Racial Identity, Attitudes, and Participation in Brazil, South Africa, and the United States

UNIVERSITY OF CALIFORNIA Los Angeles

The Political Psychology of Race in Comparative Perspective: Racial Identity, Attitudes, and Participation in Brazil, South Africa, and the United States

A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Political Science

Fabr´ıcio Mendes Fialho

2017 c Copyright by Fabr´ıcio Mendes Fialho 2017 ABSTRACT OF THE DISSERTATION

The Political Psychology of Race in Comparative Perspective: Racial Identity, Attitudes, and Participation in Brazil, South Africa, and the United States

Fabr´ıcio Mendes Fialho Doctor of Philosophy in Political Science University of California, Los Angeles, 2017 Professor David O. Sears, Chair

In this dissertation, I investigate why race is a salient political cleavage in some societies but not in others. Focusing on three countries marked by racial inequality but that differ in their racial dynamics – Brazil, South Africa, and the United States – I examine why the their racial formation processes resulted in strongly politicized racial identities in the two latter cases but not in the former.

I advance a theoretical argument that emphasizes the political roots of the development salient racial identities in these countries. I contend that, when a nation formation process has a built- in emphasis on racial hierarchies and prejudice and the state apparatus is employed for the enforcement of racial group boundaries in order to enact discriminatory policies against subordinate groups, this process unintentionally contributes to the formation of group consciousness among the members of political minorities, to reinforcement of major social cleavages, and to the emergence of political actors demanding social change. Apartheid in South Africa and the Jim Crow laws in the Southern United States, in using the state to oversee racial boundaries and to implement discriminatory practices against Blacks (and Coloureds, in the South African case) in favor of their White populations, fