Wilcox, Mark Rom, Diana Kapiszewski,

Stephen Wayne, and Jim Lengle, for the many research opportunities that helped fund my studies. Georgetown’s Department of Government likewise has my gratitude for generously funding my education and research. I am grateful as well for Maria Snyder’s assistance with grant applications. I could not thank Paula Evans enough for her years of advocacy and tireless assistance.

My PhD cohort also deserve recognition as an exceptional group of people and good friends throughout this long process. The same can be said of earlier and later arriving friends

vi and colleagues. A special thanks is owed to the many such people who attended my presentations and workshops, to Angela Jenkins and David Myles who made such events possible, and to Alex

Podkul who guided these events and who provided especially valuable feedback.

I owe a debt of gratitude to the Institute for Humane Studies and to the

Foundation for their generous contributions to my research. I am also grateful to the Roper

Center for Public Opinion Research for trusting me with their data.

Lastly, I want to thank my family whose love and support sustains me, quite literally – as in feeding me – in the case of my wife Aja who worked while I pursued this dissertation. She is my greatest advocate and my success is also hers. I am grateful for support from my parents and in-laws. In pursuing a PhD, I followed in my father’s footsteps and it is difficult to imagine having done so without the benefit of his council. Finally, I thank my children Austin and Emma who support me simply by being who they are thereby filling my life with joy.

vii

TABLE OF CONTENTS

CHAPTER 1 Introduction ...... 1 1.1 Plan of the Dissertation ...... 5 2 Conservatism, White Nationalism, and the Paradox of Rightwing Politics ...... 8 2.1 Introduction ...... 8 2.2 Literature Review ...... 11 2.2.1 American Conservatism ...... 11 2.2.2 White Nationalism ...... 12 2.2.3 The Problem of Race in Conservative Politics ...... 16 2.2.4 Ingroup Prejudice and Identification ...... 21 2.3 Theory ...... 27 2.4 Data and Methods ...... 33 2.5 Findings ...... 39 2.5.1 American and White Tribes ...... 39 2.5.2 The General Character of ...... 40 2.5.3 Conservatism: The Ally of the American Tribe ...... 42 2.5.4 Robustness: What is Conservative Identification? ...... 51 2.6 Minority Tribalism? ...... 59 2.7 Conclusion ...... 64

3 Nothing Left to Conserve: Rightwing Politics amid Social-institutional Decline . . . 67 3.1 Introduction ...... 67 3.2 The “White Working Class” ...... 73 3.3 Social-institutional Decline ...... 74 3.3.1 Social Dysfunction ...... 74 3.3.2 Social Capital ...... 77 3.4 The Appeal of ...... 79 3.5 Data and Methods ...... 81 3.6 Findings ...... 86 3.6.1 Communities ...... 86 3.6.2 Robustness ...... 95 3.6.3 Individuals ...... 98 3.7 Conclusion ...... 105

4 White-lash: How Talking About Race Polarizes Whites ...... 107 4.1 Introduction ...... 107 4.2 Literature Review ...... 110 4.2.1 White Identification ...... 110

viii 4.2.2 Racial Prejudice ...... 112 4.2.3 White Nationalism and the Alternative Right ...... 114 4.2.4 White Identity in Popular Media ...... 116 4.3 Theory ...... 120 4.4 Survey Experiment ...... 123 4.5 Methods ...... 126 4.6 Findings ...... 129 4.6.1 Prime 1 ...... 129 4.6.2 Prime 2 ...... 137 4.6.3 Prime 3 ...... 142 4.6.4 Support for President Trump ...... 147 4.7 Conclusion ...... 150

APPENDICES Appendix A: Supplemental Tables for Chapter 2 ...... 152 Appendix B: Supplemental Tables for Chapter 3 ...... 177 Robustness Checks: Rural Sorting ...... 181 Appendix C: Supplemental Tables for Chapter 4 ...... 198 Plausibility Probe: Online Search of White Identity Discourse ...... 198 Pre-treatment Questionnaire ...... 215 Survey Primes ...... 220 Post-treatment Questionnaire ...... 224

Data Sources ...... 297 References ...... 299

ix

LIST OF FIGURES

2.1 White Ingroup and Outgroup Preference, by Ideology ...... 25 2.2 Feeling Thermometer “Warmth” Towards Whites and Blacks, 2016 ...... 26 2.3 Feeling Thermometer “Warmth” Towards Whites and Preference for Whites, 2016 . 27 2.4 White and American Tribalism: Ingroup Identification and Prejudice ...... 33 2.5 Mean Ethnocentrism and Agreement that “Minorities (Should) Adapt,” by White and American Identification ...... 40 2.6 Tribalism’s “General” Character: Identification and “Cross-tribal” Prejudice . . . . . 41 2.7 Mean Racial and National Prejudice, by Ideology ...... 43 2.8 Mean Importance of American and White Identities by Ideology, 2012 and 2016 . . . 44 2.9 95% CI Plot: American Identification Ordered Logit Regression Coefficients on White Identification by Ideology, 2012 and 2016 ...... 47 2.10 95% CI Plot: Prejudice Regression Coefficients (OLS and Ordered Logit) on Ingroup Identification by Ideology, 2012 and 2016 ...... 48 2.11 Group Evaluation Coefficients on American and White Identification: Self-identified “Conservative” Respondents, 2016 ...... 50 2.12 95% CI Plot: American Identification Regression Coefficients on Principal Components by White Identification, 2012 ...... 60 2.13 95% CI Plot: American Identification Regression Coefficients on Principal Components by White Identification, 2016 ...... 60 2.14 Mean Racial Identification by American Identification, 2012 and 2016 ...... 61 2.15 Mean Racial Prejudice by Racial and American Identification, 2012 and 2016 . . . . 62 2.16 Mean National Prejudice by Racial and American Identification, 2016 ...... 62 3.1 Social Capital and Social Dysfunction in GOP Primary Counties ...... 94 4.1 Online Articles Critical of White Identity and Trends in White Identification, 2000-2016 ...... 120 4.2 “Critical” and “Anti-Critical” Discourse in Online Articles, 2000-2016 ...... 125 4.3 Interaction Effects: Prime 1 and Ideology on Concern for Whites’ Group Position . . 131 4.4 Interaction Effects: Prime 1 and Ideology / White Identification on Measures of Identification Ingroup ...... 133 4.5 Interaction Effects: Prime 1 and Conservatism / White Identification on Measures of Ingroup Prejudice ...... 136 4.6 Interaction Effects: Prime 2 and Ideology / White Identification on Measures of Ingroup Identification ...... 138 4.7 Interaction Effects: Prime 2 and Ideology / White Identification on Measures of Ingroup Prejudice ...... 141 4.8 Interaction Effects: Prime 3 and Ideology / White Identification on Measures

x of Ingroup Identification ...... 143 4.9 P1 and P3 Interactions with Ideology / White Identification on Measures of Ingroup Identification ...... 143 4.10 Interaction Effects: Prime 3 and Ideology / White Identification on Measures of Ingroup Prejudice ...... 145 4.11 Interaction Effects: Trump Support Regressed on Interactions between Survey Primes and Ideology ...... 149

xi

LIST OF TABLES

2.1 Correlations: Ingroup Identification, Prejudice, and Conservatism, 2012 and 2016 . . 21 2.2 Ordered Logit: White Identification on Conservative Identification, 2012 and 2016 . 45 2.3 Ordered Logit: American Identification on Interactions between White and Conservative Identifications, 2012 and 2016 ...... 46 2.4 Cronbach’s Alpha Tests with and without White Identity Principal Components . . . 52 2.5 Principal Components of Conservatism, 2012 and 2016 ...... 53 2.6 OLS: Conservative Identification on Principal Components of Conservatism, 2012 and 2016 ...... 58 2.7 Correlations: “PCA Conservatism” and Conservative Identification, 2012 and 2016 . 59 3.1 Social Institutions and Identification, ANES 2012-2016 ...... 71 3.2 Summary Statistics of Sampled Data: Social Capital Community Benchmark Survey 82 3.3 OLS Regression of 2016 GOP Primary Vote-shares on Membership Social Capital Measures ...... 89 3.4 OLS Regression of 2016 GOP Primary Vote-shares on Social Capital Measures: Trust and Religious Attendance ...... 90 3.5 OLS Regression of 2016 GOP Primary Vote-shares on “Work” Social Dysfunction Measures ...... 91 3.6 OLS Regression of 2016 GOP Primary Vote-share on “Family” Social Dysfunction Measures ...... 92 3.7 OLS Regression of 2016 GOP Primary Vote-share on “Behavior” Social Dysfunction Measures ...... 93 3.8 Logistic Regression of 2016 GOP Primary Candidate Support on Social Capital Measures: Trust and Religious Attendance ...... 99 3.9 Logistic Regression of 2016 GOP Primary Candidate Support on Social Capital Measures and “Working Class” Status ...... 100 3.10 Logistic Regression of 2016 GOP Primary Candidate Support on Explanations for Trump Support (Odds Ratios) ...... 103 3.11 Odds Ratios: Support for 2016 GOP Primary Candidates ...... 104 4.1 Implications of Findings for Hypotheses ...... 147 A1.1 OLS: Ethnocentrism on Identification, 2012 ...... 152 A1.2 OLS: Ethnocentrism on Identification, 2016 ...... 153 A1.3 Ordered Logit: Minorities Should Adapt to U.S. Customs & , 2016 . . . . 154 A1.4 Ordered Logit: White Identification on American Identification, 2012 and 2016 . . . 155 A1.5 Ordered Logit: American Identification on White Identification, 2012 and 2016 . . . 156 A1.6 Ordered Logit: White Identification on Interactions between American and Conservative Identifications, 2012 and 2016 ...... 157

xii A1.7 Factor Analysis Variables ...... 158 A1.8 Ordered Logit: American Identification on Interactions between White Identification and Principal Components 1 and 2, 2012 ...... 160 A1.9 Ordered Logit: American Identification on Interactions between White Identification and Principal Components 3 and 5, 2012 ...... 161 A1.10 Ordered Logit: American Identification on Interactions between White Identification and Principal Component 6, 2012 ...... 162 A1.11 Ordered Logit: American Identification on Interactions between White Identification and Principal Components 7 and 8, 2012 ...... 163 A1.12 Ordered Logit: American Identification on Interactions between White Identification and Principal Components 1 and 2, 2016 ...... 164 A1.13 Ordered Logit: American Identification on Interactions between White Identification and Principal Components 3 and 4, 2016 ...... 165 A1.14 Ordered Logit: American Identification on Interactions between White Identification and Principal Components 5 and 6, 2016 ...... 166 A1.15 Ordered Logit: American Identification on Interactions between White Identification and Principal Components 7 and 8, 2016 ...... 167 A1.16 Ordered Logit: American Identification on Black Identification, 2012 and 2016 . . . 168 A1.17 Ordered Logit: American Identification on Hispanic Identification, 2012 and 2016 . . 169 A1.18 Ordered Logit: American Identification on Asian Identification, 2012 and 2016 . . . 170 A1.19 OLS: Ethnocentrism on Interaction between Ingroup (Black and American) and Conservative Identifications, 2012 and 2016 ...... 171 A1.20 OLS: Ethnocentrism on Interaction between Ingroup (Hispanic and American) and Conservative Identifications, 2012 and 2016 ...... 172 A1.21 OLS: Ethnocentrism on Interaction between Ingroup (Asian and American) and Conservative Identifications, 2012 and 2016 ...... 173 A1.22 Ordered Logit: “Minorities Adapt” on Interaction between Ingroup (Black and American) and Conservative Identifications, 2016 ...... 174 A1.23 Ordered Logit: “Minorities Adapt” on Interaction between Ingroup (Hispanic and American) and Conservative Identifications, 2016 ...... 175 A1.24 Ordered Logit: “Minorities Adapt” on Interaction between Ingroup (Asian and American) and Conservative Identifications, 2016 ...... 176 A2.1 Summary Statistics ...... 177 A2.2 Paired, Two-way T-tests on Coefficients from OLS Regressions of Social Dysfunction on Percent White Working Class ...... 180 A2.3 OLS Regression: Change in Social Capital on Percent Rural with Demographic Controls ...... 182 A2.4 OLS Regression: Membership Social Capital on Interaction of Percent Rural and Population Change with Demographic Controls ...... 183 A2.5 OLS Regression: Trust and Religious Attendance on Interaction of Percent Rural and Population Change with Demographic Controls ...... 184 A2.6 OLS Regression: Change in Social Dysfunction on Percent Rural with Demographic Controls ...... 186

xiii A2.7 OLS Regression: Social Dysfunction on Interaction of Percent Rural and Population Change with Demographic Controls ...... 188 A2.8 OLS Regression of 2016 GOP Primary Vote- on Membership Social Capital Measures with Economic Control Variables ...... 192 A2.9 OLS Regression of 2016 GOP Primary Vote on Social Capital Measures: Trust and Religious Attendance Measures with Economic Control Variables ...... 193 A2.10 OLS Regression of 2016 GOP Primary Vote on “Family” Social Dysfunction Measures with Economic Controls ...... 194 A2.11 OLS Regression of 2016 GOP Primary Vote on “Behavior” Social Dysfunction Measures with Economic Controls ...... 195 A2.12 Logistic Regression of 2016 GOP Primary Vote on Explanations for Trump Support, Substituting Ethnocentrism for Racial Resentment (Odds Ratios) ...... 196 A2.13 Odds Ratios: 2016 GOP Primary on Explanations for Trump Support, Including “” Measure ...... 197 A3.1 Ordered Logit: Concern for Group Position Regressed on Indicator and Continuous Measures of Conservatism ...... 235 A3.2 Ordered Logit: Concern for Group Position Regressed on Indicator and Continuous Measures of White Identification ...... 236 A3.3 Ordered Logit: Concern for Group Treatment Regressed on Indicator and Continuous Measures of Conservatism ...... 237 A3.4 Ordered Logit: Concern for Group Treatment Regressed on Indicator and Continuous Measures of White Identification ...... 238 A3.5 Ordered Logit: Concern for Group Jobs Regressed on Indicator and Continuous Measures of Conservatism ...... 239 A3.6 Ordered Logit: Concern for Group Jobs Regressed on Indicator and Continuous Measures of White Identification ...... 240 A3.7 Ordered Logit: Concern for Group College Admission Regressed on Indicator and Continuous Measures of Conservatism ...... 241 A3.8 Ordered Logit: Concern for Group College Admission Regressed on Indicator and Continuous Measures of White Identification ...... 242 A3.9 OLS: White Consciousness Regressed on Indicator and Continuous Measures of Conservatism ...... 243 A3.10 OLS: White Consciousness Regressed on Indicator and Continuous Measures of White Identification ...... 244 A3.11 Ordered Logit: Regressed on Indicator and Continuous Measures of Conservatism ...... 245 A3.12 Ordered Logit: Ethnic Nationalism Regressed on Indicator and Continuous Measures of White Identification ...... 246 A3.13 Ordered Logit: “ Threatens Identity” Regressed on Indicator and Continuous Measures of Conservatism ...... 247 A3.14 Ordered Logit: “Immigration Threatens Identity” Regressed on Indicator and Continuous Measures of White Identification ...... 248 A3.15 Ordered Logit: National Unity Regressed on Indicator and Continuous Measures of Conservatism ...... 249

xiv A3.16 Ordered Logit: National Unity Regressed on Indicator and Continuous Measures of White Identification ...... 250 A3.17 Ordered Logit: “Truly American = White” Regressed on Indicator and Continuous Measures of Conservatism ...... 251 A3.18 Ordered Logit: “Truly American = White” Regressed on Indicator and Continuous Measures of White Identification ...... 252 A3.19 Ordered Logit: “Truly American = American Ancestry” Regressed on Indicator and Continuous Measures of Conservatism ...... 253 A3.20 Ordered Logit: “Truly American = American Ancestry” Regressed on Indicator and Continuous Measures of White Identification ...... 254 A3.21 Ordered Logit: “Truly American = Born in U.S.” Regressed on Indicator and Continuous Measures of Conservatism ...... 255 A3.22 Ordered Logit: “Truly American = Born in U.S.” Regressed on Indicator and Continuous Measures of White Identification ...... 256 A3.23 Ordered Logit: “Truly American = Speak English” Regressed on Indicator and Continuous Measures of Conservatism ...... 257 A3.24 Ordered Logit: “Truly American = Speak English” Regressed on Indicator and Continuous Measures of White Identification ...... 258 A3.25 Ordered Logit: “Truly American = U.S. Citizen” Regressed on Indicator and Continuous Measures of Conservatism ...... 259 A3.26 Ordered Logit: “Truly American = U.S. Citizen” Regressed on Indicator and Continuous Measures of White Identification ...... 260 A3.27 Ordered Logit: “Truly American = Live in the U.S.” Regressed on Indicator and Continuous Measures of Conservatism ...... 261 A3.28 Ordered Logit: “Truly American = Live in the U.S.” Regressed on Indicator and Continuous Measures of White Identification ...... 262 A3.29 Ordered Logit: “Truly American = Feel American” Regressed on Indicator and Continuous Measures of Conservatism ...... 263 A3.30 Ordered Logit: “Truly American = Feel American” Regressed on Indicator and Continuous Measures of White Identification ...... 264 A3.31 Ordered Logit: “Truly American = Respect Institutions and Laws” Regressed on Indicator and Continuous Measures of Conservatism ...... 265 A3.32 Ordered Logit: “Truly American = Respect Institutions and Laws” Regressed on Indicator and Continuous Measures of White Identification ...... 266 A3.33 OLS: Ethnocentrism Regressed on Indicator and Continuous Measures of Conservatism ...... 267 A3.34 OLS: Ethnocentrism Regressed on Indicator and Continuous Measures of White Identification ...... 268 A3.35 OLS: Feeling Thermometer Ratings of Whites Regressed on Indicator and Continuous Measures of Conservatism ...... 269 A3.36 OLS: Feeling Thermometer Ratings of Whites Regressed on Indicator and Continuous Measures of White Identification ...... 270 A3.37 Ordered Logit: Racial Resentment Regressed on Indicator and Continuous

xv Measures of Conservatism ...... 271 A3.38 Ordered Logit: Racial Resentment Regressed on Indicator and Continuous Measures of White Identification ...... 272 A3.39 Ordered Logit: “Minorities are Patriotic” Regressed on Indicator and Continuous Measures of Conservatism ...... 273 A3.40 Ordered Logit: “Minorities are Patriotic” Regressed on Indicator and Continuous Measures of White Identification ...... 274 A3.41 OLS: Regressed on Indicator and Continuous Measures of Conservatism ...... 275 A3.42 OLS: Affirmative Action Regressed on Indicator and Continuous Measures of White Identification ...... 276 A3.43 OLS: "Legal Immigration is Good" Regressed on Indicator and Continuous Measures of Conservatism ...... 277 A3.44 OLS: "Legal Immigration is Good" Regressed on Indicator and Continuous Measures of White Identification ...... 278 A3.45 OLS: “Increase Immigration” Regressed on Indicator and Continuous Measures of Conservatism ...... 279 A3.46 OLS: “Increase Immigration” Regressed on Indicator and Continuous Measures of White Identification ...... 280 A3.47 Ordered Logit: “Pro-Undocumented Immigrants” Regressed on Indicator and Continuous Measures of Conservatism ...... 281 A3.48 Ordered Logit: “Pro-Undocumented Immigrants” Regressed on Indicator and Continuous Measures of White Identification ...... 282 A3.49 Ordered Logit: “Blame Blacks for Police Tensions” Regressed on Indicator and Continuous Measures of Conservatism ...... 283 A3.50 Ordered Logit: “Blame Blacks for Police Tensions” Regressed on Indicator and Continuous Measures of White Identification ...... 284 A3.51 OLS: “Support School Vouchers for Racial Minorities” Regressed on Indicator and Continuous Measures of Conservatism ...... 285 A3.52 OLS: “Support School Vouchers for Racial Minorities” Regressed on Indicator and Continuous Measures of White Identification ...... 286 A3.53 Ordered Logit: Racial Inequality Guilt Regressed on Indicator and Continuous Measures of Conservatism ...... 287 A3.54 Ordered Logit: Racial Inequality Guilt Regressed on Indicator and Continuous Measures of White Identification ...... 288 A3.55 Ordered Logit: Perception of Race Privileges Regressed on Indicator and Continuous Measures of Conservatism ...... 289 A3.56 Ordered Logit: Perception of Race Privileges Regressed on Indicator and Continuous Measures of White Identification ...... 290 A3.57 Ordered Logit: Perceptions of White Advantages Regressed on Indicator and Continuous Measures of Conservatism ...... 291 A3.58 Ordered Logit: Perceptions of White Advantages Regressed on Indicator and Continuous Measures of White Identification ...... 292

xvi A3.59 OLS: Regressed on Indicator and Continuous Measures of Conservatism ...... 293 A3.60 OLS: White Guilt Regressed on Indicator and Continuous Measures of White Identification ...... 294 A3.61 OLS: Feeling Thermometer Evaluations of President Trump Regressed on Indicator and Continuous Measures of Conservatism ...... 295 A3.62 OLS: Feeling Thermometer Evaluations of President Trump Regressed on Continuous Indicator and Measures of White Identification ...... 296

xvii

CHAPTER 1 INTRODUCTION

“We condemn in the strongest possible terms this egregious display of hatred, bigotry, and violence, on many sides… It has no place in America. What is vital now is a swift restoration of and the protection of innocent lives. No citizen should ever fear for their safety and security in our society. And no child should ever be afraid to go outside and play or be with their parents and have a good time.” President Donald Trump, August 12, 2017. (Holan 2017)

“You’re changing history. You’re changing culture. And you had people – and I’m not talking about the neo-Nazis and the white nationalists – because they should be condemned totally. But you had many people in that group other than neo-Nazis and white nationalists. Okay? And the press has treated them absolutely unfairly. Now, in the other group also, you had some fine people. But you also had troublemakers, and you see them come with black outfits and with the helmets, and with the baseball bats. You had a lot of bad people in the other group.” President Donald Trump, August 15, 2017. (Holan 2019)

The label “white nationalist” has been affixed to President Trump and his supporters by critics in the press and by opponents in Congress. This charge endures for several reasons. The president’s own words – his penchant for racially inflammatory remarks and his equivocating response to the violence at Charlottesville – lend credibility to the white nationalist label in the eyes of many of his critics. The charge sticks as well because members of the Alt Right – a loosely-organized, internet-based racist community – were among the president’s earliest and

1 most enthusiastic supporters. Finally, the white nationalist label sticks because it wounds and because American politics has become a blood sport.

At the same time, the meaning of the term seems incoherent. In its public use, “white nationalism” is conflated with “;” yet, the meaning of that term is also unclear.

Conventionally, white supremacy describes a system predicated on white superiority and where oppression provides legal and social advantages to whites at the expense of other groups. 1

Historic examples include under and the American South prior to the achievements of the . “White supremacists,” in turn, support such systems or seek to (re)create them; yet, as I write this introduction in the wake of the murder of

George Floyd, protesters and rioters have targeted not only statues of Confederate generals – men who fought to preserve white supremacist institutions – but also those of men like Thomas

Jefferson with the label white supremacist.

While it is true that Jefferson was a slave owner and was born into a system that could be justly labeled white supremacist, no statue to him was ever erected for this reason. Jefferson is revered chiefly because he wrote the Declaration of Independence, a document that declares the nation’s founding values including the then-bold claim that all men are created equal. Jefferson’s influence on the nation’s founding or “creedal” values (Myrdal 1944) laid the foundation for a far more inclusive understanding of than the one he was born into. Who alive today could hope to make a comparable contribution to racial equality?

The case that monuments to Jefferson, and to other founding fathers and revered national leaders, celebrate white supremacy is based on the claim that the of America began

1 Oxford Dictionary on Lexico.com, “white supremacy,” accessed July 19, 2020, www.lexico.com/definition/white_supremacy.

2 as a white supremacist project and remains so, even post-segregation (Omi and Winant 1986,

Ansley 1988, 1997). Proponents of this perspective treat racial disparities as evidence of institutionalized racial oppression (Sowell 2019) and assert that white identity (“whiteness”) is both a creature of racial oppression (Roediger 1994) and synonymous with American identity

(Frankenberg 1997, Baldwin 1985). Until recently, such arguments gained little traction outside of heavily Marx-influenced academic fields – e.g., the various “studies” and “critical” departments. Today, this more expansive definition of white supremacy is decidedly mainstream. 2

Viewed from this standpoint, supporters of the institutions of the American system of government and society in 2020 could be “reasonably” labeled white supremacist. Presumably, this label would apply especially to those who defend the American system (“white supremacy”) professionally and at the risk of their own lives – e.g., law enforcement and military personnel – including the many such people who also happen to be of color. Certainly, those seeking to conserve American institutions would be white supremacists. The white supremacist label would thus fit the Trump administration but perhaps also the entire Republican Party – in government and in the electorate – and much of the Democratic Party as well, given that reverence for the

American project and nation is by no means exclusively felt on the political Right. White supremacists, so defined, are a diverse group.

2 The Times’ 1619 Project prominently advances this narrative of American history (Silverstein 2020). Before retracting its publication under pressure from conservative media, the Smithsonian National Museum of African American History and Culture declared working hard, delaying gratification, and forming nuclear families to be aspects of “whiteness.” The Smithsonian further stated that belief in such things as individualism, property rights, the scientific method, and “objective, linear thinking” amount to “internalizing whiteness” (see: Watts 2020).

3 But what of the term white nationalist? Is it synonymous with white supremacist?

Whatever one thinks of the merits of the arguments animating so much of the ascendant anti- racist movement, there remains the problem of where to place unambiguously racist who are clearly outside the mainstream of American politics and who primarily seek to tear down rather than to defend American institutions on the grounds that such institutions do not advance white interests. As shown by Swain (2002), these racists are not so much

“supremacists,” in that they do not seek to restore or reinforce racial hierarchy (supremacy) through legal / institutional oppression; rather, they are ethnic nationalists who seek a separate nation for .

Broadly speaking, this is the character of the Alternative Right: a movement properly labeled as white nationalist. Though alt rightists vary in their commitment to the “ethno-state”

(Hawley 2017), they retain the separatist character that defined white nationalism a generation earlier (Swain 2002). To acknowledge this is not to apologize for white nationalism; indeed, it is difficult to imagine a worse movement to have gained traction in the present moment. Though alt rightists sometimes play coy on this point, their ethnically homogenous dream-state could only be achieved through horrific means. The success of white nationalism would end the American project of self-government rooted in the ideals of liberty and equality under the law.

White nationalism is plainly not conservatism. It is not a more populist subcategory of conservatism – “” or national / rightwing – nor is it “conservatism plus ” or “conservatism plus more racism.” White nationalism, as represented today by the appropriately named Alternative Right, represents a distinct rightwing movement grounded in different premises than those on which the conservative intellectual stands. This, in my

4 judgment, is the problem with much of the current, colloquial use of the term (though not its use in Swain’s definitive work on the subject).

White nationalism’s novel characteristics can be appreciated both by those who equate conservative politics with white supremacy and by those who view the two as unrelated or as in tension. The characterization of conservatism as a fig leaf covering white supremacy (e.g., Edsall and Edsall 1992 , Walters 2003), 3 is consequently not the focus of this dissertation. I also do not attempt to prove or disprove claims made by other scholars that conservatives are more likely to harbor racial biases, though I discuss these claims in some detail. In short, this is not a

“conservatism and racism” dissertation but rather, a “conservatism and white nationalism” dissertation. I search for the rival rightwing movements’ “connective tissue,” considering question like, what does it mean to be on the Right if the Right includes both conservatism and white nationalism, and what is the role of conservatism in this context? Furthermore, where and why does white – a precursor to white nationalism – resonate more than political appeals centered on conservative mainstays like economic liberty and traditional values? Finally, if the political viability of white nationalism depends on white consciousness, how likely is it that anti-racist scholars and activists may be unwittingly enabling white nationalist politics through their efforts to increase the salience of white identity?

1.1 PLAN OF THE DISSERTATION

This dissertation addresses the relationship between white nationalism and conservatism in three stand-alone papers. The first paper considers the apparent overlap between the two rightwing philosophies on matters of “racial policy.” Conservatism’s critics on claim

3 Walters’ (2003) uses the term white nationalism in this colloquial sense.

5 that conservative policies advantage whites at the expense of people of color; yet, this is the goal of white nationalism, not conservatism. Examining conservatism and white nationalism in the context of research on intergroup attitudes (Tajfel 1978, Key 1949, Sidanius and Pratto 2001,

Kinder and Kam 2010, and Stenner 2005) – I propose that, as people, conservatives and white nationalists are of a similar kind , being more ingroup-oriented or “tribalistic” than left-leaners. I further propose that conservative ideology recategorizes (Gaertner et al. 1993) white tribalism, orienting right-leaners towards national rather than racial conceptions of the ingroup. I do this using survey data from the 2012 and 2016 American National Election Time-series (ANES) and regression methods. I employ principal component analysis as a robustness check.

The second paper addresses the sources of Trump support among non-college educated

(“working-class”) whites. Drawing on research addressing the state of white working-class

American communities (Wilcox 2010, Wilcox et al. 2012, and Murray 2012), I connect social institutional characteristics, including the prevalence of “social disfunctions” (e.g., Wilson 1987) and the strength of social bonds (Putnam 2000), to voting behavior in the 2016 GOP Primary election. I leverage data from a variety of sources, including county-level election returns and measures obtained directly and indirectly (e.g., Rupasingha et al. 2006) from the US Census. I additionally draw on individual-level data from the 2000 and 2006 Social Capital Community

Benchmark Surveys (SCCBS: Putnam et al. 2000 and 2006) and from the 2012 and 2016 ANES.

I utilize regression methods and, in the case of SCCBS data, multilevel regression with poststratification.

The final paper considers whether exposure to themes advanced by and in popular media contributes to white identification and racial prejudice on the political Right. After framing the question in the context of an eclectic group of literatures –

6 white nationalism, white identification, intergroup attitudes, and media effects – I report findings from a survey experiment in which 1,527 white respondents are primed with “critical,” “anti- critical,” and “conciliatory” discussions of race and white identity, and surveyed for effects on group attitudes. Once again, regression methods are employed.

7

CHAPTER 2

Conservatism, White Nationalism and the Paradox of Rightwing Politics

2.1 Introduction

The American Right is undergoing a profound transformation. The election of President

Trump in 2016 solidified the Republican Party’s grasp on the loyalties of America’s white working-class, shifting the party towards populist and nationalist positions on key issues.

Concurrently, a new, Alternative Right emerged eschewing conservatism and embracing white nationalism. The Republican Party in 2020 remains a , even in its more populist guise. Its defenders argue that the foundations of conservative and white nationalist politics are irreconcilable; yet, the ascendance of “Trumpism” (or “populist-nationalism”) underscores that the meaning of conservatism is subject to revision. If the party of Reagan,

Kemp, and Ryan could, almost without warning, reverse itself on formerly defining issues like trade, foreign policy, and criminal justice, one can imagine a “conservatism” that incorporates – or alternatively, lives alongside – white nationalism beneath a larger, rightwing “big tent.”

If this possibility sounds remote, consider that the salience of white identity is rising as

American whites take stock of their group’s declining numbers and power (Jardina 2019). In part for this reason, the GOP now relies on support from economically downscale whites – an increasingly secular group (Wilcox, Cherlin, Uecker, and Messel 2012) for whom laissez-faire economics typically fall flat (Douthat and Salam 2004, 10-11). How will the Republican Party

8 hold together a coalition of predominantly white voters in an era of heightened white identification? Will such a party remain a “conservative” party and if so, what would it seek to conserve?

Viewed from the outside – from the political Left – the tensions between the rival rightwing movements are less apparent. Conservatism’s detractors criticize it for, in their view, advancing the interests of white people at the expense of people of color (e.g., Edsall and Edsall

1992). They attack conservatism, in other words, for serving the goals of white nationalists. In some cases, critics go so far as to claim that conservatism is white nationalism (e.g., Walters

2003). Given the sharp philosophical differences between these movements, what explains the appearance of policy convergence on some racial issues and how is it that white nationalists successfully recruit among conservatives? It is not enough to say that these movements’ aims partially overlap because they are both of “the Right” because this answer merely rephrases the question. 4

Conversely, why do white nationalists embrace conservative positions at odds with the premises underlying their own movement? How, for example, could people simultaneously view themselves as American patriots (e.g., and Day) yet also reject the nation’s founding principles and seek to dissolve it along racial lines (Holt 2018, Beale 2016)? Given their ideological priors, why would white nationalists want to consider themselves patriots?

4What does it mean to be a movement of “the Right” if the Right includes both white nationalism and conservatism? Kaplan and Weinberg (1998, 12) assert that to be of the modern Right is to embrace racism, nationalism, and . Russel Kirk’s (1987) “Ten Conservative Principles,” omit racism, nationalism, and xenophobia, but including such things as: belief in an enduring moral order, the “principle of variety,” and the necessity of reconciling permanence and change.

9 This study considers the paradox of rightwing politics – the convergence of fundamentally antithetical movements on racial issues – and its implications for our understanding of political conservatism. 5 I consider the political significance of “tribalism:” a general ingroup orientation that unites identification with prejudice (ingroup preference). Survey data demonstrates that right-leaning whites – conservatives and white nationalists – are more inclined towards advancing the interests of their ingroups (“us”) vis-a-vis outgroups (“them”) than are left-leaning whites. Where the rival right-leaners diverge is on the definition of the ingroup. I argue that in this context, and in the context of living in a multi-ethnic society, the role played by conservatism is pivotal and largely beneficial. By orienting white “tribalistic” loyalties towards the American nation and therefore away from whites as a group, conservatism functions not as a hidden enabler of white nationalism but as an institutional bulwark against it.

I proceed as follows: Section 2.2 examines the complicated associations between conservative and white nationalist politics in theory and as measured in public opinion sentiments drawing on insights from prominent research on intergroup prejudice. Section 2.3 asserts two claims where white Americans are concerned: first, that rightwing attitudes are explicable in the context of tribalism and the “two tribes;” and second, that conservative ideology recategorizes tribalism. Section 2.4 describes the data and methods used to test this theory.

Findings are presented in Section 2.5. In laying the grounds for evaluating the role of conservatism, I begin by demonstrating tribalism’s conceptual merits: the relationship between

5 By “rightwing,” I assume conventional labels, notwithstanding objections that the meaning of “Right” and “Left” are context dependent. Alternatively, Hawley’s (2016, 12) definition of the Right as “encompassing all of those ideologies that, while not necessarily rejecting equality as a social good, do not rank it at the top of the hierarchy of values” is serviceable.

10 ingroup and outgroups sentiments (2.5.1) and the general character of this relationship (2.5.2). I then present evidence in support of this study’s central assertion: that conservative ideology recategorizes tribalism (2.5.3). I next demonstrate the appropriateness of the measures employed to test this claim (2.5.4). Lastly, I briefly consider the possibility of minority tribalism in Section

2.6 before concluding in Section 2.7.

2.2 Literature Review

2.2.1 American Conservatism

Beginning with theory, the foundations of conservative and white nationalist politics appear radically different. 6 The conservative tradition in the United States can be situated within the larger Anglo-American tradition born of Burke’s Reflections on the Revolutions in

(1789) .7 This tradition emphasizes the importance of restraining both individual passions and the exercise of political power (Kirk 1987, Acton 1887). It views traditional institutions, customs, and practices as sources of social stability (Burke 1789, Levin 2020). As such, it champions the familiar over the novel ( Oakeshott 1962 ) and prefers , variety, and voluntary private association over centralization, homogeneity and coerced public association (Tocqueville 1835).

It asserts a link between freedom, property rights, and morality (Kirk 1987, Meyer 1962). It defends economic liberty (Hayek 1944, Friedman 1962) while also proclaiming that the moral

6 I refer here to the American conservative tradition broadly and not specifically to “” and the fusion of market liberal economics, moral traditionalism, and foreign policy hawkishness (e.g., Meyer 1962). Movement conservatism coalesced in the 1950s in opposition to at home and to abroad. For two generations, it dominated conservative politics in practice and in the popular imagination; yet, even restricting our view to the United States, this was only ever a single, context-dependent example of a larger tradition. 7 Haivry and Hazony (2017) argue this tradition predates Burke (1789) by centuries.

11 order is “fixed” rather than relative (Goldwater 1960, Weaver 1948, Buckley 1951). Finally, in the conservative tradition, , and later nationalism, are viewed as natural and proper expressions of human bonds (Burke 1789, Hazony 2018).

Practically speaking, a view towards conserving American society and traditions also tethers American conservatism to 18 th Century liberal values, 8 as codified in the nation’s founding documents. 9 The weight of that tradition moreover informs the comparatively “civic”

(vs “ethnic”) character of American national identity (Kohn 1944). 10 This is not to suggest that

Americans lack any ethno-cultural ancestry beyond liberalism, nor is it to say that liberal ideas exist in a vacuum – they were carried to America’s shores by people from particular places and times, as conservatives are fond of noting (e.g., Haivry and Hazony 2017). It is rather to say that, in the context of the Anglo-American conservative tradition, to be a conservative – to value one’s customs, traditions, cultural inheritance, institutions and so forth – in America , requires embracing a set of values that are universalistic and individualistic in character. Ethnic particularism might accompany such values – consistently or otherwise – but alone, it would be insufficient.

2.2.2 White Nationalism

Although the term has been used in different contexts (e.g., Walters 2003), “white nationalism” most appropriately describes an ethnic nationalist (or “ethno-nationalist”) movement centered on the white population of the United States (Swain 2002). Much as Kurdish

8 The “American Creed” embraces liberty, individualism, populism (the rule of the people), and laissez-faire (Myrdal 1944). 9 The US Constitution, The Declaration of Independence, and Papers. 10 (e.g., Rousseau 1762) defines national belonging from a common commitment to a set of institutions and values whereas ethnic nationalism conceives of the nation in terms of a common ethnic heritage (Muller 2008, 20).

12 nationalists seek an independent homeland for Kurds, white nationalists like those in the Alt

Right seek a separate nation-state for white Americans. They acknowledge that achieving this goal would require mass ethnic cleaning (Pierce 1978, Ciaramella 2013, Johnson 2014). In the interim, the Alt Right promotes, as they see it, the interests of white Americans as a group apart from the multi-ethnic American nation as it works to raise white racial consciousness (Quinn

2016).

White nationalism was born from the demise of white supremacy. Following the end of

Jim Crow, white supremacists concluded that racial separation was the only viable path forward

(Dobratz, and Shanks-Meile 2000, 10-12). This new approach first took the form of a militia- based, white separatist movement. 11 A second, more subtle approach was taken by a small group of intellectuals working to raise white racial consciousness through their writings and through public debate. 12 Like their more militant predecessors, these “high-brow” white nationalists advocated racial separatism and often territorial partition (Nieli and Swain 2003); but they also possessed the advantage of being highly educated and articulate. As such, they were better able to convey their messages to “respectable” white audiences.

By the 1980s, those audiences had become more receptive to white identity appeals.

White Americans had grown resentful of crime in the black community, ,

Affirmative Action, and blue-collar economic stagnation (Swain 2002). Resentment continued to build in the following decade as the country’s racial composition changed and in response to perceptions of racial double standards in , political correctness, and campus

11 Prominent groups included: The Order, The Posse Comitatus, The Covenant, The Sword, and The Arm of The Lord; The World Church of the Creator, The National Alliance, and The . 12 , Michal Hart, and are among the intellectual white separatists interviewed by Swain (2002). Others include , Kevin MacDonald, and Greg Johnson.

13 speech codes (d’Souza 1991, Smelser and Alexander 1999). With the arrival of the internet, these grievances could now be aired anonymously in chat rooms and on sympathetic websites

(for review see: Bliuc, Faulkner, Jakubowicz, and McGarty 2018). White grievances metastasized for decades in these online “echo chambers” (Sunstein 1999, 2002) and mingled with other socially stigmatized perspectives. 13

By 2015, the embryonic movement described by Swain (2002), Kaplan and Weinberg

(1998), and Dobratz and Shanks-Meile (2000) had “matured” into the Alternative or “Alt”

Right. 14 As the modern face of white nationalism, the Alt Right retains the key characteristics of the earlier/proto-white nationalist movements, including a central focus on white identity and racial interests. Indeed, the Alt Right is so singularly focused on race that it champions leftwing policies where those policies could plausibly benefit whites. 15 Alt rightists display little interest in the staples of mainstream (conservative) rightwing politics: taxes, entitlement spending, individual liberty, limiting government, “supporting the troops,” etc. These heterodoxies aside, the Alt Right is, without question, a rightwing movement, insofar as opposition to equality is among its most defining characteristics (Hawley 2016, 12).

At the same time, the Alt Right is deeply anti-conservative. It holds conservatives and conservatism responsible for allowing white America’s demographic decline and for conning

13 Among them: paleo-conservatism, Neo-Reaction, and radical Libertarianism (Hawley 2017). The Men’s Rights Movement, pick-up artists, online pranksters associated with the website , and video gamers organized around the Gamer Gate controversy also trafficked in similar online circles (Nagel 2017). 14 Ironically, this more mature white is perhaps most distinguishable from its predecessors by its use of juvenile humor as a political weapon. 15 Broadly speaking, the Alt Right supports abortion to limit minority population growth (Kaufman 2017), views school vouchers as threatening (Wright 2017) and, is at least open to supporting universal healthcare (Spencer 2017a). Alt Right figurehead Richard Spencer (2017b) opposes President Trump’s merit-based immigration proposal on the grounds that admitting highly skilled immigrants threatens whites’ social position.

14 white Americans into supporting policies inimical to their racial interests (Hawley 2017, 92-93;

Beale and Red Eagle 2015). 16 More fundamentally, the Alt Right rejects the universalism of

American and Western values and with it, the American Creed and the civic nationalist tradition

(Hawley 2017, 92; Beale 2016). It substitutes racial collectivism for individualism and authoritarianism for limited, constitutional government (Tucker 2017). Alt rightists demonstrate little interest in economic liberalism or traditional morality. They are anti-feminist, not from the standpoint of cultural traditionalism, but from the standpoint of “sex realism” – an evolution- inspired ideology that claims that biological differences suit men and women to different tasks.17

Many, though not all, reject loyalty or attachment to the United States (Hawley 2017, 17-18). As stated succinctly by its putative leader,

“Should we, for instance, really be fighting for ‘’ or the Constitution,

so that the Afro-Mestizo- Melting Pot can enjoy the blessing of liberty and a

sound currency?” ("Richard Bertrand Spencer," SPLC).

Finally, it should be noted that although the Alt Right and the present crop of rightwing populist movements share some policy aims with respect to issues like immigration and a similar

“flexibility” regarding economic policy, these movements are distinct in at least two important respects. 18 First, although rightwing populism likewise focuses on identity and group interests, the identity and interests at stake are those of a virtuous “common man” struggling against a cosmopolitan governing elite. That elite is viewed as disloyal for putting its own interests and

16 The Alt Right is especially active in targeting conservative intellectuals and public figures who are Jewish (ADL 2016, Barrett 2016, Hess 2016) or who have non-white or mixed-race spouses or children (French 2016), as they view such people as racially disloyal. 17 Nagel (2017) argues the Alt Right rejects both the left-egalitarian and right-traditionalist moral traditions that typically inform gender attitudes. She argues that it instead embraces a violent and misogynistic “anti-moral” tradition embodied in the writings of the Marque de Sade. 18 Stenner and Haidt (2012) emphasize the authoritarian character of rightwing populism.

15 those of its foreign clients ahead of the interests of “the people” (Mudde 2004). Opponents of right-wing populism emphasize racial undertones in such appeals (e.g., Sides, Tesler, and Vavrek

2018, Valentino, Neuner, and Vavrek 2018); however, national populism is an order of magnitude removed from the full-throated racial identitarianism of the Alt Right. 19

Second, consistent with its full-spectrum anti-egalitarianism, the Alt Right is fundamentally elitist , not populist. Its leaders draw inspiration from past and present critics of democracy (Tucker 2017), including the French (Nagel 2017) and the obscure

Neo – a group that divided over whether American democracy should be replaced by corporate governance or by a restored absolute (e.g., Land 2013).

2.2.3 The Problem of Race in Conservative Politics

As viewed through the lens of political theory, the rival rightwing movements have little in common; indeed, they are mutually exclusive where the American Creed and the civic nationalist tradition are concerned. This sharp conceptual differentiation between conservatism and white nationalism may be challenged on theoretical and policy grounds, as well as at the individual level – for example, where sentiments expressed by self-described conservatives in survey data are concerned.

Regarding theory, while the Anglo-American tradition of ordered liberty and the liberal character of the American founding are assumed here, a competing tradition has sought to conserve the political and social hegemony of whites as a group. John C. Calhoun – an undeniably conservative thinker – went so far as to deconstruct the Declaration of Independence to construct a firmer foundation for defending slavery (1851). Given Calhoun’s rejection of the

19 European identitarian movements appears to be more closely linked to populist nationalist movements. The French Génération Identitaire has rejected association with the Alternative Right ("American Racists Work to Spread 'Identitarian' Ideology" 2015).

16 American founding, his relationship to the modern American conservative tradition may be tenuous; however, the same cannot be said of William F. Buckley Jr. who wrote an editorial in

1957 defending segregation (Felzenberg 2017). 20 Regardless, the century-long effort to conserve white supremacist institutions under Jim Crow demonstrates that conservative politics can be compatible with defending white supremacy.21

In terms of policy, a staple of contemporary Left conventional wisdom is that, whatever the origins of conservative support for limited government, individualism, industriousness, and property rights, the practical effect of advancing laissez faire economics is to advantage whites as a group vis-a-vis racial minorities: people who would disproportionately benefit from left- liberal economic policies (e.g., Hajnal and Horowitz 2014). More radical critics go further, arguing that because, in practice, conservatism stands athwart the interests of people of color, it constitutes a vehicle for maintaining white supremacy (Walters 2003, Edsall and Edsall 1992). In

Walters’ (2003) account, the majority of white America is mobilized through conservative politics to “capture” public and social resources 22 from racial minorities, targeting blacks,

Hispanics and their perceived ally: the federal government.23

Policy-based critiques emphasize so-called “racial conservatism,” or conservative positions on racially impactful public policy. The meaning of such policies, and of the associated rhetoric, is contested. The presidential candidacy of Senator is illustrative.

20 Buckley subsequently disavowed these views and went so far as to defend Affirmative Action as de facto reparations for past racial injustices. 21 White nationalism is distinct from white supremacy insofar as it is an ethnonationalist movement (Swain 2002); but the two are similar where racial attitudes are concerned. 22 The resources in question are chiefly white voters’ own tax dollars. 23 Walters (2003, 15-16) builds on the claim that the United States is a “racial state” structured to defend white supremacy (Omi and Winant 1986, Ansley 1997). Ironically, his account thus posits that conservative oppression of people of color takes the form of limiting the power of the instrument of their oppression.

17 Goldwater opposed the 1964 Civil Right Act on constitutional grounds. His position appears to have been sincere, 24 but what of his supporters? Undoubtedly many were motivated by anti-black prejudice, but to what degree? So it goes with racial conservatism, generally.

- Is opposition to Affirmative Action programs driven by prejudice or by principled

opposition to “reverse ” against whites and Asians (see Sidanius, Pratto

and Bobo 1996, and Sniderman, Piazza, Tetlock, and Kindrick 1991)?

- What of opposition to mandatory bussing policies during the 1970s (McClendon,

McKee, and Pestello 1982)?

- Are “tough on crime” policies “anti-black,” given high crime and incarceration rates

among young black men (Snyder, Cooper, and Mulako-Wangota 2020, Carson 2020,

1), or are such policies proportionate and moreover, beneficial to black

communities, 25 given that violent crime is primarily an intra-racial phenomenon

(Morgan and Oudekerk, 2019, 14)?

- Symbolically, is defense of the confederate flag defense of white supremacy or of

Southern regional identity, and can the latter ever be disentangled from the former

(Cooper and Knotts 2006)?

- Are rhetorical appeals to “real America” and “heartland values” implicitly racist,

given that center of the country and its rural areas are less diverse (whiter) than

coastal and urban areas? (Levitz 2020)

24 Goldwater was a member of the Arizona NAACP and supported the Civil Rights Acts of 1957 and 1960. He personally financed desegregation efforts in Arizona and led efforts to desegregate the armed forces and even in the Senate cafeteria: all before the Supreme Court’s decision in Brown (Whittaker 2005, Edwards 2014) . 25 As documented by Fortner (2005), many blacks, including community leaders and civil rights activists, supported such measures in the hopes of alleviating crime in their communities.

18 In contemporary American politics, the convergence of racial and economic conservatism coincided with the movement of into the Democratic Party and of Southern whites into the Republican Party. 26 Lowndes (2008) characterizes this process as a capture of the

Republican Party by white supremacists with the active participation of Republican Party officials and strategists. Interviews of prominent Republican strategists of that era (Boyd 1970,

Perlstein 2012) confirm that the Southern Strategy was a very real effort to capitalize on

Southern whites’ racial prejudices.

At the same time, Republican outreach efforts were not limited to racial issues, and the

GOP share of the Southern white vote increased steadily from 1928 to 2010 (Trende 2012).

Stated differently, the efforts of a conservative party to woo a group of conservative – and not just racially conservative – voters spanned a time period during which that party was considered

“racially liberal,” through the Southern Switch, and finally, post-Jim Crow period during which

“racial conservatism” took on new meaning. 27 In practice, however, the Southern Switch and the

Republican Party’s embrace of racial conservatism meant that principled conservative defenders of racially conservative issue positions would become associated with notoriously racist figures like Bull Connor and , fairly or otherwise.

Turning to conservative individuals, a sizable body of research links political right- leaning with racial prejudice.28 In addition, whites are known to respond to racial diversity – both

26 The convergence of racial and economic conservatism occurred first among intellectuals and subsequently among voters and members of Congress (Noel 2012). 27 On the other hand, Heersink and Jenkins (2020) find white Southern Republican support associated with the “whitening” of local Republican Party organizations during the late 18 th and early 19 th Centuries. 28 See: Bobo and Kluegel 1993, Gilens 1999, Goren 2003, Kinder and Mendelberg 2000, Sidanius, Levin, Liu, and Pratto 2000, Alesina, Glaeser, and Sacerdote 2001, Lee and Roemer 2006, Williamson, Skocpol, and Coggin 2011, Abramowitz 2012, and Parker and Baretto 2013.

19 in terms of absolute levels (Luttmer 2001, Alesina and La Ferrara 2000, Poterba 1997) and in terms of rates of change (Kruse 2005, Hopkins 2009) – by withdrawing support for public services and increasing their support for Republican candidates (Giles and Hertz 1994). Such sentiments provide an important context for Republican outreach to racially conservative whites

(e.g., Medelberg 2001) 29 and further problematize the sharp theoretical distinctions drawn between conservatism and white nationalism.

The relationship between conservative ideological self-placement and racial attitudes is apparent in recent data from the American National Election Studies Time-series. Table 2.1 presents correlations between ideological self- placement (higher = more conservative), white and American identifications (higher = more highly identifying), ethnocentrism (higher = more ethnocentric), and support for the proposition that “Minorities should adapt to the customs and traditions of the U.S.” (higher = greater agreement). 30 Given that the context concerns racial attitudes among whites, correlations are restricted to non-Hispanic white respondents.

Consistent with prior research, conservative self-placement correlates moderately with ethnocentrism in 2012 (0.21) and in 2016 (0.36). Conservative self-placement does not correlate meaningfully with white identification (0.06 in 2012 and 0.14 in 2016). Given the centrality of white identity to white nationalism, this non-association is noteworthy, as is the more substantial correlation between conservative self-placement and American identification (0.27 in 2012 and

0.36 in 2016). Finally, although the “Minorities Adapt” measure is available only in 2016, it correlates with conservative self-placement at 0.42: the strongest association present in Table

2.1.

29 It is noteworthy that this phenomenon is not exclusive to whites: non-Hispanic black and Asian voters likewise shift right in response to increases in racial diversity (Craig and Richeson 2018). 30 Measures are described in greater detail beginning on page 33

20 Table 2.1 Correlations: Ingroup Identification, Prejudice, and Conservatism, 2012 and 2016 White American “Minorities Conservative Year Identification Identification Ethnocentrism Adapt" Identification White Identification 1 American Identification 0.31 1 2012 Ethnocentrism 0.37 0.24 1 Conservative Identification 0.06 0.27 0.21 1 White Identification 1 American Identification 0.38 1 2016 Ethnocentrism 0.39 0.34 1 “Minorities Adapt” 0.22 0.35 0.37 1 Conservative Identification 0.14 0.36 0.36 0.42 1 Data. 2012 and 2016 American National Election Studies Time-series. Non-Hispanic white respondents.

It seems clear that conservatism, as understood by white survey respondents, has something to do with racial attitudes: these measures do not speak to reverence for ordered liberty, religion, subsidiarity, and so forth. The correlations displayed in Table 2.1 support race-based policy critiques of conservatism; however, the association with white nationalism looks tenuous. Ingroup preference (hereafter “prejudice”) in the form of ethnocentrism and

“Minorities Adapt” appears to be a factor, but white identity does not. Moreover, what are we to make of correlations with American identification? Why should this measure correlate positively with white identification (0.31 in 2012, 0.38 in 2016) and with ethnocentrism (0.24 and 0.34), for example? Understanding the relationship between conservatism and racial attitudes – and ultimately, white nationalism – requires further investigation. Towards this end, I turn to research on group attitudes

2.2.4 Ingroup Prejudice and Identification

21 Beginning with ethnocentrism (Taylor 1871, Sumner 1906, Levine and Campbell 1972),

Kinder and Kam (2010, 8) describe a general predisposition to divide the world between ingroups and outgroups. They state,

Members of in-groups (until they prove otherwise) are assumed to be virtuous: friendly,

cooperative, trustworthy, safe and more. Members of out-groups (until they prove

otherwise) are assumed to be the opposite: unfriendly, uncooperative, unworthy of trust,

dangerous and more.

As a disposition to categorize or pre-judge people, ethnocentrism is prejudice, “broadly conceived” (Adorno, Frenkel-Brunswik, Levinson, and Sanford 1950, 102). The ethnocentrist, in other words, is not hostile towards this or that group in particular; rather, he dislikes “the other” generally, and in comparison to those he considers to be like him.

Researchers divide on the causes of prejudice. Social identity (SI) theorists argue that individuals seek to enhance the standing of their “ingroup” vis-a-vis “outgroups.” 31 Prejudice, in this tradition, results from categorizing people into “in” and “out” groups. 32 This occurs, in part, because categorization exaggerates both intra-category similarities and inter-category differences

(Harnad 2017, 16). Realistic group conflict theory (RGC) reverses this focus, positing that the presence of conflict with outgroups mobilizes ingroup identification in defense of ingroup power

31 Under experimental conditions, research subjects direct benefits to other ingroup members and away from outgroups, even when those benefits do not accrue to them individually, and even when the basis for group assignment is trivial or random (Billig and Tajfel 1973, Rabbie 1982, Tajfel and Turner 1986). 32 See: Brewer 1979, Hogg and Abrams 1988, Messick and Mackie 1989, Mullen, Brown, and Smith 1992, Stephan and Stephan 1985, Tajfel 1978, 1982; Turner 1981, and Wilder 1986.

22 and resources (Key 1949, Sherif, Harvey, White, Hood, and Sherif 1961, Blalock 1967, Levine and Campbell 1972). 33

Much like Kinder and Kam (2010), research on authoritarianism and social dominance emphasizes the importance of stable, underlying, and largely heritable predispositions. Stenner

(2005) 34 describes a predisposition (“authoritarianism”) to defer to group authority and to prefer uniformity over individual and diversity. 35 Social dominance theory (SD) frames the discussion of ingroup identity and prejudice in the context of the natural human tendency to form and preserve group-based social hierarchies (Sidanius and Pratto 2001). SD researchers describe a predisposition (“social dominance orientation”) to prefer hierarchy and to tolerate inequality

(Pratto, Sidanius, Stallworth, and Malle 1994). 36

Given the close associations between prejudice, affect, and identification (SI, RGC,

Kinder and Kam 2010, authoritarianism, SD), especially when that ingroup is the nation

(authoritarianism and SD), it seems intuitive that greater prejudice would be found on the side of the political spectrum most closely associated with defending the nation’s social and moral

33 The realistic group conflict literature focuses primarily on racial conflict; however, as with social identity theory, the effects of group conflict on identification, mobilization, and prejudice extend further. Lau (1989), for example, finds that members of ideological groups (liberals and conservatives) residing in a district with a contested election exhibit greater group identification. 34 See also: McCourt, Buchard, Lykken, Tellegaen, and Keyes 1999, Duckitt 1989, and Adorno et al. 1950. 35 Stenner (2009a) identifies “authoritarians” – approximately one third of the population – as one of the three distinct psychological profile groups associated with modern conservatism. She further notes that authoritarians are more concerned with reducing social complexity (including diversity) than with avoiding change – the goal of “status-quo conservatives” (Stenner 2009b, 193). This latent tendency towards prejudice and ingroup conformity is heightened in the absence of widely shared perceptions of a common national identity and common values (“normative threat”): conditions that have come to define modern, multicultural Western societies. 36 SD researchers further argue that dominant group members will tend to identify with their ingroup (Levin and Sidanius 1999) and support policies that benefit it (Levin, Sidanius, Rabinowitz, and Federico, 1998).

23 traditions, economic system, constitution, borders, identity, history, etc. Given also the general character of prejudice (Kinder and Kam 2010, SI, SD, authoritarianism), one could further speculate that right-leaning individuals would evidence a greater general tendency towards ingroup identification and affect. 37

Research overwhelmingly supports this intuition.38 Conservatives identify more highly as

American and as white (Schildkraut 2014). They also report greater patriotism (“A Tale of Two

Patriotisms” 2017). White identification predicts support for rightwing leaders like President

Trump (Major, Blodorn, and Blascovich 2018, Knowles and Tropp 2018, Sides et al. 2018), and for rightwing movement like Brexit (Kaufmann 2019) and populist nationalism (Kaufmann

2018). Critically, given the notion of general ingroup identification, white identification also predicts American identification among whites (Schildkraut 2014), consistent with finding that strong national identifiers are more likely to view national identity in ethno-cultural terms

(Theiss-Morse 2009, Wong 2010).

There is an interesting exception to the association between identification and affect. As depicted in Figure 2.1, politically left-leaning whites display a marked out group bias; that is to say, on average, white liberals are prejudiced against whites (Goldberg forthcoming). 39 By contrast, right-leaning whites, whites overall, and other groups – blacks, Hispanics, Asians, and

Muslims – regardless of ideology, all display mild ingroup biases (see also Kaufmann 2018, 376-

37 This is less evident in the realistic group conflict literature (see: Taylor 1998, Duckitt and Mphuthing 1998). 38 See: Lowery, Unzueta, Knowles, and Goff 2006, Sidanius et al 1996, Weller and Junn 2018, Jackson and Smith 1999, Mullen et al. 1992, and Theiss-Morse 2009. 39 Uhlmann, Pizarro, Tannenbaum, and Ditto (2009) test the impact of race on subjects’ willingness to sacrifice an innocent person to save a greater number of people. Political liberals demonstrate greater willingness to sacrifice a person with a “white-sounding” name than one with a “black-sounding” name. No effect was found for conservatives.

24

Figure 2.1. White Ingroup and Outgroup Preference, by Ideology

8); as indeed, one would expect as a general matter, given prejudice research, and given that prejudice would have been selected for in humanity’s primitive past (Campbell 1965, 1975;

Sober and Wilson 1999). Explanations for this discrepancy focus on cognitive/moral factors – i.e., differences in how (white) liberals and conservatives are “wired” (Haidt 2012) – and, as one might expect, the influence of ideology (McWhorter 2015, Kaufmann 2018, 3, 21-4). 40

In line with leftwing criticism of conservatism, the relationship between political ideology and general racial prejudice (“ethnocentrism”) is positive, linear, and sizable among

40 Haidt (2012, 125) argues that Western liberal morality is rooted in two of the five foundational moral sentiments/dimensions: harm/care and fairness/cheating. This “WEIRD” (“Western, Educated, Industrial, Rich, and Democratic”) morality omits three dimensions common to both Western conservative and non-Western moral traditions: loyalty/betrayal, authority/submission, and sanctity/degradation.

25

Figure 2.2. White Feeling Thermometer “Warmth” Towards Whites and Blacks, 2016

whites. 41 Prejudice here is measured as average preference for whites over racial outgroups

(Kinder and Kam 2010, 48); however, it should be noted that favoring one’s ingroup more than outgroups is not the same thing as hating outgroups. SI scholars find that prejudice is driven more by positive feelings towards ingroups (Brewer 1979, 1999; Gaertner and McLaughlin

1983), a point underscored by the finding that, among white respondents, feelings towards whites and blacks are positively related (Figure 2.2). At the same time, the relationship between ingroup affect and prejudice is undeniable (Figure 2.3).

41 In the 2016 American National Elections Studies Time-series, (non-Hispanic) white ethnocentrism (measured from -100 to 100) averages at 9.7 points with a standard deviation of 19.4. Averages across ideological groups range from -10.9 (“very liberals”) to 18.2 (“very conservatives”).

26

Figure 2.3. White Feeling Thermometer “Warmth” Towards Whites and Preference for Whites, 2016

2.3 Theory

Examining conservatism and white nationalism in the context of research on prejudice, the answer to the paradox of rightwing politics takes shape. The adherents of these rival ideologies are of a similar kind in that, relative to left-leaners, they are more apt to identify with and prefer their ingroups. 42 I label this shared orientation “tribalism” in the spirit of the claim that the cognitive processes by which ingroups and outgroups are differentiated owes to evolutionary pressures operating on primitive man (Sober and Wilson 1999, Brewer 1999, 2007).

42 Alternatively, among more conservative and more white nationalist-leaning whites.

27 Obviously, for white nationalism, white tribalism is the point ; but this is also obviously not the point of conservatism: that movement is grounded in principles unrelated to race. Even so, the role of tribalism in conservative politics 43 is evident, especially where the ingroup (“tribe”) is the nation (Stenner 2009a). 44

Does this mean that conservatism, like white nationalism, is a force for prejudice in

American politics? Perhaps not. Consider conservat ism as an ideology apart from conservat ives as right-leaning people. In practice, ideologies connect distinct beliefs in system united

(“constrained”) by some common narrative (Converse 1964). The actual creation of these narratives may reflect a rationalization of mutual aims by allied interest groups and activists; however, the resulting ideological product becomes a salient frame by which some portion of voting publics interpret their political and social environments (Bawn et al. 2012). 45 Both ideology and underlying dispositions should influence the character of a given politics, in practice. It is people who decide how to translate abstractions into policy and yet, people are shaped and constrained by those same abstractions.

In this context, it may be useful to reverse the question that originally animated this inquiry and ask: given that conservatives and white nationalists are similarly “tribalistic,” what differentiates the two in practice? Returning to the policy disagreements between the

43 Haidt (2012, 134) demonstrates that, in comparison to liberals, conservative caring is less universal (more local) and blended with group loyalty. 44 Although civic nationalism resists defining national belonging in terms of immutable characteristics, it nonetheless defines and therefore limits the boundaries of national identity. Commitment to civic nationalism can associate with ethnic prejudice, particularly when an is defined in terms of religious or political values contrary to those definitive of the nation (Simonsen and Bonikowski 2020). 45 Conversely, underlying dispositions may impact ideology through the same process. If, for example, activists motivated by underlying authoritarian dispositions (Stenner 2005) favored some system of institutionalize oppression, the process of bargaining with other interested parties could work a defense of oppression into a common ideological narrative.

28 conservative and white nationalist Rights, what explains conservative opposition to abortion?

Surely not prejudice. Given the racial disparities in abortion rates between white and black

Americans (Jones and Jerman 2017) and black voting patterns (Hankin 2016), one would have to conclude that conservative whites are severely deficient in mathematical reasoning if their abortion attitudes are driven by a desire to advantage whites as a group.

Similarly, if conservatives advance a white nationalist agenda, why do they advocate for

“school choice”: a policy change that would grant millions of low-income minority children entrance to high-income, white-majority schools? How can it be that more conservative whites support this idea and less conservative whites oppose it?

Finally, even if restricting the size and scope of government disadvantages racial minorities who, being poorer on average, benefit more from government action, such a program hardly seems designed to maximally advantage whites as a group. Asian Americans earn more than whites, on average (US Bureau of Labor Statistics 2018). Broken down by national origin, eighteen groups earn more than whites – a list that includes people from South and East Asia, the

Middle East, and Africa (Perry 2016). 46 Each would presumably benefit more than whites from an economic regime that taxes and redistributes less. A more intuitive prejudice -based economics would be something along the lines of “ :” economic support for the ingroup alone (Andersen and Bjørklund 1990). Other national rightwing movements have opted for this approach. Why haven’t American conservatives followed suit?

The answer to each question lies with the influence of conservative ideology on right- leaning people. It is difficult to argue that conservatives do not care about these issues –

46 Alternatively, seventeen non-white American ancestry groups, excluding Portuguese Americans.

29 particularly abortion and limited government – rather, it is likely that conservative values (among others) constrain tribalism in these cases. Moreover, while ingroup identification is natural and perhaps unavoidable, it can be “tricked” or redirected from one ingroup to another (Haidt 2012,

239; Kaufmann 2018, 21). Might conservative ideology play this role for right-leaning whites?

There are grounds for such speculation. Gaertner, Dovidio, Anastasio, Bachman, and

Rust (1993, 6), propose that prejudice can be reduced by recategorizing group identities. 47 By enhancing the salience of an “existing, common superordinate group membership,” former outgroups are brought into a common ingroup. In effect, identification is harnessed and put it in the service of unity and tolerance (see also Wong 2010).

If conservatism could reduce prejudice among right-leaning (tribalistic) whites, the most obvious pathway would be through enhancing the salience of American identity and all that it entails: patriotism, support for national traditions and customs, etc. 48 Championing American identity would provide the added benefit of redirecting ingroup identification away from essential racial characteristics and towards a conception of the ingroup that is more broadly accessible in a multi-ethnic polity. 49 These benefits would be magnified in a country where nationalism is primarily felt and expressed along civic lines because the perception of shared

47 Gaertner, Dovidio, and Gaertner (2000, 17) emphasize that their target is “aversive racism” among liberals. 48 Stated differently, American national identity can be viewed as a source of ideological constraint (Converse 1964). 49 For example, Kaufmann (2019) finds that conservatives become less supportive of rightwing populist movements following exposure to information that racial minorities are assimilating and intermarrying with whites. This strongly suggests that the defense of American rather than white identity motivates conservative opposition to immigration. Along similar lines, Wong (2010) finds that whites who view racial minorities as members of the national community are more supportive of interracial marriage and less supportive of restricting immigration and trade.

30 beliefs is a “powerful determinant of interpersonal attraction” (Byrne 1971) 50 and because categorization homogenizes perceptions of the ingroup and encourages the perception that ingroup members share one’s own beliefs (Gaertner et al. 1993, 7). 51

Moreover, although American and white American identities are entwined, they are not identical. 52 The nation’s heroes include such figures as Fredrick Douglass and Dr. Martin Luther

King Jr. Its culture – especially music (e.g., Jazz, Blues, Rock and Roll, and Hip Hop) but also art (e.g., “the Harlem Renaissance”), theater, cuisine, dialect, sports, etc. – reflect the diversity of its people. The “American Tribe” is not simply the “White Tribe” viewed from a different angle.

This point bears emphasis because if one approaches American identity solely through the lens of civic vs ethnic nationalism it is tempting to circumscribe the nation’s ethno-cultural tradition to that of its dominant ethnic group. Doing so ignores the diversity of that tradition.

Practically speaking, distinctions between the two tribes matter because how the still numerically dominant white population conceptualizes the ingroup has important implications for racial minorities. Nations have been described as “imaginary communities” (Anderson 1983) that are both sovereign and limited. The “limited” aspect of national ingroup identity is unavoidable: something defined has boundaries. The American experience demonstrates that an

50 Conservatives championing American identity may produce ancillary benefits for liberals as well. White Americans who prioritize American identity over white identity are less typically opposed to “pro-black” government programs (Smith and Tyler 1996, Wong 2010). Transue (2007) finds that enhancing the salience of American identity counteracts “policy particularism” (Sniderman and Carmines 1997): the tendency for populations to reject paying for public goods directed towards benefiting other racial groups. 51 See also: Brown 1984, Brown and Abrams 1986, Hogg and Turner 1985, Stein, Hardyck and Smith 1965, and Wilder 1978. 52 Devos and Banaji (2005) find that Americans rate both whites and blacks as more “American” than Asians; however, using implicit association tests, they find that all groups (except blacks) primarily associate white faces with American identity. Blacks equally associated black and white faces with American identity.

31 ingroup defined in large part by and by a shared individualist-universalistic value system can incorporate newcomers, however imperfectly.53 An ingroup defined in terms of essential racial characteristics would be impermeable to those who do not share those same characteristics. One cannot learn to have fairer skin.

In summary, the paradox of rightwing politics – the irreconcilability of conservativism and white nationalism coupled with an apparent common orientation on racial issues – is resolved by the following:

1. Right-leaning (conservative and white nationalistic) white Americans are united by a

shared general tendency towards tribalism: ingroup identification combined with

ingroup prejudice.

2. There are, in an abstract sense, two tribes or distinct conceptions of white American

ingroup identity: one American and one white.

3. Conservatism champions, among other things, American identity. Conservatism

should therefore direct (recategorizes) tribalism away from the White Tribe and

towards the American Tribe.

One final point: recategorization is not de categorization (Gaertner et al. 1993, 5). It does not de-tribalize or transform right-leaners into left-leaners – people for whom ingroup identity matters little. Right-leaners retain their underlying tribalistic orientation. As depicted in Figure

2.4, movement “right” is movement towards greater identification and prejudice in general, but

53 As argued by Theiss-Morse (2009), a national ingroup will contain more and less “prototypical” members defined in relation to national (e.g., racial) . Less prototypical members – less stereotypically associated with the ingroup – can be viewed as secondary or “marginal” members of the national community. It follows that if one imagines the national community less in terms of an ethnic prototype (e.g., the White Tribe) and more in terms of principles, institutions, and shared ethno-cultural traditions, the basis for viewing some members as less prototypical than others is diminished.

32

Figure 2.4. White and American Tribalism: Ingroup Identification and Prejudice

especially as associated with the specific tribes. The differences between the two conceptions of the ingroup – and therefore, the role of conservatism – matters at the margins. American identification should predict national prejudice (preference for Americans or for America) and toa lesser extent, racial prejudice. Conversely, white identification should predict racial prejudice and to a lesser extent, national prejudice.

2.4 Data and Methods

I evaluate this theory using survey data from the 2012 and 2016 American National

Election Studies Time-series (ANES). The 2012 and 2016 ANES contain 3,509 and 3,038 non-

Hispanic white respondents, respectively. I take an empirical approach, examining the relationship between conservatism and white nationalism in practice. Doing so requires first looking at conservat ives rather than conservat ism, and white national ists rather than white national ism .

33 Towards this end, survey data is the logical choice; however, its use in this case presents some complications. First, surveys do not generally ask respondents if they are white nationalists or to what degree (e.g., 1-7) they support white nationalism. This is understandable, given that white nationalists are few 54 and given confusion surrounding the meaning of term (see: Jacobson

2017). Second, surveys like the ANES ask respondents’ ideological leanings. Such questions can be considered measures of conservative identification (Levitin and Miller 1979, Conover and

Feldman 1981) given that higher values represent more conservative self-placements; but whether more conservative identifying individuals are more ideologically conservative may depend upon contestable definitions of conservative ideology, the social identity aspects of ideological identification (Malka and Lelkes 2010, Ellis and Stimson 2012, Mason 2018), and other idiosyncratic considerations. Moreover, many white nationalists would likely identify as

“conservative” rather than as “moderate” or “liberal,” despite disagreeing with conservatives on a host of major policy issues. 55

In this context, I refer to more conservative ideological self-placement as “conservative identification” and, for now, assume only that the measure captures general political right- leaning. By making no assumptions as to the ideological substance of conservative identification,

I allow survey data to reveal what conservative identification means in practice, in the mass white public.

If the preceding theory is correct, several conditions should be present in survey data.

First, respondent attitudes should demonstrate evidence of tribalism and of the two tribes.

Respondents for whom white identity as highly important should display greater racial prejudice

54 Using survey data, Jardina (2019, 82) identifies 3-6% of white respondents as “white supremacists.” 55 Similarly, a democratic socialist would likely identify as “liberal,” given the available options.

34 than respondents for whom white identity is less important. The same dynamic should be present among American identifiers: high identifiers should be comparatively prejudiced in favor of their national ingroup.

For measures of white and American identification, I rely on the questions, “How important is being white [American] to your identity?” Answer choices range from “Not important at all” (1) to “Extremely important” (5). Racial prejudice measures are constructed from “feeling thermometer” (FT) questions asking respondents to evaluate groups from 0 to 100.

Lower values represent less favorable (“colder”) evaluations and higher values represent more favorable (“warmer”) evaluations. Averaging the difference between ingroup (white) and racial outgroup FTs yields a continuous measure of ethnocentrism or general racial prejudice (see

Kinder and Kam 2010, 55).

Ethnocentrism = ((FT whites - FT blacks) + (FT whites - FT Hispanics) + (FT whites - FT

Asians) + (FT whites - FT Muslims)) / 4

Regarding prejudice in favor of the national ingroup (the American Tribe), I leverage responses to the question, “(Agree/disagree, 1-5:) Minorities should adapt to the customs and traditions of the U.S.?” 56 The “Minorities Adapt” measure is like ethnocentrism in that it captures a pro-ingroup bias and in that the outgroup is, in most cases, non-white. “Minorities

Adapt” differs from ethnocentrism in that adapting to the customs and traditions of the United

States is assimilation: the process by which a member of an outgroup joins the ingroup.

Demanding assimilation implies that assimilation is possible thus, presumably, minorities can become full members of the national ingroup – “real” Americans – if they conform. This isn’t to

56 This question is not included in the 2012 ANES Time-series.

35 say that joining the American Tribe is cost-free, or that such demands represent a generous posture towards national inclusivity. “Minorities Adapt” appears to capture precisely the kind of ingroup bias that would associate with a more permeable ingroup like the American Tribe. 57

These conditions are tested using OLS and ordered logit regression analysis. Regression controls are included for age, sex, education, income, region (South), conservative identification, ethnocentrism, authoritarianism and egalitarianism.58 Probability weights are included.

ŶEthnocentrism = β0 + β1White Identification + β2American Identification

+ βncontrols + е

Ŷ“Minorities Adapt” = β0 + β1American Identification + β2White Identification

+ βncontrols + е

Second, if tribalism is a general tendency like ethnocentrism (Kinder and Kam 2010), authoritarianism (Stenner 2005), and social dominance orientation (Pratto et al. 1994), white and

American should be related but not the same. This means that ingroup (white and

American) identifications should correlate moderately, as depicted in Table 2.1. Weak correlations (e.g., 0.1) would suggests that the two tribes are unrelated in the of American whites. Very strong correlations (e.g. approaching 1.0) would suggest that identification measures are capturing the same underlying construct – meaning that the two tribes are, in

57 This question can be interpreted differently. In theory, a strongly agreeing respondent might prefer to see minorities segregated and subjugated: forced to acknowledge the hegemony of the American mainstream while being simultaneously excluded from it. Assuming this bleak interpretation, agreement that minorities should adapt, would run against the grain of American history where immigration and assimilation (though not always race relations) are concerned. 58 Authoritarianism is an index measure comprised from four child-rearing values questions – e.g., obedience vs self-reliance (Stenner 2005). Egalitarianism is an index measures comprised from seven equality/traditionalism questions – e.g., agree/disagree “society should make sure everyone has equal opportunity” (ANES Codebook, 2018, 1141).

36 practice, the same. The case for tribalism would remain; but absent two tribes, the argument for conservatism’s role in re-categorizing white tribalism would weaken. This same logic extends to ingroup identification coefficients in regression models like the following:

ŶWhite Identification = β0 + β1American Identification + βncontrols + е

If tribalism is a general tendency, it should also be the case that American identification predicts ethnocentrism (racial prejudice) and that white identification predicts agreement that

“Minorities (should) Adapt” (national prejudice). Associations should be positive, even though

American identity includes non-whites and even though, as evidenced by white nationalists, a greater focus on white identity is more typically associated with excluding rather than absorbing racial outgroups. Still, because the white and American Tribes are related but not the same, the expectation is that tribalism (identification and prejudice) will be more pronounced (larger coefficients) when the ingroups and prejudices are aligned:

White identification coefficients: βEthnocentrism > β“Minorities Adapt”

American identification coefficients: β“Minorities Adapt” > βEthnocentrism

Third, more conservative identifying (right-leaning) respondents should be more tribalistic than less conservative identifying (left-leaning) respondents. As such, conservative identifiers should identify more as “American” and as “white,” and they should also express greater national and racial prejudice. Yet, if the roots of conservatism are indeed planted in the

American Tribe rather than in the White Tribe, it should also be the case that as respondents identify more as “conservative,” their tribal loyalties recategorize, moving from the White Tribe

37 to the American Tribe. 59 It follows that conservative identification should negatively interact with American identification in predicting both white identification and racial prejudice:

ŶWhite Identification = β0 + β1American Identification*β2Conservative Identification +

βncontrols + е

ŶEthnocentrism = β0 + β1American Identification*β3 Conservative Identification +

β3White Identification + βncontrols + е

Conversely, conservative identification should not negatively interact with American identification in predicting national prejudice. This is because national prejudice is inherent to

American tribalism:

Ŷ”Minorities Adapt” = β0 + β1American Identification*β2Conservative Identification +

β3White Identification + βncontrols + е

Fourth, while the preceding models treat conservative identifiers as right-leaning people, conservatism is, of course, an ideology. It makes specific value claims that will appeal to some people and not to others. The validity of evidence presented in support of assertions regarding the role of conservatism (recategorization) depends on the conservative identification measure being meaningfully representative of adherence to conservative ideology. This premise is tested using exploratory principal component analysis and subsequently, by substituting principal components for conservative identification in regression models.

59 Alternatively, this process can be view as categorization – creating the American Tribe – given that, as depicted in Figure 2.4, American and White Tribes are most closely associated where neither is valued highly. This understanding of white and American identities is partially at odds with Theiss-Morse’s (2009) contention that strong national identifiers view national identity in more ethnic terms.

38 2.5 Findings

2.5.1 American and White Tribes

For conservatism to recategorize white tribalism, the idea of white and American tribes – discrete bundles of ingroup identification and preference – must be meaningful in the minds of

American whites. Figure 2.5 demonstrates this to be the case. Mean ethnocentrism and agreement that minorities should adapt are displayed by ascending categories of white and

American identification. Relationships are strongly positive and statistically significant, as shown by 95% confidence intervals.

As previously displayed in Table 2.1, correlations between the respective ingroups and prejudices are positive, comparable, and moderate. OLS and ordered logit regressions investigate these associations further alongside a battery of controls (Appendix A1.1-3, 152-154). In eight total specifications (2012 and 2016), ethnocentrism coefficients on white identification are statistically significant (p<0.01, Appendix A1.1-2, 152-153) and larger than coefficients on all controls except authoritarianism (Stenner 2005) – a questionable control, given its conceptual overlap with ethnocentrism. “Minorities Adapt” coefficients on American identification are similarly large and significant (p<0.01, Appendix A1.3, 154). Once again, coefficients on authoritarianism are larger still, as authoritarianism has even more in common with national prejudice than with ethnocentrism. 60

60 Coefficients on egalitarianism (Appendix A1.3, columns 4 and 8) are strongest predictors of (dis)agreement that minorities should adapt. This suggests a strong role for ideology; indeed, the inclusion of egalitarianism substantially reduces coefficients on conservative identification.

39

Figure 2.5. Mean Ethnocentrism and Agreement that “Minorities (Should) Adapt,” by White and American Identification

2.5.2 The General Character of Tribalism

Table 2.1 additionally provides an indication of tribalism’s general character.

Correlations between white and American identifications (0.31 in 2012, 0.38 in 2016) are in the same range as correlations between identification and prejudice measures (white identification/ethnocentrism: 0.37, 0.39; American identification/“Minorities Adapt”: 0.35).

Probing further, ingroup identification measures are strongly associated in ordered logit regressions models (Appendix A1.4 and A1.5, 155-156) with coefficients ranging from 0.44 to

0.67 (all p<0.01, see also Figure 2.6, Graph 1).

Identification measures also correlate with prejudice measures across tribes, albeit less strongly than within tribes, as expected (Table 2.1 and Figure 2.6, Graphs 2-4). This pattern repeats in regression models reported in Appendix A1.1-3 (152-154). Once again, all relevant coefficients are significant at p<0.01. Just as racial prejudice (ethnocentrism) coefficients on

40

Figure 2.6. Tribalism’s “General” Character: Identification and “Cross-tribal” Prejudice (Means)

white identification exceed those on American identification, so too national prejudice

(“Minorities Adapt”) coefficients on American identification exceed those on white identification. 61 At least in terms of attitudes towards American and white identities, tribalism appears to be a general phenomenon: identification with one ingroup predicts identification with

61 The comparative weakness of “cross-tribal” associations is less obvious at the level of summarized means, as shown in Figure 2.6. Yet, some distinctions are evident; for example, line

41 another, and both predict prejudice, generally. At the same time, how ingroup is conceptualized matters at the margins.

2.5.3 Conservatism: The Ally of the American Tribe

What can survey data reveal about conservatives and ultimately, conservatism in the context of tribalism and two tribes competing for white Americans loyalties? Recalling Table

2.1, conservative identification correlates moderately in aggregate with ethnocentrism (0.21 and

0.36) and with “Minorities Adapt” (0.42). Using regression methods, relationships between conservative identification and prejudice – racial and national – are clearly positive (Figure 2.7).

Setting aside self-identified “moderates,” relationships are also strongly linear.

Looking closer at the predictors of “Minorities Adapt,” ordered logit coefficients on conservative identification are approximately twice the size of coefficients on white identification (0.50 vs 0.22 and 0.45 vs 0.20), although this gap shrinks dramatically upon inclusion of the ideologically implicated controls: authoritarianism and egalitarianism (Appendix

A1.3, 154). 62 By contrast, ethnocentrism coefficients on conservative identification are consistently smaller than coefficients on white identification (Appendix A1.1 and A1.2, 152-

153). In short, the relationship between conservative identification and prejudice is in line with both general and specific expectations: positive associations with prejudice measures and comparatively stronger intra-tribal associations, respectively.

slopes are visibly steeper for “intra-tribal” predictors (e.g., white identification predicting ethnocentrism and American identification predicting “Minorities Adapt”). 62 “Minorities Adapt” coefficients on American identification are likewise twice as large as coefficients on white identification (Appendix A1.3, 154).

42

Figure 2.7. Mean Racial and National Prejudice, by Ideology

Regarding the relationship between conservative and ingroup identification measures, the same pattern is present: as respondents move right, identification increases. At the same time, the relationship between white and conservative identification is clearly weaker (Figure 2.8, Table

2.1). Once again, “moderates” are to blame: in 2012, “moderates” tie “conservatives” as the greatest white identifiers, even exceeding “somewhat conservatives” and “very conservatives;” in 2016, “moderates” pull slightly ahead of “somewhat conservatives.” 63 American identification, by contrast, increases linearly with rightward self-placement and the range of values is greater. Both factors demonstrate a more meaningful association between conservative and American identifications.

63 This same dynamic is present when ideological groups are broken down into three categories rather than seven. Looking at “liberals,” “moderates,” and “conservatives,” the importance of white identity increases from 2.37 to 2.63 to 2.77 in 2016. In 2012, estimates increase from 2.58 to 2.84 (liberals to moderates) but then decrease to 2.81 among conservatives.

43

Figure 2.8. Mean Importance of American and White Identities by Ideology, 2012 and 2016

The relationship between white and conservative identifications is tenuous at best.

Ordered logit regression models reported in Table 2.2 clarify the situation further. Conservative identification is weakly positively associated with white identification (p<0.01) in odd numbered specifications – those omitting American identification as a control. Controlling for American identification (even numbered specifications), the association between conservative and white identifications reverse and becomes statistically insignificant. American identification thus accounts for the connection, such as it is, between conservative and white identifications: right- leaning (conservative identifying) respondents are more white identifying insofar as they are more American identifying . Apart from its association with tribalism, conservative identification is unrelated to white identification.

To better illustrate this relationship, Table 2.3 reports ordered logit regressions of

American identification on white identification interacted with conservative identification. As has been shown (Table 2.1 and Figure 2.6), associations between American and white

44 Table 2.2. Ordered Logit: White Identification on Conservative Identification, 2012 and 2016 2012 2016 (1) (2) (3) (4) (5) (6) (7) (8) White White White White White White White. White

VARIABLES Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident.

Conservative Ident. 0.0880*** -0.0162 0.0546* -0.0339 0.1182*** -0.0223 0.0995*** -0.0285 -0.0277 -0.0289 -0.0294 -0.0301 -0.0293 -0.0323 -0.0313 -0.0333 American Ident. 0.6353*** 0.6138*** 0.6580*** 0.6773*** -0.0426 -0.0459 -0.0468 -0.0497 Male -0.0472 0.032 -0.1886** -0.1686* -0.0824 -0.0828 -0.0959 -0.0972 Education -0.1456*** -0.1229*** -0.1056** -0.073 -0.0398 -0.0395 -0.0484 -0.0498 Income 0.0026 -0.001 -0.0156** -0.0189** -0.0056 -0.0056 -0.0076 -0.0077 Age 0.0091*** 0.0018 0.0068** -0.0008 -0.0025 -0.0026 -0.0029 -0.003 South 0.1841** 0.1303 0.1342 0.0745 -0.0903 -0.0909 -0.105 -0.1105 Cut 1 -0.8684*** 1.1930*** -0.9553*** 0.7718*** -0.3789*** 1.5595*** -0.8068*** 0.9356*** -0.1231 -0.177 -0.2329 -0.2501 -0.1275 -0.1755 -0.2715 -0.2657 Cut 2 0.0604 2.1982*** -0.0027 1.7904*** 0.4016*** 2.4118*** -0.0174 1.7964*** -0.123 -0.1821 -0.2327 -0.2528 -0.128 -0.177 -0.272 -0.2644 Cut 3 1.1933*** 3.4346*** 1.1366*** 3.0203*** 1.4692*** 3.5808*** 1.0662*** 2.9764*** -0.1263 -0.192 -0.2352 -0.2589 -0.1335 -0.1866 -0.274 -0.2687 Cut 4 2.4664*** 4.7755*** 2.4196*** 4.3663*** 2.7276*** 4.9182*** 2.3455*** 4.3344*** -0.1356 -0.2019 -0.2391 -0.2649 -0.1507 -0.1959 -0.2844 -0.2735

Observations 3,081 3,081 2,973 2,973 2,157 2,155 2,040 2,038 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

identifications are positive and strong. Consistent negative interactions in Table 2.3 demonstrate that this association is attenuated by conservative identification: as respondents move right politically, white identification becomes decreasingly predictive of American identification. 64 If indeed, conservative identification represents belief in conservative ideology, this decoupling of the tribes as respondents move right is precisely what one would expect to see if conservativism recategorizes identification by moving right-leaning (more tribalistic) whites towards the

American Tribe and away from the White Tribe. Moreover, as evidenced by white identification

64 Reversing the order and regressing white identification on American-conservative identification interactions, the results are directionally the same (negative), but statistically significant only in 2016 (Appendix A1.6, 157).

45

Table 2.3. Ordered Logit: American Identification on Interactions between White and Conservative Identifications, 2012 and 2016 2012 2016 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) American American American American American American American American American American

VARIABLES Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident.

White Ident. 0.5398*** 0.5131*** 0.7394*** 0.6657*** 0.6411*** 0.5630*** 0.5465*** 1.1072*** 1.0126*** 1.0559*** -0.0355 -0.0372 -0.1211 -0.1223 -0.1235 -0.0359 -0.0393 -0.1123 -0.1153 -0.1218 Conservative Ident. 0.3404*** 0.4734*** 0.4394*** 0.4191*** 0.4266*** 0.7485*** 0.6758*** 0.6614*** -0.0323 -0.081 -0.0809 -0.0827 -0.033 -0.0734 -0.0751 -0.078 White Ident.*Cons. Ident. -0.0516** -0.0471* -0.0453* -0.1308*** -0.1261*** -0.1349*** -0.0261 -0.0263 -0.027 -0.0237 -0.024 -0.0258 Ethnocentrism 0.0125*** 0.0108*** 0.0164*** 0.0152*** -0.003 -0.003 -0.0036 -0.0036 Male -0.2604*** -0.0805 -0.0926 -0.1028 Education -0.0441 -0.0793 -0.045 -0.0553 Income 0.0045 0.0106 -0.0061 -0.0082 46 Age 0.0227*** 0.0214*** -0.0029 -0.0032 South 0.1184 0.2463** -0.0999 -0.1139 Cut 1 -2.4179*** -1.2620*** -0.7025* -0.8197** -0.1342 -1.8038*** -0.4155** 0.8582*** 0.4239 1.2457*** -0.1816 -0.2187 -0.3771 -0.3728 -0.4344 -0.1638 -0.2023 -0.33 -0.3473 -0.4383 Cut 2 -1.0574*** 0.2249 0.7933** 0.6722* 1.4153*** -0.8144*** 0.7070*** 2.0213*** 1.6196*** 2.4697*** -0.1286 -0.1821 -0.3596 -0.3569 -0.4202 -0.1282 -0.1777 -0.3265 -0.3381 -0.4352 Cut 3 0.1298 1.4686*** 2.0475*** 1.9338*** 2.6970*** 0.3629*** 1.9417*** 3.3017*** 2.9184*** 3.8227*** -0.1131 -0.1769 -0.3655 -0.3626 -0.4299 -0.1144 -0.1762 -0.3358 -0.3462 -0.442 Cut 4 1.5518*** 2.9735*** 3.5592*** 3.4495*** 4.2671*** 1.7429*** 3.5858*** 4.9759*** 4.6112*** 5.5884***

-0.1155 -0.1864 -0.3793 -0.3762 -0.4439 -0.1216 -0.1915 -0.3513 -0.359 -0.4561 Observations 3,249 3,081 3,081 3,062 2,958 2,584 2,155 2,155 2,144 2,031 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Figure 2.9. 95% CI Plot: American Identification Ordered Logit Regression Coefficients on White Identification by Ideology, 2012 and 2016

coefficients in specifications 3-6 and (especially) 8-10, the positive association between the two ingroups increases dramatically in the absence of conservative identification. Ingroup identities are now all-but indistinguishable (see also Figure 2.9).

Applying this same approach to regressions on prejudice measures, interaction models

(depicted in Figure 2.10) again demonstrate clear evidence of recategorization. Conservative identification interacts negatively with American (but not white) identification in predicting ethnocentrism in 2016. Stated differently, American identification is decreasingly predictive of ethnocentrism as respondents move right, whereas the effect of white identification on

47

Figure 2.10. 95% CI Plot: Prejudice Regression Coefficients (OLS and Ordered Logit) on Ingroup Identification by Ideology, 2012 and 2016

ethnocentrism is unrelated to ideological self-placement. 65 The same is true reverse: conservative identification interacts negatively with white (but not American) identification in predicting

“Minorities Adapt” in 2016.

White racial prejudice (racial ingroup preference) is specifically associated with the

White Tribe; it only generally associates with the American Tribe because more tribalistic whites

65 American*conservative identification interactions are insignificant in 2012. Coefficients are nonetheless displayed in Figure 2.10. Though noisier and less pronounced, American identification slopes downward in 2012, as in 2016.

48

are more generally prejudiced towards ingroups. Similarly, those who identify more highly as white display greater American national prejudice (“Minorities Adapt”), not because assimilating racial minorities is consistent with an orientation towards the White Tribe, but because of the general character of ingroup prejudice. With its roots firmly planted in American rather than white identity, conservatism attenuates White Tribe-specific racial prejudice and emphasizes

American Tribe-specific national prejudice among more tribalistic whites. Ingroup identification slopes on prejudice measures displayed in Figure 2.10 are understandable in this context.

Recategorizing white tribalism is valuable in at least two respects. First, a multi-ethnic polity requires a commonly agreed upon, multi-ethnic self-conception. Absent this, such a nation is not truly a “nation.” Integration and ultimately, territorial integrity are at risk when a “Balkanizes.” This is especially the case for democracies (Taylor, 1988, 144). Second, as noted, the cognitive biases involved in categorization operate in a pro-social direction within ingroups (Hackel, Zaki, and Van Bavel 2017). This means that a common national identity can enhance intra-national ethnic tolerance, particularly among more tribalistic people.

Evidence to this effect is presented in Figure 2.11 where coefficients on American and white identification among conservative identifiers (“somewhat” to “very” conservative) are plotted from OLS regressions on group evaluations. Group evaluations include feeling thermometer and questions: are members of the following group

“hardworking”/“lazy,” “peaceful”/“violent,” and “intelligent”/“unintelligent”? Controls are included for sex, education, income, age, and region along with probability weights. American identification overwhelmingly predicts positive attitudes towards blacks, Hispanics, and Asians, though not towards Muslims. By contrast, white identification predicts negative sentiments towards outgroups in nearly every instance.

49

Figure 2.11. Group Evaluation Coefficients on American and White Identification: Self-identified “Conservative” Respondents, 2016

Ŷ(Group Evaluations)= β0 + β1American Identification + β2White Identification

+ βncontrols + е

The benefits of recategorization are evident, but the costs are worth acknowledging as well. Categorization encourages intra-group tolerance by reducing perceived ingroup dissimilarities. It does so, in part, by emphasizing inter-group dissimilarities (Tajfel 1982). Thus creating/reinforcing the American national ingroup necessarily hardens distinctions between

50

Americans (“us”) and foreigners (“them”). In the extreme, this risks . 66 Moreover, internally, creating the ingroup involves suppressing internal diversity through assimilation and conformity. 67 For racial minorities, particularly immigrants, assimilation entails sacrificing aspects of cultures and identities. Many will understandably prefer instead the multicultural vision wherein communities coexist side-by-side but are never truly the same. 68

2.5.4 Robustness: What is Conservative Identification?

The validity of the evidence presented concerning conservatism’s role in recategorizing white tribalism rests on the assumption that conservative identification meaningfully approximates adherence to conservative ideology. To determine whether this assumption is justified, I conduct exploratory (principal component) factor analysis on questions obtained, once again, from the 2012 and 2016 ANES (descriptive statistics reported in Appendix A1.7, 158-

159). 69 Doing so attempts to uncover the common dimensions or “principal components” of conservative ideology. I subsequently assess the relationship between principal components and conservative identification using regression analysis. Finally, this study’s main findings concerning the recategorizing effect of conservatism (Table 2.3) are replicated, substituting

66 However, Gaertner et al (1993, 6) argue that recategorizing outgroups (e.g., racial minorities) within a common superordinate identity (e.g., “American”) can reduce prejudice both within the superordinate ingroup and between the new ingroup and remaining outgroups (e.g., foreigners) under certain conditions. 67 Given the implausibility of assimilating into an ingroup defined in terms of immutable racial characteristics , it is not surprisingly that “Minorities Adapt” coefficients on American identification are twice the size of coefficients on white identification (Appendix A1.3, 154). 68 Schildkraut (2010) emphasizes the costs of national identification to members who experience resentment for their (real and perceived) resistance to assimilation. 69 It is possible to extend this analysis chronologically using earlier ANES data (e.g., 2000-2008); however, doing so would not address the relationship between conservatism and white ingroup sentiments. This is because the ANES only began recording white identification in 2012.

51

Table 2.4. Cronbach’s Alpha Tests with and without White Identity Principal Components 2012 2016 Cronbach's Cronbach's Factor Label Alpha Factor Label Alpha "Racial Conservatism" 0.87 "Racial Conservatism" 0.86 "" 0.81 "Social/Religious Conservatism" 0.79 "Social/Religious Conservatism" 0.84 "Small Government" 0.58 "American Identity" 0.76 "Modern Sexism" 0.79 "Anti-egalitarianism" 0.63 "Anti-redistribution" 0.72 *"White Identity" 0.13 "American Identity" 0.65 "Authoritarianism" 0.62 "Gender Resentment 1" 0.75 "Gender Resentment 1" 0.62 "Authoritarianism" 0.55 "Gender Resentment 2" 0.61 "Anti-egalitarianism" 0.60 *"White Identity" 0.10

Total (with White Identity) 0.67 0.70 Total (without White Identity) 0.92 0.92 Data. American National Election Studies Time-series, 2012 and 2016. Non-Hispanic white respondents. *Component dropped.

principal components for conservative identification in interaction models. This final test assesses whether constructed, ideological measures recategorize tribalism in the same way as the conservative identification measure, as should be the case if both capture conservative ideology.

After confirming that survey questions are appropriate in terms of correlations and sampling adequacy,70 I run a principal component analyses on data from both years, retaining measures where component loadings exceed 0.4 and where substantial cross-loadings (>= 0.2) are absent (Stevens 2012). Given the theoretical importance of white identification and prejudice,

I retain these measures as long as possible. 71 I am ultimately unable to retain these measures in

2012: white identification loadings fall well beneath the threshold and while ethnocentrism loads

70 Bartlett’s Test of Spherisity is statistically significant in both years (p=0.000). Kaiser-Meyer- Olkin Measure of Sampling Adequacy (KMO) returns 0.949 in 2012 and 0.952 in 2016. 71 In other words, when presented with 2 or more measures that fall short in terms of either individual loadings (<0.4) or cross-loadings (>=0.2), where possible, I first drop the non-white identity/prejudice measure and then retest.

52

Table 2.5. Principal Components of Conservatism, 2012 and 2016 2012 2016 Principal Variance Eigen- Principal Variance Eigen- Label Component Explained values Component Explained values "Racial Conservatism" 1 12.61% 11.81 1 10.44% 9.57 "Small Government" 2 11.19% 2.78 3 6.82% 2.44 "Social/Religious Conservatism" 3 11.19% 2.28 2 10.12% 2.72 "American Identity" 4 5.88% 1.78 7 4.78% 1.14 "Anti-egalitarianism" 5 5.05% 1.39 8 4.40% 1.07 "Authoritarianism" 6 4.92% 1.16 9 4.17% 1.04 "Gender Resentment 1" 7 4.63% 1.14 6 4.89% 1.21

"Gender Resentment 2" 8 4.60% 1.06 "Modern Sexism" 9 4 6.47% 1.93 "Anti-redistribution" 5 5.34% 1.31

Total 60.03% 57.52% Data. American National Election Studies Time-series, 2012 and 2016. Non-Hispanic white respondents.

successfully on Component 6 alongside three immigration policy measures, this entire component is ultimately dropped following a weak Chronbach’s Alpha test for internal validity

(0.13). In 2016, white identification loads with ethnocentrism on the 10 th (weakest) component but, once again, a weak Chronbach’s Alpha test (0.10) necessitates dropping this component (see

Table 2.4).

Exploratory PCA reveals 8 principal components in 2012 and 9 in 2016. These are listed in Table 2.5 alongside proportions of variation explained and eigenvalues. Despite variations in questions asked between the 2012 and 2016 surveys, components are highly consistent across the two years. 72 Six components from 2012 repeat in 2016. These include: “Racial Conservatism,”

“Small Government,” “Social/Religious Conservatism,” “American Identity,” “Anti-

72 For example, regarding gay rights – a key element of the “Religious/” principal component – both 2012 and 2016 surveys question respondents’ views on gay marriage, adoption, and laws to protect gays and lesbians from discrimination. Additionally, in 2012 the ANES includes a question on gays and lesbians serving openly in the military. By 2016,

53

egalitarianism,” and “Authoritarianism.” In both years, gender attitudes are divided between two components. Differences between 2012 and 2016 reflect the fact that the 2016 ANES Timeseries replaces several “gender resentment” questions with new, “modern sexism” questions. By contrast, the measures comprising the 2016 “Anti-redistribution” component are present in 2012 but, in that year, these measures cross-load with “Small Government.” 73

In line with conventions, principal components are assigned a label reflecting their apparent commonalities. “Anti-egalitarianism,” for example, includes less-egalitarian responses to questions asking respondents: to rate the importance of society ensuring “that everyone has an equal opportunity to succeed,” how big a problem it is “if some people have more chances in life than others,” and whether “if people were treated more equally in this country we would have many fewer problem” (ANES Codebook 2018, 1141, 1145-47).

Principal components reflect conventional wisdom regarding the substance of conservative ideology. Obvious cases include:

- Preferring a “” to a “strong government,”

- Preferring fewer regulations on businesses,

- Holding religiously influenced, traditionalist positions on abortion, gay rights, and the

authenticity of the Bible,

- Valuing the nation and its symbols (e.g., the American flag),

- Relative indifference to equity concerns, and

this question is omitted and replaced with a new question concerning whether religious employers should be allowed to opt out of participation in same-sex wedding ceremonies. 73 These three measures refer to federal spending: “should federal spending on [welfare/childcare/aid to the poor] be increased, decreased, or kept the same?” (ANES Codebook 2018, 473-5). Before dropping these measures in 2012, “Small Government” was the largest component.

54

- Opposition to redistributive/anti-poverty spending.

Perhaps equally obvious, at least to critics of conservative politics, are the components:

Authoritarianism, Gender Resentment/Modern Sexism, and Racial Conservatism.

This last component deserves additional attention. In both 2012 and 2016, Racial

Conservatism includes the four core measures of “racial resentment” (Kinder and Sanders 1996), opposition to Affirmative Action in the workplace and in universities, and reduced support for government policies to help black Americans. In 2012, this measure also includes opposition to

“government ensuring fair jobs for blacks” (not available in 2016).

The Racial Conservatism component – highest ranked in both years – could be viewed as reflecting the link between tribalism (the prejudice aspect) and conservatism. The problem with this interpretation is that ethnocentrism and three immigration policy measures all fail to load on this same component. Rather than measuring general ingroup prejudice, Racial Conservatism seems to exclusively concern black Americans. 74 One could conclude that specifically anti-black prejudice is central to conservative ideology, at least in practice (Bobo and Kluegal 1993,

Frederico and Sidanius 2002), and that this, in turn, animates racially conservative policy positions (Kinder and Sanders 1996), yet this interpretation is problematic as well because the remaining four (non-racial resentment) measures that load on Racial Conservatism each clearly concern racial policy attitudes – the thing that racial prejudice is meant to predict. This finding fits with Carmines, Sniderman and Easter’s (2011) contention that racial resentment measures are direct measures of racial policy attitudes rather than measures of a “new racism,” as asserted by Kinder and Sanders 1996 (see also Schuman 2000).

74 This conclusion is debatable in that Affirmative Action may also tap into attitudes towards other groups.

55

Proponents of the “principled conservatism” perspective argue further that racial resentment measures conflate race-neutral political values – e.g., individualism (e.g., Kinder and

Mendelberg 2000) and the “Protestant work ethnic” (McConahay 1986) – with racial animus

(Sniderman, Piazza, Tetlock and Kendrick 1991, Sniderman and Carmines 1997). A conservative might, for example, oppose giving able-bodied people “government hand-outs,” on the grounds that individuals are responsible for their own life outcomes (Haidt 2012, 138) and/or from a commitment to limited government. S/he could oppose racial preferences in employment and education from the standpoint of procedural equality, etc.

Feldman and Huddy (2005) find that, among white liberals, racial resentment predicts opposition to government programs that benefit blacks whereas, among white conservatives, racial resentment predicts opposition to government programs regardless of the race of the recipient . Carney and Enos (2017) experimentally substitute other groups, including white ethnics, for blacks in racial resentment questions. They find “little to no difference” in resentment across treatment groups (13). Consistent with the idea that “racial resentment” taps non-racial ideological factors, they find conservatives to be more “resentful” than liberals – that is, more opposed to government assistance – but they also find conservatives to be more consistently “resentful” across racial groups.

If white conservatives are comparably opposed to government assistance for black and white Americans, opposition to racial policy might only appear to reflect anti-black sentiment because the policies in question primarily target black Americans, or because the questions themselves explicitly reference black Americans. “Racial resentment” and by extension, much of contemporary “racial conservatism,” could represent the application of (race-neutral) conservatism to racial policy (Schuman 2000, Sniderman and Piazza 1993).

56

Returning to the question of measuring conservatism, if principal components represent core dimensions of conservative ideology, how do they relate to conservative identification? OLS regression models are displayed in Table 2.6. In both years, Small Government, Religious/Social

Conservatism, Racial Conservatism, American Identification, and Anti-egalitarianism are significantly associated with conservative identification. The additional 2016 Anti-redistribution component is significantly predictive as well. Gender Resentment components fail to predict conservative identification in both years, while Modern Sexism is weakly predictive in 2016.

Authoritarianism predicts conservative identification in 2012 but not in 2016. Demographic controls have little impact on principal component coefficients.

R2 values of 0.55 in 2012 and 0.63-0.64 in 2016 indicate an impressive degree of model fit. These values change little following the inclusion of demographic controls or controls for white identification and ethnocentrism (specifications 2-5, both years). Principal components

(ideology) overwhelmingly drives conservative identification. This is especially the case for

Small Government and Social/Religious Conservatism.75 By contrast, white identification and ethnocentrism exert no statistically significant effects. Given that principal components are constructed from a broad cross-section of correlating, right-leaning political measures (39 in each year), this strong relationship with conservative identification demonstrates that the latter is an adequate approximation for conservative ideology in practice: in terms of survey public attitudes. Indeed, when measures are combined and weighted according to the proportion of the

75 In 2012, conservative identification OLS coefficients on Small Government are in the range of 0.9. In 2016, coefficients are smaller (0.42-0.45); however, coefficients on the new, conceptually related Anti-redistribution component are in the range of 0.33-0.37. Broadly speaking, support for laissez-faire political economy is the most consequential predictor of conservative identification.

57

Table 2.6. OLS: Conservative Identification on Principal Components of Conservatism, 2012 and 2016 Conservative Identification: 2012 Conservative Identification: 2016 VARIABLES (1) (2) (3) (4) (5) VARIABLES (1) (2) (3) (4) (5)

PC 1: Racial Conservatism 0.2114*** 0.2151*** 0.2047*** 0.2083*** 0.2183*** PC 1: Racial Conservatism 0.2842*** 0.3051*** 0.2798*** 0.3025*** 0.2812*** -0.0468 -0.0484 -0.0474 -0.0492 -0.0502 -0.0577 -0.0587 -0.0585 -0.0598 -0.0603 PC 2: Small Government 0.8998*** 0.9015*** 0.9074*** 0.9091*** 0.9135*** PC 2: Soc./Relig. Conserv. 0.7749*** 0.8024*** 0.7710*** 0.7968*** 0.7925*** -0.0467 -0.0481 -0.0469 -0.0483 -0.0482 -0.0487 -0.0502 -0.0491 -0.0505 -0.0507 PC 3: Soc./Relig. Conserv. 0.6296*** 0.6332*** 0.6289*** 0.6322*** 0.6453*** PC 3: Small Government 0.4458*** 0.4274*** 0.4316*** 0.4154*** 0.4263*** -0.0444 -0.0464 -0.0449 -0.0469 -0.047 -0.0503 -0.0511 -0.0517 -0.0522 -0.0529 PC 4: American Identity 0.1789*** 0.1673*** 0.1746*** 0.1631*** 0.1656*** PC 4: Modern Sexism 0.0725* 0.0433 0.0785** 0.0494 0.0428 -0.0288 -0.0312 -0.0292 -0.0313 -0.0312 -0.0392 -0.0369 -0.0399 -0.0377 -0.0377 PC 5: Anti-Egalitarianism 0.1017** 0.1011** 0.1092** 0.1072** 0.1122** PC 5: Anti-Redistribution 0.3569*** 0.3327*** 0.3676*** 0.3429*** 0.3460*** -0.043 -0.0454 -0.0433 -0.0456 -0.0454 -0.062 -0.0638 -0.0624 -0.0648 -0.065 PC 6: Authoritarianism 0.1661** 0.1588** 0.1653** 0.1558* 0.1494* PC 6: American Identity 0.3137*** 0.3278*** 0.3188*** 0.3288*** 0.3167*** -0.0768 -0.0796 -0.077 -0.0798 -0.0799 -0.0393 -0.0414 -0.0403 -0.0418 -0.0422 PC 7: Gender Resent. 1 -0.0155 -0.0198 -0.0152 -0.0204 -0.0237 PC 7: Gender Resent. 1 -0.0389 -0.0218 -0.0335 -0.0157 -0.0151 -0.0354 -0.0362 -0.0356 -0.0364 -0.0361 -0.0417 -0.0389 -0.0415 -0.0389 -0.0389 PC 8: Gender Resent. 2 0.0526 0.0618 0.0526 0.062 0.0541 PC 8: Anti-Egalitarianism 0.1232*** 0.1047*** 0.1305*** 0.1109*** 0.1070*** -0.0404 -0.0429 -0.0406 -0.0431 -0.0427 -0.0377 -0.0374 -0.0377 -0.0375 -0.0376

PC 9: Authoritarianism 0.0844 0.1061 0.0947 0.1124 0.0916

-0.0899 -0.0917 -0.0903 -0.0923 -0.0926

White Identification 0.0163 0.0174 0.0255 White Identification -0.0321 -0.0266 -0.0385 -0.0188 -0.0194 -0.0202 -0.0233 -0.0235 -0.0242 58

Ethnocentrism -0.002 Ethnocentrism 0.0031*

-0.0016 -0.0016

Male -0.0684 -0.0683 -0.0647 Male 0.1659*** 0.1592*** 0.1610***

-0.0496 -0.0498 -0.0499 -0.056 -0.0564 -0.0565

Education 0.0157 0.0142 0.0055 Education 0.0254 0.0304 0.0287

-0.0245 -0.0246 -0.0243 -0.0273 -0.0275 -0.0277

Income 0.0027 0.0027 0.0033 Income 0.0055 0.0047 0.0048

-0.0033 -0.0033 -0.0033 -0.004 -0.0041 -0.0041

Age 0.0015 0.0014 0.0013 Age -0.0001 0.0001 0.0001

-0.0015 -0.0015 -0.0015 -0.0016 -0.0016 -0.0016

South 0.0339 0.0349 0.0344 South 0.0473 0.0501 0.0553

-0.0494 -0.0496 -0.0496 -0.0569 -0.0575 -0.0573 Constant 3.8114*** 3.6843*** 3.7477*** 3.6332*** 3.6593*** Constant 3.0367*** 2.8786*** 3.0503*** 2.8689*** 2.9070*** -0.1472 -0.1963 -0.1603 -0.2048 -0.205 -0.1721 -0.2133 -0.1816 -0.2144 -0.2145

Observations 2,967 2,875 2,946 2,856 2,845 Observations 2,087 1,997 2,062 1,973 1,968 R-squared 0.5477 0.5458 0.5483 0.5464 0.5497 R-squared 0.6325 0.6399 0.6315 0.6384 0.6396 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 2.7. Correlations: “PCA Conservatism” and Conservative Identification, 2012 and 2016 PCA Conservatism Conservative Identification PCA Conservatism 1.0000 2012 Conservative Identification 0.7181 1.0000 PCA Conservatism 1.0000 2016 Conservative Identification 0.7754 1.0000 Data. American National Election Studies Time-series 2012 and 2016. Non-Hispanic white respondents.

variance they represent, the resulting ideological PCA Conservatism measure correlates very highly with conservative identification (Table 2.7).

This conclusion is further evidenced by replications of models initially reported in Table

2.3, this time replacing conservative identification with principal components in the interaction term. Statistically significant interactions are depicted in Figures 2.12 and 2.13. Full tables are available in the Appendix (Tables A1.8-15, 160-167). 76 For all statistically significant principal component measures, the recategorizing effect of conservative ideology is evident: as one moves

“right,” white identification is decreasingly predictive of American identification.

2.6 Minority Tribalism?

Given the discussion of white political and group attitudes, one could ask whether tribalism and conservatism operate similarly in minority communities? Relationships between racial and American identifications are displayed in Figure 2.14. Appendix tables (A1.16-18,

168-170) additionally report ordered logit regressions of American identification on racial identification, by racial group. Associations are positive, albeit noisy, in both years, except for

76 American identity principal components are omitted from replication tables.

59

60

Figure 2.12. 95% CI Plot: American Figure 2.13. 95% CI Plot: American Identification Regression Identification Regression Coefficients on Principal Components by White Identification, 2016 Principal Components by White Identification, 2012

Figure 2.14. Mean Racial Identification by American Identification, 2012 and 2016

Asian respondents in 2012. 77 The tendency for ingroup identifications to correlate – a feature of tribalism’s general character – is not a white-specific phenomenon.

The same is true of tribalism’s central component: the link between ingroup identification and prejudice. As with whites, racial identification consistently predicts racial prejudice

(ethnocentrism) among blacks, Hispanics and Asians (Figure 2.15, Appendix A1.19-21, 171-

173). 78 Also, as with whites, American identification predicts agreement that “minorities should adapt to the customs and traditions of the U.S.” (Figure 2.16, Appendix A1.22-24, 174-176) – an interesting result, given that the respondents are themselves racial minorities. The presence of

American tribalism in minority communities underscores the point that American identity is not

77 Sample size may be a limiting factor where Asian American estimates are concerned. 78 Black Ethnocentrism = ((FT blacks - FT whites) + (FT blacks - FT Hispanics) + (FT blacks - FT Asians) + (FT blacks - FT Muslims)) / 4

61

Figure 2.15. Mean Racial Prejudice by Racial and American Identification, 2012 and 2016

Figure 2.16. Mean National Prejudice by Racial and American Identification, 2016

62

simply white (or white-American) identity. Racial minorities, no less than whites, are members of the American Tribe. Indeed, American identification is similar for whites and racial minorities: on average, the four groups rate American identity as “moderately” to “very” important (3.73 - 4.17). In 2016, black Americans were the most highly American identifying group.

Although minority respondents display evidence of tribalism, the relationship between tribalism and the specific tribes (racial and American) looks different in these communities.

There is little evidence of “cross-tribal” prejudice among black, Hispanic, and Asian respondents: racial identification does not seem to associate with national prejudice (Figure 2.15,

Appendix A1.22-24, 174-176). 79 nor does American identification appear to associate with racial prejudice (Figure 2.16, Appendix A1.19-21, 171-173). 80 Without question, sample size is a major consideration when comparing racial groups using survey data. 81

At the same time, it is also likely reason is that, for a variety of reasons, racial identity is more salient for racial minorities than for whites.82 The clarity of conceptual boundaries between racial and American identifications could account for white/non-white differences in cross tribal prejudice; i.e., if the comparative invisibility of whiteness (McIntosh 1988, Frankenberg 1993 –

79 Black identification negatively associates with “Minorities Adapt” (p<0.01, Appendix A1.22, 174). 80 Wong (2010, 181) finds that blacks who view whites as part of their community are less racially conscious and less likely to characterize whites as racist. 81 Categories like “Asian” and “Hispanic” are quite broad, in terms of national origin. Are “Asians” in 2012 similar in this respect to “Asians” in 2016? Identity will also be experienced very differently by recent and long settled immigrants. 82 In the “white tradition of nationhood” (Kaufmann 2018, 8) majority-group identity is expressed primarily in national rather than ethnic terms (see also: Doane 1997, Terry 1981). In addition, the nation’s strong civic nationalist tradition, white America’s post-Civil Rights Era embrace of normative colorblindness, and the peculiar features of multiculturalism – its celebration of racial minority but not majority- identities (Kaufmann 2018, 338-41; Swain 2002) – all likely contribute to the relative weakness of white identity.

63

the weakness of the White Tribe, as a construct – blurs the lines between white and American

Tribes, ingroup preference would likely to “bleed over” more from one ingroup to another.

Additional research is needed in this area.

What is clear is that conservatism does not play the same role for racial minorities that it plays for whites. With the sole exception of Hispanics in 2012, conservative identification does not significantly recategorize minority racial identification (Appendix A1.16-18,167-169).

Conservative identification also fails to attenuate the relationship between American identification and racial prejudice for these groups (Appendix A1.19-21, 170-172). Strangely, among blacks, conservative identification negatively interacts with American identification in predicting “Minorities Adapt” (Appendix A1.22, 173). It is not obvious why high American- identifying blacks would become less nationally prejudiced as they become more conservative; yet, it is also not from these data what “more conservative” means to black respondents, given the discrepancy between black conservative identification and Republican voting. 83 In short, although neither tribalism nor conservatism are ethnically particular, the recategorizing effect of conservatism on tribalism appears to be particular to whites.

2.7 Conclusion

Accusations of prejudice are commonplace in American politics. Academic research and

“hot take” articles linking political conservatives to racism, xenophobia etc. are fodder in this fight. Conservatives protest that their values and policy preferences are “color-blind,” whatever

83 According to Pew Research Center, 25% of blacks identify as “conservative” (Gilberstadt and Daniller 2020). Black voters supported Republican presidential candidates at 8% in 2016 and 6% in 2012 (“How Groups Voted” 2020).

64

their distributional effects. They, in turn, accuse political liberals of cynically employing

“identity politics” to exploit racial grievances for political gain (e.g., Prager 2016).

As the public responds (and numbs) to this back and forth, an alternative Right gathers in the political wilderness. This Right embraces and expands upon the most vicious sentiments attributed to conservatives by their leftwing critics. It also pointedly rejects conservatism for failing to secure white racial interests; as indeed, conservatism never claimed to do. In this context, American public discourse stands to benefit from a richer understanding of the relationship between conservatism and prejudice.

This study has argued that a central function of conservatism relates to tribalism: a general orientation towards ingroup identification and prejudice. Among white Americans, conservatives are more tribalistic on average than liberals. At the same time, conservative ideology recategorizes tribalism thereby orienting conservative whites towards national and away from racial conceptions of the “tribe.” This finding resolves what this study has labeled as

“the paradox of rightwing politics:” the apparent association between the conservative and white nationalist Rights on racial issues.

From this novel vantagepoint, the effects of conservatism on intergroup attitudes should be viewed as salutary rather than inflammatory. An America in which conservatism recedes would be one in which white nationalism comes to define the Right. This result would be catastrophic for every American who does not (or cannot) claim membership in the “White

Tribe.” It would be catastrophic as well for every American for whom American identity is important.

This last point bears emphasis for readers for whom ingroup identity is not especially meaningful. Not every American is ingroup oriented. Some are quite comfortable embracing

65

alongside or even in lieu of national identity; but for many others, ingroup identity is felt more deeply and exclusively. America needs a common, broadly accessible national identity as a general matter but especially for this group of Americans. To this end, conservatism does yeoman’s work.

66

CHAPTER 3

Nothing Left to Conserve: Rightwing Politics amid Social-institutional Decline

3.1 Introduction

President Trump’s victories in the 2016 primary and general elections would not have been possible but for the extraordinary support he received from white, non-college-educated

(aka: “working-class”) voters. Exit polls record the president winning the white working-class vote by 50% in the crowded Republican primary (“2016 Election Center” 2016) and by two thirds in the general election (Tyson, Alec and Maniam 2016). The size and location of this population were decisive: President Trump secured the necessary Electoral College votes despite losing the popular vote and underperforming his party’s previous nominee with college-educated whites (Sides, Tesler, and Vavreck 2018).

Historically, the white working class has occupied the margins of the Republican coalition. Their past refusal to commit to the GOP more decisively has stymied Republican efforts at a -style revision of American politics (Douthat and Salam 2008, Trende

2012). In this context, 2016 was an outlier. White working-class turnout and Republican support both increased (Morgan and Lee 2017). Remarkably, 14% of Trump’s white working-class supporters previously voted for President Obama in 2012 (Huang, Jacoby Strickland and Lai

2016).

67

As white working-class voters shift Right 84 – a phenomenon occurring throughout the

West (Eatwell and Goodwin 2018) – rightwing parties becomes increasingly defined by white working-class issue preferences and values. A better understanding of white working-class

Trump support thus offers a window into the future of rightwing politics in the United States.

How was the 2016 Republican presidential nominee able to do what previous party nominees could not? What about President Trump’s message was more appealing to these voters?

During the 2016 presidential campaign, then-candidate Donald Trump railed against undocumented immigrants, Muslims, , China, major corporations, wealthy donors, hedge-fund managers, the press, cultural elites, and political correctness. Unlike past GOP candidates, and unlike his primary competitors, Trump promised to protect working-class jobs from foreign competition and to leave popular social safety-net programs un-touched (Trump

2016, James 2015). This rhetoric was fundamentally “populist” rather than “conservative.” Like foreign rightwing populists (e.g., and ), Trump combined leftwing economic and rightwing socio-cultural perspectives (Carmines, Ensley, and Wagner 2016, Oliver and Rahn 2016). He articulated a vision of “the people” as virtuous, authentic, and possessing coherent interests in conflict with those of “establishment” elites (Mudde 2004, Canovan 1999,

Greven 2016).

Prominent explanations for Trump support flesh out aspects of President Trump’s campaign rhetoric. One account highlights the role of racial prejudice and white identity, drawing on the president’s “ethnocentric,” pro-“ingroup” slogans (“America First” and “Make

84 According to PPRI’s American Values Survey, 51% of non-college-educated whites identify as Republican or Republican-leaning compared to 34% who identify as Democratic or Democratic- leaning. This gap grows to 57-29% among non-college-educated whites between the ages of 18 and 29 (Cox, Liensche, and Jones 2017).

68

America Great Again”) and numerous anti-“outgroup” verbal tirades (Sides et al. 2018, Major,

Blodorn, and Blascovich 2018, Morgan and Lee 2017, McDaniel and McElwee 2016, Pettigrew

2017). This account is typically juxtaposed with the “economic anxiety” account whereby

Trump’s positions on trade and immigration, and his criticism of “elites” resonates with people left behind by the modern economy (Irwin and Katz 2016, Kolko 2016). 85 This same populist rhetoric supports yet another account wherein Trump support represents a “materialist” challenge to the hegemony of “post-materialist” values (Inglehart and Norris 2016). 86 Finally, a fourth account emphasizes the “authoritarian” disposition of Trump supporters and the appeal of

Trump’s “strong-man” persona and disregard for political norms (MacWilliams 2016) under conditions where faith in the “normative order” has eroded (Stenner and Haidt 2018).

In focusing attention to what Donald Trump said while campaigning, current explanations miss something equally important: what he didn’t say . Typical GOP paeans to free markets, limited government, fiscal discipline, and federalism were all notably absent from

President Trump’s rhetoric, but the critical missing piece was any meaningful reference to the conservative vision of the good life. The conservative vision embraces both economic freedom and traditional social institutions, viewing the latter as a bulwark against liberalism’s rationalizing and atomizing processes. 87 Social institutions are central to this vision yet, in his

85 Internationally, support for rightwing populist movements is higher among globalization’s “losers” (Betz 1994, Ignazi 2003, Bornschier 2010). On the other hand, Rothwell and Diego- Rosell (2016) find higher Trump support among Republican primary voters living in economically distressed communities who were not themselves economically distressed. This finding suggests a more sociotropic view of economic anxiety. 86 “Materialist” values include security concerns and respect for order and tradition. “Post- materialist” values include support for multiculturalism, cosmopolitanism, equity, and environmental protection (Inglehart and Norris 2016). 87 See, for example, Burke ([1790] 1986), ’s 2 nd , 8 th and 10 th principles (1987), President Reagan’s 1981 Inaugural Address, and Levin (2014).

69

campaign, Donald Trump ignored them almost entirely. He promised to make America great again, but he never explained what it was that made America great to begin with.

To understand why white working-class Americans responded so favorably to Trump’s populist-nationalism and why they largely rejected the more conventional conservative appeals of his primary rivals we must address the dysfunctional state of white working-class communities (Murray 2012, Putnam 2016, Vance 2016). Presently, the political consequences of white working-class social dysfunction are understudied. This is not surprising. The political effects of economic conditions and institutions (e.g., economic growth, poverty, and union strength) are well known; however, the political effects of social institutions receive less attention, despite being similarly consequential.

Consider, for example, the institution of marriage; specifically, the practice of having and raising children while married. Decades of research links single parenting to a host of social ills including poverty (Mather 2010), crime (Harper and McLanahan 2004, Sourander et al. 2006), reduced life expectancy (Warner and Hayward 2006), and increased adolescent risk behaviors

(Pearson, Muller, and Frisco 2006, Pong and Ju 2000). These social maladies and others impose costs on society and on the state. As voting publics, parties, and policymakers take notice, politics is necessarily affected.

Social institutions impact politics in other measurable ways (see Table 3.1). The partisan gender gap is, to a substantial degree, a partisan marriage gap driven by high rates of Democratic affiliation among single women (Edlund and Pande 2002). 88 Other prominent examples include religious attendance, which consistently predicts Republican voting among whites (Olsen and

88 Partisan differences between married and unmarried women may also reflect age and selection factors.

70

Table 3.1. Social Institutions and Partisan Identification, ANES 2012-2016 ANES 2016: 4,270 obvs ANES 2012: 5,914 obvs Social % % Net % % Net Institution Variable GOP Dem GOP GOP Dem GOP Marriage Women: All 36.5 49.5 -13.0 30.7 55.9 -25.2 Women: Married 44.8 43.4 1.4 41.6 45.0 -3.4 Men: All 45.5 40.6 4.9 37.2 48.9 -11.7 Men: Married 51.9 37.3 14.6 44.8 42.8 2.0

Religion "Never" Attend Church 26.5 49.0 -22.5 11.8 58.8 -47.0 Attend Church "Every Week" 56.0 34.0 22.0 48.0 41.1 6.9

Work Unemployed 28.2 49.1 -20.9 23.1 57.3 -25.2

Education High School or less 36.2 44.0 -7.8 28.3 54.9 -26.6 Some College (No BA) 42.8 42.1 0.1 33.2 53.5 -20.3 46.1 44.7 1.4 44.4 44.8 -0.4 Post-Graduate Degree 35.7 56.5 -20.8 34.2 54.7 -20.5 Source: American National Elections Studies Time-series (2012 and 2016). Note: Columns 3, 4, 6, and 7 display percentages of respondents in each variable category (column 2) corresponding to social institutions (column 1). Columns 5 and 8 display the difference between the preceding two columns.

Green 2006); educational attainment, which increasingly predicts Democratic voting

(“Ideological Gap Widens Between More, Less Educated Adults” 2016); and unemployment,

which negatively affects support for Republican executives (governors and presidents) but positively affects support for Democratic executives (Wright 2012).

In this paper, I connect the deterioration of white working-class social institutions to support for Donald Trump in the 2016 GOP Primary. I argue that conservative appeals rely on a kind of “Burkean” reverence for social institutions. The “little platoons” of American – families, churches and communities – and the norms they foster, are critical contributors to wellbeing and upward mobility. Social institutions provide meaning and fulfillment by helping to bridge the gap that otherwise exists between atomized individuals and a vast, impersonal modern society. Social institutions help to sustain the socio-economic order and they help to justify efforts to conserve that order.

71

The decline of social institutions erodes the foundations for conservative politics and creates space for alternative approaches. For working-class whites, President Trump’s American version of populist-nationalism represents one such alternative. 89 White working-class social- institutional decline is thus implicated in American political conflicts over trade, immigration, and the maintenance of international alliances. Social institutional decline may also help explain the explosive growth of rightwing populism in Western (Kyle and Gultchin 2018) and with it, Brexit and the dramatic rightward shift of European politics. 90

From here, this paper proceeds as follows. Section 3.2 briefly discusses the use of education as a measure of social class. In Section 3.3, I review prominent research concerning the state of white working-class communities, focusing on social dysfunction and its inverse: social capital. In Section 3.4, I argue that collapsing social institutions erode the foundations for conservative political appeals. I then proceed, in Section 3.5, to describe the data and methods used to evaluate this argument. I present my findings in Section 3.6, beginning at the county level (3.6.1). I next consider robustness questions (3.6.2) before reporting individual level findings (3.6.3). I conclude in Section 3.7.

89 The white nationalist “Alternative Right” represents another rightwing alternative to conservative politics. I do not treat the terms “populist-nationalism” and “white nationalism” as synonymous. 90 Prominent explanations for the growth of European rightwing populism emphasize public opposition to immigration and multiculturalism (Kaufmann 2018, Eatwell and Goodwin 2018); however, rising diversity is also known to negatively impact both social capital (Putnam 2007) and social tolerance (Craig and Richeson 2014). It is possible that diversity partly affects support for rightwing populist parties through its impact on civic life and social trust.

72

3.2 The “White Working Class”

First, a note on class. Social class (alternatively, “socio-economic status”) can be conceived of in several ways; for example, in terms of occupation (e.g., Carnes 2012), income

(e.g., Stonecash 2000), or education (e.g., Franks 2004). 91 Although class measures are correlated, exceptions – e.g., high earning individuals without advanced degrees or in low-status occupations – complicate class descriptions (see: Silver 2016 , Dougherty 2016).

One reason to opt for an education-centered conception of social class is the intuition that high-income, low-education individuals are likely to have more in common culturally with low- education, low-income people than with high-educated, high-income people. As stated by

Williams (2017), 92 “(class is) expressed through cultural differences of which graduating from college is the single most important example. Americans who graduate from college typically live very different lives than those who don’t.” As an example, Williams (2019, 40-46) notes that college grads are able to leverage “entrepreneurial networks” for professional advancement

(Granovetter 1974) whereas working-class “clique networks” (Burt 1992) serve safety net functions.

In part for socio-cultural reasons, discussions of class identity among whites overwhelmingly center on education (e.g., Wilcox, Cherlin, Uecker, and Messel 2012, Case and

Deaton 2017, Douthat and Salam 2008, Jones and Cox 2012, Cox et al. 2017). This is true as well in journalistic accounts where working-class whites are sometimes treated as akin to an ethnic group (“America’s largest demographic group” per Ehrenfreund and Guo 2016).

Consequently, in lieu of income, occupation, and other accounts featuring fine-grained class

91 Though Frank (2004) devotes attention to both education and income, he makes clear (Frank 2005, 2, Bartels 2006, 204) that his definition of class is education-based. 92 Williams (2019) uses both income and education.

73

distinctions – e.g., between “working-poor,” “working-,” and “lower-middle-” classes (Gilbert

2018, Thompson and Hickey 2005)93 – I opt for the simple, commonplace, and context relevant differentiation between whites with and without Bachelor’s Degree.

3.3 Social-institutional Decline

3.3.1 Social Dysfunction

In recent years, a chorus of journalists, commentators, and scholars have directed public attention to the suffering of America’s white working-class communities. Among working-class whites, marriage, religious attendance, and indicators of health and wellbeing are declining, while non-marital (“out-of-wedlock”) childbearing, divorce, and drug and alcohol abuse are rising (Murray 2012, Vance 2016, Putnam 2016). The economic picture is similarly bleak, especially in rural areas where employment has yet to return to pre-2008 recession levels (Edsall

2017). Among white working-class men, labor-force participation is ten points lower than the national average (“The Forgotten Man Index” 2017).

The present plight of white working-class communities recalls the experience of black

America during the 1980s and 1990s. Emphasizing the impact of external economic factors,

William Julius Wilson (1987) argues that the collapse of manufacturing employment in the

1970s and 1980s deinstitutionalized work, family, and behavioral norms among African

American men. This dynamic now appears to be repeating in working-class white communities.

Indeed, other parallels are remarkable as well; for example, much as the crack boom of the 1990s

93 Americans are famously class un conscious (e.g., Grobart 2016), rendering the value of such accounts questionable, in this case.

74

devastated black communities, the ongoing opioid epidemic is wreaking havoc on working-class whites (“Opioid Overdose Deaths by Race/Ethnicity” 2017).

The explosive growth of white single parenting likewise recalls the black experience

(Cherlin 2009, Wilcox 2010). One could counter that single parenting is rising nationally; however, national trends are dominated by the non-college educated. Among college-educated

Americans, marriage remains common, non-marital childbearing is , and divorce rates have decreased since the 1970s (Hymowitz 2006, Wilcox 2010). Among white women who gave birth in 2016, only 7% of those with a bachelor’s degree or higher did so unmarried. The number rises to 34% for associate degree holders and those with “some college”; 52% for the high school educated; and 59% for those who did not finish high school (Wildsmith, Manlove, and Cook

2018).

An important aside is that many of the trends in question (e.g., single parenting) are increasing among working-class members of all racial backgrounds; however, certain worrying trends are disproportionately affecting downscale whites. These include suicide, drug and alcohol overdoses, deaths from alcohol-related conditions like chronic liver disease and cirrhosis, and physical and mental health problem (Case and Deaton 2015, 2017). Incredibly, white

American life expectancy is declining – chiefly among whites with a high school degree or less – at time in which life expectancy is rising in other rich countries and among other American racial groups (Case and Deaton 2015).

Declining church attendance is also a largely white phenomenon. This may be because, unlike predominately black and Hispanic churches, predominately white churches are less often involved in community mobilization and political activism. Such activities may help to preserve

75

the relevance religion in minority communities thereby conserving a key source of social support

(Wilcox et al. 2012, Schwadel, McCarthy, and Nelsen 2009).

Finally, it is no coincidence that foundational social institutions – those concerning work, family and behavior – are collapsing simultaneously in white working-class communities.

Research demonstrates that traditional or “bourgeois” norms tend to fit together in synergistic and reinforcing ways (Wilson 1987, 1999; Friedland 1991, Edgell 2006, Wilcox 2004, 2010;

Schwadel et al. 2009). 94 These institutions (patterns of behavior) are associated with stability, happiness, and economic mobility across income classes and communities (Haskins and Sawhill

2009, Waite and Gallagher 2002); but, this is especially true for people with moderate resources

(Haskins 2011).

What does the collapse of work, family, and behavioral norms in white working-class communities have to do with support for Donald Trump? Trump support is predicted by the

“economic distress index” which unites elements of the three norms, combining percentages of people “in poverty, unemployed, disabled, in single-parent families, living on public assistance, or living without health insurance” (Monnat 2016). Unemployment and reduced labor-force participation predict Trump support (Irwin and Katz 2016), as do drug and alcohol abuse (Jacobs

2016), poor health, mortality, and suicide (Monnat 2016) – work and behavior norms, respectively. Other than single parenting, the relationship between family norms and Trump support is unclear; however, given the state of in white working-class communities

94 Regular employment, for example, may contribute to marital and parenting success by training people to show up consistently, work diligently, display patience, and sacrifice for others (Carney 2019).

76

(Wilcox et al. 2012), declining family norms – e.g., divorce and promiscuity – may predict

Trump voting as well. 95

3.3.2 Social Capital

The term “social capital” refers both to the degree of social connectedness between individuals and to norms of trust and reciprocity that result from those connections (Putnam

2000, 19). If bourgeois norms link people to their communities in stable and productive ways, social capital is partly a measure of the strength of those linkages. As such, social capital can be roughly conceptualized as an inverse measurement of social dysfunction.

This characterization is supported by a large literature that, with few exceptions, characterizes social capital as a panacea. 96 Measured primarily in terms of organizational membership, social capital predicts improved health outcomes, higher reported happiness, lower income inequality and crime, higher rates of educational attainment (Putnam 2000), greater social tolerance (Hooghe 2003, Diamond 1999), increased political participation (Teorell 2003), faster economic growth, higher median incomes, and better scores on measures of “good” governmental performance (Putnam 1993, Helliwell and Putnam 1995, Rupasingha, Goetz, and

Freshwater 2006a, Knack 2002). Growth and good government measures (e.g., reduced corruption) are also predicted by trust measures of social capital (Fukuyama 1995, LaPorta et al.

1997, Knack and Keefer 1997, Zak and Knack 2001)

Religious attendance, another measure of social capital, is similarly associated with reductions in social dysfunctions. Frequent church goers are less likely to cheat on (Burdette,

95 As recorded in the General Social Survey, non-college-educated Americans hold increasingly permissive attitudes towards marriage, family, and sex while college-educated Americans have become more socially conservative in these areas (Wilcox et al. 2012). 96 Though see Berman (1997) for a prominent critique, as well as Skocpol (1993), Mettler (2002), and Hann (1996, 1-26).

77

Amy, Christopher Ellison, Darren Sherkat, and Kurt Gore 2007) or abuse (Wilcox 2004) their marriage partners. They are more likely to be happily married (Lichter and Carmalt 2009), less likely to divorce (Wright 2015, Wilcox 2015), and they tend to live longer lives (Idler, Blevins,

Kiser, and Hogue 2017) at least in part, because they commit suicide at lower rates (Kheriaty

2017). Children who attend religious services have more friends and better relationships, are less likely to use drugs, alcohol and tobacco, and are less prone to delinquency and other risky behaviors (Putnam 2016, 224).

Much like traditional/bourgeois norms, social capital is not evenly distributed across classes; rather, it is lowest in working-class and poor communities where it is most needed

(Putnam 2016, Carney 2019). This is particularly true with respect to religious networks which serve as critical sources of social capital in working-class communities (Verba, Schlozman, and

Henry 1995, 18; Wilcox et al. 2012).

Given the noted positive relationship between measures of social dysfunction (e.g., non- work, single parenting and drug use) and Trump support, it stands to reason that social capital – a measure of intact, functioning communities – would negatively predict Trump support. This case is recently made in Alienated America (2019) where Carney reports that Trump performed worst in highly socially capitalized communities and with highly socially capitalized groups, including

Mormons and people with Dutch ancestry.

Elsewhere, the evidence is mixed. Rothwell and Diego-Rosell (2016) find Trump support negatively predicted by voter participation and by the number of non-profit associations at the county-level; however, the authors also find Trump support positively associated with religiosity.

A survey conducted by the Public Religion Research Institute found no significant association between Trump support and either religious attendance or civic engagement (Cox et al. 2017);

78

however, the relationship between Trump support and membership social capital (e.g., civic, fraternal, and religious groups) may be obscured by the fact that Trump supporters tend to be older than other voters and thus more likely to belong to the declining number of civic associations (Putnam 2000). Additional research is needed here and with respect to the other critical component of social capital: social trust.

3.4 The Appeal of Donald Trump

To the extent that the white working class can be viewed as an established part of the

Republican coalition, the collapse of their communities represents an indictment of modern conservatism. For these Republican and Republican-leaning voters, conservatism has conserved essentially nothing. Not their economic well-being, which is stagnant and dropping relative to the rest of American society. Not any non-economic measure of well-being that comes easily to mind such as health, life expectancy, or rates of substance abuse or social connectedness. Not their faiths, which they have abandoned. Not their families, which are broken: the out-of- wedlock birth rate for white working-class women is between 34-59% (Wildsmith et al. 2018) .

Not their social status in an increasingly diverse country.

Viewed from this perspective, conservative messages concerning the importance of faith, community, in-tact families, and work and behavior norms should increasingly fall on deaf ears.

Dead institutions cannot be conserved. The conservative vision of the good life defends markets and traditional social institutions, positing that the latter enable people to capitalize on the former

(Douthat and Salam 2008, 7-8). In return, this vision asks people to buy into the status-quo. The tremendous harm caused by the demise of traditional social institutions means that the white

79

working-class now has a stake in disrupting the status-quo. If nothing else, President Trump promised to do just that.

If conservatism offers little to working-class whites, why not then and the historic home of the white working-class: the Democratic Party? The progressive vision of the good life – restrained markets and robust social safety nets – could offer this group a measure of economic, if not cultural and social security. As it stands, however, the white working-class is wedded to the GOP by the forces of demographic change. This group is not included in the

Democratic Party’s “coalition of the ascendant” (Judis and Teixeira 2004). Moreover, survey data demonstrates that progressives have shifted dramatically left on issues of race and identity

(Goldberg 2019). The impact of this shift on the tenor of progressive discourse – e.g., privilege theory and intersectionality – is likely to further alienate working-class whites from the

Democratic Party. 97

If correct, the preceding account helps to explain, in part, the growth of populist and nationalist politics on the political Right. For such an implausible and by traditional standards, un-conservative (“Against Trump” 2016) outsider-candidate like Donald Trump to have successfully secured the Republican Party nomination and then the presidency, something had to have changed in American society. I propose that the collapse of social institutions in white working-class communities erodes the foundation for conservative political appeals, thereby creating space for President Trump’s alternative vision.

97 Exposure to privilege theory (Lensmeir et al. 2013) and to multicultural curriculums (Wilton et al. 2019, Stenner 2005) is associated with negative sentiments towards racial minorities.

80

3.5 Data and Methods

I evaluate this theory drawing on election returns from the 2016 Republican presidential primaries and caucuses. 98 Using primary rather than general election data allows me to examine the voting behavior of when they had a choice between Trump’s populist-nationalist message and the more traditionally conservative messages of his rivals. I match candidate vote percentages for 2,869 counties with demographic data obtained from the US Census and health outcomes data from the Centers for Disease Control and Prevention, provided by the Robert

Wood Johnson Foundation. I specify cross-sectional ordinary least squares (OLS) regression models, regressing Trump’s share of the 2016 GOP primary vote (DV) on measures of social dysfunctions (IVs). 99

In line with the premise that working-class community deterioration is caused by/reflected in declining social capital (Putnam 2016, Carney 2019), I also regress the Trump primary vote on two groups of social capital measures (IVs). The first group draws on county- level social capital scores constructed by Rupasingha et al. (2006a, 2006b). These measures operationalize social capital in terms of organizational density and civic engagement (Putnam

1995, 2000; Brehm and Rahn 1997; Alesina and La Ferrara 2000), recording membership in civic, religious, athletic, fraternal, political, and other organizations, per 1,000 residents.

The social value of membership is also linked to the promotion of norms of trust and reciprocity (Putnam 1993, 1995, and 2000). I therefore construct county-level trust measures drawing on data from the 2000 and 2006 Social Capital Community Benchmark Surveys

98 2016 county-level primary vote totals and percentages are obtained from David Leip’s 2016 Atlas of U.S. Presidential Elections and supplemented with data the Office of the Secretary of State. 99 See Table A2.1 (Appendix, 177-179) for summary statistics.

81

Table 3.2. Summary Statistics of Sampled Data: Social Capital Community Benchmark Survey SCCBS 2000 2006 Combined Total number of survey sample groups 50 18 68 Total number of individuals, per sample 29,233 12,100 41,333 Mean number of individuals per sample 584.66 672.22 607.84 Minimum number of individuals per sample 43 200 Maximum number of individuals per sample 3,003 2,741 Number counties sampled 149 17 166 Source: Putnam et al. (2002, 2006). Note: Summary statistics on counties sampled for social capital estimates. Social capital estimates are taken from counties where the number of annual observations (individuals) is greater than or equal to 15. In counties where estimates are obtainable for both years, 2006 estimates are used. The number of usable county estimates (152) is consequently less than the combined number of estimates generated (166).

(SCCBS) conducted by the Saguaro Seminar at Harvard University (Putnam, Malkin, and

Malkin 2002, 2006; see Table 3.2). 100

To protect survey participants’ privacy, the SCCBS does not report county-identifying information in their public dataset. I work around this limitation in several ways. First, I cross- reference survey sample groups from the 2006 data with geographic information reported by the authors (Putnam et al., 2006) to identify individual and groups of surveyed counties. I then generate multi-county social capital estimates by averaging individual survey responses across state-sample combinations for counties listed as surveyed.

Second, I obtain access to the restricted 2000 SCCBS dataset, with county identifiers included. Where I am able to identify individual counties from the 2006 data, I add these

100 These data were obtained from the Roper Center and from the Association of Religion Data Archives.

82

observations to the restricted 2000 dataset. 101 I then take county-level social capital estimates after processing the combined data using multilevel regression with post-stratification (MRP: see

Park, Gelman and Bafumi 2006). 102 This involves modeling social capital measures as a function of race, gender, education, age, region, and county of residence. White respondents are further separated into “working-class” and “professional” categories.

Estimates are then post-stratified using census data. 103 Each observation is weighted by the proportion of the associated county population corresponding to the intersection of the respondent’s modeled demographic categories. This approach produces social capital estimates

(general trust, social trust, and religious attendance) for 152 counties. 104 These measures provide additional leverage on the connection between social capital, Trump support, and the white working class. Crucially, these data allow me to construct county-level white working class-only social capital estimates – something I cannot do using membership data from Rupasingha et al.

(2006b).

County regression models include demographic controls for percents: elderly (65 years or older), rural, black, Hispanic, white working-class (non-Hispanic whites, 25 years or older with less than a BA degree) and white professional (non-Hispanic whites, 25 years or older with a BA

101 Where 2000 and 2006 SCCBS social capital estimates can be taken for the same county, I use 2006 estimates. 102 MRP adjusts disaggregated subnational survey estimates by “borrowing strength” from national data along demographic and geographic dimensions. MRP is shown to improve estimates where sample sizes are small (Lax and Phillips 2009). 103 These youngest age category in these data is 20-29 years old (“Equal Employment Opportunity Tabulation 2006-2010”). For post-stratification purposes, I group 18 and 19-year- old survey respondents in with this category. See replication files (“Survey data prep.do”) for details. 104 This small number partly reflects my decision to exclude counties with fewer than 15 respondents to mitigate reliability concerns.

83

or higher degree). Additional controls include median household income, 105 political ideology

(President Obama’s 2012 net percentage of the two-party general election vote), 106 and state fixed effects. 107 Specifications further include population weights and robust standard errors, where practical. 108

There are clear advantages and disadvantages to examining white working-class Trump support at the county rather than individual level. One practical advantage of county-level regression is data availability: FIPs-coded counties are common (n=3,007) units of analysis.

County data can be combined from a wide range of sources, enabling greater leverage on research questions.

In this study, county measures confer an additional, conceptual advantage in that social- institutions are substantially community institutions with community-level effects. Places, no less than individuals, can be highly (lowly) socially capitalized and more (less) dysfunctional.

Consider for example, the effects of family structure on income mobility. The well-known negative impact of single parenting extends to the community-level: even children from married- parent homes are negatively impacted by the prevalence of single parenting in their communities

(Chetty, Hendren, Kline, Saez 2014). Or consider the neighborhood effects literature (see Jencks

105 Regression coefficients on median household income are consistently small (<0.000). I retain this measure because it is theoretically relevant to both dependent (voting) and independent (social capital and social dysfunction) variables. 106 2012 elections data are obtained from Joe Wilson (Kaggle.com). 107 In the replication files, I include state-level election controls for southern states, candidate “home states”, caucus states, the remaining number of candidates, and a rolling candidate delegate count. I omit these variables from reported models because their effects on independent variables are neutralized by the inclusion of state fixed effects. 108 The use of robust standard errors risks biasing estimation when sample sizes are small (Wooldridge 2012, 273). I omit robust standard errors from models that regress on SCCBS social capital measures.

84

and Mayer 1990) which examines the impacts of community-level norms and behavior on individual outcomes.

On the other hand, the fact that dependent variables (GOP primary voting) are also taken at the county-level raises well-known ecological inference concerns (Robinson 1950). I consequently supplement county-level models with individual-level models, drawing on data from the 2016 American National Election Studies Time-series (ANES n=4,270). Using logistic regression, I model white working-class Trump primary support as a function of general trust and church attendance (social capital). Controls are included for ideology, gender, education, income, age, marital status, and region (South), alongside probability weights. Finally, I test my theory against existing accounts of Trump support by including measures of white identification, ethnocentrism, authoritarianism, and economic anxiety.

A final note on methods, regarding the operationalization of (traditional) conservative vs. populist-nationalist politics, I treat Trump support as representative of the latter and support for

Trump’s primary rivals as the former. Practically speaking – i.e., among GOP primary voters – modern conservative politics are likely to be understood less in the Burkean terms discussed here than in terms of “movement conservatism” which is characterized by “:” the union of economic conservatives, social conservatives, and defense “hawks” (Meyer 1962). These perspectives, though distinct, are related and largely complimentary. The instrumental nature of the fusionist bargain is undeniable; but conservative philosophy frames the larger project and provides it with a sense of intellectual coherence. This is particularly the case where economic and social conservatives are concerned. 109

109 For example, Barry Goldwater’s was chiefly anti-statist, economically conservative, and anti-communist. His 1964 campaign occurred well in advance of the rise of the Religious Right; however, his defense of liberty and of the individual was explicitly grounded in

85

3.6 Findings

3.6.1 Communities

Beginning with county models and the relationship between Trump support and social capital, OLS regressions on the Trump and Cruz percentages of the GOP primary vote are displayed in Table 3.3. 110 I estimate but do not report fixed effects for all specifications. Each of the four measures of membership social capital (Rupasingha et al. 2006b) are negatively associated with Trump support (p<0.05) and positively or insignificantly associated with Cruz,

Rubio and Kasich support. 111 With each additional religious organization per 1,000 residents, the

Trump share of the GOP primary vote decreases by 8.9 percentage points (PPTs) and the Cruz share increases by 5.5 PPTs. For civic organizations, one additional organization per 1,000 residents reduces Trump support by 20.5 PPTs and increases Cruz support by 4.1 PPTs.

Coefficients should be considered alongside the actual number of organizations. In the case of civic organization, a one-unit (one organization) increase per 1,000 residents amounts to

religious themes (Goldwater 1964a). Goldwater would also decry threats posed to liberty by the nation’s “moral decline,” stating that “of all the dispositions and habits which lead to political prosperity, religion and morality are indispensable supports” (Goldwater 1964b, Busch 2012). This explicitly-Burkean linkage of economic liberty and traditional morality is echoed by conservative thinkers including (1953) and Frank Meyer (1962), the latter of whom was a central architect of the modern conservative movement (Dennis 1996). Echoing Benjamin Franklin’s (1787) observation that “only a virtuous people are capable of freedom," – a “Burkean” claim that traditional values buttress liberty – Meyer adds that forced values are cannot be virtuous (1962, 121), thereby asserting a social conservative defense of liberty. 110 Comparing the correlates of Trump and Cruz support is useful for two reasons. First, as perhaps the only serious threat to Donald Trump’s nomination, it fell to Cruz to carry the conservative banner in the GOP primary (Stein 2016). Second, Senator Cruz was a very conservative candidate along any dimension that comes easily to mind (e.g., size of government, social issues, economic issues, etc.), making the comparison of the candidates roughly akin to comparing competing philosophies: the conventional, albeit purist conservatism of Cruz vs the populist-nationalism of Trump. 111 Replication materials include candidate vote-shares for Rubio and Kasich.

86

9 times the national mean of 0.11 (see: Table A2.1, Appendix, 176-178). To put coefficients into more comparable terms, a one standard deviation increases in membership social capital predicts:

-2.2 PPTs for Trump and +0.4 PPTs for Cruz (civic organizations per 1,000),

-4.4 PPTs for Trump and +2.7 PPTs for Cruz (religious organizations per 1,000),

-4.5 PPTs for Trump and +2.0 PPTs for Cruz (all associations per 1,000), and

-4.1 PPTs for Trump and +1.4 PPTs for Cruz (county social capital index). 112

The next group of specifications replace membership social capital measures with measures of trust and religious attendance constructed from survey data (Putnam et al. 2002,

2006; see Table 3.4). Trump support is negatively associated with religious attendance among all survey respondents. 113 Among working-class whites, Trump support is negatively associated with general trust, social trust and religious attendance (p<0.05). A standard deviation increase in white working-class general and social trust decreases Trump support 2.8 and 1.5 PPTs, respectively. A one unit increase in white working-class religious attendance – e.g., increasing attendance from “once or twice a month” to “almost every week” – predicts an 11.5 PPT decrease in Trump support and an 11 PPT increase in Cruz support: a gap of 22.5 PPTs .

The negative association between community social capital and the 2016 Trump primary vote point strongly suggests that the quality of community social institutions – those

112 Rupasingha et al. (2006a) construct the social capital index variable using principal component analysis on measures of associational density constructed from U.S. Census data. Interpreting common units of change – e.g., a standard deviation increase – is more difficult for this measure than for measures recording civic, religious, and overall associational density per 1,000 residents. Interpreting general and social trust measures is similarly complicated. See Table A2.1 (Appendix, 177-179) for additional information. 113 Among all respondents, general and social trust are surprisingly negatively associated with Cruz support (see Table 3.4, columns 2 and 4).

87

approximated by county-level measures – is electorally consequential. Additional evidence to this effect is displayed in Tables 3.5, 3.6, and 3.7 where the previous specifications are again repeated, this time replacing social capital measures with measures of social dysfunction. These independent variables – fifteen in total – operationalize aspects of social-institutional decline.

Where possible, dysfunction measurements for the white population alone are used.

Trump primary voting is associated with greater social dysfunction at p<0.05 in ten of fifteen specifications and at p<0.1 in one specification. Trump support is associated with less social dysfunction only in the case of violent crime (p<0.1). 114 Coefficient magnitudes vary. At the high end: a one PPT increase in the proportion of white families that are multigenerational predicts an additional 3.2 PPTs for Trump (-0.6 PPTs for Cruz at p<0.1). 115 A one PPT increase in white unemployment predicts an additional 1.1 PPTs for Trump (-0.4 PPTs for Cruz).

Alternatively, a one standard deviation increase in white unemployment predicts a net gain for

Trump (Trump vote-share minus Cruz vote-share) of 4.4 PPTs and a standard deviation increase in drug overdose mortality nets Trump 3.3 additional PPTs.

The contrasting associations between social dysfunction on the one hand and support for

Trump and Cruz on the other, mirror associations found in the previous regressions on social capital. The resulting cross-sectional picture 116 supports this paper’s contention that social- institutional decline severs the connection between white working-class voters and conservatism.

114 The three insignificant associations include: single-mothers, white marriage, and teen births. 115 Drawing on Fischer's (1991) analysis of America's Anglo-Saxon settler communities, Bennett and Lotus (2013) argue that the absolute nuclear family structure is so deeply interwoven with the fabric of American society, economy, values, culture, and even law, that more globally- typical extended family structures are impediments to success in the United States. See also: Vance's (2016) description of modern Appalachian (extended families-typical) communities. 116 For a visual representation of these relationships, see Figure 3.1.

88

Table 3.3. OLS Regression of 2016 GOP Primary Vote-shares on Membership Social Capital Measures (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Trump Cruz Trump Cruz Trump Cruz Trump Cruz

Social Capital 2014 -3.6804*** 1.1778*** (0.8970) (0.3414) Associations 2014 -6.9601*** 3.0177*** (0.6681) (0.5243) Religious Orgs. 2014 -8.8739*** 5.4717*** (0.7765) (0.6665) Civic Orgs. 2014 -20.5341*** 4.1124** (2.8923) (2.0814) Pct. Elderly 2015 0.4677*** -0.2368*** 0.4801*** -0.2533*** 0.5000*** -0.2858*** 0.3918*** -0.2099*** (0.1028) (0.0675) (0.1048) (0.0685) (0.1066) (0.0680) (0.1031) (0.0657) Median HHI 2015 0.0001* -0.0000* 0.0000 -0.0000 0.0000 -0.0000 0.0000 -0.0000* (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Pct. Rural 2015 -0.0089 0.0192** -0.0138 0.0176** -0.0098 0.0101 -0.0402*** 0.0293*** (0.0129) (0.0087) (0.0124) (0.0085) (0.0136) (0.0086) (0.0142) (0.0092) Net Obama 2012 -0.0130 -0.0673*** -0.0175 -0.0654*** -0.0288 -0.0577*** -0.0067 -0.0685*** (0.0180) (0.0127) (0.0185) (0.0127) (0.0188) (0.0126) (0.0192) (0.0127) Pct. WWC 0.3890*** -0.2035*** 0.3790*** -0.2031*** 0.3434*** -0.1859*** 0.3722*** -0.1959*** (0.0925) (0.0500) (0.0942) (0.0501) (0.0958) (0.0490) (0.0953) (0.0496) Pct. White Prof. 2015 -0.2399* -0.3728*** -0.3751*** -0.3466*** -0.5116*** -0.2899*** -0.4781*** -0.2910*** (0.1369) (0.0636) (0.1192) (0.0608) (0.1223) (0.0616) (0.1215) (0.0631) Pct. Black 2015 0.2093*** -0.1033*** 0.1607*** -0.0910*** 0.1552*** -0.0929*** 0.1199** -0.0761** (0.0573) (0.0325) (0.0551) (0.0320) (0.0570) (0.0320) (0.0559) (0.0325) Pct. Hispanic 2015 0.0689 -0.1756*** 0.0577 -0.1709*** 0.0415 -0.1592*** 0.0579 -0.1732*** (0.0616) (0.0313) (0.0638) (0.0314) (0.0665) (0.0306) (0.0663) (0.0314) Constant 11.3780** 58.4641*** 16.3401*** 57.1289*** -3.6915 79.9840*** -4.2538 82.1228*** (4.9637) (3.0226) (4.7772) (3.0274) (6.0259) (5.3111) (5.9783) (5.3197)

Observations 2,964 2,964 2,964 2,964 2,963 2,963 2,963 2,963 R-squared 0.9269 0.9082 0.9272 0.9095 0.9254 0.9111 0.9217 0.9067 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

89

Table 3.4. OLS Regression of 2016 GOP Primary Vote-shares on Social Capital Measures: Trust and Religious Attendance (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) VARIABLES Trump Cruz Trump Cruz Trump Cruz Trump Cruz Trump Cruz Trump Cruz

General Trust -5.201 -12.6127*** (-5.1938) (-4.1581) Social Trust 1.9584 -12.0593*** (-3.9916) (-3.1125) Religious Attendance -12.042*** 2.558 (-3.2274) (-2.8629) WWC General Trust -23.6076** 2.7969 (-10.52) (-10.2132) WWC Social Trust -19.0751** 3.7633 (-7.5139) (-7.3255) WWC Relig. Attend. -11.4947*** 10.9820** (-4.3048) (-4.3146) Pct. Elderly 2015 0.6799** -0.6368*** 0.8136*** -0.6901*** 1.2519*** -1.0804*** 0.141 -0.7046* 0.2552 -0.7134* 0.3717 -0.7788** (-0.2698) (-0.216) (-0.2707) (-0.211) (-0.3369) (-0.2988) (-0.3845) (-0.3733) (-0.3714) (-0.3621) (-0.3817) (-0.3826) Median HHI 2015 0.0002*** -0.0001* 0.0001** -0.0001 0.0000 -0.0001 0.0002*** -0.0001** 0.0002*** -0.0002** 0.0002*** -0.0001** (-0.0001) (-0.0000) (-0.0001) (-0.0000) (-0.0001) (-0.0001) (-0.0001) (-0.0001) (-0.0001) (-0.0001) (-0.0001) (-0.0001) Pct. Rural 2015 -0.1015* 0.1010** -0.1036* 0.0918** -0.1148** 0.1219** -0.0562 0.0884* -0.0557 0.0877* -0.0825 0.0879* (-0.0544) (-0.0435) (-0.0545) (-0.0425) (-0.0547) (-0.0485) (-0.0522) (-0.0506) (-0.0514) (-0.0501) (-0.0514) (-0.0515)

90 Net Obama 2012 -0.1492*** -0.0262 -0.1512*** -0.0335 -0.1915*** -0.007 -0.0416 -0.0368 -0.0569 -0.0358 -0.0705* -0.0264 (-0.0326) (-0.0261) (-0.0327) (-0.0255) (-0.0318) (-0.0282) (-0.0411) (-0.0399) (-0.0396) (-0.0386) (-0.0398) (-0.0399) Pct. WWC 2015 0.2495 -0.2718** 0.1819 -0.1731 -0.1775 -0.1632 1.3134*** -0.4773** 1.1939*** -0.4667** 1.2716*** -0.4653* (-0.1575) (-0.1261) (-0.1657) (-0.1292) (-0.166) (-0.1473) (-0.2415) (-0.2344) (-0.2309) (-0.2251) (-0.2348) (-0.2353) Pct. White Prof. 2015 -0.8427*** -0.0959 -0.9060*** -0.1193 -1.0970*** -0.1181 0.3827* -0.4507** 0.2272 -0.4304** 0.1587 -0.306 (-0.1391) (-0.1114) (-0.1346) (-0.105) (-0.1294) (-0.1148) (-0.2294) (-0.2227) (-0.2203) (-0.2148) (-0.2296) (-0.2301) Pct. Black 2015 0.3449*** -0.3132*** 0.3511*** -0.2749*** 0.1589 -0.2016** 0.8168*** -0.4096*** 0.7633*** -0.4029*** 0.7995*** -0.4029*** (-0.1046) (-0.0837) (-0.1047) (-0.0816) (-0.1051) (-0.0933) (-0.1467) (-0.1424) (-0.1431) (-0.1395) (-0.1433) (-0.1436) Pct. Hispanic 2015 0.0809 -0.2413*** 0.0936 -0.2211*** -0.0451 -0.1479* 0.5177*** -0.2854** 0.4742*** -0.2821** 0.4829*** -0.2726** (-0.0852) (-0.0682) (-0.0848) (-0.0661) (-0.0855) (-0.0758) (-0.1371) (-0.1331) (-0.1328) (-0.1295) (-0.1328) (-0.1331) Constant 55.9737*** 87.8203*** 47.1632** 56.3647*** 83.3217*** 65.9190*** 25.4786 72.5437** -18.6043 77.7994*** -7.8602 45.1596* (-20.4553) (-16.3766) (-18.0788) (-14.0973) (-18.4535) (-16.3694) (-28.3877) (-27.5596) (-19.5248) (-19.0353) (-22.9542) (-23.0068)

Observations 152 152 152 152 143 143 115 115 116 116 110 110 R-squared 0.9675 0.9403 0.9673 0.943 0.9729 0.9396 0.9799 0.955 0.9802 0.9553 0.9824 0.9582 State indicator variables not shown Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 3.5. OLS Regression of 2016 GOP Primary Vote-shares on “Work” Social Dysfunction Measures (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) VARIABLES Trump Cruz Trump Cruz Trump Cruz Trump Cruz Trump Cruz Trump Cruz

Unemployment (Whites) 2015 1.0981*** -0.4254*** (0.1873) (0.1198) LFPR (Whites) 2015 -0.5033*** 0.0932 (0.1107) (0.0683) White Poverty 2015 0.3261*** -0.1111 (0.0885) (0.0809) White Child Poverty 2015 0.6069** -0.2025 (0.2670) (0.2881) Food Stamps Whites 2015 0.4488*** -0.0201 (0.1330) (0.1251) All Welfare 2015 0.1196* -0.0621 (0.0628) (0.0408) Pct. Elderly 2015 0.3624*** -0.2026*** -0.1716 -0.1052 0.3890*** -0.2120*** 0.3838*** -0.2102*** 0.3899*** -0.1600* 0.3327*** -0.1858*** (0.1028) (0.0649) (0.1810) (0.1016) (0.1097) (0.0663) (0.1099) (0.0666) (0.1248) (0.0837) (0.1169) (0.0688) Median HHI 2015 0.0001*** -0.0001*** 0.0001*** -0.0000** 0.0002*** -0.0001** 0.0001*** -0.0000** 0.0001*** -0.0001** 0.0001*** -0.0001*** (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Pct. Rural 2015 -0.0320** 0.0259*** -0.0554*** 0.0322*** -0.0517*** 0.0331*** -0.0459*** 0.0311*** -0.1139*** 0.0580*** -0.0413*** 0.0295*** (0.0139) (0.0094) (0.0154) (0.0100) (0.0146) (0.0095) (0.0143) (0.0094) (0.0305) (0.0187) (0.0152) (0.0095) Net Obama 2012 -0.0198 -0.0647*** 0.0001 -0.0697*** -0.0176 -0.0658*** -0.0120 -0.0677*** -0.0220 -0.0644*** -0.0182 -0.0648***

91 (0.0180) (0.0125) (0.0198) (0.0129) (0.0197) (0.0128) (0.0195) (0.0126) (0.0226) (0.0151) (0.0188) (0.0128) Pct. WWC 2015 0.2918*** -0.1682*** 0.4276*** -0.2059*** 0.4202*** -0.2148*** 0.3566*** -0.1932*** 0.3789*** -0.2392*** 0.3629*** -0.1976*** (0.0896) (0.0485) (0.1047) (0.0540) (0.1074) (0.0525) (0.1021) (0.0507) (0.1203) (0.0679) (0.1040) (0.0517) Pct. White Prof. 2015 -0.4057*** -0.3278*** -0.3157** -0.3204*** -0.4677*** -0.3011*** -0.4645*** -0.3018*** -0.4657*** -0.3080*** -0.4952*** -0.2979*** (0.1323) (0.0676) (0.1433) (0.0703) (0.1413) (0.0671) (0.1420) (0.0689) (0.1647) (0.0784) (0.1473) (0.0720) Pct. Black 2015 0.1385** -0.0811** 0.1583*** -0.0834** 0.2002*** -0.1018*** 0.1509** -0.0848** 0.1291* -0.0992** 0.1143* -0.0697** (0.0566) (0.0321) (0.0609) (0.0335) (0.0726) (0.0392) (0.0639) (0.0341) (0.0782) (0.0430) (0.0637) (0.0336) Pct. Hispanic 2015 0.0650 -0.1742*** 0.0708 -0.1757*** 0.1270 -0.1954*** 0.0821 -0.1800*** 0.0873 -0.2004*** 0.0585 -0.1709*** (0.0650) (0.0314) (0.0646) (0.0316) (0.0789) (0.0340) (0.0742) (0.0311) (0.0801) (0.0372) (0.0735) (0.0333) Constant -14.8214** 85.4113*** 24.8735*** 76.7911*** 5.9056 60.4774*** 14.2503** 57.6039*** 11.5067 58.1356*** 12.0199* 59.5989*** (6.2869) (5.3214) (8.6988) (6.5259) (7.4202) (4.3896) (5.7762) (3.2690) (7.1253) (4.2047) (6.6454) (3.9020)

Observations 2,963 2,963 2,963 2,963 2,964 2,964 2,964 2,964 800 800 2,963 2,963 R-squared 0.9241 0.9080 0.9250 0.9069 0.9202 0.9070 0.9195 0.9068 0.9361 0.9251 0.9196 0.9071 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 3.6. OLS Regression of 2016 GOP Primary Vote-share on “Family” Social Dysfunction Measures (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Trump Cruz Trump Cruz Trump Cruz Trump Cruz

White Ext Families 2010 3.1599*** -0.6058* (0.5052) (0.3349) 0.1971 -0.1140 Poor White Single Mothers 2015 (0.1886) (0.1624)

White Marriage 2015 0.2131 0.1047 (0.1298) (0.0649) Teen Birth Rt. 2016 0.0384 0.0129 (0.0534) (0.0232) Pct. Elderly 2015 0.5705*** -0.2439*** 0.3852*** -0.2144*** 0.2812** -0.2488*** 0.3659*** -0.2176*** (0.1032) (0.0726) (0.1088) (0.0682) (0.1358) (0.0717) (0.1118) (0.0669) Median HHI 2015 0.0000 -0.0000 0.0001** -0.0000** 0.0000 -0.0001** 0.0001** -0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0001) (0.0000) (0.0000) (0.0000) Pct. Rural 2015 -0.0167 0.0248*** -0.0406*** 0.0291*** -0.0478*** 0.0264*** -0.0402*** 0.0282*** (0.0123) (0.0090) (0.0154) (0.0095) (0.0169) (0.0100) (0.0146) (0.0093) Net Obama 2012 0.0119 -0.0720*** -0.0137 -0.0670*** 0.0056 -0.0578*** -0.0122 -0.0671*** (0.0201) (0.0131) (0.0196) (0.0127) (0.0261) (0.0132) (0.0207) (0.0129) Pct. WWC 2015 0.1836** -0.1596*** 0.3449*** -0.1871*** 0.3345*** -0.2016*** 0.3390*** -0.1841*** (0.0868) (0.0507) (0.1000) (0.0508) (0.0925) (0.0488) (0.0971) (0.0502) Pct. White Prof. 2015 -0.2609* -0.3322*** -0.5166*** -0.2865*** -0.4840*** -0.2613*** -0.5085*** -0.2731*** (0.1374) (0.0687) (0.1325) (0.0663) (0.1333) (0.0669) (0.1427) (0.0681) Pct. Black 2015 0.0671 -0.0661** 0.1358** -0.0808** 0.1053* -0.0915*** 0.1150 -0.0763** (0.0548) (0.0321) (0.0601) (0.0332) (0.0610) (0.0348) (0.0704) (0.0347) Pct. Hispanic 2015 0.0032 -0.1628*** 0.0709 -0.1771*** 0.0394 -0.1889*** 0.0533 -0.1751*** (0.0678) (0.0324) (0.0708) (0.0316) (0.0782) (0.0359) (0.0900) (0.0367) Constant -9.9426* 83.2503*** 17.2460*** 56.9275*** -13.9100** 80.3549*** 16.8560*** 54.9830*** (5.8284) (5.1854) (5.4664) (3.3392) (6.9019) (5.3695) (5.7524) (3.3706)

Observations 2,963 2,963 2,964 2,964 2,963 2,963 2,871 2,871 R-squared 0.9267 0.9070 0.9188 0.9067 0.9198 0.9070 0.9191 0.9074 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

92

Table 3.7. OLS Regression of 2016 GOP Primary Vote-share on “Behavior” Social Dysfunction Measures (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) VARIABLES Trump Cruz Trump Cruz Trump Cruz Trump Cruz Trump Cruz

Violent Crime 2016 -0.0030 0.0007 (0.0019) (0.0014) D. Driving Death Rt. 2016 0.0581** -0.0385** (0.0253) (0.0166) Drug OD Mort. Rt. 2016 0.2041*** -0.1517*** (0.0398) (0.0333) Pct. Smokers 2016 0.8138*** -0.3499** (0.2144) (0.1445) Pct. Diabetic 2016 0.9680*** 0.1663 (0.3492) (0.2489) Pct. Elderly 2015 0.3568*** -0.2140*** 0.3837*** -0.2265*** 0.3821*** -0.2035*** 0.4820*** -0.2656*** 0.1087 -0.2503*** (0.1136) (0.0681) (0.1081) (0.0665) (0.1147) (0.0699) (0.1121) (0.0739) (0.1674) (0.0888) Median HHI 2015 0.0001 -0.0000 0.0001** -0.0000* 0.0001*** -0.0001*** 0.0002*** -0.0001*** 0.0001*** -0.0000* (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Pct. Rural 2015 -0.0501*** 0.0308*** -0.0405*** 0.0273*** -0.0440** 0.0192 -0.0405*** 0.0274*** -0.0517*** 0.0279*** (0.0172) (0.0102) (0.0148) (0.0094) (0.0207) (0.0121) (0.0152) (0.0095) (0.0167) (0.0102) Net Obama 2012 -0.0107 -0.0676*** -0.0133 -0.0676*** -0.0266 -0.0599*** -0.0127 -0.0678*** -0.0181 -0.0680*** (0.0201) (0.0132) (0.0196) (0.0128) (0.0211) (0.0134) (0.0198) (0.0130) (0.0193) (0.0128) Pct. WWC 2015 0.3536*** -0.1773*** 0.3408*** -0.1682*** 0.2830*** -0.1432*** 0.3235*** -0.1641*** 0.3766*** -0.1882***

93 (0.1048) (0.0526) (0.1016) (0.0512) (0.1086) (0.0537) (0.1090) (0.0532) (0.1028) (0.0497) Pct. White Prof. 2015 -0.5150*** -0.2784*** -0.5293*** -0.2697*** -0.5947*** -0.2614*** -0.4587*** -0.3023*** -0.4257*** -0.2644*** (0.1378) (0.0672) (0.1350) (0.0661) (0.1393) (0.0692) (0.1653) (0.0747) (0.1466) (0.0676) Pct. Black 2015 0.1595** -0.0766** 0.1258** -0.0648* 0.0927 -0.0674* 0.1087 -0.0592* 0.0865 -0.0862** (0.0716) (0.0364) (0.0637) (0.0340) (0.0661) (0.0361) (0.0686) (0.0341) (0.0603) (0.0358) Pct. Hispanic 2015 0.0665 -0.1689*** 0.0661 -0.1671*** 0.0568 -0.1825*** 0.0924 -0.1792*** 0.0250 -0.1824*** (0.0738) (0.0335) (0.0740) (0.0331) (0.0759) (0.0341) (0.0794) (0.0348) (0.0745) (0.0346) Constant 21.8102*** 54.6776*** 17.1111*** 56.3313*** 17.5197*** 58.2754*** -5.7480 65.8534*** 13.4659** 55.2815*** (5.5527) (3.5954) (5.4865) (3.2420) (5.5517) (3.3285) (10.3593) (5.8676) (5.5259) (3.1814)

Observations 2,805 2,805 2,933 2,933 1,544 1,544 2,949 2,949 2,963 2,963 R-squared 0.9197 0.9081 0.9194 0.9077 0.9273 0.9175 0.9220 0.9083 0.9199 0.9067 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Source: Leip (2018), Rupasingha et al. (2006b), Robert Wood Johnson Foundation (2016), U.S. Census. Note: Figures depicts counties in which the noted candidate(s) won a plurality of county votes. “Cruz/Rubio” counties – displayed in Figures 3.1.3 and 3.1.4 – display both plurality-Cruz and plurality-Rubio counties.

Figure 3.1. Social Capital and Social Dysfunction in GOP Primary Counties

94

3.6.2 Robustness

I consider three possible challenges to my findings. First, my argument presupposes a state of social-institutional decline in white working-class communities without demonstrating such a state using available measures. Bringing additional data to bear in support of this premise is important given the noted concerns regarding the use of race and class-blind measures. If, for example, county-level social capital and dysfunction measures are primarily reflective of dynamics in black, Hispanic, or white professional communities, these measures would be ill- suited to this paper’s premise.

I address this concern by regressing dependent variables (social capital and social dysfunction) on percent white working-class with controls separately for the years 2015, 2005 and 2000. I then take the difference (2015-2000, 2015-2005) from coefficients on percent white working-class and test them for statistical significance using paired two-way t-tests. I thus model the degree to which a county’s percent white working class predicts more negative outcomes in later years as compared to earlier years. I find statistically significant increases in social dysfunction in seven specifications and statistically significant decreases in social capital in six specifications (Table A2.2, Appendix, 179). 117 Conditions in white working-class communities are deteriorating in measurable ways.

Second, a prominent narrative concerning Trump supporters centers on the plight of small towns and rural communities. It is asserted that such places are left behind by the modern economy and by their own youth who are moving to urban areas in search of better job prospects

(Carr and Kefalas 2009, Edsall 2017). Social-institutional breakdown in these disproportionately

117 I omit social dysfunction measures from the Robert Wood Johnson Foundation because available measures date back only to 2010.

95

white and working-class communities could be the result of sorting whereby the exit of people possessing better average prospects for upward mobility leaves behind a population with lower average rates of social capital and higher average rates of social dysfunction. Insofar as sorting explains the relationship between social-institutional collapse and white working-class Trump support, researchers would be justified in emphasizing the role of regional economic development over social norms.

I find that, to the contrary, less populated and more-rural counties compare favorably to more populated and less-rural counties in terms of social capital. is also generally beneficial in terms of social capital and social decline. In interaction models, the positive effects of county percent rural on social capital increase as population declines. Finally, where measures of social institutional decline are predicted by rural population loss, the effects are vanishingly small (see Appendix, 180-190 for details). In short, rural sorting does not account for declining white working-class social institutions.

Third, it could be argued that the theoretical grounding of this paper is in error because the white working class was never particularly “conservative” to begin with: they were never interested in faith, family, tradition, industriousness – the basket of linked bourgeois norms that could connect them to the defense of the socio-economic order – or if they did value such things, their voting behavior was driven instead by other considerations. Such voters, in this account, strongly prefer liberal economic policies – they were and are pro-union and pro-redistribution – because they correctly perceive such policies as serving their economic interests better than conservative economic policies. 118 This group votes Republican because they have been co-opted

118 In Williams’ (2019) account, working-class white economic attitudes are less uniformly liberal, being shaped both by resentment of the poor (i.e., “freeloaders”) and by aspirations of upward mobility.

96

by economic elites who, though their political allies, use racial appeals to divide the working class against itself. Such appeals provide working class whites a “psychological wage” – association with the dominant racial group – in lieu of greater material wages (Roediger 1991).

White working-class enthusiasm for Trump thus reflects these voters’ inherent populism in conjunction with the fact that, in 2016, they no longer had to choose between their real, material wellbeing (economic interests) and their psychological wellbeing (racial interests). As a candidate, Trump offered them a departure from rightwing economics and he doubled down on race: the perfect combination.

In this “inherent populist” account, social institutions are entirely absent. As such, the reported findings address it, in part. 119 It should be conceded that findings related to economic institutions (Table 3.5) are not helpful, practically speaking. Take for example, the white labor- force participation rate. Does this measure speak to the strength of work norms (industriousness and self-reliance) or rather to the availability of work? Or to both? It could be argued that lower labor-force participation, higher poverty etc., predict greater Trump support because such conditions increase the relative value of “the wages of whiteness” (Gest 2016). Conversely social capital (including religious attendance), and family and behavior institutions are truly disconnected from a story centered on the union of populist economic and racial appeals… except perhaps, channeling the insight that bourgeois norms are connected and reinforcing, one could claim that all non-economic measures are merely consequences of economic maladies rather than causes themselves.

119 Stated differently, if a “strong version” of the inherent populist account precludes any role for social-institutional decline, such an account would be undermined by evidence reported suggesting that social institutional characteristics are impactful. It is also worth noting here that the models reported in Tables 3.3 through 3.7 control for median income.

97

Given the seriousness of this challenge, I replicate each regression model reported in

Tables 3.3, 3.4, 3.6. and 3.7, this time alternatively adding one of the six economic institution measures as control variables. I assess for example, the effects of membership social capital on

Trump/Cruz support controlling for economic conditions, as captured by white unemployment, white labor-force participation, white poverty, white child poverty, food stamp utilization among whites, and welfare use from all sources. The most remarkable feature of these models (reported in Tables A2.8-11, Appendix 191-194). 120 is how little impact the inclusion of economic controls has on the relationship between independent and dependent variables. Whatever the merits of this perspective, it has little bearing on the relationship between social-institutional characteristics and white working-class Trump support.

3.6.3 Individuals

County-level models are limited in two important respects. First, as noted, with respect to social dysfunction (Tables 3.5 through 3.7) and membership social capital models (Table 3.3), county-level measures cannot be restricted to the white working class. Even white-only dependent variables (columns 1-10 in Table 3.5 and columns 7-10 in Table 3.6) necessarily include some whites with advanced degrees. Fortunately, six specifications leverage white working-class-only independent variables from the SCCBS (Table 3.4). Four of these fit the predicted pattern 121 and coefficients on Trump voting are negative, significant and substantial in magnitude for all three measures of white working-class social capital. As with membership

120 In the Appendix, I report models including white unemployment as a control variable I omit the remaining 95 models (19 models x 5 economic measures) for space. 121 Surprisingly, Cruz support is negatively associated with general and social trust among the general population. Cruz support is positively associated with white working-class religious attendance, however.

98

Table 3.8. Logistic Regression of 2016 GOP Primary Candidate Support on Social Capital Measures: Trust and Religious Attendance (1) (2) (3) (4) (5) (6) (7) (8) Trump Other GOP Trump Other GOP Trump Other GOP Trump Other GOP VARIABLES (WWC) (WWC) (WWC) (WWC) (WP) (WP) (WP) (WP)

General Trust -0.3047** 0.3423** -0.2962* 0.4618*** (-0.1249) (-0.1647) (-0.1709) (-0.1588) Religious Attendance -0.1087 0.0428 -0.2456** 0.1335 (-0.0947) (-0.1221) (-0.125) (-0.1014) Ideology 0.2776** 0.4379*** 0.1451 0.4890*** 0.0693 0.3920*** 0.2026 0.2969* (-0.1179) (-0.1358) (-0.1502) (-0.1575) (-0.1412) (-0.1175) (-0.1871) (-0.1548) Male 0.1335 -0.6417** 0.4855* -0.7669** 0.2501 -0.2022 0.1169 -0.2943 (-0.2318) (-0.307) (-0.2762) (-0.3574) (-0.2762) (-0.2354) (-0.3164) (-0.2651) Education 0.0627 0.2182 -0.0679 0.1739 0.3056** 0.1247 0.4319** 0.0449 (-0.102) (-0.1413) (-0.1179) (-0.1494) (-0.1487) (-0.1608) (-0.1825) (-0.17) Income 0.0379* 0.0410* 0.0216 0.0416* -0.0465** 0.0314 -0.0568** 0.0553** (-0.0194) (-0.0216) (-0.0222) (-0.0252) (-0.0223) (-0.0195) (-0.0261) (-0.0229) South -0.5751** 0.4101 -0.5370** 0.2204 -0.0499 0.4997** -0.2112 0.4639* (-0.2348) (-0.2814) (-0.2723) (-0.3231) (-0.2597) (-0.2353) (-0.2947) (-0.2682) Age 0.0298*** 0.0317*** 0.0271*** 0.0304*** 0.0294*** 0.0119* 0.0271*** 0.0155** (-0.0069) (-0.0067) (-0.008) (-0.0077) (-0.0079) (-0.0068) (-0.0088) (-0.0076) Married 0.0151 0.2528 -0.0541 0.284 0.2908 0.4842* 0.0686 0.7177** (-0.2788) (-0.2966) (-0.325) (-0.3661) (-0.321) (-0.2714) (-0.3714) (-0.3142) Constant -4.2572*** -9.7722*** -2.4041 -8.3402*** -5.4728*** -7.5873*** -7.1225*** -5.8295*** (-1.2053) (-1.5621) (-1.5831) (-1.7439) (-1.6689) (-1.9379) (-2.0841) (-2.0246)

Observations 564 564 382 382 488 488 366 366 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

social capital measures (Table 3.3), there are no instances in which trust or religious attendance are positively associated with Trump support.

County-level regression is additionally limited insofar as it offers correlative rather than causal evidence using aggregated data. Individual-level data may be preferable, assuming measures are available. Fortunately, survey data from the 2016 ANES Time Series contains two social capital measures that can be leveraged to corroborate county-level findings. Table 3.8 reports logit coefficients from regressions of GOP primary candidate support on individual-level measures of general trust and religious attendance with controls and probability weights for white Republican survey respondents. In terms of odds ratios, looking at white working-class respondents and holding other variable values fixed, higher levels of general trust decrease the probability of supporting Donald Trump by 26.3% and increase the probability of supporting one

99

Table 3.9. Logistic Regression of 2016 GOP Primary Candidate Support on Social Capital Measures and “Working Class” Status (1) (2) (3) (4) (5) (6) VARIABLES Trump Trump Trump Other GOP Other GOP Other GOP

Working Class 0.5361** 0.5484** 0.4504 -0.5697* -0.5579* -0.4943 (-0.272) (-0.2728) (-0.302) (-0.314) (-0.321) (-0.3528) General Trust -0.3078*** -0.4016*** 0.3989*** 0.4070*** (-0.1002) (-0.1197) (-0.1152) (-0.1324) Religious Attendance -0.1488** 0.0921 (-0.0748) (-0.0802) Ideology 0.2336** 0.2404*** 0.1953* 0.4149*** 0.4179*** 0.3867*** (-0.0911) (-0.0911) (-0.1187) (-0.0863) (-0.0875) (-0.1086) Male 0.1809 0.1862 0.3903* -0.4069** -0.4260** -0.5248** (-0.1741) (-0.1752) (-0.2105) (-0.184) (-0.1856) (-0.2126) Education 0.0623 0.0899 0.0438 0.1687* 0.1504 0.0893 (-0.0876) (-0.0873) (-0.1017) (-0.1023) (-0.1054) (-0.119) Income 0.0061 0.0098 -0.0015 0.0366*** 0.0332** 0.0416** (-0.0149) (-0.0154) (-0.0179) (-0.0138) (-0.014) (-0.0167) South -0.3272* -0.3757** -0.4495** 0.3794** 0.4391** 0.3900* (-0.1745) (-0.1753) (-0.2052) (-0.1773) (-0.1791) (-0.2108) Age 0.0276*** 0.0295*** 0.0311*** 0.0217*** 0.0203*** 0.0198*** (-0.0051) (-0.0051) (-0.0059) (-0.0047) (-0.0048) (-0.0055) Married 0.1031 0.1106 -0.0178 0.3744* 0.3783* 0.5116** (-0.21) (-0.2144) (-0.2518) (-0.1954) (-0.1993) (-0.243) Constant -5.0433*** -4.5343*** -2.6233* -6.9829*** -8.0467*** -7.7405*** (-1.1419) (-1.1438) (-1.355) (-1.325) (-1.3539) (-1.5681)

Observations 1,046 1,045 742 1,046 1,045 742 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

of President Trump’s primary competitors by 40.8% (p<0.05). Among white professionals, general trust similarly decreases the probability of supporting Donald Trump by 25.6% (p<0.1) and increases the probability of supporting one of his rivals by a remarkable 58.7% (p<0.01).

The robustness of general trust is further demonstrated in Table 3.9 where working-class status is treated as an independent variable among white Republicans. Doing so addresses the possibility that the negative association between social capital and Trump support is spurious, perhaps caused by the fact that these pro-Trump voters just happen to also be poorly socially capitalized. Not surprisingly, working-class status strongly predicts Trump support alongside a battery of controls. This remains true when the general trust variable is included (column 2); however, working-class status falls out of statistical significance upon including both general

100

trust and religious attendance (column 3). By contrast, general trust is consistently negatively associated with Trump support (p<0.01). Religious attendance is likewise negative and significant (P<0.05). The same pattern is evident for support for Trump’s rivals, except that religious attendance fails to achieve statistical significance. These findings from individual-level data not only corroborate county-level findings, they demonstrate that general trust – a canonical measure of social capital – is a more consistent predictor of Trump support (and opposition) than even working-class status.

Finally, using ANES data, it is possible to test the predictive power of social capital measures alongside measures representative of competing accounts of Trump support. I include the following:

- “White identification” (“How important is being white to your identity?”, 1-5),

- “White consciousness” (an average of two questions concerning the need for collective

action to protect the social position of whites, 1-5, “Proposal for the 2016 Pilot Study:

White Racial Consciousness in the U.S.”

- “Racial resentment” (an average of four questions measuring attitudes towards black

Americans, 1-5, Kinder and Sanders 1996),

- Ethnocentrism (an average of “feeling thermometer” preferences for whites over four

racial outgroups, 0-100, e.g., Kinder and Kam 2010),

- Authoritarianism (an average of four child rearing preferences, 0/1, Stenner 2005), and

- Economic Anxiety (“[In the next 12 months] do you expect the economy, in the country

as a whole, to get better, stay about the same, or get worse?”, 1-5). 122

122 See Table A2.1, Appendix, 177-179 for additional variable details.

101

With one exception, statistically significant odds ratio coefficients all predict candidate support in line with existing accounts. 123 Authoritarianism is consistently insignificant and, perhaps surprisingly, given recent work (Sides et al. 2018, Major et al. 2018, Valentino, Neuner and Vandenbroek 2018), white identification is insignificant as well. On the other hand, white consciousness strongly predicts Trump support among white professionals, but only among white professionals. White consciousness does not factor into Trump support among working- class whites, though racial resentment does. Ethnocentrism coefficients are negligible. 124 Among white working-class respondents, economic anxiety performs comparatively well with moderate, positive coefficients on Trump support (p<0.1).

As displayed in Tables 3.10 and 3.11, social capital measures perform favorably alongside measures representative of alternative accounts for Trump support. General trust consistently predicts reduced Trump support among working-class and professional whites. 125

Among professionals, general trust also strongly predicts support for Trump’s primary rivals

(p<0.01). Religious attendance likewise predicts reduced Trump support among white professionals (p<0.05).

123 Economic anxiety predicts reduced Trump support among white professionals. 124 Ethnocentrism potentially captures a more general (Kinder and Kam 2010), less policy- oriented (Schuman 2000; Carmines, Sniderman and Easter 2011), and less ideologically conflated (Sniderman, Piazza, Tetlock and Kendrick 2001, Carney and Enos 2017) form of prejudice than racial resentment. See Table A2.12, Appendix, 196 for models substituting ethnocentrism for racial resentment. 125 I omit tests pertaining to Inglehart and Norris’ (2016) fourth account emphasizing reaction to the rise of post-materialist values for two reasons. First, it is unclear what measure would best serve as a single proxy for their theory. In Table A2.13 (Appendix, 197), I add “traditionalism” to regression specifications reported in Table 3.10. This index is comprised of responses to four questions pertaining to traditional morality (“User’s Guide and Codebook for the ANES 2016 Time Series Study,” 1079-1086) – a key component of materalist values (Inglehart and Welzel 2005). Traditionalism does not significantly predict candidate choice in any specifications. Second, two measures of “materialist” values – conservative ideology and authoritarianism (Inglehart and Norris’ 2016, 47) – are already included in Table 3.10.

102

Table 3.10. Logistic Regression of 2016 GOP Primary Candidate Support on Explanations for Trump Support (Odds Ratios) (1) (2) (3) (4) (5) (6) (7) (8) Trump Trump Other GOP Other GOP Trump Trump Other GOP Other GOP VARIABLES (WWC) (WWC) (WWC) (WWC) (WP) (WP) (WP) (WP)

General Trust 0.6644*** 0.6444*** 1.2987 1.2726 0.6091** 0.6345** 1.9333*** 1.8507*** -0.1032 -0.0973 -0.2324 -0.2358 -0.1349 -0.1398 -0.4024 -0.3906 Religious Attendance 0.9484 0.9298 0.9434 0.9537 0.7299** 0.7342** 1.176 1.19 -0.0919 -0.0906 -0.1241 -0.1238 -0.0939 -0.1048 -0.1268 -0.1283 White Identification 1.0828 0.8193 1.2255 0.9416

-0.1106 -0.0998 -0.1741 -0.0996 White Consciousness 1.6229* 0.8802 0.7326 0.8251 1.1959 2.0078*** 0.8597 0.9266 -0.4422 -0.1457 -0.2273 -0.1359 -0.3494 -0.4225 -0.242 -0.159 Racial Resentment 1.7794* 0.7371 0.8915 0.8525 -0.5245 -0.2291 -0.2724 -0.2572 Authoritarianism 1.1222 1.1561 0.8518 0.8107 2.3152 1.7424 1.3815 1.4047 -0.636 -0.6865 -0.5623 -0.5291 -1.2856 -0.9905 -0.7548 -0.7761 Economic Anxiety 1.3622* 1.3221* 0.8169 0.8107 0.7986 0.7265* 1.2198 1.281 -0.2177 -0.2059 -0.1765 -0.1759 -0.1549 -0.1397 -0.2139 -0.2282 Ideology 1.0785 1.0949 1.8372*** 1.8623*** 1.2222 1.1963 1.2812 1.3284* -0.1657 -0.17 -0.3351 -0.3407 -0.2712 -0.2789 -0.1961 -0.2088 Male 2.0839** 2.0461** 0.3682*** 0.3543*** 1.0091 1.1199 0.8617 0.8849 -0.6008 -0.607 -0.1411 -0.1362 -0.3524 -0.4176 -0.2443 -0.249 Education 0.9378 0.967 1.2105 1.2163 1.7521*** 1.8902*** 0.9537 0.9543 -0.1115 -0.1197 -0.1993 -0.2034 -0.3641 -0.4186 -0.1875 -0.1901 Income 1.0306 1.0259 1.0355 1.0407 0.969 0.9696 1.0421 1.0535** -0.0242 -0.0239 -0.0272 -0.0273 -0.0271 -0.0286 -0.0277 -0.0275 South 0.5329** 0.5566** 1.4861 1.3758 0.7496 0.7893 1.765** 1.7027* -0.1531 -0.1584 -0.4617 -0.433 -0.2388 -0.2566 -0.5069 -0.4905 Age 1.0305*** 1.0323*** 1.0339*** 1.0333*** 1.0257** 1.0234** 1.0173** 1.0169* -0.0085 -0.0089 -0.0091 -0.009 -0.0103 -0.0099 -0.0087 -0.009 Married 0.9983 1.0353 1.3002 1.2001 0.9834 1.0761 2.5204*** 2.3348** -0.3366 -0.3549 -0.5042 -0.4622 -0.3813 -0.4413 -0.8798 -0.8106 Constant 0.01228** 0.0132** 0.0007*** 0.0007*** 0.0003*** 0.0001*** 0.0008*** 0.0006*** -0.0234 -0.0256 -0.0016 -0.0016 -0.0009 -0.0003 -0.0021 -0.0015

Observations 371 367 371 367 351 346 351 346 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

103

Table 3.11. Odds Ratios: Support for 2016 GOP Primary Candidates (5) (6) (7) (8) (1) (2) (3) (4) Other Other Other Other Variables Trump Trump Trump Trump GOP GOP GOP GOP

General Trust (WWC) -33.5% -32.4% -35.6% -33.2% – – – –

(p<0.01) (p<0.05) (p<0.01) (p<0.01) Religious Attendance (WWC) – – – – – – – –

Ethnocentrism (WWC) – – -2.1% -2.3% (p<0.05) (p<0.01) Racial Resentment (WWC) +62.3% +77.9% – – (p<0.1) (p<0.1) Economic Anxiety (WWC) +36.2% +36.5% +32.2% +34.4% – – – – (p<0.1) (p<0.1) (p<0.1) (p<0.1) Authoritarianism (WWC) – – – – – – – –

White Identification (WWC) – – – –

White Consciousness (WWC) – – – –

General Trust (WP) -39.1% -38% -36.6% -35.1% +93.3% +87% +85.1% +82.7% (p<0.05) (p<0.05) (p<0.05) (p<0.1) (p<0.01) (p<0.01) (p<0.01) (p<0.01) Religious Attendance (WP) -27% -26% -26.6% -25.4% – – – –

(p<0.05) (p<0.05) (p<0.05) (p<0.05) Ethnocentrism (WP) +1.7% – – – (p<0.1) Racial Resentment (WP) – – – –

Economic Anxiety (WP) – – -27.4% – – – – –

(p<0.1) Authoritarianism (WP) – – – – – – – –

White Identification (WP) – – – –

White Consciousness (WP) +100% +88.4% – – (p<0.01) (p<0.01) Source: American National Elections Studies 2016 Time-series. Note: P-values in parentheses. Cell values represent odds ratio increases (decreases) in the likelihood of supporting Donald Trump (specifications 1-4) or another GOP candidate (specifications 5-8) predicted by increased values of independent variables listed in column 1, holding constant other independent variables (see Table 3.10 and Appendix A2.12, 193). Odd numbered specifications include racial resentment and exclude ethnocentrism (reversed in even numbered specifications). Specifications 1, 2, 5, and 6 include white identification and exclude white consciousness (reversed in specifications 3, 4, 7, and 8).

Two competing accounts also find support: economic anxiety and (especially) white

identity/prejudice are both found to meaningfully impact Trump support at the individual level.

The role of economic anxiety among working-class (but not professional) whites, and the role of

white consciousness among professional (but not working-class) whites, further supports this

paper’s contention that the present state of America’s working-class white communities is

104

politically consequential. The role of racial attitudes, though certainly relevant, appears to be exogenous to this.

3.7 Conclusion

President Trump’s victories in the 2016 primary and general elections were widely credited to his outreach to white working-class voters. In this paper, I have argued that white working-class social-institutional decline enables Trump support by eroding the foundation for conventional, conservative political appeals. I report evidence to this effect. Whether conceived in terms of economic, family, and behavioral institutions, or in terms of a lack of trust and social connectedness, most measures by which one would judge a community to be in a dysfunctional state are strongly associated with support for Donald Trump. This pattern is evident at the individual level as well, where social capital measures are negatively associated with Trump support and positively associated with support for Trump’s primary rivals.

These findings add political weight to recent work documenting the deteriorating state of

America’s white working-class communities. The size of the white working-class – 42 percent of the United States – ensures that these trends will meaningfully impact American society and politics. It is equally certain, given demographic and voting patterns, that the impacts of white working-class social institutional decline will be felt primarily from the political Right .

As the vehicle for conservative politics in the American political system, the Republican

Party cannot afford to ignore the state of the communities where so many of its voters reside. If conservatism is to remain relevant to those voters, it will have to find some meaningful way to address the reality of decaying social institutions. This will likely require a restorative vision of some kind and a willingness to embrace community-oriented policies that depart from laissez-

105

faire orthodoxies. Failing this, conservatism risks being displaced in Republican politics by alternative rightwing perspectives that reach downscale and less-educated white voters on other terms. The future of American politics will be shaped by how the political Right answers the question of what it means to be a “conservative” when there is nothing left to conserve.

106

CHAPTER 4

“White-lash”: How Talking About Race Polarizes Whites

4.1 Introduction

The 2016 presidential election cycle was the first in recent memory to feature white identity center stage. The leading Republican candidate was widely accused of engaging in

“white identity politics” by his political opponents and by the press. This same election cycle saw the emergence of a new, explicitly white nationalist movement: The Alternative Right.

Though distinct, both events reflect the American political system responding to a seismic change in American society. The shrinking of America’s white population renders white identity increasingly salient. This, in turn, erodes the stigma that has, for decades, created a barrier between white identity and mainstream politics.

Whatever the results of Trump Presidency, the Republican Party faces the prospect of a declining base of increasingly race-conscious white voters. In this environment, the GOP will face electoral pressure to lean into rather than away from white identity rhetoric and to marry such rhetoric with policies directed at advantaging whites as a group. This shift in the center of gravity in American politics brings white nationalist politics into the political mainstream.

Until recently, scholars of race and American politics found little evidence of white identification in the mass public (e.g., Terry 1981, Kinder and Winter 2001, Sears and Henry

2005, Sears and Savalei 2006). The political importance of white identity likewise appeared

107

minimal (Wong and Cho 2005). It was believed that whites lacked a strong sense of racial identity due to their dominant position in American society. The renewed salience of white identity is therefore puzzling. Research associates ingroup identification and ingroup prejudice

(Key 1949, Sherif et al. 1961, Levin and Campbell 1972); however, white identification is rising at a time when white racist attitudes appear to have ebbed (Bobo, Charles, Krysan, Simmons, and Fredrickson 2012). President Trump’s rhetoric may be a contributing factor; however, rising white identification was detected well in advance of the Trump campaign (e.g., Jardina 2014). 126

Psychological explanations for authoritarianism (Stenner 2005) and ethnocentrism (Sober and

Wilson 1998, Kinder and Kam 2010) speak to identification in the context of ingroup preference, yet these accounts assume largely stable predispositions. Rising white identification suggests an external cause. Something in the social or political environment is causing white people to reevaluate their identities in relation to their larger national community.

Social dominance theory offers one prominent explanation: rising white identification reflects the dominant group rallying in defense of its status (Doane 1997, Sidanius and Pratto

2001, Levin, Sidanius, Rabinowitz, and Federico 1998). 127 Recent work draws attention to whites’ fears of demographic displacement and resentment of assertive racial-minority politicians (Schildkraut 2014, Jardina 2019). This account draws face validity from the fact that these trends are likewise, relatively new and salient.

Per social dominance theory, the visibility of status threats is key. Jardina (2019) finds that growing diversity did not trigger white anxiety – and with it, white identification

126 Indeed, the causal relationship is likely reversed in that rising white identification brought on by racial-partisan sorting during the Obama Presidency contributed to President Trump’s election victories (Sides, Tesler, and Vavrek 2018). 127 See also: group position theory (Blumer 1958, Bobo and Hutchings 1996) and intergroup emotions theory (Smith 1993).

108

– until a critical mass had been reached in recent years. The role played by media is therefore pivotal. Media attention to whites’ shrinking share of the population disseminates status-threat information to tens of millions of white media consumers. Many whites respond defensively to this information, reporting higher levels of anger, fear (Outten, Schmitt, Miller, and Garcia

2012), 128 and racial bias (Craig and Richeson 2014a).

Demographic decline, though important, is merely one path by which status-threat messages reach American whites. There are other highly salient and well-publicized topics that could have similar effects. Consider, for example, the perspectives advanced by the fields of whiteness studies and critical race theory (CRT). Prominent examples include privilege theory

(McIntosh 1988, Frankenberg 1993) and various revisions and expansions of the definition of racism: the “power plus prejudice” definition (Wellman 1993), the related claim that only white people are capable of racism (Hoyt 2012), “systemic” and “institutional” (Adams 1997,

Jones 2002), and “color-blind” racism (Bonilla-Silva 2006).

Exposure to these “critical” themes could elicit defensive reactions from white audiences. 129 Research conducted in small-scale classroom settings links exposure to privilege theory to reduced racial tolerance among whites (Branscombe, Schmitt, and Schiffhauer 2007,

Lensmire, McManimon, Lee-Nichola, Casey, Lensmire and Davis 2013). At the time of this writing, however, the effects of mass exposure to critical discourse through popular media has not been studied. The effects of critical discourse exposure on white racial identification is similarly understudied.

128 Per intergroup emotions theory, dominant groups respond to status-threats from minority groups with anger and fear (Smith 1993, Leach, Snider, and Iyer 2002). 129 These themes are colloquially labeled “woke” or “politically correct” by their proponents and opponents, respectively.

109

This paper explores whether the dissemination of critical approaches to race and white identity through popular media contributes to a white identity and prejudice backlash – a “white- lash,” in the phrasing of CNN commentator (Ryan 2016). Focusing on the looming threat of white nationalism, I report evidence from a survey experiment designed to evaluate whether these core white nationalist sentiments are mobilized by exposure to examples of these themes.

I proceed as follows: Section 4.2 reviews the literature addressing white identification, prejudice, white nationalism, and discussions of white identity in popular media. Section 4.3 explores how white identification and possibly ethnocentrism could increase in response to contact with critical discourse. I then present a survey experiment designed to evaluate this connection in Section 4.4, describe the methods employed in Section 4.5, present my findings in

Section 4.6, and conclude in Section 4.7.

4.2 Literature Review

4.2.1 White Identification

The term “identification” describes an internalized sense of psychological attachment to a group (Conover 1984, Lau 1989). This may include a sense of a group’s relative position in the social order (Blumer 1958, Bobo and Hutchings 1996) and an orientation towards protecting or promoting its interests in politics (Levin et al. 1998). Identification is a process whereby identity forms a cognitive schema or lens through which politically relevant information is processed and sorted. In this way, racial identification is like political ideology and partisanship, making it a potentially powerful determinant of political behavior (Verba and Nie 1972, Olsen 1970).

Questions such as “How important is being white to your identity?” (American National

110

Elections Studies Time-series 2012, 2016) allow survey researchers to estimate white identification, compare it to other measures of identification (e.g., American identification), and analyze its relationship with attitudes (e.g., prejudice), and demographic characteristics (e.g., education).

A closely related concept, “group consciousness,” combines identification with political awareness or ideology regarding the group’s relative position and a commitment to collective action aimed at realizing the group's interests (Jackman and Jackman 1973, Miller, Gurin, Gurin, and Malanchuk 1981). 130 Survey questions distinguish group consciousness by emphasizing the political dimension, including a sense of group deprivation: “How important is it that [members of group] work together to change laws that are unfair to [members of group]?” (ANES Pilot

2016, “White Racial Consciousness” 2015).

White identification has been linked to conservatism (Schildkraut 2014, Craig and

Richeson 2014b), authoritarianism (Schildkraut 2014, Jardina 2019, 113), racism, ethnocentrism

(Outten et al. 2012, Craig and Richeson 2014a, Schildkraut 2014), and working-class identification (Roediger 1999). White identification predicts support for President Trump in the

United States (Major, Blodorn, and Blascovich 2018, Knowles and Tropp 2018, Sides et al.

2018), and is implicated in support for Brexit in the UK (Kaufmann 2019) and for rightwing populist movements across the West (Kaufmann 2018).

As white identification rises, white Americans are likely to view politics from the perspective of being white. This has the potential to transform the political landscape given that

American whites, like other national majorities, do not typically think of themselves as an ethnic

130 Miller et al. (1981) assert that dominant groups will develop group consciousness when they perceive their position in society to be challenged by members of subordinate groups (496-7).

111

group (Doane 1997, Terry 1981). Exceptions to this rule – times and places when white identity was especially politically and socially impactful – include periods of heightened racial conflict and oppression (Higham 1963, Øverland 2004). The much-bemoaned invisibility of whiteness

(e.g., McIntosh 1988) may speak more to periods in which racial conflict has been relatively restrained and where political mobilization thus occurred primarily along other dimensions.

There is, of course, no guarantee that rising white identification foreshadows increased racial conflict, but past experience is not encouraging.

4.2.2 Racial Prejudice

When considering the likely impact of rising white identification, a key issue concerns the relationship between ingroup identification and intergroup sentiments. This turns out to be a complicated question. To begin, identification and affect are conceptually distinct: possessing a strong sense of one’s own whiteness, blackness, Americanness etc., is compatible with a range of feelings towards these identities. 131 White identity is deeply important to both the Alt Right and to the contemporary anti-racist left, for example.

Second, thinking in terms of ingroups and outgroups involves a mental process of categorization. Social identity theorists demonstrate that categorization mobilizes intergroup prejudice in experimental settings (Tajfel 1978, 1982; Turner 1981, Wilder 1986, Mullen, Brown and Smith 1992). Following assignment to groups, research subjects direct benefits to ingroup members and away from outgroup members. Remarkably, this even occurs when the basis for group assignment is trivial or random (Billig and Tajfel 1973, Rabbie 1982). In the realistic group conflict literature, the focus is reversed, and it is conflict with outgroups that creates

131 For example, the ANES “linked fate” question has been found to capture feelings of white guilt or (Dawson 2009 and Jardina 2014).

112

ingroup identification and mobilization in defense of ingroup power and resources (Key 1949,

Sherif et al. 1961, Blalock 1967, Levine and Campbell 1972).

Drawing on both traditions, Kinder and Kam (2010, 8) describe “ethnocentrism” as the predisposition to divide the world between ingroups and outgroups. 132 They state,

Members of in-groups (until they prove otherwise) are assumed to be virtuous: friendly,

cooperative, trustworthy, safe and more. Members of out-groups (until they prove

otherwise) are assumed to be the opposite: unfriendly, uncooperative, unworthy of trust,

dangerous and more.

In line with social identity theory, ingroups and outgroups are constructed as internally homogenous and sharply differentiated categories. 133 In line with realistic group conflict theory, these categories become the bases for intergroup social and political conflict.

The possibility that rising white identification could increase prejudice should concern whiteness scholars and activists who have labored for decades to raise the salience of white identity to combat racism. Indeed, if white nationalists themselves are to be believed, white identification – making whiteness “visible,” in McIntosh’s (1988) language – is sine qua non for their project. White nationalists Samuel Francis (1998) and Jared Taylor (2011) are emphatic on this point, arguing that the “invisibility” of whiteness makes whites less receptive to racial

132 Kinder and Kam’s definition of ethnocentrism is a general one, meaning that the ethnocentrist favors his/her ingroup over outgroups in general, rather than disliking a specific outgroup (2010, 8). I use the term “ethnocentrism” in this sense and otherwise use the term “prejudice” when addressing ingroup preferences vis-à-vis specific outgroups. 133 Prejudice appears to be intimately related to information processing – i.e., thinking. People interpret their environments (other people included) based on information relayed to them by their senses. To handle this workload, the mind abstracts information from sensory inputs to create relatable categories. Cognition itself is categorization… which is to say, discrimination. One byproduct of economizing information this way is the tendency underestimate within- category differences and to overestimate between-category differences (Harnad 2017, 16).

113

identity-based political appeals and to their larger project of framing political and social questions from the perspective of white racial interest.

4.2.3 White Nationalism and the Alternative Right

The term “white nationalism” most appropriately describes a white ethno-nationalist politics that conceives of the nation along racial lines.134 Put simply, white nationalism centers on the “white nation” in America. White nationalists reject the universalism of American and

Western values (Swain 2002, 16-22; Hawley 2017, 92; Beale 2016) and hold conservatism responsible, as they see it, for conning white Americans into supporting policies inimical to their racial interests (Hawley 2017, 92-93; Beale and Red Eagle 2015). 135

In Swain’s (2002) account, white nationalism emerged as the racist right’s response to the failure of white supremacy (e.g., Jim Crow and the ); namely, its inability to maintain racial separation (see also: Dobratz, and Shanks-Meile 2000, 10-12). Beginning in the

1960s, white separatist groups organized under the “Sovereign Citizen,” “” and

“Neo-Nazi” movements. Over time, these groups were succeeded by a small cadre of scientific racist (aka “racialist”) intellectuals. This group similarly favored racial separation and in some cases, territorial partition, but they were better able to convey their ideas to mass audiences because they contradicted conventional stereotypes of white racists as low-class, poorly educated, and generally inarticulate people (Swain 2002, 18-20).

The Alternative or “Alt” Right is the descendant of this latter group. Despite its recency, fragmentation, and porous boundaries, it is possible to identify at several core features of this

134 Elsewhere (e.g., Walters 2003) and sometimes colloquially, the term is treated as synonymous with “white supremacy.” Supremacy presupposes diversity, however, in that for one group to hold a position of dominance over others, there must be other groups present. 135 Contemporary white nationalists sometimes lack even symbolic attachments to the United States (e.g., patriotism) characteristic of conservatives (Hawley 2017, 91).

114

movement. 136 First and foremost is the importance of white identity (Hawley 2017, 14-16). 137

This, more than anything else, differentiates “mainstream” (conservative) and Alternative Rights.

Alt rightists reject the economically liberal and morally traditional values at the core of conservatism (Hawley 2017, 17-18) because, at a fundamental level, these movements seek to conserve different things. 138

The second essential characteristic of the Alt Right is extreme prejudice. This goes well beyond being merely “pro-white,” as some of its apologists claim (e.g., Bokhari and

Yiannopoulos 2016). Online and off, the movement is hostile to racial and religious minorities

(Heikkilä 2017, Anti- League 2016), and to whites with mixed-race families (French

2016). The Alt Right’s long-term objective is the replacement of the United States with a racially homogenous ethno-state rooted in illiberal principles. 139 Per Spencer, the ethno-state would be realized following a process of “peaceful ” (Ciaramella 2013). In social identity terms, the Alt Right displays extreme ingroup preference, intergroup prejudice, and outgroup hate. 140

136 Nagel (2017) emphasizes the misogynism of Alt Right internet trolls; however, this behavior appears to be less philosophically integral to the Alt Right than racial ideas. Anti-Semitism is also a central feature of the Alt Right, though the movement appears divided on this point (Hawley 2017, 28; Sisyphus 2018). 137 As stated by figurehead Richard Spencer, “Identity is the most important question to answer. Who are we racially? Who are we historically? Who are we in terms of our experience? Who are we in terms of our community?" (Millard 2015). 138 The divide over racial and civic conceptions of the nation (the ingroup) is visible even in the borderlands between conservative and white nationalist politics (aka: the “Alt Light”) where one finds figures like , Gavin McInness, , and . These commentators are willing to discuss white identity and advocate for whites as a group in ways that other conservative public figures generally will not; but they typically stop short of revising American identity along racial lines. 139 Hawley argues that the ambitions of many Alt Rightist are more modest (2017, 16). 140 Outgroup hatred is distinct from ingroup love. As such, it does not necessarily accompany prejudice (Brewer 1999).

115

The Alt Right thus represents a kind of endpoint towards which the bundled sentiments of identification and prejudice move. Less extreme examples include the current crop of rightwing populist movements. 141 Indeed, even conventional conservative parties are comparatively ingroup identity-focused in their emphasis on patriotism/nationalism, and (broadly-speaking) ethnocentric in seeking to limit immigration, bolster defense, restrict benefits from noncitizens

(“”), and favor native traditions, customs, and values. In short, the link between for ingroup identification and prejudice – evidenced in the social identity, realistic group conflict, and ethnocentrism literatures, among others 142 – finds expression broadly in the political Right 143 but especially in Alt Right where the ingroup is conceived in explicitly racial terms. It follows that, notwithstanding the small number of “woke” antiracists for whom white identity is also highly salient, growing white identification portends greater racial prejudice. I turn now to an underexamined potential cause of rising white identification.

4.2.4 White Identity in Popular Media

Beginning in 2010, there was a striking shift in media attention to the topic of white identity, little if any of which was positive in tone. Terms like “whiteness,” “white privilege,”

“white fragility,” “white tears,” and “whitesplaining” were introduced to the lexicon as public criticism of white identity and people proliferated. Examples from online media include,

141 Populism, as described by Mudde (2004), combines identification and prejudice by positing a virtuous, authentic, and coherent ingroup (“the people”) possess interests at odds with those of cosmopolitan elites and their (external and internal) foreign client-groups. 142 Research on authoritarianism (e.g., Stenner 2005) and social dominance (Sidanius and Pratto 2001) likewise link ingroup identification with prejudice. 143 See: Bobo and Kluegel 1993, Gilens 1999, Kinder and Mendelberg 2000, Sidanius, Levin, Liu, and Pratto 2000, Alesina, Glaeser, and Sacerdote 2001, Goren 2003, Lee and Roemer 2006, Williamson, Skocpol, and Coggin 2011, Abramowitz 2012, and Parker and Barreto 2013.

116

- : “It’s Time to Stop Talking about Racism with White People: No

More Arguing with Disingenuous Folks Who Have Nothing to Lose” (Linly 2016).

- The Huffington Post: “Ten Things White People Need to Quit Saying” (Moezzi 2016).

- Salon Magazine : “White Men must be Stopped, the Very Future of Humanity Depends

Upon it” (Joyce 2015) and “10 Ways White People are More Racist than they Realize”

(Holloway 2015),

- Vice: “Dear White People, Please Stop Pretending is Real” (Krishnan

2016).

- : “21 Things White People Ruined in 2015, Besides Everything” (Fequiere

2015).

The reach of critical discourse extends well beyond the internet. These themes are expressed in television (e.g., MTV’s “New Year’s Resolutions for White Guys,” Plainview

2016) and in film (“Dear White People,” Simien 2014). They are echoed by politicians (Bacon

2016), celebrities (Fry and Pizzello 2019), and corporations (Young 2019). Even comic books prominently advocate for critical themes despite hostile reactions from their readers (Cain 2017).

This sudden mainstreaming of radical perspectives on race and white identity followed decades of research under the banners of whiteness studies and critical race theory. Whiteness or critical whiteness studies examines whiteness as a social construct and as a resource of socio- economic power (Frankenberg 1993, Stowe 1996, Hartigan 1997). Critical race theory applies critical theory perspectives from the Frankfurt School to the study of race relations (Delgado and

Stefancic 2012, Bohman 2005).

Many of the associated ideas are incendiary. These include claims that: white people, regardless of socioeconomic status, carry with them unearned privileges derived at the expense

117

of people of color (McIntosh 1988, Frankenberg 1993); that normative colorblindness, the decades-old standard of anti-racism, is itself a form of racism (Bonilla-Silva 2006);145 that only whites are capable of racism because racism is not racial prejudice or hate but is rather

“prejudice plus power ,” with power referring to one’s racial group (Chesler 1976); that all whites are racist due to being socialized “white” in a racist society (Di Angelo 2018); that white identity is uniquely pathological and deserving of abolition because it was created to oppress African

Americans (Roediger 1994, Garvey and Ignatiev 1997); that racism is structural (Bonilla-Silva

2001); and that the United States is a “racial state” structured to advance white supremacy (Omi and Winant 2014, Ansley 1997). An implicit corollary to the last point is that patriotism, military service, and reverence for American symbols and values – e.g., the American flag, liberty, and individualism – amounts to support for white supremacy (Starr 2017). The same can be said of mainstream Right and, in some cases, Left politics (Walters 2003).

Until recently, these perspectives were largely contained to universities. This changed between 2005 and 2015 when usage accelerated (Perrin 2015) bringing critical themes to larger audiences. Returning to the online news/commentary sources listed on page

117, it is possible to get a sense of the reach of this discourse. During the month of September in

2016, for example, The Washington Post recorded 83.1 million unique visitors, Vox recorded 71 million, and Buzzfeed recorded 72.9 million (WashPostPRBlog 2016). According to the Pew

Research Center , 38% of American adults report frequently consuming news online and 62% consume news through social media (Matsa and Lu 2016). It is likely that these figures dramatically understate exposure to critical discourse, given the ease with which a single article

145 Proponents of colorblindness reference Martin Luther King’s 1963 “I Have a Dream” speech. Opponents of colorblindness argue that proponents deliberately misinterpret King (Turner 1996).

118

posted to a news or opinion website becomes a launch pad for discussions in other forums, including television news, social media, and person-to-person conversations. The reach of this discourse is vast and attempting to catalogue it fully would be a Sisyphean exercise.

Getting a sense of its growth over time is more manageable. Figure 4.1 displays results from a plausibility probe for evidence that online criticism of white identity – a subset of what I have name “critical discourse” – has increased in recent years. For the sake of consistency, I search the terms “white people,” “whites,” and “white men” in the years 2000, 2005, 2010, 2015, and 2016 using Google. I then record results explicitly addressing white people and white identity from a transparently critical or hostile perspective. As suggested by the articles previously listed, a title alone can be sufficient indication of tone; however, I take the additional precaution of reviewing and verifying the content to ensure that misleading titles such as the conservative Federalist Paper’s “Why White People Will Always Be Racists” (McAllister 2015) were not miscoded (see Appendix, 197 for additional details).

Beneath plausibility probe results, Figure 4.1 display ANES Time-series summary measures of white identification for self-identified “liberals,” “moderates” and “conservatives.”

The proportion of conservatives who rated their white identities as “very” or “extremely” important (“high identifiers”) increased from 32.1% in 2012 to 36.6% in 2016. High-identifying liberals increased slightly from 23.2 to 24.6% during this same period, while high-identifying moderates decreased from 32.7 to 29.4%. Unfortunately, these measures are available only for the years 2012 and 2016; however, this time-period overlaps with the rapid growth in online critical discourse, the Trump election and the emergence of the Alt Right.

119

Figure 4.1. Online Articles Critical of White Identity and Trends in White Identification, 2000-2016

4.3 Theory

Notwithstanding the limitations of ANES data, the combined trendlines in Figure 4.1 indicate growing polarization on white identity. Given that critical discourse originates from the political Left, it is likely that liberal and conservative whites are elevating their racial identities for different reasons. In the case of liberals, rising online news consumption during the early

2010s – a proxy for exposure to critical discourse – coincided with leftward on issues of race and gender (Campbell and Manning 2018, Goldberg forthcoming). Rightwing

120

radicalization followed shortly thereafter (Nagel 2017, Kaufmann 2018). 146 If the Trump election and the Alt Right are indicative of rightwing responses to leftwing racial discourse (including

“P.C. Culture”), rising white identification among conservatives suggests a reaction against this discourse.

What of spurious causation? Returning to the view from social dominance theory, it could be argued that the more important trendline is the demographic one (rising diversity) and widespread dissemination of information to that effect (Jardina 2019). Perhaps but for a sense of insecurity driven by rapid demographic change, whites would simply shrug off criticism of their identities and values.

The social dominance account is compelling in many ways; however, the importance of demographic change does not preclude other factors. This point is important because, as a practical matter, rising diversity is inevitable. The decision as to whether whites would lose their majority share of the US population was made over a half century ago when immigration laws were loosened and in the following decades when white fertility rates dropped. No current change to immigration policy could plausibly alter the movement of white America to minority status. If rising white identification is caused by white demographic decline combined with exposure to critical discourse, only the latter factor can (to say nothing of should ) be changed.

Although it is conceivable that the mass dissemination of critical themes could be unrelated to trends in white identification, there is reason to suspect that many whites would respond defensively. As noted, these themes and their presentation are inherently provocative in taking aim at an ethnic group’s identity and core values, including patriotism and normative

146 Given that the source material (whiteness studies and CRT) animating the discourse in question are decades-old, reverse causation – white identification mobilizing critical discourse – is unlikely.

121

colorblindness. If white Americans perceive themselves as “under siege” by “anti-white” themes in popular media, many would likely adopt a “siege mentality” and rally to rather than against their white identities. Lord, Ross, and Lepper (1979) and Redlawsk (2002) document that, when confronted with information contradicting previously held beliefs, those beliefs harden. In this case, hostility directed at one’s – perhaps only moderately valued – racial identity could trigger a defensive, upward revaluation of that identity, resulting in increased identification and/or group consciousness.

Perceptions of unfair treatment are known to nurture ingroup and aggression

(Allport 1954, Trivers 1971, Tajfel, Turner, Austin, and Worchel 1979, Branscombe, Schmitt, and Harvey 1999). Viewing critical discourse in this light helps to account for how, in a majority white country with a long history of mistreating African Americans, most whites now believe that anti-white discrimination is comparable to (Piacenza 2014) or more prevalent than (Norton and Sommers 2011) anti-black discrimination. The perception of anti-white racial hostility in media and more broadly in the culture may be substituting for tangible experiences of in the minds of many whites. Given the noted connections between ingroup identification and prejudice (Tajfel 1970, Effron and Knowles 2015), it seems likely that widespread engagement with critical discourse could worsen race relations by encouraging prejudice and racial hostility.

Donald Trump’s upset electoral victories and the rise of the Alternative Right are interpretable in this context. Though different in important respects, both events represent recent high watermarks for the politicization of white identity in American politics and both occurred shortly after the surge in critical discussions of white identity and the subsequent polarization of white identification. Of course, the timing of these events could be coincidental. What is

122

therefore needed is a test of whether media narratives concerning race and white identity can mobilize ingroup identification and prejudice. Such a test could additionally provide greater insight into the role of backlash in media effects.

4.4 Survey Experiment

Towards this end, I partnered with Dynata to field a survey experiment from August 1-

29, 2019. In total, 1,527 white respondents were treated with survey primes representative of contemporary discussions of race. Respondents were then surveyed to determine the effects of primes on white identification and intergroup attitudes.

The survey experiment contained three back-to-back phases. The first (“pre-survey”) phase collected basic demographics and information on respondents’ political dispositions, including: ideology, authoritarianism, egalitarianism, social dominance orientation, and white identification. Respondents were randomly assigned to three “treatment” and one “control” groups, asked to read a short article, and then asked to assess whether the article was well-written

(phase two). Treatment interventions were excerpted from online commentary articles and obtained with the permission of their authors. Real-world articles were used in lieu of constructed primes as a precaution against external validity concerns. 147

The first prime is titled “10 Things White People Seem to Not Understand (Because,

White Privilege)” (Domingo 2014). 148 This article pointedly articulates the concept of white privilege while touching on related themes like and cultural appropriation.

147 Author and publication names are removed to avoid priming respondents on those factors. 148 The full article is titled “18 Things White People Seem to Not Understand (Because, White Privilege).”

123

The second prime is titled “The Fallacy of White Privilege” (Prager 2016). It asserts a conservative perspective on privilege theory while disputing claims of systemic racism.

Including an alternative, oppositional prime helps to address the concerns that respondents are affected more by the tone (positive or negative) than by the substance of treatment interventions.

The second prime also addresses selective exposure concerns: the tendency of people to discount information presented by “unfriendly” media sources (Vallone, Ross, and Lepper 1985) and to limit exposure to such sources (Hart, Albarracín, Eagly, Brechan, Lindberg, and Merrill 2009).

In theory, critical discourse could be quarantined within a left-leaning ideological community to which conservatives would have little exposure. 149

Of course, conservatives do not need to directly engage with liberal news and opinion sources to learn about critical discourse as they have their own friendly news media sources to relay and interpret these themes for them. Illustrative examples include,

- The American Conservative . “The Left’s Anti-White Racism” (Dreher 2015).

- . “Buzzfeed Celebrates Anti-White Racism” (Prestigiacomo 2016).

- . “Drexel University Professor’s Wish: ‘All I Want for Christmas is White

Genocide’” (Huston 2016).

- The Federalist. “How Anti-White Rhetoric is Fueling White Nationalism” (Markus

2016).

Replicating the plausibility probe search criteria described on page 119, the number of articles

149 Though perhaps intuitive, this scenario is unlikely. Sunstein (2002, 2009) shows that online “echo chambers” do not contain ideologically extreme ideas; rather, they radicalize these ideas before exporting them to the wider public. Consequently, social media echo chambers both increase polarization and exposure to opposing viewpoints (Flaxman, Goe, and Rao 2016).

124

Figure 4.2. “Critical” and “Anti-Critical” Discourse in Online Articles, 2000-2016

presenting conservative – not to be confused with white nationalist – “anti-critical” perspectives largely mirrors the trend for critical articles (see Figure 4.2).

The third prime is titled “Racism and the Indelible Impact of Personal Experience”

(French 2016). The source article presents a more balanced and conciliatory discussion of racial issues. The author identifies himself as a white, Christian conservative: a source that right- leaning, white readers might consider “friendly.” This prime is included to assess whether greater racial tolerance and more inclusive identification can be advanced by using an alternative rhetorical approach.

125

The fourth article is a control piece titled “Social Media Outstrips TV as News Source for

Young People” (Wakefield 2016). It is included to provide a baseline with which to compare the effects of treatment primes.

In the third phase, following treatment exposure, respondents received the final “post survey.” Question topics include feeling thermometer evaluations of individuals and groups, racial resentment, and other questions addressing identity and public policy. The majority of questions are taken from the 2016 ANES Time-series. I report findings in figures listed below and in additional tables referenced in Appendix .

The approach taken here differs in some important respects from those of earlier inquiries into the effects of media frames on racial attitudes. Research in the racial threat (Hopkins 2011,

Sniderman, Crosby, and Howell. 2000, Sniderman and Hagendoorn 2007) and social dominance traditions (Sidanius and Pratto 2001, Levin et al. 1998, Jardina 2019) directs white attention towards racial outgroups for the purpose of mobilizing the predicted threat or dominance response. Less attention has been paid to the effects of public discourse on white identity when that discourse concerns white identity itself rather than the non-white “other.” This limitation likely reflects the fact that white identification has been historically under-studied.

4.5 Methods

I evaluate six hypotheses separately for “conservative” (n=559) and “high white identifying” (n=434) respondents. Conservatives include respondents who self-describe as

“slightly conservative,” “conservative,” or “very conservative.” High white identifiers include respondents who rate white identities as “very” or “extremely” important. I focus on these two groups because they are more natural audiences for white nationalist appeals than liberals and

126

low white identifiers. Moreover, the primes employed and questions asked may not be appropriate for other respondents. 150

I hypothesize that, within the first and second treatment groups (P1 and P2), target groups should report increased white identification [H1a, H2a] and prejudice [H1b, H2b], as well as reduced support for “racially liberal” policies (e.g., affirmative action, immigration, and welfare spending) [H1c, H2c]. 151 Regarding the third treatment group (P3), I expect the reverse: target groups should report decreased white identification [H3a] and prejudice [H3b], and increased support for racially liberal policies [H3c].

I test these hypotheses on the collected survey data, regressing identification and prejudice measures (DVs) on treatment prime indicator variables (IVs) interacted with measures of conservatism and white identification. I use ordinary least squares regression where dependent variables are continuous (e.g., ethnocentrism: 0-100) and ordered logit where they are categorical

(e.g., agreement that one must speak English to be “truly American”: 1-5).

Survey primes are alternatively interacted with continuous and indicator measures of conservatism and white identification. Continuous measures treat conservatism (1-7) as a general tendency that increases in a linear fashion from one measurement category to another as respondents move “right” – e.g., from “somewhat liberal” to “moderate” and from “moderate” to

150 For example, the third treatment prime (P3) is unmistakably directed at a conservative audience. This “friendly” framing would not be appropriate for liberal readers. In addition, interpretations of responses to questions like “How important is being white to being ‘truly American?’” might differ across ideological groups. For right-leaning respondents, uniting white and American identities suggests white nationalist sympathies; however, for left-leaning respondents, a willingness to equate American identity with white identity might represent an indictment of the former for failing to fully include people of color. 151 I make no assumptions as to whether policy liberalism measures per se capture prejudice; rather, I infer that changes in policy liberalism following exposure to race-priming survey interventions would be attributable to priming racial attitudes.

127

“somewhat conservative,” and so on. The same is true for white identification (1-5). Interactions with continuous measures have the advantage of leveraging the conservatism (white identification) of less conservative (white identifying) respondents.

Interactions with indicator measures treat conservatives and high white identifiers as discrete categories of people, as distinct from non-conservatives (“very liberal” to “moderate” respondents) and low-to-moderate white identifiers. This approach may be appropriate insofar as political polarization is group identity-based and “affective,” rather than ideological (Iyengar,

Sood, and Lelkes 2012, Mason 2015) or by extension, dispositional.152

Ŷ = β0 + β1P1*conservatism (1-7) + β2P2*conservatism (1-7) + β3P3*conservatism (1-7) +

βncontrols + е

Ŷ = β0 + β1P1*conservatism (0/1) + β2P2*conservatism (0/1) + β3P3*conservatism (0/1) +

βncontrols + е

Ŷ = β0 + β1P1*white identification (1-7) + β2P2*white identification (1-7) + β3P3*white

identification (1-7) + βncontrols + е

Ŷ = β0 + β1P1*white identification (0/1) + β2P2*white identification (0/1) + β3P3*white

identification (0/1) + βncontrols + е

In both groups of models and in all specifications, I exclude respondents who failed the provided attention check (see Appendix, 226, question 16) and those who completed the survey in less than five minutes. Probability weights are constructed using census data and raking procedures. Control variables are added progressively with specifications. Controls include

152 Ideological extremism and partisanship are distinct. A moderate ideologue may be highly partisan. An ideological extremist could have weak partisan ties, perhaps due to perceptions of his/her party’s ideological moderation.

128

respondent age, gender, income, education, white identification (alternatively, conservatism), authoritarianism, egalitarianism, and social dominance orientation.

4.6 Findings

Below, I report statistically significant interactions, by prime, describing measures along the way. I begin with models regressing identification measures on survey primes*conservatism/white identification. I then do the same for prejudice measures and for measures of policy liberalism. Finally, I separately extend this same approach to feeling thermometer evaluations of President Trump, revisiting the connection between “white-lash” and

Trump support in 2016.

4.6.1 Prime 1

Beginning with the critical prime (P1: “10 Things White People Seem to Not Understand,

Because White Privilege”), I find positive and statistically significant interactions with conservatism (higher values of “ideology”) and/or white identification in 12 models (62 specifications) predicting measures of ingroup identification (see Figure 4.4).153 No statistically significant interactions are contrary to expectations. Findings, in this case, are entirely in line with expectations: exposure to critical discourse consistently associates with a pro-white identity backlash [H1a].

Among the findings, “Group position” stands out as the most important. This measure is among those used to capture white “group consciousness” or politicized white racial identification (e.g., Jardina 2019). Group consciousness is central to the white nationalist project

153 Here and elsewhere, I exclude misleading interactions from reported findings: see discussion on pages 133-134.

129

of framing political questions through the lens of white identity and racial interest. This question asks, “How important is it that white people work together to improve the position of their group?” Answers are coded 1-5, with responses ranging from “not at all important” (1) to

“extremely important” (5).

More conservative respondents in the P1 treatment group are moderately more likely to express concern for group position in comparison to such respondents in the Control group (five specifications, p<0.1). This association is depicted in Figure 4.3. To ease interpretation, respondent ideological categories are compressed from seven to three: liberals, moderates, and conservatives. Conservatives display greater concern for “Group position,” even in the control group; however, in the P1 treatment group, the gap between liberals and conservatives increases by approximately one third.

Readers will note that the greater distance (polarization) between liberal and conservative points in the P1 treatment condition is driven more by the negative effects of P1 on moderates (-

0.26) and liberals (-0.13) than by the positive effect of P1 on conservatives (0.08). Two considerations are worth noting. First, research has shown that whites typically report more racially liberal attitudes in experiments following exposure to primes explicitly addressing race.

This is attributed to social desirability concerns in the context of racially egalitarian norms that have dominated since the end of the Civil Rights Era (Mendelberg 2001, though see Valentino,

Neuner, and Vandenbroek 2018). Second, recent research shows that backlash effects are difficult to mobilize in survey experiments (Guess and Coppock 2018). Given these “thumbs on the scale,” small increases in ingroup identification among conservative whites constitute a compelling evidence of a white-identity backlash.

130

Figure 4.3. Interaction Effects: Prime 1 and Ideology on Concern for Whites’ Group Position

In theory, a more ideal approach would be to compare P1 treatment effects for conservatives with those for some non-ideological group, if such a group existed. This is because the categories “moderate” and “liberal” presumably represent more than just less conservatism.

Interacting P1 with conservatism could therefore obscure other influences of “liberalism” and

“moderateness” by folding them into a single dimension. Notwithstanding this limitation, by treating moderates are roughly equivalent to this hypothetical non-ideological baseline – an admittedly imperfect workaround – the positive effect of P1 on conservative concern for “Group position" (0.56) nearly doubles the negative effect of P1 on liberal concern for “Group position”

(-0.3).

131

Proceeding to the next group of identification measures, questions ask the importance of

(_) to being “truly American.” These measures assess the extent to which respondents draw the boundaries of American identity narrowly/exclusively. As with group consciousness measures, answer choices range from “not at all important” (1) to “extremely important” (5). Two cases are especially noteworthy: conservatism (three specifications, p<0.05-0.1) and white identification

(eight specifications, p<0.01) both strongly positively interact with P1 in predicting agreement that one must have “American ancestry” to be “truly American” (Figure 4.4, Graphs 2 and 3).

This claim asserts an ethnonationalist view of American identity; indeed, “American ancestry” may substitute for “white,” for some respondents (Devos and Banaji 2005). Other examples –

“Born in the U.S.” (Graph 4) and “Speak English” (Graph 5) – similarly suggest stronger ethno- cultural boundaries around the national ingroup. By contrast, restricting American identity to those who respect U.S. “Institutions and laws” would suggest a stronger civic nationalist differentiation between outgroups and the ingroup (Graph 10). In the case of circumscribing

American identity to “U.S citizen(s)” (Graph 6), to those “Living in the U.S” (Graphs 8 and 9), and to those who “Feel American” (Graph 7), findings speak more to a general narrowing/exclusionary effect on attitudes towards the national ingroup. 155

155 Significant interactions between conservatism and P1 are present in eight specifications in the case of “Live in the U.S.” (p< 0.01-0.05). Significant interactions between white identification and P1 are present in eight specifications in the case of “Born in U.S.” (p<0.05-0.1), two specifications in the case of “Speak English” (p<0.1), four specifications in the case of “U.S. citizen” (p<0.05), eight specifications in the case of “Live in the U.S.” (p<0.01-0.05), four specifications in the case of “Feel American” (p<0.05-0.1), and eight specifications in the case of respecting U.S. “Institutions and laws” (p<0.05-0.1).

132

Figure 4.4. Interaction Effects: Prime 1 and Ideology / White Identification on Measures of Ingroup Identification

In several cases, proper interpretation of findings requires deeper probing. For example, as shown in Figure 4.4, Graphs 5 and 6, increases in ingroup identification (“Speak English” and

“U.S. citizen”) are minimal among high white identifiers. Statistically significant interactions are

133

here dominated by decreased identification among moderate and low white identifiers. In these cases, Prime 1 widens the identification gap between respondents (polarization) without meaningfully increasing identification among high white identifiers. In other cases (not depicted), significant interactions are clearly misleading. For example, P1 negatively interacts with measures of white guilt (see Tables A3.53, A3.55, A3.57, A3.59, Appendix, 287, 289, 291, and 293); however, P1 exposure increases white guilt among conservatives and high white identifiers (insignificantly) while increasing white guilt more among other groups.

Turning to the effects of critical discourse exposure on prejudice, I begin with direct measures of outgroup sentiments. The first of these is “Ethnocentrism,” operationalized here with positive values representing greater preference for whites over other groups. In line with

Kinder and Kam (2010), I construct a continuous ethnocentrism measure from group feeling thermometer (FT) evaluations ranging from 0 (very “cold”/unfavorable) to 100 (very

“warm”/favorable).

Ethnocentrism = ((FT whites - FT blacks) + (FT whites - FT Hispanics) + (FT whites - FT

Asians) + (FT whites - FT Muslims)) / 4

The second direct prejudice measure is “Minority patriotism.” In this case, negative findings represent more prejudiced views towards racial minorities. Respondents are asked to select one of five answer choices ranging from “strongly favor Statement A over Statement B” to

“strongly favor Statement B over Statement A” in response to the prompt,

Which of the two statements comes closer to your view?

Statement A. Racial minorities are, on average, less patriotic than white Americans.

Statement B. Racial minorities are, on average, just as patriotic as white Americans.

134

Surprisingly, positive interactions are present in regressions on both measures: P1 interacts positively with conservatism in predicting “Ethnocentrism” in three specifications

(p<0.1) and with white identification in predicting “Minority patriotism” in one specification

(p<0.1). These findings are interesting for several reasons. First, they are mixed, contrary to expectations. Second, they differ by target groups: conservatives become more prejudiced white high white identifiers become less so. Third, responses from non-target groups

(liberals/moderates and low/moderate white identifiers) look different as well. In the case of

“Ethnocentrism,” P1 has a sizable negative effect on liberals and moderates; indeed, larger than the positive effect on conservatives (see Figure 4.5, Graph 1). This is not the case with “Minority patriotism” (Graph 2). Here, P1 has a stronger effect on high white identifiers and a negative effect on low white identifiers.

It is unclear why low white identifiers would become more prejudiced following exposure to critical discourse. It is possible that “Minority patriotism” is a flawed measure of prejudice in the context of P1, which asserts a list of grievances against society on behalf of racial minorities. Low white identifiers may not perceive lack of patriotism as condemnatory, under these circumstances. Conversely, it may be that in reporting higher perceptions of minority patriotism, high white identifiers are saying something akin to “No, this article is all wrong.

Minorities are doing just fine and they therefore love America no less than whites!”

The next group of prejudice measures are formed from responses to questions concerning racially implicated public policies. I find negative interactions between conservatism and P1 in models predicting favorable attitudes towards legal immigration (“Immigration is good”, 5 specifications, p<0.05-0.1) and models predicting the number of immigrants respondents would like to see admitted annually (“Immigration numbers”, 7 specifications, p<0.05-0.1). Both

135

Figure 4.5. Interaction Effects: Prime 1 and Conservatism / White Identification on Measures of Ingroup Prejudice

findings are in line with expectations; however, P1*Conservatism also significantly predicts increased support for “School vouchers” to help low-income minority children acquire better educational opportunities in one specification (p<0.1).

To summarize P1 prejudice interactions, significant coefficients on “Ethnocentrism”

[H1b], “Immigration is good”, and “Immigration numbers” [H1c] – 15 total specifications – are consistent with expectations. Significant interaction coefficients on “Minority patriotism” [H1b] and support for “School vouchers” – two specifications – are contrary to expectations [H1c].

Experimental evidence, while mixed, overwhelmingly supports the white-lash hypothesis that critical discourse exposure mobilizes increases prejudice among the target groups [H1b and

H1c].

136

4.6.2 Prime 2

Turning next to the “anti-critical” prime (P2: “The Fallacy of ‘White Privilege”), identification findings differ markedly from P1. Beginning with group consciousness measures,

P2*white identification coefficients are negative for concern for “Group position” (eight specifications, p<0.01-0.1), “Group treatment” (four specifications, p<0.05-0.1), and “Group jobs” (seven specifications, p<0.05-0.1): all contrary to expectations. Like “Group position,”

“Group treatment” and “Group jobs” speak to white group consciousness. Respondents are asked

“How important is it that whites work together to change laws that are unfair to whites?” (1-5) and “How likely is it that many whites are unable to find a job because employers are hiring minorities instead?” (1-5).

Given the relevance of group consciousness measures, findings here strongly argue against this paper’s contention that anti-critical conservative rhetoric mobilizes ingroup identification, in this case, among high white identifiers. This finding is surprising, given the tone P2 takes towards pro-minority, “social justice” claims. One possible explanation is that by attacking group-conscious on behalf of racial minorities (“critical discourse”), P2 demobilizes white group consciousness by delegitimizing group consciousness, per se. If correct, it is possible that conservative, anti-critical counter narratives on race and identity could be demobilizing white identification, thereby impeding rather than abetting the growth of white nationalist sentiments.

At the same time, as shown in Figure 4.6, P2 is associated with sizable increases in group consciousness among moderate white identifiers (Graphs 1-3) and small increases among low white identifiers (Graphs 1 and 2). For example, at 2.23 and 2.3, control group moderate white identifiers generally describe concerns regarding whites’ social position and comparative access

137

Figure 4.6. Interaction Effects: Prime 2 and Ideology / White Identification on Measures of Ingroup Identification

to jobs as “A little important” (2). At 2.76 and 2.78, P2 moderate white identifiers generally describe such concerns as “Moderately important” (3). P2 compresses (depolarizes) white group consciousness again, opposite to the effects of P1. Presumably, depolarization is a desirable; however, this pro-white identity effect on moderate and low white identifiers may be cause for concern.

Other measures of ingroup identification tell a different story. P2 negatively interacts with conservatism in predicting support for “National unity” (four specifications, p<0.01-0.05) and positively interacts with white identification in predicting agreement that to be “truly

American” one must have “American ancestry” (one specification, p<0.1), be a “U.S. citizen”

(two specifications, p<0.1; see Figure 4.6, Graphs 4 and 5), and respect U.S. “Institutions and

138

laws” (seven specifications, p<0.05-0.1). These findings are in line with expectations that anti- critical discourse should mobilize ingroup identification among target groups. As noted, “U.S. citizen” and “Institutions and laws” measures are among the least theoretically relevant measures considered whereas “American ancestry” is more meaningful. The “National unity” measure asks respondents to indicate their level a preference for one of the two statements (1-5):

Statement A. Americans are one people with a common national identity. Race shouldn’t

be an important consideration.

Statement B. Racial distinctions between white, black, Hispanic and Asian Americans are

important. It is a mistake to pretend these groups share the same identity.

Statement A is the more unifying answer option. Conservatives rate this option less favorably following exposure to P2 (four specifications, p<0.01-0.05). Interestingly, liberals become much more supportive of “National unity” following exposure to P2 (Figure 4.6, Graph 4).

P2 identification findings are thus mixed; however, I judge them to weigh against this paper’s expectations, on balance [H2a]. Group consciousness measures are the most theoretically valuable, in terms of identification, due to the importance of politicized racial consciousness to white nationalist politics. Supportive findings for “National unity,” “American ancestry” and to a lesser extent, “U.S. citizen” and “Institutions and laws” complicate this picture.

Regarding prejudice measures, P2 findings are again mixed, but this time generally in line with expectations (see Figure 4.7). High white identifiers record even more positive appraisals of white people as a group, following exposure to P2 (four specifications, p<0.05-0.1).

While this is not prejudice per se – prejudice being the difference between appraisals of whites

139

and non-white groups – it is indicative. 159 On the other hand, “Racial resentment” dips slightly among high white identifiers, while increasing substantially among moderate white identifiers.

This is another case of depolarization and it is contrary to expectations; however, it should be noted that the net result of P2 exposure in this case is that high white identifiers still pick the second most “resentful” answer on average (4.0: “somewhat agree” that blacks should work their way up without special treatment) and, at 3.68, moderate white identifiers are now largely in agreement with them (Figure 4.7, Graph 2; Table A3.37, Appendix, 271).

In terms of policy questions, P2 high white identifiers are increasingly likely to “Blame blacks” for tensions with police (one specification, p<0.1), and are decreasingly likely to support

“Affirmative Action” (one specification, p<0.1) and “School vouchers” for racial minorities

(four specifications, p<0.1). P2 conservatives desire reduced annual “Immigration numbers” (one specification, p<0.1). On the other hand, P2 conservatives are more likely to report recognizing

“Racial privilege” (one specification, p<0.05); however, the effect is negligible. The positive interaction with ideology in this case is driven by a backlash among liberals and, to a lesser degree, moderates (see Figure 4.7, Graph 6). Policy measures are overwhelmingly as predicted in their associations with P2 among target groups.

Although P2 identification findings are generally contrary to expectations, the combination of mixed findings from direct prejudice measures [H2b] and more consistent reductions in racial liberalism from policy measures [H2c] suggests that conservative, anti- critical responses to critical discourse are also a cause for concern. As with identification measures, significant P2 interaction effects are present primarily among high white identifiers

159 Low white identifiers display the opposite: reducing the “warmth” of their appraisals of white people as a group. This is an apparent case of backlash in the other direction.

140

Figure 4.7. Interaction Effects: Prime 2 and Ideology / White Identification on Measures of Ingroup Prejudice

(less so among conservatives). The separation of identification and prejudice findings in the case of P2 – contrary to P1 – is additionally interesting in that it echoes research emphasizing the

141

disconnect between these sentiments, at least among whites, contrary to what might be expected from research in the of social identity and realistic group conflict traditions, among others. 160

4.6.3 Prime 3

If exposure to critical themes is associated with white-lash responses – identification and prejudice – and exposure to anti-critical themes is partially associated with white-lash responses

– chiefly prejudice – how might target groups respond to less abrasive discussions of racial justice issues? Like P2, findings from P3 (“Racism and the Indelible Impact of Personal

Experience) are surprising and, in part, contrary to expectations [H3a]. P3 interacts positively with conservatism in the case of white consciousness – an index of the four group consciousness measures (one specification, p<0.1) – and with white identification in the cases of “American ancestry” (eight specifications, p<0.05-0.1), “Born in the U.S.” (eight specifications, p<0.05-

0.1), and “Live in the U.S.” (three specifications, p<0.1).

Once again, interactions tell only part of the story. As shown in Figure 4.8, P3 identification interactions are substantively weak and driven primarily by opposite trends among liberals and low white identifiers. 161 As shown in Figure 4.9, P3’s effects on target group identification are consistently weaker than the effects of P1. In Graph 2, for example, the average high white identifying respondent in the control group reports that having American ancestry is moderately important to being truly American (b=3.0). In the P1 condition, the average such

160 It is noteworthy that Brewer (1979, 1999) emphasizes the independence of ingroup love and outgroup hate – both of which contribute to prejudice. Identification is yet another matter, being distinct from ingroup love. 161 Graphs 2 and 4 display slight declines in identification; however, findings from regression models record slight increases . The discrepancy here owes to slight differences between regression models reported in Appendix tables (A3.19 and A3.28, 253 and 262) and those used to estimate effects. The former includes control variables and, in many cases, are estimated using ordered logit. The latter are estimated without control, using OLS for the sake of simplicity and consistency.

142

Figure 4.8. Interaction Effects: Prime 3 and Ideology / White Identification on Measures of Ingroup Identification

Figure 4.9. P1 and P3 Interactions with Ideology / White Identification on Measures of Ingroup Identification

143

respondent moves 6/10 th of the way towards believing that having American ancestry is very important (4.0) to being truly American. At b=3.22, the effect of P3 is approximately one third that of P1.

P3 identification findings are mildly disappointing; however, prejudice findings offer cause for optimism. Regarding direct measures, P3 is associated with reduced “Ethnocentrism” among conservatives (one specification, p<0.1) and increased agreement that minorities are patriotic among high white identifiers (five specifications, p<0.1). As shown in Figure 4.10

Graphs 1 and 2, these interactions are dominated by the target groups; for example, all identifiers view minorities as more patriotic following P3 exposure, but the increase among high white identifiers (0.3) exceeds increases among moderate white identifiers (0.2) by a third and dwarfs increases among low white identifiers (0.05). P3 is unambiguously associated with reduced prejudice, as expected [H3b].

In terms of policy liberalism, P3 associates with reduced “Blaming blacks” for police tensions (three specifications, p<0.05-0.1) among high white identifiers but also strangely, with a sharp increase in “Blaming blacks” among moderate white identifiers, and a moderate increase among low white identifiers (Figure 4.10, Graph 4). Similarly, P3 associates with increased recognition of “Race privileges” among conservatives (three specifications p<0.1-0.05) but reduced recognition among moderates and liberals (Figure 4.10, Graph 6). This depolarization of attitudes, in both cases, may be attributable to the author’s approach of emphasizing both sides of racial justice issues.

Other policy liberalism findings are contrary to expectations. Among conservatives, P3 is associated with moderate reductions in support for “Affirmative Action” (eight specifications, p<0.1) , small reductions in support for immigration (“Immigration is good,” eight specifications,

144

Figure 4.10. Interaction Effects: Prime 3 and Ideology / White Identification on Measures of Ingroup Prejudice

p<0.05-0.1), and reduced guilt regarding black-white inequality (“Guilt inequality,” eight specifications, p<0.05-0.1). Taken together, policy liberalism findings are mixed and therefore not in line with expectations [H3c].

Diminished policy liberalism (on balance) among target group respondents in the P3 condition suggests that the kind of discussions represented by P3 may mobilizes prejudice, in some cases. As noted, the assumption underlying the inclusion of policy liberalism measures is that differences between prime and control groups should be attributable to the effects of primes on racial attitudes. Thus, while conservative respondents might oppose say, Affirmative Action programs for a variety of reasons, including non-prejudiced reasons, if conservative respondents

145

in the P3 condition oppose Affirmative Action programs more than conservatives in the Control group, it is reasonable to attribute cause to the (racial attitudes) treatment intervention. In the case of P3, the contrast between direct prejudice and policy liberalism findings suggests another plausible interpretation. Given P3’s “both sides” rhetoric, it may be that, rather than mobilizing prejudice, P3 reduces white guilt, giving (in this case) conservatives greater moral license to reject public policies at odds with their ideological commitments.

Viewed from the standpoint of the political Left, P3 is a decidedly “mixed bag.” Even still, this prime would be preferable to either of the alternative primes considered. P3 is certainly preferable to P1, given that the two are similar (mixed-negative) in terms of policy liberalism, while P1 is much more strongly associated with increased identification and positively associated

(on balance) with direct measures of prejudice. A case could be made that P2 is the preferable survey prime from a liberal perspective, on the grounds that it alone reduces white identification again, on balance. This would be an ironic conclusion, given the substance of that prime. This interpretation also seems like a stretch in that P2 is associated with net increases in direct prejudice and with overwhelming net decreases in policy liberalism.

Viewed from a more political neutral perspective – one concerned solely with reducing the mobilization of white nationalist sentiments – P3 is even more clearly preferable to P1 and

P2. Here, findings from direct prejudice measures would naturally dominate findings from policy liberalism in terms of importance. P3 alone associates with net reductions in measures of direct prejudice. In terms of ingroup identification, P3 is situated between P1 and P2. Again, a case could be made the P2 represents the best of the three rhetorical approaches, but the downsides of

“anti-critical” rhetoric in terms of increased prejudice seem prohibitive. For its part, critical

146

Table 4.1. Implications of Findings for Hypotheses Hypothesis Description Do Findings Support Hypothesis? H1a P1 -> increased identification Supported: 12-0 measures, 62 specifications in support. H1b P1 -> increased prejudice (direct measures) Mixed-supported: 1-1 measures, 3-1 specifications in support. H1c P1 -> increased prejudice (policy liberalism) Mixed-supported: 3-2 measures, 11-1 specifications in support. H2a P2 -> increased identification Mixed-opposed: 6-4 measures, 26-13 specifications in support. H2b P2 -> increased prejudice (direct measures) Mixed-supported: 1-1 measures, 4-1 specifications in support. H2c P2 -> increased prejudice (policy liberalism) Mixed-supported: 5-1 measures, 10-1 specifications in support. H3a P3 -> decreased identification Opposed: 4-0, 20 specifications in support.

H3b P3 -> decreased prejudice (direct measures) Supported: 2 measures, 6 specifications in support. H3c P3 -> decreased prejudice (policy liberalism) Mixed-opposed: 3-2 measures, 24-6 specifications opposed.

discourse (P1) increases ingroup identification and prejudice in both direct and policy liberalism

measures. P1 alone offers nothing but downsides

4.6.4 Support for President Trump

I turn finally to the effects of survey prime exposure on target group support for President

Trump, as measured by feeling thermometer evaluations. I treat Trump support separately from

ingroup identification and prejudice because it isn’t obvious to which discrete category it belongs. At the same time, the relationship between critical discourse and Trump support is

theoretically important; indeed, this study proceeds from the speculation that critical discussions

of race fueled a backlash that contributed to President Trump’s election in 2016.

I find significant positive interactions between conservatism and both P1 (four

specifications, p<0.01) and P3 (seven specifications, p<0.05-p<0.1). Interactions with P2, though

147

positive and significant (p<0.1) in one specification are illusory: P2 reduces feeling thermometer evaluations of President Trump among conservatives while reducing evaluations more among moderates and liberals. Surprisingly, given recent research (Major et al. 2018, Knowles and

Tropp 2018, Sides et al. 2018), no survey prime interactions with white identification are statistically significant (Appendix Table A3.61 and A3.62, 295-296). Findings are displayed in

Figure 4.11, separated by ideological group.162 As shown in Graph 1, P1 is associated with a 4.23 percentage point (PPT) increase in Trump support among conservatives. In the P3 condition, conservative Trump support increases 2.61 PPTs.

In previous figures, I purposefully omit statistically insignificant findings; however, in this case, I include P2 estimates for reference because the contrasting effects of survey primes on conservative Trump support warrant additional consideration (Graph 1). In the case of moderates and liberals, all survey primes are negatively associated with Trump support. It is as if any discussion of race primes respondents against President Trump… unless those respondents are conservatives, in which case critical and conciliatory conservative discourse both increase

Trump support. Associations with P1 are entirely consistent with expectations; as indeed, findings are for P1, throughout. Associations with P3 are perhaps surprising yet smaller than P1 associations. It is worth considering as well that conservatives may not view President Trump’s racially insensitive statements in the same light as his progressive critics. In this context, it may be appropriate to view P3 simply as a conservative prime that pushes conservatives to support a conservative president.

162 As one might expect, the effects of survey primes on Trump support are minimal by comparison to the effects of ideology.

148

Figure 4.11. Interaction Effects: Trump Support Regressed on Interactions between Survey Primes and Ideology

What is remarkable is that P2 doesn’t do this , much as it is remarkable that P2 decreases ingroup identification. Speculating, if Trump support has more to do with white identification than with prejudice,163 this strange non-finding for P2 on Trump support is yet another indication that anti-critical (or “anti-identity politics”) conservative rhetoric does not increase – and in some cases, inhibits – white identification among target groups, contrary to expectations.

163 Goldberg (Forthcoming, 10) finds that Trump 2016 voters were no more likely to endorse anti-black stereotypes than were Romney 2012 voters. Moreover, Trump voters and were significantly less likely to endorse anti-Hispanic stereotypes than Romney voters. He finds further that white conservatives became more racially liberal in 2016, in terms of support for government assistance for racial minorities and Affirmative Action (13).

149

4.7 Conclusion

Rising white identification is poised to reconfigure American politics; indeed, this process is underway. As white Americans decline numerically, they observe their nation’s culture, values, and symbols – what it means to be “American” – defined more by other groups.

Whites are likely to respond to this change by thinking of themselves more as an ethnic group and less as deracinated, generic Americans. The fading of the stigma surrounding white identity creates new opportunities for politicians to leverage white identity appeals for political gain.

Given the well-established association between ingroup identification and prejudice, rising white identification risks opening the “Overton Window” to white nationalism in mainstream

American politics.

In this context, this paper has considered the role of other, potentially avoidable factors that similarly abet the mainstreaming of white nationalism. I report experimental evidence linking exposure to whiteness studies and critical race theory inspired themes (critical discourse) to increases in core white nationalist sentiments among the natural audiences for such appeals: conservatives and high white identifiers. The evidence suggests that exposure to critical discourse mobilizes identification and prejudice among these target groups. Surprisingly, conservative anti-critical counter-rhetoric does not appear to increase identification – just the opposite – however, it does increase prejudice. Results for conciliatory conservative rhetoric are more promising. This less-abrasive approach to issues of racial equity and justice appears to reduce racial prejudice among target groups at the cost of mild increases in identification.

Extrapolating, the country would benefit from a shift away from critical themes in popular media and in the public square, generally. As evidenced by contrasting findings from the first and third survey primes, less caustic and more balanced perspectives on race and identity

150

appear to be more effective in promoting tolerance. These conciliatory approaches also pose far less risk, in terms of mobilizing hostile backlash responses from white audiences.

Given the popularity of critical discourse and well-founded concerns for racial justice, many will understandably resist this conclusion. For defenders of critical perspectives – people who are likewise opposed to white nationalism – the problem is a practical one. These messages are heard not only by the faithful and by the persuadable, but also by their opponents.

Polarization is a reciprocal process and it would be a dangerous gamble to polarize American politics and the still dominant white majority on white identity and racial interest. Our national conversation about race needs a different approach.

151

APPENDIX A: SUPPLEMENTAL TABLES FOR CHAPTER 2

Table A1.1. OLS: Ethnocentrism on Identification, 2012 (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc.

White Ident. 5.6025*** 5.2472*** 4.9756*** 4.8874*** -0.3222 -0.3331 -0.3355 -0.3348 American Ident. 4.6366*** 3.8016*** 3.4839*** 3.1653*** -0.3681 -0.3924 -0.4169 -0.4235 Conservative Ident. 2.3388*** 2.0567*** 0.9346*** 1.9773*** 1.7024*** 0.7603** -0.2486 -0.2589 -0.36 -0.2735 -0.2805 -0.3836 Male 0.52 0.6073 0.9193 0.9254 -0.7644 -0.755 -0.8072 -0.7987 Education -2.2420*** -1.6492*** -2.6593*** -1.9950*** -0.3498 -0.3697 -0.37 -0.3903 Income 0.011 0.0116 0.0112 0.017 -0.0523 -0.0522 -0.055 -0.0549 Age 0.0317 0.0242 0.0221 0.0208 -0.0243 -0.0242 -0.0264 -0.0264 South 2.1011** 1.7403** 2.4847*** 2.1292** -0.8275 -0.8189 -0.8783 -0.873 Authoritarianism 6.6744*** 7.7309*** -1.4616 -1.5592 Egalitarianism -3.0656*** -2.3083*** -0.7836 -0.8466 Constant 0.3628 -8.9458*** -2.7024 6.2774 -3.3312** -8.7107*** -0.51 5.357 -0.8754 -1.3441 -2.144 -4.4076 -1.5149 -1.7317 -2.3363 -4.6984

Observations 3,226 3,062 2,958 2,953 3,245 3,081 2,975 2,970 R-squared 0.1566 0.1802 0.2013 0.2237 0.0654 0.0846 0.1141 0.1353 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

152

Table A1.2. OLS: Ethnocentrism on Identification, 2016 (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc.

White Ident. 6.1120*** 5.1624*** 4.8461*** 4.3082*** -0.3802 -0.3685 -0.38 -0.3756 American Ident. 5.9293*** 4.3924*** 4.2367*** 3.5158*** -0.5042 -0.5735 -0.5901 -0.5616 Conservative Ident. 3.8284*** 3.6862*** 1.4921*** 3.2683*** 3.1563*** 0.8630** -0.2855 -0.3025 -0.3875 -0.3222 -0.3319 -0.3957 Male -0.5257 -0.5707 -0.8278 -0.8204 -0.9504 -0.9227 -0.9892 -0.9551 Education -0.9451* -0.0277 -1.0731* -0.0942 -0.5274 -0.5198 -0.5499 -0.5359 Income -0.1413 -0.0801 -0.1933** -0.116 -0.0866 -0.08 -0.0904 -0.0836 Age 0.0659** 0.0374 0.0444 0.0184 -0.0325 -0.0301 -0.0346 -0.0318 South 0.172 -0.2839 0.4012 -0.0815 -0.9399 -0.9118 -0.9891 -0.9494 Authoritarianism 10.4574*** 11.2143*** -1.6251 -1.7127 Egalitarianism -4.2554*** -4.7019*** -0.8897 -0.8891 Constant -6.2085*** -21.4251*** -17.5992*** -0.8599 -14.0737*** -23.4463*** -17.5095*** 1.6017

-0.9617 -1.6408 -3.1589 -4.866 -2.1082 -2.3318 -3.6356 -5.2799 Observations 2,570 2,146 2,033 2,030 2,589 2,162 2,048 2,045 R-squared 0.1628 0.2486 0.2589 0.3052 0.1031 0.1788 0.1988 0.2536 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

153

Table A1.3. Ordered Logit: Minorities Should Adapt to U.S. Customs & Traditions, 2016 (1) (2) (3) (4) (5) (6) (7) (8) Minorities Minorities Minorities Minorities Minorities Minorities Minorities Minorities VARIABLES Adapt Adapt Adapt Adapt Adapt Adapt Adapt Adapt

White Ident. 0.2509*** 0.2221*** 0.1954*** 0.1376*** -0.0321 -0.0362 -0.0381 -0.038 American Ident. 0.5644*** 0.4537*** 0.4150*** 0.3522*** -0.0443 -0.0485 -0.0494 -0.0507 Conservative Ident. 0.5024*** 0.4453*** 0.1540*** 0.4270*** 0.3845*** 0.1105** -0.0308 -0.0326 -0.0429 -0.0324 -0.0336 -0.0434 Male 0.0642 0.0595 0.0688 0.0687 -0.0989 -0.0998 -0.0982 -0.0996 Education -0.1847*** -0.1127** -0.1738*** -0.1058** -0.0501 -0.054 -0.0495 -0.0539 Income -0.0079 -0.0036 -0.0124* -0.0067 -0.0068 -0.0072 -0.0067 -0.0071 Age 0.0237*** 0.0207*** 0.0199*** 0.0175*** -0.0027 -0.0028 -0.0027 -0.0028 South 0.0514 -0.0008 0.0173 -0.0311 -0.1095 -0.1108 -0.1107 -0.1124 Authoritarianism 0.6732*** 0.6215*** -0.1864 -0.1868 Egalitarianism -0.7910*** -0.7741*** -0.0999 -0.0984 Cut 1 -1.9383*** -0.0997 -0.0176 -3.6229*** -0.4550** 0.7565*** 0.5901** -2.9618*** -0.1322 -0.1763 -0.2887 -0.5656 -0.1912 -0.206 -0.2858 -0.5781 Cut 2 -0.8837*** 1.0981*** 1.1985*** -2.3318*** 0.6455*** 1.9913*** 1.8409*** -1.6371*** -0.1063 -0.1614 -0.2772 -0.5613 -0.179 -0.1943 -0.2759 -0.5749 Cut 3 0.0972 2.0594*** 2.2081*** -1.2732** 1.6800*** 2.9814*** 2.8775*** -0.5561 -0.0995 -0.1634 -0.2864 -0.5617 -0.1836 -0.2023 -0.2878 -0.5783 Cut 4 1.7050*** 3.9196*** 4.1765*** 0.794 3.3716*** 4.8785*** 4.8661*** 1.5249*** -0.1068 -0.1817 -0.3015 -0.5613 -0.1957 -0.2214 -0.3032 -0.5801

Observations 2,581 2,154 2,038 2,035 2,603 2,173 2,054 2,051 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

154

Table A1.4. Ordered Logit: White Identification on American Identification, 2012 and 2016 2012 2016 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) VARIABLES White Ident. White Ident. White Ident. White Ident. White Ident. White Ident. White Ident. White Ident. White Ident. White Ident.

American Ident. 0.6626*** 0.6353*** 0.5336*** 0.5277*** 0.5307*** 0.6737*** 0.6580*** 0.5621*** 0.5838*** 0.5785*** -0.0401 -0.0426 -0.0443 -0.0471 -0.0472 -0.0401 -0.0468 -0.0489 -0.0538 -0.0537 Conservative Ident. -0.0162 -0.0985*** -0.1024*** -0.0428 -0.0223 -0.1454*** -0.1401*** -0.1786*** -0.0289 -0.0291 -0.0301 -0.0391 -0.0323 -0.0334 -0.0347 -0.0471 Ethnocentrism 0.0389*** 0.0382*** 0.0390*** 0.0372*** 0.0351*** 0.0338*** -0.0029 -0.0029 -0.003 -0.0035 -0.0037 -0.0039 Male -0.0002 -0.0131 -0.1465 -0.1446 -0.0825 -0.0827 -0.0971 -0.0973 Education -0.0391 -0.0303 -0.0343 -0.0202 -0.04 -0.0417 -0.0532 -0.0548 Income -0.0025 -0.0011 -0.0124 -0.0116 -0.0057 -0.0057 -0.0078 -0.0078 Age 0.0004 0.0014 -0.0022 -0.0027 -0.0026 -0.0027 -0.003 -0.003 South 0.0455 0.0428 0.0857 0.0747 -0.0889 -0.0889 -0.1101 -0.1104 Authoritarianism 0.1659 0.1501 -0.1497 -0.1875

155 Egalitarianism 0.2769*** -0.0973 -0.0892 -0.1005 Cut 1 1.3565*** 1.1930*** 0.8874*** 0.7196*** 2.0002*** 1.6818*** 1.5595*** 0.8791*** 0.4864* 0.0801 -0.1553 -0.177 -0.1785 -0.2497 -0.4853 -0.1473 -0.1755 -0.1928 -0.2758 -0.5893 Cut 2 2.3574*** 2.1982*** 1.9596*** 1.8005*** 3.0819*** 2.4950*** 2.4118*** 1.7859*** 1.3954*** 0.9917* -0.1606 -0.1821 -0.181 -0.2504 -0.486 -0.1497 -0.177 -0.193 -0.2756 -0.5863 Cut 3 3.5805*** 3.4346*** 3.3206*** 3.1454*** 4.4347*** 3.6878*** 3.5808*** 3.0558*** 2.6661*** 2.2659*** -0.1709 -0.192 -0.1911 -0.257 -0.4894 -0.1589 -0.1866 -0.2024 -0.2799 -0.5875 Cut 4 4.8732*** 4.7755*** 4.8055*** 4.6308*** 5.9327*** 4.9279*** 4.9182*** 4.5203*** 4.1320*** 3.7331*** -0.1818 -0.2019 -0.2035 -0.2643 -0.4941 -0.1678 -0.1959 -0.2117 -0.2873 -0.5921

Observations 3,249 3,081 3,062 2,958 2,953 2,584 2,155 2,144 2,031 2,028 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A1.5. Ordered Logit: American Identification on White Identification, 2012 and 2016 2012 2016 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) American American American American American American American American American American

VARIABLES Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident.

White Ident. 0.5398*** 0.5131*** 0.4586*** 0.4421*** 0.4413*** 0.5630*** 0.5465*** 0.4695*** 0.4782*** 0.4677*** -0.0355 -0.0372 -0.0393 -0.0411 -0.0411 -0.0359 -0.0393 -0.0422 -0.0452 -0.0453 Conservative Ident. 0.3404*** 0.3186*** 0.3029*** 0.2184*** 0.4266*** 0.3641*** 0.3319*** 0.2583*** -0.0323 -0.0326 -0.0336 -0.0418 -0.033 -0.0341 -0.0355 -0.0451 Ethnocentrism 0.0127*** 0.0111*** 0.0091*** 0.0168*** 0.0155*** 0.0131*** -0.003 -0.003 -0.0031 -0.0036 -0.0036 -0.0037 Male -0.2619*** -0.2528*** -0.0891 -0.095 -0.0928 -0.0933 -0.1032 -0.1034 Education -0.0442 -0.0009 -0.0931* -0.0598 -0.0451 -0.0463 -0.0565 -0.0572 Income 0.0047 0.0056 0.0111 0.0133 -0.0061 -0.0062 -0.0083 -0.0083 Age 0.0227*** 0.0225*** 0.0210*** 0.0200*** -0.0029 -0.0029 -0.0032 -0.0032 South 0.1158 0.0986 0.2196* 0.1973* -0.0998 -0.0994 -0.1147 -0.1153 Authoritarianism 0.5457*** 0.3857*

156 -0.1704 -0.1969 Egalitarianism -0.2081** -0.1691 -0.0961 -0.1034 Cut 1 -2.4179*** -1.2620*** -1.3269*** -0.6175* -1.2124** -1.8038*** -0.4155** -0.8143*** -0.1184 -0.7577 -0.1816 -0.2187 -0.2159 -0.317 -0.5363 -0.1638 -0.2023 -0.2189 -0.3354 -0.6154 Cut 2 -1.0574*** 0.2249 0.1565 0.9232*** 0.3302 -0.8144*** 0.7070*** 0.3413* 1.0591*** 0.4224 -0.1286 -0.1821 -0.1812 -0.2948 -0.5139 -0.1282 -0.1777 -0.1871 -0.3191 -0.6077 Cut 3 0.1298 1.4686*** 1.4088*** 2.1962*** 1.6127*** 0.3629*** 1.9417*** 1.5969*** 2.3643*** 1.7332*** -0.1131 -0.1769 -0.1756 -0.2978 -0.5131 -0.1144 -0.1762 -0.1845 -0.3194 -0.605 Cut 4 1.5518*** 2.9735*** 2.9188*** 3.7616*** 3.1910*** 1.7429*** 3.5858*** 3.2627*** 4.1020*** 3.4823*** -0.1155 -0.1864 -0.1855 -0.3066 -0.5149 -0.1216 -0.1915 -0.1963 -0.333 -0.6053

Observations 3,249 3,081 3,062 2,958 2,953 2,584 2,155 2,144 2,031 2,028 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A1.6. Ordered Logit: White Identification on Interactions between American and Conservative Identifications, 2012 and 2016 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) White White White White White White White White White White VARIABLES Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident.

American Ident. 0.6626*** 0.6353*** 0.5771*** 0.4531*** 0.4455*** 0.6737*** 0.6580*** 0.9685*** 0.7863*** 0.7775***

-0.0401 -0.0426 -0.1084 -0.1072 -0.1099 -0.0401 -0.0468 -0.1155 -0.1237 -0.1259

Conservative Ident. -0.0162 -0.0748 -0.1799* -0.1854* -0.0223 0.2930*** 0.0813 0.0614

-0.0289 -0.1014 -0.1035 -0.1055 -0.0323 -0.1032 -0.1069 -0.1139

American Ident.*Cons. Ident. 0.0144 0.0199 0.0204 -0.0795*** -0.0567** -0.0503*

-0.0254 -0.0255 -0.0261 -0.027 -0.0277 -0.0292

Ethnocentrism 0.0389*** 0.0383*** 0.0369*** 0.0349***

-0.0029 -0.0029 -0.0035 -0.0037

Male -0.0004 -0.1439

-0.0824 -0.097

Education -0.0395 -0.0341

-0.0399 -0.0533

Income -0.0024 -0.012

-0.0057 -0.0078

Age 0.0004 -0.0022

-0.0026 -0.003

South 0.0459 0.0921

-0.0889 -0.11 157 Cut 1 1.3565*** 1.1930*** 0.9618** 0.5669 0.394 1.6818*** 1.5595*** 2.7438*** 1.7416*** 1.2445**

-0.1553 -0.177 -0.4091 -0.4075 -0.4409 -0.1473 -0.1755 -0.4133 -0.4509 -0.5048 Cut 2 2.3574*** 2.1982*** 1.9667*** 1.6387*** 1.4746*** 2.4950*** 2.4118*** 3.6010*** 2.6519*** 2.1564***

-0.1606 -0.1821 -0.4117 -0.4092 -0.4422 -0.1497 -0.177 -0.4151 -0.4511 -0.5044 Cut 3 3.5805*** 3.4346*** 3.2032*** 2.9999*** 2.8197*** 3.6878*** 3.5808*** 4.7719*** 3.9229*** 3.4279***

-0.1709 -0.192 -0.4172 -0.4146 -0.4469 -0.1589 -0.1866 -0.4186 -0.4584 -0.5101 Cut 4 4.8732*** 4.7755*** 4.5447*** 4.4859*** 4.3062*** 4.9279*** 4.9182*** 6.1077*** 5.3859*** 4.8920*** -0.1818 -0.2019 -0.4219 -0.4195 -0.4501 -0.1678 -0.1959 -0.4241 -0.4647 -0.5165

Observations 3,249 3,081 3,081 3,062 2,958 2,584 2,155 2,155 2,144 2,031 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A1.7. Factor Analysis Variables 2012 2016 Variable Description Obvs Mean SD Obvs Mean SD

R1 Agree/disagree: blacks should work way up w/o special favors. 1-5 3,265 3.8698 1.1351 2,625 3.5901 1.3425 R2 Agree/disagree: past slavery make more diff for blacks. 1-5 3,268 3.4523 1.2863 2,624 3.1574 1.4107 R3 Agree/disagree: blacks have gotten less than deserve. 1-5 3,259 3.8291 1.0685 2,622 3.4344 1.2758 R4 Agree/disagree: blacks must try harder to get ahead. 1-5 3,266 3.4032 1.1939 2,618 3.0943 1.3355 min_adapt Agree/disagree: "Minorities should adapt to customs/traditions of US." 1-5 2,618 3.6635 1.1853 customs_traditions Agree/disagree: "Truly American: follow America’s customs/traditions." 1-4 2,617 3.0539 0.8725 govt_blks Scale govt assistance to blacks scale: self-placement. 1-7 3,157 2.7596 1.5584 2,705 3.2436 1.7905 AA_univ Favor/oppose affirmative action in universities. 1-7 3,479 2.624 1.6248 3,007 3.0206 1.7699 AA_hire Favor preferential hiring and promotion of blacks. 1-4 3,196 1.5106 0.8105 2,572 1.6384 0.9161 FairJob Opinion about govt ensuring fair jobs for blacks. 1-4 2,885 2.1705 1.2536 deport_illegals U.S. government policy toward unauthorized immigrants. 1-4 3,490 2.5493 0.8836 3,015 2.4484 0.8964 imm_levels "What should immigration levels be?" 1-5 3,218 2.4214 1.0881 2,616 2.4281 1.15 imm_jobs "How likely immigration will take away jobs?" 1-4 3,252 2.4305 0.9751 2,620 2.3359 0.9833 word_God "Is Bible word of God or men?" 1-3 3,470 1.9922 0.7124 2,998 1.986 0.7199 rel_import "Is religion important part of R life?" 0/1 3,499 0.6388 0.4804 4,243 0.6554 0.4753 rel_daily Agree/disagree "Religion provides guidance in day-to-day living." 1-3 2,233 2.2459 0.7897 1,893 2.281 0.7979 abortion Abortion: self-placement. 1-4 3,493 2.9619 1.0772 3,004 2.9381 1.095 gays_discrim Support/oppose: Laws to protect gays and lesbians from job discrimination. 1-4 3,424 3.0786 1.0901 2,983 3.3637 1.0259 anti_trans policy. 1-6 2,913 3.723 2.0517 gay_mil "Allow gays/lesbians serve in US armed forces?" 3,448 3.3707 0.9429

158 deny_gay Support/oppose: Allow companies to deny services? 1-6 2,990 3.7054 2.0896 gay_adopt "Should gay and lesbian couples be allowed to adopt?" 0/1 3,439 0.6418 0.4796 2,976 0.7507 0.4327 gay_marriage Position on gay marriage. 0/1 3,486 2.1727 0.7902 2,999 2.4268 0.756 anti_pc "Need to be more sensitive talking or people too easily offended?" 1-4 2,994 2.8146 1.0599 trad_gender "Better if man works and woman takes care of home?" 1-7 3,247 5.081 1.398 ET_lifestyle Agree/disagree: "Newer lifestyles breaking down society." 1-5. 3,269 2.4849 1.2955 2,626 2.7292 1.3694 ET_tolerant Agree/disagree: "Be more tolerant of other moral standards..." 1-5. 3,267 3.371 1.1879 2,622 3.5572 1.1909 too_involved "Govt bigger because too involved or bigger problems?" 0/1 3,247 0.623 0.4847 2,615 0.5916 0.4916 free_makt "Need strong govt for complex problems or free market?" 0/1 3,234 0.4626 0.4987 2,615 0.3943 0.4888 least_best "Less govt better or more the govt should be doing?" 0/1 3,226 0.6259 0.484 2,612 0.557 0.4968 no_reg_biz More/less: Regulation of business. 1-5 3,255 3.1303 0.7912 2,618 3.0798 0.7751 govt_sevices Spending and services self-placement. 1-7 3,227 3.4868 1.5464 2,639 3.676 1.6621 govt_hc Scale govt-private medical insur scale: self-plmt. 1-7 3,331 3.6647 1.9406 2,742 3.7567 2.045 pro_gun "Should fed govt make it more difficult to buy a gun?" 1-3 3,501 1.6398 0.5988 3,025 1.5722 0.6103 anthro_chng Anthropogenic . 1-3 3,483 2.1209 0.7274 3,023 2.1991 0.7199 env_jobs Scale environment-jobs tradeoff self-placement. 1-7 3,069 4.5627 1.7653 2,568 4.7161 1.8259 sex1 Agree/disagree: "Women interpret innocent remarks as sexist." 1-5 2,973 3.1053 1.0833 sex2 Agree/disagree: "Women fail to appreciate what men do for them." 1-5 2,972 2.6336 1.1454 sex3 Agree/disagree: "Women seek to gain power by getting control over men." 1-5 2,967 2.5635 1.1624 sex4 Agree/disagree: "Women put men on a tight leash." 1-5 2,968 2.3416 1.1482 feminist "How well does feminist describe you?" 1-5 2,996 2.0698 1.1778 incr_welfa~d Federal Budget Spending: welfare programs. 1-3 3,481 1.5789 0.6628 3,013 1.617 0.7234 incr_aidpoor Federal Budget Spending: aid to poor. 1-3 3,475 2.0455 0.7208 3,007 2.1576 0.7303 incr_ccare Federal Budget Spending: childcare. 1-3 3,469 2.0219 0.7245 3,001 2.2839 0.7095 Flag_good "How good/bad does R feel to see American flag?" 1-7 3,258 4.1111 1.0169 2,630 6.2125 1.1812 Import_Amer "How important is being American to your identity?" 1-5 3,269 4.1851 1.009 2,610 4.0398 1.0953

Table A1.7. (Cont.) inc_defense Scale defense spending self-placement. 1-7 3,227 4.1429 1.4751 2,702 4.5714 1.5646 patriotic Love of country. 1-5 3,270 4.6193 0.6485 Media_disc "How much attn media should pay to discrim against women?" 1-7 3,242 4.3122 1.5139 2,593 4.4759 1.7047 Women_spec "Do women demanding equality seek special favors?" 1-5 3,214 2.2194 0.9178 2,586 2.0905 0.9769 Women_compl "Do women complaining about discrim cause more problems?" 1-5 3,221 2.4489 0.9223 2,592 2.2419 0.995 Women_opp Opportunities compared for women and men. 1-7 3,250 2.9028 1.3015 hiring_disc "How often do hiring and promotion discriminate agst women?" 1-5 3,210 2.2866 0.7115 Auth_R "Child trait more important: independence or respect." 1-3 3,271 0.7709 0.4127 2,618 0.7273 0.4405 Auth_M "Child trait more important: curiosity or good manners." 1-3 3,269 0.6568 0.468 2,621 0.6032 0.4834 Auth_O "Child trait more important: self-reliance or obedience." 1-3 3,266 0.5155 0.4925 2,615 0.4373 0.491 Auth_B "Child trait more important: considerate or well-behaved." 1-3 3,272 0.3131 0.4521 2,620 0.2761 0.4387 pro_dp Support/oppose: death penalty. 1-4 3,446 3.2179 1.0612 2,975 3.1486 1.1313 inc_crimespending Federal Budget Spending: dealing with crime. 1-3 3,478 2.3772 0.642 3,024 2.5493 0.6344 ET_treat Agree/disagree: "If people were treated more fairly would be fewer probs." 1-5. 3,266 3.3484 1.1481 2,618 3.6165 1.117 ET_opp Agree/disagree: "Society should make sure everyone has equal opportunity." 1-5. 3,271 3.941 1.157 2,623 4.1098 1.0568 ET_worry Agree/disagree: "We'd be better off if people worried less about equality." 1-5. 3,265 2.8744 1.214 2,619 2.9798 1.3588 ET_change Agree/disagree: "World is always changing and we should adjust." 1-5. 3,269 2.6213 1.3623 2,628 2.9098 1.3979 ET_tradfam Agree/disagree: "More emphasis on traditional " 1-5. 3,268 2.1778 1.2336 2,624 2.3213 1.286 ET_chances Agree/disagree: (is/is not) "a big problem if some have more chance in life." 1-5. 3,263 2.6859 1.1092 2,617 3.4425 1.1602 Import_white "How important is being white to your identity?" 1-5 3,249 2.7649 1.299 2,586 2.6419 1.341 white_conscious Constructed from white group consciousness measures: V162316 and V162317. 1-5 2,563 2.9758 1.3499 whites_infl "How much influence do whites have in U.S. politics?" 1-3 3,253 2.071 0.5349 2,583 2.1885 0.5437

159 Ethnocentrism Constructed from feeling thermometer measures: ((whites - blacks) + (whites - 3,248 15.806 18.0732 2,592 9.6581 19.3886 Hispanics) + (whites - Asians) + (whites - Muslims) / 4). Range: -100 - 100.

Table A1.8. Ordered Logit: American Identification on Interactions between White Identification and Principal Components 1 and 2, 2012 (1) (2) (3) (4) (5) (6) (7) (8) American American American American American American American American VARIABLES Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident.

White Ident. 0.5218*** 0.5863*** 0.5382*** 0.5139*** 0.5588*** 0.5843*** 0.5187*** 0.4956*** -0.0362 -0.0405 -0.0423 -0.0442 -0.0365 -0.0381 -0.0399 -0.0416 PC 1: Racial Conservatism 0.6691*** 1.2365*** 1.1629*** 1.1017***

-0.0682 -0.1723 -0.1744 -0.1761

White Ident*Racial Conserv. -0.2221*** -0.2284*** -0.2109***

-0.0576 -0.0589 -0.0609 PC 2: Small Government 0.4980*** 0.8510*** 0.7625*** 0.7819***

-0.067 -0.1612 -0.1609 -0.1666

White Ident.*Small Govt. -0.1392** -0.1300** -0.1362**

-0.0542 -0.055 -0.0578

Ethnocentrism 0.0113*** 0.0096*** 0.0142*** 0.0122***

-0.0029 -0.003 -0.0029 -0.003

Male -0.2323*** -0.3022***

-0.0898 -0.0918

Education -0.0124 -0.0486

-0.0439 -0.0445 Income 0.004 0.0045

160

-0.0061 -0.0061

Age 0.0248*** 0.0246***

-0.0028 -0.0028

South 0.1458 0.2043**

-0.0979 -0.0992 Cut 1 -2.4620*** -2.3542*** -2.3340*** -1.3597*** -2.4434*** -2.3879*** -2.3762*** -1.4972*** -0.1828 -0.1859 -0.1864 -0.2839 -0.1848 -0.1871 -0.1865 -0.2893 Cut 2 -1.0651*** -0.9416*** -0.9267*** 0.0979 -0.9930*** -0.9316*** -0.9190*** -0.0164 -0.1289 -0.1345 -0.1357 -0.2537 -0.1305 -0.1334 -0.1337 -0.2597 Cut 3 0.1663 0.3126*** 0.3316*** 1.3694*** 0.2127* 0.2820** 0.3007** 1.2123*** -0.1119 -0.1201 -0.1216 -0.2537 -0.1157 -0.1197 -0.1199 -0.2588 Cut 4 1.6444*** 1.8066*** 1.8277*** 2.9216*** 1.6916*** 1.7680*** 1.7906*** 2.7650*** -0.115 -0.1265 -0.128 -0.2597 -0.1188 -0.1246 -0.1252 -0.2639

Observations 3,234 3,234 3,212 3,101 3,188 3,188 3,166 3,055 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A1.9. Ordered Logit: American Identification on Interactions between White Identification and Principal Components 3 and 5, 2012 (1) (2) (3) (4) (5) (6) (7) (8) American American American American American American American American VARIABLES Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident.

White Ident. 0.5210*** 0.5204*** 0.4704*** 0.4514*** 0.5362*** 0.7627*** 0.5985*** 0.6565*** -0.0358 -0.0357 -0.0377 -0.0395 -0.0358 -0.1629 -0.167 -0.1688 PC 3: Social/Religious Conservatism 0.6317*** 0.9809*** 0.9485*** 0.8085***

-0.0684 -0.1729 -0.1755 -0.183 White Ident.*Soc./Rel. Conserv. -0.1350** -0.1522*** -0.1248**

-0.0553 -0.0566 -0.0596 PC 5: Anti-Egalitarianism -0.0577 0.1713 0.0803 0.1987 -0.0748 -0.1914 -0.192 -0.1956 White Ident.*Anti-Egal. -0.0848 -0.0533 -0.0816 -0.0589 -0.0602 -0.0612 Ethnocentrism 0.0121*** 0.0112*** 0.0181*** 0.0156***

-0.003 -0.003 -0.0029 -0.003 Male -0.2617*** -0.2293** -0.0898 -0.0896 Education -0.0231 -0.0539 -0.0439 -0.0441 Income 0.0085 0.0081 161 -0.0061 -0.0061 Age 0.0224*** 0.0243*** -0.0028 -0.0028 South 0.1356 0.1943** -0.0974 -0.0974 Cut 1 -2.6157*** -2.6475*** -2.6074*** -1.6861*** -2.5910*** -1.9738*** -2.1903*** -0.9609 -0.188 -0.1912 -0.1923 -0.2919 -0.2816 -0.5513 -0.5506 -0.5947 Cut 2 -1.2224*** -1.2471*** -1.2106*** -0.2419 -1.2249*** -0.6078 -0.8255 0.4519 -0.13 -0.1317 -0.1333 -0.2556 -0.2522 -0.5375 -0.5374 -0.5842 Cut 3 0.0045 -0.0085 0.0352 1.0113*** -0.036 0.5815 0.3767 1.6696*** -0.1139 -0.1149 -0.1163 -0.2534 -0.2415 -0.5357 -0.5362 -0.5866 Cut 4 1.4894*** 1.4862*** 1.5315*** 2.5548*** 1.3862*** 2.0042*** 1.8134*** 3.1669*** -0.1151 -0.1156 -0.1172 -0.2553 -0.2419 -0.5377 -0.5383 -0.5899

Observations 3,229 3,229 3,207 3,097 3,243 3,243 3,220 3,106 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A1.10. Ordered Logit: American Identification on Interactions between White Identification and Principal Component 6, 2012 (1) (2) (3) (4) American American American American VARIABLES Ident. Ident. Ident. Ident.

White Ident. 0.5098*** 0.5830*** 0.5266*** 0.4722*** -0.0358 -0.0756 -0.0771 -0.0793 PC 6: Authoritarianism 1.1434*** 1.4246*** 1.3293*** 1.2119*** -0.1235 -0.3136 -0.3184 -0.3315 White Ident.*Author. -0.1097 -0.1168 -0.0563

-0.1046 -0.1074 -0.1099 Ethnocentrism 0.0144*** 0.0132***

-0.003 -0.0031 Male -0.2105** -0.0898 Education 0.0472 -0.0442 Income 0.008 -0.006 Age 0.0246*** -0.0028 South 0.1507 -0.0969

Cut 1 -1.8416*** -1.6658*** -1.6906*** -0.5099 -0.182 -0.239 -0.2385 -0.3329 Cut 2 -0.4717*** -0.2934 -0.3212 0.9091*** -0.1331 -0.2126 -0.2136 -0.3175 Cut 3 0.7404*** 0.9225*** 0.9036*** 2.1495*** -0.1216 -0.2106 -0.2113 -0.3213 Cut 4 2.2112*** 2.3961*** 2.3831*** 3.6871*** -0.1259 -0.2182 -0.2189 -0.3281

Observations 3,247 3,247 3,224 3,109 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

162

Table A1.11. Ordered Logit: American Identification on Interactions between White Identification and Principal Components 7 and 8, 2012 (1) (2) (3) (4) (5) (6) (7) (8) American American American American American American American American VARIABLES Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident.

White Ident. 0.5268*** 0.7445*** 0.6925*** 0.6384*** 0.5266*** 0.5384*** 0.4573*** 0.4394*** -0.0362 -0.1167 -0.1213 -0.1285 -0.036 -0.0373 -0.039 -0.0407 PC 7: Gender Resentment 1 0.1453** 0.3785*** 0.3449** 0.3083*

-0.0604 -0.1467 -0.1507 -0.1577 White Ident.*Gender Resent. 1 -0.0903** -0.0991** -0.0852*

-0.0455 -0.0477 -0.0507 PC 8: Gender Resentment 2 -0.0307 0.1883 0.138 0.2199 -0.0629 -0.1578 -0.1553 -0.1629 White Ident.*Gender Resent. 2 -0.0808* -0.0789* -0.0857* -0.0474 -0.0479 -0.0498 Ethnocentrism 0.0169*** 0.0148*** 0.0181*** 0.0157***

-0.0029 -0.003 -0.0029 -0.003 Male -0.2195** -0.2269** -0.091 -0.0936 Education -0.0621 -0.0633 -0.0444 -0.0446 Income 0.0094 0.0085 163 -0.0061 -0.0062 Age 0.0243*** 0.0245*** -0.0028 -0.0029 South 0.1876* 0.1979** -0.0986 -0.098 Cut 1 -2.2474*** -1.6960*** -1.7380*** -0.8785* -2.5289*** -2.4956*** -2.4767*** -1.5842*** -0.2237 -0.3799 -0.3854 -0.462 -0.1916 -0.1937 -0.1927 -0.2915 Cut 2 -0.7722*** -0.2179 -0.2622 0.6167 -1.1001*** -1.0659*** -1.0488*** -0.1027 -0.191 -0.3728 -0.3799 -0.4594 -0.1331 -0.1363 -0.1356 -0.2568 Cut 3 0.4239** 0.9825*** 0.9491** 1.8398*** 0.0904 0.1261 0.155 1.1275*** -0.1791 -0.3679 -0.3748 -0.4589 -0.1168 -0.1208 -0.1199 -0.255 Cut 4 1.8484*** 2.4105*** 2.3903*** 3.3381*** 1.5070*** 1.5434*** 1.5888*** 2.6226*** -0.1833 -0.3722 -0.3797 -0.4652 -0.1185 -0.123 -0.1224 -0.2585

Observations 3,163 3,163 3,142 3,034 3,177 3,177 3,157 3,053 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A1.12. Ordered Logit: American Identification on Interactions between White Identification and Principal Components 1 and 2, 2016 (1) (2) (3) (4) (5) (6) (7) (8) American American American American American American American American VARIABLES Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident.

White Ident. 0.5305*** 0.5640*** 0.5096*** 0.5217*** 0.5347*** 0.5362*** 0.4547*** 0.4562*** -0.036 -0.0364 -0.0407 -0.0441 -0.0358 -0.0354 -0.0393 -0.0422 PC 1: Racial Conservatism 0.9830*** 1.4646*** 1.3446*** 1.3654***

-0.0719 -0.1526 -0.1565 -0.1628 White Ident.*Racial Conserv. -0.2030*** -0.1959*** -0.2256***

-0.0506 -0.0503 -0.0527 PC 2: Religious/Social Conservatism 0.8185*** 1.4692*** 1.3250*** 1.2286*** -0.0753 -0.1644 -0.1649 -0.1684 White Ident.*Rel./Soc. Conserv. -0.2653*** -0.2595*** -0.2573*** -0.053 -0.0531 -0.0551 Ethnocentrism 0.0105*** 0.0107*** 0.0170*** 0.0172***

-0.0037 -0.0036 -0.0036 -0.0035 Male -0.004 0.0168 -0.0963 -0.0961 Education -0.009 -0.0488 -0.0508 -0.0506 Income 0.0130* 0.0132* 164 -0.0075 -0.0074 Age 0.0183*** 0.0166*** -0.003 -0.003 South 0.1932* 0.1982* -0.108 -0.1072 Cut 1 -2.0687*** -2.0644*** -2.1433*** -1.1122*** -2.0846*** -2.1628*** -2.2728*** -1.4237*** -0.1651 -0.1658 -0.1721 -0.3015 -0.1597 -0.1586 -0.1652 -0.2981 Cut 2 -1.0286*** -0.9979*** -1.0631*** 0.0102 -1.0559*** -1.1105*** -1.1971*** -0.3154 -0.1277 -0.1285 -0.1312 -0.2823 -0.1245 -0.1248 -0.1271 -0.2828 Cut 3 0.2594** 0.3240*** 0.2671** 1.3968*** 0.1862* 0.1645 0.0988 1.0276*** -0.1122 -0.1135 -0.1159 -0.2806 -0.1109 -0.1117 -0.1136 -0.2841 Cut 4 1.7937*** 1.8726*** 1.8223*** 3.0075*** 1.6602*** 1.6604*** 1.6151*** 2.5916*** -0.1155 -0.1173 -0.1179 -0.287 -0.1186 -0.1199 -0.1194 -0.2932

Observations 2,581 2,581 2,565 2,434 2,564 2,564 2,550 2,423 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A1.13. Ordered Logit: American Identification on Interactions between White Identification and Principal Components 3 and 4, 2016 (1) (2) (3) (4) (5) (6) (7) (8) American American American American American American American American VARIABLES Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident.

White Ident. 0.5813*** 0.6039*** 0.4939*** 0.4915*** 0.5294*** 0.8407*** 0.7675*** 0.7515*** -0.0366 -0.0372 -0.0412 -0.0442 -0.0361 -0.111 -0.1141 -0.1201 PC 3: Small Government 0.5380*** 1.1110*** 0.9566*** 0.8697***

-0.0684 -0.1625 -0.1661 -0.174 White Ident.*Small Govt. -0.2312*** -0.2057*** -0.2033***

-0.0513 -0.0531 -0.0569 PC 4: Modern Sexism 0.3377*** 0.6194*** 0.5310*** 0.5256*** -0.0548 -0.1234 -0.1242 -0.1299 White Ident.*Modern Sexism -0.1154*** -0.1241*** -0.1161*** -0.0393 -0.0399 -0.0419 Ethnocentrism 0.0207*** 0.0196*** 0.0216*** 0.0202***

-0.0035 -0.0035 -0.0035 -0.0035 Male -0.0165 -0.0551 -0.0979 -0.0998 Education -0.0862* -0.0539 -0.0517 -0.0529

165 Income 0.0121 0.0147* -0.0076 -0.0075 Age 0.0194*** 0.0195*** -0.0031 -0.0031 South 0.2761** 0.3178*** -0.1074 -0.1065 Cut 1 -1.8061*** -1.7829*** -1.9704*** -1.1597*** -1.0595*** -0.3401 -0.7236** 0.2161 -0.1633 -0.1627 -0.1702 -0.3224 -0.2 -0.3431 -0.3491 -0.4564 Cut 2 -0.7965*** -0.7592*** -0.9177*** -0.0741 -0.0482 0.6789** 0.3288 1.3079*** -0.1271 -0.1277 -0.1308 -0.3047 -0.1733 -0.329 -0.3326 -0.4416 Cut 3 0.4157*** 0.4751*** 0.3451*** 1.2360*** 1.1534*** 1.8921*** 1.5728*** 2.6166*** -0.1138 -0.1167 -0.1191 -0.3049 -0.1655 -0.3309 -0.3341 -0.4439 Cut 4 1.8415*** 1.9190*** 1.8210*** 2.7690*** 2.5674*** 3.3138*** 3.0286*** 4.1353*** -0.1217 -0.1267 -0.1258 -0.3134 -0.1745 -0.3406 -0.3416 -0.4511

Observations 2,570 2,570 2,555 2,424 2,550 2,550 2,538 2,424 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A1.14. Ordered Logit: American Identification on Interactions between White Identification and Principal Components 5 and 6, 2016 (1) (2) (3) (4) (5) (6) (7) (8) American American American American American American American American VARIABLES Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident.

White Ident. 0.5730*** 1.1673*** 1.0082*** 1.0449*** 0.5152*** 0.7242*** 0.6427*** 0.6469*** -0.0365 -0.1302 -0.1393 -0.1484 -0.0364 -0.0716 -0.0733 -0.0771 PC 5: Anti-Redistribution 0.7665*** 1.5244*** 1.3066*** 1.2668***

-0.0852 -0.1957 -0.2069 -0.2124 White Ident.*Anti-Redist. -0.3049*** -0.2729*** -0.2918***

-0.0614 -0.0645 -0.0686 PC 6: Authoritarianism 1.2524*** 2.1731*** 1.8794*** 1.7755*** -0.1449 -0.3494 -0.3527 -0.3637 White Ident.*Author. -0.3659*** -0.3775*** -0.3672*** -0.1108 -0.1119 -0.1179 Ethnocentrism 0.0183*** 0.0169*** 0.0199*** 0.0191***

-0.0035 -0.0035 -0.0035 -0.0035 Male 0.015 0.0195 -0.0967 -0.0964 Education -0.0914* -0.0389 -0.0502 -0.0525 Income 0.0073 0.0150** 166 -0.0075 -0.0075 Age 0.0191*** 0.0191*** -0.0031 -0.0031 South 0.2977*** 0.2905*** -0.1079 -0.1077 Cut 1 -0.4151* 1.0077** 0.4354 1.0959** -1.3388*** -0.8700*** -1.1506*** -0.1552 -0.232 -0.4119 -0.4439 -0.551 -0.1681 -0.2311 -0.2391 -0.3711 Cut 2 0.5965*** 2.0408*** 1.4920*** 2.1754*** -0.3345** 0.1467 -0.1066 0.9267*** -0.2113 -0.4054 -0.4327 -0.5445 -0.135 -0.2092 -0.2128 -0.3561 Cut 3 1.8284*** 3.3028*** 2.7755*** 3.5085*** 0.8695*** 1.3668*** 1.1409*** 2.2285*** -0.2061 -0.4104 -0.4366 -0.5525 -0.1229 -0.2057 -0.2081 -0.3575 Cut 4 3.2794*** 4.7738*** 4.2706*** 5.0622*** 2.2988*** 2.8067*** 2.6074*** 3.7563*** -0.2165 -0.4228 -0.4456 -0.5658 -0.1302 -0.2162 -0.2163 -0.3665

Observations 2,542 2,542 2,527 2,406 2,579 2,579 2,563 2,434 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A1.15. Ordered Logit: American Identification on Interactions between White Identification and Principal Components 7 and 8, 2016 (1) (2) (3) (4) (5) (6) (7) (8) American American American American American American American American VARIABLES Ident. Ident. Ident. Ident. Ident. Ident. Ident. Ident.

White Ident. 0.5465*** 0.7566*** 0.6578*** 0.6206*** 0.5490*** 0.9364*** 0.8110*** 0.8080*** -0.0368 -0.0913 -0.0939 -0.0985 -0.0356 -0.1095 -0.1146 -0.1216 PC 7: Gender Resentment 0.1775*** 0.4182*** 0.3285** 0.2959**

-0.0594 -0.13 -0.1292 -0.1342 White Ident.*Gender Resent. -0.0946** -0.0985** -0.0786*

-0.0389 -0.0392 -0.0411 PC 8: Anti-Egalitarianism 0.2504*** 0.6667*** 0.5259*** 0.5308*** -0.0626 -0.1413 -0.1447 -0.149 White Ident.*Anti-Egal. -0.1680*** -0.1599*** -0.1566*** -0.0443 -0.0461 -0.0493 Ethnocentrism 0.0240*** 0.0220*** 0.0229*** 0.0207***

-0.0034 -0.0034 -0.0034 -0.0034 Male 0.0293 0.0063 -0.0977 -0.0968 Education -0.0822 -0.0935* -0.0527 -0.0503

167 Income 0.0139* 0.0126* -0.0076 -0.0075 Age 0.0205*** 0.0200*** -0.0031 -0.0031 South 0.2916*** 0.3036*** -0.106 -0.1063 Cut 1 -1.4863*** -0.9795*** -1.3477*** -0.4748 -1.2972*** -0.38 -0.8666** -0.0098 -0.1967 -0.3078 -0.3117 -0.4349 -0.2125 -0.3597 -0.37 -0.4651 Cut 2 -0.4818*** 0.0278 -0.3033 0.6053 -0.3007 0.6260* 0.1707 1.0639** -0.1677 -0.289 -0.2899 -0.4216 -0.1871 -0.3432 -0.3504 -0.4513 Cut 3 0.7013*** 1.2170*** 0.9233*** 1.8912*** 0.8841*** 1.8237*** 1.4029*** 2.3514*** -0.154 -0.2874 -0.2879 -0.4239 -0.1743 -0.3422 -0.3488 -0.4535 Cut 4 2.0963*** 2.6182*** 2.3694*** 3.4029*** 2.2771*** 3.2284*** 2.8460*** 3.8620*** -0.1628 -0.2956 -0.2933 -0.4309 -0.1823 -0.3494 -0.3526 -0.4596

Observations 2,536 2,536 2,525 2,399 2,581 2,581 2,566 2,435 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A1.16. Ordered Logit: American Identification on Black Identification, 2012 and 2016 2012 2016 (1) (2) (3) (4) (5) (6) American American American American American American VARIABLES Ident. Ident. Ident. Ident. Ident. Ident.

Black Ident. 0.9471*** 0.8631*** 0.2359 0.9890*** 0.9713*** 1.2986* -0.1406 -0.1499 -0.4411 -0.2128 -0.2552 -0.7044 Conservative Ident. -0.0063 -0.7386 0.2813 0.6568 -0.089 -0.4861 -0.1944 -0.8006 Black Ident.*Cons. Ident. 0.1809 -0.0876 -0.1107 -0.1876 Male 0.0665 0.1178 0.464 0.4043 -0.2379 -0.2374 -0.3874 -0.3549 Education -0.0267 -0.0361 0.1326 0.1205 -0.126 -0.1314 -0.2057 -0.1917 Income -0.0041 -0.0033 -0.0438* -0.0430* -0.0197 -0.02 -0.0264 -0.0258 Age 0.0356*** 0.0353*** 0.0221* 0.0212* -0.0073 -0.0074 -0.0118 -0.0125 South 0.1573 0.1173 -0.3915 -0.4065 -0.232 -0.2392 -0.3845 -0.3805 Cut 1 -0.3307 0.7269 -1.8617 0.3205 1.7263 2.9757 -0.5552 -0.9323 -2.1799 -0.8697 -1.3918 -2.5531

Cut 2 0.6453 1.7231* -0.8646 1.5867* 3.2328* 4.5183 -0.5123 -0.9125 -2.1496 -0.874 -1.6832 -3.0377 Cut 3 2.6433*** 3.8581*** 1.2879 2.8705*** 4.7761*** 6.0818** -0.5776 -0.9473 -2.1309 -0.896 -1.6267 -3.075 Cut 4 4.1374*** 5.3885*** 2.8337 4.2640*** 6.0588*** 7.3656** -0.6385 -1.0208 -2.1666 -0.9633 -1.6864 -3.0754

Observations 951 718 718 334 189 189 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

168

Table A1.17. Ordered Logit: American Identification on Hispanic Identification, 2012 and 2016 2012 2016 (1) (2) (3) (4) (5) (6) American American American American American American VARIABLES Ident. Ident. Ident. Ident. Ident. Ident.

Hispanic Ident. 0.2899*** 0.2958*** 0.8983*** 0.5010*** 0.4432*** 1.0037** -0.1008 -0.1148 -0.2753 -0.1345 -0.1498 -0.4598 Conservative Ident. 0.0924 0.5894** 0.2259* 0.6940* -0.0735 -0.2408 -0.116 -0.3899 Hispanic Ident.*Cons. Ident. -0.1529** -0.1333 -0.0631 -0.0959 Male 0.0731 0.0639 -0.4372 -0.3907 -0.2247 -0.2244 -0.2955 -0.2945 Education 0.0784 0.0964 0.1661 0.1839 -0.1034 -0.1018 -0.1673 -0.1548 Income 0.0487*** 0.0480*** 0.0420* 0.0413* -0.0144 -0.0142 -0.0231 -0.0219 Age 0.0210*** 0.0196*** 0.0123 0.0122 -0.0071 -0.0072 -0.0102 -0.0101 South 0.3860* 0.4120* 0.0226 0.0426 -0.2265 -0.2233 -0.3311 -0.3219 Cut 1 -2.0658*** 0.0884 2.0082* -1.5032*** 0.5483 2.5389 -0.4269 -0.7836 -1.1954 -0.5711 -0.9359 -1.8802

Cut 2 -1.1540*** 0.9618 2.8912** -0.4785 1.5470* 3.5600* -0.3923 -0.7619 -1.1935 -0.4838 -0.8548 -1.8434 Cut 3 0.0469 2.2704*** 4.2114*** 1.0483** 2.9789*** 5.0229*** -0.3876 -0.7584 -1.2099 -0.5204 -0.9434 -1.9481 Cut 4 1.2828*** 3.6099*** 5.5778*** 2.3243*** 4.3649*** 6.4345*** -0.406 -0.7892 -1.2336 -0.571 -1.0228 -2.0218

Observations 918 732 732 352 236 236 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

169

Table A1.18. Ordered Logit: American Identification on Asian Identification, 2012 and 2016 2012 2016 (1) (2) (3) (4) (5) (6) American American American American American American VARIABLES Ident. Ident. Ident. Ident. Ident. Ident.

Asian Ident. -0.3896* -0.3527 0.176 0.7429** 0.8975** 0.2676 -0.2077 -0.279 -0.6754 -0.3296 -0.4293 -0.6034 Conservative Ident. 0.6202*** 1.0191** -0.0633 -0.7195 -0.2364 -0.4748 -0.1516 -0.8596 Asian Ident.*Cons. Ident. -0.1208 0.1856 -0.1445 -0.2291 Male 1.3014** 1.3796** -0.2864 -0.2184 -0.6326 -0.6386 -0.5938 -0.6421 Education 0.3102 0.3196 -0.0712 -0.0866 -0.2783 -0.2777 -0.1807 -0.1757 Income -0.0018 -0.0062 -0.0221 -0.0198 -0.0381 -0.038 -0.034 -0.032 Age -0.0037 0.0017 0.0275 0.0265 -0.0224 -0.0231 -0.0174 -0.0164 South 0.6202 0.6029 -0.3117 -0.2007 -0.7469 -0.7822 -0.6915 -0.6691 Cut 1 -6.9359*** -2.8661 -0.8616 -1.462 0.3449 -1.9659 -1.2427 -2.107 -2.816 -0.9928 -1.7062 -2.6645

Cut 2 -3.2133*** 0.7465 2.7524 0.0244 3.0511 0.8236 -0.917 -1.8085 -2.8251 -1.131 -1.9041 -2.4231 Cut 3 -1.5302** 2.8986 4.8876* 2.2001* 4.6299** 2.4199 -0.7349 -1.8728 -2.7829 -1.2476 -2.0609 -2.3779 Cut 4 -0.0289 4.8635** 6.8703** 3.7205*** -0.7106 -1.9699 -2.7387 -1.3564

Observations 76 68 68 101 72 72 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

170

Table A1.19. OLS: Ethnocentrism on Interaction between Ingroup (Black and American) and Conservative Identifications, 2012 and 2016 2012 2016 (1) (2) (3) (4) (5) (6) VARIABLES Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc.

Black Ident. 2.7744*** 2.4484*** 0.5271 3.2863*** 3.0351* -0.5264 -0.8202 -0.9308 -2.0885 -1.2162 -1.6839 -3.1597 Black Ident.*Cons. Ident. 0.5682 0.9758 -0.6011 -0.7675 American Ident. -0.8672 -1.0152 -2.3381 0.5162 0.2502 4.3477 -1.0068 -1.1692 -3.0496 -1.2731 -1.6041 -3.2192 American Ident.*Cons. Ident. 0.3136 -1.2444 -0.7089 -0.8101 Conservative Ident. -0.0509 -3.673 -0.5668 0.1314 -0.8308 -4.4645 -1.0445 -2.9148 Male -1.5274 -1.445 12, -3.338 -3.1534 -2.0861 -2.0831 -3.0457 -3.157 Education -2.6672*** -2.6756*** 0.1199 0.3333 -0.8976 -0.8938 -1.612 -1.6808 Income -0.1137 -0.1124 0.263 0.2497 -0.1513 -0.1509 -0.2159 -0.2162 Age 0.0436 0.0451 0.1091 0.0962 -0.0703 -0.0708 -0.0886 -0.0899 South -0.197 -0.2492 1.8 1.3034 -2.1474 -2.125 -3.1369 -3.0867 Constant 10.7172** 20.5870*** 33.7691** 1.133 -4.0987 -4.2328 -4.8549 -6.967 -16.9144 -6.4134 -7.8863 -12.792

Observations 938 711 711 329 187 187 R-squared 0.0276 0.0642 0.0682 0.0403 0.0853 0.096 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

171

Table A1.20. OLS: Ethnocentrism on Interaction between Ingroup (Hispanic and American) and Conservative Identifications, 2012 and 2016 2012 2016 (1) (2) (3) (4) (5) (6) VARIABLES Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc.

Hispanic Ident. 4.1558*** 3.6264*** 3.0235 4.7264*** 5.0381*** 2.4984 -0.6647 -0.7906 -1.8983 -0.8462 -0.9507 -2.3546 Hispanic Ident.* Cons. Ident. 0.1783 0.6128 -0.4096 -0.5579 American Ident. 0.774 0.4945 -1.6225 -0.1804 -1.1563 -1.225 -0.6932 -0.8781 -2.1041 -1.0105 -1.1533 -3.0032 American Ident.* Cons. Ident. 0.5458 0.0548 -0.4848 -0.838 Conservative Ident. 0.1804 -2.5577 1.1727 -1.1736 -0.5583 -2.4607 -0.8113 -3.928 Male -4.0476** -3.8963** 3.3495 3.26 -1.6322 -1.6408 -2.0991 -2.1058 Education -1.6742** -1.7123** -0.6252 -0.7674 -0.8429 -0.8459 -1.143 -1.1198 Income 0.0763 0.0658 -0.0746 -0.0683 -0.1212 -0.1215 -0.1598 -0.1568 Age 0.0185 0.0209 0.0548 0.0525 -0.0515 -0.0518 -0.0693 -0.0696

South 3.6292** 3.5640** 0.4207 0.2229 -1.7657 -1.7612 -2.4051 -2.4403 Constant -0.6044 4.5012 14.8654 -3.0188 -7.3696 2.2931 -3.1733 -5.1357 -10.3863 -3.9132 -6.074 -14.641

Observations 909 728 728 350 235 235 R-squared 0.1096 0.1417 0.1455 0.1291 0.1882 0.1942 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

172

Table A1.21. OLS: Ethnocentrism on Interaction between Ingroup (Asian and American) and Conservative Identifications, 2012 and 2016 2012 2016 (1) (2) (3) (4) (5) (6) VARIABLES Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc.

Asian Ident. 6.9910** 6.0116*** 12.5809* 2.5091** 2.8031** 1.3971 -2.8257 -1.9333 -6.7653 -1.1491 -1.1623 -3.1601 Asian Ident.*Cons. Ident. -1.4791 0.3373 -1.3384 -0.8714 American Ident. 2.204 3.8937 13.8008* -2.8452* -2.1426 2.6805 -2.1095 -2.4122 -7.3168 -1.4529 -1.5789 -5.6484 American Ident.*Cons. Ident. -2.5645 -1.1447 -1.5503 -1.2524 Conservative Ident. -1.5699 13.6774 0.2759 3.4349 -1.8793 -8.2408 -1.1942 -5.4145 Male 1.9535 2.7666 4.163 4.0499 -5.3537 -5.4326 -3.2301 -3.2176 Education -5.3734 -4.5876 -0.9662 -1.155 -3.4166 -3.4908 -1.3827 -1.5 Income 0.5564 0.5717 0.2644 0.252 -0.3885 -0.3874 -0.233 -0.2394 Age -0.2507 -0.2074 0.0471 0.0315 -0.2059 -0.2009 -0.0984 -0.1033 South -9.6643* -9.6914** 2.011 1.7447 -4.9377 -4.7491 -4.3984 -4.5618 Constant -11.1006 12.8677 -53.955 15.5270** 5.1561 -6.5137 -9.4526 -20.0891 -44.9169 -7.0929 -8.721 -20.279

Observations 74 67 67 101 72 72 R-squared 0.1879 0.3491 0.385 0.0437 0.1152 0.1261 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

173

Table A1.22. Ordered Logit: “Minorities Adapt” on Interaction between Ingroup (Black and American) and Conservative Identifications, 2016 (1) (2) (3) Minorities Minorities Minorities VARIABLES Adapt Adapt Adapt

Black Ident. -0.1891 -0.3608* -0.3627 -0.1497 -0.2004 -0.6943 Black Ident.*Cons. Ident. -0.0035 -0.1638 American Ident. 0.4778*** 0.3825** 1.2612** -0.1415 -0.1824 -0.5181 American Ident.*Cons. Ident. -0.2647** -0.1218 Conservative Ident. 0.1023 1.1863* -0.111 -0.6455 Male -0.1265 -0.2478 -0.3449 -0.3693 Education -0.1627 -0.1695 -0.1494 -0.1424 Income -0.0149 -0.0198 -0.0211 -0.0215 Age 0.0407*** 0.0383*** -0.0114 -0.0114

South 0.048 -0.1567 -0.32 -0.348 Cut1 -0.7873 -0.4548 2.6178 -0.5277 -1.0488 -2.533 Cut2 0.2767 0.8634 3.9758 -0.5262 -1.0344 -2.5246 Cut3 1.5696*** 2.0534* 5.2066** -0.5177 -1.0694 -2.5032 Cut 4 3.2356*** 4.2270*** 7.4602*** -0.5483 -1.1966 -2.5483

Observations 333 188 188 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

174

Table A1.23. Ordered Logit: “Minorities Adapt” on Interaction between Ingroup (Hispanic and American) and Conservative Identifications, 2016 (1) (2) (3) Minorities Minorities Minorities VARIABLES Adapt Adapt Adapt

Hispanic Ident. -0.0866 0.0317 0.1287 -0.087 -0.1126 -0.3229 Hispanic Ident*Cons. Ident. -0.0217 -0.0715 American Ident. 0.4067*** 0.2661* -0.0956 -0.1179 -0.1504 -0.4487 American Ident.*Cons. Ident. 0.0961 -0.1212 Conservative Ident. 0.3586*** 0.0336 -0.114 -0.5739 Male 0.4626 0.4805* -0.2856 -0.2826 Education -0.0366 -0.0442 -0.1232 -0.126 Income 0.0181 0.0175 -0.0193 -0.0192 Age 0.0180** 0.0178** -0.0074 -0.0075

South 0.2874 0.3209 -0.3878 -0.3989 Cut1 -1.0050* 1.6238* 0.4492 -0.5939 -0.8633 -2.1878 Cut2 0.1098 2.6140*** 1.4389 -0.5676 -0.8402 -2.1928 Cut3 1.3369** 3.8241*** 2.651 -0.5792 -0.8603 -2.202 Cut 4 2.8978*** 5.7079*** 4.5491**

-0.6124 -0.9163 -2.2167 Observations 351 235 235 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

175

Table A1.24. Ordered Logit: “Minorities Adapt” on Interaction between Ingroup (Asian and American) and Conservative Identifications, 2016 (1) (2) (3) Minorities Minorities Minorities VARIABLES Adapt Adapt Adapt

Asian Ident. -0.2883 -0.1288 -1.9328*** -0.2312 -0.3513 -0.6419 Asian Ident.*Cons. Ident. 0.4916*** -0.1809 American Ident. 0.2214 -0.1084 2.0115* -0.266 -0.4873 -1.1853 American Ident.*Cons. Ident. -0.5360* -0.2809 Conservative Ident. 0.3961** 0.7198 -0.1644 -0.6898 Male 1.2832* 1.4032* -0.732 -0.7508 Education -0.0235 -0.1867 -0.1875 -0.2257 Income -0.058 -0.0548 -0.0366 -0.0361 Age 0.0133 0.0105 -0.0162 -0.016

South -0.4069 -0.4894 -0.7721 -0.6972 Cut1 -2.3083*** -1.5273 -0.6264 -0.6679 -1.2752 -3.0218 Cut2 -1.1184 -0.258 0.8228 -0.754 -1.3034 -3.0163 Cut3 -0.0964 0.6396 1.8217 -0.7505 -1.3043 -3.064 Cut 4 2.0231*** 3.3255** 4.6919

-0.7688 -1.3724 -3.0231 Observations 100 71 71 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

176

APPENDIX B: SUPPLEMENTAL INFORMATION FOR CHAPTER 3

Table A2.1. Summary Statistics Dependent Vars Description Year Obvs Mean SD Trump 2016 county percent of GOP primary votes cast for Donald Trump 2016 2,966 45.31 16.28 Cruz 2016 county percent of GOP Primary votes cast for . 2016 2,966 28.93 13.75 Trump (ANES) Respondent voted for Donald Trump in 2016 GOP primary?” (0/1) 2016 4,270 0.104 0.306 Other GOP (ANES) Respondent voted for another Republican in 2016 GOP primary (0/1) 2016 4,270 0.097 0.296 Independent. Vars Description 2014 (N=2,966) 2009 (N=2,961) 2005 (N=2,961) 1997 (N=2,960) Mean SD Mean SD Mean SD Mean SD

Social Capital Total county social capital score -0.055 1.197 -0.081 1.255 -0.071 1.34 -0.078 1.382 Associations County associations per 1,000 population 1.352 0.653 1.375 0.656 1.401 0.657 1.393 0.626 Religious Organizations County religious organizations per 1,000 population 0.946 0.498 0.934 0.489 0.916 0.477 0.886 0.458

177 Civic Organizations County civic organizations per 1,000 population 0.109 0.13 0.119 0.137 0.135 0.144 0.151 0.149

Independent Vars Description Year(s) Obvs Mean SD General Trust (SCCBS) County average response to question: "Generally speaking, would you say that most people can be trusted or 2000, 2006 152 2.1 0.16 that you can't be too careful in dealing with people?" (1-3) Religious Attendance (SCCBS) County average response to question: "Not including weddings and funerals, how often do you attend religious 2000, 2006 143 3.422 0.27 services?" (1-5) Social Trust (SCCBS) County continuous index of standardized mean responses to six questions concerning trust (general trust, trust in 2000, 2006 152 0.089 0.162 neighbors, co-workers, co-religionists, workers and police officers WWC General Trust (SCCBS) County average response to question: "Generally speaking, would you say that most people can be trusted or that you can't be too careful in dealing with people?", among non-Hispanic whites with less than a BA degree (1-3) 2000, 2006 115 2.049 0.118 WWC Social Trust (SCCBS) County continuous index of standardized mean responses to six “trust” questions: general trust, trust in neighbors, co-workers, co-religionists, workers and police officers, among non-Hispanic whites with less than a BA degree. 2000, 2006 116 0.107 0.08 WWC Religious Attendance County average response to question: "Not including weddings and funerals, how often do you attend religious 2000, 2006 110 3.285 0.247 (SCCBS) services?", among non-Hispanic whites with less than a BA degree (1-5)

General Trust (ANES) Response to question: “Generally speaking, how often can you trust other people?” (1-5) 2016 4,255 3.127 0.919 Religious Attendance (ANES) Responses to questions: “Do you go to religious services, [never … every week]?” and “Would you say you go 2016 2,547 3.656 1.442 to religious services once a week or more often than once a week?” (1-6) Racial Resentment (ANES) Average of responses to four questions measuring attitudes towards black Americans. (1-5) 2016 3,631 3.19 0.684 Ethnocentrism (ANES) Average of four feeling thermometer evaluations of racial minorities (blacks, Hispanics, Asians and Muslims) 24.35 subtracted from feeling thermometer evaluations of whites (0-100) 2016 2,592 8.926 9 Authoritarianism (ANES) Average responses to four questions: “Child trait most important, [independence or respect, curiosity or good 2016 3,616 0.547 0.324 manners, obedience or self-reliance, and considerate or well-behaved] (0-1) Economic Anxiety (ANES) Response to question: “What about the next 12 months? Do you expect the economy, in the country as a whole, to get better, stay about the same, or get worse?” (1-5) 2016 4,184 2.977 0.935

Table A2.1. (Cont.)

White Identification (ANES) Response to question: “How important is being White to your identity?” (1-5) 2016 2,891 2.63 1.337 White Consciousness (ANES) Average of responses to questions: “How likely is it that many whites are unable to find a job because 2016 2,559 2.765 1.072 employers are hiring minorities instead?” and “How important whites work together to change laws unfair to whites” (1-5) Traditionalism (ANES) Average of responses to four questions addressing morally traditionalist values (1-5) 2016 3,638 3.079 0.95

Independent Vars Description 2016 2010

Obvs Mean SD Obvs Mean SD

Teen Birth Rt. County teen birth rate per 1,000 female population, ages 15-19 2,873 41.828 18.959 2,846 48.695 20.79

Violent Crime Rt. County number of reported violent crime offenses per 100,000 population 2,809 251.728 197.362 1,450 327.363 247.82

Drug OD Mort. Rt. County drug overdose mortality rate 1,545 17.217 9.353

Pct. Uninsured County percentage of population under age 65 without health insurance 2,966 21.052 6.561 2,966 17.944 6.079

Pct. Smokers County percentage of adults who are current smokers 2,951 18.493 3.715 2,313 22.533 5.821

Pct. Obese County percentage of adults that report a BMI of 30 or more 2,951 31.218 4.256 2,966 28.521 3.388

D Driving Death Rt. County percentage of driving deaths with alcohol involvement 2,935 31.02 15.331

Chlamydia Rt. County number of newly diagnosed chlamydia cases per 100,000 population 2,811 347.027 230.252 2,966 261.868 244.27 Pct. Diabetic County percent of total population: diabetic 2,965 11.284 2.267

White Extended Families County percent of non-Hispanic white households, with three generations present. 2,965 2.738 1.061

Independent Vars Description 2015 2005 1999 Obvs Mean SD Obvs Mean SD Obvs Mean SD

Median HHI County median household income 2,964 48,236.21 12,210.86 757 46,842.5 11,954.07 2,965 33,929.64 8,352.02 All Welfare Percent of under-18 county population living in family 2,964 30.195 11.758 736 19.078 9.221 household receiving Supplemental Security Income (SSI), 178 cash public assistance, or food stamps/SNAP in the past 12 months White Poverty Percent of county total population: non-Hispanic whites with 2,965 13.357 5.147 756 9.443 4.194 2,968 11.105 4.825 income below the poverty line Food Stamps, Whites Percent of county non-Hispanic white-headed households 800 9.287 4.385 758 5.718 3.566 who received Food Stamps/SNAP in past 12 months Poor White Single Mothers Percent of county total population: non-Hispanic white 2,965 3.559 1.93 757 7.359 2.303 2,966 2.501 1.226 female householder, no husband present, with related children under 18 years of age White Child Poverty Percent of county non-Hispanic white children living below 2,965 3.574 1.826 756 2.5 1.439 2,966 3.228 1.663 poverty rate White Marriage Married percentage of county non-Hispanic white 2,964 55.272 5.42 757 57.411 4.754 population, ages 15+ (excluding separated) White LFPR County labor-force participation rate: non-Hispanic whites 2,964 58.769 7.419 739 65.691 5.573 2,965 61.503 6.616 White Unemployment County unemployment rate: non-Hispanic whites 2,964 6.727 2.866 739 5.609 1.727 2,965 2.922 1.187 Demo. Controls Description 2015 2005 2000

Obvs Mean SD Obvs Mean SD Obvs Mean SD Pct. White Working-Class Percent of total county population: non-Hispanic whites over 2,966 43.317 13.485 757 37.871 11.936 2,966 45.765 12.395 25 years without a Bachelor of Arts degree Pct. White Professionals Percent of total county population: non-Hispanic whites over 2,966 11.862 5.115 757 14.177 5.596 2,966 9.612 4.446 25 years with a Bachelor of Arts degree Percent of total county population: black of African 2,965 10.231 14.867 737 11.27 12.564 2,965 9.504 14.848 Pct. Black American Pct. Hispanic Percent of total county population: Hispanic or Latino (any 2,965 8.743 13.558 682 9.42 12.757 2,965 6.096 12.003 race) Pct. Elderly Percent of total county population: age 65 and older 2,965 17.146 4.278 757 12.286 3.417 2,965 14.743 3.959

Table A2.1. (Cont.) Population County total population 2,968 106,009 337,397 760 309,320 556,024 2,968 92,965 299,859 Pct. Rural Percent of total county population not included within 2,966 105,941 337,421 census- designated urban areas. South County located in a "Southern" state, (0/1) 2,966 0.479 0.5 Election Controls Description Year Obvs Mean SD Caucus GOP caucus election state, (0/1) 2016 2,966 0.159 0.366 Net Obama [(Votes for Obama) / (total votes cast)] - [(Votes for Romney)/(total votes cast)] 2012 2,966 -21.13 29.451 Home State Candidate (Donald Trump or Ted Cruz) home-state, (0-1) 2016 2,966 0.04 0.195 Delegate Share (Delegates accumulated) / (1237: Delegates needed to secure GOP nomination) 2016 2,966 17.32 21.543 N Candidates 1 - (number of candidates in the contest) 2016 2,966 -3.031 1.9 ANES Controls Description Year Obvs Mean SD Age 2016 – (respondent date of birth), (18-90) 2016 4,149 49.582 17.578 Education category: “What is the highest level of school you have completed or the highest degree you have received?” (1- 2016 4,231 10.671 1.84 Education 14) Working Class Respondent possesses less than a BA education and (if under 25 years old) is not currently a student. (0/1) 2016 4,270 0.583 0.493 Income “Pre income summary”, (1-28) 2016 4,068 15.39 8.078 Male Response to question: “?”, (0/1) 2016 4,270 0.465 0.499 Married Response to question: “Are you now married, divorced, separated or never married?”, (0/1) 2016 4,270 0.502 0.5 Ideology Response to question: “Where would you place yourself on this scale…?”, (1-7) 2016 3.303 4.176 1.6 Republican Party affiliation is “Republican”, (0/1) 2016 4,270 0.405 0.491 White Self-identified race is “White, non-Hispanic”, (0/1) 2016 4,270 0.711 0.453 South Respondent lives in a “Southern” state, (0/1) 2016 4,270 0.389 0.488

179 Source: Leip (2018), Rupasingha et al. (2006b), Putnam et al. (2002, 2006), Robert Wood Johnson Foundation (2016), U.S. Census, 2016 American National Elections Studies

Table A2.2. Paired, Two-way T-tests on Coefficients from OLS Regressions of Social Dysfunction on Percent White Working Class Regression Coefficients WWC WWC WWC Difference Difference Dependent Variables (2015) (2005) (2000) (2015-2005) (2015-2000)

Unemployment (Whites) 0.0457 -0.0221 -0.0112 0.0678 0.0569 (p=0.000) (p=0.006) (p=0.001) (p=0.000) (p=0.000) LPFR (Whites) 0.0965 0.107 0.1306 -0.0106 -0.0342 (p=0.000) (p=0.001) (p=0.000) (p=0.000) (p=0.000) All Welfare -0.149 -0.1174 -0.0315 (p=0.001) (p=0.125) (p=0.000) Median HHI ($798.90) ($419.73) $155.34 ($397.17) ($954.24) (p=0.000) (p=0.000) (p=0.007) (p=0.000) (p=0.000) White Poverty -0.2308 -0.2322 -0.2066 0.0014 -0.0242 (p=0.000) (p=0.000) (p=0.000) (p=0.058) (p=0.000) White Child Poverty -0.0006 -0.0118 -0.0029 0.0113 0.0024 (p=0.948) (p=0.291) (p=0.622) (p=0.000) (p=0.000) Food Stamp (Whites) 0.1056 -0.0469 0.1526 (p=0.000) (p=0.036) (p=0.000) Poor White Single Moms 0.0415 0.0591 0.0227 -0.0175 0.0189 (p=0.000) (p=0.001) (p=0.000) (p=0.000) (p=0.000) White Marriage 0.2596 0.2242 0.0354 (p=0.000) (p=0.000) (p=0.000) Social Capital 0.0093 0.0327 0.0279 -0.0234 -0.0186 (p=0.000) (p=0.000) (p=0.000) (p=0.000) (p=0.000) Associations 0.0047 0.0061 0.009 -0.0014 -0.0043 (p=0.000) (p=0.000) (p=0.000) (p=0.000) (p=0.000) Civic Orgs. 0.0029 0.003 0.0077 -0.0001 -0.0077 (p=0.258) (p=0.431) (p=0.000) (p=0.002) (p=0.000) Religious Orgs. 0.002 0.0018 0.0026 0.0002 -0.006 (p=0.000) (p=0.007) (p=0.000) (p=0.000) (p=0.000) P-values in parentheses

180

Robustness Checks: Rural Sorting

Context: The following provides further explanation for assessments of rural sorting as a cause of social institutional decline in white working-class communities (Chapter 3, 95).

A prominent narrative concerning Trump supporters centers on the plight of small towns and rural communities. It is asserted that such places are left behind by the modern economy, as well as by their own youth, who are moving to urban areas in high numbers in search of better job prospects (Edsall 2017, Carr and Kefalas 2009). It is possible that social-institutional breakdown in these disproportionately white and working-class communities could be the result of sorting , whereby the exit of people possessing better average prospects for upward mobility leaves behind a population with lower average rates of social capital and higher average rates of social dysfunction. Insofar as this is true, the relationship between social-institutional collapse and white working-class Trump support could be understood within the context of larger regional economic development issues. If a sorting process is producing white working-class social-institutional decline, two conditions should be present:

1. Rural areas should evidence social-institutional decline overtime as compared to non-

rural areas.

2. Social-institutional decline should be predicted by depopulation and specifically, by

depopulation in rural areas.

Beginning with social capital, I test for condition one by regressing change in social capital measures on percent rural, alongside the standard battery of controls (Table A2.3). I find that, where significant (columns 1-3, 7 and 8), county percent rural is associated with positive change in social capital in all but one case (column 8), contrary to what should be the case under the sorting hypothesis. The fact that these relationships persist with demographic controls

181

Table A2.3. OLS Regression: Change in Social Capital on Percent Rural with Demographic Controls (1) (2) (3) (4) (5) (6) (7) (8) Social Capital Social Capital Associations Associations Civic Orgs. Civic Orgs. Religious Orgs. Religious Orgs. VARIABLES 1997-2014 2005-2014 1997-2014 2005-2014 1997-2014 2005-2014 1997-2014 2005-2014

Pct. Rural 0.9925** 7.1338*** 0.0793*** -0.0191 0.0013 0.0802 0.1394*** -0.0444** (0.3975) (1.4089) (0.0233) (0.0146) (0.0401) (0.0530) (0.0280) (0.0193) WWC 2000-2015 1.9089** 0.4742*** 0.4416*** 0.4979*** (0.7858) (0.0544) (0.1270) (0.0705) White Profs. 2000-2015 -0.1795 -0.0186 -0.1132** -0.0726** (0.2424) (0.0324) (0.0485) (0.0361) Hispanics 2000-2015 -0.1578*** -0.0127*** 0.0161 -0.0298*** (0.0596) (0.0039) (0.0148) (0.0051) Blacks 2000-2015 -0.0208 0.0032 -0.0034 0.0038* (0.0185) (0.0023) (0.0031) (0.0023) Pct. Elderly 2000-2015 -0.0271 0.1360*** 0.0567 0.1569*** (0.5466) (0.0300) (0.0543) (0.0345) Median HHI 2000-2015 0.0594 -0.1347*** -0.1617*** -0.1609*** (0.3361) (0.0367) (0.0568) (0.0430) WWC 2005-2015 -11.0349*** 0.2207*** 0.0877 0.3968*** (2.7917) (0.0403) (0.1329) (0.0524) 182 White Profs. 2005-2015 -4.0383** -0.0332* -0.1449** -0.0429** (1.6489) (0.0174) (0.0651) (0.0203) Hispanics 2005-2015 1.0590* 0.0167* 0.0255 0.0064 (0.6142) (0.0093) (0.0200) (0.0089) Blacks 2005-2015 0.2959** -0.0042 0.0034 0.0030 (0.1321) (0.0029) (0.0060) (0.0042) Pct. Elderly 2005-2015 -4.5438*** 0.0975*** 0.0680 0.0774*** (1.1866) (0.0184) (0.0645) (0.0209) Median HHI 2005-2015 -1.9577 0.0625** 0.4003*** 0.0196 (1.2272) (0.0247) (0.0891) (0.0317) Constant -126.9834*** -192.9894*** 1.0033 -4.1773** -21.7933*** -22.7445*** 10.1011*** -0.6000 (32.3623) (72.1237) (2.0191) (1.6736) (3.0361) (7.4810) (2.5401) (1.3061)

Observations 7,190 2,490 7,166 2,494 6,154 2,490 7,090 2,494 R-squared 0.0675 0.0777 0.1696 0.2115 0.0735 0.1544 0.1640 0.2172 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A2.4. OLS Regression: Membership Social Capital on Interaction of Percent Rural and Population Change with Demographic Controls (1) (2) (3) (4) (5) (6) (7) (8) Social Social Associations Associations Civic Orgs. Civic Orgs. Religious Religious VARIABLES Capital 2014 Capital 2014 2014 2014 2014 2014 Orgs. 2014 Orgs. 2014

Pct. Rural 0.0083*** 0.0095*** 0.0036*** 0.0044*** 0.0000 0.0001*** 0.0034*** 0.0038*** (0.0005) (0.0005) (0.0002) (0.0003) (0.0000) (0.0000) (0.0001) (0.0001) Pop. Change 2000-2015 -0.0067*** -0.0036*** -0.0046*** -0.0026*** -0.0005*** -0.0002*** -0.0030*** -0.0019*** (0.0004) (0.0005) (0.0002) (0.0002) (0.0000) (0.0000) (0.0001) (0.0002) Pct. Rural * Pop. Change 2000-2015 -0.0002*** -0.0001*** -0.0000*** -0.0001*** (0.0000) (0.0000) (0.0000) (0.0000) Pct. WWC 2015 0.0095*** 0.0089*** 0.0048*** 0.0044*** 0.0003 0.0002 0.0021*** 0.0019*** (0.0019) (0.0019) (0.0009) (0.0008) (0.0002) (0.0002) (0.0005) (0.0005) Pct. White Prof. 2015 0.0754*** 0.0760*** 0.0202*** 0.0206*** 0.0021*** 0.0022*** 0.0007 0.0009 (0.0038) (0.0037) (0.0019) (0.0019) (0.0004) (0.0004) (0.0008) (0.0008) Pct. Black 2015 0.0180*** 0.0175*** 0.0017*** 0.0014** -0.0006*** -0.0007*** 0.0002 0.0000 (0.0014) (0.0014) (0.0006) (0.0006) (0.0002) (0.0002) (0.0004) (0.0004) Pct. Hispanic 2015 0.0001 -0.0003 -0.0015** -0.0018*** -0.0005*** -0.0006*** -0.0028*** -0.0030*** (0.0015) (0.0015) (0.0007) (0.0007) (0.0002) (0.0002) (0.0003) (0.0003)

183 Pct. Elderly 2015 0.0176*** 0.0188*** 0.0086*** 0.0094*** 0.0009*** 0.0010*** 0.0079*** 0.0083*** (0.0030) (0.0029) (0.0016) (0.0015) (0.0003) (0.0003) (0.0010) (0.0010) Median HHI 2015 -0.0000*** -0.0000*** -0.0000*** -0.0000*** -0.0000*** -0.0000*** -0.0000*** -0.0000*** (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Constant -1.8078*** -1.7616*** -0.2632*** -0.2338*** 0.2691*** 0.2365*** 0.5973*** 0.5600*** (0.1255) (0.1233) (0.0617) (0.0608) (0.0136) (0.0161) (0.0343) (0.0386)

Observations 11,858 11,858 11,858 11,858 11,854 11,854 11,854 11,854 R-squared 0.6589 0.6648 0.6512 0.6601 0.4548 0.4624 0.6924 0.6974 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A2.5. OLS Regression: Trust and Religious Attendance on Interaction of Percent Rural and Population Change with Demographic Controls (3) (4) (7) (8) (11) (12) (1) (2) WWC WWC (5) (6) WWC WWC (9) (10) WWC WWC General General General General Social Social Social Social Religious Religious Religious Religious VARIABLES Trust Trust Trust Trust Trust Trust Trust Trust Attendance Attendance Attendance Attendance

Pct. Rural 0.0006** 0.0014*** 0.0005*** 0.0005*** -0.0003 0.0005 0.0007** 0.0007*** -0.0013* -0.0019** -0.0003 -0.0011** (0.0003) (0.0003) (0.0002) (0.0002) (0.0005) (0.0005) (0.0003) (0.0003) (0.0008) (0.0008) (0.0006) (0.0006) Pop. Change 2000-2015 0.0011* 0.0020*** -0.0004 -0.0003 0.0016** 0.0024** -0.0006* -0.0004 -0.0014*** -0.0021*** -0.0005 -0.0030*** (0.0006) (0.0007) (0.0002) (0.0004) (0.0008) (0.0010) (0.0003) (0.0005) (0.0005) (0.0007) (0.0004) (0.0007) Pct. Rural * Pop. Change 2000-2015 -0.0001*** -0.0000 -0.0001*** -0.0000 0.0001*** 0.0001*** (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Pct. WWC 2015 0.0050*** 0.0043*** 0.0026*** 0.0026** 0.0151*** 0.0144*** 0.0004 0.0001 -0.0040* -0.0031 -0.0001 0.0025 (0.0011) (0.0011) (0.0010) (0.0011) (0.0019) (0.0019) (0.0016) (0.0016) (0.0024) (0.0025) (0.0025) (0.0026) Pct. White Prof. 2015 0.0086*** 0.0082*** 0.0032*** 0.0032*** 0.0076*** 0.0072*** -0.0020 -0.0023 -0.0087*** -0.0082*** -0.0118*** -0.0090*** (0.0013) (0.0012) (0.0011) (0.0011) (0.0020) (0.0019) (0.0016) (0.0017) (0.0021) (0.0022) (0.0029) (0.0029) Pct. Black 2015 -0.0012 -0.0016* 0.0019** 0.0019** 0.0017 0.0013 -0.0004 -0.0005 -0.0061*** -0.0056*** -0.0029 -0.0016 (0.0010) (0.0009) (0.0008) (0.0008) (0.0015) (0.0015) (0.0012) (0.0012) (0.0018) (0.0019) (0.0019) (0.0019) Pct. Hispanic 2015 -0.0031*** -0.0031*** 0.0014** 0.0014** -0.0011 -0.0012 0.0006 0.0006 0.0009 0.0011 -0.0015 -0.0012

184 (0.0008) (0.0008) (0.0006) (0.0006) (0.0013) (0.0013) (0.0010) (0.0010) (0.0019) (0.0019) (0.0020) (0.0020) Pct. Elderly 2015 -0.0129*** -0.0115*** -0.0059*** -0.0056*** -0.0186*** -0.0172*** -0.0050* -0.0041 -0.0012 -0.0031 -0.0005 -0.0109** (0.0027) (0.0028) (0.0019) (0.0021) (0.0038) (0.0041) (0.0030) (0.0032) (0.0058) (0.0061) (0.0050) (0.0050) Median HHI 2015 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0000*** -0.0000** -0.0000** -0.0000 -0.0000 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Constant 1.6894*** 1.6701*** 1.7780*** 1.7336*** -0.8207*** -0.7810*** 0.1300 -0.0354 3.5657*** 3.9670*** 3.4662*** 3.6100*** (0.0850) (0.0805) (0.0743) (0.0677) (0.1343) (0.1336) (0.1103) (0.1109) (0.1302) (0.1390) (0.2688) (0.2102)

Observations 610 610 462 462 610 610 466 466 574 574 442 442 R-squared 0.8731 0.8779 0.9231 0.9231 0.8407 0.8441 0.7030 0.7036 0.8062 0.8082 0.9211 0.9265 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

included is noteworthy, given that higher levels of social capital are typically associated with older people (Putnam 1995) and with whites (Hero 2003, Putnam 2007) – two groups typically overrepresented in rural areas

To test for condition two, I regress annual social capital measures on the interaction between population growth (2000-2015) and percent rural. The negative and significant interactions in membership social capital (Table A2.4, columns 2, 4, 6 and 8) and trust models

(Table A2.5, columns 2 and 6) mean that, as county populations decline , percent rural becomes increasingly positive in its effects on social capital. Again, this finding runs counter to what would be predicted by the sorting hypothesis. Positive interaction effects for religious attendance

(total and white working-class-only: Table A2.5, columns 10 and 12) are in line with the sorting hypothesis, however.

Turning to social dysfunction, tests for conditions one and two yield mixed results (Table

A2.6). Percent rural is associated with change in the direction of more social dysfunction in eight specifications and with change in the direction of less social dysfunction in five specifications.164

With respect to condition two (Table A2.7), the sorting hypothesis is bolstered by negative interactions in seven specifications and undermined by positive interactions in ten specifications.165

Evidence for rural sorting driving social-institutional collapse in white working-class communities is thus minimal. On net, rural areas fare better than non-rural in some respects and population decline is, at best, associated with a very small portion of the growth of social

165 For the sake of interpretability, interaction coefficients on median income, marriage and labor- force participation are flipped in this sentence such that "negative" coefficients represent less dysfunctional outcomes (the direction in which most “dysfunction” measures are coded) and "positive" coefficients represent more dysfunctional outcomes.

185

Table A2.6. OLS Regression: Change in Social Dysfunction on Percent Rural with Demographic Controls

(1) (2) (3) (4) (5) (6) (7) White White White Child White Child Poor White Food Stamps White Poverty Poverty Poverty Poverty Single Moms Whites Marriage VARIABLES 1999-2015 2005-2015 1999-2015 2005-2015 1999-2015 2005-2015 2005-2015

Pct. Rural -0.1486*** 0.0737 -0.1218*** 0.8761*** 0.2181*** -0.6134*** -0.0487*** (-0.0213) (-0.0495) (-0.0363) (-0.1717) (-0.0735) (-0.119) (-0.0086) WWC 2000-2015 0.0251 0.3451*** 0.5669*** (-0.0769) (-0.1252) (-0.1779) White Profs. 2000-2015 -0.1043*** -0.0691* -0.4386*** (-0.0242) (-0.0392) (-0.0825) Hispanics 2000-2015 0.0002 0.0271** -0.008 -0.0046* (-0.0049) (-0.0128) (-0.0234) (-0.0025) Blacks 2000-2015 -0.0004 -0.0057** 0.0002 0.0061*** (-0.0019) (-0.0028) (-0.0044) (-0.0023) Pct. Elderly 2000-2015 0.1319*** -0.2499*** -0.1457** (-0.0274) (-0.041) (-0.0741) Median HHI 2000-2015 -0.6964*** -0.7098*** -0.4307*** (-0.0335) (-0.055) (-0.1021) WWC 2005-2015 -0.5541*** -2.6533*** 0.7989** 0.0761*** (-0.1823) (-0.4992) (-0.3444) (-0.0228) White Profs 2005-2015 -0.0908 -0.2166 -0.3117** 0.0702*** (-0.0579) (-0.1866) (-0.1457) (-0.0097) Hispanics 2005-2015 -0.1012*** -0.2735*** -0.1001 (-0.0309) (-0.076) (-0.0629) Blacks 2005-2015 0.0347*** 0.0690** -0.0352 (-0.013) (-0.0322) (-0.0226) Pct. Elderly 2005-2015 0.1423** 0.3999*** 0.4590*** 0.1437*** (-0.0577) (-0.1482) (-0.1472) (-0.0089) Median HHI 2005-2015 -0.8683*** -0.8251*** -0.1929 0.0901*** (-0.0713) (-0.2026) (-0.1598) (-0.0108) Constant 66.5062*** 40.0092*** 49.4278*** -36.8493*** 50.3490*** 77.6276*** -13.1624*** (-2.3505) (-8.2453) (-3.4917) (-7.3849) (-4.9105) (-15.9282) (-0.9823)

Observations 7,210 2,506 7,210 2,502 7,182 2,506 2,806 R-squared 0.4253 0.3406 0.2521 0.1973 0.1231 0.4153 0.3383 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

dysfunction. Consider, for example, the negative interactions between percent rural and population increase displayed in columns 22 (B= 0.0003, p=0.01) and 30 (B=-0.0003, p=0.05) in

Table A2.7. As county population declines, the effect of county percent rural on social dysfunction – in this case, white poverty and white food stamp use – increases by just three ten thousandths of one PPT. This means that a 50 percent population decrease would be associated with an increase in the effect of percent rural on social dysfunction of approximately 0.017 PPTs.

In the case of median household income, the interaction effect amounts to two dollars and six cents.

186

Table A2.6. (Cont.)

(9) (10) (11) (12) (13) (14) (8) Median Median White White White White All Welfare HHI HHI LFPR LFPR Unemployment Unemployment. VARIABLES 2005-2015 1998-2015 1998-2015 2000-2015 2005-2015 . 2000-2015 2005-2015

Pct. Rural 0.1122 0.1957*** 0.0741*** -0.0425*** -0.1014*** 0.5095*** 0.4904*** (-0.0785) (-0.0123) (-0.019) (-0.0086) (-0.0064) (-0.0822) (-0.0905) WWC 2000-2015 -0.6410*** 0.0027 0.0879 (-0.0355) (-0.0175) (-0.2485) White Profs. 2000-2015 0.2029*** -0.0053 0.6416*** (-0.0282) (-0.0149) (-0.0854) Hispanics 2000-2015 -0.0078*** 0.0035* 0.1364*** (-0.0025) (-0.002) (-0.034) Blacks 2000-2015 -0.0013 -0.0038*** -0.0240*** (-0.0008) (-0.0008) (-0.0059) Pct. Elderly 2000-2015 -0.0967*** -0.1192*** 0.5701*** (-0.0217) (-0.0098) (-0.0927) Median HHI 2000-2015 0.0762*** -0.3855*** (-0.0122) (-0.1145) WWC 2005-2015 -0.1714 -0.4336*** -0.0934*** 0.0162 (-0.1861) (-0.0501) (-0.0215) (-0.1792) White Profs 2005-2015 -0.043 0.0998*** 0.0602*** -0.3836*** (-0.117) (-0.0234) (-0.0085) (-0.1108) Hispanics 2005-2015 -0.1979*** -0.0206** 0.0162*** -0.021 (-0.0378) (-0.0088) (-0.0054) (-0.0388) Blacks 2005-2015 -0.0061 0.0036* -0.0054*** -0.0051 (-0.0115) (-0.0021) (-0.0009) (-0.014) Pct. Elderly 2005-2015 1.2666*** -0.1399*** -0.0586*** 0.1836** (-0.1384) (-0.026) (-0.0078) (-0.076) Median HHI 2005-2015 -0.6985*** 0.0204 -0.5716*** (-0.1423) (-0.0128) (-0.1059) Constant 75.7639*** 23.7771*** 27.0377*** 1.063 -4.2033*** 78.2269*** 27.9590*** (-6.5403) (-2.3794) (-1.1569) (-1.4053) (-0.5467) (-16.77) (-8.2167)

Observations 2,458 7,210 2,506 7,210 2,466 7,206 2,466 R-squared 0.3662 0.4659 0.3588 0.3764 0.4287 0.448 0.3788 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

187

Table A2.7. OLS Regression: Social Dysfunction on Interaction of Percent Rural and Population Change with Demographic Controls

(5) (6) (7) (8) (9) (10) (1) (2) (3) (4) Drug OD Drug OD Pct. Pct. Pct. Pct. Teen Birth Teen Birth Violent Crime Violent Crime Mort Rt. Mort Rt. Uninsured Uninsured Smokers Smokers VARIABLES Rt. 2016 Rt. 2016 Rt. 2016 Rt. 2016 2016 2016 2016 2016 2016 2016

Pct. Rural -0.0283*** -0.0407*** -2.4145*** -2.6890*** -0.0827*** -0.0839*** 0.0152*** 0.0124*** -0.0008 -0.0029 (-0.009) (-0.0091) (-0.1499) (-0.1593) (-0.0063) (-0.0071) (-0.0019) (-0.0019) (-0.0017) (-0.0018) Pop. Change 2000-2015 -0.0832*** -0.1148*** -0.6099*** -1.2768*** -0.0350*** -0.0369*** -0.0121*** -0.0193*** 0.0001 -0.0053** (-0.0137) (-0.0148) (-0.2158) (-0.2594) (-0.0057) (-0.0074) (-0.0037) (-0.0046) (-0.0018) (-0.0025) Pct. Rural * Pop. Change 2000-2015 0.0017*** 0.0354*** 0.0001 0.0004*** 0.0003*** (-0.0003) (-0.0055) (-0.0002) (-0.0001) (-0.0001) Pct. WWC 2015 0.5166*** 0.5211*** -3.6083*** -3.5129*** 0.3711*** 0.3717*** -0.1601*** -0.1590*** 0.0422*** 0.0430*** (-0.0544) (-0.0541) (-1.1273) (-1.1114) (-0.0284) (-0.0283) (-0.0176) (-0.0175) (-0.0155) (-0.0155) Pct. White Prof. 2015 -0.2783*** -0.2864*** 1.1898 1.0153 0.2019*** 0.2017*** -0.1533*** -0.1551*** -0.0736*** -0.0749*** (-0.0785) (-0.0785) (-1.5637) (-1.5544) (-0.0303) (-0.0303) (-0.0241) (-0.024) (-0.0208) (-0.021) Pct. Black 2015 0.4441*** 0.4484*** 8.7995*** 8.8759*** 0.1334*** 0.1333*** -0.0584*** -0.0574*** 0.0308*** 0.0315*** (-0.0378) (-0.0378) (-0.8392) (-0.8285) (-0.0205) (-0.0205) (-0.0125) (-0.0124) (-0.0109) (-0.0109) Pct. Hispanic 2015 0.4443*** 0.4475*** -1.5047* -1.4415* 0.0358** 0.0359** 0.0985*** 0.0993*** -0.0271*** -0.0265*** (-0.0427) (-0.0426) (-0.7935) (-0.7834) (-0.0181) (-0.0181) (-0.0129) (-0.0128) (-0.0097) (-0.0098) Pct. Elderly 2015 -0.0869 -0.0949 -2.7202** -2.8989** -0.0533 -0.0554 0.2684*** 0.2667*** -0.1408*** -0.1421*** (-0.0879) (-0.0849) (-1.2201) (-1.212) (-0.0367) (-0.0365) (-0.0244) (-0.0248) (-0.015) (-0.015) Median HHI 2015 -0.0003*** -0.0003*** -0.0065*** -0.0064*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001*** 188 (-0.0000) (-0.0000) (-0.0003) (-0.0003) (-0.0000) (-0.0000) (-0.0000) (-0.0000) (-0.0000) (-0.0000) Constant 37.5080*** 37.1065*** 1,222.3657*** 1,215.0388*** 10.2006*** 10.2127*** 29.0460*** 28.9464*** 29.5325*** 29.4583***

(-3.1029) (-3.1042) (-64.9531) (-64.0894) (-1.6832) (-1.6863) (-1.0177) (-1.0104) (-0.9572) (-0.9595) Observations 11,487 11,487 11,223 11,223 6,177 6,177 11,799 11,799 11,799 11,799 R-squared 0.7513 0.7528 0.6971 0.6997 0.5148 0.5148 0.8973 0.8979 0.8754 0.8762 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A2.7. (Cont.) (17) (18) (19) (20) (11) (12) (13) (14) (15) (16) Pct. Pct. White Ext. White Ext. Pct. Obese Pct. Obese DD Death DD Death Chlamydia Rt. Chlamydia Rt. Diabetic Diabetic Families Families VARIABLES 2016 2016 Rt. 2016 Rt. 2016 2016 2016 2016 2016 2010 2010

Pct. Rural -0.0014 -0.0031 -0.0102 -0.0002 -1.4934*** -1.7510*** 0.0096*** 0.0106*** -0.0073*** -0.0097*** (-0.0021) (-0.0022) (-0.0067) (-0.0067) (-0.1179) (-0.1199) (-0.0007) (-0.0007) (-0.0005) (-0.0005) Pop. Change 2000-2015 0.0197*** 0.0154*** 0.0288*** 0.0545*** -0.275 -0.9148*** -0.0062*** -0.0036*** 0.0044*** -0.0019* (-0.0036) (-0.0045) (-0.0072) (-0.0088) (-0.1766) (-0.1882) (-0.001) (-0.0013) (-0.0008) (-0.0011) Pct. Rural * Pop. Change 2000-2015 0.0002** -0.0014*** 0.0339*** -0.0001*** 0.0003*** (-0.0001) (-0.0002) (-0.0035) (-0.0000) (-0.0000) Pct. WWC 2015 0.2544*** 0.2550*** 0.2657*** 0.2621*** -5.4121*** -5.3246*** -0.0311*** -0.0316*** 0.0691*** 0.0703*** (-0.0149) (-0.0149) (-0.0282) (-0.0279) (-0.8933) (-0.8935) (-0.0055) (-0.0054) (-0.004) (-0.0041) Pct. White Prof. 2015 -0.2942*** -0.2953*** 0.1832*** 0.1896*** 1.8488 1.6748 -0.1037*** -0.1032*** -0.0813*** -0.0825*** (-0.0184) (-0.0183) (-0.05) (-0.0496) (-1.3186) (-1.3261) (-0.0063) (-0.0062) (-0.0047) (-0.0046) Pct. Black 2015 0.1548*** 0.1554*** 0.1471*** 0.1434*** 8.0244*** 8.1061*** 0.0472*** 0.0468*** 0.0182*** 0.0192*** (-0.0098) (-0.0099) (-0.0225) (-0.0223) (-0.628) (-0.6285) (-0.0039) (-0.0039) (-0.0028) (-0.0028) Pct. Hispanic 2015 0.0322*** 0.0326*** 0.0657*** 0.0629*** -2.2856*** -2.2252*** 0.0437*** 0.0433*** 0.0220*** 0.0229*** (-0.0119) (-0.0119) (-0.0225) (-0.0224) (-0.66) (-0.6588) (-0.0039) (-0.0039) (-0.0028) (-0.0028) Pct. Elderly 2015 -0.1897*** -0.1907*** -0.2232*** -0.2169*** -6.6563*** -6.8308*** 0.2676*** 0.2686*** -0.0699*** -0.0723*** (-0.021) (-0.0207) (-0.0409) (-0.0409) (-1.0178) (-1.003) (-0.0066) (-0.0064) (-0.0044) (-0.0042) Median HHI 2015 -0.0000*** -0.0000** 0.0000* -0.0000 -0.0077*** -0.0076*** -0.0000*** -0.0000*** 0.0000*** 0.0000***

189 (-0.0000) (-0.0000) (-0.0000) (-0.0000) (-0.0003) (-0.0003) (-0.0000) (-0.0000) (-0.0000) (-0.0000) Constant 26.1780*** 26.1189*** 21.1135*** 21.4622*** 1,578.9348*** 1,571.5090*** 5.6387*** 5.6765*** -0.4740** -0.0828 (-0.9151) (-0.9152) (-2.0287) (-2.0257) (-50.3457) (-50.3323) (-0.3522) (-0.3518) (-0.2386) (-0.2652)

Observations 11,799 11,799 11,735 11,735 11,239 11,239 11,854 11,854 11,854 11,854 R-squared 0.8015 0.8018 0.3304 0.3334 0.7825 0.7854 0.8811 0.8818 0.6435 0.6608 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A2.7. (Cont.) (23) (24) (29) (30) (21) (22) White White Food Food White White Child Child (25) (26) (27) (28) Stamps Stamps Poverty Poverty Poverty Poverty Median HHI Median HHI All Welfare All Welfare (Whites) (Whites) VARIABLES 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015

Pct. Rural 0.0311*** 0.0335*** 0.0075*** 0.0083*** -22.9661** -9.6136 -0.0021 -0.0012 0.0143*** 0.0195*** (-0.002) (-0.0021) (-0.0008) (-0.0009) (-8.9472) (-9.3571) (-0.0053) (-0.0055) (-0.0041) (-0.0048) Pop. Change 2000-2015 -0.0024 0.004 0.0005 0.0027* 145.5790*** 179.1552*** 0.0018 0.004 0.0045 0.0100** (-0.0027) (-0.0032) (-0.0012) (-0.0014) (-13.1265) (-16.3638) (-0.0071) (-0.009) (-0.0036) (-0.0046) Pct. Rural * Pop. Change 2000-2015 -0.0003*** -0.0001*** -1.7768*** -0.0001 -0.0003** (-0.0001) (0.0000) (-0.3046) (-0.0002) (-0.0001) Pct. WWC 2015 -0.2307*** -0.2320*** -0.0006 -0.001 -751.2091*** -753.3602*** -0.1490*** -0.1494*** 0.1053*** 0.1031*** (-0.0224) (-0.0224) (-0.0088) (-0.0087) (-90.6304) (-90.3422) (-0.046) (-0.0459) (-0.0214) (-0.0213) Pct. White Prof. 2015 -0.1802*** -0.1790*** -0.0996*** -0.0992*** 677.7875*** 680.1878*** -0.2516*** -0.2512*** -0.1854*** -0.1852*** (-0.0274) (-0.0273) (-0.0109) (-0.0109) (-102.5944) (-102.1125) (-0.0731) (-0.0731) (-0.0247) (-0.0247) Pct. Black 2015 -0.2052*** -0.2062*** -0.0326*** -0.0329*** -672.4201*** -673.7352*** 0.1711*** 0.1707*** -0.0159 -0.0152 (-0.0151) (-0.0151) (-0.005) (-0.005) (-64.1511) (-64.0683) (-0.033) (-0.033) (-0.0142) (-0.0143) Pct. Hispanic 2015 -0.1868*** -0.1876*** -0.0240*** -0.0243*** -512.6095*** -514.0833*** 0.0693** 0.0690** -0.0351** -0.0354** (-0.0146) (-0.0146) (-0.0057) (-0.0056) (-62.9552) (-62.7927) (-0.0309) (-0.0308) (-0.0144) (-0.0144) Pct. Elderly 2015 -0.0420** -0.0395** -0.0262*** -0.0254*** -140.4064* -126.3691* 0.3703*** 0.3711*** -0.1288*** -0.1202*** (-0.0189) (-0.0188) (-0.0078) (-0.0078) (-74.8302) (-74.6343) (-0.0483) (-0.0482) (-0.0205) (-0.0204) 190 Median HHI 2015 -0.0003*** -0.0003*** -0.0000*** -0.0000*** -0.0005*** -0.0005*** -0.0001*** -0.0001*** (-0.0000) (-0.0000) (-0.0000) (-0.0000) (-0.0000) (-0.0000) (-0.0000) (-0.0000) Constant 39.8861*** 39.9799*** 6.9515*** 6.9829*** 103,703.1569*** 103,575.3116*** 57.1351*** 57.1677*** 16.7216*** 16.5950*** (-1.5056) (-1.5043) (-0.5621) (-0.5596) (-5,138.04) (-5,128.72) (-2.6416) (-2.6312) (-1.4222) (-1.4295)

Observations 11,858 11,858 11,858 11,858 11,858 11,858 11,854 11,854 3,202 3,202 R-squared 0.7362 0.737 0.6746 0.6754 0.7352 0.7368 0.8141 0.8141 0.754 0.7545 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A2.7. (Cont.) (31) (32) (33) (34) (37) (38) Poor White Poor White White White (35) (36) White White Single Single Marriage Marriage White White Unemployment Unemployment VARIABLES Moms 2015 Moms 2015 Rt. 2015 Rt. 2015 LFPR 2015 LFPR 2015 2015 2015

Pct. Rural -0.0045*** -0.0040*** 0.0367*** 0.0393*** -0.0313*** -0.0341*** -0.0076*** -0.0085*** (-0.0007) (-0.0008) (-0.0042) (-0.0043) (-0.0032) (-0.0033) (-0.001) (-0.001) Pop. Change 2000-2015 -0.0036*** -0.0024** 0.0543*** 0.0612*** -0.0350*** -0.0424*** 0.0136*** 0.0114*** (-0.0009) (-0.0011) (-0.0041) (-0.0047) (-0.0045) (-0.006) (-0.0015) (-0.002) Pct. Rural * Pop. Change 2000-2015 -0.0001** -0.0004*** 0.0004*** 0.0001*** (-0.0000) (-0.0001) (-0.0001) (0.0000) Pct. WWC 2015 0.0416*** 0.0414*** 0.2581*** 0.2568*** 0.0974*** 0.0988*** 0.0454*** 0.0458*** (-0.005) (-0.005) (-0.0243) (-0.0242) (-0.0189) (-0.0188) (-0.0057) (-0.0057) Pct. White Prof. 2015 -0.0452*** -0.0450*** -0.1674*** -0.1661*** 0.4033*** 0.4019*** -0.1048*** -0.1052*** (-0.0071) (-0.0071) (-0.0391) (-0.0392) (-0.0263) (-0.0263) (-0.0073) (-0.0073) Pct. Black 2015 -0.0214*** -0.0216*** 0.0998*** 0.0987*** 0.0520*** 0.0531*** 0.0035 0.0039 (-0.0036) (-0.0036) (-0.0176) (-0.0176) (-0.0144) (-0.0143) (-0.0042) (-0.0042) Pct. Hispanic 2015 -0.0186*** -0.0187*** 0.1496*** 0.1487*** 0.001 0.002 0.002 0.0022 (-0.0034) (-0.0034) (-0.0186) (-0.0186) (-0.0164) (-0.0163) (-0.0044) (-0.0044) Pct. Elderly 2015 -0.0816*** -0.0811*** 0.3526*** 0.3552*** -1.0829*** -1.0857*** 0.0341*** 0.0333*** (-0.0055) (-0.0055) (-0.0253) (-0.0252) (-0.0231) (-0.0232) (-0.0079) (-0.0079) Median HHI 2015 -0.0000*** -0.0000*** 0.0003*** 0.0003*** 0.0001*** 0.0001*** -0.0000*** -0.0000***

191 (-0.0000) (-0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (-0.0000) (-0.0000) Constant 6.8940*** 6.9114*** 21.8416*** 20.4830*** 71.5440*** 68.0542*** 3.9500*** 5.6250*** (-0.3127) (-0.3125) (-1.4432) (-1.5314) (-1.1331) (-1.1752) (-0.3229) (-0.3377)

Observations 11,858 11,858 11,854 11,854 11,854 11,854 11,854 11,854 R-squared 0.6882 0.6884 0.674 0.6746 0.7884 0.7889 0.6189 0.6193 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A2.8. OLS Regression of 2016 GOP Primary Vote- on Membership Social Capital Measures with Economic Control Variables (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Trump Cruz Trump Cruz Trump Cruz Trump Cruz

Social Capital 2014 -3.10*** 0.93*** (-0.81) (-0.32) Associations 2014 -5.81*** 2.60*** (-0.63) (-0.55) Religious Orgs. 2014 -7.2572*** 5.0046*** (-0.7868) (-0.6823) Civic Orgs. 2014 -14.9953*** 1.7461 (-2.8275) (-2.1712) Unemployment (Whites) 2015 0.80*** -0.33*** 0.76*** -0.27** 0.8282*** -0.2393** 0.9574*** -0.4090*** (-0.18) (-0.12) (-0.18) (-0.12) (-0.1872) (-0.1158) (-0.1861) (-0.124) Pct. Elderly 2015 0.45*** -0.23*** 0.46*** -0.24*** 0.4708*** -0.2774*** 0.3796*** -0.2046*** (-0.1) (-0.07) (-0.1) (-0.07) (-0.1066) (-0.0682) (-0.1016) (-0.0653) Median HHI 2015 0.00** -0.00** 0.00* 0 0.0001** 0 0.0001** -0.0000** (0) (0) (0) (0) (0) (0) (0) (0) Pct. Rural 2015 -0.01 0.02** -0.01 0.02** -0.0085 0.0097 -0.0323** 0.0260*** (-0.01) (-0.01) (-0.01) (-0.01) (-0.0132) (-0.0087) (-0.0134) (-0.0094) Net Obama 2012 -0.02 -0.07*** -0.02 -0.06*** -0.0309* -0.0571*** -0.0141 -0.0654*** (-0.02) (-0.01) (-0.02) (-0.01) (-0.0179) (-0.0125) (-0.0177) (-0.0125) Pct. WWC 0.34*** -0.18*** 0.33*** -0.19*** 0.2986*** -0.1729*** 0.3133*** -0.1707***

192 (-0.09) (-0.05) (-0.09) (-0.05) (-0.0889) (-0.0479) (-0.0876) (-0.0482) Pct. White Prof. 2015 -0.2 -0.39*** -0.32*** -0.37*** -0.4240*** -0.3152*** -0.3867*** -0.3300*** (-0.13) (-0.06) (-0.12) (-0.06) (-0.1266) (-0.0646) (-0.1253) (-0.0662) Pct. Black 2015 0.20*** -0.10*** 0.16*** -0.09*** 0.1560*** -0.0931*** 0.1290** -0.0799** (-0.06) (-0.03) (-0.05) (-0.03) (-0.0554) (-0.0317) (-0.0548) (-0.0322) Pct. Hispanic 2015 0.07 -0.17*** 0.06 -0.17*** 0.0444 -0.1600*** 0.0584 -0.1734*** (-0.06) (-0.03) (-0.06) (-0.03) (-0.0635) (-0.0308) (-0.0633) (-0.0316) Constant -13.48** 85.01*** -7.9 82.31*** -9.3018 81.6050*** -10.8757* 84.9518*** (-5.97) (-5.34) (-6.1) (-5.45) (-6.2501) (-5.3907) (-6.2562) (-5.4052)

Observations 2,963 2,963 2,963 2,963 2,963 2,963 2,963 2,963 R-squared 0.93 0.91 0.93 0.91 0.9283 0.9116 0.9257 0.908 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A2.9. OLS Regression of 2016 GOP Primary Vote on Social Capital Measures: Trust and Religious Attendance Measures with Economic Control Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12 ) VARIABLES Trump Cruz Trump Cruz Trump Cruz Trump Cruz Trump Cruz Trump Cruz

General Trust -11.6923** -11.8875*** (-5.3186) (-4.4681) Social Trust -2.9441 -12.1350*** (-4.2538) (-3.4301) Religious Attendance -10.8530*** 1.8049 (-3.0959) (-2.8202) WWC Gen. Trust -25.3080** 2.7781 (-10.4332) (-10.3261) WWC Social Trust -20.1193*** 3.7619 (-7.4484) (-7.3979) WWC Rel. Attendance -11.1150** 11.0723** (-4.3056) (-4.3549) Unemployment (White) 1.7497*** -0.1955 1.4991*** 0.0231 1.6180*** -1.0248** 0.9864* 0.0109 0.9614* 0.0013 0.7192 0.1711 2015 (-0.5126) (-0.4307) (-0.5353) (-0.4316) (-0.4754) (-0.4331) (-0.5706) (-0.5647) (-0.563) (-0.5591) (-0.6123) (-0.6193) Pct. Elderly 2015 0.5166* -0.6185*** 0.6595** -0.6924*** 1.3584*** -1.1479*** 0.2613 -0.7033* 0.3812 -0.7132* 0.4167 -0.7681* (-0.2624) (-0.2205) (-0.2686) (-0.2166) (-0.3226) (-0.2939) (-0.386) (-0.382) (-0.3743) (-0.3717) (-0.3826) (-0.387) Median HHI 2015 0.0002*** -0.0001* 0.0002*** -0.0001 0 -0.0001 0.0002*** -0.0001** 0.0002*** -0.0002** 0.0002*** -0.0001** (-0.0001) 0 (-0.0001) 0 (-0.0001) (-0.0001) (-0.0001) (-0.0001) (-0.0001) (-0.0001) (-0.0001) (-0.0001) Pct. Rural 2015 -0.0970* 0.1005** -0.1037* 0.0918** -0.1235** 0.1274*** -0.055 0.0885* -0.0546 0.0877* -0.0777 0.0891* (-0.052) (-0.0437) (-0.0529) (-0.0427) (-0.0522) (-0.0475) (-0.0515) (-0.051) (-0.0508) (-0.0505) (-0.0514) (-0.052) Net Obama 2012 -0.1409*** -0.0271 -0.1472*** -0.0334 -0.1887*** -0.0087 -0.0513 -0.0369 -0.0676* -0.0358 -0.0751* -0.0275 193 (-0.0313) (-0.0263) (-0.0318) (-0.0256) (-0.0303) (-0.0276) (-0.0409) (-0.0405) (-0.0397) (-0.0394) (-0.0399) (-0.0403) Pct. WWC 2015 0.1212 -0.2575* 0.1089 -0.1743 -0.3697** -0.0415 1.1510*** -0.4790* 1.0263*** -0.4669* 1.1635*** -0.4910* (-0.1552) (-0.1304) (-0.1631) (-0.1315) (-0.168) (-0.1531) (-0.2562) (-0.2536) (-0.2483) (-0.2466) (-0.2516) (-0.2545) Pct. White Prof. 2015 -0.6413*** -0.1184 -0.7470*** -0.1168 -0.9778*** -0.1937 0.4074* -0.4504** 0.2407 -0.4304* 0.1965 -0.297 (-0.1456) (-0.1223) (-0.1425) (-0.1149) (-0.1282) (-0.1168) (-0.2269) (-0.2246) (-0.2178) (-0.2163) (-0.2312) (-0.2339) Pct. Black 2015 0.2320** -0.3006*** 0.2707** -0.2761*** 0.0452 -0.1295 0.7606*** -0.4103*** 0.7049*** -0.4030*** 0.7615*** -0.4120*** (-0.1053) (-0.0885) (-0.1056) (-0.0852) (-0.1056) (-0.0962) (-0.1485) (-0.1469) (-0.1454) (-0.1444) (-0.1465) (-0.1482) Pct. Hispanic 2015 -0.0721 -0.2242*** -0.0283 -0.2229*** -0.1912** -0.0553 0.4469*** -0.2862** 0.4017*** -0.2822** 0.4379*** -0.2833** (-0.093) (-0.0782) (-0.0931) (-0.0751) (-0.0921) (-0.0839) (-0.1415) (-0.14) (-0.1379) (-0.137) (-0.1379) (-0.1395) Constant 64.1251*** 86.9096*** 40.5257** 56.2622*** 76.6545*** 70.1418*** 28.8574 72.5811** -18.3859 77.7997*** -12.0062 44.1730* (-19.7072) (-16.5559) (-17.7186) (-14.2878) (-17.6973) (-16.1214) (-28.0961) (-27.8077) (-19.2897) (-19.1589) (-23.1645) (-23.4294)

Observations 152 152 152 152 143 143 115 115 116 11 6 110 110 R-squared 0.9706 0.9404 0.9694 0.943 0.9756 0.9427 0.9807 0.955 0.9809 0.9553 0.9828 0.9582 State indicator variables not shown Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A2.10. OLS Regression of 2016 GOP Primary Vote on “Family” Social Dysfunction Measures with Economic Controls (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Trump Cruz Trump Cruz Trump Cruz Trump Cruz

White Extended. Families 2010 2.4932*** -0.1906 (-0.4759) (-0.3635) Poor White Single Mothers 2015 -0.0947 -0.0021 (-0.2075) (-0.1662) White Marriage 2015 0.2173* 0.1031 (-0.1219) (-0.0664) Teen Birth Rt. 2016 0.0333 0.0149 (-0.0489) (-0.0239) Unemployment (Whites) 2015 0.6249*** -0.3892*** 1.1089*** -0.4252*** 1.1025*** -0.4233*** 1.1060*** -0.4413*** (-0.1674) (-0.1205) (-0.1986) (-0.1225) (-0.1851) (-0.1167) (-0.1845) (-0.1197) Pct. Elderly 2015 0.5240*** -0.2150*** 0.3549*** -0.2028*** 0.2723** -0.2454*** 0.3689*** -0.2188*** (-0.1045) (-0.0734) (-0.1055) (-0.0679) (-0.1292) (-0.0719) (-0.1073) (-0.066) Median HHI 2015 0 -0.0000** 0.0001*** -0.0001** 0 -0.0001*** 0.0001*** -0.0000** (0) (0) (0) (0) (-0.0001) (0) (0) (0) Pct. Rural 2015 -0.0165 0.0247*** -0.0323** 0.0259*** -0.0385** 0.0229** -0.0313** 0.0246*** (-0.0124) (-0.0092) (-0.0142) (-0.0095) (-0.0155) (-0.0102) (-0.0138) (-0.0094) Net Obama 2012 0.0029 -0.0664*** -0.0198 -0.0647*** -0.0005 -0.0555*** -0.0188 -0.0645*** (-0.0187) (-0.0127) (-0.018) (-0.0125) (-0.0239) (-0.0134) (-0.0189) (-0.0127) Pct. WWC 2015 0.1843** -0.1600*** 0.2954*** -0.1681*** 0.2720*** -0.1776*** 0.2729*** -0.1578***

194 (-0.086) (-0.0507) (-0.0904) (-0.0495) (-0.086) (-0.0476) (-0.0902) (-0.0487) Pct. White Prof. 2015 -0.2487* -0.3398*** -0.4087*** -0.3279*** -0.3633*** -0.3077*** -0.3974*** -0.3174*** (-0.1374) (-0.0699) (-0.1312) (-0.0675) (-0.1346) (-0.0697) (-0.1432) (-0.0699) Pct. Black 2015 0.0846 -0.0769** 0.1367** -0.0811** 0.1115* -0.0939*** 0.1203* -0.0784** (-0.0549) (-0.0327) (-0.0565) (-0.0324) (-0.0586) (-0.0343) (-0.066) (-0.0338) Pct. Hispanic 2015 0.0154 -0.1704*** 0.0633 -0.1742*** 0.0364 -0.1878*** 0.0483 -0.1732*** (-0.0655) (-0.0327) (-0.0661) (-0.0317) (-0.0731) (-0.0363) (-0.0833) (-0.037) Constant -13.2150** 85.2885*** -14.3185** 85.4227*** -20.3041*** 82.8099*** -16.0230** 84.0570*** (-6.0146) (-5.2732) (-6.3865) (-5.4374) (-7.1112) (-5.4657) (-7.0742) (-5.5126)

Observations 2,963 2,963 2,963 2,963 2,963 2,963 2,870 2,870 R-squared 0.9281 0.908 0.9241 0.908 0.9252 0.9084 0.9244 0.9087 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A2.11. OLS Regression of 2016 GOP Primary Vote on “Behavior” Social Dysfunction Measures with Economic Controls (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) VARIABLES Trump Cruz Trump Cruz Trump Cruz Trump Cruz Trump Cruz

Violent Crime 2016 -0.0024 0.0004 (-0.0017) (-0.0014) D. Driving Death Rt. 2016 0.0564** -0.0379** (-0.0237) (-0.0164) Drug OD Mort. Rt. 2016 0.1438*** -0.1283*** (-0.0392) (-0.0331) Pct. Smokers 2016 0.6104*** -0.2716* (-0.2014) (-0.1435) Pct. Diabetic 2016 0.7830** 0.2424 (-0.3175) (-0.2466) Unemployment (Whites) 2015 1.0995*** -0.4301*** 1.1084*** -0.4350*** 1.2794*** -0.4956*** 0.9714*** -0.3743*** 1.0623*** -0.4371*** (-0.1904) (-0.1241) (-0.1893) (-0.122) (-0.2517) (-0.1434) (-0.1839) (-0.1202) (-0.1827) (-0.1184) Pct. Elderly 2015 0.3613*** -0.2158*** 0.3866*** -0.2276*** 0.3457*** -0.1894*** 0.4567*** -0.2559*** 0.1516 -0.2679*** (-0.1089) (-0.0672 (-0.1041 (-0.0655 (-0.1127 (-0.0696 (-0.1073 (-0.0727 (-0.1565 (-0.0891 Median HHI 2015 0.0001*** -0.0000** 0.0001*** -0.0000** 0.0001*** -0.0001*** 0.0002*** -0.0001*** 0.0001*** -0.0000** (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) Pct. Rural 2015 -0.0394** 0.0266*** -0.0313** 0.0236** -0.0443** 0.0193 -0.0325** 0.0244** -0.0407*** 0.0233** (-0.0157) (-0.0103) (-0.0138) (-0.0095) (-0.0199) (-0.0124) (-0.0142) (-0.0096) (-0.0154) (-0.0103) Net Obama 2012 -0.0177 -0.0648*** -0.0199 -0.0650*** -0.0279 -0.0594*** -0.0187 -0.0655*** -0.0234 -0.0658***

195 (-0.0186) (-0.0131) (-0.018) (-0.0126) (-0.0196) (-0.0133) (-0.0182) (-0.0127) (-0.0178) (-0.0126) Pct. WWC 2015 0.2860*** -0.1509*** 0.2725*** -0.1413*** 0.2372** -0.1254** 0.2714*** -0.1440*** 0.3123*** -0.1618*** (-0.0962) (-0.0509) (-0.093) (-0.0495) (-0.0984) (-0.0527) (-0.0999) (-0.0513) (-0.094) (-0.0485) Pct. White Prof. 2015 -0.4036*** -0.3220*** -0.4161*** -0.3142*** -0.4397*** -0.3214*** -0.3747** -0.3347*** -0.3292** -0.3041*** (-0.1377) (-0.0688) (-0.1352) (-0.0676) (-0.144) (-0.0732) (-0.1577) (-0.0743) (-0.1464) (-0.0694) Pct. Black 2015 0.1566** -0.0755** 0.1290** -0.0661** 0.0938 -0.0679* 0.1176* -0.0626* 0.1014* -0.0923*** (-0.0676) (-0.0353) (-0.0606) (-0.033) (-0.0623) (-0.0352) (-0.0639) (-0.033) (-0.0576) (-0.0351) Pct. Hispanic 2015 0.06 -0.1664*** 0.0591 -0.1643*** 0.0507 -0.1802*** 0.0806 -0.1746*** 0.0307 -0.1847*** (-0.0697) (-0.0334) (-0.0695) (-0.0331) (-0.0703) (-0.0343) (-0.0745) (-0.0343) (-0.0693) (-0.0345) Constant 15.9859** 33.1285*** -16.0062** 85.4672*** -17.8518*** 89.1981*** -29.8403*** 91.3240*** -20.4544*** 83.6601*** (-6.2658) (-3.7492) (-6.685) (-5.4064) (-6.577) (-5.6401) (-10.1081) (-6.8618) (-7.0772) (-5.5207)

Observations 2,804 2,804 2,932 2,932 1,543 1,543 2,948 2,948 2,962 2,962 R-squared 0.9249 0.9093 0.9247 0.909 0.9323 0.9187 0.9259 0.9092 0.9249 0.9081 State indicator variables not shown Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table A2.12. Logistic Regression of 2016 GOP Primary Vote on Explanations for Trump Support, Substituting Ethnocentrism for Racial Resentment (Odds Ratios) (1) (2) (3) (4) (5) (6) (7) (8) Trump Trump Other GOP Other GOP Trump Trump Other GOP Other GOP VARIABLES (WWC) (WWC) (WWC) (WWC) (WP) (WP) (WP) (WP)

General Trust 0.6757** 0.6683*** 1.2663 1.2371 0.6201** 0.6487* 1.870*** 1.8269*** (-0.1046) (-0.1012) (-0.2298) (-0.2318) (-0.1382) (-0.1446) (-0.3903) (-0.3829) Religious Attendance 0.9406 0.9255 0.9509 0.9568 0.7397** 0.7456** 1.1732 1.1819 (-0.0911) (-0.0907) (-0.1253) (-0.1244) (-0.0948) (-0.1035) (-0.1304) (-0.1294) White Identification 1.0824 0.8762 1.1655 0.9683

(-0.1155) (-0.1103) (-0.1704) (-0.1058) White Consciousness 0.9164 0.8777 1.8836*** 0.9406

(-0.1481) (-0.147) (-0.3892) (-0.1549) Ethnocentrism 1.0059 1.0101 0.9794** 0.9773** 1.0165* 1.0116 0.9878 0.9881 (-0.008) (-0.0086) (-0.009) (-0.0093) (-0.0092) (-0.009) (-0.0079) (-0.008) Authoritarianism 1.1999 1.1473 1.0667 1.0746 2.1829 1.548 1.4848 1.5114 (-0.6882) (-0.6938) (-0.7433) (-0.748) (-1.2128) (-0.8921) (-0.8033) (-0.8392) Economic Anxiety 1.3648* 1.344* 0.7945 0.7889 0.8072 0.7321 1.1782 1.241 (-0.2177) (-0.2111) (-0.1669) (-0.1665) (-0.1579) (-0.1442) (-0.2061) (-0.2196) Ideology 1.1078 1.128 1.7906*** 1.8114*** 1.1999 1.15 1.274 1.3234* (-0.1685) (-0.1726) (-0.3272) (-0.3331) (-0.2682) (-0.2594) (-0.2012) (-0.2135) Male 2.0655** 2.0893** 0.3615** 0.3495*** 1.0035 1.1229 0.8693 0.8914 (-0.5934) (-0.6151) (-0.1431) (-0.1388) (-0.3471) (-0.4128) (-0.2479) (-0.2515) Education 0.93 0.9625 1.2226 1.2243 1.8091*** 1.9264*** 0.9542 0.9547 (-0.1107) (-0.1192) (-0.2014) (-0.203) (-0.3729) (-0.4294) (-0.1903) (-0.1912) Income 1.0299 1.0255 1.0376 1.0408 0.9698 0.9706 1.04 1.0517* (-0.024) (-0.0239) (-0.0285) (-0.0284) (-0.0272) (-0.029) (-0.0274) (-0.0277) South 0.5277** 0.5463** 1.4886 1.4108 0.714 0.7473 1.8787** 1.8013** (-0.1529) (-0.1569) (-0.4625) (-0.444) (-0.2335) (-0.2488) (-0.5326) (-0.5122) Age 1.0305*** 1.0317*** 1.0333*** 1.0329*** 1.0245** 1.0229** 1.016* 1.0162* (-0.0084) (-0.0088) (-0.009) (-0.009) (-0.0102) (-0.0101) (-0.0086) (-0.009) Married 1.0548 1.1274 1.1458 1.0625 0.9655 1.0573 2.6047*** 2.4122** (-0.3601) (-0.3946) (-0.445) (-0.4151) (-0.383) (-0.4335) (-0.9187) (-0.8446) Constant 0.0553* 0.0597 0.0003*** 0.0003*** 0.0004*** 0.0001*** 0.0007*** 0.0004*** (-0.0967) (-0.1088) (-0.0006) (-0.0007) (-0.0011) (-0.0002) (-0.0017) (-0.0011)

Observations 369 365 369 365 348 348 348 346 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

196

Table A2.13. Odds Ratios: 2016 GOP Primary on Explanations for Trump Support, Including “Traditionalism” Measure (1) (2) (3) (4) (5) (6) (7) (8) Trump Trump Other GOP Other GOP Trump Trump Other GOP Other GOP VARIABLES (WWC) (WWC) (WWC) (WWC) (WP) (WP) (WP) (WP)

General Trust 0.6661*** 0.6459*** 1.2977 1.271 0.6085** 0.6328** 1.9386*** 1.8492*** (-0.1046) (-0.098) (-0.2342) (-0.2378) (-0.1344) (-0.1387) (-0.4015) (-0.3881) Religious Attendance 0.9625 0.9394 0.9398 0.9493 0.7347** 0.7492* 1.1171 1.129 (-0.0989) (-0.0968) (-0.1224) (-0.1234) (-0.0998) (-0.1123) (-0.1328) (-0.1336) White Identification 1.0853 0.8188* 1.2253 0.9472

(-0.1114) (-0.0992) (-0.174) (-0.1012) White Consciousness 0.8833 0.8235 2.0208*** 0.9212

(-0.1474) (-0.1347) (-0.4271) (-0.1583) Racial Resentment 1.6395* 1.7885** 0.7302 0.7339 1.2009 0.8991 0.8385 0.8329 (-0.4458) (-0.5231) (-0.2254) (-0.228) (-0.355) (-0.2755) (-0.2349) (-0.2509) Authoritarianism 1.1589 1.1864 0.8491 0.8085 2.3491 1.8157 1.185 1.2098 (-0.6652) (-0.7157) (-0.5616) (-0.5283) (-1.3406) (-1.0535) (-0.6741) (-0.6905) Economic Anxiety 1.3738* 1.3302* 0.8153 0.8085 0.7988 0.7263* 1.2143 1.2718 (-0.2177) (-0.2062) (-0.1778) (-0.177) (-0.1553) (-0.1402) (-0.2096) (-0.2227) Traditionalism 0.9051 0.9327 1.0251 1.0307 0.9673 0.9043 1.332 1.3475 (-0.1905) (-0.1975) (-0.2577) (-0.2493) (-0.1887) (-0.1857) (-0.288) (-0.2971) Ideology 1.1042 1.1135 1.8266*** 1.8492*** 1.2326 1.2315 1.1786 1.2148 (-0.1778) (-0.182) (-0.3544) (-0.3574) (-0.2802) (-0.3032) (-0.1886) (-0.2005) Male 2.0731** 2.0394** 0.3685*** 0.3544*** 1.0085 1.118 0.8527 0.8713 (-0.5962) (-0.6028) (-0.1421) (-0.1366) (-0.3524) (-0.4158) (-0.2426) (-0.2457) Education 0.9353 0.9655 1.2117 1.2179 1.7547*** 1.8977*** 0.9338 0.9325 (-0.1111) (-0.1195) (-0.1991) (-0.2035) (-0.3635) (-0.4209) (-0.1852) (-0.1879) Income 1.0312 1.0262 1.0355 1.0409 0.9691 0.9699 1.0434 1.0543** (-0.0241) (-0.0238) (-0.0272) (-0.0272) (-0.0272) (-0.0287) (-0.0275) (-0.0274) South 0.54** 0.5623** 1.4798 1.3678 0.7492 0.7837 1.780** 1.7169* (-0.156) (-0.1617) (-0.4523) (-0.4209) (-0.2383) (-0.2537) (-0.5134) (-0.496) Age 1.0314*** 1.0329*** 1.0337*** 1.0331*** 1.0259** 1.0243** 1.0153* 1.0148* (-0.0085) (-0.0089) (-0.0093) (-0.0091) (-0.0104) (-0.0101) (-0.0088) (-0.009) Married 0.998 1.0372 1.2984 1.1982 0.9849 1.0822 2.5043*** 2.3234** (-0.3359) (-0.3547) (-0.5056) (-0.4633) (-0.3829) (-0.4451) (-0.8779) (-0.81) Constant 0.0131** 0.0137** 0.0007*** 0.0007*** 0.0003*** 0.0001*** 0.0009*** 0.0007*** (-0.0251) (-0.0267) (-0.0015) (-0.0016) (-0.0009) (-0.0003) (-0.0023) (-0.0018)

Observations 371 367 371 367 351 346 351 346 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

197

APPENDIX C: SUPPLEMENTAL INFORMATION TO CHAPTER 4

Plausibility Probe: Online Search of White Identity Discourse

Background : Figures 4.1 and 4.2 display results from an online search for evidence that discussions of white identity have increased in recent years. Terms searched: “whites”, “white people”, and “white men”. Years searched: 2000, 2005, 2010, 2015, and 2016.

Strategy : For each successful “hit” on a searched term, I read the article as far as necessary to establish the author’s perspective. I label an article “critical” (representative of progressive racial discourse) if it: 1. Returns a “hit” on one or more of the searched terms, 2. Is explicitly critical of or hostile towards white people or “whiteness”, 3. Is not satirical or humorous in tone – e.g., “basic” white girls drinking “pumpkin- spiced lattes,” 4. Is not primarily historical – e.g., focused on Jim Crow, or slavery, 5. Is not a “current events” or news-article – e.g., a police shooting of a young black man – and 6. Does not duplicate another article – e.g., a second article discussing a previously recorded video of Samantha Bee criticizing white people.

I label an article “anti-critical” if it satisfies conditions 1, 3-6, and is explicitly opposed to progressive racial discourse. I ignore white nationalist/supremacist, neo-Nazi, and other explicitly white racist content as the intention is to record responses to critical/progressive racial discourse from conservative sources.

Results : 2016 “Critical” Content

Chicoyo. “I've Grown to Hate White People.” The Race Card Project , 9 Oct. 2016, theracecardproject.com/ive-grown-hate-white-people/.

198

Conforti, Michael. “White People, It's OK to Not Feel Guilty About Police Killing .” Long Island News from the Long Island Press , 21 Sept. 2016, www.longislandpress.com/2016/09/21/white-people-its-ok-to-not-feel-guilty/.

Drayton, Tiffanie. “18 Things White People Need to Know and Do before Discussing Racism.” Matador Network , 21 Mar. 2016, matadornetwork.com/pulse/18-things-white-people-need- know-discussing-racism/.

Elliott, Greg. “10 Ways White Supremacy Wounds White People: A Tale of Mutuality.” American Friends Service Committee , 21 July. 2016, www.afsc.org/blogs/acting-in- faith/10-ways-white-supremacy-wounds-white-people-tale-mutuality.

Encalada, Debbie. “White People: 'Luke Cage' Is Racist.” Complex , 3 Oct. 2016, www.complex.com/pop-culture/2016/10/luke-cage-racist-says-white-people.

Enjeti, Anjali. “Ghosts of White People Past: Witnessing from an Asian Ethnoburb.” Pacific Standard , 25 Aug. 2016, psmag.com/ghosts-of-white-people-past- witnessing-white-flight-from-an-asian-ethnoburb-b550ba986cdb.

Hains, Tim. “Samantha Bee: White People Have Ruined America.” RealClearPolitics , 11 Nov. 2016, www.realclearpolitics.com/video/2016/11/11/samantha_bee_white_people_have_ruined_a merica.html.

Harris, Jalynn. “Column: White People, This Ain't for You.” The Daily Tar Heel , 3 Mar. 2016, www.dailytarheel.com/article/2016/03/column-your-critiques-are-boring.

Hillman, Melissa. “White People: Shut Up About Beyoncé.” Bitter Gertrude , 9 Feb. 2016, bittergertrude.com/2016/02/08/white-people-shut-up-about-beyonce/.

Hina. “Fuck You, White People.” Medium , Bullshit.IST, 23 Nov. 2016, bullshit.ist/fuck-you- white-people-f0907289ad44.

Johnson, Jazzi. “'Luke Cage' Is Unapologetically Black and White People Are Seeing Red About It.” Oxygen , 3 Oct. 2016, www.oxygen.com/very-real/luke-cage-is-unapologetically-black- and-white-people-are-seeing-red-about-it.

Kel, TaLynn. “When White People Consume Blackness for Personal Gain.” Medium , , 23 Dec. 2016, medium.com/the-establishment/when-white-people- consume-blackness-for-personal-gain-f5051d820c01.

Kirkinis, Katherine, and Sarah Birdsong. “The Scientific Way to Train White People to Stop Being Racist.” Quartz , 11 Apr. 2016, qz.com/656159/the-scientific-way-to-train-white- people-to-stop-being-racist/.

199

Kreider, Tim. “A Letter to My Fellow White People.” The Week , 22 Apr. 2016, theweek.com/articles/619242/letter-fellow-white-people.

Linly, Zack. "It’s Time to Stop Talking about Racism with White People." The Washington Post, 7 Sep. 2016. https://www.washingtonpost.com/posteverything/wp/2016/09/07/its-time-to- stop-talking-about-racism-with-white-people/.

Loubriel, Jennifer. “4 Ways White People Can Process Their Emotions Without Bringing the White Tears.” Everyday Feminism , 12 Feb. 2016, everydayfeminism.com/2016/02/white- people-emotions-tears/.

Luvvie. “9 Things White People Can Do to Fight Racism Now.” Awesomely Luvvie , 22 Sept. 2016, www.awesomelyluvvie.com/2016/09/white-people-anti-racism.html.

Mathis-Lilley, Ben. “A Quick Summary of Why Only White People Want to Make America Great ‘Again’". Slate , 22 Feb. 2016, slate.com/news-and-politics/2016/02/white-people- want-to-make-america-great-again.html.

Mirk, Sarah, and Bitch Media. “Donald Trump Is a White People Problem.” Bitch Media , 9 Nov. 2016, bitchmedia.org/article/donald-trump-white-people-problem.

Moezzi, Melody. “Ten Things White People Need to Quit Saying.” The Huffington Post, Apr 27. 2016. https://www.huffpost.com/entry/ten-things-white-people-n_b_9765436.

Moss, Candida. “ Owes Very Little to 'White' People.” , 24 July 2016, www.thedailybeast.com/articles/2016/07/24/christianity-owes-very-little-to-white- people.html.

*http://www.freepresshouston.com/dear-fellow-white-people-shut-up-right-now/ This link has been taken down. The article is referenced here: Newstral.com. “The Huffington Post (UK): ‘Dear White People.’” Newstral , newstral.com/en/article/en/1032523095/dear-white-people.

Ogunyinka, Taiwo. “Dreadlocks Are Not OK for White People to Have. Period.” The Tab , 2016, thetab.com/2016/04/05/dreadlocks-white-people-83996.

Packnett, Brittany. “White People: What Is Your Plan for the Trump Presidency?” Vox , 14 Nov. 2016, www.vox.com/first-person/2016/11/14/13626404/trump-election-protest.

Pickens, Josie. “Dear White People: Locs Are Not ‘Just Hair.’” EBONY , 06 Apr. 2016, www.ebony.com/style/justin-bieber-locs#axzz4cv6SchoR.

The Race Card. “I Told Y'all-White People Don't Give A Fuck.” AFROPUNK , 16 Nov. 2016, afropunk.com/2016/11/i-told-yall-white-people-dont-give-a-fuck/.

200

Radke, Bill, et al. “White People, It's Time to Realize You're White. And That Matters.” KUOW , 25 Oct. 2018, kuow.org/post/white-people-its-time-realize-youre-white-and-matters.

Rigby, Kevin. “White People Have No Place in Black Liberation.” Racebaitr.com , 31 Mar. 2016, racebaitr.com/2016/03/31/white-people-no-place-black-liberation/.

Rosario, Justin. “White People Have Gone Insane.” The Daily Banter , 28 Nov. 2016, thedailybanter.com/2016/11/white-people-insane/.

Shackelford, , and Da'Shaun Harrison. “For White People Who Want to Attend #BlackLivesMatter Protests.” Wear Your Voice , 13 Jul. 2016, wearyourvoicemag.com/identities/race/white-people-blacklivesmatter-protests.

Young, Damon. “Dear White People: Here's 10 Ways to Tell If A Black Person Actually Likes You.” Very Smart Brothas , 4 Mar. 2016, verysmartbrothas.theroot.com/dear-white- people-heres-10-ways-to-tell-if-a-black-per-1822523163.

Young, Damon. “I'm Tired of Good White People.” GQ , 11 Nov. 2016, www..com/story/damon-young-the-good-white-people.

Warfield, Zenobia Jeffries. “White People, It's the Bigots Where You Live That Need You the Most.” Yes! Magazine , 13 Nov. 2016, www.yesmagazine.org/opinion/2016/11/13/white- people-it2019s-the-bigots-where-you-live-that-need-you-the-most/.

Watkins, D. “Dear Hard-Working White People: Congratulations, You Played Yourself.” Salon , 20 Nov. 2016, www.salon.com/2016/11/20/dear-hard-working-white-people- congratulations-you-played-yourself/.

Wood, Zachary. “Why White People Need to Talk About Racism Too.” The Nation , 5 Aug. 2016, www.thenation.com/article/why-white-people-need-to-talk-about-racism-too/.

2016 “Anti-Critical” Content

Ciccotta, Tom. “ Student Art Display Tells White People to 'Do Something'.” Breitbart , 26 Sept. 2016, www.breitbart.com/tech/2016/09/26/temple-university-student- art-display-tells-white-people-to-do-something/.

Dash, Stacey. Racist Speech at BET Awards Attacked White People . Patheos , 29 June 2016, www.patheos.com/blogs/staceydash/2016/06/racist-speech-at-bet-awards-attacked-white- people/.

Goins-Phillips, Tre. “Caught on Video: Literally Orders 'White People' to the 'Back' of Protest.” TheBlaze , 10 Jan. 2020, www.theblaze.com/news/2016/07/27/caught- on-video-black-lives-matter-literally-orders-white-people-to-the-back-of-protest.

201

Kovacs, Joe. “Milwaukee Madness: White People 'Hunted' for Attacks.” WND , 16 Aug. 2016, www.wnd.com/2016/08/milwaukee-madness-white-people-targeted-for-attacks/.

O'Neill Editor, Brendan. “Everyone Needs to Stop Talking about 'White People'.” Spiked , 18 Nov. 2016, www.spiked-online.com/2016/11/18/everyone-needs-to-stop-talking-about- white-people/.

Pfeiffer, Alex. “Drexel Professor Has A History of Hating White People and Wishing for Their Genocide.” , 26 Dec. 2016, dailycaller.com/2016/12/25/drexel-professor- has-a-history-of-hating-white-people-and-wishing-for-their-genocide/.

Prestigiacomo, Amanda. “Can White People Be Victims of Racism? Here's What You Learn If You Google That.” The Daily Wire , 4 Oct. 2016, www.dailywire.com/news/can-white- people-be-victims-racism-heres-what-amanda-prestigiacomo.

The Real Strategy. “Police Killed 5 White People the Same Day as Keith Scott.” ' InfoWars , 26 Sept. 2016, www..com/police-killed-5-white-people-the-same-day- as-keith-scott/.

*https://heatst.com/culture-ward/mass-effect-designer-manveer-heir-hates-whites-people-and- wants-the-world-to-know/ This link has been taken down. The article is referenced here: “r/KotakuInAction - Mass Effect Designer Manveer Heir Hates White People and Wants the World to Know.” , 2017, www.reddit.com/r/KotakuInAction/comments/4wt6k9/mass_effect_designer_manveer_hei r_hates_white/.

2016 Ambivalent / Random / Not “Critical” Content

“15 White People Really Doubling Down on Their Whiteness: Funny Roasts, Roast Me, Hilarious.” , 2016, www.pinterest.com/pin/395402042270366659/.

*https://ageofshitlords.com/there-are-people-who-believe-that-a-black-scientist-created-white- people/ Taken down. Unable to locate.

Adams, Genetta. “The 8 Wokest White People We Know.” The Root , 7 Jul. 2016, www.theroot.com/the-8-wokest-white-people-we-know-1790856096.

Bates, Grigsby. “This Week in Race: Sisterhood, Immigration, And The 'Official Shoe Of White People'.” NPR , 18 Nov. 2016, www..org/sections/codeswitch/2016/11/18/502565211/this-week-in-race-sisterhood- immigration-and-the-official-shoe-of-white-people.

202

Burnham, Bo. “Bo Burnham – White People/Faggot.” Genius , 2016, genius.com/Bo-burnham- white-people-annotated.

* http://www.collegehumor.com/post/7039003/15-white-people-really-doubling-down-on-their -whiteness This link has been taken down. The article is referenced here: “CollegeHumor.” YouTube , www.collegehumor.com/post/7039003/15-white-people- really-doubling-down-on-their-whiteness.

Crucchiola, Jordan. “Why White People Need Beyoncé.” GOOD , GOOD, 29 Apr. 2016, www.good.is/articles/why-white-people-need-beyonce.

Egan, Timothy. “The Other White People.” , 18 Nov. 2016, www.nytimes.com/2016/11/19/opinion/the-other-white-people.html.

Emrys, Ruthanna, and Anne M. Pillsworth. “Looking for an Original Sin: Arthur Machen's ‘The White People.’” Tor.com , 23 Sept. 2016, www.tor.com/2016/06/22/looking-for-an- original-sin-arthur-machens-the-white-people/.

Farsad, Negin. 2016. “How To Make White People Laugh.” Grand Central Publishing: New York. https://www.amazon.com/dp/B0169ATKEG/ref=dp-kindle- redirect?_encoding=UTF8&btkr=1

Fidel, Emma. “White People Voted to Elect Donald Trump.” Vice , 9 Nov. 2016, news.vice.com/story/white-people-voted-to-elect-donald-trump.

Giorgis, Hannah. “How White People Can Support People of Color Now.” BuzzFeed News , BuzzFeed News, 22 Nov. 2016, www.buzzfeed.com/hannahgiorgis/how-white-people-can- support-people-of-color-right-now?utm_term=.wxgQQZVg4D#.cfx77AbZ26.

Glasser, Susan B., and Glenn Thrush. “What's Going on With America's White People?” Magazine , 2016, www.politico.com/magazine/story/2016/09/problems-white- people-america-society-class-race-214227.

* http://historybuff.com/robert-mugabes-love-hate-relationship-with-white-people-1- ONIKD06kAaMB Taken down. Unable to locate

Iandoli, Kathy. “White People Who Love Black Culture: Now Is the Time to Speak Up.” BET.com , 7 July 2016, www.bet.com/music/2016/07/07/white-people-black-culture-speak- up.html.

Mather, Katie. “42 Things All Generic White People Love to Do.” Thought Catalog , 29 Sept. 2016, thoughtcatalog.com/katie-mather/2016/09/42-things-all-generic-white-people-love- to-do/.

203

McGill, Andrew. “Why White People Don't Use White Emoji.” , Atlantic Media Company, 10 May 2016, www.theatlantic.com/politics/archive/2016/05/white-people- dont-use-white-emoji/481695/.

McWhorter, John. “We Need a 'PC' That Includes White People.” CNN , 25 Nov. 2016, www..com/2016/11/25/opinions/new-pc-should-include-white-people-mcwhorter/.

Morse, Brandon. “1990's Kenyan Video Surfaces of Obama Complaining About White People, Refusing to Tour with Them.” RedState , 4 Oct. 2016, www..com/brandon_morse/2016/10/04/1990s-kenyan-video-surfaces-obama- complaining-white-people-refusing-tour/.

“NZ Wine Navigator.” NZ Wine Navigator , 2016, wherewhitepeoplemeet.com/.

Quarles, Derius. “To Be A Young Black Man Working In An Office Full of White People.” Hackernoon , 5 Apr. 2016, hackernoon.com/to-be-a-young-black-man-working-in-an- office-full-of-white-people-fcf137d152c8.

Sanburn, Josh. “White Deaths Exceed Births in 17 US States.” Time , 29 Nov. 2016, time.com/4585232/white-deaths-exceed-births-united-states/.

Sanders, Topher. “'Only White People,' Said the Little Girl.” ProPublica , 10 Oct. 2016, www.propublica.org/article/only-white-people-said-the-little-girl.

*http://www.showingupforracialjustice.org/six_ways Taken down. Unable to locate.

Snider, Mike. “New Balance Sprint from 'White People' Shoes Tag Spotlights PR Dilemma.” USA Today , 17 Nov. 2016, www.usatoday.com/story/money/business/2016/11/17/new- balance-sprint-white-people-shoes-tag-spotlights-pr-dilemma/93954192/.

Stafford, Zach. “That Dating Site for White People? It's Racist, No Matter How It's Justified.” , 8 Jan. 2016, www.theguardian.com/commentisfree/2016/jan/08/where- white-people-meet-online-dating-relationship-racism.

Ufc. “White People Dancing GIFs - Find & Share on GIPHY.” GIPHY , giphy.com/search/white- people-dancing.

Wire, SI. “Embiid Learned to Shoot from Videos of 'White People'.” Sports Illustrated , 26 Oct. 2016, www.si.com/nba/2016/10/26/joel-embiid-76ers-learned-shoot-videos-white-people.

Workneh, Lilly. “6 Eye-Opening Facts About How Differently Black and White People View Race.” The Huffington Post , 6 Jun. 2016, www.huffingtonpost.com/entry/6-eye-opening- facts-about-how-differently-black-and-white-people-view- race_us_5773f678e4b0eb90355d1234.

204

“Www.WhiteAccomplices.org.” Whiteaccomplices , 2016, www.whiteaccomplices.org/.

Young, Zoe. “Nine Ways White People Can Support #BlackLivesMatter.” Medium , The Bold Italic, 25 Aug. 2016, thebolditalic.com/nine-ways-white-people-can-support- blacklivesmatter-bc2920b904cf.

2016 News / History Content

Faulders, Katherine. “Rep. Clarifies Remarks About 'White People' Doing More for Civilization.” ABC News , 19 Jul. 2016, abcnews.go.com/Politics/rep-steve-king-clarifies- remarks-white-people-civilization/story?id=40687050.

Goldberg, Lesley. “'Dear White People' TV Series Based on Movie a Go at Netflix.” The Reporter , 5 May. 2016, www.hollywoodreporter.com/live-feed/dear-white- people-tv-series-890882.

Kristof, Nicholas. “It's Time for White People to Wake up from Mass Delusion.” The Seattle Times , 14 Jul. 2016, www.seattletimes.com/opinion/its-time-for-white-people-to-wake-up- from-mass-delusion/.

“Students Selling 'All White People Are Racist' Hoodies Sparks Controversy.” ABC13 Houston , 14 Oct. 2016, abc13.com/1554761/.

“'What Have White People Ever Done for Us?'.” BBC News , 11 Jul. 2016, www..com/news/blogs-trending-36765111.

2016 White Nationalist Content NA

2015 “Critical” Content

Bell, Kamau. “Analysis: In 2016, White People Must Take Responsibility for Donald Trump.” Times , 30 Dec. 2015, www.latimes.com/politics/la-pol-prez-white-people- responsibility-trump-20151230-story.html.

Biddle, Sam. “White People Control All the Power in Silicon Valley.” Gawker , 6 Oct. 2015, gawker.com/white-people-control-all-the-power-in-silicon-valley-1735006694.

Brown, Kara. “Notable Moments in White People Taking Credit for Discovering Things (Columbusing).” Jezebel , 12 Oct. 2015, jezebel.com/notable-moments-in-white-people- taking-credit-for-disco-1735529106.

205

Clayton, Tracy. “35 Questions Black People Have for White People.” BuzzFeed , 9 Apr. 2015, www.buzzfeed.com/tracyclayton/how-and-why-and-what- for?utm_term=.yvMaapNPG4#.osLEEzQ8Mm.

D'Amour, Zon. “Confused White People Are Shocked ‘The Wiz!" Has an All Black Cast.” NewsOne , 3 Dec. 2015, newsone.com/3281350/confused-white-people-are-shocked-the- wiz-has-an-all-black-cast/.

Desmond-Harris, Jenee. “White People Have a Race - but Everyone Flips out When We Talk about It.” Vox , 24 Jul. 2015, www.vox.com/2015/7/24/9023721/white-whiteness-race- identity.

DiAngelo, Robin. “Why It's So Hard to Talk to White People About Racism.” The Huffington Post , 30 Apr. 2015, www.huffingtonpost.com/good-men-project/why-its-so-hard-to-talk- to-white-people-about-racism_b_7183710.html.

Dolan, Keyy. “Why White People Downplay Their Individual Racial Privileges.” Stanford Graduate School of Business , 27 Aug. 2015, www.gsb.stanford.edu/insights/why-whites- downplay-their-individual-racial-privileges.

Dwyer, Dustin. “Why All White People Are Racist, but Can't Handle Being Called Racist: the Theory of White Fragility.” State of Opportunity , 25 Mar. 2015, stateofopportunity.michiganradio.org/post/why-all-white-people-are-racist-cant-handle- being-called-racist-theory-white-fragility.

Ericksen, Adam. “Dear White People: Why I Am Racist and So Are You.” Sojourners , 24 Jun. 2015, sojo.net/articles/dear-white-people-why-i-am-racist-and-so-are-you.

Fequiere, Pedro. “21 Things White People Ruined In 2015.” BuzzFeed , 3 Dec. 2015, www.buzzfeed.com/pedrofequiere/stop-white-ppl- 2015?utm_term=.qywddrlDjv#.hqz77zG0BX.

Goldstein, Emily. “Yes, Diversity Is About Getting Rid of White People (And That's A Good Thing).” Thought Catalog , 26 May 2015, thoughtcatalog.com/emily- goldstein/2015/05/get-rid-of-white-people/.

Holloway, Kali. “10 Ways White People Are More Racist than They Realize.” Salon , 5 Mar. 2015, www.salon.com/2015/03/04/10_ways_white_people_are_more_racist_than_they_realize_p artner/.

Hughes, Jazmine. “How Many White People Does It Take to Ruin a Good Joke?” , 6 Feb. 2015, newrepublic.com/article/120916/white-people-are-co-opting-black- peoples-jokes-about-white-people.

206

Jensen, Robert. “Make It Right: Two Things White People Should Never Say.” Yes Magazine , 17 Jun. 2015, www.yesmagazine.org/issues/make-it-right/two-things-white-people-should- never-say.

Koutonin, Mawuna. “Why Are White People Expats When the Rest of Us Are Immigrants?” The Guardian , 13 Mar. 2015, www.theguardian.com/global-development-professionals- network/2015/mar/13/white-people-expats-immigrants-migration.

Lucas, Quentin. “Why I Don'tTalk To White People.” Racebaitr.com , 9 Oct. 2015, racebaitr.com/2015/10/09/why-i-dont-talk-to-white-people/#.

Massey, Alana. “Transforming White People Is Not the Job of Minority Students.” Pacific Standard , 10 Apr. 2015, psmag.com/transforming-white-people-is-not-the-job-of-minority- students-789b17a3df4c.

Metta, John. “I Don't Discuss Racism with White People.” PopularResistance.Org , 12 Jul. 2015, popularresistance.org/i-dont-discuss-racism-with-white-people/.

Porras, Franco Eugene Herminio. “White People, Please Don't Paint A Sugar Skull on Your Face This Halloween.” The Odyssey Online , 26 Oct. 2015, www.theodysseyonline.com/white- people-please-dont-paint-sugar-skull-on-your-face-this-halloween.

Schless-Meie, Adrien. “White People and The Zero-Sum Game of Racial Politics.” Talking Points Memo , 23 Apr. 2015, talkingpointsmemo.com/cafe/whiteness-and-the-zero-sum- game-of-racial-politics.

Seltzer, Sarah. “White People Are Flipping Out Because 'Selma' Isn't About Them.” Flavorwire , 9 Jan. 2015, flavorwire.com/498002/white-people-are-flipping-out-because-selma-isnt- about-them.

“Think About the White People?!” , 10 Mar. 2015, www.dailykos.com/story/2015/3/10/1369797/-Think-About-the-White-People.

Thomas, Michael. “New Study Explains Why White People Deny Their Privilege.” Exposing the Truth , 18 Jul. 2015, www.exposingtruth.com/new-study-explains-why-white-people-deny- their-privilege/.

Touré. “White People Explain Why They Feel Oppressed.” Vice , 17 Sept. 2015, www.vice.com/en_us/article/white-people-told-me-why-they-feel-they-oppressed-456.

Tran, Diep. “Keep Your Hands Off of My Kimono, White People.” AMERICAN THEATRE , 6 Jan. 2017, www.americantheatre.org/2015/09/18/keep-your-hands-off-of-my-kimono- white-people/.

207

Utt, Jamie. “Here Are the Real Reasons Why We White People Struggle to Admit That Racism Still Exists.” Everyday Feminism , 1 Jul. 2015, everydayfeminism.com/2015/07/why-white- people-struggle-with-racism/.

Weiss, Suzannah. “Why Is White Privilege So Hard to Accept?” Bustle , 14 Jul. 2015, www.bustle.com/articles/97064-why-white-people-dont-like-admitting-that-white- privilege-exists-according-to-science.

Woods, Baynard. “Only White People Can Save Themselves from Racism and White Supremacism.” The Washington Post , 19 June 2015, www.washingtonpost.com/posteverything/wp/2015/06/19/only-white-people-can-save- themselves-from-racism-and-white-supremacism/?utm_term=.229bd7013ce9.

Young, Damon. “The Difference Between 'White People' and 'White People'.” The Root , 3 Dec. 2015, www.theroot.com/the-difference-between-white-people-and-white-people- 1790861926.

Young, Damon. “White Tears, Explained, For White People Who Don't Get It.” Very Smart Brothas , 11 July 2015, verysmartbrothas.com/white-tears-explained-for-white-people-who- dont-get-it/.

2015 Anti-critical Content deWolf Smith, Nancy. “'White People' Review: Let's Talk About Race, or Not.” , Dow Jones & Company, 16 July 2015, www.wsj.com/articles/white-people- review-lets-talk-about-race-or-not-1437085200.

Anderson, Brian. “Rapper Azealia Banks Hates White People, Especially the Fat Ones.” SEO Tools , 17 Mar. 2015, downtrend.com/71superb/rapper-azealia-banks-hates-white-people- especially-the-fat-ones.

Bear, Fencing. “Talking Points: Three Cheers for White Men.” Talking Points: Three Cheers for White Men , 5 June 2015, fencingbearatprayer.blogspot.com/2015/06/talking-points-three- cheers-for-white.html.

Bennett, Casey. “This ‘No More White Men’ in Universities Article Disappeared Pretty Damn Quick.” The Libertarian Republic , 29 Mar. 2015, thelibertarianrepublic.com/this-no-more- white-men-in-universities-article-disappeared-pretty-damn-quick/.

Hasson, Peter. “Northeastern Allegedly Tells RAs That White Men Can't Be Oppressed.” Campus Reform , 14 Aug. 2015, www.campusreform.org/?ID=6728.

Heath, Krystal. “Baltimore Professor: White People Need to Personally Give All Their Money to Black People...” Louder With Crowder , 5 June 2015, louderwithcrowder.com/baltimore- professor-white-people-need-to-personally-give-all-their-money-to-black-people/.

208

McAllister, D.C. “Why White People Will Always Be Racists.” The Federalist , 15 July 2015, thefederalist.com/2015/07/10/why-white-people-will-always-be-racists/.

Meyers, Laura. “White Professor Calls All White People to Mass Suicide Over Slavery.” The Libertarian Republic , 2 July 2015, thelibertarianrepublic.com/white-professor-calls-all- white-people-to-mass-suicide-over-slavery/.

Neff, Blake. “Columbia Student Claims to Be Traumatized by Reading About White People.” The Daily Caller , 20 Nov. 2015, dailycaller.com/2015/11/20/columbia-student-claims-to- be-traumatized-by-reading-about-white-people/.

Peterson, Jesse Lee. “Dear White People: Your Days Are Numbered.” WND , 17 Nov. 2015, www.wnd.com/2015/11/dear-white-people-your-days-are-numbered/.

Richardson, Valerie. “Police Kill More Whites than Blacks, but Minority Deaths Generate More Outrage.” , 21 Apr. 2015, www.washingtontimes.com/news/2015/apr/21/police-kill-more-whites-than-blacks-but- minority-d/.

Robinson, Wills. “MTV to Air White People Documentary Hosted by Jose Antonio Vargas.” Online , Associated Newspapers, 10 July 2015, www.dailymail.co.uk/news/article-3155570/MTV-set-air-controversial-new-documentary- hosted-illegal-immigrant-called-White-People-challenging-whiteness-slams-privileged- whites-America.html.

Shapiro, Ben. “Why White People Seek Black Privilege.” Breitbart , 20 Aug. 2015, www.breitbart.com/big-government/2015/08/19/why-white-people-seek-black-privilege- 2/.

Soave, Robby. “Diversity Officer Who Tweeted 'Kill All White Men' Deserves Ridicule, Not Arrest.” Reason.com , 19 May 2015, reason.com/2015/05/19/diversity-officer-who- tweeted-kill-all-w/.

Southern, Lauren. “White People: a Bad Racial Group?” Spiked , 28 July 2015, www.spiked- online.com/newsite/article/white-people-a-bad-racial-group/17236#.WOJoTG8rJac.

Timpf, Katherine. “University Report: A Room Full of White People Is a Microaggression.” , 12 May 2015, www.nationalreview.com/article/418273/university- report-room-full-white-people-microaggression-katherine-timpf.

*http://www.vocativ.com/206128/white-men-pictured-face-down-on-the-ground-seek-to-make-a a-statement/ Taken down. Unable to locate.

209

“White Student Turned Away from 'Stop White People' Multicultural Luncheon.” The College Fix , 9 Nov. 2015, www.thecollegefix.com/post/24980/.

Yiannopoulos, Milo. “Why Equality and Diversity Departments Should Only Hire Rich, Straight White Men.” Breitbart , 29 Dec. 2015, www.breitbart.com/tech/2015/12/29/why-equality- and-diversity-departments-should-only-hire-rich-straight-white-men/.

(Stopped recording Ambivalent/Random/Not Critical,” “News/History” results)

2015 White Nationalist Content

Le, Patrick. “Patrick Le Brun, ‘Career Advice for Young White Men’: Counter-Currents Publishing.” Counter , 6 Feb. 2015, www.counter-currents.com/2015/02/career-advice-for- young-white-men/.

*http://www.radixjournal.com/journal/2015/2/14/the-roots-of-the-white-man Taken down. Unable to locate.

Reed, Fred. “Are White Men Gods? (II).” The Unz Review , 25 June 2015, www.unz.com/freed/are-white-men-gods-ii/.

2010 “Critical” Content

Abagond. “Why Some Black Women Prefer Not to Date Some White Men.” Abagond , 9 Feb. 2010, abagond.wordpress.com/2010/02/08/why-some-black-women-prefer-not-to-date- some-white-men/.

Ambirge, Ash. “White Men Can't Jump, But They've Got Other Tricks Up Their Sleeves: The (Unearned) Privilege of Being White.” The Middle Finger Project , 26 Apr. 2010, www.themiddlefingerproject.org/white-men-cant-jump-but-theyve-got-other-tricks-up- their-sleeves-the-unearned-privilege-of-being-white/.

Cheadle, H. “Why White People in My Neighborhood Suck.” Why White People in My Neighborhood Suck , 7 June 2010, cheadlesucks.blogspot.com/2010/06/why-white-people- in-my-neighborhood.html.

Dreyfuss, Joel. “White People Have Lost Their Minds.” The Root , 29 Sept. 2010, www.theroot.com/white-people-have-lost-their-minds-1790881074.

Horn, Heather. “Have White People Gone 'Insane'?” The Atlantic , 7 Oct. 2010, www.theatlantic.com/national/archive/2010/10/have-white-people-gone-insane/343891/.

210

Jensen, Robert. “What White People Fear.” Yes! Magazine , 5 Mar. 2010, www.yesmagazine.org/issue/america-remix/opinion/2010/03/05/what-white-people-fear/.

“What Are White People So Afraid Of?” NewsOne , 12 Oct. 2010, newsone.com/801505/what- are-white-people-so-afraid-of/.

“Why Americans Suck: #1: White People.” Why Americans Suck , 6 July 2010, whyamericanssuck.blogspot.com/2010/07/1-white-people.html.

Wade, Lisa. “Should White Men Feel Guilty? - Sociological Images.” Sociological Images Should White Men Feel Guilty Comments , 6 Dec. 2010, thesocietypages.org/socimages/2010/12/06/should-white-men-feel-guilty-for-the-sins-of- their-fathers/.

“White People Embarrass Me.” Scavenger , 8 Mar. 2010, www.thescavenger.net/glbtiq-sp- 1239/glbtiq/208-white-people-embarrass-me-95345.html.

“White People Lack Empathy for Brown People, Brain Research Shows.” Restructure! , 8 May 2010, restructure.wordpress.com/2010/05/04/white-people-lack-empathy-for-brown- people-brain-research-shows/.

White Women Are Rejecting White Men . 2010, www.your-inner- voice.com/White_Women_are_Rejecting _White_Men.html.

Wise, Tim. “An Open Letter to the White Right, On the Occasion of Your Recent, Successful Temper Tantrum.” Tim Wise , 5 Nov. 2010, www.timwise.org/2010/11/an-open-letter-to- the-white-right-on-the-occasion-of-your-recent-successful-temper-tantrum/.

2010 “Anti-Critical” Content

“EDITORIAL: Blame the White Man.” The Washington Times , 5 May 2010, www.washingtontimes.com/news/2010/may/05/blame-the-white-man/.

Joyner, James. “Does Bill Gates Hate White People?” Outside the Beltway , 22 Apr. 2010, www.outsidethebeltway.com/does_bill_gates_hate_white_people/.

2010 “White Nationalist” Content

“Black Police Officers Organizing Gang of White Men in South Africa.” Conservative Headlines , 15 Mar. 2010, conservative-headlines.com/2010/03/black-police-officers- organizing-gang-rapes-of-white-men-in-south-africa/.

211

“Botswana Daily Paper Asks: ‘What If There Were No White People?".” Conservative Headlines , 5 Feb. 2010, conservative-headlines.com/2010/02/botswana-daily-paper-asks- what-if-their-were-no-white-people/.

Duke, David. “Civil Rights for White People.” Davidduke.com, 21 Apr. 2010, http://davidduke.com/civil-rights-for-white-people/. Incog Man. “Arm Yourselves Now White People!” Incogman.net, 25. Oct. 2010, http://incogman.net/2010/10/arm-yourselves-now-white-people/.

2005 “Critical” Content

Bernabe, Sandy. “Racist Alliance - Whiteness.” Anti-Racist Alliance , 2005, antiracistalliance.com/whiteness.html.

Hardisky, Jean. “ and Structural Racism: A Letter to White People: Past Years: Opinion: News & Events Wellesley Centers for Women.” Wellesley Centers for Women , Oct. 2005, www.wcwonline.org/Past-years/hurricane-katrina-and-structural-racism-a- letter-to-white-people.

Jensen, Robert. “The Fears of White People.” Mercer Community College , 8 Sept. 2005, www.mccc.edu/pdf/cmn214/Class%208/The%20fears%20of%20white%20people.pdf.

Jones, Bomani. “‘Gee Baba, Why Don't White People Write About Racism?" ~ Bomani Jones: TV Host: Sports Radio: Media Personality.” Bomani Jones , 3 Nov. 2005, www.bomanijones.com/gee-baba-why-dont-white-people-write-about-racism/.

Kirkey, Jason. “ for White People - Jason Kierkey.” Healing from Whiteness , 8 Dec. 2005, healingfromwhiteness.blogspot.com/2005/12/decolonization-for-white-people- jason.html.

*http://www.makezine.enoughenough.org/mohawksdreads.htm Taken down. Unable to locate.

Moore, Michael. “Excerpts from the Book Stupid White Men and Other Sorry Excuses for the State of the Nation by Michael Moore.” Third World Traveler , 2005, www.thirdworldtraveler.com/Michael_Moore/Stupid_White_Men.html.

Thompson, Cooper. “White Men and the Denial of Racism.” Reading for Diversity and Social Justice , 2000, nuernbergcounseling.com/wp- content/uploads/2019/02/White_Men_And_The_Denial_Of_Racism.pdf.

Tony. “More Proof That White People Are the Biggest Thieves on the Planet.” More Proof That White People Are the Biggest Thieves on the Planet , 28 Dec. 2005, www.tonyskansascity.com/2005/12/more-proof-that-white-people-are.html.

212

“White People and Dreadlocks: A Problematic Union.” Angry White Kid , 22 June 2005, angrywhitekid.blogs.com/weblog/2005/06/white_people_an.html.

2005 “Anti-Critical” Content “It's Not Racist, Unless You're White [Taking Back the Moral High Ground].” Ironbark Resources , 2005, ironbarkresources.com/taking/takingback16.htm.

WND. “Professor: Exterminate White People.” WND , 22 Oct. 2005, www.wnd.com/2005/10/32984/.

2005 White Nationalist Content

H., William. “Jews Murdered 600,000,000 White Men in the Twentieth Century!!!” Jews Murdered 600,000,000 White Men in the Twentieth Century!!! , 13 July 2005, www.vanguardnewsnetwork.com/v1/2005/WilliamH600m071305.htm.

Sailer, Steve. “Blond Bad Guys-Hollywood's Other Obsession: Articles.” VDARE.com , 19 June 2005, www.vdare.com/articles/blond-bad-guys-hollywoods-other-obsession.

The Shadow. “Why White People Don't Stick Together.” Why White People Don't Stick Together , 28 Sept. 2005, www.vanguardnewsnetwork.com/v1/2005/ShadowStick0905.htm.

Slattery, Patrick. “White People Discovered in .” .com , 25 Aug. 2005, davidduke.com/white-people-discovered-in-michigan/.

2000 “Critical” Content

Kivel, Paul. “Young White Men: Scared, Entitled, and Cynical -- A Deadly Combination.” Challenging Male Supremacy.org , Apr. 2000, challengingmalesupremacy.org/wp- content/uploads/2015/04/Young-White-Men-Paul-Kivel.pdf.

Moore, Michael. “Stupid White Men and Other Sorry Excuses for the State of the Nation.” Sajtichek Narod , 2000, sajtichek.narod.ru/books/without_translation/stupid_white_men_michael_moore.pdf.

Walker, Alice. “What Can the White Man Say to the Black Woman?” Alice Walker, What Can the White Man Say to the Black Woman? 25 Feb. 2000, www.hartford- hwp.com/archives/45a/584.html.

213

Wise, Tim. “Getting Serious About White Deviance: An Open Letter to the Pioneer Fund.” Tim Wise , 3 Nov. 2000, www.timwise.org/2000/11/getting-serious-about-white-deviance-an- open-letter-to-the-pioneer-fund/.

Wise, Tim. “Santa, Jesus and the Symbolism of White Supremacy.” Race, Racism and the Law , 20 Dec. 2000, racism.org/index.php?option=com_content&view=article&id=374%3Awhite07a2&catid=6 6%3Awhite-european-american&Itemid=237.

2000 “Anti-Critical” Content

Walsh, Joan. “Confessions of a Former Self-Hating White Person.” Salon , 17 Feb. 2000, www.salon.com/2000/02/17/guilty/.

2000 White Nationalist Content

Morse, John. “Will Whites Commit Race Suicide? .” Spearhead.co , 2000, www.spearhead.co.uk/0101-jm.html.

The National Alliance. “The Killing Of Whites In South Africa...And America's Silence.” Rense.com , 28 Sept. 2000, www.rense.com/general29/silence.htm.

214

Pre-treatment Questionnaire:

Please tell us about yourself.

1. Please indicate your gender. - Male - Female

2. In what year were you born?

3. In which state do you live?

4. Please indicate your primary race/ethnicity. - White - Black or African American - Hispanic or Latino(a) - Asian - Native Hawaiian or Pacific Islander - American Indian or Alaska Native - Middle Eastern or North African - Other

5. Please indicate your family’s approximate annual household income. - Less than $20,000 - $20,000 to $29,999 - $30,000 to $39,999 - $40,000 to $49,999 - $50,000 to $59,999 - $60,000 to $74,999 - $75,000 to $99,999 - $100,000 to $149,999 - $150,000 or more

6. Please indicate the highest level of education you have completed. - Less than high school - High school graduate - high school diploma - Some college, no degree - Associate degree - Bachelor’s degree - Masters or professional degree - Doctorate degree

7. In general, do you think of yourself as… - Extremely liberal

215

- Liberal - Slightly liberals - Moderate / middle of the road - Slightly conservatives - Conservative - Extremely conservative

Although there are a number of qualities that people feel that children should have, every person thinks that some are more important than others. We are going to show you pairs of desirable qualities. Please say which items are more important for children to have:

8. Obedience or self-reliance - Obedience - Self-reliance

9. Independence or respect for elders - Independence - Respect for elders

10. Curiosity or good manners - Curiosity - Good manners

11. Considerate or well behaved - Considerate - Well behaved

The following questions speak to the topic of identity. 12. How important is being white to your identity? - Extremely important - Very important - Moderately important - A little important - Not important at all

13. How important is being American to your identity? - Extremely important - Very important - Moderately important - A little important - Not important at all

Please answer the following values questions:

216

14. If people were treated more equally in this country we would have many fewer problems. - Strongly agree - Agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Disagree - Strongly disagree

15. Our society should do whatever is necessary to make sure that everyone has an equal opportunity to succeed. - Strongly agree - Agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Disagree - Strongly disagree

16. This country would be better off if we worried less about how equal people are. - Strongly agree - Agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Disagree - Strongly disagree

17. The world is always changing and we should adjust our view of moral behavior to those changes. - Strongly agree - Agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Disagree - Strongly disagree

18. The newer lifestyles are contributing to the breakdown of our society. - Strongly agree - Agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Disagree - Strongly disagree

217

19. We should be more tolerant of people who choose to live according to their own moral standards, even if they are very different from our own. - Strongly agree - Agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Disagree - Strongly disagree

20. This country would have many fewer problems if there were more emphasis on traditional family ties. - Strongly agree - Agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Disagree - Strongly disagree

21. We should try to get ahead by any means necessary. - Strongly agree - Agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Disagree - Strongly disagree

22. Sometimes war is necessary to put other nations in their place. - Strongly agree - Agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Disagree - Strongly disagree

23. Winning is more important than how the game is played. - Strongly agree - Agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Disagree - Strongly disagree

218

24. Inferior groups should stay in their place. - Strongly agree - Agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Disagree - Strongly disagree

219

Survey Primes

Please read the following and tell us if you think it is written well.

“10 Things White People Seem to Not Understand (Because, White Privilege)” By contributing author

I don’t wake up every morning with the intention of pissing you off, I swear, and whether or not you believe it, I’m here to help you. I want you to recognize that on a daily basis, you hold a set of advantages and immunities that are a direct result of the oppression of people of color. That doesn’t sound nice, does it? Makes you squirm in your chair a bit and maybe feel a little uncomfortable, right?

But here’s the thing – I’m not here to make you feel comfortable, that’s not my job. I’m here to erase the invisibility of the privileges you have that continue to help maintain white supremacy. I’m here to show you what your White Privilege is. 1. White Privilege is being able to move into a new neighborhood and being fairly sure that your neighbors will be pleasant to you and treat you with respect.

2. White Privilege is being able to watch a movie, read a book and open the front page of a newspaper and see yourself and your race widely represented and spoken for.

3. White Privilege is being able to seek legal, financial and medical help without having your race work against you.

4. White Privilege is living in a world where you are taught that people with your skin tone hold the standard for beauty.

5. White Privilege is arrogantly believing that reverse racism actually exists.

6. White Privilege is being pulled over or taken aside and knowing that you are not being singled out because of your race/color.

7. White Privilege is not having to teach your children to be aware of systematic racism for their own protection.

8. White Privilege is not having to acknowledge the fact that we live in a system that treat people of color unfairly politically, socially and economically and choosing, instead, to believe that people of color are inherently less capable.

9. White Privilege is not having your people and their culture appropriated, romanticized or eroticized for the gain and pleasure of other white people.

10. White Privilege is being able to ignore the consequences of race.

220

Please read the following and tell us if you think it is written well.

“The Fallacy of White Privilege” By contributing author A pillar of contemporary Leftism is the notion of “white privilege.” It turns out to be largely meaningless and, more significantly, it does great harm to blacks. First, no reasonable person can argue that white privilege applies to the great majority of whites, let alone to all whites. There are simply too many variables other than race that determine individual success in America. And if it were true, why would whites commit suicide at twice the rate of blacks? Second, there are a host of privileges that dwarf “white privilege.” A huge one is Two-Parent Privilege. If you are raised by a father and mother, you enter adulthood with more privileges than anyone else in American society, irrespective of race, ethnicity, or sex. That’s why the poverty rate among two-parent black families is only 7 percent. Compare that with a 22 percent poverty rate among whites in single-parent homes. Obviously the two-parent home is the decisive “privilege.” There are even times when there is “minority privilege” in America today. Every high-school student knows that given similar scholastic and extra-curricular records, one’s chances of being accepted into a prestigious college are considerably greater if one is a member of a minority. So then why all this left-wing talk about white privilege? The major reason is in order to portray blacks as victims. The Left knows that the only way to retain political power in America is to perpetuate the belief among black Americans that their primary problem is white racism. White privilege is another left-wing attempt, and a successful one, to keep America from focusing on what will truly help black America — a resurrection of the black family, for example — and instead to focus on an external problem: white privilege. In doing so, the Left has become the only real enemy blacks have in America today.

221

Please read the following and tell us if you think it is written well.

“Racism and the Indelible Impact of Personal Experience” By contributing author

Let’s begin with a thought experiment. Imagine for a moment that one out of every 20 Americans was an outright, died-in-the-wool racist. That estimate may be low, or it may be high, but we know that some people are in fact bigots.

If you’re black, by the simple law of averages you’ll end up interacting with racists at some point in your daily life, and some of those bigots will make their views plain. They may treat you differently in a store, they may drive past you in a taxi (by the way, many taxi-driver bigots aren’t white), or they may actively discriminate against you at work. Those encounters will naturally make you suspicious that racial motivations may lurk behind other negative life events.

If you’re white, by contrast, unless your social circle includes open racists, the odds are that you’ll not only never see racism — you may never even have a conversation with a person who admits to their bigotry. Expressing overtly discriminatory thoughts is a ticket straight out of polite white society, so even racists tend to keep their thoughts to themselves. Having never seen bigotry — and, in contrast, often seeing affirmative-action programs that actually disadvantage you or your kids — many white Americans are naturally suspicious of claims that racism is a material factor holding back black Americans.

In the years after my wife and I (we are both white) adopted a black child, we’ve experienced more racially charged moments than in our previous 41 years combined — and it runs both ways. Yes, there’s the vicious online hate — first from the identity-politics Left expressing disgust at white conservatives raising a black child, then from the alt-right sharing the same view — but there have also been a number of surprising in-person incidents. There was the father who warned his child not to play with black kids. There was the lady who demanded proof that our child belonged in a neighborhood pool — a pool that had guarded entry, and every child wore an armband.

I don’t want to give a wrong impression. Our largely white and Christian community has thoroughly embraced our daughter. She’s surrounded with love and friendship. But those few racially charged moments do hurt, and she’s starting to notice when they happen. My daughter is in the 71 percent of black Americans who can report experiencing racism, and it does indeed color her view of the world.

I don’t know the path forward, but I know that America is a place where my daughter will likely enjoy more freedom and opportunity than in any other nation on earth. She will also almost certainly confront racism and rejection simply because of the color of her skin. Both things can be true at the same time, and we can thus be encouraged and discouraged.

222

Please read the following and tell us if you think it is written well.

“Social media 'outstrips TV' as news source for young people” By contributing author Social media has overtaken television as young people's main source of news, according to a report.

Of the 18-to-24-year-olds surveyed, 28% cited social media as their main news source, compared with 24% for TV. The Institute for the Study of research also suggests 51% of people with online access use social media as a news source. This trend and the rising use of mobile phones to access news are undermining traditional business models. The report, now in its fifth year, is based on a YouGov survey of about 50,000 people across 26 countries, including 2,000 Britons.

In its introduction, the report says "a second wave of disruption" has hit news organizations around the world, with "potentially profound consequences both for publishers and the future of news production". For older media organizations struggling to find a profitable path in the online era, there is little comfort to be found in this report. Under 10% of readers in English-speaking countries have paid anything for online news in the past year - so advertising looks the only sustainable business model. No wonder, then, that the march of the ad-blockers is seen by some news businesses as a threat to their very survival. And while there still seems to be a big appetite for news, it is to social-media platforms that users are increasingly turning to find it.

The report also suggests users are noticing the original news brand behind social media content less than half of the time, something that is likely to worry traditional media outlets. Such outlets "cannot afford to ignore social media", said Mr. Newman, but he admitted that created a dilemma.

"In doing so, they risk losing control of content and that relationship with the reader which can drive them to other content, so they have to balance using social media platforms with building up a loyal user base of their own," he said.

223

Post-treatment Questionnaire

1. How would you rate what you just read ? - It was poorly written - It was moderately well written - It was well written - It was very well written

Some people say the following things are important for being truly American. Others say they are not important. How important do you think each of the following is?

2. To have been born in America - Extremely important - Very important - Moderately important - A little important - Not important at all

3. To have lived in America for most of one's life - Extremely important - Very important - Moderately important - A little important - Not important at all

4. To be a Christian - Extremely important - Very important - Moderately important - A little important - Not important at all

5. To be able to speak English - Extremely important - Very important - Moderately important - A little important - Not important at all

6. To have American citizenship - Extremely important - Very important - Moderately important - A little important

224

- Not important at all

7. To feel American - Extremely important - Very important - Moderately important - A little important - Not important at all

8. To respect America's political institutions and laws - Extremely important - Very important - Moderately important - A little important - Not important at all

9. To have American ancestry - Extremely important - Very important - Moderately important - A little important - Not important at all

10. To be white - Extremely important - Very important - Moderately important - A little important - Not important at all

Please answer the following questions about race in America today: 11. How important is it that whites work together to improve the position of their group? - Extremely important - Very important - Moderately important - A little important - Not important at all

12. How important is it that whites work together to change laws that are unfair to whites? - Extremely important - Very important - Moderately important - A little important - Not important at all

225

13. How likely is it that many whites are unable to find a job because employers are hiring minorities instead? - Extremely likely - Very likely - Moderately likely - Slightly likely - Not likely at all

14. How likely is it that many whites are not accepted to some colleges because these colleges are admitting minorities instead? - Extremely likely - Very likely - Moderately likely - Slightly likely - Not likely at all

15. Irish, , Jewish and many other minorities overcame prejudice and worked their way up. Blacks should do the same without any special favors. - Strongly agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Strongly disagree

16. Survey researchers are interested in whether or not respondents carefully read survey questions. Please mark “Somewhat agree” for this question. - Strongly agree - Somewhat agree - Neither agree nor disagree - Somewhat disagree - Strongly disagree

Please look at the graphic below. We'll show the name of a person or group and we'd like you to rate that person or group using something we call the feeling thermometer.

226

17. How would you rate whites? [1-100] 18. How would you rate blacks? [1-100] 19. How would you rate Hispanics? [1-100] 20. How would you rate Asians? [1-100] 21. How would you rate Muslims? [1-100] 22. How would you rate Donald Trump? [1-100] 23. How would you rate Social Justice Activists? [1-100] 24. How would you rate the Alternative or “Alt” Right? [1-100] 25. How would you rate the group Black Lives Matter? [1-100]

Please answer the following questions about race in America today: 26. How guilty do you feel about social inequality between white and black Americans? - Extremely guilty - Very guilty - Moderately guilty - A little guilty - Not guilty at all

27. How much does being white grant you unearned privileges in today’s society? - A great deal - A lot

227

- A moderate amount - A little - None

28. How many advantages do white people have that minorities do not have in this society? - A great many - A lot - A moderate number - A few - None

Please answer the following public policy questions: 29. How do you feel about affirmative action policies in universities? - Favor a great deal - Favor moderately - Favor a little - Neither favor nor oppose - Oppose a little - Oppose moderately - Oppose a great deal

30. How do you feel about affirmative action policies in the workplace? - Favor a great deal - Favor moderately - Favor a little - Neither favor nor oppose - Oppose a little - Oppose moderately - Oppose a great deal

31. When people from other countries legally move to the United States to live and work, is this generally good for the U.S., generally bad for the U.S., or neither good nor bad? - Extremely good - Moderately good - A little good - Neither good nor bad - A little bad - Moderately bad - Extremely bad

32. Should the number of people who are allowed to legally move to the United States to live and work be increased, decreased, or kept the same as it is now? - Increased a lot - Increased a moderate amount - Increased a little

228

- Kept the same - Decreased a little - Decreased a moderate amount - Decreased a lot

33. Which comes closest to your view about what government policy should be toward unauthorized immigrants now living in the United States? - Arrest unauthorized immigrants and send them back to their home country. - Have a guest worker program that allows unauthorized immigrants to remain. - Allow unauthorized immigrants to remain in the United States provided that they meet certain requirements - Allow unauthorized immigrants to remain in the United States without requirements

34. Are tensions between black communities and police departments the result of unfair policing or are they the result of higher crime rates in the black community? - Primarily due to unfair policing - Both, but more due to unfair policing - Equal amounts unfair policing and higher black crime rates - Both, but more due to higher black crime rates - Primarily due to higher black crime rates

35. Do you support school vouchers to help low-income minority children acquire better educational opportunities? - Support a great deal - Support a moderate amount - Support a little - Neither support nor oppose - Oppose a little - Oppose a moderate amount - Oppose a great deal

The following questions as you to pick which of two statements (“A” or “B”) you agree with most and to indicate how strongly you prefer one over the other.

36. Which of the two statements comes closer to your view? Statement A. Americans are one people with a common national identity. Race shouldn’t be an important consideration. OR Statement B. Racial distinctions between white, black, Hispanic and Asian Americans are important. It is a mistake to pretend these groups share the same identity. - Strongly favor Statement A over Statement B - Somewhat favor Statement A over Statement B - Agree with Statements A and B equally

229

- Somewhat favor Statement B over Statement A - Strongly favor Statement B over Statement A

37. Which of the two statements comes closer to your view? Statement A. Racial minorities are, on average, just as patriotic as white Americans. OR Statement B. Racial minorities are, on average, less patriotic than white Americans. - Strongly favor Statement A over Statement B - Somewhat favor Statement A over Statement B - Agree with Statements A and B equally - Somewhat favor Statement B over Statement A - Strongly favor Statement B over Statement A

38. Which of the two statements comes closer to your view? Statement A. Assimilation is a two-way street. New immigrant groups adopt American culture and values but they also change America by adding aspects of their own cultures to it. OR Statement B. Assimilation is a one-way street. New immigrant should groups adopt American culture and values without changing America in the process. - Strongly favor Statement A over Statement B - Somewhat favor Statement A over Statement B - Agree with Statements A and B equally - Somewhat favor Statement B over Statement A - Strongly favor Statement B over Statement A

39. Which of the two statements comes closer to your view? Statement A. Racial conflict is likely to get worse in the future. OR Statement B. Racial conflict is likely to diminish in the future. - Strongly favor Statement A over Statement B - Somewhat favor Statement A over Statement B - Agree with Statements A and B equally - Somewhat favor Statement B over Statement A - Strongly favor Statement B over Statement A

The Census projects that, as a result of immigration, whites will become a minority in the United States by 2040. 40. Which of the two statements comes closer to your view?

230

Statement A. It will not matter if the country becomes less white due to immigration so long as new groups see themselves as “American” first and foremost. OR Statement B. Immigration will make America less American. - Strongly favor Statement A over Statement B - Somewhat favor Statement A over Statement B - Agree with Statements A and B equally - Somewhat favor Statement B over Statement A - Strongly favor Statement B over Statement A

41. Which of the two statements comes closer to your view? Statement A. American national identity comes from a shared commitment to American political values and institutions. OR Statement B. American national identity comes from a shared ethnic and cultural identity including Americans’ common ancestry, language, religion, customs, and cultural traditions. - Strongly favor Statement A over Statement B - Somewhat favor Statement A over Statement B - Agree with Statements A and B equally - Somewhat favor Statement B over Statement A - Strongly favor Statement B over Statement A

Please answer the following questions 42. Generally speaking, do you usually think of yourself as a Republican, a Democrat, an independent, or what? - Republican - Democrat - Independent - Something else [please specify]

43. [If Democrat] Would you call yourself a STRONG Democrat or a NOT VERY STRONG Democrat? - Strong - Not very strong

44. [If Republican] Would you call yourself a STRONG Republican or a NOT VERY STRONG Republican? - Strong - Not very strong

45. [If Independent] Do you think of yourself as CLOSER to the Republican Party or to the Democratic Party?

231

- Closer to the Republican Party - Closer to the Democratic Party

From which of the following sources do you often get information about politics. The list includes sources that may be linked to on social media platforms. Please select all that apply: - National newspapers (print or online) Ex: New York Times, Washington Post, USA Today, Wall Street Journal, LA Times - Local newspaper (print or online) Ex: Fox5 , NBC 10 - Cable news (CNN, Channel, MSNBC): Tonight The Show The Five Special Report with The Last Word with Lawrence O’Donnell The Story with Martha MacCallum All in with The 11 th Hour with Brian Williams Other (please specify) - “Nightly” network news Ex: ABC, CBS, NBC - Sports news Ex: ESPN - “Local” network news Ex: NBC4 Washington - Public broadcasting (TV or radio) Ex: PBS, NPR, BBC, C-Span - “Commercial” (not including PBS or C-Span): The Show The Show The Program The Show The Hugh Hewitt Show Other (please specify) - “Late night” comedy shows:

232

Ex: , Jimmy Kimmel Live, The Late Show, Politically Incorrect with Bill Marr - Online news or commentary Atlantic Buzzfeed The Blaze Breitbart Huffington Post Daily Beast Daily Caller Daily Mail Daily Wire The Federalist Gawker Guardian Infowars Salon Slate Mother Jones , Politico Redstate Upworthy Vice Vox Washington Times Other (please specify) - YouTube Bloomberg Politics The Daily Wire Roaming Millennial Liberal Viewer The Majority Report Other (please specify) - Other The Daily Pod Save America The Show Political Gabfest

233

Other (please specify) - Other news source (please specify)

234

Table A3.1. Ordered Logit: Concern for Group Position Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Group Group Group Group Group Group Group Group

VARIABLES position position position position position position position position

Prime 1 -0.2386 -0.3097 -0.2713 -0.1693 -0.7116 -0.8675* -0.8520* -0.5649

-0.1972 -0.1911 -0.1907 -0.1787 -0.4384 -0.4535 -0.4521 -0.4306 Conservatism 0.4423** 0.5437*** 0.5808*** 0.019 0.1710*** 0.1671** 0.1728** -0.0421

-0.1927 -0.2009 -0.2019 -0.2224 -0.0629 -0.0685 -0.0685 -0.0739 Prime 1*Cons. 0.4739 0.5538* 0.5142* 0.323 0.1501 0.1741* 0.1760* 0.1195

-0.2945 -0.3004 -0.303 -0.3188 -0.0943 -0.0993 -0.0993 -0.1001 Prime 2 0.2176 0.1286 0.1456 0.2024 0.1467 -0.014 0.003 -0.183

-0.1794 -0.1761 -0.1765 -0.1708 -0.4261 -0.4263 -0.4274 -0.4148 Prime 2*Cons. -0.0411 0.0054 -0.0223 0.1169 0.0128 0.034 0.0313 0.1016

-0.2794 -0.2772 -0.2774 -0.2977 -0.0928 -0.0938 -0.0938 -0.0961 Prime 3 0.0051 0.0031 0.0123 -0.0121 -0.222 -0.3316 -0.3489 -0.5832

-0.1938 -0.196 -0.1953 -0.1919 -0.4383 -0.4502 -0.4531 -0.4695 Prime 3*Cons. 0.24 0.2355 0.235 0.4726 0.0746 0.0992 0.1053 0.1773*

-0.2897 -0.2994 -0.3008 -0.3061 -0.0951 -0.0988 -0.0996 -0.1045 Age -0.001 -0.0012 -0.0018 -0.0008 -0.0012 -0.0013

-0.0038 -0.0039 -0.0043 -0.0039 -0.004 -0.0043 Male 0.0438 0.0289 -0.1182 0.0438 0.0283 -0.1229

-0.1271 -0.1286 -0.1403 -0.1292 -0.1309 -0.1418 Income -0.0862*** -0.0864*** -0.0746*** -0.0817*** -0.0827*** -0.0725***

-0.0207 -0.021 -0.0226 -0.0209 -0.0212 -0.0225 Education -0.1789*** -0.1675*** -0.0882** -0.1655*** -0.1539*** -0.0883**

-0.0377 -0.038 -0.0415 -0.038 -0.0384 -0.0414 North East 0.4965** 0.3055 0.4915** 0.3003

-0.2048 -0.2021 -0.2073 -0.2007 South 0.1548 -0.0192 0.1063 -0.0265

-0.1886 -0.18 -0.1931 -0.1804 Mid-West 0.2215 0.0073 0.1816 0.0016

-0.1944 -0.1851 -0.1982 -0.1844 Mountain 0.2221 0.1836 0.2008 0.1813

-0.2451 -0.2464 -0.2472 -0.2459 White Ident. 0.4420*** 0.4433***

-0.048 -0.0481 Authoritarianism 0.1883 0.1684

-0.175 -0.1753 Egalitarianism -0.2268*** -0.2355***

-0.0677 -0.0716 Social Dominance 0.4203*** 0.4181***

-0.0587 -0.0585 Cut 1 -0.3454** -1.4613*** -1.2112*** 0.1626 0.1922 -0.8983** -0.6779 -0.0461

-0.1426 -0.2182 -0.3104 -0.5506 -0.3002 -0.3493 -0.4172 -0.6544 Cut 2 0.2877** -0.7978*** -0.5450* 0.9738* 0.8365*** -0.2268 -0.0035 0.7653

-0.1442 -0.2177 -0.3098 -0.5499 -0.3021 -0.3508 -0.4178 -0.6534 Cut 3 1.2692*** 0.2275 0.4844 2.2443*** 1.8271*** 0.8017** 1.0296** 2.0335***

-0.152 -0.2188 -0.3109 -0.5525 -0.311 -0.3567 -0.4221 -0.6575 Cut 4 2.2774*** 1.2627*** 1.5232*** 3.4828*** 2.8376*** 1.8338*** 2.0653*** 3.2699***

-0.1637 -0.2242 -0.3139 -0.565 -0.3195 -0.3626 -0.4262 -0.6672

Observations 1,525 1,522 1,522 1,522 1,525 1,522 1,522 1,522 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

235

Table A3.2. Ordered Logit: Concern for Group Position Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Group Group Group Group Group Group Group Group VARIABLES position position position position position position position position

Prime 1 -0.056 -0.0919 -0.0596 0.0849 -0.2358 -0.1852 -0.2208 0.0411 -0.175 -0.1735 -0.1733 -0.1782 -0.3777 -0.366 -0.367 -0.3778 White Ident. 2.0674*** 2.0685*** 2.1004*** 1.6238*** 0.6706*** 0.7070*** 0.7015*** 0.5470*** -0.2068 -0.2094 -0.2099 -0.2278 -0.0801 -0.0777 -0.0783 -0.0845 Prime 1*White Ident. -0.0326 -0.0807 -0.1288 -0.3081 0.0527 0.0153 0.0226 -0.0332 -0.3214 -0.3161 -0.318 -0.3339 -0.1189 -0.1163 -0.1159 -0.1202 Prime 2 0.5641*** 0.5327*** 0.5535*** 0.6383*** 0.7791** 0.8476** 0.8023** 0.9959*** -0.1648 -0.1616 -0.161 -0.1654 -0.3471 -0.3327 -0.3344 -0.3431 Prime 2*White Ident. -1.0561*** -1.1252*** -1.1713*** -1.0934*** -0.2041* -0.2537** -0.2387** -0.2661** -0.3021 -0.3027 -0.2996 -0.3017 -0.1091 -0.1071 -0.1079 -0.1093 Prime 3 0.2425 0.2593 0.2646 0.2965 0.4161 0.5116 0.4937 0.5151 -0.1753 -0.1784 -0.1762 -0.1832 -0.3599 -0.3558 -0.358 -0.3643 Prime 3*White Ident. -0.3793 -0.3801 -0.3781 -0.3868 -0.0917 -0.1265 -0.1207 -0.1168 -0.2988 -0.3117 -0.3112 -0.3184 -0.1112 -0.1126 -0.1132 -0.1158 Age -0.004 -0.0042 -0.0016 -0.0041 -0.0039 -0.002 -0.0039 -0.0039 -0.0042 -0.004 -0.004 -0.0043 Male 0.1356 0.1271 -0.0736 0.0509 0.0596 -0.1095 -0.1341 -0.1345 -0.1411 -0.1356 -0.1347 -0.1418 Income -0.0612*** -0.0623*** -0.0623*** -0.0736*** -0.0734*** -0.0702*** -0.0214 -0.0215 -0.022 -0.0215 -0.0212 -0.0223 Education -0.1564*** -0.1478*** -0.0871** -0.1494*** -0.1581*** -0.0902** -0.0383 -0.0383 -0.0408 -0.0389 -0.0386 -0.0414 North East 0.4227** 0.3479* 0.3865* 0.3207 -0.1996 -0.199 -0.2013 -0.1995 South 0.0762 -0.0591 0.1176 -0.0206 -0.1821 -0.1787 -0.1872 -0.1812 Mid-West 0.0674 -0.0372 0.1097 -0.0076 -0.1869 -0.1834 -0.192 -0.1858 Mountain 0.2586 0.2133 0.2172 0.177 -0.238 -0.2443 -0.2464 -0.2481 Conservatism 0.0499 0.047 -0.047 -0.0476 Authoritarianism 0.2233 0.208 -0.1758 -0.1753 Egalitarianism -0.2395*** -0.2440*** -0.0715 -0.0721 Social Dominance 0.4387*** 0.4145*** -0.0582 -0.0588 Cut 1 0.036 -0.9981*** -0.8398*** -0.2074 1.2268*** 0.3426 0.1541 0.5801 -0.1203 -0.2176 -0.2949 -0.6227 -0.264 -0.3611 -0.3021 -0.6503 Cut 2 0.7354*** -0.2761 -0.1149 0.5964 1.9412*** 1.0868*** 0.8962*** 1.3947** -0.1217 -0.217 -0.2947 -0.6206 -0.268 -0.3626 -0.3042 -0.6489 Cut 3 1.8584*** 0.8793*** 1.0458*** 1.8650*** 3.0653*** 2.2574*** 2.0636*** 2.6651*** -0.1309 -0.2178 -0.2977 -0.6225 -0.2771 -0.3679 -0.3092 -0.6521 Cut 4 2.9796*** 2.0224*** 2.1953*** 3.0944*** 4.1917*** 3.4185*** 3.2206*** 3.9002*** -0.1526 -0.2301 -0.3059 -0.6305 -0.2959 -0.3795 -0.3236 -0.6616

Observations 1,525 1,522 1,522 1,522 1,525 1,522 1,522 1,522 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

236

Table A3.3. Ordered Logit: Concern for Group Treatment Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Group Group Group Group Group Group Group Group

VARIABLES Treatment Treatment Treatment Treatment Treatment Treatment Treatment Treatment

Prime 1 -0.1966 -0.2696 -0.2311 -0.1012 -0.4034 -0.5692 -0.5498 -0.1963 -0.1891 -0.1814 -0.1823 -0.1725 -0.4516 -0.4538 -0.4535 -0.4147 Conservatism 0.3948** 0.4215** 0.4481** -0.1039 0.1911*** 0.1720** 0.1756** -0.0074 -0.191 -0.1936 -0.1959 -0.2079 -0.0668 -0.0707 -0.0712 -0.0736 Prime 1*Cons. 0.3186 0.39 0.353 0.1279 0.0745 0.1018 0.1036 0.0343 -0.2779 -0.28 -0.2814 -0.2866 -0.0957 -0.0979 -0.098 -0.0933 Prime 2 0.0297 -0.0539 -0.0421 0.0156 0.0102 -0.1917 -0.1782 -0.2025 -0.1793 -0.1732 -0.1731 -0.17 -0.4227 -0.4267 -0.4301 -0.4058 Prime 2*Cons. 0.0136 0.09 0.0635 0.1113 0.0079 0.0432 0.0404 0.0635 -0.2712 -0.272 -0.2725 -0.285 -0.0907 -0.0929 -0.0937 -0.0919 Prime 3 0.054 0.0578 0.0711 0.0851 -0.1409 -0.2159 -0.2432 -0.2393 -0.2047 -0.2029 -0.2035 -0.2008 -0.4819 -0.4904 -0.4944 -0.4769 Prime 3*Cons. 0.2467 0.2504 0.2543 0.4324 0.0667 0.0859 0.0959 0.1162 -0.2929 -0.2998 -0.3018 -0.3079 -0.1004 -0.1036 -0.1047 -0.1032 Age 0.0024 0.0024 0.0012 0.0019 0.0019 0.0012 -0.0036 -0.0036 -0.0037 -0.0037 -0.0037 -0.0037 Male 0.1386 0.122 0.0133 0.1303 0.1109 0.0067 -0.117 -0.1182 -0.1256 -0.119 -0.1204 -0.1268 Income -0.0906*** -0.0881*** -0.0743*** -0.0879*** -0.0862*** -0.0753*** -0.0205 -0.0209 -0.0217 -0.0206 -0.0211 -0.0217 Education -0.1361*** -0.1275*** -0.0568 -0.1228*** -0.1135*** -0.0581 -0.0374 -0.0375 -0.0386 -0.0377 -0.0378 -0.0385 North East 0.4576** 0.228 0.4805** 0.2347 -0.2054 -0.1876 -0.2089 -0.1884 South 0.222 0.053 0.1842 0.0413 -0.1855 -0.1703 -0.1883 -0.1717 Mid-West 0.3435* 0.1274 0.3278* 0.1293 -0.1877 -0.1723 -0.1909 -0.1727 Mountain 0.1763 0.0543 0.1688 0.0509 -0.2309 -0.2199 -0.2341 -0.2204 White Ident. 0.3623*** 0.3630*** -0.0451 -0.0452 Authoritarianism 0.0606 0.0468 -0.1669 -0.1674 Egalitarianism -0.2868*** -0.2640*** -0.0655 -0.0691 Social Dominance 0.3300*** 0.3282*** -0.0535 -0.0534 Cut 1 -0.7858*** -1.5771*** -1.2718*** -0.7656 -0.1445 -0.9875*** -0.6992* -0.6836 -0.1407 -0.2177 -0.2987 -0.5407 -0.3175 -0.3684 -0.4238 -0.6615 Cut 2 -0.0983 -0.8685*** -0.5591* 0.0573 0.5573* -0.267 0.0258 0.1402 -0.1394 -0.2152 -0.2973 -0.537 -0.3189 -0.3687 -0.4248 -0.6588 Cut 3 0.8899*** 0.1492 0.4628 1.2434** 1.5556*** 0.7568** 1.0544** 1.3256** -0.1428 -0.2151 -0.2985 -0.5386 -0.325 -0.3722 -0.4286 -0.6617 Cut 4 1.9196*** 1.2025*** 1.5178*** 2.4480*** 2.5907*** 1.8119*** 2.1117*** 2.5291*** -0.1534 -0.2241 -0.3062 -0.5466 -0.334 -0.3804 -0.4369 -0.6688

Observations 1,522 1,519 1,519 1,519 1,522 1,519 1,519 1,519 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

237

Table A3.4. Ordered Logit: Concern for Group Treatment Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Group Group Group Group Group Group Group Group

VARIABLES Treatment Treatment Treatment Treatment Treatment Treatment Treatment Treatment

Prime 1 -0.1399 -0.1804 -0.1574 -0.0549 -0.4123 -0.4217 -0.4163 -0.2811 -0.1637 -0.1632 -0.1651 -0.1665 -0.3536 -0.3437 -0.3455 -0.3485 White Ident. 1.5876*** 1.5294*** 1.5277*** 1.0877*** 0.5311*** 0.5332*** 0.5316*** 0.3890*** -0.2056 -0.2065 -0.2082 -0.2212 -0.0787 -0.0765 -0.0768 -0.0825 Prime 1*White 0.1778 0.1643 0.1544 0.0944 0.1125 0.0981 0.1026 0.0847 Ident. -0.2965 -0.2935 -0.2967 -0.3095 -0.1105 -0.1082 -0.109 -0.1112 Prime 2 0.2502 0.2126 0.2241 0.2492 0.3475 0.3257 0.3473 0.3896 -0.1619 -0.1599 -0.1615 -0.1616 -0.3302 -0.322 -0.3236 -0.3294 Prime 2 *White -0.6590** -0.6534** -0.6806** -0.5494* -0.1108 -0.1202 -0.1279 -0.1195 Ident. -0.3012 -0.3037 -0.3033 -0.3053 -0.1056 -0.1044 -0.1045 -0.1067 Prime 3 0.2355 0.2322 0.243 0.2535 0.4301 0.4747 0.4986 0.4615 -0.1779 -0.1794 -0.1791 -0.1854 -0.361 -0.3559 -0.3575 -0.3682 Prime 3 *White -0.1536 -0.0991 -0.0946 -0.052 -0.0738 -0.0873 -0.0928 -0.0738 Ident. -0.3112 -0.3156 -0.3174 -0.3146 -0.1146 -0.1141 -0.1149 -0.1167 Age 0.0003 0.0004 0.0013 0.0003 0.0004 0.0007 -0.0035 -0.0035 -0.0036 -0.0036 -0.0036 -0.0037 Male 0.2053* 0.1938 0.033 0.16 0.1507 0.0202 -0.1193 -0.1202 -0.1228 -0.1237 -0.1254 -0.1268 Income -0.0679*** -0.0667*** -0.0672*** -0.0783*** -0.0757*** -0.0741*** -0.0211 -0.0214 -0.0215 -0.0211 -0.0216 -0.0217 Education -0.1161*** -0.1102*** -0.0545 -0.1165*** -0.1102*** -0.0583 -0.037 -0.0371 -0.038 -0.0371 -0.0373 -0.0383 North East 0.3337* 0.2475 0.3175 0.242 -0.1969 -0.1864 -0.1995 -0.1888 South 0.1446 0.0104 0.1935 0.0536 -0.1787 -0.1691 -0.1829 -0.1728 Mid-West 0.1861 0.0922 0.2253 0.123 -0.1813 -0.1715 -0.1849 -0.1739 Mountain 0.123 0.069 0.0933 0.0424 -0.2252 -0.2206 -0.23 -0.2222 Conservatism 0.0422 0.0388 -0.0435 -0.0433 Authoritarianism 0.0838 0.0635 -0.1663 -0.1671 Egalitarianism -0.2613*** -0.2697*** -0.0698 -0.0695 Social Dominance 0.3503*** 0.3226*** -0.0524 -0.0536 Cut 1 -0.5364*** -1.2201*** -1.0283*** -0.9629 0.4178 -0.3274 -0.1108 -0.4634 -0.1197 -0.2191 -0.2874 -0.5991 -0.2544 -0.3119 -0.3637 -0.6344 Cut 2 0.1915 -0.4779** -0.284 -0.1477 1.1636*** 0.434 0.6528* 0.3635 -0.1188 -0.2172 -0.286 -0.595 -0.2575 -0.313 -0.3647 -0.6308 Cut 3 1.2755*** 0.6247*** 0.8209*** 1.0333* 2.2554*** 1.5482*** 1.7696*** 1.5498** -0.123 -0.2186 -0.2884 -0.5949 -0.2647 -0.3188 -0.3705 -0.6315 Cut 4 2.4007*** 1.7688*** 1.9663*** 2.2363*** 3.3842*** 2.7002*** 2.9223*** 2.7543*** -0.1429 -0.2331 -0.2995 -0.5991 -0.2836 -0.3355 -0.3848 -0.6365

Observations 1,522 1,519 1,519 1,519 1,522 1,519 1,519 1,519 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

238

Table A3.5. Ordered Logit: Concern for Group Jobs Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Group Job Group Job Group Job Group Job Group Job Group Job Group Job Group Job

Prime 1 -0.103 -0.19 -0.1714 -0.0827 -0.05 -0.268 -0.24 -0.0613 -0.2021 -0.1958 -0.1979 -0.1862 -0.4274 -0.4308 -0.4331 -0.4092 Conservatism 0.7393*** 0.7700*** 0.7772*** -0.1791 0.3625*** 0.3433*** 0.3473*** 0.0389 -0.2126 -0.2174 -0.2216 -0.2282 -0.0678 -0.0704 -0.0711 -0.0733 Prime 1*Cons. 0.1461 0.2395 0.2087 0.1511 0.0039 0.042 0.0375 0.0109 -0.2907 -0.2955 -0.2991 -0.2982 -0.0921 -0.0944 -0.0949 -0.0909 Prime 2 -0.1312 -0.2544 -0.2475 -0.2534 0.0924 -0.1599 -0.158 -0.4034 -0.2048 -0.2008 -0.2013 -0.1921 -0.448 -0.4516 -0.4517 -0.4264 Prime 2 *Cons. -0.0897 0.0088 -0.0012 0.2284 -0.0571 -0.0168 -0.016 0.06 -0.2972 -0.302 -0.303 -0.3174 -0.0955 -0.0977 -0.0977 -0.0957 Prime 3 -0.2896 -0.3268 -0.3276 -0.3762* -0.0132 -0.164 -0.1704 -0.2803 -0.2118 -0.2101 -0.2115 -0.2072 -0.4459 -0.4454 -0.4478 -0.4333 Prime 3*Cons. 0.081 0.0857 0.0877 0.355 -0.0577 -0.0293 -0.0279 0.0124 -0.2809 -0.2881 -0.2901 -0.3022 -0.091 -0.0927 -0.0933 -0.0928 Age 0.0038 0.0038 0.0045 0.0032 0.003 0.0044 -0.0041 -0.0042 -0.0043 -0.0042 -0.0042 -0.0043 Male -0.0837 -0.077 -0.2934** -0.0921 -0.0876 -0.2984** -0.138 -0.1392 -0.1439 -0.1371 -0.1383 -0.1437 Income -0.0725*** -0.0734*** -0.0421* -0.0709*** -0.0733*** -0.0450** -0.0207 -0.0208 -0.022 -0.021 -0.0211 -0.0221 Education -0.1482*** -0.1433*** -0.0679* -0.1280*** -0.1230*** -0.0682* -0.0378 -0.038 -0.0382 -0.039 -0.0392 -0.0386 North East 0.2014 -0.0961 0.2058 -0.085 -0.1877 -0.179 -0.1889 -0.1784 South 0.1025 -0.0755 0.0252 -0.0982 -0.1689 -0.1614 -0.1705 -0.1623 Mid-West -0.0466 -0.3467** -0.1105 -0.3474** -0.1804 -0.1724 -0.1812 -0.1724 Mountain 0.1101 -0.0354 0.0794 -0.0444 -0.2327 -0.2071 -0.2283 -0.2062 White Ident. 0.2121*** 0.2110*** -0.0451 -0.0456 Authoritarianism 0.0108 -0.0079 -0.1706 -0.1719 Egalitarianism -0.5282*** -0.4796*** -0.0615 -0.0658 Social Dominance 0.4747*** 0.4749*** -0.0552 -0.055 Cut 1 -0.7329*** -1.5324*** -1.4398*** -2.0743*** 0.481 -0.3622 -0.3168 -1.6695** -0.1588 -0.2577 -0.3134 -0.5474 -0.3245 -0.3855 -0.4241 -0.6596 Cut 2 0.3689** -0.3945 -0.2997 -0.7013 1.6284*** 0.8166** 0.8652** -0.2938 -0.1592 -0.2535 -0.3111 -0.5456 -0.3295 -0.3855 -0.4247 -0.6577 Cut 3 1.4627*** 0.7301*** 0.8265*** 0.646 2.7495*** 1.9645*** 2.0153*** 1.0521 -0.1676 -0.2549 -0.3131 -0.5485 -0.3385 -0.3907 -0.4299 -0.6599 Cut 4 2.5319*** 1.8103*** 1.9074*** 1.8999*** 3.8311*** 3.0539*** 3.1058*** 2.3024*** -0.1857 -0.2675 -0.3193 -0.5593 -0.3563 -0.4057 -0.4408 -0.6692

Observations 1,526 1,523 1,523 1,523 1,526 1,523 1,523 1,523 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

239

Table A3.6. Ordered Logit: Concern for Group Jobs Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Group Job Group Job Group Job Group Job Group Job Group Job Group Job Group Job

Prime 1 -0.0407 -0.0659 -0.0573 0.115 -0.1127 -0.068 -0.0571 0.1802 -0.1678 -0.1682 -0.1697 -0.1719 -0.3624 -0.358 -0.3594 -0.3563 White Ident. 1.5587*** 1.5000*** 1.5135*** 0.8056*** 0.5195*** 0.5272*** 0.5304*** 0.3043*** -0.2263 -0.225 -0.228 -0.2527 -0.0802 -0.0778 -0.0787 -0.0854 Prime 1*White Ident. -0.2072 -0.2432 -0.2626 -0.3571 -0.0035 -0.033 -0.0369 -0.0694 -0.3187 -0.3158 -0.3171 -0.3264 -0.1165 -0.1152 -0.1157 -0.1165 Prime 2 0.0726 0.0251 0.0335 0.0564 0.2802 0.2837 0.2995 0.3198 -0.1675 -0.1693 -0.171 -0.1756 -0.3351 -0.3345 -0.3371 -0.3453 Prime 2*White Ident. -0.8082** -0.8435** -0.8535** -0.6479* -0.1682 -0.1930* -0.1974* -0.1729 -0.3376 -0.3386 -0.3386 -0.347 -0.112 -0.1119 -0.1126 -0.1157 Prime 3 -0.0853 -0.1107 -0.1131 -0.1376 0.2067 0.2275 0.2317 0.1363 -0.1637 -0.165 -0.1665 -0.1773 -0.3258 -0.3224 -0.3243 -0.3397 Prime 3*White Ident. -0.4385 -0.3928 -0.4025 -0.311 -0.1459 -0.1597 -0.164 -0.1325 -0.316 -0.3195 -0.3219 -0.3416 -0.1069 -0.1063 -0.107 -0.1123 Age 0.0034 0.0033 0.0051 0.0031 0.0031 0.0043 -0.004 -0.004 -0.0042 -0.0039 -0.0039 -0.0042 Male -0.0151 0 -0.2936** -0.0582 -0.0413 -0.3010** -0.1381 -0.1395 -0.1445 -0.135 -0.1365 -0.1428 Income -0.0438** -0.0459** -0.0408* -0.0523** -0.0538** -0.0441** -0.0209 -0.021 -0.0219 -0.0209 -0.0211 -0.0219 Education -0.1319*** -0.1293*** -0.0674* -0.1338*** -0.1318*** -0.0696* -0.0364 -0.0365 -0.0382 -0.0365 -0.0367 -0.0382 North East 0.0606 -0.0632 0.0292 -0.07 -0.1813 -0.1789 -0.1822 -0.1783 South 0.0524 -0.1183 0.0785 -0.0963 -0.1635 -0.162 -0.1667 -0.1632 Mid-West -0.1979 -0.3571** -0.181 -0.3474** -0.1769 -0.1738 -0.1782 -0.1736 Mountain 0.0528 -0.0363 0.0158 -0.0567 -0.232 -0.2085 -0.2313 -0.2067 Conservatism 0.055 0.0545 -0.0432 -0.0435 Authoritarianism 0.0153 -0.0012 -0.1728 -0.1727 Egalitarianism -0.4816*** -0.4826*** -0.0649 -0.0656 Social Dominance 0.4966*** 0.4730*** -0.0543 -0.0555 Cut 1 -0.6040*** -1.1856*** -1.1881*** -1.7936*** 0.3299 -0.3026 -0.2929 -1.3654** -0.1241 -0.2454 -0.2914 -0.5915 -0.2516 -0.3208 -0.3543 -0.6266 Cut 2 0.5180*** -0.0419 -0.0416 -0.4246 1.4729*** 0.8652*** 0.8776** 0.0121 -0.1244 -0.2421 -0.288 -0.5894 -0.256 -0.322 -0.3543 -0.6253 Cut 3 1.6770*** 1.1361*** 1.1395*** 0.9215 2.6353*** 2.0530*** 2.0681*** 1.3610** -0.1345 -0.2421 -0.2867 -0.5921 -0.2663 -0.3263 -0.3568 -0.6286 Cut 4 2.8021*** 2.2671*** 2.2716*** 2.1692*** 3.7649*** 3.1929*** 3.2088*** 2.6126*** -0.1595 -0.2586 -0.2963 -0.6006 -0.2862 -0.344 -0.3695 -0.6377

Observations 1,526 1,523 1,523 1,523 1,526 1,523 1,523 1,523 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

240

Table A3.7. Ordered Logit: Concern for Group College Admission Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Group Group Group Group Group Group Group Group VARIABLES College College College College College College College College

Prime 1 -0.1175 -0.1596 -0.1491 -0.0772 -0.272 -0.3775 -0.3722 -0.2493 -0.1832 -0.1787 -0.1798 -0.1755 -0.4088 -0.4065 -0.4094 -0.387 Conservatism 0.8231*** 0.8172*** 0.8309*** -0.0677 0.3119*** 0.2946*** 0.3011*** 0.003 -0.2204 -0.2226 -0.227 -0.2407 -0.0672 -0.0679 -0.069 -0.0715 Prime 1*Cons. 0.2795 0.3217 0.3114 0.3036 0.0576 0.0773 0.0781 0.0673 -0.2998 -0.3032 -0.3051 -0.307 -0.093 -0.0936 -0.0942 -0.091 Prime 2 0.1105 0.0675 0.0689 0.0748 0.2264 0.1248 0.1184 -0.0594 -0.1909 -0.1878 -0.1885 -0.1908 -0.4256 -0.4252 -0.4259 -0.4133 Prime 2*Cons. 0.0053 0.051 0.0538 0.2443 -0.0255 -0.006 -0.0037 0.0551 -0.301 -0.3043 -0.3047 -0.3203 -0.0947 -0.0954 -0.0954 -0.0954 Prime 3 -0.0264 -0.0334 -0.0296 -0.02 -0.1876 -0.2315 -0.2264 -0.2195 -0.1992 -0.1978 -0.2 -0.1937 -0.437 -0.4359 -0.4405 -0.4205 Prime 3*Cons. 0.0365 0.033 0.0273 0.236 0.0367 0.0461 0.0452 0.0687 -0.2961 -0.3004 -0.3034 -0.3086 -0.0937 -0.0941 -0.095 -0.0936 Age 0.0039 0.0037 0.0019 0.0037 0.0033 0.0021 -0.0039 -0.004 -0.0042 -0.004 -0.004 -0.0042 Male 0.0368 0.0379 -0.107 0.0405 0.0426 -0.105 -0.1273 -0.129 -0.135 -0.1271 -0.1288 -0.135 Income -0.0410* -0.0433** -0.015 -0.0354 -0.0390* -0.0145 -0.0215 -0.0216 -0.0221 -0.0216 -0.0217 -0.0222 Education -0.0790** -0.0767** -0.01 -0.0607 -0.0576 -0.0098 -0.0379 -0.0376 -0.0386 -0.0382 -0.0378 -0.0385 North East 0.1153 -0.2008 0.1241 -0.1896 -0.1768 -0.1695 -0.1767 -0.1694 South -0.0663 -0.2680* -0.1164 -0.2701* -0.1547 -0.1508 -0.1557 -0.1511 Mid-West -0.117 -0.4264*** -0.1705 -0.4253*** -0.1649 -0.1588 -0.165 -0.1584 Mountain 0.0641 -0.141 0.0313 -0.142 -0.2127 -0.1986 -0.21 -0.199 White Ident. 0.1976*** 0.2001*** -0.0473 -0.0477 Authoritarianism -0.3344** -0.3559** -0.1693 -0.1699 Egalitarianism -0.5542*** -0.5396*** -0.0615 -0.0659 Social Dominance 0.3475*** 0.3460*** -0.0543 -0.0545 Cut 1 -0.9014*** -1.2030*** -1.2367*** -2.7133*** 0.0569 -0.223 -0.2773 -2.6096*** -0.1457 -0.2398 -0.2797 -0.5152 -0.3014 -0.3556 -0.3811 -0.6179 Cut 2 0.2775* -0.0121 -0.0446 -1.3404*** 1.2680*** 0.9967*** 0.9448** -1.2352** -0.1444 -0.2364 -0.2803 -0.513 -0.3053 -0.3566 -0.3848 -0.6169 Cut 3 1.2882*** 1.0055*** 0.9744*** -0.1743 2.2962*** 2.0283*** 1.9789*** -0.0697 -0.1507 -0.237 -0.2823 -0.5126 -0.315 -0.3632 -0.3918 -0.619 Cut 4 2.4614*** 2.1817*** 2.1525*** 1.1489** 3.4786*** 3.2110*** 3.1642*** 1.2515** -0.1626 -0.2433 -0.2866 -0.5172 -0.3243 -0.37 -0.3978 -0.6225

Observations 1,524 1,521 1,521 1,521 1,524 1,521 1,521 1,521 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

241

Table A3.8. Ordered Logit: Concern for Group College Admission Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Group Group Group Group Group Group Group Group VARIABLES College College College College College College College College

Prime 1 -0.1331 -0.1418 -0.1311 0.0192 -0.4943 -0.4923 -0.4733 -0.219 -0.1656 -0.1676 -0.1686 -0.1727 -0.3545 -0.3547 -0.3559 -0.3541 White Ident. 1.0804*** 1.0295*** 1.0590*** 0.4518* 0.3571*** 0.3471*** 0.3534*** 0.1682** -0.2251 -0.2292 -0.233 -0.249 -0.0785 -0.0794 -0.0804 -0.0854 Prime 1*White Ident. 0.1602 0.1607 0.1268 0.0892 0.1397 0.1355 0.1279 0.0899 -0.3109 -0.3119 -0.3143 -0.3219 -0.1128 -0.1127 -0.1131 -0.113 Prime 2 0.2305 0.2264 0.2369 0.2394 0.1862 0.1828 0.2001 0.1956 -0.1673 -0.1701 -0.1712 -0.1799 -0.3358 -0.3377 -0.3382 -0.3548 Prime 2*White Ident. -0.3484 -0.348 -0.3695 -0.1945 -0.0262 -0.0279 -0.033 -0.0102 -0.3425 -0.3425 -0.342 -0.357 -0.1117 -0.1114 -0.1114 -0.1157 Prime 3 0.0689 0.0554 0.0559 0.0685 0.0162 0.0001 -0.0017 -0.0423 -0.1703 -0.1727 -0.1739 -0.1828 -0.345 -0.3461 -0.3468 -0.3588 Prime 3*White Ident. -0.1286 -0.0716 -0.084 -0.0169 0.0143 0.0207 0.0196 0.0416 -0.3243 -0.3286 -0.3291 -0.3358 -0.1129 -0.1131 -0.1132 -0.1157 Age 0.0036 0.0032 0.0027 0.0028 0.0025 0.0017 -0.0039 -0.0039 -0.0041 -0.0039 -0.0039 -0.0042 Male 0.1023 0.1161 -0.0944 0.0759 0.0903 -0.0926 -0.1277 -0.1294 -0.1351 -0.1277 -0.1292 -0.1348 Income -0.0157 -0.0182 -0.0107 -0.022 -0.0243 -0.014 -0.0217 -0.0218 -0.022 -0.0218 -0.0218 -0.0221 Education -0.0623* -0.0617* -0.0084 -0.0573 -0.0576 -0.0075 -0.0371 -0.0368 -0.0387 -0.0371 -0.0368 -0.0385 North East -0.0178 -0.1727 -0.0603 -0.1871 -0.1722 -0.1714 -0.1729 -0.1699 South -0.0746 -0.2775* -0.0619 -0.2649* -0.1514 -0.1509 -0.1534 -0.1503 Mid-West -0.2359 -0.4307*** -0.231 -0.4227*** -0.1625 -0.1593 -0.164 -0.1585 Mountain 0.0323 -0.137 0.005 -0.1486 -0.2121 -0.1995 -0.2121 -0.1979 Conservatism 0.0476 0.0491 -0.0446 -0.0444 Authoritarianism -0.3251* -0.3426** -0.1695 -0.1697 Egalitarianism -0.5395*** -0.5395*** -0.0656 -0.0658 Social Dominance 0.3739*** 0.3449*** -0.0536 -0.0545 Cut 1 -0.8991*** -0.9885*** -1.0793*** -2.6530*** -0.2746 -0.4445 -0.53 -2.4996*** -0.1299 -0.2417 -0.2727 -0.5618 -0.2524 -0.3221 -0.3409 -0.5946 Cut 2 0.2752** 0.1929 0.1036 -1.2885** 0.9265*** 0.7626** 0.6784** -1.1240* -0.1275 -0.2369 -0.2704 -0.5607 -0.2551 -0.3214 -0.3412 -0.5935 Cut 3 1.2966*** 1.2159*** 1.1287*** -0.1268 1.9583*** 1.7966*** 1.7142*** 0.041 -0.1343 -0.237 -0.2707 -0.5614 -0.2627 -0.324 -0.3435 -0.5951 Cut 4 2.4877*** 2.4066*** 2.3217*** 1.1913** 3.1578*** 2.9964*** 2.9156*** 1.3612** -0.1529 -0.2463 -0.2769 -0.5664 -0.2778 -0.3343 -0.3517 -0.6009

Observations 1,524 1,521 1,521 1,521 1,524 1,521 1,521 1,521 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

242

Table A3.9. OLS: White Consciousness Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) White White White White White White White White

VARIABLES Cons. Cons. Cons. Cons. Cons. Cons. Cons. Cons.

Prime 1 -0.5037 -0.6859 -0.6254 -0.1436 -1.0324 -1.5092 -1.4655 -0.7012 -0.4653 -0.4296 -0.4297 -0.3183 -1.0313 -0.9806 -0.9814 -0.7381 Conservatism 1.6710*** 1.7777*** 1.8187*** 0.1643 0.6695*** 0.6192*** 0.6297*** 0.0071 -0.4984 -0.5004 -0.5058 -0.4027 -0.1609 -0.1615 -0.1632 -0.1372 Prime 1*Cons. 0.9673 1.1844* 1.1058 0.1873 0.2032 0.2916 0.2903 0.157 -0.7154 -0.7092 -0.7127 -0.5345 -0.225 -0.2209 -0.2215 -0.1761 Prime 2 0.0804 -0.1442 -0.1378 0.1348 0.1194 -0.353 -0.3651 -0.5512 -0.453 -0.4263 -0.4258 -0.3232 -1.0334 -0.9814 -0.9817 -0.7356 Prime 2*Cons. 0.0398 0.1704 0.146 -0.0717 -0.0046 0.0696 0.0719 0.1702 -0.71 -0.6925 -0.6921 -0.5261 -0.2273 -0.2206 -0.2204 -0.1778 Prime 3 -0.238 -0.2648 -0.2574 0.0292 -0.6008 -0.8882 -0.9166 -0.8337 -0.4714 -0.4529 -0.4533 -0.3336 -1.0349 -0.9888 -1.0006 -0.7569 Prime 3*Cons. 0.5171 0.5117 0.5328 0.4954 0.1242 0.1876 0.1977 0.2473 -0.6843 -0.6886 -0.6923 -0.5353 -0.219 -0.2149 -0.2176 -0.1736 Age 0.0046 0.0043 0.0003 0.0045 0.0039 0.0019 -0.0089 -0.009 -0.0071 -0.009 -0.0091 -0.0078 Male 0.0868 0.064 -0.2969 0.0797 0.0515 -0.2819 -0.2943 -0.2957 -0.231 -0.2937 -0.2951 -0.2607 Income -0.2186*** -0.2187*** -0.1306*** -0.2082*** -0.2105*** -0.1536*** -0.0479 -0.0486 -0.0383 -0.0481 -0.0488 -0.0412 Education -0.3829*** -0.3651*** -0.1438** -0.3380*** -0.3197*** -0.1310* -0.0872 -0.0878 -0.0686 -0.0879 -0.0885 -0.0729 North East 0.9012** 0.229 0.9286** 0.2317 -0.4516 -0.3296 -0.4533 -0.3457 South 0.2693 -0.1607 0.133 -0.1834 -0.4116 -0.301 -0.4154 -0.3228 Mid-West 0.2558 -0.4031 0.1421 -0.4175 -0.4318 -0.3221 -0.4347 -0.3334 Mountain 0.4395 -0.2509 0.3716 -0.0288 -0.5431 -0.3999 -0.536 -0.4121 White Ident. 0.8118*** 0.7951*** -0.0709 -0.0873 Authoritarianism 0.1758 -0.1566 -0.3035 -0.3342 Egalitarianism -0.8924*** -0.8340*** -0.0981 -0.1277 Social Dominance 0.8827*** 0.9033*** -0.0842 -0.1093 Constant 9.8293*** 12.0370*** 11.6271*** 10.6460*** 7.6870*** 9.9362*** 9.6015*** 10.5388*** -0.3446 -0.5184 -0.6783 -0.8742 -0.7565 -0.8406 -0.9343 -1.2554

Observations 1,519 1,516 1,516 1,516 1,519 1,516 1,516 1,516 R-squared 0.0526 0.1032 0.1071 0.3789 0.081 0.1238 0.1287 0.3724 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

243

Table A3.10. OLS: White Consciousness Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) White White White White White White White White

VARIABLES Cons. Cons. Cons. Cons. Cons. Cons. Cons. Cons.

Prime 1 -0.1861 -0.2364 -0.1953 0.1745 -0.5738 -0.5047 -0.4719 0.1552 -0.3764 -0.3688 -0.3715 -0.324 -0.7656 -0.7306 -0.7358 -0.6456 White Ident. 4.6575*** 4.5182*** 4.5443*** 3.0100*** 1.4217*** 1.4323*** 1.4358*** 0.9589*** -0.4778 -0.4698 -0.4746 -0.4721 -0.1621 -0.1507 -0.1519 -0.1488 Prime 1*White Ident. -0.0258 -0.133 -0.1895 -0.518 0.116 0.0561 0.0512 -0.0721 -0.7059 -0.687 -0.6908 -0.6446 -0.2415 -0.2313 -0.2331 -0.212 Prime 2 0.8081** 0.7019* 0.7231* 0.7912** 0.9369 0.9678 1.0257 1.1759* -0.3841 -0.3722 -0.3744 -0.3326 -0.7129 -0.6805 -0.682 -0.601 Prime 2*White Ident. -2.1136*** -2.1590*** -2.2215*** -1.8504*** -0.3179 -0.3790* -0.4002* -0.3760* -0.7406 -0.7244 -0.7219 -0.649 -0.2344 -0.2253 -0.2249 -0.2013 Prime 3 0.3282 0.2965 0.3043 0.4142 0.6111 0.6979 0.7267 0.809 -0.3736 -0.3681 -0.3691 -0.336 -0.7027 -0.668 -0.67 -0.607 Prime 3*White Ident. -0.8261 -0.7374 -0.746 -0.7075 -0.1814 -0.2196 -0.2295 -0.2219 -0.6877 -0.691 -0.6961 -0.6228 -0.2237 -0.2158 -0.2169 -0.1977 Age -0.0001 -0.0003 0.0024 -0.0009 -0.0008 0.0014 -0.0081 -0.0081 -0.0076 -0.0082 -0.0082 -0.0077 Male 0.3165 0.3157 -0.2196 0.1932 0.1984 -0.2733 -0.2869 -0.2878 -0.2592 -0.2878 -0.2895 -0.2616 Income -0.1390*** -0.1414*** -0.1358*** -0.1646*** -0.1653*** -0.1513*** -0.046 -0.0464 -0.0414 -0.0452 -0.0459 -0.0413 Education -0.3082*** -0.2981*** -0.1266* -0.3066*** -0.2966*** -0.1326* -0.081 -0.0812 -0.0731 -0.0799 -0.0805 -0.0727 North East 0.1372 0.138 -0.0856 -0.0855 South -0.0831 -0.1166 -0.3389 -0.3344 Mid-West -0.8514*** -0.8438*** -0.129 -0.1287 Mountain 0.9427*** 0.8978*** -0.1089 -0.11 Conservatism 0.5353 0.2795 0.4965 0.2615 -0.4056 -0.3442 -0.41 -0.3451 Authoritarianism 0.1337 -0.2801 0.2714 -0.1861 -0.3715 -0.3187 -0.384 -0.3264 Egalitarianism -0.1519 -0.4938 -0.0562 -0.4322 -0.3907 -0.333 -0.397 -0.3365 Social Dominance 0.288 -0.0288 0.2338 -0.0648 -0.4869 -0.409 -0.4936 -0.413 Constant 8.9997*** 10.7236*** 10.5573*** 10.9964*** 6.5969*** 8.4787*** 8.2282*** 9.5970*** -0.2716 -0.5108 -0.6225 -1.1949 -0.5432 -0.6829 -0.7669 -1.2314

Observations 1,519 1,516 1,516 1,516 1,519 1,516 1,516 1,516 R-squared 0.1722 0.1989 0.2014 0.3673 0.1865 0.2182 0.22 0.3734 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

244

Table A3.11. Ordered Logit: Ethnic Nationalism Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Ethnic Ethnic Ethnic Ethnic Ethnic Ethnic Ethnic Ethnic VARIABLES Nationalism Nationalism Nationalism Nationalism Nationalism Nationalism Nationalism Nationalism

Prime 1 0.2092 0.1593 0.1986 0.219 -0.2244 -0.3517 -0.3103 -0.1995 -0.1787 -0.1794 -0.1783 -0.178 -0.3893 -0.3898 -0.3859 -0.389 Conservatism 0.0663 0.0972 0.1388 0.0195 0.0361 0.0267 0.0326 -0.0113 -0.2066 -0.2126 -0.2132 -0.2233 -0.0669 -0.0681 -0.0676 -0.0732 Prime 1*Cons. 0.0317 0.0751 0.0047 -0.0941 0.1114 0.133 0.1265 0.0959 -0.28 -0.2847 -0.2858 -0.2908 -0.0864 -0.087 -0.0863 -0.0873 Prime 2 0.2884 0.2198 0.2325 0.2254 0.2622 0.1305 0.1548 0.1534 -0.1841 -0.1833 -0.1828 -0.1832 -0.4294 -0.4228 -0.4168 -0.4229 Prime 2*Cons. -0.0991 -0.0758 -0.1274 -0.1076 -0.0016 0.015 0.0076 0.0071 -0.2962 -0.2967 -0.2965 -0.2967 -0.0956 -0.0946 -0.0935 -0.0945 Prime 3 0.1803 0.1596 0.1657 0.187 0.2342 0.1654 0.1442 0.247 -0.1901 -0.1919 -0.1887 -0.1919 -0.4635 -0.4642 -0.4572 -0.4668 Prime 3*Cons. 0.2285 0.1962 0.1974 0.1541 0.0081 0.0164 0.0229 -0.0004 -0.3058 -0.3091 -0.3065 -0.3083 -0.1039 -0.1046 -0.1036 -0.1052 Age 0.0031 0.0029 0.0012 0.0036 0.0034 0.0017 -0.004 -0.0039 -0.0041 -0.0039 -0.0039 -0.0041 Male -0.1444 -0.1552 -0.1376 -0.1707 -0.1841 -0.1545 -0.1293 -0.1287 -0.131 -0.1298 -0.1296 -0.1314 Income -0.015 -0.0152 -0.0111 -0.0155 -0.0161 -0.0125 -0.0209 -0.0208 -0.0211 -0.0208 -0.0208 -0.021 Education -0.1239*** -0.1134*** -0.0850** -0.1222*** -0.1124*** -0.0886** -0.0374 -0.0373 -0.0388 -0.0374 -0.0372 -0.0384 North East 0.5718*** 0.5462*** 0.5515*** 0.5292*** -0.1886 -0.1917 -0.1889 -0.1916 South 0.226 0.1866 0.1852 0.1664 -0.1702 -0.1714 -0.1719 -0.1724 Mid-West 0.2231 0.184 0.1971 0.1715 -0.1861 -0.1892 -0.1849 -0.1876 Mountain 0.0776 0.0743 0.0635 0.0724 -0.2267 -0.222 -0.225 -0.2213 White Ident. 0.0291 0.0268 -0.042 -0.0421 Authoritarianism 0.7774*** 0.7611*** -0.1704 -0.1724 Egalitarianism -0.0271 -0.0176 -0.0624 -0.0659 Social Dominance 0.0149 0.0109 -0.052 -0.0523 Cut 1 -0.9882*** -1.4492*** -1.1806*** -0.7984 -0.8661*** -1.3660*** -1.1203*** -0.8563 -0.1424 -0.2444 -0.2821 -0.5209 -0.3078 -0.364 -0.3846 -0.6287 Cut 2 -0.1426 -0.5901** -0.3144 0.0835 -0.0168 -0.5028 -0.2501 0.0267 -0.1388 -0.2404 -0.28 -0.5204 -0.308 -0.3638 -0.3853 -0.6291 Cut 3 0.9971*** 0.5627** 0.8498*** 1.2682** 1.1263*** 0.6535* 0.9175** 1.2115* -0.1425 -0.2383 -0.2799 -0.5203 -0.3119 -0.3643 -0.3867 -0.6296 Cut 4 2.1999*** 1.7674*** 2.0599*** 2.4891*** 2.3297*** 1.8589*** 2.1278*** 2.4315*** -0.1544 -0.2441 -0.2865 -0.5248 -0.3157 -0.3669 -0.39 -0.6324

Observations 1,506 1,503 1,503 1,503 1,506 1,503 1,503 1,503 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

245

Table A3.12. Ordered Logit: Ethnic Nationalism Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Ethnic Ethnic Ethnic Ethnic Ethnic Ethnic Ethnic Ethnic VARIABLES Nationalism Nationalism Nationalism Nationalism Nationalism Nationalism Nationalism Nationalism

Prime 1 0.3008* 0.2851* 0.3015* 0.3164** 0.3733 0.4264 0.4365 0.4593 -0.1594 -0.1606 -0.16 -0.1585 -0.3195 -0.3192 -0.3196 -0.3182 White Ident. 0.4097* 0.3908 0.3826 0.2483 0.11 0.1231 0.1229 0.0785 -0.2431 -0.2439 -0.2446 -0.2553 -0.0799 -0.0806 -0.0805 -0.0834 Prime 1*White -0.2872 -0.3537 -0.3655 -0.4229 -0.0595 -0.093 -0.092 -0.1001 Ident. -0.316 -0.3202 -0.3196 -0.3252 -0.1076 -0.109 -0.109 -0.1101 Prime 2 0.1876 0.1467 0.1513 0.1473 0.0529 0.0813 0.1271 0.1375 -0.1664 -0.1649 -0.1649 -0.164 -0.3301 -0.326 -0.3285 -0.3277 Prime 2*White 0.2467 0.1929 0.1502 0.1717 0.0691 0.0372 0.0179 0.0167 Ident. -0.3242 -0.3266 -0.3285 -0.3334 -0.1086 -0.1099 -0.1108 -0.1119 Prime 3 0.3962** 0.3666** 0.3772** 0.3630** 0.529 0.5470* 0.5909* 0.5673* -0.1765 -0.1779 -0.1757 -0.1744 -0.3243 -0.3243 -0.3223 -0.3199 Prime 3*White -0.4242 -0.4264 -0.4462 -0.3878 -0.0961 -0.115 -0.13 -0.121 Ident. -0.333 -0.3354 -0.3338 -0.3386 -0.1116 -0.1126 -0.1122 -0.1131 Age 0.0028 0.0028 0.0014 0.0031 0.003 0.0015 -0.004 -0.0039 -0.0041 -0.004 -0.0039 -0.0041 Male -0.1301 -0.1391 -0.1442 -0.1431 -0.1503 -0.1471 -0.1287 -0.128 -0.1323 -0.1283 -0.1276 -0.1317 Income -0.0113 -0.0115 -0.0123 -0.0128 -0.0128 -0.0127 -0.021 -0.0209 -0.0211 -0.0211 -0.021 -0.0212 Education -0.1173*** -0.1090*** -0.0841** -0.1203*** -0.1120*** -0.0869** -0.038 -0.0379 -0.039 -0.0383 -0.0381 -0.0393 North East 0.5179*** 0.5263*** 0.5239*** 0.5311*** -0.1907 -0.1897 -0.1915 -0.1908 South 0.2076 0.1683 0.2182 0.1741 -0.1729 -0.1717 -0.1736 -0.1723 Mid-West 0.1689 0.1622 0.1851 0.1724 -0.1863 -0.1854 -0.1868 -0.1857 Mountain 0.06 0.078 0.0548 0.0728 -0.2283 -0.2197 -0.2289 -0.2204 Conservatism 0.0175 0.0169 -0.0434 -0.0436 Authoritarianism 0.7774*** 0.7732*** -0.1712 -0.171 Egalitarianism -0.0121 -0.0123 -0.0653 -0.0654 Social Dominance 0.0207 0.017 -0.0513 -0.052 Cut 1 -0.8981*** -1.3407*** -1.1168*** -0.6671 -0.7232*** -1.1391*** -0.9029*** -0.5491 -0.122 -0.2355 -0.2714 -0.5639 -0.2317 -0.2993 -0.3231 -0.5876 Cut 2 -0.0481 -0.4791** -0.2493 0.2161 0.1272 -0.2765 -0.0344 0.3342 -0.1187 -0.2324 -0.2698 -0.5636 -0.2312 -0.298 -0.3227 -0.5877 Cut 3 1.0983*** 0.6779*** 0.9173*** 1.4033** 1.2714*** 0.8793*** 1.1311*** 1.5196*** -0.1221 -0.2314 -0.2698 -0.5646 -0.2335 -0.2982 -0.3237 -0.5886 Cut 4 2.3063*** 1.8869*** 2.1305*** 2.6272*** 2.4756*** 2.0850*** 2.3412*** 2.7405*** -0.1353 -0.2375 -0.2763 -0.5697 -0.2374 -0.3007 -0.3273 -0.5924

Observations 1,506 1,503 1,503 1,503 1,506 1,503 1,503 1,503 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

246

Table A3.13. Ordered Logit: “Immigration Threatens Identity” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Immig. Immig. Immig. Immig. Immig. Immig. Immig. Immig.

VARIABLES Threat Threat Threat Threat Threat Threat Threat Threat

Prime 1 -0.303 -0.4049** -0.3804** -0.3390* -0.7645* -1.0065** -0.9810** -0.8047* -0.1908 -0.1915 -0.1925 -0.1943 -0.4115 -0.4329 -0.4361 -0.4381 Conservatism 0.167 0.24 0.2519 -0.4147 0.1517** 0.1479** 0.1490** -0.0685 -0.2218 -0.2276 -0.2271 -0.253 -0.0696 -0.0729 -0.0724 -0.0803 Prime 1*Cons. 0.3838 0.4907 0.4539 0.291 0.1463 0.1869* 0.1842* 0.1378 -0.302 -0.3058 -0.3075 -0.3231 -0.0925 -0.0967 -0.0974 -0.0986 Prime 2 -0.2547 -0.3663* -0.3521* -0.3766** -0.3901 -0.6167 -0.5948 -0.7741* -0.1895 -0.1926 -0.1934 -0.1852 -0.4127 -0.4247 -0.427 -0.4313 Prime 2*Cons. 0.3247 0.3888 0.3499 0.4688 0.0651 0.0984 0.093 0.1372 -0.3088 -0.3125 -0.3133 -0.326 -0.0945 -0.0964 -0.0965 -0.0994 Prime 3 -0.2753 -0.3321 -0.322 -0.3231 -0.4024 -0.5937 -0.5993 -0.5872 -0.2015 -0.2052 -0.2038 -0.2065 -0.4519 -0.4705 -0.4709 -0.5176 Prime 3*Cons. 0.33 0.3448 0.3572 0.4478 0.0588 0.0918 0.0968 0.1028 -0.3263 -0.3374 -0.3353 -0.353 -0.1033 -0.1076 -0.1075 -0.1177 Age -0.0023 -0.002 -0.0035 -0.0029 -0.0028 -0.0036 -0.0039 -0.0039 -0.0044 -0.0039 -0.0039 -0.0043 Male -0.2103 -0.2127 -0.3334** -0.2229 -0.2313* -0.3484** -0.1368 -0.1379 -0.1429 -0.1365 -0.138 -0.1431 Income -0.0268 -0.0245 -0.0107 -0.026 -0.0246 -0.0143 -0.0232 -0.0233 -0.0236 -0.0231 -0.0232 -0.0234 Education -0.1631*** -0.1557*** -0.0904** -0.1549*** -0.1474*** -0.0939** -0.0411 -0.041 -0.0416 -0.0414 -0.0415 -0.0415 North East 0.3600* 0.1759 0.3683* 0.1881 -0.1876 -0.2013 -0.1894 -0.2012 South 0.3067* 0.1355 0.2399 0.1234 -0.1692 -0.1821 -0.1724 -0.1834 Mid-West 0.2334 -0.0248 0.2142 -0.0031 -0.1839 -0.1961 -0.1878 -0.1963 Mountain 0.1152 -0.0658 0.0765 -0.0637 -0.2339 -0.258 -0.236 -0.2578 White Ident. 0.2144*** 0.2152*** -0.0458 -0.0459 Authoritarianism 0.5570*** 0.5335*** -0.1801 -0.1822 Egalitarianism -0.3863*** -0.3422*** -0.065 -0.0719 Social Dominance 0.2646*** 0.2639*** -0.056 -0.0558 Cut 1 -0.2771* -1.2468*** -0.9531*** -1.2344** 0.2834 -0.7347* -0.4821 -1.2288* -0.1484 -0.2627 -0.305 -0.5248 -0.3134 -0.3951 -0.4227 -0.669 Cut 2 0.6638*** -0.28 0.0167 -0.1346 1.2438*** 0.2518 0.5069 -0.1293 -0.1511 -0.2606 -0.3034 -0.5278 -0.3175 -0.3949 -0.4222 -0.6725 Cut 3 1.3207*** 0.3978 0.6968** 0.6221 1.9112*** 0.9408** 1.1978*** 0.6272 -0.1578 -0.2604 -0.3037 -0.5305 -0.3239 -0.3971 -0.4244 -0.6767 Cut 4 2.3672*** 1.4642*** 1.7657*** 1.7779*** 2.9670*** 2.0163*** 2.2754*** 1.7829*** -0.1765 -0.2716 -0.312 -0.5353 -0.3392 -0.4079 -0.4337 -0.6834

Observations 1,503 1,500 1,500 1,500 1,503 1,500 1,500 1,500 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

247

Table A3.14. Ordered Logit: “Immigration Threatens Identity” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Immig. Immig. Immig. Immig. Immig. Immig. Immig. Immig.

VARIABLES Threat Threat Threat Threat Threat Threat Threat Threat

Prime 1 -0.1174 -0.1619 -0.1568 -0.061 -0.2387 -0.214 -0.2269 -0.0515 -0.1729 -0.1732 -0.1739 -0.1798 -0.3394 -0.3441 -0.3422 -0.3577 White Ident. 1.1312*** 1.1194*** 1.1082*** 0.6211** 0.3692*** 0.3940*** 0.3920*** 0.2427** -0.2497 -0.2454 -0.2454 -0.2701 -0.0898 -0.0895 -0.0891 -0.095 Prime 1*White -0.2455 -0.2905 -0.2774 -0.471 0.0131 -0.0206 -0.0128 -0.0612 Ident. -0.3311 -0.3287 -0.3293 -0.3492 -0.1151 -0.1164 -0.1159 -0.1213 Prime 2 -0.0463 -0.1089 -0.1092 -0.1136 -0.1815 -0.1952 -0.1943 -0.1547 -0.1706 -0.1729 -0.173 -0.1762 -0.3478 -0.3524 -0.3517 -0.3577 Prime 2*White -0.2392 -0.2849 -0.2833 -0.1749 0.0096 -0.016 -0.0162 -0.0144 Ident. -0.3406 -0.339 -0.3405 -0.3515 -0.1184 -0.1189 -0.1186 -0.1212 Prime 3 -0.0853 -0.1322 -0.1245 -0.1257 -0.0139 -0.0101 0.0148 -0.028 -0.1816 -0.1851 -0.1855 -0.191 -0.3522 -0.3557 -0.3564 -0.3666 Prime 3*White -0.1689 -0.1525 -0.1583 -0.1181 -0.0362 -0.0555 -0.0639 -0.0448 Ident. -0.3651 -0.367 -0.3637 -0.3913 -0.1242 -0.1249 -0.124 -0.13 Age -0.0029 -0.0028 -0.0027 -0.0035 -0.0033 -0.0037 -0.0038 -0.0038 -0.0042 -0.0038 -0.0038 -0.0043 Male -0.1366 -0.1314 -0.3330** -0.1859 -0.1789 -0.3419** -0.1362 -0.1375 -0.1432 -0.1334 -0.1341 -0.1413 Income -0.0095 -0.0082 -0.0114 -0.0138 -0.0118 -0.0136 -0.0228 -0.0229 -0.0232 -0.0228 -0.023 -0.0233 Education -0.1494*** -0.1427*** -0.0879** -0.1484*** -0.1423*** -0.0898** -0.0406 -0.0406 -0.0419 -0.0404 -0.0403 -0.0417 North East 0.2668 0.2136 0.2377 0.1921 -0.1893 -0.1989 -0.1927 -0.201 South 0.2755 0.1216 0.2911* 0.1277 -0.1688 -0.1824 -0.1717 -0.1836 Mid-West 0.102 -0.0055 0.1122 -0.0044 -0.1852 -0.1969 -0.1855 -0.197 Mountain 0.0506 -0.0346 0.0054 -0.0704 -0.2364 -0.2557 -0.2404 -0.2583 Conservatism 0.0233 0.0274 -0.0501 -0.0508 Authoritarianism 0.5873*** 0.5555*** -0.1792 -0.1809 Egalitarianism -0.3402*** -0.3360*** -0.0723 -0.0723 Social Dominance 0.3024*** 0.2686*** -0.0547 -0.0561 Cut 1 -0.0199 -0.8685*** -0.6555** -0.9725 0.6284** -0.2274 -0.0141 -0.6764 -0.1283 -0.2493 -0.2936 -0.6003 -0.2621 -0.3302 -0.3624 -0.6497 Cut 2 0.9557*** 0.1252 0.3409 0.1171 1.6183*** 0.7857** 1.0022*** 0.4225 -0.1306 -0.2484 -0.292 -0.602 -0.2664 -0.3318 -0.3627 -0.652 Cut 3 1.6389*** 0.8246*** 1.0423*** 0.8671 2.3075*** 1.4949*** 1.7135*** 1.1780* -0.1381 -0.248 -0.2919 -0.6051 -0.2712 -0.3327 -0.3636 -0.6556 Cut 4 2.7112*** 1.9119*** 2.1318*** 2.0160*** 3.3848*** 2.5909*** 2.8115*** 2.3311*** -0.1595 -0.2597 -0.3008 -0.6118 -0.2834 -0.3427 -0.3719 -0.6624

Observations 1,503 1,500 1,500 1,500 1,503 1,500 1,500 1,500 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

248

Table A3.15. Ordered Logit: National Unity Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) National National National National National National National National VARIABLES Unity Unity Unity Unity Unity Unity Unity Unity

Prime 1 -0.0525 -0.0652 -0.0843 -0.1109 0.3683 0.3313 0.3307 0.2587 -0.1829 -0.183 -0.1826 -0.1856 -0.4015 -0.4041 -0.4069 -0.4226 Conservatism 0.2525 0.2314 0.1981 0.4588* 0.1749*** 0.1649*** 0.1618** 0.2868*** -0.2184 -0.2205 -0.2221 -0.2371 -0.0631 -0.0638 -0.0638 -0.0735 Prime 1*Cons. 0.0119 0.0275 0.053 0.0981 -0.1039 -0.0964 -0.0983 -0.0854 -0.3007 -0.3036 -0.3067 -0.3112 -0.0911 -0.0919 -0.0932 -0.0961 Prime 2 0.1553 0.1404 0.1333 0.1302 0.9376** 0.8906** 0.8876** 0.9920** -0.1929 -0.1913 -0.1911 -0.1905 -0.3933 -0.3941 -0.3954 -0.4087 Prime 2*Cons. -0.424 -0.4297 -0.4034 -0.4746 -0.2289** -0.2213** -0.2200** -0.2501*** -0.3006 -0.3019 -0.3023 -0.3073 -0.089 -0.0895 -0.0898 -0.0929 Prime 3 0.1149 0.0976 0.0912 0.1062 0.4735 0.4438 0.466 0.5301 -0.1959 -0.1951 -0.1937 -0.1925 -0.4356 -0.4337 -0.4364 -0.4452 Prime 3*Cons. -0.2466 -0.2622 -0.2599 -0.3642 -0.1116 -0.11 -0.1165 -0.1391 -0.3132 -0.312 -0.3117 -0.3207 -0.0971 -0.0964 -0.0973 -0.1001 Age 0.0023 0.0025 0.0023 0.0014 0.0015 0.0021 -0.0038 -0.0038 -0.0042 -0.0038 -0.0038 -0.0043 Male -0.0975 -0.0849 -0.0365 -0.0956 -0.0834 -0.0344 -0.129 -0.1292 -0.1312 -0.1288 -0.1291 -0.1315 Income 0.0447** 0.0445** 0.0374* 0.0433** 0.0430** 0.0352* -0.0217 -0.0217 -0.0216 -0.0216 -0.0216 -0.0214 Education -0.0907** -0.0958** -0.1233*** -0.0799** -0.0851** -0.1114*** -0.039 -0.0387 -0.04 -0.0392 -0.039 -0.0403 North East -0.2558 -0.1827 -0.2455 -0.1606 -0.177 -0.1799 -0.1782 -0.1815 South -0.0605 -0.0312 -0.0724 -0.0545 -0.1667 -0.1684 -0.1674 -0.1691 Mid-West -0.1964 -0.1394 -0.1963 -0.1328 -0.1715 -0.1731 -0.1721 -0.1734 Mountain -0.0194 0.0444 -0.027 0.0455 -0.2239 -0.2282 -0.2257 -0.228 White Ident. -0.1332*** -0.1316*** -0.0427 -0.0431 Authoritarianism 0.1928 0.1388 -0.173 -0.1765 Egalitarianism 0.1223** 0.2050*** -0.0617 -0.0675 Social Dominance -0.1121** -0.1090** -0.0523 -0.0531 Cut 1 -2.5302*** -2.5944*** -2.7262*** -2.8338*** -1.9134*** -2.0217*** -2.1614*** -1.4699** -0.1626 -0.2567 -0.2929 -0.5612 -0.2864 -0.3462 -0.3717 -0.6707 Cut 2 -1.2855*** -1.3465*** -1.4772*** -1.5716*** -0.6651** -0.7708** -0.9096** -0.2002 -0.1502 -0.245 -0.2874 -0.553 -0.285 -0.342 -0.3711 -0.6654 Cut 3 -0.5499*** -0.6073** -0.7370** -0.8203 0.0744 -0.0286 -0.1665 0.5573 -0.1431 -0.2436 -0.2868 -0.5501 -0.2832 -0.3423 -0.3719 -0.6644 Cut 4 0.2756* 0.221 0.093 0.0295 0.9033*** 0.8026** 0.6660* 1.4139** -0.1416 -0.2425 -0.2862 -0.5467 -0.2837 -0.3424 -0.3723 -0.6625

Observations 1,504 1,501 1,501 1,501 1,504 1,501 1,501 1,501 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

249

Table A3.16. Ordered Logit: National Unity Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) National National National National National National National National VARIABLES Unity Unity Unity Unity Unity Unity Unity Unity

Prime 1 -0.0709 -0.0701 -0.081 -0.1031 -0.0142 0.027 0.0157 -0.0309 -0.1708 -0.1718 -0.1722 -0.1751 -0.3449 -0.3475 -0.3487 -0.3573 White Ident. -0.1793 -0.1917 -0.1953 -0.0376 -0.1084 -0.1082 -0.1104 -0.0791 -0.2437 -0.2418 -0.2413 -0.2441 -0.079 -0.079 -0.0788 -0.08 Prime 1*White Ident. -0.0083 -0.0352 -0.0243 -0.0192 -0.0198 -0.0374 -0.0354 -0.025 -0.3229 -0.3221 -0.3221 -0.3219 -0.1128 -0.1133 -0.1136 -0.115 Prime 2 0.04 0.0315 0.0282 0.0142 0.4116 0.4347 0.4166 0.3453 -0.1728 -0.1728 -0.1731 -0.1743 -0.3266 -0.329 -0.3286 -0.3299 Prime 2*White Ident. -0.1911 -0.2261 -0.2017 -0.2015 -0.153 -0.1681 -0.1601 -0.1385 -0.3352 -0.3352 -0.3355 -0.3329 -0.1087 -0.1094 -0.109 -0.1085 Prime 3 0.0648 0.0431 0.0362 -0.0144 0.1034 0.1037 0.0816 -0.0128 -0.1796 -0.179 -0.1786 -0.1808 -0.3348 -0.3354 -0.3362 -0.3403 Prime 3*White Ident. -0.163 -0.1674 -0.1584 -0.0787 -0.0367 -0.0449 -0.0378 -0.0111 -0.3404 -0.3375 -0.3369 -0.3324 -0.1128 -0.1124 -0.1126 -0.1121 Age 0.0033 0.0034 0.0011 0.0039 0.004 0.0022 -0.0037 -0.0037 -0.0041 -0.0038 -0.0038 -0.0042 Male -0.1012 -0.0876 -0.0269 -0.0863 -0.0734 -0.0336 -0.1311 -0.131 -0.1304 -0.1318 -0.1316 -0.1311 Income 0.0443** 0.0440** 0.0348 0.0461** 0.0457** 0.0364* -0.0216 -0.0216 -0.0213 -0.0217 -0.0217 -0.0215 Education -0.1007** -0.1042*** -0.1165*** -0.1083*** -0.1112*** -0.1177*** -0.0392 -0.0389 -0.0398 -0.0395 -0.0392 -0.04 North East -0.2234 -0.1807 -0.1881 -0.1549 -0.1763 -0.1785 -0.1782 -0.1803 South -0.0264 -0.0597 -0.023 -0.0594 -0.1648 -0.1676 -0.167 -0.1693 Mid-West -0.1777 -0.1507 -0.1804 -0.1517 -0.1711 -0.1727 -0.1735 -0.1744 Mountain -0.003 0.0282 0.0132 0.0394 -0.2225 -0.2246 -0.2246 -0.227 Conservatism 0.1608*** 0.1587*** -0.0444 -0.0448 Authoritarianism 0.107 0.1384 -0.1761 -0.1755 Egalitarianism 0.2035*** 0.1957*** -0.0671 -0.0673 Social Dominance -0.1493*** -0.1134** -0.0523 -0.0529 Cut 1 -2.6874*** -2.7382*** -2.8333*** -1.8507*** -2.9414*** -2.9685*** -3.0595*** -1.9174*** -0.1523 -0.2565 -0.2884 -0.6153 -0.2543 -0.3323 -0.3591 -0.6437 Cut 2 -1.4415*** -1.4885*** -1.5825*** -0.5896 -1.6904*** -1.7136*** -1.8035*** -0.652 -0.1353 -0.2424 -0.2806 -0.6069 -0.2456 -0.3222 -0.3534 -0.6361 Cut 3 -0.7049*** -0.7478*** -0.8408*** 0.1606 -0.9496*** -0.9682*** -1.0572*** 0.1018 -0.1278 -0.2415 -0.2802 -0.6058 -0.2401 -0.3203 -0.3518 -0.6343 Cut 4 0.1219 0.0827 -0.0088 1.0093* -0.1149 -0.1292 -0.2169 0.9554 -0.1268 -0.2404 -0.2796 -0.6039 -0.2389 -0.3192 -0.3509 -0.6329

Observations 1,504 1,501 1,501 1,501 1,504 1,501 1,501 1,501 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

250

Table A3.17. Ordered Logit: “Truly American = White” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American" VARIABLES = "White" = "White" = "White" = "White" = "White" = "White" = "White" = "White"

Prime 1 0.1651 0.0965 0.1197 0.3939* 0.2349 -0.0109 0.0037 0.586 -0.2138 -0.2095 -0.2088 -0.2313 -0.4525 -0.4675 -0.4663 -0.4719 Conservatism 0.2929 0.2056 0.2427 -0.4770* 0.1252* 0.0711 0.0799 -0.1830** -0.2324 -0.2451 -0.2475 -0.281 -0.069 -0.0752 -0.0755 -0.0862 Prime 1*Cons. -0.0011 0.0994 0.0712 -0.2505 -0.0171 0.0338 0.0335 -0.0645 -0.333 -0.3467 -0.3484 -0.397 -0.0974 -0.1031 -0.103 -0.1078 Prime 2 0.1145 0.0207 0.0261 0.1401 0.1278 -0.1256 -0.1059 -0.3276 -0.214 -0.2131 -0.2128 -0.2253 -0.4707 -0.4892 -0.4896 -0.4822 Prime 2*Cons. -0.0835 0.0191 -0.006 0.2229 -0.0098 0.0372 0.0315 0.1294 -0.323 -0.3355 -0.3374 -0.3612 -0.1006 -0.1056 -0.1059 -0.106 Prime 3 -0.0443 -0.0478 -0.0379 -0.0366 0.0385 -0.0675 -0.0699 -0.1918 -0.2322 -0.2367 -0.2346 -0.2438 -0.5051 -0.5311 -0.5317 -0.5771 Prime 3*Cons. -0.0637 -0.0757 -0.0817 0.2974 -0.026 -0.0028 -0.0005 0.0656 -0.3423 -0.3566 -0.3579 -0.3923 -0.1072 -0.1143 -0.115 -0.1308 Age 0.0155*** 0.0155*** 0.0236*** 0.0155*** 0.0154*** 0.0234*** -0.0045 -0.0045 -0.0052 -0.0045 -0.0046 -0.0052 Male 0.1408 0.1248 -0.0807 0.136 0.1188 -0.0851 -0.149 -0.148 -0.1677 -0.1481 -0.1473 -0.1674 Income -0.0672*** -0.0668*** -0.0591** -0.0668*** -0.0667*** -0.0596** -0.0237 -0.024 -0.0272 -0.0237 -0.024 -0.0271 Education -0.1829*** -0.1773*** -0.069 -0.1769*** -0.1709*** -0.0811 -0.0452 -0.045 -0.0524 -0.0453 -0.0452 -0.0518 North East 0.3247 -0.1197 0.3337 -0.1488 -0.2302 -0.2153 -0.2301 -0.2126 South 0.0661 -0.2749 0.0498 -0.2759 -0.2091 -0.1978 -0.2095 -0.196 Mid-West 0.2669 -0.1007 0.2573 -0.1123 -0.2192 -0.2147 -0.2193 -0.2137 Mountain 0.1172 -0.1022 0.121 -0.1216 -0.2714 -0.2825 -0.2713 -0.2792 White Ident. 0.6836*** 0.6783*** -0.0575 -0.0567 Authoritarianism 0.4446** 0.4877** -0.2213 -0.2224 Egalitarianism -0.0938 -0.132 -0.0746 -0.0811 Social Dominance 0.6717*** 0.6776*** -0.064 -0.0644 Cut 1 0.6735*** 0.4948* 0.6694* 4.8107*** 1.0885*** 0.7314* 0.9211* 4.0174*** -0.164 -0.2698 -0.3569 -0.6378 -0.3344 -0.4162 -0.4711 -0.7762 Cut 2 1.1439*** 0.9838*** 1.1596*** 5.4971*** 1.5614*** 1.2221*** 1.4129*** 4.7045*** -0.1677 -0.2749 -0.3632 -0.6453 -0.3377 -0.4209 -0.4772 -0.7834 Cut 3 2.1359*** 2.0008*** 2.1792*** 6.9591*** 2.5561*** 2.2404*** 2.4341*** 6.1751*** -0.1823 -0.2825 -0.3713 -0.6527 -0.3516 -0.4309 -0.4878 -0.7894 Cut 4 2.8863*** 2.7631*** 2.9436*** 8.0128*** 3.3066*** 3.0022*** 3.1982*** 7.2390*** -0.2092 -0.3027 -0.3868 -0.6684 -0.3714 -0.4493 -0.504 -0.8015

Observations 1,524 1,521 1,521 1,521 1,524 1,521 1,521 1,521 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

251

Table A3.18. Ordered Logit: “Truly American = White” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American" VARIABLES = "White" = "White" = "White" = "White" = "White" = "White" = "White" = "White"

Prime 1 0.3219 0.3025 0.3151 0.4175* 0.4736 0.4533 0.4806 0.6017 -0.2127 -0.2159 -0.2178 -0.2241 -0.4967 -0.4805 -0.483 -0.4602 White Ident. 2.1855*** 2.1060*** 2.1395*** 1.5221*** 0.9053*** 0.8885*** 0.8964*** 0.6917*** -0.2578 -0.2633 -0.2646 -0.2648 -0.1124 -0.112 -0.1116 -0.1048 Prime 1*White -0.0676 -0.0771 -0.115 -0.1711 -0.0529 -0.0617 -0.0717 -0.0884 Ident. -0.3715 -0.3706 -0.3705 -0.3733 -0.1555 -0.151 -0.1508 -0.1436 Prime 2 0.3574* 0.3282 0.3387 0.3549 0.2736 0.2375 0.2601 0.189 -0.213 -0.2186 -0.2209 -0.2207 -0.4903 -0.485 -0.4855 -0.46 Prime 2*White -0.4282 -0.4342 -0.4731 -0.1725 -0.037 -0.0426 -0.0516 0.0095 Ident. -0.3613 -0.3676 -0.3676 -0.3611 -0.148 -0.147 -0.1459 -0.1385 Prime 3 -0.0147 -0.0352 -0.0325 -0.0032 0.0732 0.0132 0.0194 0.0438 -0.2259 -0.2325 -0.2337 -0.2364 -0.531 -0.5322 -0.5312 -0.493 Prime 3*White 0.0829 0.1788 0.1706 0.171 0.0024 0.0169 0.0146 0.0174 Ident. -0.3831 -0.387 -0.3853 -0.3858 -0.1639 -0.1638 -0.1625 -0.1527 Age 0.0095** 0.0091* 0.0239*** 0.0093** 0.0089* 0.0238*** -0.0047 -0.0047 -0.0051 -0.0047 -0.0047 -0.0051 Male 0.2844* 0.2765* -0.0641 0.2032 0.1938 -0.1047 -0.1659 -0.1654 -0.17 -0.1615 -0.1607 -0.1673 Income -0.0616** -0.0632** -0.0526** -0.0737*** -0.0755*** -0.0620** -0.0251 -0.0252 -0.0263 -0.0264 -0.0266 -0.0272 Education -0.1361*** -0.1373*** -0.0746 -0.1362*** -0.1376*** -0.08 -0.0474 -0.0474 -0.0496 -0.0495 -0.0499 -0.0517 North East 0.0615 -0.0807 -0.0129 -0.1429 -0.2239 -0.2137 -0.2283 -0.2124 South -0.2198 -0.3058 -0.2096 -0.2838 -0.205 -0.1955 -0.212 -0.1975 Mid-West -0.0232 -0.1028 -0.0166 -0.0967 -0.2133 -0.211 -0.2213 -0.2134 Mountain -0.0641 -0.0153 -0.1522 -0.0961 -0.2683 -0.278 -0.2703 -0.2785 Conservatism -0.1349*** -0.1478*** -0.052 -0.053 Authoritarianism 0.4763** 0.4863** -0.2172 -0.2223 Egalitarianism -0.1245 -0.1272 -0.0795 -0.0809 Social Dominance 0.7147*** 0.6727*** -0.0633 -0.0648 Cut 1 1.3087*** 1.1151*** 1.0041*** 3.0565*** 3.1917*** 2.8376*** 2.7260*** 4.2227*** -0.1623 -0.2962 -0.3535 -0.7071 -0.378 -0.4302 -0.467 -0.7489 Cut 2 1.8725*** 1.6931*** 1.5831*** 3.7097*** 3.7898*** 3.4528*** 3.3417*** 4.9092*** -0.1649 -0.3013 -0.3586 -0.7127 -0.3806 -0.4337 -0.4703 -0.7549 Cut 3 3.0803*** 2.9217*** 2.8161*** 5.1346*** 5.0321*** 4.7222*** 4.6150*** 6.3755*** -0.1795 -0.3054 -0.3631 -0.7148 -0.3964 -0.4434 -0.4785 -0.7608 Cut 4 3.9340*** 3.7891*** 3.6869*** 6.1588*** 5.9168*** 5.6256*** 5.5216*** 7.4331*** -0.2077 -0.3246 -0.3775 -0.7202 -0.414 -0.4609 -0.4943 -0.769

Observations 1,524 1,521 1,521 1,521 1,524 1,521 1,521 1,521 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

252

Table A3.19. Ordered Logit: “Truly American = American Ancestry” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American" ======VARIABLES "Ancestry" "Ancestry" "Ancestry" "Ancestry" "Ancestry" "Ancestry" "Ancestry" "Ancestry"

Prime 1 -0.1811 -0.3019 -0.2953 -0.1579 -0.4222 -0.705 -0.6806 -0.2736 -0.1914 -0.1886 -0.1904 -0.2051 -0.427 -0.4456 -0.4498 -0.434 Conservatism 0.4095** 0.4850** 0.4783** -0.0794 0.2279*** 0.2100*** 0.2073*** 0.0196 -0.207 -0.2156 -0.2165 -0.2554 -0.064 -0.0696 -0.0701 -0.0765 Prime 1*Cons. 0.5593* 0.6697** 0.6427** 0.4482 0.1025 0.1499 0.1439 0.0663 -0.2951 -0.2953 -0.2985 -0.3245 -0.0932 -0.097 -0.0978 -0.0975 Prime 2 0.1505 0.0191 0.0237 0.0643 0.5894 0.328 0.3303 0.2235 -0.1873 -0.1895 -0.1903 -0.2007 -0.4184 -0.4368 -0.4399 -0.4266 Prime 2*Cons. -0.2937 -0.2495 -0.2702 -0.1464 -0.1302 -0.0942 -0.0954 -0.0497 -0.2862 -0.2912 -0.2912 -0.3185 -0.0918 -0.0951 -0.0955 -0.0964 Prime 3 -0.2342 -0.2783 -0.2859 -0.2521 -0.119 -0.3347 -0.3508 -0.2773 -0.2021 -0.2103 -0.2112 -0.2219 -0.4373 -0.4597 -0.4642 -0.4602 Prime 3*Cons. 0.1651 0.1684 0.1758 0.3462 -0.0132 0.0292 0.0322 0.0397 -0.3006 -0.3166 -0.3164 -0.3378 -0.0942 -0.0992 -0.0999 -0.1014 Age 0.0057 0.0061 0.0093** 0.006 0.0063 0.0096** -0.0037 -0.0038 -0.0043 -0.0038 -0.0039 -0.0043 Male -0.1526 -0.1466 -0.3603** -0.1659 -0.1609 -0.3639** -0.1285 -0.1293 -0.1412 -0.1315 -0.1325 -0.1422 Income -0.0806*** -0.0780*** -0.0604** -0.0793*** -0.0777*** -0.0604** -0.022 -0.0223 -0.0237 -0.0221 -0.0224 -0.0237 Education -0.2327*** -0.2296*** -0.1628*** -0.2193*** -0.2153*** -0.1609*** -0.0402 -0.0406 -0.0441 -0.0405 -0.0409 -0.0438 North East 0.1837 -0.1436 0.2192 -0.1279 -0.1935 -0.1865 -0.1959 -0.1867 South 0.2639 0.0303 0.2257 0.028 -0.1821 -0.1733 -0.1846 -0.1747 Mid-West 0.1377 -0.1284 0.1197 -0.121 -0.194 -0.1893 -0.1961 -0.1899 Mountain -0.038 -0.1817 -0.0321 -0.1692 -0.2338 -0.2459 -0.2386 -0.2483 White Ident. 0.4020*** 0.4000*** -0.0486 -0.0487 Authoritarianism 0.4071** 0.4140** -0.1912 -0.1905 Egalitarianism -0.1603** -0.1492** -0.0657 -0.0701 Social Dominance 0.5485*** 0.5487*** -0.057 -0.0571 Cut 1 -0.2472* -1.3293*** -1.1294*** 1.0383* 0.5422* -0.5758 -0.4041 1.2228* -0.1393 -0.2329 -0.301 -0.5702 -0.2928 -0.3733 -0.423 -0.6707 Cut 2 0.4209*** -0.6239*** -0.4227 1.9215*** 1.2220*** 0.1384 0.3115 2.1051*** -0.1402 -0.2309 -0.2992 -0.572 -0.2948 -0.3737 -0.423 -0.6726 Cut 3 1.2886*** 0.292 0.4948* 3.1023*** 2.1003*** 1.0615*** 1.2358*** 3.2835*** -0.145 -0.2303 -0.2981 -0.5754 -0.3018 -0.3771 -0.4256 -0.6786 Cut 4 2.1041*** 1.1412*** 1.3459*** 4.1474*** 2.9197*** 1.9116*** 2.0873*** 4.3255*** -0.1592 -0.2373 -0.302 -0.5812 -0.3138 -0.3857 -0.4322 -0.683

Observations 1,525 1,522 1,522 1,522 1,525 1,522 1,522 1,522 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

253

Table A3.20. Ordered Logit: “Truly American = American Ancestry” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American" ======VARIABLES "Ancestry" "Ancestry" "Ancestry" "Ancestry" "Ancestry" "Ancestry" "Ancestry" "Ancestry"

Prime 1 -0.2986* -0.3674** -0.3841** -0.2931 -0.9927*** -0.9693*** -1.0165*** -0.9134** -0.1791 -0.1775 -0.1788 -0.1874 -0.3734 -0.3604 -0.3636 -0.3744 White Ident. 1.1487*** 1.1231*** 1.1026*** 0.3931 0.4122*** 0.4411*** 0.4368*** 0.2072** -0.2347 -0.2414 -0.2439 -0.2525 -0.0894 -0.0872 -0.0886 -0.0917 Prime 1*White 1.0470*** 0.9593*** 0.9966*** 0.9650*** 0.3584*** 0.3129*** 0.3276*** 0.3232*** Ident. -0.3126 -0.3173 -0.3202 -0.3365 -0.1168 -0.1151 -0.1158 -0.1196 Prime 2 -0.0215 -0.1045 -0.1119 -0.1061 -0.4882 -0.4941 -0.5099 -0.5531 -0.1795 -0.177 -0.1776 -0.1818 -0.3661 -0.3558 -0.3592 -0.3698 Prime 2*White 0.3214 0.2682 0.2858 0.5046 0.1877 0.1482 0.1537 0.2016* Ident. -0.3246 -0.33 -0.3318 -0.3294 -0.1157 -0.1141 -0.1153 -0.1171 Prime 3 -0.2992* -0.3578* -0.3687** -0.3704* -0.6946* -0.7128** -0.7249** -0.8051** -0.1809 -0.1836 -0.185 -0.1947 -0.3699 -0.363 -0.3676 -0.3731 Prime 3*White 0.6041* 0.6618* 0.6603* 0.7305** 0.2257* 0.2140* 0.2139* 0.2493** Ident. -0.3433 -0.3455 -0.347 -0.3473 -0.1218 -0.1196 -0.1208 -0.1209 Age 0.0011 0.0014 0.0100** 0.0012 0.0015 0.0094** -0.0036 -0.0037 -0.0042 -0.0037 -0.0037 -0.0043 Male -0.017 -0.0062 -0.3215** -0.0904 -0.0755 -0.3531** -0.1294 -0.1303 -0.1424 -0.1275 -0.129 -0.1417 Income -0.0573** -0.0558** -0.0540** -0.0690*** -0.0667*** -0.0605** -0.0224 -0.0225 -0.0238 -0.0221 -0.0224 -0.0237 Education -0.2047*** -0.2059*** -0.1521*** -0.2067*** -0.2097*** -0.1546*** -0.0407 -0.041 -0.0432 -0.0407 -0.0414 -0.0435 North East -0.0036 -0.1022 -0.053 -0.1376 -0.1872 -0.186 -0.189 -0.1863 South 0.1764 0.0328 0.1978 0.0461 -0.1744 -0.1723 -0.1786 -0.1754 Mid-West -0.0208 -0.1086 -0.0287 -0.1184 -0.186 -0.188 -0.1878 -0.1893 Mountain -0.1236 -0.1249 -0.1615 -0.1597 -0.221 -0.2396 -0.2309 -0.2448 Conservatism 0.0254 0.0306 -0.044 -0.0438 Authoritarianism 0.4122** 0.4115** -0.1907 -0.189 Egalitarianism -0.1567** -0.1543** -0.0707 -0.0702 Social Dominance 0.5892*** 0.5524*** -0.0561 -0.0565 Cut 1 -0.1234 -1.1782*** -1.1143*** 0.4882 0.6308** -0.4526 -0.4005 0.7298 -0.1335 -0.2375 -0.288 -0.629 -0.2796 -0.3193 -0.3597 -0.6585 Cut 2 0.6127*** -0.4156* -0.3509 1.3542** 1.3903*** 0.3397 0.3928 1.6140** -0.1361 -0.2357 -0.2858 -0.631 -0.2851 -0.3207 -0.3592 -0.6605 Cut 3 1.6086*** 0.6168*** 0.6828** 2.5210*** 2.3984*** 1.3970*** 1.4525*** 2.7984*** -0.1431 -0.235 -0.2842 -0.6341 -0.2907 -0.3227 -0.3586 -0.664 Cut 4 2.5239*** 1.5596*** 1.6276*** 3.5598*** 3.3218*** 2.3570*** 2.4157*** 3.8487*** -0.1618 -0.243 -0.2879 -0.6367 -0.3082 -0.3361 -0.3672 -0.6688

Observations 1,525 1,522 1,522 1,522 1,525 1,522 1,522 1,522 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

254

Table A3.21. Ordered Logit: “Truly American = Born in U.S.” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American"

VARIABLES = "Born" = "Born" = "Born" = "Born" = "Born" = "Born" = "Born" = "Born"

Prime 1 -0.2225 -0.3338* -0.3195* -0.2001 -0.5055 -0.8013* -0.7893* -0.3301 -0.1912 -0.1848 -0.1859 -0.1956 -0.4245 -0.4394 -0.44 -0.4192 Conservatism 0.4497** 0.4924** 0.4976** -0.0531 0.2346*** 0.2097*** 0.2108*** 0.0265 -0.2007 -0.2092 -0.2108 -0.2324 -0.0615 -0.0678 -0.0683 -0.0702 Prime 1*Cons. 0.3192 0.4718 0.4483 0.1634 0.094 0.1498 0.1489 0.0464 -0.2853 -0.2869 -0.2895 -0.3084 -0.0929 -0.0963 -0.0964 -0.0936 Prime 2 0.1117 -0.0252 -0.0219 0.0362 0.4153 0.1146 0.1267 0.1262 -0.1839 -0.1877 -0.1882 -0.1988 -0.387 -0.4088 -0.4115 -0.4072 Prime 2*Cons. -0.0953 -0.0042 -0.0229 0.0626 -0.0778 -0.0297 -0.0336 -0.0148 -0.2837 -0.2856 -0.286 -0.3114 -0.0861 -0.0894 -0.0899 -0.0912 Prime 3 -0.3914* -0.4334** -0.4371** -0.5079** -0.5539 -0.7867 -0.8139 -0.8358* -0.208 -0.2147 -0.2152 -0.2146 -0.4722 -0.5009 -0.5038 -0.4678 Prime 3*Cons. 0.2504 0.2864 0.2957 0.4232 0.0569 0.106 0.1126 0.116 -0.2989 -0.3152 -0.3148 -0.3191 -0.101 -0.1074 -0.1079 -0.102 Age 0.0057 0.006 0.0094** 0.0055 0.0056 0.0094** -0.0038 -0.0039 -0.0039 -0.0038 -0.0039 -0.0039 Male -0.2347* -0.2386* -0.4573*** -0.2466** -0.2538** -0.4626*** -0.1266 -0.1262 -0.1303 -0.1256 -0.1255 -0.1299 Income -0.0806*** -0.0786*** -0.0677*** -0.0792*** -0.0782*** -0.0688*** -0.0223 -0.0224 -0.0222 -0.0221 -0.0223 -0.0222 Education -0.1973*** -0.1928*** -0.1095*** -0.1823*** -0.1773*** -0.1085*** -0.0395 -0.0395 -0.0406 -0.0397 -0.0399 -0.0406 North East 0.2375 0.0629 0.2735 0.074 -0.1879 -0.1735 -0.1895 -0.1736 South 0.1891 0.0307 0.142 0.0211 -0.1713 -0.1591 -0.1733 -0.1608 Mid-West 0.2138 -0.0009 0.1956 0.0052 -0.1806 -0.1669 -0.1837 -0.168 Mountain 0.1324 0.1013 0.135 0.1027 -0.2315 -0.2395 -0.2346 -0.2408 White Ident. 0.2966*** 0.2973*** -0.0465 -0.0468 Authoritarianism 0.4612*** 0.4444** -0.173 -0.173 Egalitarianism -0.2226*** -0.1966*** -0.0651 -0.0711 Social Dominance 0.5445*** 0.5428*** -0.0534 -0.0531 Cut 1 -0.5771*** -1.5850*** -1.3802*** 0.2808 0.2156 -0.8638** -0.6821* 0.513 -0.1465 -0.2447 -0.2982 -0.5611 -0.2903 -0.3747 -0.4116 -0.6634 Cut 2 -0.0125 -0.9977*** -0.7922*** 0.9961* 0.7957*** -0.2631 -0.0806 1.2302* -0.1461 -0.2413 -0.296 -0.5614 -0.2916 -0.3737 -0.4113 -0.6646 Cut 3 0.8703*** -0.0727 0.1346 2.1469*** 1.6967*** 0.6767* 0.8605** 2.3813*** -0.1496 -0.2405 -0.2958 -0.5691 -0.2973 -0.3755 -0.413 -0.6728 Cut 4 1.7060*** 0.8006*** 1.0090*** 3.2096*** 2.5418*** 1.5561*** 1.7411*** 3.4421*** -0.1571 -0.2431 -0.2995 -0.5727 -0.3057 -0.3798 -0.4182 -0.6765

Observations 1,525 1,522 1,522 1,522 1,525 1,522 1,522 1,522 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

255

Table A3.22. Ordered Logit: “Truly American = Born in U.S.” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American"

VARIABLES = "Born" = "Born" = "Born" = "Born" = "Born" = "Born" = "Born" = "Born"

Prime 1 -0.3416** -0.4027** -0.4017** -0.2802* -0.9603*** -0.9489*** -0.9666*** -0.7488** -0.1633 -0.1603 -0.1605 -0.1653 -0.3574 -0.3456 -0.347 -0.3499 White Ident. 1.1004*** 1.0631*** 1.0506*** 0.3255 0.3559*** 0.3733*** 0.3686*** 0.1389 -0.2476 -0.2538 -0.2566 -0.2699 -0.0875 -0.0859 -0.0868 -0.091 Prime 1*White 0.6823** 0.6405* 0.6492* 0.5758* 0.2869** 0.2541** 0.2618** 0.2258* Ident. -0.3369 -0.3308 -0.3335 -0.3462 -0.1187 -0.1159 -0.1168 -0.1195 Prime 2 0.0373 -0.0429 -0.0475 0.0078 -0.3182 -0.3403 -0.3517 -0.3004 -0.1661 -0.1653 -0.1661 -0.1734 -0.344 -0.3381 -0.3397 -0.3452 Prime 2*White 0.1004 0.0688 0.0736 0.3264 0.1296 0.1015 0.1043 0.1386 Ident. -0.3277 -0.3338 -0.3346 -0.3387 -0.1133 -0.1133 -0.1139 -0.1157 Prime 3 -0.4856*** -0.5264*** -0.5301*** -0.5844*** -0.9096*** -0.9082*** -0.9144*** -1.0492*** -0.1712 -0.1774 -0.1777 -0.1815 -0.3487 -0.3456 -0.3465 -0.3489 Prime 3*White 0.6299* 0.6781* 0.6860* 0.7802** 0.2378** 0.2269* 0.2281* 0.2659** Ident. -0.3435 -0.3514 -0.3523 -0.3593 -0.1173 -0.1172 -0.1176 -0.1197 Age 0.0027 0.0029 0.0097** 0.0031 0.0033 0.0095** -0.0037 -0.0037 -0.0039 -0.0037 -0.0037 -0.0039 Male -0.1234 -0.1217 -0.4298*** -0.1878 -0.184 -0.4574*** -0.1256 -0.1255 -0.1313 -0.1262 -0.1266 -0.1313 Income -0.0609*** -0.0601*** -0.0633*** -0.0735*** -0.0721*** -0.0693*** -0.0218 -0.0218 -0.0217 -0.0221 -0.0222 -0.0221 Education -0.1778*** -0.1751*** -0.1074*** -0.1679*** -0.1654*** -0.1034** -0.0391 -0.0391 -0.0398 -0.0402 -0.0402 -0.0404 North East 0.1293 0.0755 0.1062 0.059 -0.1728 -0.1698 -0.1765 -0.1732 South 0.1318 0.0072 0.1657 0.0239 -0.1616 -0.1589 -0.1658 -0.1631 Mid-West 0.0549 -0.0131 0.0734 -0.0078 -0.1718 -0.1667 -0.1727 -0.1686 Mountain 0.0649 0.1387 0.0144 0.1005 -0.2206 -0.2359 -0.2253 -0.2393 Conservatism 0.0577 0.0584 -0.0442 -0.0445 Authoritarianism 0.4701*** 0.4619*** -0.1734 -0.173 Egalitarianism -0.2010*** -0.1981*** -0.0701 -0.0709 Social Dominance 0.5655*** 0.5424*** -0.0527 -0.0532 Cut 1 -0.5187*** -1.4662*** -1.3602*** 0.0677 0.1015 -0.8403*** -0.7298** 0.2393 -0.1227 -0.2382 -0.2748 -0.6098 -0.2555 -0.3119 -0.3384 -0.6398 Cut 2 0.0802 -0.8507*** -0.7443*** 0.7793 0.7149*** -0.2094 -0.0984 0.9575 -0.1218 -0.2334 -0.2705 -0.6098 -0.2577 -0.3104 -0.3363 -0.6409 Cut 3 1.0557*** 0.1568 0.2643 1.9300*** 1.6977*** 0.8092*** 0.9220*** 2.1116*** -0.1264 -0.2295 -0.2674 -0.6141 -0.2652 -0.3136 -0.3395 -0.6479 Cut 4 1.9759*** 1.1086*** 1.2166*** 2.9893*** 2.6223*** 1.7707*** 1.8842*** 3.1747*** -0.1363 -0.2312 -0.271 -0.6166 -0.2733 -0.3174 -0.3443 -0.6506

Observations 1,525 1,522 1,522 1,522 1,525 1,522 1,522 1,522 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

256

Table A3.23. Ordered Logit: “Truly American = Speak English” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American" = "Speak = "Speak = "Speak = "Speak = "Speak = "Speak = "Speak = "Speak VARIABLES English" English" English" English " English " English " English" English"

Prime 1 -0.1511 -0.2062 -0.1926 -0.0962 -0.325 -0.4633 -0.4393 -0.2196 -0.1936 -0.194 -0.1957 -0.1958 -0.4507 -0.4546 -0.4582 -0.409 Conservatism 1.2933*** 1.2006*** 1.1972*** 0.2484 0.4442*** 0.4043*** 0.4042*** 0.0823 -0.2162 -0.2243 -0.2311 -0.2454 -0.0708 -0.074 -0.0753 -0.0744 Prime 1*Cons. 0.0864 0.138 0.1137 -0.0197 0.0399 0.0685 0.0646 0.0271 -0.3099 -0.3175 -0.3208 -0.3306 -0.1003 -0.101 -0.1017 -0.0943 Prime 2 0.1551 0.0854 0.0889 0.1466 0.3583 0.2192 0.217 0.0409 -0.1844 -0.1837 -0.1845 -0.1826 -0.4207 -0.4303 -0.4336 -0.3833 Prime 2*Cons. -0.1671 -0.1098 -0.1251 0.0888 -0.0616 -0.0353 -0.0351 0.0336 -0.3004 -0.3083 -0.3122 -0.3197 -0.0944 -0.0969 -0.0978 -0.0902 Prime 3 -0.0667 -0.0707 -0.071 -0.0587 -0.138 -0.1128 -0.1185 -0.2024 -0.1839 -0.1847 -0.1857 -0.1971 -0.4186 -0.431 -0.4335 -0.4429 Prime 3*Cons. -0.2706 -0.3533 -0.3464 -0.2339 -0.0124 -0.0223 -0.0208 0.0119 -0.3011 -0.3113 -0.3125 -0.3211 -0.0932 -0.0963 -0.0967 -0.0989 Age 0.0267*** 0.0271*** 0.0268*** 0.0266*** 0.0268*** 0.0268*** -0.0041 -0.0041 -0.0042 -0.0043 -0.0044 -0.0043 Male -0.2862** -0.2815** -0.4225*** -0.2870** -0.2840** -0.4165*** -0.1218 -0.1228 -0.1273 -0.1262 -0.1268 -0.1284 Income -0.0336 -0.032 -0.0109 -0.0306 -0.0303 -0.0115 -0.0219 -0.022 -0.0226 -0.0227 -0.0227 -0.0227 Education -0.1165*** -0.1114*** 0.0019 -0.0896** -0.0847** 0.0045 -0.0401 -0.0399 -0.0406 -0.0408 -0.0407 -0.0411 North East 0.2795 0.0488 0.2772 0.0647 -0.1995 -0.1989 -0.2025 -0.1999 South 0.2553 0.075 0.1668 0.0662 -0.1778 -0.1827 -0.1824 -0.1828 Mid-West 0.0892 -0.2623 0.0148 -0.2625 -0.1887 -0.1925 -0.1942 -0.1931 Mountain 0.1453 -0.0282 0.0967 -0.0361 -0.2389 -0.2363 -0.245 -0.2387 White Ident. 0.2986*** 0.3027*** -0.0472 -0.0472 Authoritarianism 0.5775*** 0.5320*** -0.1737 -0.1746 Egalitarianism -0.5793*** -0.5366*** -0.0658 -0.0716 Social Dominance 0.3283*** 0.3276*** -0.055 -0.0548 Cut 1 -2.1586*** -1.6871*** -1.4695*** -2.4865*** -0.8881*** -0.4241 -0.2757 -2.0634*** -0.1641 -0.2531 -0.3066 -0.5352 -0.3309 -0.415 -0.4582 -0.6833 Cut 2 -1.1430*** -0.6482*** -0.4299 -1.2551** 0.168 0.654 0.8027* -0.8264 -0.144 -0.2426 -0.301 -0.5307 -0.3214 -0.4095 -0.4557 -0.68 Cut 3 -0.1588 0.3689 0.5904** 0.0164 1.1909*** 1.7078*** 1.8592*** 0.4473 -0.1371 -0.2393 -0.3007 -0.5276 -0.3187 -0.4088 -0.4561 -0.6757 Cut 4 0.8935*** 1.4579*** 1.6827*** 1.3470** 2.2721*** 2.8228*** 2.9773*** 1.7781*** -0.1395 -0.2418 -0.3035 -0.5271 -0.325 -0.4154 -0.4622 -0.6752

Observations 1,525 1,522 1,522 1,522 1,525 1,522 1,522 1,522 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

257

Table A3.24. Ordered Logit: “Truly American = Speak English” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American" = "Speak = "Speak = "Speak = "Speak = "Speak = "Speak = "Speak = "Speak VARIABLES English" English" English" English " English " English " English" English"

Prime 1 -0.3265* -0.3203* -0.3168* -0.1872 -0.7741** -0.6936* -0.7113** -0.5235 -0.173 -0.181 -0.1814 -0.177 -0.3508 -0.3617 -0.3628 -0.3529 White Ident. 1.3938*** 1.3440*** 1.3390*** 0.7744*** 0.4351*** 0.4265*** 0.4256*** 0.2401*** -0.2338 -0.2434 -0.2467 -0.2852 -0.0789 -0.0811 -0.0817 -0.088 Prime 1*White Ident. 0.504 0.447 0.4602 0.3998 0.2122* 0.1784 0.1853 0.1613 -0.3398 -0.3535 -0.3586 -0.4066 -0.1113 -0.1146 -0.1156 -0.1235 Prime 2 0.0948 0.0945 0.0917 0.179 -0.2902 -0.2473 -0.252 -0.2025 -0.1647 -0.1695 -0.1697 -0.1647 -0.3369 -0.341 -0.3416 -0.3432 Prime 2*White Ident. 0.0546 -0.0133 -0.0122 0.1323 0.1381 0.116 0.1161 0.149 -0.3424 -0.3492 -0.3521 -0.3869 -0.1104 -0.1114 -0.1123 -0.1225 Prime 3 -0.0612 -0.1097 -0.1133 -0.1028 0.0288 -0.0071 -0.0019 -0.0249 -0.1727 -0.1723 -0.1727 -0.1746 -0.3446 -0.3385 -0.3385 -0.337 Prime 3*White Ident. -0.3393 -0.2409 -0.2412 -0.2692 -0.0579 -0.052 -0.0558 -0.0521 -0.3293 -0.3319 -0.3335 -0.3692 -0.1104 -0.1082 -0.1085 -0.1166 Age 0.0267*** 0.0270*** 0.0265*** 0.0270*** 0.0274*** 0.0264*** -0.004 -0.004 -0.0043 -0.0039 -0.0039 -0.0043 Male -0.1767 -0.1618 -0.3861*** -0.2222* -0.2072* -0.4052*** -0.1236 -0.1237 -0.1293 -0.1212 -0.1215 -0.1282 Income -0.0016 -0.0011 -0.0054 -0.0133 -0.0121 -0.0126 -0.0213 -0.0214 -0.0228 -0.0211 -0.0212 -0.0229 Education -0.0884** -0.0850** 0.0131 -0.0881** -0.0845** 0.0093 -0.0386 -0.0384 -0.0413 -0.0377 -0.0376 -0.0408 North East 0.1555 0.082 0.1224 0.0682 -0.1836 -0.1949 -0.192 -0.1981 South 0.256 0.0478 0.3049* 0.0824 -0.1719 -0.1801 -0.1786 -0.1819 Mid-West -0.0595 -0.2816 -0.0062 -0.2443 -0.1804 -0.1901 -0.1852 -0.1917 Mountain 0.0622 -0.0467 0.0684 -0.0416 -0.2356 -0.234 -0.2403 -0.2384 Conservatism 0.1002** 0.1033** -0.0462 -0.0461 Authoritarianism 0.5597*** 0.5277*** -0.1743 -0.1745 Egalitarianism -0.5380*** -0.5346*** -0.0712 -0.0721 Social Dominance 0.3455*** 0.3282*** -0.0548 -0.0548 Cut 1 -2.2639*** -1.4499*** -1.3079*** -2.4291*** -1.5439*** -0.8073** -0.6334* -2.1297*** -0.1504 -0.2471 -0.2853 -0.6137 -0.2652 -0.3322 -0.3683 -0.6451 Cut 2 -1.2489*** -0.4082* -0.265 -1.2119** -0.5059** 0.2596 0.4347 -0.8912 -0.129 -0.2372 -0.2786 -0.6119 -0.2545 -0.3262 -0.3641 -0.6438 Cut 3 -0.2595** 0.6201*** 0.7673*** 0.0484 0.5106** 1.3141*** 1.4936*** 0.3868 -0.1234 -0.2383 -0.2812 -0.6079 -0.254 -0.3311 -0.3698 -0.6412 Cut 4 0.8191*** 1.7373*** 1.8897*** 1.3806** 1.6028*** 2.4438*** 2.6287*** 1.7225*** -0.1258 -0.2429 -0.2845 -0.6069 -0.259 -0.3367 -0.3741 -0.6395

Observations 1,525 1,522 1,522 1,522 1,525 1,522 1,522 1,522 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

258

Table A3.25. Ordered Logit: “Truly American = U.S. Citizen” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American"

VARIABLES = "Citizen" = "Citizen" = "Citizen" = "Citizen" = "Citizen" = "Citizen" = "Citizen" = "Citizen"

Prime 1 -0.1694 -0.2487 -0.2482 -0.2265 -0.4061 -0.6174 -0.6125 -0.4843 -0.1835 -0.1805 -0.181 -0.1791 -0.4143 -0.41 -0.4106 -0.3663 Conservatism 1.0207*** 0.9397*** 0.9255*** 0.1628 0.3765*** 0.3303*** 0.3268*** 0.0911 -0.2122 -0.2197 -0.2229 -0.2331 -0.0635 -0.065 -0.0656 -0.0677 Prime 1*Cons. 0.287 0.3757 0.3669 0.2771 0.0697 0.1115 0.1106 0.0838 -0.3071 -0.3157 -0.3171 -0.3174 -0.094 -0.0934 -0.0936 -0.0866 Prime 2 0.1195 0.0318 0.0327 -0.0221 0.227 0.0337 0.0269 -0.1839 -0.1889 -0.1857 -0.1863 -0.1861 -0.4171 -0.4138 -0.4171 -0.3891 Prime 2*Cons. -0.1689 -0.1149 -0.1118 0.0671 -0.0413 -0.0084 -0.0062 0.0469 -0.2943 -0.3024 -0.3036 -0.3049 -0.0931 -0.0933 -0.0938 -0.0897 Prime 3 0.0041 -0.0362 -0.036 -0.0377 -0.2406 -0.3554 -0.3623 -0.384 -0.1966 -0.1928 -0.1946 -0.2032 -0.4304 -0.4376 -0.4375 -0.4667 Prime 3*Cons. -0.0587 -0.0879 -0.0756 0.0269 0.0457 0.0628 0.0657 0.0831 -0.3131 -0.3241 -0.3242 -0.3319 -0.0951 -0.0978 -0.0976 -0.1037 Age 0.0165*** 0.0168*** 0.0119*** 0.0164*** 0.0165*** 0.0121*** -0.0043 -0.0042 -0.0044 -0.0043 -0.0043 -0.0044 Male -0.1305 -0.1271 -0.1807 -0.1422 -0.1415 -0.1796 -0.1326 -0.1331 -0.142 -0.1357 -0.1357 -0.1425 Income 0.0094 0.0113 0.0329 0.0148 0.0155 0.032 -0.0233 -0.0235 -0.0233 -0.0234 -0.0234 -0.0232 Education -0.2031*** -0.1988*** -0.1412*** -0.1838*** -0.1805*** -0.1379*** -0.0406 -0.0406 -0.0407 -0.0412 -0.0412 -0.041 North East 0.1313 -0.1093 0.1313 -0.0777 -0.1911 -0.1934 -0.191 -0.194 South 0.2152 0.0454 0.1367 0.0341 -0.1717 -0.177 -0.1708 -0.1759 Mid-West 0.1424 -0.1536 0.0713 -0.1442 -0.1886 -0.1924 -0.1894 -0.1909 Mountain 0.3556 0.173 0.2972 0.175 -0.2425 -0.2544 -0.2464 -0.2568 White Ident. 0.2917*** 0.2977*** -0.0446 -0.0448 Authoritarianism 0.6364*** 0.5853*** -0.1858 -0.1864 Egalitarianism -0.4695*** -0.4009*** -0.0682 -0.0719 Social Dominance 0.0746 0.0753 -0.0553 -0.0555 Cut 1 -2.8593*** -2.9178*** -2.7235*** -4.1623*** -1.7827*** -1.9039*** -1.7841*** -3.5535*** -0.1714 -0.255 -0.2974 -0.5845 -0.2887 -0.3477 -0.3787 -0.6755 Cut 2 -1.7840*** -1.8299*** -1.6348*** -2.9871*** -0.6769** -0.7859** -0.6657* -2.3673*** -0.144 -0.2384 -0.289 -0.5807 -0.2783 -0.3403 -0.3755 -0.6749 Cut 3 -0.6246*** -0.6453*** -0.4487 -1.6550*** 0.5261* 0.4399 0.5614 -1.0252 -0.1379 -0.2394 -0.2902 -0.5746 -0.283 -0.3482 -0.3832 -0.6715 Cut 4 0.5651*** 0.5729** 0.7715*** -0.2799 1.7520*** 1.6892*** 1.8123*** 0.3535 -0.1395 -0.2388 -0.2906 -0.5682 -0.288 -0.3512 -0.386 -0.6662

Observations 1,524 1,521 1,521 1,521 1,524 1,521 1,521 1,521 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

259

Table A3.26. Ordered Logit: “Truly American = U.S. Citizen” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American"

VARIABLES = "Citizen" = "Citizen" = "Citizen" = "Citizen" = "Citizen" = "Citizen" = "Citizen" = "Citizen"

Prime 1 -0.2488 -0.2673 -0.2796 -0.1962 -0.8884*** -0.8547** -0.8926*** -0.8464*** -0.173 -0.1761 -0.176 -0.1679 -0.335 -0.3391 -0.3389 -0.3186 White Ident. 1.1393*** 1.0852*** 1.0572*** 0.6796*** 0.2992*** 0.2935*** 0.2844*** 0.1539** -0.2253 -0.2265 -0.2291 -0.254 -0.0741 -0.0744 -0.0747 -0.0773 Prime 1*White Ident. 0.3208 0.2879 0.3185 0.248 0.2689** 0.2436** 0.2564** 0.2685** -0.3198 -0.3186 -0.323 -0.3461 -0.1046 -0.1049 -0.1056 -0.1071 Prime 2 0.0062 -0.0174 -0.025 -0.0527 -0.4478 -0.4148 -0.4505 -0.5408 -0.1689 -0.1706 -0.1702 -0.1642 -0.3359 -0.3354 -0.338 -0.3328 Prime 2*White Ident. 0.3255 0.2574 0.2937 0.4426 0.1855* 0.1567 0.1704 0.2121* -0.3245 -0.3275 -0.3338 -0.3688 -0.1052 -0.1056 -0.1073 -0.1139 Prime 3 0.0727 0.0107 0.0022 -0.04 -0.1168 -0.131 -0.1529 -0.2772 -0.183 -0.1831 -0.1846 -0.1861 -0.3425 -0.3405 -0.3431 -0.3347 Prime 3*White Ident. -0.2618 -0.2068 -0.1824 -0.0784 0.0527 0.04 0.0475 0.0876 -0.3389 -0.343 -0.3444 -0.3642 -0.1106 -0.1103 -0.1112 -0.1139 Age 0.0161*** 0.0164*** 0.0122*** 0.0161*** 0.0165*** 0.0116*** -0.0041 -0.0041 -0.0044 -0.0041 -0.0041 -0.0044 Male -0.0273 -0.0153 -0.1476 -0.066 -0.0514 -0.1586 -0.1343 -0.1337 -0.1426 -0.1344 -0.1341 -0.1424 Income 0.0380* 0.0396* 0.0371 0.0292 0.0313 0.0327 -0.0231 -0.0232 -0.0232 -0.0228 -0.0229 -0.023 Education -0.1821*** -0.1802*** -0.1263*** -0.1866*** -0.1843*** -0.1311*** -0.0396 -0.0395 -0.0413 -0.0388 -0.0388 -0.0411 North East 0.0083 -0.078 -0.0087 -0.0817 -0.181 -0.1917 -0.1847 -0.1932 South 0.2211 0.0177 0.2614 0.058 -0.1702 -0.175 -0.1722 -0.175 Mid-West 0.0194 -0.1533 0.046 -0.1264 -0.1824 -0.1894 -0.1848 -0.1897 Mountain 0.317 0.2021 0.2981 0.18 -0.2372 -0.2487 -0.2455 -0.2537 Conservatism 0.1440*** 0.1478*** -0.0443 -0.0451 Authoritarianism 0.6178*** 0.5946*** -0.1823 -0.1844 Egalitarianism -0.3987*** -0.3969*** -0.0717 -0.0721 Social Dominance 0.1016* 0.0755 -0.0547 -0.0554 Cut 1 -2.9233*** -2.6986*** -2.5634*** -3.7184*** -2.4881*** -2.3425*** -2.1985*** -3.6618*** -0.169 -0.2561 -0.2924 -0.6414 -0.2679 -0.3287 -0.3616 -0.6628 Cut 2 -1.8476*** -1.6102*** -1.4736*** -2.5482*** -1.3960*** -1.2376*** -1.0917*** -2.4723*** -0.1356 -0.237 -0.2811 -0.6387 -0.248 -0.3138 -0.3524 -0.6597 Cut 3 -0.6816*** -0.4201* -0.2812 -1.2177* -0.2131 -0.0313 0.1172 -1.1264* -0.1267 -0.2385 -0.2816 -0.6321 -0.2441 -0.3159 -0.3536 -0.6535 Cut 4 0.5262*** 0.8113*** 0.9538*** 0.1623 1.0038*** 1.2089*** 1.3619*** 0.2581 -0.1271 -0.2397 -0.283 -0.6277 -0.2459 -0.3182 -0.3552 -0.6484

Observations 1,524 1,521 1,521 1,521 1,524 1,521 1,521 1,521 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

260

Table A3.27. Ordered Logit: “Truly American = Live in the U.S.” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American" = "Live in = "Live in = "Live in = "Live in = "Live in = "Live in = "Live in = "Live in VARIABLES U.S." U.S." U.S." U.S." U.S." U.S." U.S." U.S."

Prime 1 -0.3989** -0.5116*** -0.5053*** -0.4251** -1.1534*** -1.4341*** -1.4218*** -1.0657*** -0.1801 -0.1763 -0.1773 -0.1813 -0.4006 -0.4015 -0.4047 -0.3927 Conservatism 0.2236 0.2729 0.2731 -0.2359 0.1284** 0.1066* 0.1053* -0.0583 -0.1846 -0.1902 -0.1919 -0.2189 -0.0565 -0.0587 -0.0592 -0.0681 Prime 1*Cons. 0.7167** 0.8494*** 0.8343*** 0.6371** 0.2396*** 0.2893*** 0.2870*** 0.2094** -0.2821 -0.2815 -0.2845 -0.2947 -0.088 -0.0886 -0.0893 -0.0886 Prime 2 -0.0901 -0.2226 -0.2202 -0.2173 -0.1093 -0.3686 -0.3587 -0.491 -0.1762 -0.1819 -0.1824 -0.1787 -0.3875 -0.3939 -0.3977 -0.3871 Prime 2*Cons. -0.05 0.0148 -0.0049 0.143 0.0009 0.0372 0.0336 0.0803 -0.2707 -0.2721 -0.2733 -0.2872 -0.0855 -0.0861 -0.0869 -0.0874 Prime 3 -0.2763 -0.3268* -0.3245* -0.3652* -0.3937 -0.5965 -0.6087 -0.7043* -0.1872 -0.1884 -0.1887 -0.1931 -0.4003 -0.4057 -0.4089 -0.4173 Prime 3*Cons. 0.204 0.2391 0.2419 0.4092 0.0449 0.0843 0.0879 0.119 -0.2862 -0.2947 -0.2942 -0.3048 -0.0885 -0.0901 -0.0907 -0.0933 Age 0.0038 0.0041 0.0073* 0.004 0.0042 0.0077* -0.0036 -0.0036 -0.004 -0.0036 -0.0037 -0.004 Male -0.2211* -0.2213* -0.4036*** -0.2425* -0.2456* -0.4136*** -0.1261 -0.1259 -0.1295 -0.1274 -0.1273 -0.1307 Income -0.0576*** -0.0553** -0.0426* -0.0562*** -0.0545** -0.0442** -0.0215 -0.0217 -0.0226 -0.0213 -0.0215 -0.0224 Education -0.1918*** -0.1888*** -0.1131*** -0.1789*** -0.1755*** -0.1143*** -0.0381 -0.038 -0.0401 -0.0383 -0.0382 -0.0399 North East 0.1543 -0.0033 0.1788 0.0146 -0.1791 -0.1672 -0.18 -0.167 South 0.2047 0.0624 0.1596 0.0569 -0.1537 -0.1501 -0.1567 -0.1506 Mid-West 0.1989 0.0695 0.1756 0.0812 -0.1648 -0.1633 -0.1664 -0.1625 Mountain 0.0249 0.0552 0.0378 0.0703 -0.2145 -0.2132 -0.2186 -0.2143 White Ident. 0.2924*** 0.2944*** -0.0466 -0.0466 Authoritarianism 0.5913*** 0.5699*** -0.1713 -0.1723 Egalitarianism -0.1180* -0.0949 -0.0614 -0.0656 Social Dominance 0.4393*** 0.4373*** -0.0533 -0.0534 Cut 1 -0.9905*** -1.9586*** -1.7736*** 0.1274 -0.5567** -1.5791*** -1.4236*** 0.0733 -0.134 -0.2309 -0.2773 -0.5198 -0.2634 -0.333 -0.3682 -0.6258 Cut 2 -0.2676** -1.2091*** -1.0235*** 0.9894* 0.1796 -0.8185** -0.6629* 0.9369 -0.1324 -0.2266 -0.2735 -0.519 -0.263 -0.3304 -0.3658 -0.6248 Cut 3 0.7485*** -0.1522 0.0338 2.2265*** 1.2082*** 0.2475 0.4031 2.1725*** -0.135 -0.2242 -0.2717 -0.5251 -0.2664 -0.3307 -0.3658 -0.6307 Cut 4 1.6826*** 0.8096*** 0.9968*** 3.3442*** 2.1492*** 1.2132*** 1.3696*** 3.2879*** -0.1464 -0.2297 -0.2744 -0.5333 -0.2753 -0.3374 -0.3704 -0.6374

Observations 1,526 1,523 1,523 1,523 1,526 1,523 1,523 1,523 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

261

Table A3.28. Ordered Logit: “Truly American = Live in the U.S.” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American" = "Live in = "Live in = "Live in = "Live in = "Live in = "Live in = "Live in = "Live in VARIABLES U.S." U.S." U.S." U.S." U.S." U.S." U.S." U.S."

Prime 1 -0.4100*** -0.4768*** -0.4823*** -0.4379*** -1.0616*** -1.0488*** -1.0751*** -0.9965*** -0.1592 -0.1577 -0.1579 -0.1622 -0.3535 -0.3387 -0.3401 -0.343 White Ident. 1.0490*** 1.0271*** 1.0110*** 0.4035* 0.2997*** 0.3252*** 0.3194*** 0.112 -0.2122 -0.2144 -0.2157 -0.2273 -0.0792 -0.0766 -0.0772 -0.081 Prime 1*White 0.9243*** 0.9057*** 0.9242*** 0.8722*** 0.3261*** 0.2946*** 0.3039*** 0.2917** Ident. -0.3129 -0.3064 -0.3079 -0.3133 -0.1156 -0.1109 -0.1115 -0.1133 Prime 2 -0.1031 -0.1871 -0.1939 -0.2129 -0.5824* -0.6086* -0.6311* -0.7368** -0.1603 -0.1616 -0.1621 -0.1628 -0.3285 -0.3238 -0.3268 -0.3256 Prime 2*White 0.0869 0.0511 0.0623 0.3077 0.1734 0.1457 0.1524 0.2154** Ident. -0.3094 -0.313 -0.3137 -0.3117 -0.1081 -0.1068 -0.1079 -0.1084 Prime 3 -0.2641 -0.2971* -0.2966* -0.3150* -0.7326** -0.6819** -0.6818** -0.8023** -0.1658 -0.1675 -0.1677 -0.174 -0.3345 -0.3343 -0.3352 -0.3416 Prime 3*White 0.3084 0.3316 0.3281 0.3376 0.2161* 0.1855* 0.1851* 0.2204* Ident. -0.326 -0.3316 -0.3316 -0.33 -0.1117 -0.1117 -0.112 -0.1136 Age 0.0009 0.0011 0.0069* 0.0014 0.0016 0.0069* -0.0035 -0.0036 -0.0039 -0.0035 -0.0036 -0.0039 Male -0.15 -0.1471 -0.3616*** -0.2063 -0.2004 -0.3867*** -0.127 -0.1272 -0.13 -0.1257 -0.1264 -0.1296 Income -0.0412* -0.0399* -0.0366 -0.0518** -0.0500** -0.0431* -0.0215 -0.0216 -0.0224 -0.0216 -0.0217 -0.0223 Education -0.1660*** -0.1649*** -0.1045*** -0.1618*** -0.1607*** -0.1027*** -0.0378 -0.0378 -0.0396 -0.0383 -0.0384 -0.0397 North East 0.0687 0.0169 0.0556 0.0069 -0.1725 -0.166 -0.1736 -0.166 South 0.1435 0.0571 0.1799 0.0725 -0.1533 -0.1512 -0.1544 -0.1512 Mid-West 0.0897 0.0659 0.1205 0.0811 -0.1639 -0.1633 -0.1645 -0.1628 Mountain -0.0363 0.0495 -0.0237 0.0587 -0.2159 -0.2149 -0.2157 -0.2138 Conservatism 0.0357 0.0427 -0.0414 -0.0414 Authoritarianism 0.5889*** 0.5976*** -0.1728 -0.1717 Egalitarianism -0.0964 -0.0916 -0.0657 -0.0654 Social Dominance 0.4651*** 0.4451*** -0.0529 -0.0532 Cut 1 -0.8346*** -1.7463*** -1.6480*** -0.0553 -0.3507 -1.2371*** -1.1269*** 0.0772 -0.1175 -0.2255 -0.269 -0.5719 -0.24 -0.2919 -0.3268 -0.5944 Cut 2 -0.0729 -0.9646*** -0.8660*** 0.7998 0.4253* -0.4395 -0.3286 0.9414 -0.1166 -0.2208 -0.2647 -0.5707 -0.2423 -0.2916 -0.3256 -0.5935 Cut 3 1.0326*** 0.1714 0.2703 2.0341*** 1.5295*** 0.6996** 0.8112** 2.1768*** -0.1221 -0.2182 -0.2618 -0.574 -0.249 -0.2952 -0.3293 -0.5976 Cut 4 2.0509*** 1.2119*** 1.3113*** 3.1561*** 2.5440*** 1.7407*** 1.8532*** 3.2972*** -0.1368 -0.2253 -0.2663 -0.5808 -0.2604 -0.3043 -0.3366 -0.6056

Observations 1,526 1,523 1,523 1,523 1,526 1,523 1,523 1,523 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

262

Table A3.29. Ordered Logit: “Truly American = Feel American” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American"

VARIABLES = "Feel" = "Feel" = "Feel" = "Feel" = "Feel" = "Feel" = "Feel" = "Feel"

Prime 1 -0.0461 -0.1111 -0.1213 -0.0583 -0.339 -0.5969 -0.608 -0.509 -0.1944 -0.1923 -0.1927 -0.1923 -0.4448 -0.433 -0.4336 -0.4121 Conservatism 0.7737*** 0.6650*** 0.6612*** 0.2905 0.2514*** 0.1961*** 0.2006*** 0.0794 -0.214 -0.2184 -0.2214 -0.2377 -0.066 -0.0643 -0.0648 -0.071 Prime 1*Cons. 0.3691 0.4702 0.4871 0.3906 0.0936 0.1496 0.1514 0.1372 -0.2988 -0.3023 -0.3022 -0.3057 -0.0957 -0.0932 -0.0931 -0.0901 Prime 2 0.0093 -0.092 -0.101 -0.0816 0.0724 -0.1657 -0.1808 -0.2746 -0.189 -0.1871 -0.1883 -0.1914 -0.4026 -0.3931 -0.3935 -0.3833 Prime 2*Cons. -0.0049 0.0771 0.0981 0.1563 -0.0149 0.0259 0.0292 0.0605 -0.2933 -0.2974 -0.2979 -0.3078 -0.0878 -0.0861 -0.0858 -0.0855 Prime 3 0.0735 0.035 0.0288 0.0204 -0.0828 -0.1961 -0.1906 -0.2475 -0.2061 -0.2023 -0.2021 -0.2027 -0.4555 -0.4541 -0.4547 -0.4266 Prime 3*Cons. -0.0673 -0.0923 -0.0912 0.0514 0.0274 0.0433 0.0404 0.0668 -0.3024 -0.3095 -0.3107 -0.3111 -0.0966 -0.0974 -0.0976 -0.0925 Age 0.0214*** 0.0213*** 0.0212*** 0.0217*** 0.0214*** 0.0216*** -0.0043 -0.0042 -0.0042 -0.0043 -0.0043 -0.0043 Male -0.2400* -0.2354* -0.3229** -0.2427* -0.2394* -0.3175** -0.1396 -0.1396 -0.1425 -0.14 -0.1401 -0.1426 Income 0.0035 0.001 0.0183 0.0095 0.0064 0.0207 -0.0224 -0.0227 -0.0232 -0.0224 -0.0226 -0.0231 Education -0.1902*** -0.1928*** -0.1412*** -0.1786*** -0.1805*** -0.1391*** -0.0397 -0.0394 -0.0396 -0.0396 -0.0393 -0.0394 North East -0.1269 -0.2902 -0.1016 -0.2726 -0.19 -0.1856 -0.1904 -0.1857 South -0.1549 -0.2524 -0.1872 -0.2618 -0.1752 -0.1714 -0.1771 -0.1723 Mid-West -0.2258 -0.3638** -0.2576 -0.3686** -0.1888 -0.183 -0.1911 -0.1838 Mountain -0.0489 -0.1184 -0.0729 -0.1286 -0.2424 -0.2441 -0.2477 -0.2484 White Ident. 0.2771*** 0.2857*** -0.0439 -0.0441 Authoritarianism 0.1739 0.1155 -0.1738 -0.1737 Egalitarianism -0.1389** -0.1111 -0.064 -0.0685 Social Dominance 0.2007*** 0.1952*** -0.0538 -0.0535 Cut 1 -2.2164*** -2.0675*** -2.2332*** -1.5057*** -1.4858*** -1.4393*** -1.6084*** -1.1607* -0.1699 -0.2651 -0.3128 -0.5757 -0.3211 -0.3786 -0.4086 -0.6821 Cut 2 -1.4362*** -1.2725*** -1.4383*** -0.6665 -0.6958** -0.6347* -0.8035** -0.3157 -0.1578 -0.2497 -0.2995 -0.563 -0.3173 -0.3702 -0.4007 -0.6721 Cut 3 -0.2613* -0.0664 -0.2317 0.6199 0.4911 0.5816 0.4139 0.9744 -0.1499 -0.2468 -0.296 -0.5583 -0.3138 -0.3686 -0.3986 -0.6674 Cut 4 0.9284*** 1.1605*** 0.9971*** 1.9307*** 1.6863*** 1.8117*** 1.6464*** 2.2833*** -0.1529 -0.2497 -0.2969 -0.5577 -0.3183 -0.3733 -0.402 -0.6664

Observations 1,525 1,522 1,522 1,522 1,525 1,522 1,522 1,522 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

263

Table A3.30. Ordered Logit: “Truly American = Feel American” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American"

VARIABLES = "Feel" = "Feel" = "Feel" = "Feel" = "Feel" = "Feel" = "Feel" = "Feel"

Prime 1 -0.1003 -0.1035 -0.1069 -0.0226 -0.6512* -0.5877 -0.5937 -0.4864 -0.1761 -0.1814 -0.1812 -0.1772 -0.3555 -0.3688 -0.3698 -0.3616 White Ident. 1.0660*** 0.9938*** 1.0103*** 0.6868*** 0.3369*** 0.3227*** 0.3249*** 0.2173*** -0.2254 -0.2343 -0.2343 -0.2427 -0.0783 -0.0816 -0.0815 -0.083 Prime 1*White 0.4731 0.4593 0.4481 0.3511 0.2445** 0.2186* 0.2177* 0.2017* Ident. -0.3204 -0.3218 -0.323 -0.3339 -0.1108 -0.1131 -0.1138 -0.1151 Prime 2 0.0494 0.026 0.0266 0.0609 -0.0593 -0.0284 -0.042 -0.018 -0.1734 -0.1785 -0.178 -0.1775 -0.3518 -0.3615 -0.3598 -0.3608 Prime 2*White -0.1811 -0.2609 -0.2592 -0.1785 0.0064 -0.0203 -0.0153 -0.0006 Ident. -0.3115 -0.3146 -0.314 -0.3223 -0.1082 -0.1103 -0.1101 -0.1135 Prime 3 0.084 0.018 0.012 0.0095 -0.0292 -0.0846 -0.1058 -0.1647 -0.1882 -0.1903 -0.1903 -0.1895 -0.3621 -0.3717 -0.3716 -0.3665 Prime 3*White -0.0745 0.0064 0.0002 0.0428 0.0386 0.0443 0.0494 0.0729 Ident. -0.3175 -0.3258 -0.3266 -0.3286 -0.1109 -0.1135 -0.1137 -0.1146 Age 0.0202*** 0.0201*** 0.0213*** 0.0200*** 0.0200*** 0.0208*** -0.0042 -0.0042 -0.0042 -0.0042 -0.0042 -0.0043 Male -0.1446 -0.131 -0.2819** -0.1788 -0.1631 -0.2959** -0.1402 -0.1396 -0.1408 -0.1417 -0.1411 -0.1416 Income 0.0327 0.0307 0.0286 0.0237 0.0221 0.0216 -0.0228 -0.0229 -0.0233 -0.0225 -0.0226 -0.0231 Education -0.1740*** -0.1776*** -0.1352*** -0.1705*** -0.1742*** -0.1347*** -0.0396 -0.0392 -0.0404 -0.0387 -0.0382 -0.0397 North East -0.2232 -0.2482 -0.2425 -0.2598 -0.1863 -0.1858 -0.1878 -0.1865 South -0.1247 -0.2678 -0.0877 -0.2357 -0.1726 -0.1731 -0.1735 -0.1731 Mid-West -0.2821 -0.3765** -0.2618 -0.3543* -0.1838 -0.1844 -0.1836 -0.1833 Mountain -0.0566 -0.1182 -0.0662 -0.1271 -0.2458 -0.2503 -0.245 -0.249 Conservatism 0.1405*** 0.1431*** -0.0443 -0.0446 Authoritarianism 0.151 0.1361 -0.1718 -0.1731 Egalitarianism -0.1141* -0.1143* -0.0683 -0.0694 Social Dominance 0.2160*** 0.1902*** -0.0527 -0.0542 Cut 1 -2.2229*** -1.8451*** -2.0162*** -1.3196** -1.6677*** -1.3603*** -1.5120*** -1.0915* -0.1552 -0.2627 -0.3033 -0.6306 -0.2703 -0.3502 -0.3787 -0.6621 Cut 2 -1.4396*** -1.0462*** -1.2174*** -0.487 -0.8683*** -0.5461 -0.6974* -0.2463 -0.1417 -0.2473 -0.2898 -0.6193 -0.2614 -0.3388 -0.3683 -0.6515 Cut 3 -0.2503* 0.172 0.0016 0.7927 0.3393 0.6877** 0.5377 1.0451 -0.1345 -0.2462 -0.2878 -0.6147 -0.2582 -0.3397 -0.3679 -0.6477 Cut 4 0.9686*** 1.4227*** 1.2555*** 2.0987*** 1.5673*** 1.9461*** 1.7995*** 2.3558*** -0.138 -0.2471 -0.2887 -0.6131 -0.2639 -0.3416 -0.3697 -0.6456

Observations 1,525 1,522 1,522 1,522 1,525 1,522 1,522 1,522 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

264

Table A3.31. Ordered Logit: “Truly American = Respect Institutions and Laws” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American" = "Resp. = "Resp. = "Resp. = "Resp. = "Resp. = "Resp. = "Resp. = "Resp. VARIABLES Institutions" Institutions" Institutions" Institutions" Institutions" Institutions" Institutions" Institutions"

Prime 1 -0.0845 -0.1127 -0.1107 -0.0509 -0.1526 -0.3018 -0.3083 -0.1261 -0.1803 -0.1781 -0.1786 -0.1833 -0.3984 -0.4081 -0.4067 -0.3929 Conservatism 1.2095*** 1.0796*** 1.0686*** 0.5891** 0.3330*** 0.2859*** 0.2829*** 0.1237* -0.2184 -0.2257 -0.227 -0.2424 -0.0601 -0.0632 -0.0635 -0.0702 Prime 1*Cons. -0.0969 -0.0583 -0.0659 -0.1772 -0.0021 0.0335 0.0355 -0.0004 -0.3042 -0.3148 -0.3144 -0.3181 -0.0894 -0.0926 -0.0922 -0.0898 Prime 2 -0.0458 -0.082 -0.0832 -0.1089 -0.1856 -0.3152 -0.3244 -0.4303 -0.1758 -0.1769 -0.1781 -0.1806 -0.3883 -0.4011 -0.4031 -0.3971 Prime 2*Cons. -0.2896 -0.2342 -0.2312 -0.1593 0.0087 0.0397 0.0418 0.0673 -0.3176 -0.3249 -0.3256 -0.332 -0.0922 -0.0952 -0.0955 -0.0955 Prime 3 -0.1404 -0.1447 -0.1469 -0.1296 -0.4218 -0.4237 -0.4423 -0.3593 -0.1914 -0.1891 -0.1907 -0.1949 -0.4167 -0.4254 -0.4308 -0.4263 Prime 3*Cons. 0.0896 0.0657 0.082 0.09 0.0716 0.0708 0.0759 0.0623 -0.3193 -0.3297 -0.3314 -0.3357 -0.0929 -0.0962 -0.0973 -0.0967 Age 0.0197*** 0.0199*** 0.0149*** 0.0197*** 0.0198*** 0.0153*** -0.0042 -0.0042 -0.0043 -0.0043 -0.0043 -0.0044 Male -0.1696 -0.1709 -0.1639 -0.1554 -0.1549 -0.1545 -0.1389 -0.1404 -0.1467 -0.1425 -0.1435 -0.1481 Income 0.0315 0.0326 0.0480** 0.0368 0.0371 0.0506** -0.0231 -0.0233 -0.0234 -0.0231 -0.0232 -0.0233 Education -0.0964** -0.0922** -0.0458 -0.0800* -0.0761* -0.0442 -0.0411 -0.0414 -0.0419 -0.0412 -0.0414 -0.0417 North East 0.1457 0.0014 0.1438 0.0023 -0.1892 -0.1889 -0.1899 -0.1882 South 0.1523 0.0529 0.1019 0.0336 -0.1655 -0.1684 -0.1662 -0.1673 Mid-West 0.0923 -0.0718 0.0252 -0.0998 -0.1857 -0.1859 -0.1864 -0.1842 Mountain 0.3535 0.2583 0.3336 0.2457 -0.2489 -0.2497 -0.252 -0.2502 White Ident. 0.1885*** 0.1916*** -0.0458 -0.046 Authoritarianism 0.6496*** 0.5952*** -0.1809 -0.1812 Egalitarianism -0.2964*** -0.2796*** -0.064 -0.0667 Social Dominance -0.0172 -0.0196 -0.0553 -0.0555 Cut 1 -3.4542*** -2.8613*** -2.7073*** -3.6131*** -2.5605*** -2.0100*** -1.9105*** -3.2536*** -0.2053 -0.2767 -0.3171 -0.5514 -0.3017 -0.3714 -0.3974 -0.6561 Cut 2 -2.1495*** -1.5473*** -1.3937*** -2.2688*** -1.2481*** -0.6884** -0.589 -1.9049*** -0.1487 -0.2465 -0.2885 -0.541 -0.2643 -0.3482 -0.3738 -0.6422 Cut 3 -0.8352*** -0.2118 -0.0582 -0.8781* 0.0794 0.6601* 0.7598** -0.5096 -0.1294 -0.2387 -0.2823 -0.5325 -0.2604 -0.3475 -0.373 -0.6374 Cut 4 0.6325*** 1.2899*** 1.4459*** 0.7079 1.5603*** 2.1760*** 2.2784*** 1.0763* -0.1298 -0.2403 -0.2861 -0.5273 -0.265 -0.351 -0.3778 -0.6328

Observations 1,526 1,523 1,523 1,523 1,526 1,523 1,523 1,523 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

265

Table A3.32. Ordered Logit: “Truly American = Respect Institutions and Laws” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "American" "American" "American" "American" "American" "American" "American" "American" = "Resp. = "Resp. = "Resp. = "Resp. = "Resp. = "Resp. = "Resp. = "Resp. VARIABLES Insts" Insts" Insts" Insts" Insts" Insts" Insts" Insts"

Prime 1 -0.3712** -0.3640** -0.3723** -0.2972* -0.7315** -0.6825** -0.7093** -0.6610** -0.169 -0.1709 -0.1709 -0.1735 -0.3151 -0.3214 -0.3238 -0.3312 White Ident. 0.4981** 0.4569** 0.4391* 0.182 0.1550** 0.1398* 0.1343* 0.0463 -0.2267 -0.2325 -0.2344 -0.2499 -0.0725 -0.0746 -0.0752 -0.0817 Prime 1*White 0.7127** 0.7134** 0.7280** 0.6849* 0.2064** 0.1898* 0.1978* 0.2044* Ident. -0.3216 -0.3269 -0.3289 -0.3518 -0.1029 -0.1049 -0.1057 -0.1125 Prime 2 -0.3416** -0.3154* -0.3205* -0.3356** -0.7019** -0.6473* -0.6723** -0.6736** -0.1666 -0.1705 -0.1705 -0.1686 -0.3216 -0.3313 -0.3335 -0.3332 Prime 2*White 0.7146** 0.6567* 0.6818* 0.7728** 0.1947* 0.1774 0.1872* 0.1947* Ident. -0.3373 -0.3441 -0.3479 -0.3643 -0.1059 -0.1082 -0.1093 -0.114 Prime 3 -0.1346 -0.1579 -0.1637 -0.22 -0.4368 -0.4579 -0.4718 -0.5708* -0.1803 -0.1794 -0.1801 -0.1828 -0.3269 -0.3291 -0.3299 -0.3353 Prime 3*White 0.1975 0.2523 0.2733 0.3859 0.1404 0.1454 0.151 0.1779 Ident. -0.3414 -0.3525 -0.3553 -0.3659 -0.1089 -0.1108 -0.1115 -0.1153 Age 0.0203*** 0.0206*** 0.0148*** 0.0208*** 0.0212*** 0.0150*** -0.0039 -0.0039 -0.0044 -0.0039 -0.0039 -0.0044 Male -0.0815 -0.0743 -0.1215 -0.1155 -0.1077 -0.1474 -0.1357 -0.137 -0.1471 -0.1365 -0.1378 -0.1471 Income 0.0549** 0.0558** 0.0545** 0.0472** 0.0485** 0.0496** -0.0227 -0.0228 -0.0233 -0.0226 -0.0227 -0.0233 Education -0.0817** -0.0793** -0.0369 -0.0812** -0.0782** -0.037 -0.0395 -0.0397 -0.042 -0.0392 -0.0393 -0.0417 North East 0.0099 -0.0134 0.0106 -0.0121 -0.1818 -0.1865 -0.1857 -0.1882 South 0.1741 0.0196 0.204 0.0404 -0.1622 -0.1666 -0.1653 -0.1681 Mid-West 0.0052 -0.105 0.0284 -0.0881 -0.1798 -0.1836 -0.1822 -0.1853 Mountain 0.3466 0.2384 0.3618 0.2514 -0.2503 -0.254 -0.2485 -0.252 Conservatism 0.1597*** 0.1587*** -0.0451 -0.0449 Authoritarianism 0.5848*** 0.5952*** -0.1811 -0.1809 Egalitarianism -0.2773*** -0.2768*** -0.0663 -0.0671 Social Dominance -0.0167 -0.0221 -0.0542 -0.0553 Cut 1 -3.7067*** -2.8408*** -2.7216*** -3.5224*** -3.4433*** -2.6345*** -2.5010*** -3.4811*** -0.2039 -0.2797 -0.3118 -0.6058 -0.2723 -0.3378 -0.3665 -0.6413 Cut 2 -2.4038*** -1.5264*** -1.4064*** -2.1773*** -2.1369*** -1.3160*** -1.1814*** -2.1312*** -0.1479 -0.2516 -0.2832 -0.5923 -0.238 -0.3183 -0.3457 -0.6301 Cut 3 -1.1033*** -0.1958 -0.0746 -0.7814 -0.8343*** 0.0158 0.1517 -0.7326 -0.1247 -0.2425 -0.2756 -0.5865 -0.2211 -0.3102 -0.3383 -0.6236 Cut 4 0.3297*** 1.2844*** 1.4090*** 0.8146 0.5949*** 1.4902*** 1.6298*** 0.8568 -0.1221 -0.2448 -0.2793 -0.5826 -0.221 -0.3133 -0.3423 -0.6196

Observations 1,526 1,523 1,523 1,523 1,526 1,523 1,523 1,523 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

266

Table A3.33. OLS: Ethnocentrism Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc.

Prime 1 -6.5356* -7.6212** -7.2876** -5.5038** -12.0506 -15.2556** -15.2606** -11.2102* -3.3732 -3.2418 -3.2473 -2.7748 -7.3983 -7.3819 -7.3684 -6.1598 Conservatism 9.9989*** 9.0621*** 9.0713*** -1.7924 3.8754*** 3.2662*** 3.1454*** -0.4543 -3.1874 -3.2284 -3.2295 -2.9105 -1.142 -1.1627 -1.1711 -1.0906 Prime 1*Cons. 7.3238 8.6741* 8.1065* 5.4043 1.9104 2.5512 2.5941* 1.8581 -4.5777 -4.6028 -4.6037 -4.0809 -1.536 -1.5588 -1.554 -1.3221 Prime 2 0.2848 -1.2716 -1.1472 -0.8352 2.7741 -0.5884 -0.652 -2.2995 -3.2757 -3.2003 -3.1998 -2.6409 -7.9142 -7.8404 -7.8562 -6.159 Prime 2*Cons. -0.4455 0.9413 0.4453 1.9849 -0.6484 -0.0642 -0.0643 0.5367 -4.9762 -5.0062 -4.992 -4.3005 -1.7109 -1.7059 -1.7042 -1.3637 Prime 3 -1.7337 -2.13 -2.0714 -1.6752 -0.4956 -2.0879 -2.6327 -2.8874 -3.1636 -3.0747 -3.042 -2.801 -7.2021 -7.1608 -7.0801 -6.9421 Prime 3*Cons. -2.4461 -2.4069 -2.0844 0.7396 -0.5614 -0.263 -0.0867 0.3565 -4.0842 -4.1249 -4.0496 -3.5932 -1.4512 -1.4657 -1.4444 -1.4 Age 0.1908*** 0.1967*** 0.1666*** 0.1875*** 0.1929*** 0.1707*** -0.0639 -0.0636 -0.0569 -0.0649 -0.0647 -0.0575 Male -2.1139 -2.2436 -4.3424** -2.0941 -2.2625 -4.3887** -2.1664 -2.1825 -1.9551 -2.1364 -2.1524 -1.9391 Income -0.6782** -0.6101* -0.2491 -0.6277* -0.574 -0.2572 -0.3441 -0.3486 -0.3029 -0.3463 -0.3501 -0.305 Education -2.1406*** -1.9607*** -0.7664 -1.9039*** -1.7294*** -0.7649 -0.6533 -0.6465 -0.5569 -0.6562 -0.6504 -0.5613 North East 6.8057*** 3.5367 7.0183*** 3.7542 -2.6295 -2.3559 -2.5788 -2.3743 South 6.7176*** 4.7594** 6.0829*** 4.8106** -2.2395 -2.187 -2.2264 -2.1647 Mid-West 7.0687*** 3.402 6.5683*** 3.553 -2.578 -2.4303 -2.544 -2.4245 Mountain 6.4171* 3.9976 6.4499* 4.1531 -3.479 -2.8936 -3.371 -2.8873 White Ident. 4.7935*** 4.8217*** -0.6554 -0.6504 Authoritarianism 2.0435 1.8479 -2.2433 -2.2221 Egalitarianism -5.0675*** -4.9019*** -0.754 -0.8876 Social Dominance 4.1557*** 4.1503*** -0.8684 -0.8565 Constant 6.8922*** 10.1475** 3.1678 0.2905 -5.3716 -0.9167 -6.9612 0.5645 -2.5647 -4.1325 -4.5565 -6.5502 -5.7581 -6.7469 -6.8665 -9.0053

Observations 1,391 1,388 1,388 1,388 1,391 1,388 1,388 1,388 R-squared 0.0502 0.0811 0.0895 0.3162 0.0743 0.1002 0.1079 0.3169 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

267

Table A3.34. OLS: Ethnocentrism Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc. Ethnoc.

Prime 1 -6.0947** -6.3575*** -6.3720*** -4.3811** -9.0225* -8.4174 -8.9572* -6.6238 -2.4124 -2.3791 -2.387 -2.1538 -5.4258 -5.2207 -5.2513 -4.7641 White Ident. 22.1940*** 21.0018*** 20.4745*** 12.1302*** 7.4795*** 7.3810*** 7.2359*** 4.6381*** -3.8101 -3.7425 -3.7775 -3.6427 -1.3396 -1.2896 -1.2987 -1.2593 Prime 1*White 4.4623 4.0498 4.3796 2.6033 1.5831 1.1961 1.4212 1.0787 Ident. -5.3631 -5.2297 -5.2674 -4.9419 -1.8808 -1.8078 -1.8257 -1.7003 Prime 2 0.5744 -0.0215 -0.1511 0.1078 -5.4045 -5.4243 -5.8207 -5.25 -2.6095 -2.5876 -2.5895 -2.3491 -5.4639 -5.3662 -5.3624 -5.0472 Prime 2*White 0.1248 -0.1486 0.1027 1.7374 1.9068 1.6425 1.7681 1.898 Ident. -5.2807 -5.2236 -5.2188 -4.6982 -1.8365 -1.8031 -1.8029 -1.6895 Prime 3 0.6116 -0.0335 0.0391 0.4392 5.3771 4.986 5.2957 5.0757 -2.1846 -2.1791 -2.1708 -1.9035 -4.8934 -4.7614 -4.7446 -4.2421 Prime 3*White -8.3417* -7.2399 -7.1234 -6.5983 -2.6253 -2.6039 -2.6815 -2.4404 Ident. -4.9306 -4.8742 -4.809 -4.6764 -1.7363 -1.6997 -1.6815 -1.6026 Age 0.1454** 0.1527** 0.1712*** 0.1435** 0.1511*** 0.1584*** -0.0608 -0.0607 -0.0574 -0.0578 -0.0576 -0.0554 Male -0.7945 -0.7879 -3.9126** -1.5853 -1.5449 -4.0893** -2.0338 -2.0473 -1.9672 -1.9198 -1.9272 -1.878 Income -0.3403 -0.2831 -0.1693 -0.4682 -0.3938 -0.2515 -0.3284 -0.3331 -0.3104 -0.313 -0.3177 -0.3022 Education -1.5615*** -1.4424** -0.5938 -1.5745*** -1.4412** -0.653 -0.6046 -0.601 -0.5751 -0.5855 -0.5777 -0.5581 North East 4.9108** 3.734 5.0577** 3.8781* -2.4535 -2.2962 -2.4257 -2.2832 South 6.3710*** 4.5239** 7.3675*** 5.2456** -2.2402 -2.1491 -2.2352 -2.1395 Mid-West 4.6852* 3.1807 5.6031** 3.8333 -2.5097 -2.4401 -2.4118 -2.362 Mountain 5.1305 4.2677 4.9082 4.1182 -3.1487 -2.8719 -3.0934 -2.8443 Conservatism 0.2068 0.2248 -0.6794 -0.6734 Authoritarianism 2.1874 1.8846 -2.1929 -2.1894 Egalitarianism -4.8965*** -4.8324*** -0.8877 -0.8727 Social Dominance 4.8354*** 4.2481*** -0.8274 -0.8501 Constant 3.5334** 4.541 -1.0384 3.6568 -9.9941** -7.8865* -13.9701*** -2.6123 -1.6996 -3.5799 -4.0585 -8.1423 -3.8794 -4.755 -4.9988 -8.4147

Observations 1,391 1,388 1,388 1,388 1,391 1,388 1,388 1,388 R-squared 0.1491 0.1636 0.1699 0.2992 0.1869 0.2034 0.2116 0.3223 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

268

Table A3.35. OLS: Feeling Thermometer Ratings of Whites Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES FT: Whites FT: Whites FT: Whites FT: Whites FT: Whites FT: Whites FT: Whites FT: Whites

Prime 1 -2.9749 -3.5141 -3.3267 -2.7075 -6.459 -8.3321 -7.9877 -6.6188 -2.4728 -2.4154 -2.4318 -2.3923 -5.1919 -5.1046 -5.0824 -4.9863 Conservatism 4.0354 3.5049 3.339 1.9597 0.7013 0.314 0.2112 -0.5338 -2.5927 -2.6177 -2.6435 -2.7028 -0.7579 -0.761 -0.7644 -0.8036 Prime 1*Cons. 4.7939 5.4986 5.0532 3.6161 1.1856 1.5731 1.4996 1.2103 -3.6431 -3.6551 -3.682 -3.6004 -1.116 -1.1119 -1.1094 -1.0868 Prime 2 1.3967 0.4658 0.5531 0.7294 0.2706 -1.7478 -1.7869 -2.4722 -2.4939 -2.4443 -2.4423 -2.4087 -5.3568 -5.3199 -5.2873 -5.0611 Prime 2*Cons. -2.5651 -1.98 -2.3133 -1.927 0.0358 0.3588 0.3593 0.591 -3.9614 -4.023 -4.0036 -3.8536 -1.1917 -1.1997 -1.1918 -1.1277 Prime 3 0.2669 -0.1457 -0.1616 -0.2329 -2.4464 -3.4994 -3.6332 -4.2041 -2.4508 -2.3947 -2.3919 -2.4241 -5.2381 -5.1415 -5.1154 -5.3696 Prime 3 *Cons. 1.771 1.5258 1.6041 2.8293 0.8109 0.944 0.9813 1.227 -3.6453 -3.665 -3.6413 -3.6015 -1.1211 -1.1119 -1.1073 -1.1465 Age 0.1196** 0.1245** 0.1076** 0.1291** 0.1341** 0.1141** -0.0542 -0.0542 -0.0547 -0.0549 -0.0549 -0.055 Male -3.5190* -3.3685* -3.7181** -3.4327* -3.2814* -3.6667** -1.8485 -1.8729 -1.8298 -1.8598 -1.8805 -1.8288 Income 0.3076 0.3473 0.4197 0.3429 0.3791 0.4594 -0.286 -0.2869 -0.2794 -0.2895 -0.2905 -0.2826 Education -1.6954*** -1.6361*** -1.1976** -1.6803*** -1.6264*** -1.2261** -0.5096 -0.5059 -0.4989 -0.5118 -0.5076 -0.4991 North East 2.6689 1.7343 2.7985 1.7055 -2.3266 -2.268 -2.321 -2.2797 South 4.1714** 3.6691* 4.2174** 3.7613* -2.107 -2.0856 -2.1042 -2.0879 Mid-West 1.2007 0.4378 1.0913 0.2679 -2.3418 -2.3041 -2.3345 -2.3014 Mountain 0.8183 0.2152 1.0279 0.3154 -2.7696 -2.673 -2.7866 -2.6983 White Ident. 2.9562*** 2.9789*** -0.5431 -0.5401 Authoritarianism 3.5235* 3.4542 -2.1082 -2.1382 Egalitarianism 0.3644 -0.1061 -0.6919 -0.746 Social Dominance 0.8841 0.8406 -0.6163 -0.6075 Constant 71.6600*** 72.5052*** 69.4762*** 55.0907*** 70.3033*** 71.7934*** 69.1270*** 59.7771*** -1.763 -3.3582 -3.8288 -6.4575 -3.5603 -4.5357 -4.7656 -7.6465

Observations 1,409 1,406 1,406 1,406 1,409 1,406 1,406 1,406 R-squared 0.0169 0.0331 0.0381 0.0862 0.0118 0.0279 0.0331 0.0827 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

269

Table A3.36. OLS: Feeling Thermometer Ratings of Whites Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES FT: Whites FT: Whites FT: Whites FT: Whites FT: Whites FT: Whites FT: Whites FT: Whites

Prime 1 -3.3645 -3.3618 -3.4019 -2.9943 -6.6416 -5.9263 -6.2058 -5.7491 -2.3154 -2.3105 -2.3021 -2.3117 -4.3587 -4.3181 -4.2949 -4.308 White Ident. 8.4726*** 8.2172*** 8.0799*** 6.2179** 2.6947*** 2.7314*** 2.7177*** 2.1886** -2.597 -2.6133 -2.6508 -2.7081 -0.8934 -0.8839 -0.8838 -0.8971 Prime 1*White 5.2861 4.6909 4.923 4.3239 1.8128 1.4743 1.5854 1.4854 Ident. -3.6731 -3.6692 -3.7009 -3.6996 -1.3054 -1.2892 -1.2913 -1.2922 Prime 2 -0.507 -0.8589 -0.8947 -0.787 -7.5164 -7.3926 -7.4169 -7.2624 -2.4396 -2.4321 -2.4185 -2.4118 -4.6366 -4.6519 -4.58 -4.6143 Prime 2*White 4.0144 3.3873 3.4279 3.8477 2.8694** 2.6180* 2.6171** 2.6469** Ident. -3.6304 -3.6795 -3.6954 -3.6862 -1.3285 -1.3387 -1.3263 -1.3359 Prime 3 1.9641 1.4296 1.4374 1.4222 4.579 4.1969 4.3943 4.2201 -2.2457 -2.2256 -2.2124 -2.2074 -4.2325 -4.1772 -4.124 -4.0931 Prime 3*White -2.3415 -2.1116 -2.2246 -1.9755 -1.2005 -1.2336 -1.3161 -1.2491 Ident. -3.7636 -3.762 -3.7523 -3.731 -1.3142 -1.2991 -1.2879 -1.2773 Age 0.0964* 0.1004* 0.1115** 0.0944* 0.0988* 0.1055* -0.0528 -0.0529 -0.0544 -0.0527 -0.0527 -0.0546 Male -2.908 -2.7107 -3.2820* -3.2439* -3.0365* -3.4193* -1.7923 -1.8087 -1.8285 -1.7726 -1.787 -1.8087 Income 0.4669* 0.4967* 0.5144* 0.4008 0.439 0.4596 -0.2836 -0.2845 -0.2853 -0.2787 -0.2794 -0.282 Education -1.4114*** -1.3757*** -1.1198** -1.3958*** -1.3550*** -1.1321** -0.5071 -0.5034 -0.5105 -0.5013 -0.4957 -0.504 North East 1.6095 1.5025 1.6298 1.5544 -2.2689 -2.269 -2.2629 -2.2608 South 3.8870* 3.5146* 4.3831** 3.9853* -2.087 -2.0962 -2.0806 -2.0919 Mid-West 0.0523 -0.0518 0.5863 0.4541 -2.3137 -2.3225 -2.2724 -2.2829 Mountain 0.1896 0.3269 0.1571 0.3105 -2.7061 -2.6852 -2.6917 -2.6686 Conservatism 0.1947 0.2249 -0.5246 -0.5151 Authoritarianism 3.5899* 3.4381 -2.1093 -2.1232 Egalitarianism -0.0956 -0.0402 -0.7541 -0.7451 Social Dominance 1.1771* 0.9088 -0.6091 -0.6115 Constant 70.4849*** 70.1927*** 67.8954*** 61.2508*** 65.7455*** 65.8213*** 63.1435*** 58.1789*** -1.6323 -3.3851 -3.8076 -7.3083 -2.9927 -4.1841 -4.3852 -7.4625

Observations 1,409 1,406 1,406 1,406 1,409 1,406 1,406 1,406 R-squared 0.0515 0.0626 0.0684 0.0776 0.0651 0.0764 0.0831 0.0897 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

270

Table A3.37. Ordered Logit: Racial Resentment Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Resentment Resentment Resentment Resentment Resentment Resentment Resentment Resentment

Prime 1 -0.1512 -0.2504 -0.2277 -0.1524 -0.3634 -0.702 -0.6789 -0.5017 -0.1889 -0.1809 -0.1817 -0.1869 -0.457 -0.4473 -0.4459 -0.3988 Conservatism 1.1953*** 1.1311*** 1.1577*** 0.0142 0.4878*** 0.4310*** 0.4317*** 0.048 -0.2055 -0.2001 -0.2035 -0.2094 -0.0756 -0.0741 -0.0743 -0.0697 Prime 1*Cons. 0.146 0.2744 0.223 0.0874 0.064 0.1313 0.1284 0.0944 -0.2807 -0.2871 -0.2919 -0.2967 -0.0971 -0.097 -0.0968 -0.0878 Prime 2 0.0596 -0.0301 -0.0158 0.0142 0.031 -0.2789 -0.2594 -0.45 -0.1962 -0.1879 -0.1876 -0.198 -0.4792 -0.47 -0.4717 -0.4509 Prime 2*Cons. -0.0848 0.0231 -0.0322 0.1518 0.0051 0.0681 0.0618 0.1273 -0.288 -0.2867 -0.2891 -0.3059 -0.1014 -0.1016 -0.1019 -0.0988 Prime 3 -0.0884 -0.1248 -0.105 -0.0289 -0.1882 -0.4184 -0.4316 -0.3229 -0.1937 -0.1923 -0.192 -0.1977 -0.471 -0.4629 -0.4644 -0.4199 Prime 3*Cons. -0.0097 0.039 0.0246 0.1202 0.0191 0.0696 0.0759 0.0791 -0.2801 -0.2875 -0.2885 -0.2894 -0.0984 -0.0984 -0.0985 -0.0893 Age 0.0137*** 0.0146*** 0.0150*** 0.0142*** 0.0148*** 0.0155*** -0.0039 -0.004 -0.004 -0.004 -0.0041 -0.004 Male 0.1991 0.1874 0.0176 0.1984 0.1782 0.0135 -0.1246 -0.1257 -0.126 -0.1274 -0.1287 -0.1269 Income -0.0648*** -0.0614*** -0.0368 -0.0665*** -0.0644*** -0.0416* -0.0224 -0.0225 -0.0228 -0.0227 -0.0229 -0.023 Education -0.2321*** -0.2250*** -0.1452*** -0.2089*** -0.2020*** -0.1459*** -0.0383 -0.0385 -0.039 -0.0387 -0.0392 -0.0393 North East 0.3751* 0.1443 0.3895** 0.1576 -0.1934 -0.1915 -0.196 -0.1907 South 0.3788** 0.1926 0.2685 0.1614 -0.1755 -0.173 -0.1804 -0.1742 Mid-West 0.4788*** 0.1601 0.4103** 0.1622 -0.1807 -0.1792 -0.1864 -0.1793 Mountain 0.2071 0.0416 0.1778 0.0462 -0.2152 -0.2102 -0.2136 -0.21 White Ident. 0.1830*** 0.1867*** -0.042 -0.0421 Authoritarianism 0.3258* 0.2751 -0.1752 -0.1746 Egalitarianism -0.8567*** -0.7775*** -0.0661 -0.0704 Social Dominance 0.4162*** 0.4160*** -0.0562 -0.0562 Cut 1 -1.6684*** -2.1734*** -1.7692*** -4.3994*** -0.2023 -0.8235** -0.499 -3.9123*** -0.1536 -0.239 -0.2988 -0.5415 -0.3564 -0.4043 -0.4439 -0.665 Cut 2 -0.8053*** -1.2694*** -0.8598*** -3.1857*** 0.7345** 0.1551 0.4841 -2.6875*** -0.1449 -0.2267 -0.2939 -0.5337 -0.3579 -0.4022 -0.4452 -0.66 Cut 3 0.2295 -0.1797 0.2377 -1.7464*** 1.8461*** 1.3180*** 1.6532*** -1.2373* -0.1457 -0.228 -0.2981 -0.5319 -0.3656 -0.4093 -0.453 -0.6598 Cut 4 1.4683*** 1.1108*** 1.5344*** -0.1508 3.1410*** 2.6594*** 2.9981*** 0.3624 -0.1569 -0.2344 -0.3077 -0.5358 -0.381 -0.4212 -0.4666 -0.6655

Observations 1,526 1,523 1,523 1,523 1,526 1,523 1,523 1,523 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

271

Table A3.38. Ordered Logit: Racial Resentment Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Resentment Resentment Resentment Resentment Resentment Resentment Resentment Resentment

Prime 1 -0.2234 -0.2532 -0.2701* -0.0613 -0.6048* -0.5682* -0.6337* -0.3834 -0.1656 -0.1633 -0.1641 -0.1666 -0.3448 -0.3384 -0.3412 -0.3337 White Ident. 1.4872*** 1.3840*** 1.3316*** 0.6877*** 0.3890*** 0.3725*** 0.3557*** 0.1414* -0.2312 -0.2297 -0.2355 -0.2485 -0.077 -0.073 -0.0748 -0.0783 Prime 1*White -0.0021 -0.0394 0.0119 -0.12 0.1316 0.104 0.1269 0.1029 Ident. -0.3034 -0.3079 -0.3147 -0.3378 -0.1071 -0.1068 -0.1085 -0.111 Prime 2 0.1392 0.1302 0.1085 0.1838 -0.0942 -0.0467 -0.113 -0.0527 -0.172 -0.167 -0.1683 -0.1748 -0.3474 -0.333 -0.3398 -0.3434 Prime 2*White -0.4907 -0.5615* -0.517 -0.3245 0.0199 -0.0059 0.0151 0.0492 Ident. -0.321 -0.3244 -0.33 -0.3449 -0.108 -0.1062 -0.1086 -0.114 Prime 3 0.0545 0.0049 0.0015 0.0791 0.0088 -0.0138 -0.0279 -0.0454 -0.1716 -0.1686 -0.1688 -0.1699 -0.3235 -0.3121 -0.3174 -0.3205 Prime 3*White -0.5064 -0.3686 -0.3474 -0.2928 -0.0262 -0.0198 -0.0143 0.0207 Ident. -0.3167 -0.3249 -0.3284 -0.3333 -0.1046 -0.1036 -0.1055 -0.1091 Age 0.0149*** 0.0156*** 0.0151*** 0.0149*** 0.0157*** 0.0149*** -0.0038 -0.0038 -0.004 -0.0039 -0.0039 -0.004 Male 0.2651** 0.2666** 0.025 0.2343* 0.2380* 0.0238 -0.1241 -0.1253 -0.1254 -0.1264 -0.128 -0.1265 Income -0.0318 -0.0287 -0.0355 -0.0422* -0.0383* -0.0408* -0.0227 -0.0227 -0.0231 -0.0226 -0.0226 -0.0231 Education -0.2053*** -0.2002*** -0.1402*** -0.2060*** -0.2002*** -0.1404*** -0.0389 -0.0393 -0.0396 -0.039 -0.0392 -0.0393 North East 0.2516 0.165 0.2481 0.1632 -0.1866 -0.19 -0.1906 -0.1906 South 0.3955** 0.142 0.4507** 0.1772 -0.1777 -0.1744 -0.1824 -0.1756 Mid-West 0.3348* 0.1478 0.3873** 0.1756 -0.1836 -0.1789 -0.1884 -0.1802 Mountain 0.176 0.0389 0.1835 0.0401 -0.2132 -0.209 -0.2161 -0.2105 Conservatism 0.1232*** 0.1235*** -0.0441 -0.0442 Authoritarianism 0.2893* 0.2845 -0.1742 -0.174 Egalitarianism -0.7741*** -0.7767*** -0.0707 -0.0707 Social Dominance 0.4275*** 0.4148*** -0.056 -0.0565 Cut 1 -1.7022*** -1.8314*** -1.5111*** -3.7951*** -1.1047*** -1.3130*** -0.9825*** -3.6989*** -0.1313 -0.2375 -0.2865 -0.6106 -0.243 -0.3066 -0.3451 -0.6318 Cut 2 -0.8477*** -0.9462*** -0.6220** -2.5827*** -0.2368 -0.4134 -0.0781 -2.4728*** -0.1222 -0.2274 -0.2821 -0.6054 -0.2404 -0.3009 -0.344 -0.6274 Cut 3 0.172 0.1166 0.447 -1.1347* 0.7858*** 0.6527** 0.9954*** -1.0232 -0.1217 -0.2306 -0.2865 -0.6045 -0.2426 -0.3052 -0.3486 -0.6261 Cut 4 1.4138*** 1.4002*** 1.7355*** 0.4649 2.0242*** 1.9346*** 2.2842*** 0.5749 -0.1304 -0.238 -0.2954 -0.6098 -0.2514 -0.3138 -0.3582 -0.6311

Observations 1,526 1,523 1,523 1,523 1,526 1,523 1,523 1,523 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

272

Table A3.39. Ordered Logit: “Minorities are Patriotic” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "Minorities "Minorities "Minorities "Minorities "Minorities "Minorities "Minorities "Minorities are are are are are are are are VARIABLES Patriotic" Patriotic" Patriotic" Patriotic" Patriotic" Patriotic" Patriotic" Patriotic"

Prime 1 -0.176 -0.1205 -0.1541 -0.2229 -0.1167 -0.0657 -0.0716 -0.2066 -0.1939 -0.1958 -0.1939 -0.1951 -0.414 -0.4242 -0.4207 -0.4342 Conservatism -0.8888*** -0.9891*** -1.0420*** -0.2682 -0.3403*** -0.3515*** -0.3567*** -0.1155 -0.2348 -0.2387 -0.2376 -0.2475 -0.0736 -0.0757 -0.0755 -0.0821 Prime 1*Cons. 0.2977 0.2999 0.3611 0.4624 0.0133 0.0152 0.0128 0.0355 -0.2913 -0.2947 -0.2967 -0.296 -0.0897 -0.0922 -0.0916 -0.0933 Prime 2 -0.3196 -0.2697 -0.2916 -0.2918 -0.5647 -0.4962 -0.5495 -0.4402 -0.1965 -0.1997 -0.1965 -0.2026 -0.4544 -0.462 -0.4604 -0.4791 Prime 2*Cons. 0.323 0.3269 0.407 0.2965 0.0864 0.0819 0.0962 0.0613 -0.3227 -0.3237 -0.3245 -0.3329 -0.1027 -0.1038 -0.1035 -0.106 Prime 3 0.1023 0.1496 0.1176 0.1266 -0.213 -0.1096 -0.1152 -0.0767 -0.2122 -0.2137 -0.2106 -0.2192 -0.4568 -0.4571 -0.4613 -0.4739 Prime 3*Cons. 0.22 0.2213 0.2557 0.1422 0.099 0.085 0.0817 0.0618 -0.3211 -0.3263 -0.3244 -0.3358 -0.0986 -0.1 -0.1007 -0.1027 Age 0.0118*** 0.0113*** 0.0145*** 0.0123*** 0.0120*** 0.0144*** -0.0038 -0.0037 -0.0043 -0.0038 -0.0038 -0.0043 Male -0.2945** -0.2677** -0.1764 -0.2753** -0.2447* -0.1593 -0.1342 -0.1336 -0.1434 -0.1384 -0.1382 -0.1452 Income 0.0133 0.0076 -0.0179 0.0097 0.0047 -0.0136 -0.0225 -0.0228 -0.0249 -0.0227 -0.0231 -0.0247 Education 0.1380*** 0.1285*** 0.0721* 0.1198*** 0.1111*** 0.0705 -0.0415 -0.0416 -0.0437 -0.0429 -0.043 -0.0441 North East -0.4221** -0.2839 -0.4186** -0.2751 -0.1797 -0.1885 -0.1814 -0.1868 South -0.3878** -0.2726 -0.3026* -0.2375 -0.1629 -0.1733 -0.1653 -0.1725 Mid-West -0.6829*** -0.5070*** -0.6398*** -0.5028*** -0.1698 -0.181 -0.1714 -0.1784 Mountain -0.2035 -0.0793 -0.1647 -0.0725 -0.2301 -0.2337 -0.2324 -0.2344 White Ident. -0.1245*** -0.1226*** -0.0461 -0.0464 Authoritarianism -0.2668 -0.2356 -0.1779 -0.1787 Egalitarianism 0.5045*** 0.4538*** -0.0592 -0.0621 Social Dominance -0.1967*** -0.1922*** -0.0584 -0.0584 Cut 1 -3.0089*** -2.0467*** -2.5441*** -1.3669** -4.1309*** -3.2166*** -3.6557*** -1.8941*** -0.2003 -0.2794 -0.3202 -0.543 -0.3772 -0.4403 -0.4621 -0.6913 Cut 2 -1.6341*** -0.6617** -1.1519*** 0.0972 -2.7441*** -1.8243*** -2.2566*** -0.4317 -0.172 -0.2584 -0.2981 -0.5334 -0.353 -0.4178 -0.438 -0.6776 Cut 3 -0.5942*** 0.4048 -0.0767 1.2588** -1.6824*** -0.7400* -1.1647*** 0.731 -0.1595 -0.2564 -0.2977 -0.533 -0.3394 -0.4098 -0.4312 -0.6729 Cut 4 0.3357** 1.3591*** 0.8873*** 2.3341*** -0.7222** 0.242 -0.1737 1.8081*** -0.1561 -0.2601 -0.3003 -0.5364 -0.3332 -0.4079 -0.4291 -0.6737

Observations 1,506 1,503 1,503 1,503 1,506 1,503 1,503 1,503 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

273

Table A3.40. Ordered Logit: “Minorities are Patriotic” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) "Minorities "Minorities "Minorities "Minorities "Minorities "Minorities "Minorities "Minorities are are are are are are are are VARIABLES Patriotic" Patriotic" Patriotic" Patriotic" Patriotic" Patriotic" Patriotic" Patriotic"

Prime 1 -0.1773 -0.1315 -0.1257 -0.2409 -0.4556 -0.4048 -0.3465 -0.522 -0.1685 -0.1725 -0.1735 -0.1767 -0.3504 -0.3529 -0.3542 -0.3554 White Ident. -1.1233*** -1.1142*** -1.0703*** -0.5855** -0.3676*** -0.3718*** -0.3600*** -0.2094** -0.2551 -0.2485 -0.2468 -0.2605 -0.0913 -0.0897 -0.0893 -0.0938 Prime 1*White Ident, 0.5289 0.5143 0.4614 0.5776* 0.1625 0.1611 0.1364 0.1696 -0.3217 -0.3166 -0.3183 -0.3214 -0.1156 -0.1146 -0.1154 -0.1154 Prime 2 -0.2747 -0.2405 -0.2333 -0.2655 -0.1735 -0.1518 -0.1131 -0.1653 -0.1771 -0.1793 -0.1795 -0.1881 -0.3555 -0.3583 -0.3607 -0.3792 Prime 2*White Ident. 0.3058 0.2961 0.2902 0.1992 0.0044 0.0108 -0.0011 -0.007 -0.3412 -0.3354 -0.3346 -0.3551 -0.1161 -0.1157 -0.116 -0.122 Prime 3 -0.0086 0.0272 0.0157 0.0432 -0.3901 -0.3751 -0.3912 -0.2895 -0.1856 -0.1894 -0.188 -0.1953 -0.3571 -0.361 -0.362 -0.3723 Prime 3*White Ident. 0.6193* 0.6058* 0.5808* 0.48 0.2036* 0.2122* 0.2122* 0.1765 -0.3495 -0.3512 -0.3513 -0.3669 -0.1186 -0.1186 -0.1188 -0.1226 Age 0.0110*** 0.0103*** 0.0140*** 0.0107*** 0.0100*** 0.0141*** -0.0038 -0.0037 -0.0043 -0.0038 -0.0037 -0.0043 Male -0.3591*** -0.3367** -0.1556 -0.3170** -0.2959** -0.1451 -0.1357 -0.1349 -0.1452 -0.1338 -0.1327 -0.1438 Income -0.0081 -0.0136 -0.0149 -0.0028 -0.0091 -0.0127 -0.022 -0.0223 -0.0248 -0.0221 -0.0224 -0.0249 Education 0.1310*** 0.1228*** 0.0705 0.1320*** 0.1231*** 0.0714 -0.0399 -0.04 -0.0444 -0.0398 -0.04 -0.0445 North East -0.3563** -0.2771 -0.3455* -0.2658 -0.1797 -0.1865 -0.1828 -0.1883 South -0.4141** -0.2126 -0.4392*** -0.2232 -0.1633 -0.1718 -0.1673 -0.1747 Mid-West -0.6037*** -0.4742*** -0.6411*** -0.4928*** -0.1718 -0.177 -0.1758 -0.1801 Mountain -0.2391 -0.088 -0.2165 -0.0747 -0.2304 -0.2364 -0.229 -0.2356 Conservatism -0.0741* -0.0770* -0.0437 -0.0444 Authoritarianism -0.2495 -0.2262 -0.1783 -0.1775 Egalitarianism 0.4547*** 0.4497*** -0.0616 -0.062 Social Dominance -0.2152*** -0.1993*** -0.0575 -0.0583 Cut 1 -3.0113*** -2.2176*** -2.6707*** -1.6622*** -3.6835*** -2.8665*** -3.3225*** -2.0068*** -0.1778 -0.2706 -0.3157 -0.5902 -0.3109 -0.3815 -0.4163 -0.6513 Cut 2 -1.6287*** -0.8255*** -1.2732*** -0.1953 -2.2980*** -1.4720*** -1.9212*** -0.5369 -0.1472 -0.2492 -0.2932 -0.5785 -0.2921 -0.3643 -0.397 -0.6398 Cut 3 -0.5807*** 0.244 -0.1958 0.9671* -1.2442*** -0.396 -0.8356** 0.629 -0.135 -0.2485 -0.2932 -0.5768 -0.2807 -0.3593 -0.3926 -0.6367 Cut 4 0.3531*** 1.1955*** 0.7633*** 2.0410*** -0.3014 0.5653 0.1348 1.7074*** -0.1331 -0.2529 -0.2953 -0.58 -0.2763 -0.3596 -0.3917 -0.639

Observations 1,506 1,503 1,503 1,503 1,506 1,503 1,503 1,503 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

274

Table A3.41. OLS: Affirmative Action Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Affirmative Affirmative Affirmative Affirmative Affirmative Affirmative Affirmative Affirmative

VARIABLES Action Action Action Action Action Action Action Action

Prime 1 0.3198 0.3025 0.2945 0.283 0.3522 0.3559 0.3468 0.4261 -0.2849 -0.283 -0.2832 -0.2738 -0.5821 -0.5868 -0.5855 -0.5823 Conservatism -2.1724*** -1.8923*** -1.8973*** -0.4466 -0.7854*** -0.7211*** -0.7227*** -0.3302*** -0.3538 -0.3519 -0.356 -0.3405 -0.1046 -0.1044 -0.1049 -0.1098 Prime 1*Cons. 0.0382 0.0563 0.083 -0.0367 0.0043 -0.0052 -0.0032 -0.0385 -0.5116 -0.5024 -0.5046 -0.4626 -0.1401 -0.1396 -0.1395 -0.1349 Prime 2 -0.2232 -0.2543 -0.2604 -0.1159 -0.3637 -0.3399 -0.355 -0.1628 -0.2892 -0.2872 -0.2885 -0.2816 -0.5964 -0.5958 -0.5969 -0.5807 Prime 2*Cons. -0.2413 -0.3201 -0.2818 -0.4494 0.0139 -0.011 -0.0054 -0.032 -0.4844 -0.4752 -0.4783 -0.4371 -0.1377 -0.1358 -0.136 -0.1285 Prime 3 0.1928 0.1691 0.1636 0.1669 0.9157 0.8524 0.8557 0.8872 -0.2903 -0.2887 -0.2901 -0.2854 -0.6122 -0.6033 -0.6049 -0.5908 Prime 3*Cons. -0.8660* -0.9483** -0.9456* -1.0181** -0.2391* -0.2395* -0.2406* -0.2575** -0.4973 -0.4827 -0.485 -0.4468 -0.1424 -0.1372 -0.1378 -0.1308 Age -0.0124** -0.0128** -0.0012 -0.0115* -0.0116** -0.0019 -0.0059 -0.0059 -0.0059 -0.0059 -0.0059 -0.0059 Male -0.8882*** -0.8846*** -0.8627*** -0.8680*** -0.8598*** -0.8461*** -0.2121 -0.2136 -0.2069 -0.2088 -0.2106 -0.2063 Income 0.001 -0.003 -0.0365 -0.0097 -0.0115 -0.0325 -0.0346 -0.0346 -0.0328 -0.0338 -0.0337 -0.0324 Education 0.0445 0.0402 0.0259 0.0008 -0.0019 0.0177 -0.06 -0.0603 -0.0564 -0.0586 -0.0587 -0.0562 North East -0.174 -0.0144 -0.1459 -0.0344 -0.2845 -0.2765 -0.2755 -0.2689 South -0.2999 -0.161 -0.1128 -0.0855 -0.2607 -0.2517 -0.2546 -0.247 Mid-West -0.3687 -0.0607 -0.2381 -0.0533 -0.2727 -0.2639 -0.2631 -0.256 Mountain 0.0065 0.3254 0.1326 0.3632 -0.3434 -0.3175 -0.3304 -0.3097 White Ident. 0.1245** 0.1188* -0.0632 -0.0624 Authoritarianism 0.2961 0.4561* -0.2559 -0.2541 Egalitarianism 1.1636*** 1.0004*** -0.0861 -0.0911 Social Dominance 0.1886** 0.1931** -0.0832 -0.0816 Constant 8.7725*** 9.5978*** 9.8743*** 2.6259*** 11.1995*** 12.0269*** 12.1591*** 4.4581*** -0.2131 -0.339 -0.4075 -0.7233 -0.451 -0.5135 -0.5453 -0.9019

Observations 1,513 1,510 1,510 1,510 1,513 1,510 1,510 1,510 R-squared 0.1407 0.1716 0.1734 0.2804 0.1919 0.2234 0.2243 0.2939 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

275

Table A3.42. OLS: Affirmative Action Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Affirmative Affirmative Affirmative Affirmative Affirmative Affirmative Affirmative Affirmative

VARIABLES Action Action Action Action Action Action Action Action

Prime 1 0.3436 0.2999 0.3283 0.1201 -0.2361 -0.2247 -0.1505 -0.3464 -0.2879 -0.2844 -0.2862 -0.256 -0.5362 -0.5128 -0.514 -0.4441 White Ident. -0.413 -0.2877 -0.2227 0.2699 -0.1445 -0.0715 -0.0554 0.1204 -0.3785 -0.3623 -0.3646 -0.3358 -0.127 -0.12 -0.1205 -0.1042 Prime 1*White 0.5784 0.5568 0.4818 0.5053 0.2802 0.2581 0.2326 0.2257 Ident. -0.5549 -0.5242 -0.5234 -0.4796 -0.1802 -0.1694 -0.169 -0.15 Prime 2 -0.0796 -0.184 -0.1588 -0.0874 0.393 0.2986 0.3699 0.4393 -0.286 -0.2786 -0.2803 -0.2502 -0.5477 -0.5204 -0.5245 -0.4388 Prime 2*White -0.6363 -0.5589 -0.6235 -0.7139 -0.2356 -0.2311 -0.2556 -0.2734* Ident. -0.5515 -0.5348 -0.5371 -0.4847 -0.1823 -0.1742 -0.1756 -0.1499 Prime 3 -0.3013 -0.315 -0.31 -0.1894 -0.6836 -0.6418 -0.6376 -0.3813 -0.299 -0.292 -0.2941 -0.259 -0.5589 -0.5299 -0.532 -0.4449 Prime 3*White 0.3939 0.2621 0.2405 -0.0509 0.1876 0.1544 0.1521 0.0685 Ident. -0.5845 -0.553 -0.5512 -0.4853 -0.1901 -0.1793 -0.179 -0.1518 Age -0.0216*** -0.0224*** -0.0017 -0.0225*** -0.0233*** -0.0029 -0.006 -0.006 -0.0058 -0.0059 -0.0059 -0.0058 Male -0.9658*** -0.9768*** -0.8312*** -0.9451*** -0.9572*** -0.8135*** -0.2141 -0.2146 -0.2059 -0.2124 -0.213 -0.2035 Income -0.0398 -0.0448 -0.0298 -0.0391 -0.0445 -0.0325 -0.0364 -0.0365 -0.0322 -0.0362 -0.0362 -0.0319 Education 0.0718 0.0679 0.0157 0.0785 0.074 0.0165 -0.0639 -0.064 -0.0556 -0.0636 -0.0636 -0.0554 North East -0.074 0.0114 -0.0693 0.0221 -0.302 -0.2678 -0.302 -0.2671 South -0.5021* -0.0653 -0.5147* -0.0575 -0.2777 -0.2448 -0.2762 -0.2435 Mid-West -0.3532 -0.0276 -0.3783 -0.0343 -0.2956 -0.2556 -0.2947 -0.2541 Mountain -0.1081 0.3728 -0.1165 0.3643 -0.3654 -0.3069 -0.3631 -0.3045 Conservatism -0.4168*** -0.4182*** -0.0615 -0.0617 Authoritarianism 0.4534* 0.4534* -0.2523 -0.2521 Egalitarianism 0.9824*** 0.9819*** -0.0915 -0.0909 Social Dominance 0.2036** 0.1743** -0.0802 -0.0812 Constant 8.0549*** 9.5604*** 9.9093*** 5.0542*** 8.3184*** 9.6701*** 10.0086*** 4.9747*** -0.2164 -0.3561 -0.4257 -0.7997 -0.3965 -0.4674 -0.5248 -0.817

Observations 1,513 1,510 1,510 1,510 1,513 1,510 1,510 1,510 R-squared 0.0155 0.0738 0.0777 0.2934 0.0179 0.0762 0.0803 0.2976 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

276

Table A3.43. OLS: "Legal Immigration is Good" Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Immig. is Immig. is Immig. is Immig. is Immig. is Immig. is Immig. is Immig. is

VARIABLES Good Good Good Good Good Good Good Good

Prime 1 0.2742 0.3035* 0.2897* 0.1968 0.418 0.5437 0.5324 0.336 -0.1775 -0.1722 -0.1729 -0.1657 -0.3309 -0.3316 -0.333 -0.3155 Conservatism 0.1386 0.0478 0.0411 0.3528* -0.0362 -0.0389 -0.0383 0.0451 -0.1906 -0.1848 -0.183 -0.1962 -0.0477 -0.0493 -0.049 -0.0561 Prime 1*Cons. -0.5586** -0.6378** -0.6158** -0.5053* -0.0864 -0.1155 -0.1142 -0.0791 -0.2777 -0.2689 -0.2693 -0.267 -0.0735 -0.0734 -0.0735 -0.0705 Prime 2 0.3026* 0.3880** 0.3805** 0.3369** 0.6164** 0.7791** 0.7718** 0.7784** -0.1718 -0.1716 -0.1707 -0.1636 -0.3024 -0.3128 -0.3109 -0.3033 Prime 2*Cons. -0.4013 -0.4667* -0.4412* -0.5333** -0.1157 -0.1403* -0.1376* -0.1582** -0.2735 -0.2689 -0.2669 -0.2534 -0.0713 -0.0724 -0.0718 -0.0692 Prime 3 0.4650*** 0.4668*** 0.4655*** 0.4013** 0.8010** 0.9104*** 0.9200*** 0.8057** -0.1714 -0.1659 -0.165 -0.1582 -0.3363 -0.3345 -0.3332 -0.3242 Prime 3*Cons. -0.5825** -0.5815** -0.5835** -0.5980** -0.1354 -0.1611** -0.1638** -0.1530* -0.2866 -0.2731 -0.2707 -0.2612 -0.0825 -0.0808 -0.0805 -0.0785 Age 0.0035 0.0032 0.003 0.0039 0.0036 0.0028 -0.0028 -0.0028 -0.0029 -0.0028 -0.0028 -0.0029 Male 0.2860** 0.2952** 0.3530*** 0.2861** 0.2969** 0.3560*** -0.1204 -0.1192 -0.1137 -0.1201 -0.119 -0.1143 Income 0.0546*** 0.0520*** 0.0384** 0.0536*** 0.0515*** 0.0401** -0.0189 -0.019 -0.0179 -0.0188 -0.0188 -0.0178 Education 0.0682** 0.0630* 0.0083 0.0637* 0.0586* 0.0096 -0.0341 -0.0339 -0.0315 -0.0341 -0.0339 -0.0317 North East -0.2339* -0.1297 -0.2452* -0.1463 -0.1306 -0.1307 -0.1296 -0.1293 South -0.2037* -0.1577 -0.1728 -0.1507 -0.1161 -0.1177 -0.1174 -0.1179 Mid-West -0.2396* -0.1531 -0.2191* -0.1588 -0.1298 -0.1277 -0.1313 -0.1277 Mountain -0.0775 -0.0373 -0.0477 -0.0283 -0.1589 -0.1642 -0.1612 -0.1651 White Ident. -0.0777** -0.0795** -0.0387 -0.0389 Authoritarianism -0.5486*** -0.5419*** -0.1288 -0.1275 Egalitarianism 0.0717 0.0368 -0.0504 -0.0543 Social Dominance -0.1780*** -0.1800*** -0.0448 -0.0447 Constant 5.7175*** 4.8786*** 5.0999*** 5.9497*** 5.9247*** 5.0630*** 5.2556*** 6.0570*** -0.1464 -0.2384 -0.2477 -0.4128 -0.2386 -0.3242 -0.3291 -0.5077

Observations 1,251 1,248 1,248 1,248 1,251 1,248 1,248 1,248 R-squared 0.0155 0.066 0.0695 0.1473 0.0267 0.0774 0.0808 0.1489 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

277

Table A3.44. OLS: "Legal Immigration is Good" Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Immig. is Immig. is Immig. is Immig. is Immig. is Immig. is Immig. is Immig. is

VARIABLES Good Good Good Good Good Good Good Good

Prime 1 0.0046 -0.0026 -0.0019 -0.0886 0.025 -0.0203 -0.0014 -0.1406 -0.1391 -0.1344 -0.1349 -0.132 -0.2805 -0.2746 -0.2752 -0.273 White Ident. -0.5555** -0.5479** -0.5287** -0.1621 -0.1932** -0.2032*** -0.2000*** -0.0974 -0.2323 -0.2197 -0.22 -0.2262 -0.0792 -0.0758 -0.0757 -0.0789 Prime 1*White 0.1828 0.2405 0.2195 0.3079 0.011 0.0307 0.0213 0.0524 Ident. -0.3246 -0.3137 -0.3145 -0.3124 -0.1071 -0.1042 -0.1043 -0.1045 Prime 2 0.0486 0.1045 0.1079 0.0713 0.0259 0.0627 0.0698 0.0258 -0.1352 -0.1343 -0.1341 -0.1309 -0.28 -0.2779 -0.2767 -0.2735 Prime 2*White 0.256 0.255 0.2516 0.1483 0.0395 0.0463 0.0439 0.0352 Ident. -0.3233 -0.3143 -0.3111 -0.3048 -0.1065 -0.1045 -0.1033 -0.1022 Prime 3 0.1628 0.1669 0.1679 0.1461 0.1909 0.2124 0.2012 0.1694 -0.1414 -0.1382 -0.1389 -0.1329 -0.2931 -0.2864 -0.2868 -0.2834 Prime 3*White 0.1043 0.0897 0.0836 0.0721 -0.0052 -0.014 -0.0101 -0.0037 Ident. -0.3518 -0.3362 -0.3357 -0.3309 -0.1162 -0.1115 -0.111 -0.1109 Age 0.0033 0.0029 0.0019 0.0037 0.0034 0.0027 -0.0029 -0.0029 -0.0028 -0.0029 -0.0028 -0.0029 Male 0.2546** 0.2608** 0.3636*** 0.2824** 0.2862** 0.3634*** -0.1184 -0.1174 -0.1138 -0.1165 -0.1155 -0.113 Income 0.0429** 0.0404** 0.0393** 0.0441** 0.0414** 0.0395** -0.019 -0.019 -0.0179 -0.0186 -0.0186 -0.0177 Education 0.0628* 0.0586* 0.0088 0.0571* 0.0529 0.0075 -0.0339 -0.0337 -0.0321 -0.0333 -0.0331 -0.0318 North East -0.2042 -0.1579 -0.1783 -0.1416 -0.1315 -0.1291 -0.1316 -0.1302 South -0.2244* -0.1385 -0.2358* -0.1398 -0.1191 -0.1186 -0.1206 -0.1206 Mid-West -0.2081 -0.1659 -0.2062 -0.1555 -0.1301 -0.1274 -0.1311 -0.1288 Mountain -0.0567 -0.0372 -0.0297 -0.0155 -0.1644 -0.1649 -0.1645 -0.1665 Conservatism -0.0523 -0.0531 -0.0402 -0.0402 Authoritarianism -0.5822*** -0.5546*** -0.1266 -0.1265 Egalitarianism 0.0378 0.034 -0.0549 -0.0551 Social Dominance -0.2084*** -0.1806*** -0.0435 -0.0444 Constant 5.9603*** 5.1926*** 5.3974*** 6.4506*** 6.3186*** 5.5594*** 5.7606*** 6.5444*** -0.1037 -0.1983 -0.2104 -0.4554 -0.214 -0.2483 -0.2601 -0.473

Observations 1,251 1,248 1,248 1,248 1,251 1,248 1,248 1,248 R-squared 0.0046 -0.0026 -0.0019 -0.0886 0.025 -0.0203 -0.0014 -0.1406 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

278

Table A3.45. OLS: “Increase Immigration” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Increase Increase Increase Increase Increase Increase Increase Increase

VARIABLES Immig. Immig. Immig. Immig. Immig. Immig. Immig. Immig.

Prime 1 0.3050* 0.3609** 0.3569** 0.2969** 0.6720** 0.8601** 0.8501** 0.7255** -0.1603 -0.1565 -0.1571 -0.1474 -0.3345 -0.3342 -0.334 -0.3257 Conservatism -0.4575** -0.4044** -0.4132** 0.2964 -0.1892*** -0.1560*** -0.1566*** 0.0575 -0.1777 -0.1819 -0.1826 -0.1822 -0.053 -0.0549 -0.0552 -0.0625 Prime 1*Cons. -0.4026 -0.4835* -0.4633* -0.3967* -0.1223* -0.1619** -0.1591** -0.1399** -0.2489 -0.249 -0.25 -0.2345 -0.0732 -0.0733 -0.0732 -0.0709 Prime 2 -0.0272 0.0439 0.0388 0.0674 0.2162 0.3817 0.3681 0.4421 -0.1613 -0.158 -0.1581 -0.1489 -0.3418 -0.339 -0.3395 -0.3223 Prime 2*Cons. -0.1507 -0.2141 -0.181 -0.2885 -0.0733 -0.1027 -0.0977 -0.1176* -0.2479 -0.2477 -0.2486 -0.2327 -0.0742 -0.0739 -0.0741 -0.0701 Prime 3 0.3056* 0.3241* 0.3214* 0.3084** 0.5861 0.6730* 0.6744* 0.6424* -0.1665 -0.1666 -0.1656 -0.1557 -0.3571 -0.3653 -0.3646 -0.3625 Prime 3*Cons. -0.1787 -0.1769 -0.1701 -0.2464 -0.0795 -0.0964 -0.0967 -0.1018 -0.2517 -0.2612 -0.2603 -0.2451 -0.0775 -0.0803 -0.0801 -0.0792 Age -0.0126*** -0.0128*** -0.0088*** -0.0123*** -0.0124*** -0.0089*** -0.0034 -0.0033 -0.0032 -0.0033 -0.0033 -0.0032 Male 0.2165** 0.2178** 0.2684*** 0.2245** 0.2281** 0.2737*** -0.109 -0.1085 -0.1033 -0.1059 -0.1058 -0.1028 Income 0.0382** 0.0358* 0.0163 0.0366** 0.0349** 0.0194 -0.0182 -0.0183 -0.0172 -0.0176 -0.0178 -0.017 Education 0.1017*** 0.0992*** 0.0530* 0.0885*** 0.0863** 0.0551* -0.0341 -0.0341 -0.0314 -0.0335 -0.0336 -0.0316 North East -0.0954 0.0481 -0.1037 0.0283 -0.1475 -0.142 -0.1436 -0.1402 South -0.1767 -0.0665 -0.1193 -0.0613 -0.1322 -0.1314 -0.1298 -0.1302 Mid-West -0.2149 -0.0346 -0.1839 -0.0493 -0.1405 -0.1374 -0.1389 -0.1364 Mountain 0.1055 0.2193 0.1249 0.2096 -0.179 -0.1743 -0.1753 -0.1742 White Ident. -0.0703** -0.0732** -0.0348 -0.0348 Authoritarianism -0.2632* -0.2395* -0.1359 -0.1357 Egalitarianism 0.4399*** 0.3997*** -0.043 -0.0478 Social Dominance -0.0678 -0.0679 -0.0456 -0.0457 Constant 4.3246*** 4.2494*** 4.4025*** 2.7004*** 4.9345*** 4.7794*** 4.8924*** 2.7309*** -0.1217 -0.1934 -0.2297 -0.3672 -0.2538 -0.3076 -0.3283 -0.487

Observations 1,501 1,498 1,498 1,498 1,501 1,498 1,498 1,498 R-squared 0.0484 0.0755 0.0793 0.1984 0.0861 0.1094 0.1122 0.2003 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

279

Table A3.46. OLS: “Increase Immigration” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Increase Increase Increase Increase Increase Increase Increase Increase

VARIABLES Immig. Immig. Immig. Immig. Immig. Immig. Immig. Immig.

Prime 1 0.1475 0.1536 0.1681 0.073 0.2612 0.2089 0.246 0.1197 -0.1403 -0.1421 -0.1422 -0.1287 -0.2794 -0.2843 -0.2837 -0.2611 White Ident. -0.6008*** -0.5386*** -0.5108** -0.1553 -0.1592** -0.1517** -0.1447** -0.0319 -0.2041 -0.2035 -0.2041 -0.2145 -0.0666 -0.068 -0.068 -0.0704 Prime 1*White 0.1849 0.2341 0.2028 0.2701 -0.019 0.0081 -0.0034 0.0132 Ident. -0.2817 -0.2773 -0.276 -0.2687 -0.0953 -0.0952 -0.0948 -0.0904 Prime 2 -0.0293 -0.0073 0.0058 0.0096 0.2295 0.2017 0.2345 0.1905 -0.1332 -0.1368 -0.1367 -0.1266 -0.261 -0.266 -0.2667 -0.2483 Prime 2*White -0.22 -0.1685 -0.1909 -0.2283 -0.1106 -0.0869 -0.0967 -0.0874 Ident. -0.2871 -0.279 -0.2788 -0.2756 -0.0914 -0.0902 -0.0902 -0.087 Prime 3 0.2497* 0.2791** 0.2827** 0.2908** 0.3935 0.401 0.4033 0.4363* -0.1346 -0.1409 -0.1405 -0.1288 -0.2646 -0.2768 -0.277 -0.2612 Prime 3*White -0.128 -0.1693 -0.1741 -0.2376 -0.0705 -0.0663 -0.0662 -0.0815 Ident. -0.2972 -0.3001 -0.299 -0.2973 -0.0965 -0.0981 -0.0976 -0.0954 Age -0.0132*** -0.0135*** -0.0087*** -0.0132*** -0.0135*** -0.0086*** -0.0034 -0.0033 -0.0032 -0.0034 -0.0033 -0.0032 Male 0.171 0.1671 0.2644*** 0.1923* 0.1871* 0.2708*** -0.1091 -0.1086 -0.1018 -0.1096 -0.1089 -0.1023 Income 0.0227 0.0206 0.0185 0.0253 0.0228 0.0188 -0.0186 -0.0188 -0.0171 -0.0186 -0.0187 -0.0171 Education 0.0924*** 0.0909*** 0.0498 0.0918*** 0.0901*** 0.0497 -0.0341 -0.0341 -0.0321 -0.0339 -0.0339 -0.0319 North East -0.0394 0.0227 -0.0215 0.0371 -0.1489 -0.1395 -0.1524 -0.1418 South -0.1995 -0.0529 -0.2145 -0.0588 -0.1374 -0.1296 -0.1402 -0.1315 Mid-West -0.1614 -0.0396 -0.1761 -0.0447 -0.1461 -0.1358 -0.1481 -0.1375 Mountain 0.076 0.2058 0.0871 0.2161 -0.1816 -0.1727 -0.1844 -0.1749 Conservatism -0.0389 -0.0371 -0.0362 -0.0363 Authoritarianism -0.2735** -0.2588* -0.1352 -0.1349 Egalitarianism 0.3915*** 0.3930*** -0.0478 -0.0479 Social Dominance -0.0785* -0.073 -0.0451 -0.0456 Constant 4.3396*** 4.4364*** 4.5690*** 3.0850*** 4.5837*** 4.6556*** 4.7853*** 3.0734*** -0.0969 -0.1807 -0.2227 -0.416 -0.1901 -0.2453 -0.2765 -0.4347

Observations 1,501 1,498 1,498 1,498 1,501 1,498 1,498 1,498 R-squared 0.0448 0.0667 0.0704 0.2004 0.0455 0.0679 0.0724 0.1987 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

280

Table A3.47. Ordered Logit: “Pro-Undocumented Immigrants” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Pro-Undoc, Pro-Undoc. Pro-Undoc. Pro-Undoc. Pro-Undoc. Pro-Undoc. Pro-Undoc. Pro-Undoc.

Prime 1 0.3863** 0.4282** 0.4285** 0.3958** 0.4759 0.6079 0.5903 0.4719 -0.1884 -0.1848 -0.1859 -0.1975 -0.4402 -0.4409 -0.4406 -0.429 Conservatism -1.1198*** -1.0237*** -1.0148*** -0.1564 -0.3989*** -0.3601*** -0.3604*** -0.0855 -0.247 -0.2475 -0.2496 -0.2602 -0.0779 -0.0779 -0.078 -0.0811 Prime 1*Cons. -0.337 -0.3848 -0.3749 -0.3509 -0.0422 -0.0698 -0.065 -0.0458 -0.3274 -0.3256 -0.3274 -0.3352 -0.1013 -0.1012 -0.1013 -0.0996 Prime 2 0.2614 0.2939 0.2912 0.2841 0.5263 0.6412 0.6416 0.7357* -0.1837 -0.1846 -0.1852 -0.1936 -0.4274 -0.4324 -0.4314 -0.4326 Prime 2*Cons. -0.2675 -0.3338 -0.3268 -0.5304 -0.0862 -0.1139 -0.1139 -0.1558 -0.3286 -0.3317 -0.3336 -0.3424 -0.0994 -0.1002 -0.1 -0.1008 Prime 3 0.3659* 0.3809** 0.3857** 0.3891* 0.5943 0.6837 0.6853 0.7066 -0.1927 -0.1917 -0.1918 -0.2003 -0.4619 -0.4601 -0.4596 -0.4773 Prime 3*Cons. -0.1942 -0.2522 -0.256 -0.3985 -0.0676 -0.0921 -0.0911 -0.1103 -0.3438 -0.3469 -0.3463 -0.3435 -0.1067 -0.1073 -0.1072 -0.1098 Age -0.0072* -0.0073* -0.0069 -0.0076* -0.0076* -0.0073 -0.0041 -0.0042 -0.0046 -0.0042 -0.0042 -0.0046 Male -0.3151** -0.3291** -0.2345 -0.3238** -0.3355** -0.2415 -0.1394 -0.1408 -0.1505 -0.1407 -0.1418 -0.1506 Income 0.0221 0.0217 0.0018 0.015 0.016 -0.0015 -0.0234 -0.0236 -0.0245 -0.0236 -0.0237 -0.0245 Education 0.1104** 0.1087** 0.0351 0.0928** 0.0912** 0.0351 -0.043 -0.0429 -0.0447 -0.0431 -0.0432 -0.045 North East -0.0152 0.1417 -0.0202 0.1371 -0.1986 -0.2031 -0.2024 -0.2042 South -0.1208 0.0209 -0.037 0.0375 -0.1646 -0.1711 -0.1721 -0.1724 Mid-West 0.0459 0.3068 0.1159 0.317 -0.1875 -0.1924 -0.1935 -0.1932 Mountain -0.032 0.0737 0.0297 0.0937 -0.2178 -0.2423 -0.2251 -0.2442 White Ident. -0.0990** -0.1035** -0.0464 -0.0469 Authoritarianism -0.298 -0.2332 -0.1929 -0.1935 Egalitarianism 0.5185*** 0.4826*** -0.066 -0.0702 Social Dominance -0.2519*** -0.2521*** -0.0577 -0.0578 Cut 1 -0.9630*** -0.9426*** -1.0035*** 0.1123 -2.2074*** -2.1940*** -2.1892*** -0.3499 -0.1415 -0.2272 -0.2829 -0.557 -0.3386 -0.3915 -0.4283 -0.6802 Cut 2 0.0475 0.086 0.0257 1.2609** -1.1720*** -1.1406*** -1.1352*** 0.7991 -0.1375 -0.227 -0.2826 -0.5593 -0.3318 -0.3866 -0.4241 -0.6829 Cut 3 3.2905*** 3.3604*** 3.3029*** 4.8036*** 2.1349*** 2.1961*** 2.2033*** 4.3539*** -0.2065 -0.2877 -0.3344 -0.6089 -0.3649 -0.4245 -0.4598 -0.725

Observations 1,483 1,480 1,480 1,480 1,483 1,480 1,480 1,480 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

281

Table A3.48. Ordered Logit: “Pro-Undocumented Immigrants” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES Pro-Undoc. Pro-Undoc. Pro-Undoc. Pro-Undoc. Pro-Undoc. Pro-Undoc. Pro-Undoc. Pro-Undoc.

Prime 1 0.3190* 0.3162* 0.3294* 0.2561 0.6023* 0.5721 0.6035* 0.5253 -0.1818 -0.1837 -0.1833 -0.1953 -0.3541 -0.3576 -0.3552 -0.3709 White Ident. -1.1415*** -1.1183*** -1.1192*** -0.5601** -0.2941*** -0.2808*** -0.2806*** -0.0816 -0.2332 -0.2297 -0.2308 -0.2708 -0.077 -0.0769 -0.0761 -0.086 Prime 1*White -0.0568 -0.0183 -0.0343 0.0441 -0.099 -0.084 -0.0917 -0.0896 Ident. -0.3344 -0.3321 -0.3318 -0.3648 -0.112 -0.1119 -0.1112 -0.12 Prime 2 -0.0047 -0.0356 -0.0333 -0.0141 0.1129 0.05 0.066 0.1222 -0.1758 -0.1822 -0.1816 -0.1908 -0.3369 -0.3434 -0.3407 -0.3554 Prime 2*White 0.4477 0.5209 0.5093 0.3488 0.0163 0.0369 0.0309 -0.0081 Ident. -0.3415 -0.3411 -0.3436 -0.3634 -0.1108 -0.111 -0.1109 -0.1159 Prime 3 0.0709 0.0833 0.089 0.144 0.0106 0.0142 0.0183 0.1711 -0.1836 -0.1866 -0.1857 -0.1939 -0.3467 -0.3469 -0.3454 -0.363 Prime 3*White 0.5294 0.4699 0.4785 0.3645 0.0818 0.0792 0.0808 0.0311 Ident. -0.3577 -0.3514 -0.351 -0.373 -0.1151 -0.1136 -0.1135 -0.1209 Age -0.0089** -0.0091** -0.0068 -0.0089** -0.0091** -0.007 -0.004 -0.004 -0.0046 -0.004 -0.0041 -0.0046

Male -0.4023*** -0.4268*** -0.2551* -0.3705*** -0.3953*** -0.2441 -0.1362 -0.137 -0.1505 -0.1369 -0.1377 -0.1496 Income -0.0112 -0.0116 -0.0053 -0.0048 -0.0057 -0.0021 -0.0236 -0.0237 -0.0246 -0.0234 -0.0235 -0.0244 Education 0.1041** 0.1034** 0.0324 0.1067** 0.1057** 0.0316 -0.0425 -0.0422 -0.0456 -0.0422 -0.0418 -0.0452 North East 0.0832 0.1406 0.0736 0.1301 -0.195 -0.2031 -0.1972 -0.2032 South -0.1687 0.0485 -0.2144 0.0214 -0.1657 -0.1729 -0.1669 -0.1727 Mid-West 0.1394 0.3326* 0.0937 0.3009 -0.1854 -0.1926 -0.1858 -0.1917 Mountain -0.0442 0.107 -0.0579 0.0948 -0.2346 -0.2436 -0.2379 -0.245 Conservatism -0.1609*** -0.1625*** -0.0458 -0.0461 Authoritarianism -0.2222 -0.2387 -0.1938 -0.1938 Egalitarianism 0.4837*** 0.4825*** -0.0706 -0.0705 Social Dominance -0.2418*** -0.2487*** -0.0586 -0.0579 Cut 1 -0.8892*** -1.2286*** -1.2790*** -0.5086 -1.3288*** -1.5765*** -1.6638*** -0.6079 -0.1388 -0.2333 -0.2792 -0.6184 -0.2525 -0.3119 -0.3449 -0.6294 Cut 2 0.0709 -0.2448 -0.2925 0.6424 -0.3734 -0.5998* -0.6839** 0.5389 -0.1355 -0.2327 -0.279 -0.6217 -0.2495 -0.3113 -0.3443 -0.6329 Cut 3 3.2530*** 2.9800*** 2.9409*** 4.1862*** 2.8159*** 2.6284*** 2.5537*** 4.0924*** -0.2043 -0.29 -0.3301 -0.661 -0.291 -0.3544 -0.3854 -0.6698

Observations 1,483 1,480 1,480 1,480 1,483 1,480 1,480 1,480 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

282

Table A3.49. Ordered Logit: “Blame Blacks for Police Tensions” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Blame Blame Blame Blame Blame Blame Blame Blame

VARIABLES Blacks Blacks Blacks Blacks Blacks Blacks Blacks Blacks

Prime 1 -0.1929 -0.2121 -0.196 -0.1626 -0.1958 -0.2936 -0.2839 -0.1926 -0.185 -0.1865 -0.1886 -0.1905 -0.4186 -0.4269 -0.4278 -0.4405 Conservatism 1.7694*** 1.5537*** 1.5537*** 0.4703** 0.6785*** 0.6279*** 0.6259*** 0.2951*** -0.2242 -0.23 -0.2327 -0.2345 -0.0722 -0.0736 -0.0735 -0.079 Prime 1*Cons. 0.1041 0.1127 0.0844 0.0682 0.0033 0.0262 0.0251 0.0124 -0.3064 -0.3122 -0.3148 -0.3102 -0.0933 -0.096 -0.096 -0.0964 Prime 2 0.1466 0.0988 0.1089 0.11 0.6204 0.4748 0.4936 0.3667 -0.2 -0.1957 -0.1964 -0.1973 -0.4291 -0.4194 -0.4181 -0.4267 Prime 2*Cons. -0.3217 -0.2095 -0.2333 -0.1266 -0.1264 -0.0894 -0.0933 -0.0645 -0.3023 -0.3105 -0.3125 -0.3143 -0.0947 -0.0951 -0.0948 -0.0953 Prime 3 0.053 0.0457 0.066 0.1181 0.296 0.2883 0.2942 0.2929 -0.1974 -0.1961 -0.1973 -0.1989 -0.4025 -0.4074 -0.4081 -0.4155 Prime 3*Cons. -0.1916 -0.1511 -0.1411 -0.0845 -0.0746 -0.07 -0.0671 -0.051 -0.2929 -0.2955 -0.2966 -0.2959 -0.0881 -0.0898 -0.0898 -0.0894 Age 0.0244*** 0.0250*** 0.0184*** 0.0245*** 0.0250*** 0.0187*** -0.0042 -0.0042 -0.0043 -0.0042 -0.0042 -0.0043 Male 0.1667 0.156 0.1167 0.1721 0.1586 0.1275 -0.1319 -0.1344 -0.1358 -0.1321 -0.1342 -0.137 Income 0.0284 0.0335 0.0786*** 0.0398* 0.0432** 0.0754*** -0.0216 -0.0219 -0.0226 -0.0213 -0.0217 -0.0225 Education -0.1175*** -0.1111*** -0.0779** -0.0895** -0.0857** -0.0695* -0.0393 -0.0396 -0.0384 -0.0389 -0.0392 -0.0388 North East 0.1796 -0.0596 0.17 -0.0292 -0.1896 -0.2054 -0.1873 -0.1974 South 0.3260** 0.2091 0.1906 0.1628 -0.1628 -0.1859 -0.1642 -0.1791 Mid-West 0.4052** 0.1293 0.2957* 0.126 -0.1801 -0.1977 -0.1794 -0.1902 Mountain 0.3619 0.1316 0.2668 0.1143 -0.2267 -0.2572 -0.2324 -0.252 White Ident. 0.1121*** 0.1181*** -0.0434 -0.0439 Authoritarianism 0.2287 0.1417 -0.1784 -0.1795 Egalitarianism -0.9287*** -0.8063*** -0.0693 -0.0733 Social Dominance -0.0313 -0.0377 -0.0613 -0.0602 Cut 1 -1.6763*** -0.8283*** -0.4851 -5.1593*** 0.3076 1.1955*** 1.4174*** -3.6485*** -0.1658 -0.244 -0.3065 -0.5787 -0.3281 -0.3785 -0.4172 -0.6984 Cut 2 -0.3480** 0.5541** 0.9019*** -3.5526*** 1.7722*** 2.7131*** 2.9369*** -2.0014*** -0.1475 -0.2359 -0.2988 -0.5617 -0.3298 -0.3829 -0.4189 -0.6869 Cut 3 1.2110*** 2.1945*** 2.5474*** -1.6347*** 3.4369*** 4.4725*** 4.6992*** -0.0509 -0.1513 -0.2421 -0.3087 -0.557 -0.3416 -0.3968 -0.434 -0.6867 Cut 4 2.4190*** 3.4462*** 3.8039*** -0.1756 4.6875*** 5.7822*** 6.0111*** 1.4162** -0.165 -0.259 -0.3229 -0.5582 -0.3519 -0.4117 -0.4482 -0.6887

Observations 1,485 1,482 1,482 1,482 1,485 1,482 1,482 1,482 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

283

Table A3.50. Ordered Logit: “Blame Blacks for Police Tensions” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Blame Blame Blame Blame Blame Blame Blame Blame

VARIABLES Blacks Blacks Blacks Blacks Blacks Blacks Blacks Blacks

Prime 1 -0.3153* -0.2784 -0.2929 -0.1261 -0.4236 -0.3443 -0.4061 -0.2653 -0.1723 -0.1787 -0.1797 -0.1805 -0.3591 -0.3632 -0.3653 -0.3564 White Ident. 0.7992*** 0.7406*** 0.6947*** 0.3376 0.2642*** 0.2323*** 0.2157** 0.0777 -0.2346 -0.2351 -0.2359 -0.2382 -0.088 -0.0883 -0.0888 -0.0866 Prime 1*White Ident. 0.0447 0.0359 0.0856 -0.062 0.0414 0.0251 0.048 0.0422 -0.3309 -0.3159 -0.3181 -0.3317 -0.1169 -0.1142 -0.1148 -0.1151 Prime 2 -0.085 -0.0588 -0.0741 0.0036 -0.4202 -0.3832 -0.4451 -0.4077 -0.1804 -0.1824 -0.1832 -0.1873 -0.3693 -0.3663 -0.3688 -0.359 Prime 2*White Ident. 0.2988 0.2831 0.3361 0.3872 0.1511 0.1471 0.1692 0.1904* -0.322 -0.3233 -0.3248 -0.3281 -0.118 -0.1165 -0.1168 -0.1151 Prime 3 0.197 0.2056 0.2153 0.2185 0.3797 0.3864 0.3775 0.2325 -0.1771 -0.1771 -0.181 -0.1753 -0.356 -0.3526 -0.3565 -0.3363 Prime 3*White Ident. -0.6417** -0.5883* -0.5745* -0.5304 -0.1332 -0.1267 -0.1182 -0.0627 -0.3226 -0.3203 -0.3197 -0.329 -0.1155 -0.1136 -0.1138 -0.1121 Age 0.0273*** 0.0280*** 0.0191*** 0.0274*** 0.0283*** 0.0188*** -0.0042 -0.0042 -0.0042 -0.0041 -0.0041 -0.0043 Male 0.2200* 0.2222 0.1315 0.196 0.1968 0.1292 -0.1332 -0.1356 -0.139 -0.1308 -0.133 -0.1382 Income 0.0610*** 0.0654*** 0.0767*** 0.0552*** 0.0600*** 0.0735*** -0.0212 -0.0214 -0.0225 -0.021 -0.0212 -0.0226 Education -0.1040*** -0.0980*** -0.0649* -0.1066*** -0.0998*** -0.0671* -0.0374 -0.0377 -0.0389 -0.0368 -0.0371 -0.0388 North East 0.0584 -0.0333 0.0652 -0.0302 -0.1818 -0.1951 -0.1848 -0.1971 South 0.3991** 0.1631 0.4215** 0.1792 -0.1624 -0.1781 -0.1647 -0.1797 Mid-West 0.2982* 0.1215 0.3326* 0.1432 -0.1773 -0.19 -0.1782 -0.1906 Mountain 0.3947 0.1061 0.3926 0.1179 -0.2417 -0.2514 -0.2411 -0.2514 Conservatism 0.2696*** 0.2729*** -0.0465 -0.0466 Authoritarianism 0.1359 0.131 -0.1791 -0.1797 Egalitarianism -0.8046*** -0.8007*** -0.074 -0.0744 Social Dominance -0.0135 -0.033 -0.0581 -0.0601 Cut 1 -1.9528*** -0.6801*** -0.3632 -3.8502*** -1.4909*** -0.3242 -0.0148 -3.8067*** -0.1583 -0.2513 -0.3044 -0.6242 -0.292 -0.3592 -0.3958 -0.6698 Cut 2 -0.6917*** 0.6524*** 0.9745*** -2.2111*** -0.216 1.0206*** 1.3353*** -2.1582*** -0.1374 -0.2455 -0.2972 -0.611 -0.2807 -0.355 -0.3899 -0.6567 Cut 3 0.6880*** 2.1543*** 2.4838*** -0.2555 1.1743*** 2.5277*** 2.8503*** -0.2025 -0.1353 -0.2539 -0.307 -0.6107 -0.2821 -0.3641 -0.4003 -0.6569 Cut 4 1.7807*** 3.3143*** 3.6496*** 1.2220** 2.2699*** 3.6854*** 4.0143*** 1.2703* -0.1441 -0.2714 -0.322 -0.6142 -0.2901 -0.3783 -0.4136 -0.6598

Observations 1,485 1,482 1,482 1,482 1,485 1,482 1,482 1,482 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

284

Table A3.51. OLS: “Support School Vouchers for Racial Minorities” Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Support Support Support Support Support Support Support Support

VARIABLES Vouchers Vouchers Vouchers Vouchers Vouchers Vouchers Vouchers Vouchers

Prime 1 0.0812 0.0588 0.057 0.0366 -0.3987 -0.3638 -0.3502 -0.4042 -0.1622 -0.1554 -0.1568 -0.1582 -0.3566 -0.3477 -0.3513 -0.3612 Conservatism -0.5219*** -0.3192 -0.3268 -0.0549 -0.1683*** -0.1206** -0.1195** -0.0243 -0.1998 -0.2025 -0.2035 -0.2127 -0.0561 -0.0558 -0.056 -0.0607 Prime 1*Cons. 0.2313 0.2356 0.2399 0.269 0.1424* 0.1257 0.1223 0.1312 -0.2735 -0.2782 -0.2792 -0.2809 -0.0803 -0.0806 -0.0811 -0.0827 Prime 2 -0.2781 -0.3095* -0.3113* -0.3039* -0.6193 -0.5992* -0.6045* -0.5737 -0.1735 -0.1615 -0.1623 -0.1606 -0.3822 -0.3608 -0.361 -0.3583 Prime 2*Cons. 0.3682 0.3233 0.3358 0.3035 0.1171 0.0993 0.1013 0.0929 -0.2806 -0.2831 -0.2837 -0.2796 -0.0863 -0.0846 -0.0846 -0.0833 Prime 3 -0.014 -0.0317 -0.0386 -0.0396 -0.2306 -0.2673 -0.2597 -0.271 -0.1669 -0.1559 -0.1557 -0.1572 -0.3668 -0.3399 -0.3395 -0.3476 Prime 3*Cons. 0.3151 0.2876 0.2877 0.2527 0.0837 0.0845 0.0811 0.0784 -0.2867 -0.2783 -0.278 -0.2787 -0.0847 -0.0803 -0.0803 -0.0811 Age -0.0175*** -0.0176*** -0.0161*** -0.0170*** -0.0171*** -0.0157*** -0.0032 -0.0032 -0.0033 -0.0032 -0.0032 -0.0033 Male -0.1245 -0.1112 -0.0853 -0.1302 -0.117 -0.0897 -0.1155 -0.115 -0.1131 -0.1146 -0.1142 -0.1131 Income -0.0243 -0.0263 -0.0339* -0.0246 -0.0265 -0.0335* -0.0197 -0.0197 -0.0194 -0.0196 -0.0196 -0.0194 Education -0.0201 -0.0229 -0.0418 -0.0258 -0.0284 -0.0431 -0.0349 -0.035 -0.0347 -0.0349 -0.035 -0.0347 North East -0.1177 -0.0533 -0.1144 -0.0474 -0.1654 -0.1649 -0.1641 -0.1636 South -0.0944 -0.0487 -0.0839 -0.0537 -0.1472 -0.1486 -0.1476 -0.1486 Mid-West -0.256 -0.1769 -0.2427 -0.1746 -0.1576 -0.1588 -0.1567 -0.158 Mountain -0.1226 -0.0601 -0.1159 -0.0609 -0.1988 -0.2031 -0.2003 -0.204 White Ident. -0.056 -0.0542 -0.0351 -0.0351 Authoritarianism 0.0263 -0.0061 -0.15 -0.1507 Egalitarianism 0.1715*** 0.1816*** -0.053 -0.0568 Social Dominance -0.0424 -0.0446 -0.0436 -0.0435 Constant 5.5295*** 6.6056*** 6.7470*** 6.1344*** 6.0246*** 6.9843*** 7.1092*** 6.1742*** -0.1228 -0.1962 -0.2405 -0.4272 -0.2489 -0.2716 -0.2992 -0.5095

Observations 1,491 1,488 1,488 1,488 1,491 1,488 1,488 1,488 R-squared 0.0136 0.0661 0.0683 0.0858 0.0145 0.0677 0.0698 0.087 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

285

Table A3.52. OLS: “Support School Vouchers for Racial Minorities” Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Support Support Support Support Support Support Support Support

VARIABLES Vouchers Vouchers Vouchers Vouchers Vouchers Vouchers Vouchers Vouchers

Prime 1 0.1656 0.1278 0.1317 0.0967 0.3484 0.3225 0.3392 0.2819 -0.1541 -0.1474 -0.1479 -0.1482 -0.2835 -0.27 -0.2709 -0.2704 White Ident. -0.1334 -0.0773 -0.0621 0.064 -0.0508 -0.0188 -0.0134 0.0176 -0.222 -0.2185 -0.219 -0.2246 -0.0717 -0.0692 -0.0694 -0.0709 Prime 1*White Ident. 0.0966 0.0901 0.0819 0.121 -0.0573 -0.0626 -0.0679 -0.0549 -0.2986 -0.2988 -0.3008 -0.299 -0.097 -0.0949 -0.0953 -0.0943 Prime 2 -0.0377 -0.1036 -0.0994 -0.1075 0.3609 0.3103 0.3294 0.2841 -0.1559 -0.1485 -0.1483 -0.1476 -0.3008 -0.2815 -0.281 -0.278 Prime 2*White Ident. -0.3386 -0.3019 -0.3068 -0.2928 -0.1813* -0.1838* -0.1900* -0.1746* -0.32 -0.315 -0.3158 -0.3086 -0.104 -0.1002 -0.1 -0.0975 Prime 3 0.1507 0.1438 0.1404 0.1199 0.2138 0.2445 0.2427 0.2142 -0.1573 -0.1499 -0.15 -0.1511 -0.2859 -0.2677 -0.2679 -0.2696 Prime 3*White Ident. -0.154 -0.2259 -0.2351 -0.2099 -0.0428 -0.0642 -0.0656 -0.0582 -0.3202 -0.3028 -0.3026 -0.3021 -0.0999 -0.0936 -0.0934 -0.0931 Age -0.0173*** -0.0175*** -0.0168*** -0.0168*** -0.0169*** -0.0160*** -0.0032 -0.0032 -0.0033 -0.0032 -0.0031 -0.0033 Male -0.1365 -0.1251 -0.0773 -0.1291 -0.1191 -0.0848 -0.1155 -0.1151 -0.1135 -0.1144 -0.1138 -0.1125 Income -0.026 -0.0278 -0.0322* -0.0254 -0.0274 -0.0321* -0.0194 -0.0194 -0.0192 -0.0194 -0.0194 -0.0192 Education -0.0248 -0.0267 -0.0395 -0.0313 -0.0331 -0.0426 -0.0346 -0.0348 -0.0345 -0.0346 -0.0348 -0.0345 North East -0.0906 -0.0541 -0.065 -0.0329 -0.164 -0.1635 -0.1637 -0.1634 South -0.087 -0.052 -0.0903 -0.0561 -0.1453 -0.1483 -0.1458 -0.1484 Mid-West -0.2328 -0.1819 -0.236 -0.1848 -0.1563 -0.1568 -0.157 -0.1572 Mountain -0.1407 -0.0914 -0.1252 -0.0792 -0.1993 -0.2016 -0.2001 -0.2024 Conservatism 0.0492 0.0472 -0.038 -0.0378 Authoritarianism -0.008 0.0215 -0.1507 -0.1508 Egalitarianism 0.1813*** 0.1759*** -0.0563 -0.0562 Social Dominance -0.065 -0.0458 -0.0438 -0.0435 Constant 5.3676*** 6.5295*** 6.6517*** 5.8054*** 5.4644*** 6.5457*** 6.6553*** 5.7033*** -0.1152 -0.1953 -0.2336 -0.4668 -0.2154 -0.249 -0.2774 -0.4803

Observations 1,491 1,488 1,488 1,488 1,491 1,488 1,488 1,488 R-squared 0.0114 0.0684 0.0703 0.0851 0.0194 0.0737 0.0757 0.0876 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

286

Table A3.53. Ordered Logit: Racial Inequality Guilt Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Inequality Inequality Inequality Inequality Inequality Inequality Inequality Inequality

VARIABLES Guilt Guilt Guilt Guilt Guilt Guilt Guilt Guilt

Prime 1 0.5961*** 0.6378*** 0.6270*** 0.6624*** 0.5442 0.6547 0.6353 0.7076* -0.183 -0.1847 -0.187 -0.1964 -0.4114 -0.4071 -0.4078 -0.4233 Conservatism -0.9741*** -0.8283*** -0.8313*** 0.1398 -0.4292*** -0.3951*** -0.3910*** -0.122 -0.2329 -0.2435 -0.2499 -0.2761 -0.0775 -0.0786 -0.0795 -0.0951 Prime 1*Cons. -0.5492* -0.5663* -0.5286 -0.6437* -0.0209 -0.0428 -0.0387 -0.0587 -0.3299 -0.3385 -0.344 -0.3379 -0.0994 -0.1003 -0.1002 -0.1014 Prime 2 0.139 0.1414 0.1415 0.2746 -0.2656 -0.2224 -0.2114 -0.0166 -0.1976 -0.1995 -0.2002 -0.2142 -0.4539 -0.4439 -0.4437 -0.4672 Prime 2*Cons. 0.0442 -0.0149 0.0112 -0.1409 0.1057 0.0875 0.0865 0.0612 -0.3301 -0.3395 -0.343 -0.3509 -0.1056 -0.1047 -0.1045 -0.1087 Prime 3 0.3466* 0.3503* 0.3488* 0.3988** 0.8897** 0.9891** 1.0148** 1.1048** -0.1998 -0.2021 -0.2011 -0.2031 -0.4516 -0.4432 -0.4426 -0.469 Prime 3*Cons. -0.6334* -0.7395** -0.7533** -0.9033** -0.1802* -0.2163** -0.2237** -0.2472** -0.333 -0.3441 -0.3439 -0.351 -0.1075 -0.1081 -0.1076 -0.1135 Age -0.0213*** -0.0218*** -0.0153*** -0.0217*** -0.0219*** -0.0155*** -0.0048 -0.0048 -0.0048 -0.0048 -0.0047 -0.0049 Male -0.3729** -0.3757** -0.4039*** -0.3874*** -0.3858*** -0.4132*** -0.1466 -0.1463 -0.1463 -0.1441 -0.1444 -0.1456 Income 0.0554** 0.0515** 0.0358 0.0529** 0.0504** 0.0403* -0.0237 -0.0238 -0.0242 -0.0232 -0.0234 -0.0241 Education 0.0416 0.0335 -0.0035 0.0036 -0.0037 -0.0111 -0.0427 -0.0427 -0.0437 -0.0423 -0.0427 -0.0441 North East -0.3194 -0.2083 -0.3197* -0.2498 -0.1951 -0.2067 -0.1932 -0.2062 South -0.4052** -0.3692* -0.2901 -0.3408* -0.1764 -0.1894 -0.1793 -0.191 Mid-West -0.3450* -0.0958 -0.2607 -0.1199 -0.1889 -0.2034 -0.1915 -0.2026 Mountain -0.2507 -0.061 -0.1845 -0.0561 -0.2409 -0.2454 -0.2429 -0.2454 White Ident. -0.0038 -0.0085 -0.0512 -0.0514 Authoritarianism 0.4662** 0.5071** -0.2007 -0.1971 Egalitarianism 0.8503*** 0.7566*** -0.0762 -0.0848 Social Dominance 0.1431** 0.1550** -0.0679 -0.0684 Cut 1 -0.2017 -0.9944*** -1.3694*** 3.6143*** -1.6054*** -2.4918*** -2.7620*** 2.6806*** -0.1405 -0.2594 -0.3133 -0.5889 -0.3286 -0.4075 -0.4395 -0.7632 Cut 2 0.5544*** -0.203 -0.5743* 4.4920*** -0.8235** -1.6725*** -1.9407*** 3.5623*** -0.1411 -0.2585 -0.3113 -0.5942 -0.3262 -0.4035 -0.4359 -0.7674 Cut 3 1.5511*** 0.8500*** 0.485 5.6521*** 0.2175 -0.5689 -0.8336* 4.7346*** -0.1512 -0.2655 -0.3162 -0.6022 -0.3323 -0.4074 -0.4393 -0.7768 Cut 4 2.5104*** 1.8451*** 1.4835*** 6.7164*** 1.2164*** 0.4746 0.2125 5.8117*** -0.179 -0.2854 -0.3295 -0.616 -0.3463 -0.4182 -0.4471 -0.7899

Observations 1,518 1,515 1,515 1,515 1,518 1,515 1,515 1,515 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

287

Table A3.54. Ordered Logit: Racial Inequality Guilt Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Inequality Inequality Inequality Inequality Inequality Inequality Inequality Inequality

VARIABLES Guilt Guilt Guilt Guilt Guilt Guilt Guilt Guilt

Prime 1 0.5088*** 0.5445*** 0.5669*** 0.4712** 0.8083** 0.8750** 0.9485*** 0.8816** -0.1687 -0.1781 -0.1795 -0.1935 -0.3492 -0.3628 -0.3666 -0.3957 White Ident. -0.5012** -0.3823 -0.3315 -0.1224 -0.0912 -0.0389 -0.0232 0.0759 -0.2326 -0.2511 -0.2526 -0.2778 -0.0791 -0.0868 -0.0877 -0.101 Prime 1*White Ident. 0.0076 -0.039 -0.0907 0.0278 -0.1081 -0.1259 -0.1507 -0.1507 -0.343 -0.3497 -0.3516 -0.3756 -0.1169 -0.119 -0.1202 -0.1303 Prime 2 0.1565 0.1192 0.145 0.2139 0.4379 0.4226 0.4797 0.6045 -0.1797 -0.1888 -0.1897 -0.2107 -0.3517 -0.3691 -0.3714 -0.4211 Prime 2*White Ident. 0.0288 0.0588 0.0129 0.0173 -0.0982 -0.1041 -0.1207 -0.1437 -0.3407 -0.3513 -0.3509 -0.3788 -0.1152 -0.1192 -0.1194 -0.1364 Prime 3 -0.0388 -0.0241 -0.0196 0.0357 -0.0039 0.0394 0.0392 0.2015 -0.1854 -0.1931 -0.1935 -0.2001 -0.3479 -0.3659 -0.3703 -0.3875 Prime 3*White Ident. 0.5072 0.3883 0.3686 0.3631 0.0407 0.0176 0.0175 -0.0211 -0.3473 -0.3672 -0.3638 -0.3648 -0.1176 -0.1234 -0.1238 -0.1284 Age -0.0239*** -0.0245*** -0.0155*** -0.0239*** -0.0246*** -0.0155*** -0.0048 -0.0048 -0.0049 -0.0048 -0.0048 -0.0049 Male -0.3724*** -0.3808*** -0.4017*** -0.3684** -0.3780*** -0.4111*** -0.1437 -0.1434 -0.1467 -0.1441 -0.1439 -0.1467 Income 0.0298 0.0254 0.0394 0.0321 0.0272 0.0396 -0.0237 -0.0238 -0.0243 -0.0235 -0.0237 -0.0242 Education 0.0481 0.0417 -0.0103 0.0479 0.0408 -0.0109 -0.0432 -0.0432 -0.0448 -0.0429 -0.0428 -0.0444 North East -0.2258 -0.2253 -0.2367 -0.2352 -0.1941 -0.2035 -0.1971 -0.2057 South -0.4630*** -0.3300* -0.4896*** -0.3497* -0.1795 -0.1909 -0.1825 -0.193 Mid-West -0.2851 -0.0887 -0.3102 -0.1086 -0.1905 -0.2009 -0.1922 -0.2022 Mountain -0.2758 -0.041 -0.293 -0.0561 -0.2437 -0.243 -0.2469 -0.2435 Conservatism -0.1727*** -0.1758*** -0.0545 -0.0547 Authoritarianism 0.5092*** 0.5113*** -0.1975 -0.1961 Egalitarianism 0.7589*** 0.7542*** -0.0853 -0.0854 Social Dominance 0.1488** 0.1489** -0.0672 -0.068 Cut 1 0.0324 -1.0165*** -1.3732*** 2.4930*** -0.0654 -0.9979*** -1.3527*** 2.6772*** -0.1291 -0.2573 -0.3058 -0.6731 -0.2515 -0.3488 -0.3805 -0.7112 Cut 2 0.7402*** -0.2679 -0.6199** 3.3703*** 0.6417** -0.2503 -0.5998 3.5544*** -0.1303 -0.258 -0.3062 -0.6794 -0.253 -0.3512 -0.3826 -0.7187 Cut 3 1.6869*** 0.7406*** 0.3951 4.5325*** 1.5896*** 0.7588** 0.4166 4.7175*** -0.1376 -0.2638 -0.3109 -0.6879 -0.2554 -0.3547 -0.3857 -0.7267 Cut 4 2.6162*** 1.7087*** 1.3661*** 5.6028*** 2.5228*** 1.7306*** 1.3917*** 5.7911*** -0.1669 -0.2832 -0.3246 -0.6991 -0.2744 -0.3712 -0.399 -0.7403

Observations 1,518 1,515 1,515 1,515 1,518 1,515 1,515 1,515 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

288

Table A3.55. Ordered Logit: Perception of Race Privileges Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Race Race Race Race Race Race Race Race

VARIABLES Privileges Privileges Privileges Privileges Privileges Privileges Privileges Privileges

Prime 1 0.4815** 0.5625*** 0.5679*** 0.6095*** 0.4115 0.6558* 0.6408* 0.6313 -0.1895 -0.1829 -0.1836 -0.1881 -0.3842 -0.3821 -0.3849 -0.3902 Conservatism -0.8831*** -0.8160*** -0.8086*** -0.0888 -0.4554*** -0.4075*** -0.4045*** -0.2239*** -0.2198 -0.2193 -0.2217 -0.2409 -0.0634 -0.0627 -0.0629 -0.0713 Prime 1*Cons. -0.4856 -0.5534* -0.5326* -0.6259** -0.0177 -0.0616 -0.0558 -0.0543 -0.3037 -0.31 -0.3126 -0.3112 -0.0895 -0.0904 -0.091 -0.0915 Prime 2 -0.1162 0.008 0.0088 0.0876 -0.7695* -0.4776 -0.5 -0.4529 -0.189 -0.1834 -0.1838 -0.1907 -0.3944 -0.3788 -0.3789 -0.3914 Prime 2*Cons. 0.1963 0.0803 0.1102 0.0444 0.1774** 0.1255 0.133 0.1343 -0.2917 -0.2932 -0.2951 -0.3031 -0.0881 -0.0858 -0.0858 -0.0883 Prime 3 0.2797 0.3284* 0.3302* 0.3232* 0.5925 0.7952** 0.7919** 0.7456* -0.1982 -0.1957 -0.1952 -0.1925 -0.3861 -0.3902 -0.391 -0.404 Prime 3*Cons. -0.2498 -0.2748 -0.28 -0.2659 -0.0898 -0.1291 -0.1289 -0.119 -0.3041 -0.3146 -0.3155 -0.3147 -0.0893 -0.0922 -0.0923 -0.0954 Age -0.0112*** -0.0119*** -0.0056 -0.0101** -0.0106*** -0.0053 -0.004 -0.0039 -0.0041 -0.004 -0.0039 -0.0042 Male -0.1681 -0.1704 -0.1826 -0.1693 -0.1688 -0.189 -0.1294 -0.1298 -0.1305 -0.131 -0.1315 -0.1325 Income 0.0561** 0.0531** 0.036 0.0543** 0.0521** 0.0415* -0.0229 -0.0229 -0.0235 -0.0226 -0.0227 -0.0234 Education 0.1841*** 0.1814*** 0.1781*** 0.1640*** 0.1621*** 0.1708*** -0.0398 -0.0399 -0.0408 -0.0398 -0.0402 -0.0417 North East -0.0608 0.0224 -0.0632 -0.0214 -0.18 -0.1826 -0.1834 -0.1836 South -0.3259** -0.2661 -0.2325 -0.2346 -0.1632 -0.1666 -0.1689 -0.1693 Mid-West -0.3282* -0.1998 -0.263 -0.2136 -0.1779 -0.1834 -0.1828 -0.1844 Mountain -0.0963 0.0259 -0.0449 0.0262 -0.2269 -0.2306 -0.226 -0.2294 White Ident. 0.1189*** 0.1116** -0.0446 -0.0445 Authoritarianism -0.2126 -0.1363 -0.1804 -0.1806 Egalitarianism 0.6313*** 0.5052*** -0.0658 -0.0695 Social Dominance 0.1295** 0.1257** -0.058 -0.057 Cut 1 -1.0136*** -0.6834*** -0.9498*** 2.9104*** -2.6050*** -2.1213*** -2.3096*** 1.4830** -0.1525 -0.2509 -0.297 -0.5542 -0.2899 -0.3512 -0.3828 -0.6661 Cut 2 -0.056 0.3031 0.0413 3.9749*** -1.6025*** -1.0948*** -1.2806*** 2.5587*** -0.1473 -0.2477 -0.2927 -0.5589 -0.2828 -0.3462 -0.3773 -0.6686 Cut 3 0.9143*** 1.3084*** 1.0526*** 5.0638*** -0.5739** -0.0359 -0.2182 3.6666*** -0.1487 -0.2478 -0.2912 -0.5657 -0.2799 -0.3436 -0.3742 -0.6739 Cut 4 1.9102*** 2.3311*** 2.0788*** 6.1504*** 0.4793* 1.0414*** 0.8605** 4.7740*** -0.1593 -0.2559 -0.2965 -0.5794 -0.2823 -0.3488 -0.3786 -0.6838

Observations 1,519 1,516 1,516 1,516 1,519 1,516 1,516 1,516 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

289

Table A3.56. Ordered Logit: Perception of Race Privileges Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) Race Race Race Race Race Race Race Race

VARIABLES Privileges Privileges Privileges Privileges Privileges Privileges Privileges Privileges

Prime 1 0.3809** 0.4205** 0.4537*** 0.3999** 0.6265* 0.5976* 0.6715* 0.6889* -0.1698 -0.1712 -0.1713 -0.1758 -0.3491 -0.3537 -0.3552 -0.3545 White Ident. -0.2509 -0.1213 -0.0609 0.1932 0.0221 0.0476 0.0652 0.1690** -0.2265 -0.2249 -0.225 -0.2452 -0.0783 -0.0793 -0.0802 -0.0844 Prime 1*White Ident. 0.0594 0.0965 0.0361 0.0697 -0.0807 -0.0526 -0.0745 -0.1003 -0.3358 -0.3385 -0.3352 -0.343 -0.1164 -0.1171 -0.1166 -0.1169 Prime 2 -0.0178 0.04 0.0746 0.1159 0.29 0.2929 0.3737 0.4562 -0.1694 -0.1752 -0.176 -0.1815 -0.327 -0.3357 -0.3378 -0.3503 Prime 2*White Ident. -0.0113 0.0348 -0.0293 -0.0708 -0.1101 -0.0866 -0.1107 -0.1341 -0.303 -0.2968 -0.2977 -0.3236 -0.1048 -0.104 -0.1041 -0.1113 Prime 3 -0.0369 0.025 0.0336 0.1093 0.0112 0.0239 0.0403 0.2149 -0.1751 -0.1806 -0.1802 -0.182 -0.3451 -0.3543 -0.3587 -0.3537 Prime 3*White Ident. 0.6697** 0.5846* 0.5681* 0.4549 0.0587 0.0676 0.0626 0.0151 -0.3266 -0.3322 -0.3319 -0.3402 -0.114 -0.1155 -0.1159 -0.1155 Age -0.0154*** -0.0162*** -0.0053 -0.0156*** -0.0164*** -0.0055 -0.0039 -0.0038 -0.0041 -0.0039 -0.0038 -0.0041 Male -0.1949 -0.2016 -0.1787 -0.2025 -0.2101* -0.194 -0.1275 -0.1277 -0.132 -0.1265 -0.1269 -0.1314 Income 0.037 0.0339 0.0424* 0.0381* 0.0348 0.0420* -0.0229 -0.0228 -0.0233 -0.0227 -0.0226 -0.0231 Education 0.1929*** 0.1896*** 0.1716*** 0.1942*** 0.1900*** 0.1689*** -0.0397 -0.0398 -0.0411 -0.0395 -0.0396 -0.0412 North East -0.0669 0.0016 -0.082 -0.0112 -0.1776 -0.1825 -0.1766 -0.1817 South -0.4352*** -0.2345 -0.4488*** -0.2376 -0.1624 -0.1683 -0.1614 -0.1674 Mid-West -0.3491** -0.1963 -0.3640** -0.2043 -0.1758 -0.1836 -0.175 -0.1832 Mountain -0.1492 0.0508 -0.1715 0.0318 -0.2286 -0.2291 -0.2282 -0.2289 Conservatism -0.2243*** -0.2290*** -0.0432 -0.044 Authoritarianism -0.126 -0.1426 -0.1799 -0.18 Egalitarianism 0.5059*** 0.5019*** -0.0694 -0.0696 Social Dominance 0.1274** 0.1204** -0.0567 -0.057 Cut 1 -0.6980*** -0.6537*** -0.9671*** 1.2776** -0.5548** -0.4872 -0.7859** 1.5744** -0.1331 -0.2497 -0.2929 -0.6094 -0.2465 -0.3289 -0.3582 -0.6209 Cut 2 0.21 0.2849 -0.0213 2.3481*** 0.3509 0.4509 0.1596 2.6476*** -0.1309 -0.2481 -0.2901 -0.6126 -0.2466 -0.3299 -0.358 -0.6251 Cut 3 1.1301*** 1.2448*** 0.9474*** 3.4538*** 1.2678*** 1.4092*** 1.1270*** 3.7524*** -0.1336 -0.2459 -0.2861 -0.6174 -0.2481 -0.3281 -0.3548 -0.63 Cut 4 2.0935*** 2.2387*** 1.9470*** 4.5593*** 2.2300*** 2.4021*** 2.1257*** 4.8553*** -0.1441 -0.253 -0.2898 -0.6261 -0.2544 -0.334 -0.3587 -0.6389

Observations 1,519 1,516 1,516 1,516 1,519 1,516 1,516 1,516 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

290

Table A3.57. Ordered Logit: Perceptions of White Advantages Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) White White White White White White White White

VARIABLES Advantages Advantages Advantages Advantages Advantages Advantages Advantages Advantages

Prime 1 0.5464*** 0.6155*** 0.6126*** 0.6496*** 0.499 0.6908* 0.6763* 0.6937* -0.1894 -0.1823 -0.1843 -0.186 -0.406 -0.4042 -0.4039 -0.4021 Conservatism -0.9957*** -0.9194*** -0.9091*** 0.119 -0.5020*** -0.4562*** -0.4509*** -0.1563** -0.2293 -0.2287 -0.2304 -0.2384 -0.0706 -0.0703 -0.0704 -0.0777 Prime 1*Cons. -0.5651* -0.6220** -0.5909* -0.6921** -0.0284 -0.0634 -0.0596 -0.0688 -0.309 -0.3143 -0.3155 -0.3103 -0.0943 -0.0955 -0.0951 -0.0939 Prime 2 -0.1837 -0.0996 -0.098 -0.0111 -0.539 -0.3256 -0.3266 -0.1563 -0.194 -0.1884 -0.1899 -0.1925 -0.4109 -0.4063 -0.4076 -0.4006 Prime 2*Cons. -0.0179 -0.1119 -0.0887 -0.2676 0.0826 0.0425 0.0446 0.0083 -0.2979 -0.2964 -0.2985 -0.2999 -0.0912 -0.0907 -0.0907 -0.0893 Prime 3 0.2419 0.268 0.2671 0.2445 0.4268 0.5589 0.5658 0.5749 -0.206 -0.2024 -0.2023 -0.2017 -0.4195 -0.4182 -0.4158 -0.4327 Prime 3*Cons. -0.1792 -0.2056 -0.2125 -0.2561 -0.0508 -0.0797 -0.0824 -0.095 -0.3026 -0.308 -0.3075 -0.3034 -0.0929 -0.0943 -0.0936 -0.0964 Age -0.0112*** -0.0118*** -0.0063 -0.0101*** -0.0106*** -0.0059 -0.0039 -0.0039 -0.0039 -0.0039 -0.0039 -0.004 Male -0.2127* -0.2199* -0.1598 -0.2123* -0.2142* -0.1717 -0.1255 -0.1267 -0.1284 -0.1239 -0.1249 -0.1289 Income 0.0383* 0.035 0.0143 0.0304 0.0285 0.0175 -0.0227 -0.0226 -0.0229 -0.0226 -0.0225 -0.0227 Education 0.1876*** 0.1843*** 0.1641*** 0.1677*** 0.1641*** 0.1599*** -0.0392 -0.039 -0.0388 -0.039 -0.039 -0.0394 North East -0.1367 -0.0065 -0.1798 -0.0704 -0.1677 -0.1705 -0.1671 -0.1692 South -0.3726** -0.3091** -0.2965** -0.2941* -0.1479 -0.1573 -0.1507 -0.156 Mid-West -0.2525 -0.071 -0.2118 -0.0984 -0.1577 -0.166 -0.1611 -0.1653 Mountain -0.1942 -0.0201 -0.1484 -0.0284 -0.209 -0.2166 -0.2067 -0.2145 White Ident. 0.0961** 0.0869* -0.0453 -0.0462 Authoritarianism -0.1387 -0.0665 -0.1734 -0.1755 Egalitarianism 0.8301*** 0.7076*** -0.0648 -0.07 Social Dominance 0.0466 0.0468 -0.0554 -0.0561 Cut 1 -1.8358*** -1.6271*** -1.9156*** 2.4513*** -3.6203*** -3.2995*** -3.5238*** 1.2332* -0.1635 -0.2473 -0.2836 -0.493 -0.3277 -0.3781 -0.3989 -0.6431 Cut 2 -0.5811*** -0.3449 -0.6274** 3.8873*** -2.3127*** -1.9710*** -2.1914*** 2.6708*** -0.1545 -0.2398 -0.2772 -0.5004 -0.3169 -0.3672 -0.389 -0.6477 Cut 3 0.5079*** 0.7785*** 0.5018* 5.1575*** -1.1549*** -0.7853** -1.0023*** 3.9502*** -0.1535 -0.2407 -0.277 -0.5117 -0.3119 -0.3644 -0.3861 -0.6573 Cut 4 1.5240*** 1.8257*** 1.5511*** 6.3270*** -0.0609 0.3354 0.1191 5.1371*** -0.1625 -0.2492 -0.2831 -0.5235 -0.3116 -0.3667 -0.3866 -0.6656

Observations 1,519 1,516 1,516 1,516 1,519 1,516 1,516 1,516 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

291

Table A3.58. Ordered Logit: Perceptions of White Advantages Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) White White White White White White White White

VARIABLES Advantages Advantages Advantages Advantages Advantages Advantages Advantages Advantages

Prime 1 0.5891*** 0.6115*** 0.6369*** 0.5637*** 1.0976*** 1.0528*** 1.1126*** 1.1068*** -0.1734 -0.175 -0.1761 -0.1786 -0.3529 -0.3589 -0.3567 -0.3587 White Ident. 0.0277 0.1677 0.223 0.6478** 0.069 0.1045 0.1192 0.2813*** -0.227 -0.2278 -0.2268 -0.2543 -0.0797 -0.0803 -0.0794 -0.0848 Prime 1*White Ident. -0.4502 -0.4064 -0.4544 -0.4406 -0.2325** -0.2054* -0.2236* -0.2537** -0.3163 -0.3175 -0.3137 -0.3288 -0.1142 -0.1155 -0.1141 -0.1156 Prime 2 -0.0522 -0.0308 0.0025 0.0166 0.4379 0.4192 0.4929 0.5638* -0.1724 -0.1759 -0.1773 -0.1809 -0.3348 -0.3366 -0.3349 -0.3379 Prime 2*White Ident. -0.3381 -0.3031 -0.3578 -0.4202 -0.2142** -0.1982* -0.2197** -0.2537** -0.3114 -0.3049 -0.3059 -0.3266 -0.1087 -0.1065 -0.106 -0.1091 Prime 3 0.1387 0.1864 0.1924 0.2783 0.5607 0.5759* 0.5831* 0.8640** -0.1757 -0.178 -0.1776 -0.1786 -0.343 -0.3456 -0.3456 -0.3416 Prime 3*White Ident. -0.016 -0.13 -0.1372 -0.3358 -0.159 -0.1606 -0.162 -0.2571** -0.3232 -0.3349 -0.333 -0.3519 -0.1139 -0.1154 -0.1149 -0.1165 Age -0.0158*** -0.0164*** -0.0061 -0.0156*** -0.0161*** -0.0058 -0.0038 -0.0038 -0.0039 -0.0039 -0.0038 -0.004 Male -0.2473** -0.2618** -0.1709 -0.2490** -0.2646** -0.1882 -0.122 -0.1227 -0.1279 -0.1219 -0.1225 -0.128 Income 0.0165 0.0129 0.02 0.0175 0.0137 0.0192 -0.0226 -0.0224 -0.0227 -0.0224 -0.0223 -0.0227 Education 0.1895*** 0.1875*** 0.1589*** 0.1852*** 0.1827*** 0.1537*** -0.0385 -0.0382 -0.0394 -0.0385 -0.0381 -0.0396 North East -0.1019 -0.0607 -0.092 -0.0485 -0.1638 -0.1685 -0.1655 -0.1709 South -0.4790*** -0.3112** -0.4805*** -0.2964* -0.148 -0.1552 -0.1492 -0.1569 Mid-West -0.2606* -0.1089 -0.2610* -0.0953 -0.1569 -0.1647 -0.1575 -0.1664 Mountain -0.2437 -0.0238 -0.251 -0.0271 -0.2134 -0.2148 -0.2152 -0.217 Conservatism -0.1934*** -0.1996*** -0.0433 -0.0437 Authoritarianism -0.0821 -0.079 -0.1748 -0.1731 Egalitarianism 0.7092*** 0.7064*** -0.0697 -0.0699 Social Dominance 0.0394 0.0455 -0.0551 -0.0561 Cut 1 -1.3313*** -1.4805*** -1.7982*** 1.0128* -1.1520*** -1.2471*** -1.5432*** 1.5478*** -0.1453 -0.2463 -0.277 -0.5612 -0.2575 -0.3254 -0.3472 -0.5913 Cut 2 -0.1541 -0.2715 -0.5787** 2.4518*** 0.026 -0.0374 -0.3228 2.9906*** -0.1359 -0.2378 -0.2705 -0.5661 -0.252 -0.32 -0.3429 -0.5961 Cut 3 0.8530*** 0.7757*** 0.4783* 3.7327*** 1.0346*** 1.0106*** 0.7352** 4.2729*** -0.1366 -0.2362 -0.2682 -0.5741 -0.2521 -0.3184 -0.3407 -0.6048 Cut 4 1.8178*** 1.7765*** 1.4827*** 4.9229*** 2.0046*** 2.0147*** 1.7430*** 5.4641*** -0.1472 -0.2446 -0.2746 -0.583 -0.259 -0.3261 -0.3473 -0.6149

Observations 1,519 1,516 1,516 1,516 1,519 1,516 1,516 1,516 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

292

Table A3.59. OLS: White Guilt Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) White White White White White White White White

VARIABLES Guilt Guilt Guilt Guilt Guilt Guilt Guilt Guilt

Prime 1 1.2073*** 1.3192*** 1.3079*** 1.2650*** 1.1301* 1.5234** 1.5069** 1.5003** -0.3513 -0.3367 -0.3384 -0.3166 -0.6574 -0.6378 -0.638 -0.6213 Conservatism -1.9873*** -1.6755*** -1.6539*** 0.0335 -0.9053*** -0.7872*** -0.7767*** -0.3298*** -0.3687 -0.3661 -0.371 -0.3574 -0.1005 -0.0988 -0.0997 -0.1106 Prime 1*Cons. -1.1383** -1.2884** -1.2425** -1.3106*** -0.0689 -0.1549 -0.151 -0.1672 -0.5204 -0.5151 -0.518 -0.4678 -0.1461 -0.1437 -0.1432 -0.1351 Prime 2 -0.1185 0.0137 0.004 0.1464 -0.9938 -0.6149 -0.6254 -0.4441 -0.3521 -0.3358 -0.3363 -0.3184 -0.6904 -0.6509 -0.6505 -0.6315 Prime 2*Cons. 0.1496 -0.042 0.0128 -0.1749 0.2239 0.142 0.1472 0.1215 -0.5161 -0.5077 -0.5116 -0.4709 -0.1473 -0.1406 -0.1404 -0.1332 Prime 3 0.6379* 0.6558* 0.6488* 0.6419** 1.4544** 1.6050** 1.6194** 1.6135** -0.3726 -0.3583 -0.3553 -0.3257 -0.6551 -0.6401 -0.6348 -0.644 Prime 3*Cons. -0.7551 -0.8295 -0.8398 -0.9110** -0.2481* -0.2898** -0.2956** -0.3056** -0.5121 -0.5137 -0.5123 -0.4635 -0.1379 -0.1389 -0.1374 -0.1369 Age -0.0318*** -0.0330*** -0.0200*** -0.0295*** -0.0303*** -0.0199*** -0.0076 -0.0075 -0.0069 -0.0071 -0.007 -0.0068 Male -0.5021** -0.5052** -0.4751** -0.4936** -0.4892** -0.4709** -0.2429 -0.243 -0.2222 -0.2295 -0.2304 -0.2193 Income 0.1001** 0.0910** 0.0516 0.0928** 0.0863** 0.0610* -0.0399 -0.0401 -0.0374 -0.0374 -0.0375 -0.0364 Education 0.2717*** 0.2624*** 0.2264*** 0.2124*** 0.2043*** 0.2097*** -0.0688 -0.0688 -0.0637 -0.0653 -0.0656 -0.0637 North East -0.3417 -0.1545 -0.3761 -0.2458 -0.3163 -0.302 -0.3019 -0.2929 South -0.7458*** -0.5816** -0.5586** -0.5240* -0.2887 -0.2816 -0.2803 -0.2745 Mid-West -0.6664** -0.311 -0.5551* -0.3513 -0.3089 -0.2982 -0.2994 -0.2897 Mountain -0.3372 -0.0031 -0.2229 -0.0039 -0.414 -0.3885 -0.3976 -0.3795 White Ident. 0.1416** 0.1302* -0.0678 -0.0667 Authoritarianism -0.0261 0.1258 -0.29 -0.2836 Egalitarianism 1.3075*** 1.0823*** -0.0893 -0.0986 Social Dominance 0.1639* 0.1623* -0.0898 -0.0885 Constant 7.8978*** 8.1281*** 8.7678*** 1.0206 10.8974*** 10.8922*** 11.3609*** 3.3505*** -0.262 -0.447 -0.5304 -0.7984 -0.4781 -0.5923 -0.6509 -1.0157

Observations 1,516 1,513 1,513 1,513 1,516 1,513 1,513 1,513 R-squared 0.1307 0.1812 0.1872 0.3106 0.2128 0.2536 0.257 0.3286 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

293

Table A3.60. OLS: White Guilt Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) White White White White White White White White

VARIABLES Guilt Guilt Guilt Guilt Guilt Guilt Guilt Guilt

Prime 1 1.1078*** 1.1060*** 1.1502*** 0.9137*** 1.8246*** 1.7346*** 1.8563*** 1.6475*** -0.3336 -0.3274 -0.3284 -0.2859 -0.6523 -0.6428 -0.6441 -0.5625 White Ident. -0.4387 -0.1701 -0.0478 0.6001* 0.0052 0.0823 0.1125 0.3289*** -0.3972 -0.3849 -0.3829 -0.3549 -0.1409 -0.1376 -0.1378 -0.1239 Prime 1*White Ident. -0.3194 -0.2197 -0.346 -0.2855 -0.297 -0.2547 -0.2979 -0.3048* -0.6157 -0.5979 -0.5897 -0.521 -0.2131 -0.207 -0.2057 -0.1796 Prime 2 0.0725 0.074 0.1186 0.1771 0.8494 0.7445 0.8637 0.9029* -0.3335 -0.33 -0.3307 -0.295 -0.6255 -0.6096 -0.6123 -0.5419 Prime 2*White Ident. -0.3318 -0.1962 -0.3088 -0.4012 -0.3144 -0.2645 -0.3053* -0.3157* -0.56 -0.5292 -0.5285 -0.4832 -0.1942 -0.1838 -0.1841 -0.1663 Prime 3 0.0641 0.1271 0.1353 0.2578 0.4275 0.4714 0.4774 0.7539 -0.3412 -0.3325 -0.3313 -0.278 -0.6433 -0.6282 -0.631 -0.5289 Prime 3*White Ident. 0.8001 0.5701 0.5205 0.2056 -0.044 -0.0622 -0.0671 -0.1574 -0.606 -0.5971 -0.5853 -0.5159 -0.2115 -0.2057 -0.2033 -0.1747 Age -0.0405*** -0.0421*** -0.0201*** -0.0402*** -0.0417*** -0.0195*** -0.0078 -0.0076 -0.0068 -0.0078 -0.0076 -0.0068 Male -0.6136** -0.6294** -0.4615** -0.6239** -0.6425*** -0.4966** -0.2498 -0.2484 -0.2201 -0.2488 -0.2475 -0.2195 Income 0.0545 0.045 0.0614* 0.0564 0.0463 0.0599* -0.0418 -0.0418 -0.0363 -0.0415 -0.0415 -0.0362 Education 0.2952*** 0.2853*** 0.2080*** 0.2914*** 0.2801*** 0.2015*** -0.0726 -0.0724 -0.0638 -0.0722 -0.0718 -0.0638 North East -0.3039 -0.2134 -0.3002 -0.2119 -0.3317 -0.2924 -0.3344 -0.2951 South -0.9943*** -0.5375* -1.0137*** -0.5371* -0.3047 -0.2754 -0.3067 -0.2771 Mid-West -0.6686** -0.3327 -0.6921** -0.3401 -0.325 -0.2901 -0.3262 -0.2914 Mountain -0.4515 0.0227 -0.4728 -0.002 -0.4435 -0.3803 -0.4458 -0.3812 Conservatism -0.4049*** -0.4128*** -0.0698 -0.0706 Authoritarianism 0.0859 0.0869 -0.2848 -0.2819 Egalitarianism 1.0813*** 1.0740*** -0.0993 -0.0997 Social Dominance 0.1499* 0.1576* -0.088 -0.0888 Constant 7.2637*** 8.1681*** 8.9021*** 3.8961*** 7.1092*** 7.8834*** 8.5918*** 3.2481*** -0.2455 -0.4635 -0.5401 -0.9046 -0.4561 -0.6061 -0.6631 -0.9359

Observations 1,516 1,513 1,513 1,513 1,516 1,513 1,513 1,513 R-squared 0.0218 0.0909 0.1016 0.3245 0.0231 0.0918 0.1031 0.3255 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

294

Table A3.61. OLS: Feeling Thermometer Evaluations of President Trump Regressed on Indicator and Continuous Measures of Conservatism Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES FT: Trump FT: Trump FT: Trump FT: Trump FT: Trump FT: Trump FT: Trump FT: Trump

Prime 1 -10.9779*** -11.9134*** -11.7922*** -9.7381*** -12.2368* -14.5072** -13.9929** -9.9219* -3.531 -3.4026 -3.4302 -3.0067 -6.9614 -6.9079 -6.9037 -5.8564 Conservatism 36.1179*** 35.6843*** 35.1254*** 16.8669*** 12.5829*** 12.0271*** 11.8467*** 6.3989*** -4.2424 -4.103 -4.1021 -3.8848 -1.1784 -1.1699 -1.1665 -1.1095 Prime 1*Cons. 15.2223*** 16.2125*** 15.6559*** 13.5873*** 1.3169 1.7681 1.64 1.0167 -5.6359 -5.5519 -5.5792 -5.0574 -1.548 -1.5442 -1.5372 -1.3497 Prime 2 -7.5911** -8.9172** -8.8217** -8.8879*** -8.8413 -11.4838 -11.5464 -12.5777** -3.6363 -3.5724 -3.569 -2.9664 -7.2489 -7.1173 -7.0867 -6.0065 Prime 2*Cons. 6.4203 7.5485 7.0619 9.1926* 0.8918 1.3935 1.3891 1.7825 -5.6572 -5.5409 -5.5086 -5.1377 -1.6134 -1.5899 -1.5806 -1.4657 Prime 3 -7.4767** -7.5146** -7.5922** -6.4296** -13.8096** -14.8730** -14.9783** -12.7829** -3.7783 -3.7209 -3.6942 -3.0646 -7.0223 -6.9487 -6.9246 -5.5602 Prime 3*Cons, 10.1067* 9.8932* 10.1585* 11.1267** 2.2835 2.5330* 2.5578* 2.4572** -5.7995 -5.7447 -5.6899 -4.8684 -1.5167 -1.5108 -1.5007 -1.2463 Age 0.0538 0.0684 0.0107 0.0565 0.0671 0.0288 -0.0691 -0.0689 -0.0617 -0.0686 -0.0689 -0.0637 Male 3.3969 3.7363 1.6477 3.7774 4.1023* 2.0645 -2.3419 -2.3347 -2.0964 -2.2953 -2.2934 -2.1519 Income -0.7262* -0.628 -0.0839 -0.4471 -0.386 0.05 -0.3892 -0.3877 -0.345 -0.3748 -0.3768 -0.3427 Education -2.9636*** -2.8626*** -1.4761** -2.2254*** -2.1479*** -1.2204** -0.7142 -0.7102 -0.6129 -0.6957 -0.6923 -0.616 North East 3.1261 -0.8429 3.0735 -0.5384 -3.2294 -2.8824 -3.1737 -2.8268 South 9.2364*** 6.4184** 7.2174** 5.8156** -2.9181 -2.6092 -2.8611 -2.5327 Mid-West 4.51 -0.4611 2.3348 -1.2962 -3.1531 -2.862 -3.1098 -2.7997 Mountain 3.5626 0.3009 2.0122 -0.3427 -3.4501 -3.2139 -3.4758 -3.2629 White Ident. 0.7641 0.9738 -0.6947 -0.709 Authoritarianism 5.4684* 2.9596 -2.9467 -3.0182 Egalitarianism -11.0400*** -9.9228*** -0.9596 -1.0338 Social Dominance 4.3993*** 4.3316*** -0.9214 -0.9413 Constant 30.9827*** 41.3240*** 34.5465*** 69.6945*** -7.3522 0.637 -3.7871 43.4043*** -2.8628 -4.3881 -5.2778 -8.5102 -5.6157 -6.8655 -7.4056 -10.5257

Observations 1,488 1,486 1,486 1,486 1,488 1,486 1,486 1,486 R-squared 0.302 0.3223 0.3295 0.4717 0.3468 0.3579 0.3626 0.4658 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

295

Table A3.62. OLS: Feeling Thermometer Evaluations of President Trump Regressed on Indicator and Continuous Measures of White Identification Indicator Models Continuous Models (1) (2) (3) (4) (5) (6) (7) (8) VARIABLES FT: Trump FT: Trump FT: Trump FT: Trump FT: Trump FT: Trump FT: Trump FT: Trump

Prime 1 -7.7050** -7.7124** -8.2571** -3.5932 -5.638 -4.9298 -6.4267 -0.8215 -3.5827 -3.5092 -3.5121 -2.6904 -6.7527 -6.5896 -6.5465 -5.099 White Ident. 23.0384*** 21.4062*** 20.1179*** 5.4243 6.7224*** 6.3059*** 5.9992*** 1.0488 -4.7958 -4.6575 -4.6126 -4.0803 -1.5608 -1.4946 -1.4743 -1.2874 Prime 1*White -4.2821 -4.8868 -3.3283 -6.678 -1.3342 -1.6898 -1.1638 -1.7968 Ident. -6.8268 -6.7465 -6.6651 -5.5995 -2.237 -2.1983 -2.1685 -1.8157 Prime 2 -4.0772 -4.1404 -4.6693 -4.7119* -5.0369 -4.2172 -5.9003 -5.72 -3.6424 -3.5667 -3.5345 -2.6496 -6.7464 -6.5368 -6.4799 -5.028 Prime 2*White -5.7459 -6.7552 -5.0541 -1.0061 -0.5304 -0.9805 -0.3659 0.1954 Ident. -6.9038 -6.8119 -6.7328 -5.6494 -2.2146 -2.1591 -2.135 -1.8007 Prime 3 -1.3369 -1.7765 -2.0106 -3.0097 -2.587 -2.5354 -2.9056 -5.5319 -3.7943 -3.6972 -3.69 -2.6328 -6.8717 -6.6259 -6.5707 -4.8587 Prime 3*White -3.0121 -1.9312 -1.3889 2.0665 0.2037 0.1121 0.2233 1.1838 Ident. -7.1502 -7.0619 -6.9546 -5.661 -2.258 -2.1977 -2.1664 -1.7461 Age 0.1767** 0.1939** 0.0265 0.1823** 0.1989** 0.0339 -0.079 -0.0781 -0.0635 -0.0792 -0.0781 -0.0634 Male 6.2300** 6.9269** 2.1064 5.5446** 6.2872** 1.8453 -2.7583 -2.7128 -2.1572 -2.7499 -2.6989 -2.135 Income 0.1727 0.2652 0.0665 0.061 0.1648 0.0534 -0.4593 -0.4572 -0.3436 -0.4574 -0.4548 -0.3436 Education -3.0673*** -2.9628*** -1.2012** -3.1257*** -3.0069*** -1.2154** -0.8066 -0.7963 -0.6111 -0.8068 -0.7926 -0.6092 North East 0.8888 -0.6654 0.6581 -0.8204 -3.7144 -2.8224 -3.7903 -2.8356 South 12.4729*** 5.3910** 13.0411*** 5.4865** -3.359 -2.5289 -3.4312 -2.5377 Mid-West 2.3093 -1.6478 2.7373 -1.5425 -3.6698 -2.7864 -3.697 -2.7983 Mountain 5.1595 -0.3547 4.9253 -0.4421 -4.5081 -3.2163 -4.5828 -3.2279 Conservatism 7.7646*** 7.7328*** -0.7154 -0.7136 Authoritarianism 3.2288 3.1783 -2.9857 -2.9997 Egalitarianism -9.8087*** -9.8612*** -1.0266 -1.0207 Social Dominance 4.2940*** 4.4086*** -0.9367 -0.9478 Constant 37.7454*** 36.5496*** 29.1904*** 38.3201*** 26.7076*** 27.0131*** 19.7439*** 37.0760*** -2.7078 -4.8646 -5.6236 -9.5346 -4.9914 -6.2308 -6.8569 -9.8801

Observations 1,488 1,486 1,486 1,486 1,488 1,486 1,486 1,486 R-squared 0.0617 0.0866 0.105 0.4666 0.061 0.085 0.1052 0.4659 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

296

DATA SOURCES

American National Election Studies. 2019. "User’s Guide and Codebook for the ANES 2016

Time Series Study." Ann Arbor.Leip, David. 2018. Dave Leip's Atlas of U.S. Presidential

Elections [dataset] . Accessed: December 2018. http://uselectionatlas.org.

Minnesota, Office of the Secretary of State. 2016. “Election Results.” Accessed December 2018.

https://electionresults.sos.state.mn.us/Results/MediaResult/98?mediafileid=96.

Putnam, Robert, Peter Malkin, and Isabel Malkin. 2002. Social Capital Community Benchmark

Survey, 2000 [restricted dataset]. The Roper Center for Public Opinion Research.

Accessed: March 2019. https://ropercenter.cornell.edu/featured-collections/2000-social-

capital-community-benchmark-survey. Accessed: March 2019.

Putnam, Robert, Peter Malkin, and Isabel Malkin. Social Capital Community Survey, 2006

[dataset]. The Association of Religion Data Archives (The ARDA). Accessed: March

2019. www.thearda.com/archive/files/Descriptions/SCCS.asp.

Robert Wood Johnson Foundation and University of Wisconsin Population Health Institute.

2016. County Health Ranking and Roadmaps, 2016 [dataset]. Accessed: March 2019.

http://www.countyhealthrankings.org/.

The American National Election Studies (ANES). ANES 2012 Time Series Study. ICPSR35157-

v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research

[distributor], 2016-05-17. http://doi.org/10.3886/ICPSR35157.v1.

297

The American National Election Studies (ANES). ANES 2016 Time Series Study. ICPSR36824.

University of Michigan, and Stanford University. Ann Arbor, MI: Inter-university

Consortium for Political and Social Research [distributor], 2017-09-19.

https://doi.org/10.3886/ICPSR36824.v2.

The American National Election Studies, Stanford University, and University of Michigan.

American National Election Study: 2016 Pilot Study. ICPSR36390-v1. Ann Arbor, MI:

Inter-university Consortium for Political and Social Research [distributor], 2016-03-16.

http://doi.org/10.3886/ICPSR36390.v1.

U.S. Census Bureau, 2000-2015 American Community Survey 5-Year Estimates. Accessed:

March 2019. https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml.

U.S. Census Bureau. EEO 7r. Educational Attainment (6) by Younger Age Groups, Sex, and

Race/Ethnicity (Part I) for Residence Geography, Total Population, Number. 2006-2010

[data file]. Accessed: Accessed: March 2019. https://factfinder.census.gov/faces/

tableservices/jsf/pages/productview.xhtml?pid=EEO_10_5YR_EEOALL7RN1&prodTyp

e=table.

Wilson, Joe. Kaggle.com. 2012 General Election Data, version 2 [dataset]. Accessed: March 16,

2017. https://www.kaggle.com/joelwilson/2012-2016-presidential-elections.

298

REFERENCES

“2016 Election Center.” CNN. Accessed February 15, 2019.

http://www.cnn.com/election/primaries/polls.

Abramowitz, Alan. 2012. "Grand Old Tea Party: Partisan Polarization and the Rise of the Tea

Party Movement." In Steep: The Precipitous Rise of the Tea Party, eds. Christine Trost

and Lawrence Rosenthal, 195–211. : University of California.

Acton, Lord John. "Letter to Bishop Mandell Creighton [1887]." Online Library of Liberty.

2004. https://oll.libertyfund.org/titles/acton-acton-creighton-correspondence.

Adams, Maurianne, Lee Anne Bell, Dianne Goodman, and Khyati Joshi. 1997. Teaching for

Diversity and Social Justice , 3 rd edition. New York: Routledge.

"ADL Task Force Issues Report Detailing Widespread Anti-Semitic Harassment of Journalists

on During 2016 Campaign." 2016. Anti-Defamation League , October 19.

https://www.adl.org/news/press-releases/adl-task-force-issues-report-detailing-

widespread-anti-semitic-harassment-of.

Adorno, Theodor, Else Frenkel-Brunswik, Daniel Levinson, and R. Nevitt Sanford. 1950. The

Authoritarian Personality. New York: Harper and Row.

“Against Trump.” National Review . January 21, 2016. http://www.nationalreview.com/article/

430137/donald-trump-conservative-movement-shouldnt-support-him.

Alesina, Alberto, Edward Glaeser, and Bruce Sacerdote. 2001. “Why Doesn't the US have a

European-Style welfare System?” No. w8524. National Bureau of Economic Research .

Alesina, Alberto, and Eliana La Ferrara. 2000 "Participation in Heterogeneous Communities."

299

The Quarterly Journal of Economics 115, (3): 847 –904.

Allport, Gordon Willard, Kenneth Clark, and Thomas Pettigrew. 1954. The Nature of Prejudice.

Massachusetts: Addison-Wesley.

“American Racists Work to Spread 'Identitarian' Ideology.” 2015. Southern Poverty Law Center ,

October 12. www.splcenter.org/hatewatch/2015/10/12/american-racists-work-spread-

%E2%80%98identitarian%E2%80%99-ideology.

Andersen, Jørgen, and Tor Bjørklund. 1990. "Structural Changes and New Cleavages: The

Progress Parties in Denmark and Norway." Acta Sociologica 33, (3): 195–217.

Anderson, Benedict. 1983. Imagined Communities: Reflections on the Origin and Spread of

Nationalism . New York: Verso.

Ansley, Frances Lee. 1988. "Stirring the Ashes: Race Class and the Future of Civil Rights

Scholarship." Cornell Law Review 74, (6): 993-1077.

Ansley, Frances. 1997. "White Supremacy (and What We Should Do About it)." In Critical

White Studies: Looking Behind the Mirror, eds Richard Delgado and Jean Stefancic,

592–595. : Temple University.

Anti-Defamation League. 2016. "ADL Task Force Issues Report Detailing Widespread Anti-

semitic Harassment of Journalists on Twitter During 2016 Campaign." Anti-Defamation

League .

“Asian Women and Men Earned More than Their White, Black, and Hispanic Counterparts in

2017.” 2018. U.S. Bureau of Labor Statistics , August 29.

www.bls.gov/opub/ted/2018/asian-women-and-men-earned-more-than-their-white-black-

and-hispanic-counterparts-in-2017.htm.

300

“A Tale of Two Patriotisms.” 2017. Ear to the Ground , American Culture and Faith Institute.

www.eartotheground.us/a-tale-of-two-patriotisms.

Bacon, Perry. 2016. “Clinton and Kaine Are Challenging White Americans on Racial Issues.”

NBCNews.com , August 14. www.nbcnews.com/news/nbcblk/clinton-kaine-are-

challenging-white-americans-racial-issues-n628531.

Baldwin, James. 1985. The Price of the Ticket: Collected Nonfiction, 1948-1985 . New York:

Macmillan.

Barrett, James. 2016. "Report: Daily Wire's Ben Shapiro #1 Journalistic Target of Anti-

Semitism." Daily Wire , October 20. https://www.dailywire.com/news/report-daily-wires-

ben-shapiro-1-journalistic-james-barrett.

Bartels, Larry. 2006. "What's the Matter with What's the Matter with Kansas?", Quarterly

Journal of Political Science, 1 (2): 201– 226.

Bawn, Kathleen, Martin Cohen, David Karol, Seth Masket, Hans Noel, and John Zaller. 2012.

"A Theory of Political Parties: Groups, Policy Demands and Nominations in American

Politics." Perspectives on Politics : 571-597.

Beale, Theodore Robert (). 2016. “America Is Not an Idea: And Americans Are Not

Proposition People.” Vox Popoli , May 8. www.voxday.blogspot.com/2016/05/america-is-

not-idea.html.

Beale, Theodore (Vox Day) and John Red Eagle. 2015. : How “Conservatives”

Betrayed America. EBook. .com. Finland: Castalia House.

Bennett, James, and Michael Lotus. 2013 America 3.0: Rebooting American Prosperity in the

21 st Century: Why America's Greatest Days are Yet to Come . New York: Encounter

Books.

301

Berman, Sheri. 1997. "Civil Society and the Collapse of the Weimar Republic." World

Politics 49, (3): 401–429.

Betz, Hans-Georg. 1994. Radical Rightwing Populism in Western Europe. New York: St

Martin’s.

Billig, Michael and Henri Tajfel. 1973. "Social Categorization and Similarity in Intergroup

Behaviour." European Journal of Social Psychology 3, (1): 27–52.

Blalock, Hubert. 1967. Toward a Theory of Minority-Group Relations . New York: John

Wiley and Sons.

Bliuc, Ana-Maria, Nicholas Faulkner, Andrew Jakubowicz, and Craig McGarty. 2018. "Online

Networks of Racial Hate: A Systematic Review of 10 Years of Research on Cyber-

Racism." Computers in Human Behavior 87: 75–86

Blumer, Herbert. 1958. "Race Prejudice as a Sense of Group Position." Pacific Sociological

Review 1, (1): 3–7.

Bobo, Lawrence, and James Kluegel. 1993. "Opposition to Race-targeting: Self-interest,

Stratification Ideology, or Racial Attitudes?" American Sociological Review : 443–464.

Bobo, Lawrence, Camille Charles, Maria Krysan, Alicia Simmons, and George Fredrickson.

2012. "The Real Record on Racial Attitudes." In Social Trends in American Life: Findings

from the General Social Survey Since 1972, ed Peter Marsden: 38–83. New Jersey:

Princeton University.

Bobo, Lawrence and Vincent Hutchings. 1996. “Perceptions of Racial Group Competition:

Extending Blumer’s Theory of Group Position to a Multiracial Social Context.”

American Sociological Review 61, (6): 951–972.

302

Bohman, James. 2005. "Critical Theory." Stanford Encyclopedia of Philosophy . Stanford

University, March 08. https://plato.stanford.edu/entries/critical-theory/.

Bokhari, Allum and Milo Yiannopoulos. 2016. "An Establishment Conservative’s Guide to the

Alt-Right." Breitbart News, March 29. https://www.breitbart.com/tech/2016/03/29/an-

establishment-conservatives-guide-to-the-alt-right/.

Bonilla-Silva, Eduardo. 2001. White Supremacy & Racism in the Post-Civil Rights Era .

Colorado: Lynne Rienner.

Bonilla-Silva, Eduardo. 2006. Racism Without Racists: Color-blind Racism and the Persistence

of Racial Inequality in the United States . Maryland: Rowman & Littlefield Publishers.

Bornschier, Simon. 2010. Cleavage Politics and the Populist Right: The New Cultural Conflict

in Western Europe . Philadelphia: Temple University.

Boyd, James. 1970. “Nixon's Southern Strategy.” The New York Times , May 17.

www.nytimes.com/1970/05/17/archives/nixons-southern-strategy-its-all-in-the-charts.html.

Branscombe, Nyla, Michael Schmitt, and Richard Harvey. 1999. "Perceiving Pervasive

Discrimination among African Americans: Implications for Group Identification and

Well-being." Journal of Personality and Social Psychology 77, (1): 135–149.

Branscombe, Nyla, Michael Schmitt, and Kristin Schiffhauer. 2007. "Racial Attitudes in

Response to Thoughts of White Privilege." European Journal of Social Psychology 37,

(2): 203–215.

Brehm, John, and Wendy Rahn. 1997. "Individual-level Evidence for the Causes and

Consequences of Social Capital." American Journal of Political Science 41, (3): 999–

1023.

Brewer, Marilynn. 1979. "In-group Bias in the Minimal Intergroup Situation: A Cognitive-

303

motivational Analysis." Psychological Bulletin 86, (2): 307–324.

Brewer, Marilynn. 1999. "The Psychology of Prejudice: Ingroup Love and Outgroup Hate?"

Journal of Social Issues 55, (3): 429 –444.

Brewer, Marilynn. 2007. "The Importance of Being We: Human Nature and Intergroup

Relations." American Psychologist 62, (8): 728–738.

Brown, Rupert. 1984. "The Effects of Intergroup Similarity and Cooperative vs. Competitive

Orientation on Intergroup Discrimination." British Journal of Social Psychology 23, (1):

21–33.

Brown, Rupert, and Dominic Abrams. 1986. "The Effects of Intergroup Similarity and Goal

Interdependence on Intergroup Attitudes and Task Performance." Journal of

Experimental Social Psychology 22, (1): 78–92.

Buckley Jr, William. 1951. God and Man at Yale: The Superstitions of Academic Freedom.”

Illinois: Regnery.

Burdette, Amy, Christopher Ellison, Darren Sherkat, and Kurt Gore. 2007. “Are there Religious

Variations in Marital Infidelity?" Journal of Family Issues 28, (12): 1553–1581.

Burke, Edmund. 1789. "Reflections on the Revolution in France." Everyman's Library (1986).

Burt, Ronald. 1992. Structual Holes: The Social Structure of Competition. :

Harvard University.

Busch, Andrew. 2012. “Social Conservatives and Economic Conservatives.” Society 49: 13–23.

https://doi.org/10.1007/s12115-011-9498-4.

Byrne, Donn. 1971. The Attraction Paradigm . New York: Academic Press.

304

Cain, Sian. 2017. “Marvel Executive Says Emphasis on Diversity May Have Alienated Readers.”

The Guardian , April 3. www.theguardian.com/books/2017/apr/03/marvel-executive-says-

emphasis-on-diversity-may-have-alienated-readers.

Calhoun, John Caldwell. 1851. A disquisition on government . Vol. 1. Press of Walker and James.

Campbell, Bradley, and Jason Manning. 2018. The Rise of Victimhood Culture:

Microaggressions, Safe Spaces, and the New Culture Wars . Basingstoke, UK: Springer.

Campbell, Donald. 1965. "Ethnocentrism and other Altruistic Motives." In Symposium

on Motivation : 283–311.

Campbell, Donald. 1975. "On the Conflicts between Biological and Social Evolution and

Between Psychology and Moral Tradition." American Psychologist 30, (12): 1103–1126.

Canovan, Margaret. 1999. "Trust the People! Populism and the Two Faces of Democracy.”

Political Studies 47, (1): 2–16.

Carmines, Edward, Michael Ensley, and Michael Wagner. 2016. "Ideological Heterogeneity and

the Rise of Donald Trump." The Forum 14, (4): 385–397.

Carmines, Edward, Paul Sniderman, and Beth Easter. 2011. "On the Meaning, Measurement, and

Implications of Racial Resentment." The Annals of the American Academy of Political

and Social Science 634, (1): 98–116.

Carnes, Nicholas. 2012. "Does the Numerical Underrepresentation of the Working Class in

Congress Matter?" Legislative Studies Quarterly 37, (1): 5 –34.

Carney, Timothy. 2019. Alienated America: Why Some Places Thrive While Others Collapse.

New York: Harper Collins.

Carney, Riley and Ryan Enos. 2017. "Conservatism and Fairness in Contemporary Politics:

305

Unpacking the Psychological Underpinnings of Modern Racism." In NYU CESS

Experiments Conference .

Carr, Patrick and Maria Kefalas. 2009 Hollowing out the Middle: The Rural Brain Drain and

What it Means for America . Boston: Beacon.

Carson, Ann. Prisoners in 2018. Washington D.C.: U.S. Department of Justice, Bureau of Justice

Statistics, 2020. https://bjs.gov/content/pub/pdf/p18.pdf.

Case, Anne, and Angus Deaton. 2015. "Rising Morbidity and Mortality in Midlife among White

Non-Hispanic Americans in the 21 st Century." Proceedings of the National Academy of

Sciences 112, (49): 15078–15083.

Case, Anne, and Angus Deaton. 2017. "Mortality and Morbidity in the 21st Century." Brookings

Papers on Economic Activity (1): 397–476.

Cherlin Andrew. 2009. The Marriage-go-round: The State of Marriage and the Family in

America Today. New York: Knopf.

Chesler, Mark. 1976. "Contemporary Sociological Theories of Racism." In Towards the

Elimination of Racism, ed Phyllis Katz: 21–72. New York: Pergamon.

Chetty, Raj, Nathaniel Hendren, Patrick Kline, and Emmanuel Saez. 2014. "Where is the Land of

Opportunity? The Geography of Intergenerational Mobility in the United States." The

Quarterly Journal of Economics 129, (4): 1553–1623.

Ciaramella, CJ. 2013. “Some Well-Dressed White Nationalists Gathered in DC Last Weekend.”

Vice , October 29. www.vice.com/en_us/article/kwpadw/some-well-dressed-white-

nationalists-gathered-in-dc-last-weekend.

Conover, Pamela. 1984. “The Influence of Group Identifications on Political Perception and

Evaluation.” The Journal of Politics 46, (3): 760–85.

306

Conover, Pamela, and Stanley Feldman. 1981. "The Origins and Meaning of

Liberal/Conservative Self-identifications." American Journal of Political Science : 617–

645.

Converse, Philip. 2006. “The Nature of Belief Systems in Mass Publics (1964), Critical Review

18, (1-3): 1–74.

Cooper, Christopher, and H. Gibbs Knotts. 2006. "Region, Race, and Support for the South

Carolina Confederate Flag." Social Science Quarterly 87, (1): 142-154.

Cox, Daniel, Rachel Liensche, and Robert Jones. 2017. “Beyond Economics: Fears of Cultural

Displacement Pushed the White Working Class to Trump | PRRI/The Atlantic

Report.” PRRI . May 9. http://www.prri.org/research/white-working-class-attitudes-

economy-trade-immigration-election-donald-trump/.

Craig, Maureen, and Jennifer Richeson. 2014. "More Diverse yet Less Tolerant? How the

Increasingly Diverse Racial Landscape Affects White Americans’ Racial Attitudes.”

Personality and Social Psychology Bulletin 40, (6): 750–761.

Craig, Maureen and Jennifer Richeson. 2018. "Hispanic Population Growth Engenders

Conservative Shift among Non-Hispanic Racial Minorities." Social Psychological and

Personality Science 9, (4): 383–392.

Dawson, Michael. 2009. “Black and Blue: Black Identity and Black Solidarity in an Era of

Conservative Triumph.” In Measuring Identity: A Guide for Social Scientists, eds.

Abdelal Rawi, Yoshiko Herrera, Alastair Johnston, and Rose McDermott: 175–99. New

York: Cambridge University.

Delgado, Richard, and Jean Stefancic. 2012. Critical Race Theory: An Introduction . New York:

NYU.

307

Dennis, William. 1996. "Foreword" in Frank S. Meyer, In Defense of Freedom and Other

Essays , Indianapolis, Liberty Fund.

Devos, Thierry, and Mahzarin Banaji. 2005. "American = White?" Journal of Personality and

Social Psychology 88, (3): 447–466.

Diamond, Larry. 1999. Developing Democracy . Baltimore MD: Johns Hopkins University Press.

Doane, Ashley. 1997. "Dominant Group Ethnic Identity in the United States: The Role of

“Hidden” Ethnicity in Intergroup Relations." The Sociological Quarterly 38, (3): 375–

397.

Dobratz, Betty and Stephanie Shanks-Meile. 2000. The White Separatist Movement in the

United States: White Power, White ! Maryland: JHU.

Domingo, Macy. 2014. “18 Things White People Seem To Not Understand (Because, White

Privilege).” Thought Catalog , April 5. www.thoughtcatalog.com/macy-sto-

domingo/2014/04/18-things-white-people-seem-to-not-understand-because-white-

privilege/.

Doughtery, Brendan Michael. 2016. “The Myth of Donald Trump's Upper-Class Support.”

The Week, 12 May. theweek.com/articles/623729/myth-donald-trumps-upperclass-

support.

Douthat, Ross, and Reihan Salam. 2008. Grand New Party: How Republicans Can Win the

Working Class and Save the American Dream . New York: Doubleday.

Dreher, Rod. 2015. "The Left’s Anti-White Racism." The American Conservative . December 23.

https://www.theamericanconservative.com/dreher/the-lefts-anti-white-racism/. d'Souza, Dinesh. Illiberal Education: 1991. The Politics of Race and Sex on Campus . Simon and

Schuster.

308

Duckitt, John. 1989. "Authoritarianism and Group Identification: A New View of an Old

Construct." Political Psychology : 63–84.

Duckitt, John, and Thobi Mphuthing. 1998. "Group Identification and Intergroup Attitudes: a

Longitudinal Analysis in South Africa." Journal of Personality and Social Psychology

74, (1): 80–85.

Eatwell, Roger, and Matthew Goodwin. 2018. National Populism: The Revolt Against Liberal

Democracy . UK: Penguin.

Edgell, Penny. 2006. Religion and Family in a Changing Society . Princeton: Princeton

University Press.

Edlund, Lena, and Rohini Pande. 2002. "Why Have Women Become Left-wing? The Political

Gender Gap and the Decline in Marriage." The Quarterly Journal of Economics 117, (3):

917–961.

Edsall, Thomas. 2017. “Reaching Out to the Voters the Left Left Behind.” The New York Times,

April 13. http://www.nytimes.com/2017/04/13/opinion/reaching-out-to-the-voters-the-

left-left-behind.html.

Edsall, Thomas, and Mary Edsall. 1992. Chain Reaction: The Impact of Race, Rights, and Taxes

on American Politics . New York: WW Norton & Company.

Edwards, Lee. 2014 “In Barry Goldwater, the Conscience of a Conservative.” Miami Herald,

July 2. https://www.miamiherald.com/latest-news/article1973798.html.

Effron, Daniel and Eric Knowles. 2015. "Entitativity and Intergroup Bias: How Belonging to a

Cohesive Group Allows People to Express their Prejudices." Journal of Personality and

Social Psychology 108, (2): 234–253.

Ehrenfreund, Max, and Jeff Guo. 2016. “If You've Ever Described People as ‘White Working

309

Class,’ Read This.” The Washington Post, 23 November.

www.washingtonpost.com/news/wonk/wp/2016/11/22/who-exactly-is-the-white-

working-class-and-what-do-they-believe-good-questions/?utm_term=.8312747719b1.

Ellis, Christopher, and James Stimson. 2012. Ideology in America . New York: Cambridge

University.

Federico, Christopher, and Jim Sidanius. 2002. "Racism, Ideology, and Affirmative Action

Revisited: the Antecedents and Consequences of "Principled Objections" to Affirmative

Action." Journal of Personality and Social Psychology 82, (4): 488–502.

Feldman, Stanley, and Leonie Huddy. 2005. "Racial Resentment and White Opposition to

Race‐conscious Programs: Principles or Prejudice?" American Journal of Political

Science 49, (1): 168–183.

Felzenberg, Alvin. 2017. “How William F. Buckley Jr. Changed His Mind on Civil Rights.”

Politico , May 13. https://www.politico.com/magazine/story/2017/05/13/william-f-

buckley-civil-rights-215129.

Fequiere, Pedro. 2015. "21 Things White People Ruined In 2015, Besides Everything.”

BuzzFeed, December 3. https://www.buzzfeed.com/pedrofequiere/stop-white-ppl-2015.

Flaxman, Seth, Sharad Goel, and Justin Rao. 2016. "Filter Bubbles, Echo Chambers, and Online

News Consumption." Public Opinion Quarterly 80, (1): 298–320.

Fischer, David. 1991. Albion's Seed: Four British Folkways in America . Oxford: Oxford

University.

Fortner, Michael. 2015. Black Silent Majority: The Rockefeller Drugs Laws and the Politics of

Punishment . Boston: Harvard University.

Francis, Samuel. 1998. “Whose Future?” The Social Contract 8, (4).

310

Frank, Thomas. 2004. What’s the Matter with Kansas? How Conservatives Won the Heart of

America. New York: Henry Holt & Company.

Frankenberg, Ruth. 1993. White Women, Race Matters: The Social Construction of Whiteness.

Minnesota: University of Minnesota.

Frankenburg, Ruth. 1997. “Introduction: Local Whitenesses, Localizing Whiteness”, in

Frankenberg, Ruth (Ed.), Displacing Whiteness: Essays in Social and Cultural Criticism ,

1-35. North Carolina: Duke University.

Franklin, Benjamin. 1787. “Letter to the Abbes Chalut and Arnoux.”

French, David. 2016. "The Price I’ve Paid for Opposing Donald Trump." National Review,

October 21. https://www.nationalreview.com/2016/10/donald-trump-alt-right-internet-

abuse-never-trump-movement/.

Friedland, Roger, and Robert Alford. 1991. "Bringing Society Back in: Symbols, Practices and

Institutional Contradictions." In The New Institutionalism in Organizational Analysis,

eds . Paul DiMaggio and Walter Powell, 232–263. Chicago, IL: University of Chicago.

Friedman, Milton. 1962. and Freedom. Illinois: University of Chicago.

Fry, Madeline, and Chris Pizzello. 2019. “Brie Larson Doesn't Want You Reviewing Her

Superhero Movie, White Males.” Washington Examiner , February 13.

www.washingtonexaminer.com/opinion/brie-larson-doesnt-want-you-reviewing-her-

-movie-white-males.

Fukuyama, Francis. 1995. Trust: The Social Virtues and the Creation of Prosperity . Vol. 99.

New York: Free Press.

311

Gaertner, Samuel, John Dovidio, Phyllis Anastasio, Betty Bachman, and Mary Rust. 1993. "The

Common Ingroup Identity Model: Recategorization and the Reduction of Intergroup Bias."

European Review of Social Psychology 4, (1): 1–26.

Gaertner, Samuel, John Dovidio, and Gaertner Samuel. 2000. Reducing Intergroup Bias: The

Common Ingroup Identity Model . Pennsylvania: Psychology Press.

Gaertner, Samuel, and John McLaughlin. 1983 "Racial Stereotypes: Associations and

Ascriptions of Positive and Negative Characteristics." Social Psychology Quarterly : 23–

30.

Garvey, John and Noel Ignatiev. 1997. "Toward A New Abolitionism: A Race Traitor

Manifesto." Whiteness: A Critical Reader : 346–49.

Gest, Justin. 2016. The New Minority: White Working Class Politics in an Age of Immigration

and Inequality. New York: Oxford University.

Gilberstadt, Hannah, and Andrew Daniller. 2020. Liberals Make up the Largest Share of

Democratic Voters, but Their Growth Has Slowed in Recent Years . Pew Research Center,

January 17, www.pewresearch.org/fact-tank/2020/01/17/liberals-make-up-largest-share-of-

democratic-voters/.

Gilbert, Dennis. 2018. The American Class Structure in an Age of Growing Inequality .

California: SAGE.

Gilens, Martin. 1999. Why Americans Hate Welfare: Race Media, and the Politics of Antipoverty

Policy . Chicago: University of Chicago.

Giles, Micheal, and Kaenan Hertz. 1994. "Racial Threat and Partisan Identification." American

Political Science Review 88, (2): 317–326.

312

Goldberg, Zach “2016 and The Left’s Lurch Left.” Working Paper.

https://drive.google.com/file/d/1-InKu3jR-J6LNrulbje-r4tIo25NTqoS/view.

Goldberg, Zachary. 2019. “America's White Saviors.” Tablet Magazine . June 6, 2019.

http://www.tabletmag.com/jewish-news-and-politics/284875/americas-white-saviors.

Goldwater, Barry. 1960. The Conscience of a Conservative (CC Goldwater, Ed.). New Jersey:

Princeton University.

Goldwater, Barry. 1964a. “Address Accepting the Presidential Nomination at the Republican

National Convention.” Speech, , California, July 16,

http://www.presidency.ucsb.edu/ws/index.php?pid=25973.

Goldwater, Barry. 1964b. “Nationwide TV Address on ‘Morality and Government’ Over the

CBS Network, 9:30–10 P.M.,” Republican National Committee News, October 20. Barry

Goldwater Papers, University of at Austin.

Goren, Paul. 2003. "Race, Sophistication, and White Opinion on Government Spending."

Political Behavior 25, (3): 201–220.

Granovetter, Mark. 1974. Getting a Job: A Study of Contacts and Careers. Illinois: University of

Chicago.

Greven, Thomas. 2016. "The Rise of Right-wing Populism in Europe and the United States , A

Comparative Perspective.” Friedrich Ebert Foundation, Washington D.C. Office, May

2016. https://library.fes.de/pdf-files/id/12892.pdf.

Grobart, Sam. 2016. “Everybody Thinks They're Middle-Class.” Bloomberg.com , 15 September.

www.bloomberg.com/features/2016-america-divided/middle-class/.

313

Guess, Andrew, and Alexander Coppock. 2018. "Does Counter-attitudinal Information Cause

Backlash? Results from Three Large Survey Experiments." British Journal of Political

Science : 1-19.

Hackel, Leor, Jamil Zaki, and Jay Van Bavel. 2017. "Social Identity Shapes Social Valuation:

Evidence from Prosocial Behavior and Vicarious Reward." Social Cognitive and

Affective Neuroscience 12, (8): 1219–1228.

Haidt, Jonathan. 2012. The Righteous Mind: Why Good People are Divided by Politics and

Religion . New York: Pantheon Books.

Haivry, Ofir, and Yoram Hazony. 2017. “What Is Conservatism?” Journal ,

May 20. americanaffairsjournal.org/2017/05/what-is-conservatism/.

Hajnal, Zoltan, and Jeremy Horowitz. 2014. "Racial Winners and Losers in

Politics." Perspectives on Politics 12, (1): 100–118.

Hankin, Stefan. 2016. “The Past Ten Presidential Elections In Today's Electorate.” Lincoln Park

Strategies , February 18. lpstrategies.com/white-papers/the-past-ten-presidential-

elections/218,2016.

Hann, Chris. 1996. “Introduction: Political Society and Civil Anthropology.” In Civil Society:

Challenging Western Models , eds . Chris Hann and Elizabeth Dunn, 1–26. and

New York: Routledge.

Harnad, Stevan. 2017. "To Cognize is to Categorize: Cognition is Categorization." In Handbook

of Categorization in Cognitive Science , 2 nd edition, eds. Henri Cohen and Claire Lefebvre:

21–54. New York: Elsevier.

Harper, Cynthia, and Sara McLanahan. 2004. "Father Absence and Youth Incarceration.”

Journal of Research on Adolescence 14, (3): 369–397.

314

Hart, William, Dolores Albarracín, Alice Eagly, Inge Brechan, Matthew Lindberg, and Lisa

Merrill. 2009. "Feeling Validated Versus Being Correct: A Meta-analysis of Selective

Exposure to Information." Psychological Bulletin 135, (4): 555–88.

Hartigan, John. 1997. “Establishing the Fact of Whiteness.” American Anthropologist 99, (3):

495–505.

Haskins, Ron. 2013. "Three Simple Rules Poor Teens Should Follow to Join the Middle Class.”

Brookings Institution, March 13. https://www.brookings.edu/opinions/three-simple-rules-

poor-teens-should-follow-to-join-the-middle-class/.

Haskins, Ron, and Isabel Sawhill. 2009. Creating an Opportunity Society . Washington D.C.:

Brookings Institution.

Hawley, George. 2016. Right-wing Critics of American Conservatism . Kansas: University Press

of Kansas.

Hawley, George. 2017. Making Sense of the Alt-right . New York: Columbia University.

Hayek Friedrich, von. 1944. The Road to Serfdom . Illinois: University of Chicago.

Hazony, Yoram. 2018. The Virtue of Nationalism . New York: Basic Books.

Heersink, Boris, and Jeffery Jenkins. 2020. Republican Party Politics and the American South,

1865–1968 . Massachusetts: Cambridge University.

Heikkilä, Niko. 2017. "Online Antagonism of the Alt-right in the 2016 Election." European

Journal of American Studies 12, 12–2.

Helliwell, John, and Robert Putnam. 1995. "Economic Growth and Social Capital in Italy."

Eastern Economic Journal 21, (3): 295–307.

Hess, Amanda. 2016. "For the Alt Right, the Message Is in the Punctuation." The New

315

York Times , June 10. https://www.nytimes.com/2016/06/11/arts/for-the-alt-right-the-

message-is-in-the-punctuation.html.

Higham, John. 1963. Strangers in the Land, Patterns in American : 1860–1925 . New

Jersey: Rutgers.

Hogg, Michael, and Dominic Abrams. 1988. Social Identification: A Social Psychology of

Intergroup Relations and Group Processes . London, UK: Routledge.

Hogg, Michael, and John Turner. 1985. "Interpersonal Attraction, Social Identification and

Psychological Group Formation." European Journal of Social Psychology 15, (1): 51–66.

Holan, Angie. 2017. “PolitiFact - In Context: President Donald Trump's Statement on 'Many

Sides' in Charlottesville, Va.” Politifact , 14 August,

www..com/article/2017/aug/14/context-president-donald-trumps-saturday-

statement/.

Holan, Angie. 2019. “PolitiFact - In Context: Donald Trump's 'Very Fine People on Both Sides'

Remarks (Transcript).” Politifact , 26 April,

www.politifact.com/article/2019/apr/26/context-trumps-very-fine-people-both-sides-

remarks/.

Holloway, Kali. 2015. "10 Ways White People Are More Racist than They Realize." Salon,

March 4. https://www.salon.com/2015/03/04/10_ways_white_people_are_more_racist_

than_they_realize_partner/.

Holt, Jared. 2018. “Nick Fuentes: Racial Hostility Toward Minorities a Fair 'Trade Off' to End

'White Genocide'.” Right Wing Watch , October 1. www.rightwingwatch.org/post/nick-

fuentes-racial-hostility-toward-minorities-a-fair-trade-off-to-end-white-genocide/.

316

Hooghe, Marc. 2003. “Value Congruence and Convergence within Voluntary Associations:

Ethnocentrism in Belgian Organization.” Political Behavior 25, (2): 151–75.

Hopkins, Daniel. 2009. "The Diversity Discount: When Increasing Ethnic and Racial Diversity

Prevents Tax Increases." The Journal of Politics 71, (1): 160–177.

Hopkins, Daniel. 2011. "National Debates, Local Responses: The Origins of Local Concern

about Immigration in Britain and the United States." British Journal of Political

Science 41, (3): 499–524.

Hoyt, Carlos. 2012. "The Pedagogy of the Meaning of Racism: Reconciling a Discordant

Discourse." Social Work 57, (3): 225–234.

Huston, Warner. 2016. “Drexel University Professor's Christmas Wish: 'All I Want for Christmas

Is White Genocide'.” Breitbart News , December 26. www.breitbart.com/politics/2016

/12/25/drexel-univ-professors-christmas-wish-want-christmas-white-genocide/.

“How Groups Voted.” 2020. Roper Center for Public Opinion Research , Cornell University,

ropercenter.cornell.edu/how%20groups%20voted.

Hymowitz, Kay. 2006. Marriage and Caste in America: Separate and Unequal Families in a

Post-Marital Age . Chicago: Ivan R. Dee Publisher.

Huang, Jon, Samuel Jacoby, Michael Strickland and K.K. Rebecca Lai. 2016. “Election 2016:

Exit Polls.” New York Times , November 9. http://www.nytimes.com/interactive/2016/11

/08/us/politics/election-exit-polls.html.

“Ideological Gap Widens Between More, Less Educated Adults.” Pew Research Center for the

People and the Press. June 3, 2016. http://www.people-press.org/2016/04/26/a-wider-

ideological-gap-between-more-and-less-educated-adults/.

317

Idler, Ellen, John Blevins, Mimi Kiser, and Carol Hogue. 2017. "Religion, a Social Determinant

of Mortality? A 10-year follow-up of the Health and Retirement Study." PloS ONE 12,

(12): e0189134. https://doi.org/10.1371/journal.pone.0189134

Ignazi, Pierre. 2003. Extreme Right Parties in Western Europe . Oxford: Oxford University Press.

Inglehart, Ronald, and Pippa Norris. 2016. "Trump, Brexit, and the Rise of Populism: Economic

Have-nots and Cultural Backlash". Faculty Working Paper. RWP16-026.

https://faculty.uml.edu/sgallagher/Trump_Populism_Norris.pdf.

Inglehart, Ronald, and Christian Welzel. 2005. “Modernizational Cultural Change and

Democracy.” New York: Cambridge University.

Irwin, Neil, and Josh Katz. 2016. "The Geography of Trumpism." The New York Times, March 14.

https://www.nytimes.com/2016/03/13/upshot/the-geography-of-trumpism.html.

Iyengar, Shanto, Gaurav Sood, and Yphtach Lelkes. 2012. "Affect, Not ideology: A Social

Identity Perspective on Polarization." Public Opinion Quarterly 76, (3): 405-431.

Jackman, Mary and Robert Jackman. 1973. An Interpretation of the Relation Between Objective

and Subjective Social Status. American Sociological Review , 38 (October): 569–582.

Jacobs, Harrison. 2016. "The Revenge of the 'Oxy Electorate' Helped Fuel Trump's Election

Upset." , November 23. https://www.greenwichtime.com/technology/

businessinsider/article/The-revenge-of-the-Oxy-electorate-helped-fuel-10632622.php

Jackson, Jay, and Eliot Smith. 1999. "Conceptualizing Social Identity: A New Framework and

Evidence for the Impact of Different Dimensions." Personality and Social Psychology

Bulletin 25, (1): 120–135.

Jacobson, Louis. 2017. “Are There White Nationalists in the White House?” Politifact , August

15. www.politifact.com/article/2017/aug/15/are-there-white-nationalists-white-house/.

318

James, Bendan. 2015. “Donald Trump Slams Fellow GOPers For 'Attacking Social Security'

(VIDEO).” Talking Points Memo . April 20. https://talkingpointsmemo.com /livewire

/trump-gop-candidates-social-security.

Jardina, Ashley. 2014. "Demise of Dominance: Group Threat and the New Relevance of White

Identity for American Politics." PhD diss. University of Michigan.

Jardina, Ashley. 2019. White Identity Politics. Cambridge, UK: Cambridge University Press.

Jencks, Christopher, and Susan Mayer. 1990. "The Social Consequences of Growing up in a

Poor Neighborhood." In Inner-city Poverty in the United States, National Research

Council, 111: 186. Washington D.C.: National Academies.

Johnson, Greg. 2014. “Restoring White Homelands.” Counter-Currents Publishing , June 24.

www.counter-currents.com/2014/06/the-slow-cleanse/.

Jones, Bernie. 2002. "Critical Race Theory: New Strategies for Civil Rights in the New

Millenium." Harv. BlackLetter LJ, 18: 1.

Jones, Rachel, and Jenna Jerman. 2017. "Abortion Incidence and Service Availability in the

United States, 2014." Perspectives on Sexual and Reproductive Health 49, (1): 17–27.

Jones, Robert, and Daniel Cox. 2012. “Beyond Guns and God: Understanding the Complexity of

the White Working Class in America.” PRRI . http://www.prri.org/research/race-class-

culture-survey-2012/.

Joyce, Frank. 2015. "White Men Must Be Stopped: The Very Future of Mankind Depends on

It." Salon, December 22. https://www.salon.com/2015/12/22/white_men_must_be_

stopped_the_very_future_of_the_planet_depends_on_it_partner/.

Judis, John, and Ruy Teixeira. 2004. The Emerging Democratic Majority . Simon and Schuster.

319

Kaplan, Jeffrey, and Leonard Weinberg. 1998. The Emergence of a Euro-American Radical

Right . New Jersey: .

Kaufman, Elliot. 2017. “The Alt-Right Carries on Margaret Sanger's Legacy of .”

National Review , August 25. www.nationalreview.com/2017/08/alt-right-abortion-richard-

spencer-upholds-margaret-sanger-eugenicist-legacy/.

Kaufmann, Eric. 2018. Whiteshift: Populism, Immigration and the Future of White Majorities .

New York: Penguin.

Kaufmann, Eric. 2019. "Can Narratives of White Identity Reduce Opposition to Immigration and

Support for Hard Brexit? A Survey Experiment." Political Studies 67, (1): 31–46.

Key, V.O. 1949. Southern Politics in State and Nation. New York: A.A. Knopf.

Kheriaty, Aaron. 2017. "Dying of Despair." 275: 1–11.

Kinder, Donald and Cindy Kam. 2010. Us Against Them: Ethnocentric Foundations of American

Opinion . Illinois: University of Chicago.

Kinder, Donald and Lynn Sanders. 1996. Divided by Color: Racial Politics and Democratic

Ideals . Illinois: University of Chicago.

Kinder, Donald and Nicholas Winter. 2001. “Exploring the Racial Divide: Blacks, Whites, and

Opinion on National Policy.” American Journal of Political Science 45, (2): 439–56.

Kinder, Donald and Tali Mendelberg. 2000. "Individualism Reconsidered: Principles and

Prejudice in Contemporary American Opinion." In Racialized Politics: The Debate about

Racism in America , eds. David Sears, John Hetts, Jim Sidanius, Lawrence Bobo, 44–74.

Illinois: University of Chicago.

King, Martin Luther. 1963. “I Have a Dream.” March on Washington for Jobs and Freedom.

March on Washington for Jobs and Freedom, August 28. Washington, D.C.

320

Kirk, Russell. 1987. Ten Conservative Principles . Washington, DC: Heritage Foundation.

Knack, Stephen. 2002. "Social Capital and the Quality of Government: Evidence from the

States." American Journal of Political Science 46, (4): 772–785.

Knack, Stephen, and Philip Keefer. 1997. "Does Social Capital have an Economic Payoff? A

Cross-Country Investigation." The Quarterly Journal of Economics 112, (4): 1251–1288.

Knowles, Eric and Linda Tropp. 2018. "The Racial and Economic Context of Trump Support:

Evidence for Threat, Identity, and Contact Effects in the 2016 Presidential Election."

Social Psychological and Personality Science 9, (3): 275–284.

Kohn, Hans. 1944. The Idea of Nationalism : A Study in its Origins and Background. New York:

Macmillian.

Kolko, Jed. 2016. “Trump Was Stronger Where the Economy Is Weaker.” FiveThirtyEight

[], November 10. https://fivethirtyeight.com/features/trump-was-stronger-where-the-

economy-is-weaker/.

Krishnan, Manisha. 2016. “Dear White People, Please Stop Pretending Reverse Racism is Real”

Vice . October 2. https://www.vice.com/en_us/article/kwzjvz/dear-white-people-please-

stop-pretending-reverse-racism-is-real.

Kruse, Kevin. 2005. White Flight: and the Making of Modern Conservatism . New

Jersey: Princeton University.

Land, Nick. 2013. " The ." http://www.thedarkenlightenment.

com/the-dark-enlightenment-by-nick-land/

LaPorta, Rafael, Florencio Lopez-De-Silane, Andrei Shleifer, and Robert Vishny. 1996. “Trust

in Large Organizations.” No. w5864. National Bureau of Economic Research .

Lau, Richard 1989. "Individual and Contextual Influences on Group Identification." Social

321

Psychology Quarterly : 220–231.

Lax, Jeffrey, and Justin Phillips. 2009. "How Should We Estimate Public Opinion in the

States?" American Journal of Political Science 53, (1): 107–121.

Leach, Colin, Nastia Snider, Aarti Iyer. 2002. "Poisoning the Consciences of the Fortunate: The

Experience of Relative Advantage and Support for Social Equality. In Relative

Deprivation: Specification, Development and Integration , eds. Iain Walker and Heather

Smith: 136–163. Cambridge, UK: Cambridge University.

Lee, Woojin, and John Roemer. 2006. "Racism and Redistribution in the United States: A

Solution to the Problem of ." Journal of Public Economics 90,

(6-7): 1027–1052.

Lensmire, Timothy, Shannon McManimon, Jessica Dockter Tierney, Mary Lee-Nichols, Zachary

Casey, Audrey Lensmire, and Bryan Davis. 2013. "McIntosh as Synecdoche: How

Teacher Education's Focus on White Privilege Undermines Antiracism." Harvard

Educational Review 83, (3): 410-431.

Levine, Robert and Donald Campbell. 1972. Ethnocentrism: Theories of Conflict, Ethnic

Attitudes, and Group Behavior. Oxford, UK: John & Sons.

Levin, Shana, and Jim Sidanius. 1999. "Social Dominance and Social Identity in the United

States and : Ingroup Favoritism or Outgroup Derogation?" Political Psychology 20,

(1): 99–126.

Levin, Shana, Jim Sidanius, Joshua Rabinowitz, and Christopher Federico. 1998. “Ethnic

Identity, Legitimizing Ideologies, and Social Status: A Matter of Ideological

Asymmetry.” Political Psychology 19, (2): 373–404.

322

Levin, Yuval. 2014. "A Conservative Governing Vision." In Room to Grow: Conservative

Reforms for a Limited Government and a Thriving Middle Class, eds. Yuval Levin and

Peter Wehner: 15-21. Washington D.C.: YG Network.

Levin, Yuval. 2020. A Time to Build: From Family and Community to Congress and the

Campus, How Recommitting to Our Institutions Can Revive the American Dream . New

York: Basic Books.

Levitin, Teresa and Warren Miller. 1979. "Ideological Interpretations of Presidential Elections."

American Political Science Review 73, (3): 751–771.

Levitz, Eric. 2020. “Why Pete Buttigieg’s Paeans to the ‘Heartland’ Are Nonsensical.”

Intelligencer January 30. https://nymag.com/intelligencer/2020/01/why-pete-buttigiegs-

paens-to-the-heartland-are-nonsense.html.

Lichter, Daniel and Julie Carmalt. 2009. "Religion and Marital Quality among Low-income

Couples." Social Science Research 38, (1): 168–187.

Linly, Zack. 2016. "It’s Time to Stop Talking about Racism with White People." The

Washington Post, September 7. https://www.washingtonpost.com/posteverything/

wp/2016/09/07/its-time-to-stop-talking-about-racism-with-white-people/.

Lord, Charles, Lee Ross, and Mark Lepper. 1979. "Biased Assimilation and Attitude

Polarization: The Effects of Prior Theories on Subsequently Considered Evidence.”

Journal of Personality and Social Psychology 37, (11): 2098–2109.

Lowndes, Joseph. 2008. From the New Deal to the : Race and the Southern Origins of

Modern Conservatism . : Yale University.

Lowery, Brian, Miguel Unzueta, Eric Knowles, and Phillip Goff. 2006. "Concern for the In-

group and Opposition to Affirmative Action." Journal of Personality and Social

323

Psychology 90, (6): 961–974.

Luttmer, Erzo. 2001. "Group Loyalty and the Taste for Redistribution." Journal of Political

Economy 109, (3): 500–528.

MacWilliams, Matthew. 2016. “Who Decides when the Party Doesn’t? Authoritarian Voters and

the Rise of Donald Trump.” PS: Political Science & Politics 49, (4): 716–721

Major, Brenda, Alison Blodorn, and Gregory Blascovich. 2018. "The Threat of Increasing

Diversity: Why many White Americans Support Trump in the 2016 Presidential

Election." Group Processes & Intergroup Relations 21, (6): 931–940.

Malka, Ariel, and Yphtach Lelkes. 2010. "More Than Ideology: Conservative–Liberal Identity

and Receptivity to Political Cues." Social Justice Research 23, (2-3): 156–188.

Markus, David. 2016. "How Anti-White Rhetoric Is Fueling White Nationalism." The

Federalist, May 26 . https://thefederalist.com/2016/05/23/how-anti-white-rhetoric-is-

fueling-white-nationalism/.

Mason, Lilliana. 2015. "‘I Disrespectfully Agree’: The Differential Effects of Partisan Sorting on

Social and Issue Polarization." American Journal of Political Science 59, (1): 128 –145.

Mason, Lilliana. 2018. Uncivil Agreement: How Politics Became our Identity. Illinois:

University of Chicago.

Mather, Mark. 2010. "US Children in Single-mother Families." Population Reference Bureau,

Data Brief (May). Washington, D.C.

Matsa, Katerina and Kristine Lu. 2016. “10 Facts about the Change Digital News Landscape.”

Pew Research Center, September 14. https://www.pewresearch.org/fact-tank/2016/09/14

/facts-about-the-changing-digital-news-landscape/.

324

McAllister, D.C. 2015. "Why White People Will Always Be Racists." The Federalist, July 10 .

https://thefederalist.com/2015/07/10/why-white-people-will-always-be-racists/.

McClendon, McKee and Fred Pestello. 1982. "White Opposition: To Busing or to

Desegregation?" Social Science Quarterly 63, (1): 70 –82.

McConahay, John. 1986. “Modern Racism, Ambivalence, and the Modern Racism Scale." In

Prejudice, Discrimination, and Racism, eds John Dovidio and Samuel Gaertner: 91–125.

Florida: Academic.

McCourt, Kathryn, Thomas Bouchard Jr., David Lykken, Auke Tellegen, and Margaret Keyes.

1999. "Authoritarianism Revisited: Genetic and Environmental Influences Examined in

Twins Reared Apart and Together." Personality and Individual Differences 27, (5): 985–

1014.

McDaniel, Jason, and Sean McElwee. 2016. "Racial Resentment and the Rise of Donald

Trump." The New West [blog] , April 03. https://thewpsa.wordpress.com/2016/03

/27/racial-resentment-and-the-rise-of-donald-trump/.

McIntosh, Peggy. 1988 "White Privilege: Unpacking the Invisible Knapsack." Peace and

Freedom Magazine : 10–12.

McWhorter, John. 2015. “Antiracism, Our Flawed New Religion.” The Daily Beast , July 27.

www.thedailybeast.com/antiracism-our-flawed-new-religion.

Mendelberg, Tali. 2001. The Race Card: Campaign Strategy, Implicit Messages, and the Norm

of Equality . New Jersey: Princeton University.

Messick, David and Diane Mackie. 1989. "Intergroup Relations." Annual Review of Psychology

40, (1): 45–81.

325

Mettler, Suzanne. 2002. "Bringing the State back in to Civic Engagement: Policy Feedback

Effects of the GI Bill for World War II Veterans." American Political Science Review 96,

(2): 351–365.

Meyer, Frank. 1962. In Defense of Freedom: A Conservative Credo . Illinois: H. Regnery Co.

Millard, Drew. 2015. “We Asked a White Supremacist What He Thinks of Donald Trump.”

Vice , December 10. www.vice.com/en_ca/article/bnp33d/we-asked-a-white-supremacist-

what-he-thought-of-donald-trump-1210.

Miller, Arthur, Patricia Gurin, Gerald Gurin and Oksana Malanchuk. 1981. "Group

Consciousness and Political Participation." American Journal of Political Science 25, (3) :

494–511.

Moezzi, Melody. 2016. “Ten Things White People Need to Quit Saying.” The Huffington Post,

April 27. https://www.huffpost.com/entry/ten-things-white-people-n_b_9765436.

Monnat, Shannon. 2016. "Deaths of Despair and Support for Trump in the 2016 Presidential

Election." Pennsylvania State University Department of Agricultural Economics

Research Brief 5 . https://aese.psu.edu/directory/smm67/Election16.pdf

Morgan, Stephen, and Jiwon Lee. 2017. "The White Working Class and Voter Turnout in US

Presidential Elections, 2004 to 2016." Sociological Science 4: 656–685.

Morgan, Rachel, and Barbara Oudekerk. Criminal Victimization, 2018. Washington D.C.: U.S.

Department of Justice, Bureau of Justice Statistics, 2019.

https://www.bjs.gov/content/pub/pdf/cv18.pdf.

Mudde, Cas. 2004. "The Populist ." Government and Opposition 39, (4): 541–563.

Mullen, Brian, Rupert Brown, and Colleen Smith. 1992. "Ingroup Bias as a Function of Salience,

326

Relevance, and Status: An Integration." European Journal of Social Psychology 22, (2):

103–122.

Muller, Jerry. 2008. "Us and Them: The Enduring Power of Ethnic Nationalism." Foreign

Affairs : 18–35.

Murray, Charles. 2012. Coming Apart: The State of White America, 1960-2010. New York:

Crown Forum.

Myrdal, Gunnar. 1944. An American Dilemma; the Negro Problem and Modern Democracy. (2

vols.) New York: Harper and Row."

Nagle, Angela. 2017. : Online Culture Wars from 4chan and Tumblr to Trump

and the Alt-right . UK, John Hunt Publishing.

Nieli, Russell, and Carol Swain. 2003. Contemporary Voices of White Nationalism in America.

Massachusetts: Cambridge University.

Nisbet Robert. 1953 . The Quest for Community . New York: Oxford University Press. Noel, Hans. 2012. "The Coalition Merchants: The Ideological Roots of the Civil Rights

Realignment." The Journal of Politics 74, (1): 156–173.

Norton, Michael and Samuel Sommers. 2011. "Whites See Racism as a Zero-sum Game That

They Are Now Losing." Perspectives on Psychological Science 6, (3): 215–218.

Oakeshott, Michael. 1962. “On Being Conservative.” Rationalism in Politics: and Other Essays .

London, UK: Methuen.

Oliver, Eric, and Wendy Rahn. 2016. "Rise of the Trumpenvolk: Populism in the 2016

Election." The ANNALS of the American Academy of Political and Social Science 667,

(1): 189–206.

Olson, Laura, and John Green. 2006. "The Religion Gap." PS: Political Science & Politics 39,

(3): 455–459.

327

Olsen, Marvin. 1970. "Social and Political Participation of Blacks." American Sociological

Review 35, (4): 682–697.

Omi, Michael, and Howard Winant. 1986. Racial Formation in the United States: From the

1960s to the 1980s. New York: Routledge and Kegan Paul.

“Opioid Overdose Deaths by Race/Ethnicity.” Kaiser Family Foundation . August 3, 2017.

http://www.kff.org/other/state-indicator/opioid-overdose-deaths-by-raceethnicity/.

Outten, Robert, Michael Schmitt, Daniel Miller, and Amber Garcia. 2012. "Feeling Threatened

About the Future: Whites’ Emotional Reactions to Anticipated Ethnic Demographic

Changes." Personality and Social Psychology Bulletin 38, (1): 14–25.

Øverland, Orm. 2004. "Becoming White in 1881: An Immigrant Acquires an American

Identity." Journal of American Ethnic History 23, (4): 132–141.

Park, David, Andrew Gelman, and Joseph Bafumi. 2006. "State-level Opinions from National

Surveys: Poststratification Using Multilevel Logistic Regression." Public Opinion in

State Politics : 209–28.

Parker, Christopher and Matt Barreto. 2013. Change They Can’t Believe In: The Tea Party and

Reactionary Politics in America. New Jersey: Princeton University.

Pearson, Jennifer, Chandra Muller, and Michelle Frisco. 2006. "Parental Involvement, Family

Structure, and Adolescent Sexual Decision Making." Sociological Perspectives 49, (1):

67–90.

Perlstein, Rick. 2018. “Exclusive: 's Infamous 1981 Interview on the Southern

Strategy.” The Nation , December 7. www.thenation.com/article/archive/exclusive-lee-

atwaters-infamous-1981-interview-southern-strategy/.

328

Perrin, Andrew. 2015. "Social Media Usage." Pew Research Center, October 8.

https://www.pewinternet.org/2015/10/08/social-networking-usage-2005-2015/.

Perry, Mark. 2016. “Chart of the Day.” American Enterprise Institute , March 17.

www.aei.org/carpe-diem/chart-of-the-day-4/.

Pettigrew, Thomas. 2017. "Social Psychological Perspectives on Trump Supporters." Journal of

Social and Political Psychology 5, (1): 107–116.

Piacenza, Joanna. 2014. "Americans’ Racial Disconnect on Fairness and Discrimination." Public

Religion Research Institute, October 6. https://www.prri.org/spotlight/graphic-of-the-

week-americans-racial-disconnect-on-fairness-and-discrimination/.

Pierce, William. 1978. . West Virginia: National Vanguard Books.

Plainview. 2016. “2017 New Years Resolutions for White Guys | MTV News.” YouTube ,

December 20. www..com/watch?v=SBluYsydAVc&feature=youtube.

Pong, Suet-Ling, and Dong-Beom Ju. 2000. "The Effects of Change in Family Structure and

Income on Dropping out of Middle and High School." Journal of Family Issues 21, (2):

147–169.

Poterba, James. 1997. "Demographic Structure and the Political Economy of Public Education."

Journal of Policy Analysis and Management: The Journal of the Association for Public

Policy Analysis and Management 16, (1): 48–66.

Prager, Dennis. 2016. “The Fallacy of 'White Privilege'.” National Review , February 16.

www.nationalreview.com/2016/02/white-privilege-myth-reality/.

Pratto, Felicia, Jim Sidanius, Lisa Stallworth, and Bertram Malle. 1994. "Social Dominance

Orientation: A Personality Variable Predicting Social and Political Attitudes." Journal of

Personality and Social Psychology 67, (4): 741–763.

329

Prestigiacomo, Amanda. 2016. "'Buzzfeed' Celebrates Anti-White Racism." Daily Wire,

December 7. https://www.dailywire.com/news/buzzfeed-celebrates-anti-white-racism-

amanda-prestigiacomo.

“Proposal for the 2016 Pilot Study: White Racial Consciousness in the U.S.” Online Commons ,

American National Elections Studies Forum, February 18, 2016. forum.electionstudies.org

/proposal-for-the-2016-pilot-study-white-racial-consciousness-in-the-u-s/.

Putnam, Robert. 1993. Making Democracy Work. Civic Traditions in Modern Italy. Princeton:

Princeton University.

Putnam, Robert. 1995. "Tuning In, Tuning Out: The Strange Disappearance of Social Capital in

America." PS: Political Science & Politics 28, (4): 664–683.

Putnam, Robert. 2000. Bowling Alone: The Collapse and Revival of American Community . New

York: Simon and Schuster.

Putnam, Robert. 2016. Our Kids: The American Dream in Crisis. New York: Simon and

Schuster.

Putnam, Robert. 2007. "E Pluribus Unum: Diversity and Community in the Twenty‐first

Century the 2006 Johan Skytte Prize Lecture." Scandinavian Political Studies 30, (2):137–

174.

Quinn, Spencer. 2016. “The Best Ever out of the West.” Counter-Currents

Publishing , July 8. www.counter-currents.com/2016/07/the-best-ever-white-ethnostate-

out-of-the-west/.

Rabbie, Jacob. 1982. "The Effects of Intergroup Competition and Cooperation on Intragroup

330

and Intergroup Relationships." In Cooperation and Helping Behavior: Theories and

Research, eds Valerian Derlega and Janusz Grzelak: 123–149. New York: Academic

Press.

Reagan, Ronald. "First Inaugural Address” (speech, Washington, DC, January 21, 1981).

American Rhetoric. https://www.americanrhetoric.com/speeches/ronaldreagand

firstinaugural.html.

Redlawsk, David. 2002. "Hot Cognition or Cool Consideration? Testing the Effects of Motivated

Reasoning on Political Decision Making." The Journal of Politics 64, (4): 1021–1044.

“Richard Bertrand Spencer.” Southern Poverty Law Center , www.splcenter.org/fighting-

hate/extremist-files/individual/richard-bertrand-spencer-0.

Robinson, William. 1950. "Ecological Correlations and the Behavior of Individuals.” American

Sociological Review 15: 337–341.

Roediger, David. 1994. Towards the Abolition of Whiteness: Essays on Race, Politics, and

Working Class History . New York: Verso Press.

Roediger, David. 1999. The Wages of Whiteness: Race and the Making of the American Working

Class . New York: Verso.

Rothwell, Jonathan, and Pablo Diego-Rosell. 2016. “Explaining Nationalist Political Views: The

Case of Donald Trump.” Gallup . Draft working paper. http://pelg.ucsd.edu/2.rothwell_

2016.pdf.

Rousseau, Jean-Jacques. 1762. The Social Contract. Trans. Maurice Cranston Harmondsworth:

Penguin Books (1968).

Rupasingha, Anil, Stephan Goetz and David Freshwater. 2006a. “The Production of Social

Capital in US Counties.” Journal of Socio-Economics , 35: 83–101

331

Rupasingha, Anil, Stephan Goetz, and David Freshwater. 2006b, with updates. County-level

Measure of Social Capital [dataset]. Northeast Regional Center for Rural Development.

Pennsylvania State University. Accessed: March 2019.

https://aese.psu.edu/nercrd/community/social-capital-resources.

Ryan, Josiah. 2016. "‘This Was a Whitelash’: Van Jones’ Take on the Election Results." CNN

Politics, November 9. https://www.cnn.com/2016/11/09/politics/van-jones-results-

disappointment-cnntv/index.html.

Schildkraut, Deborah. 2010. Americanism in the Twenty-first Century: Public Opinion in the

Age of Immigration. New York: Cambridge University.

Schildkraut, Deborah. 2014. “‘The White Establishment Is Now the Minority': White Identity,

Discrimination, and Linked Fate in the United States." 2014 Annual Meeting of the

American Political Science Association . Washington D.C.

Schuman, Howard. 2000. "The Perils of Correlation, the Lure of Labels, and the Beauty of

Negative Results." In Racialized Politics: The Debate about Racism in America , eds

James Sidanius, and Lawrence Bobo: 302–323. Illinois: University of Chicago Press.

Schwadel Phillip, John McCarthy, Hart Nelsen. 2009. “The Continuing Relevance of Family

Income for Religious Participation: U.S. White Catholic Church Attendance in the Late

20 th Century.” Social Forces 87 (4): 1997–2030.

Sears, David and Victoria Savalei. 2006. “The Political Color Line in America: Many ‘Peoples

of Color’ or Black Exceptionalism?” Political Psychology 27 (6): 895–924.

Sears, David and Patrick Henry. 2005. "Over Thirty Years Later: A Contemporary Look at

Symbolic Racism." Advances in Experimental Social Psychology 37: 95–125.

332

Sherif, Muzafer, O.J. Harvey, B. Jack White, William Hood, and Caroline Sherif. 1961.

Intergroup Conflict and Cooperation: The Robbers Cave Experiment : Oklahoma:

The University Book Exchange.

Sidanius, Jim and Felicia Pratto. 2001. Social Dominance: An Intergroup Theory of Social

Hierarchy and Oppression . Cambridge, UK: Cambridge University Press.

Sidanius, Jim, Felicia Pratto, and Lawrence Bobo. 1996. "Racism, Conservatism, Affirmative

Action, and Intellectual Sophistication: A Matter of Principled Conservatism or Group

Dominance?" Journal of Personality and Social Psychology 70, (3): 476–490.

Sidanius, Jim, Shana Levin, James Liu, and Felicia Pratto. 2000. "Social Dominance Orientation,

Anti‐egalitarianism and the Political Psychology of Gender: An Extension and Cross‐

cultural Replication." European Journal of Social Psychology 30, (1): 41–67.

Sides, John, Michael Tesler, and Lynn Vavreck. 2018. Identity Crisis: The 2016 Presidential

Campaign and the Battle for the Meaning of America . New Jersey: Princeton University.

Silver, Nate. 2016. “The Mythology Of Trump's ‘Working Class’ Support.” FiveThirtyEight , 3

May, fivethirtyeight.com/features/the-mythology-of-trumps-working-class-support/.

Silverstein, Jake. “Why We Published The 1619 Project.” The New York Times , 20 Dec. 2019,

www.nytimes.com/interactive/2019/12/20/magazine/1619-intro.html.

Simien, Justin, director. 2014. Dear White People . Code Red Films.

Simonsen, Kristina Bakkær, and Bart Bonikowski. 2020. "Is Civic Nationalism Necessarily

Inclusive? Conceptions of Nationhood and Anti‐Muslim Attitudes in Europe." European

Journal of Political Research 59, (1): 114–136.

Sisyphus, Mortal. 2018. “RightRealist.” RightRealist.com , July 9. www.rightrealist.com/#q17.

333

Skocpol, Theda. 1993. “How Americans Became Civic.” In Civic Engagement in American

Democracy, eds. Theda Skocpol and Morris Fiorina, 27–80. Washington, D.C.: The

Brookings Institution Press and the Russell Sage Foundation.

Smelser, Neil and Jeffrey Alexander. 1999. Diversity and its Discontents: Cultural Conflict and

Common Ground in Contemporary American Society . New Jersey: Princeton University

Press.

Smith, Eliot. 1993. "Social Identity and Social Emotions: Toward New Conceptualizations of

Prejudice." In Affect, Cognition and Stereotyping , eds. Diane Mackie and David

Hamilton: 297–315. California: Academic.

Smith, Heather and Tom Tyler. 1996. "Justice and Power: When will Justice Concerns

Encourage the Advantaged to Support Policies which Redistribute Economic Resources

and the Disadvantaged to Willingly Obey the Law?" European Journal of Social

Psychology 26, (2): 171–200.

Sniderman, Paul, and Edward Carmines. 1997. "Reaching Beyond Race." PS: Political Science

& Politics 30, (3): 466–471.

Sniderman, Paul, Gretchen Crosby, and William Howell. 2000. “The Politics of Race.” In

Racialized Politics: The Debate about Racism in America , eds. David Sears, James

Sidanius, and Lawrence Bobo: 236–279. Illinois: University of Chicago.

Sniderman, Paul and Louk Hagendoorn. 2007. When Ways of Life Collide: Multiculturalism and

its Discontents in the . New Jersey: Princeton University.

Sniderman, Paul, and Thomas Piazza. 1993. The Scar of Race . Massachusetts: Harvard

University.

334

Sniderman, Paul, Thomas Piazza, Philip Tetlock, and Ann Kendrick. 1991. "The New Racism."

American Journal of Political Science : 423–447.

Snyder, Howard, Alexia Cooper, and Joseph Mulako-Wangota. 2020. Bureau of Justice

Statistics. Trend Tables By Race. July, 21. Generated using the Arrest Data Analysis

Tool at www.bjs.gov.

Sober, Elliott, and David Wilson. 1999. Unto Others: The Evolution and Psychology of Unselfish

Behavior . Massachusetts: Harvard University Press.

Sourander, Andre, Henrik Elonheimo, Solja Niemla, Nuutila Ari-Matti, Hans Helenius, Lauri

Sillanmaki, Jorma Phia, Tuula Tamminen, Kristi Kumpulainen, Irma Moilanen, and

Frederik Almqvist. 2006. "Childhood Predictors of Male Criminality: A Prospective

Population-based Follow-up Study from Age 8 to Late Adolescence." Journal of the

American Academy of Child & Adolescent Psychiatry 45, (5): 578–586.

Sowell, Thomas. 2019. Discrimination and Disparities . New York: Basic Books. Spencer, Richard. 2017a “Why Trump Must Champion National Healthcare.” AltRight.com ,

March 23. www.altright.com/2017/03/23/why-trump-must-champion-universal-

healthcare/.

Spencer, Richard. 2017b. “Why I Oppose the RAISE Act.” YouTube , August 2.

www.youtube.com/watch?v=dgLrwhlzlOk.

Starr, Terrell. 2017. “Patriotism is for White People.” The Root, November.

https://www.theroot.com/patriotism-is-for-white-people-1818724099 .

Stein, David, Jane Hardyck, and M. Brewster Smith. 1965. "Race and Belief: An Open and Shut

Case." Journal of Personality and Social Psychology 1, (4): 281–289.

Stein, Jeff. 2016. “National Review: Ted Cruz Is the Only Hope to Stop Donald Trump.”

Vox.com , March 11. http://www.vox.com/2016/3/11/11208266/national-review-ted-cruz.

335

Stenner, Karen. 2005. The Authoritarian Dynamic . New York: Cambridge University Press.

Stenner, Karen. 2009a. "Three Kinds of “Conservatism”." Psychological Inquiry 20, (2-3):

142–159.

Stenner, Karen. 2009b. "‘Conservatism,’ Context-Dependence, and Cognitive Incapacity."

Psychological Inquiry 20, (2-3): 189–195.

Stenner, Karen, and Jonathan Haidt. 2018. "Authoritarianism is Not a Momentary Madness, but

an Eternal Dynamic within Liberal Democracies." In Can it Happen Here :

Authoritarianism in America , ed Cass Sunstein, 175–220. New York: Harper Collins.

Stephan, Walter, and Cookie Stephan. 1985. "Intergroup Anxiety." Journal of Social Issues 41,

(3): 157–175.

Stevens, James. 2012. Applied Multivariate Statistics for the Social Sciences (5 th ed.). New York:

Taylor & Francis Group.

Stonecash, Jeffrey. 2000. Class and Party in American Politics . Colorado: Westview.

Stowe, David. 1996 "Uncolored People: The Rise of Whiteness Studies." Lingua Franca 6, (6):

68–77.

Sumner, William. Folkways: A Study of the Sociological Importance of Usages Manners,

Customs, Mores, and Morals. Massachusetts: Ginn & Co.

Sunstein, Cass. 1999. "The Law of Group Polarization." University of Chicago Law School, John

M. Olin Law & Economics Working Paper 91.

Sunstein, Cass. 2002. Republic.com . New Jersey: Princeton University Press.

Sunstein, Cass. 2009. Republic. com 2.0. Nova Jersey: Princeton University Press.

Swain, Carol. 2002. The New White Nationalism in America: Its Challenge to Integration.

Massachusetts: Cambridge University.

336

Swain, Carol, and Russ Nieli. 2003. Contemporary Voices of White Nationalism in America.

Massachusetts: Cambridge University.

Tajfel, Henri. 1970. "Experiments in Intergroup Discrimination." Scientific American 223, (5):

96–103.

Tajfel, Henri. 1978. Differentiation between Social Groups: Studies in the Social Psychology of

Intergroup Relations. Oxford, UK: Academic.

Tajfel, Henri. 1982. "Social Psychology of Intergroup Relations." Annual Review of

Psychology 33, (1): 1–39.

Tajfel, Henri, and John Turner. 1986. "The Social Identity Theory of Intergroup Behaviour:

Worchel S. Austin Psychology of Intergroup Relations." Illinois: Nelson Hall : 7–24.

Taylor, Edward. 1871 . Primitive Culture: Researches into the Development of Mythology,

Philosophy, Religion, Art, and Custom. New York: Holt (1889).

Taylor, Jared. 2011. "Jared Taylor on White Identity: Racial Consciousness in the 21 st Century.”

VDARE Premier News Outlet for Patriotic , May 5 .

https://vdare.com/solicitations/limited-edition-vdare-2020-flag.

Taylor, Marylee." 1998. How White Attitudes Vary with the Racial Composition of Local

Populations: Numbers Count." American Sociological Review : 512–535.

Teorell, Jan. 2003. "Linking Social Capital to Political Participation: Voluntary Associations and

Networks of Recruitment in Sweden1." Scandinavian Political Studies 26, (1): 49–66.

Terry, Robert. 1981. “The Negative Impact on White Values .” In Impacts of Racism on White

Americans, eds Benjamin Bowser and Raymond Hunt: 119–15. California: Sage.

337

“The Forgotten Man Index: Tracking the Fortunes of the White Working-Class.” Economist.

February 18, 2017. http://www.economist.com/news/united-states/21717068-our-new-

labour-market-index-tracking-fortunes-white-working-class?frsc=dg%7Ca.

Theiss-Morse, Elizabeth. 2009. Who Counts as an American?: The Boundaries of National

Identity . Massachusetts: Cambridge University.

Thompson, William, and Joseph Hickey. 2005. Social Stratification in the US Class

System. Society in Focus: 183 –187.

Tocqueville, Alexis de. 1835. Democracy in America. Trans. Harvey C. Mansfield and Delba

Winthrop. Illinois: University of Chicago (2000).

Transue, John. 2007. "Identity Salience, Identity Acceptance, and Racial Policy Attitudes:

American National Identity as a Uniting Force." American Journal of Political Science

51, (1): 78–91.

Trende, Sean. 2012. The Lost Majority: Why the Future of Government Is Up for Grabs-and

Who Will Take It . New York: St. Martin's.

Trivers, Robert. 1971. "The Evolution of Reciprocal Altruism." The Quarterly Review of

Biology 46, (1): 35–57.

Trump, Donald. 2016. “Donald Trump: Disappearing Middle Class Needs Better Deal on

Trade.” USA Today , Satellite Information Network, March 14.

www.usatoday.com/story/opinion/2016/03/14/donald-trump-tpp-trade-american-

manufacturing-jobs-workers-column/81728584/.

Tucker, Jeffrey. 2017. Right-Wing Collectivism: The Other Threat to Liberty . :

Foundation for Economic Education.

338

Turner, John. 1981. "The Experimental Social Psychology of Intergroup Behavior." Intergroup

Behaviour 66: 1–32.

Turner, Ronald. 1996. "The Dangers of Misappropriation: Misusing Martin Luther King, Jr.'s

Legacy to Prove the Colorblind Thesis." Mich. J. Race & L. 2: 101.

Tyson, Alec, and Shiva Maniam. 2016. “Behind Trump's Victory: Divisions by Race, Gender,

Education.” Pew Research Center . November 9. http://www.pewresearch.org/fact-tank

/2016/11/09/behind-trumps-victory-divisions-by-race-gender-education/.

Uhlmann, Eric, David Pizarro, David Tannenbaum, and Peter Ditto. 2009. "The Motivated Use

of Moral Principles." Judgment and Decision Making 4, (6): 479–491.

Valentino, Nicholas, Fabian Neuner, and L. Matthew Vandenbroek. 2018. "The Changing Norms

of Racial Political Rhetoric and the End of Racial Priming." The Journal of Politics 80,

(3): 757–771.

Vallone, Robert, Lee Ross, and Mark Lepper. 1985 "The Hostile Media Phenomenon: Biased

Perception and Perceptions of Media Bias in Coverage of the Beirut Massacre." Journal

of Personality and Social Psychology 49, (3): 577–585.

Vance, J. D. 2016. Hillbilly Elegy: A Memoir of a Family and Culture in Crisis. New York:

Harper Collins.

Verba Sidney, Kay Schlozman, and Brady Henry. 1995. Voice and Equality: Civic

Voluntarism in American Politics. Cambridge: Harvard University Press.

Verba, Sidney and Norma Nie. 1972. Participation in America: Political Democracy and Social

Equality . New York: Harper and Row.

339

Young, Toby. 2019. “The Woke Corporation: How Campus Madness Entered the Workplace.”

The Spectator , March 7. www.spectator.co.uk/2019/03/the-woke-corporation-how-

campus-madness-entered-the-workplace/.

Wakefield, Jane. 2016. “Social Media 'Outstrips TV' as News Source for Young People.” BBC

News . June 15. www.bbc.com/news/uk-36528256.

Waite, Linda, and . 2002. The Case for Marriage: Why Married People are

Happier, Healthier and Better Off Financially . New York: Broadway Books.

Walters, Ronald. 2003. White Nationalism, Black Interests: Conservative Public Policy and the

Black Community . Michigan: Wayne State University Press.

Warner, David, and Mark Hayward. 2006. "Early-life Origins of the Race Gap in Men's

Mortality." Journal of Health and Social Behavior 47, (3): 209–226.

WashPostPR Blog. “The Washington Post Reached New Traffic Record of 83.1 Million Visitors

in September 2016.” Washington Post, October 14. https://www.washingtonpost.com

/pr/wp/2016/10/14/the-washington-post-reached-new-traffic-record-of-83-1-million-

visitors-in-september-2016/.

Watts, Marina. “In Smithsonian Race Guidelines, Hard Work and Rational Thinking Are White

Values.” , 17 July 2020, www.newsweek.com/smithsonian-race-guidelines-

rational-thinking-hard-work-are-white-values-1518333.

Weaver, Richard. 1984. "Ideas Have Consequences." Illinois: University Chicago Press .

Weller, Nicholas, and Jane Junn. 2018. "Racial Identity and Voting: Conceptualizing White

Identity in Spatial Terms." Perspectives on Politics 16, (2): 436–448.

Wellman, David. 1993. Portraits of White Racism (2nd ed.). Cambridge, England: Cambridge

University.

340

"White Racial Consciousness in the U.S. 2016 ANES Pilot Study Proposal." September 4, 2015.

Online Commons, American National Election Studies Forum . http://forum.election

studies.org/proposal-for-the-2016-pilot-study-white-racial-consciousness-in-the-u-s/.

Whittaker, Mathew. 2005. Race Work: The Rise of Civil Rights in the Urban West. Nebraska:

University of Nebraska.

Wilcox, W. Bradford. 2004. Soft Patriarchs, New Men: How Christianity Shapes Fathers and

Husbands. Chicago: University of Chicago.

Wilcox, W. Bradford. 2010. "When Marriage Disappears: The Retreat from Marriage in Middle

America." The State of our Unions: Marriage in America : 13-60.

Wilcox, W. Bradford. 2015. “The Latest Social Science Is Wrong. Religion Is Good for

Families and Kids.” The Washington Post , December 15.

www.washingtonpost.com/posteverything/wp/2015/12/15/the-latest-social-science-is-

wrong-religion-is-good-for-families-and-kids/.

Wilcox, Bradford, Andrew Cherlin, Jeremy Uecker, Mathew Messel. 2012. “No Money, No

Honey, No Church: The Deinstitutionalization of Religious Life Among the White

Working Class.” Research in the Sociology of Work . 23: 227–250.

Wilder, David. 1978. "Reduction of Intergroup Discrimination through Individuation of the Out-

Group." Journal of Personality and Social Psychology 36, (12): 1361–1374.

Wilder, David. 1986. "Social Categorization: Implications for Creation and Reduction of

Intergroup Bias." Advances in Experimental Social Psychology 19: 291–355.

Wildsmith, Elizabeth, Jennifer Manlove, and Elizabeth Cook. 2018. “Dramatic Increase in the

Proportion of Births Outside of Marriage in the United States from 1990 to 2016.” Child

341

Trends , August 8. www.childtrends.org/publications/dramatic-increase-in-percentage-of-

births-outside-marriage-among-whites-hispanics-and-women-with-higher-education-levels.

Williams, Joan. 2017. “We Need to Redefine What 'Working Class' Means.” Time , 14 August.

time.com/4899906/donald-trump-white-working-class/.

Williams, Joan. 2019. White Working Class, With a New Foreword by Mark Cuban and a New

Preface by the Author: Overcoming Class Cluelessness in America . Massachusetts:

Harvard Business Press.

Williamson, Vanessa, Theda Skocpol, and John Coggin. 2011. "The Tea Party and the Remaking

of Republican Conservatism." Perspectives on Politics 9, (1): 25–43.

Wilson, William. 1987. The Truly Disadvantaged: The Inner City, the Underclass, and Public

Policy . Chicago: University of Chicago.

Wilson, William. 1997. When Work Disappears: The World of the New Urban Poor. New York:

Vintage.

Wilton, Leigh, Evan Apfelbaum, and Jessica Good. 2019. "Valuing Differences and Reinforcing

them: Multiculturalism Increases Race Essentialism." Social Psychological and

Personality Science 10, (5): 681–689.

Wooldridge, Jeffrey. 2015. Introductory Econometrics: A Modern Approach . Ontario: Nelson

Education.

Wong, Cara. 2010. Boundaries of Obligation in American Politics: Geographic, National, and

Racial Communities . New York: Cambridge University.

Wong, Cara and Grace Cho. 2005 "Two‐Headed Coins or Kandinskys: White Racial

Identification." Political Psychology 26, (5): 699–720.

342

Wright, Bradley. 2015. “What God Has Joined Together: Religion and the Risk of Divorce.”

Institute for Family Studies , August 20. ifstudies.org/blog/what-god-has-joined

together-religion-and-the-risk-of-divorce/.

Wright, Bruce. 2017. “Richard Spencer On Betsy DeVos: 'Black ' Won't Change With

'School Choice,' Alt-Right Leader Tells New Secretary of State.” International Business

Times, April 10. www.ibtimes.com/richard-spencer-betsy-devos-black-ghetto-wont-

change-school-choice-alt-right-leader-2488186.

Wright, John. 2012. "Unemployment and the Democratic Electoral Advantage." American

Political Science Review 106, (4): 685–702.

Zak, Paul, and Stephen Knack. 2001. "Trust and Growth." The Economic Journal 111, (470):

295–321.

343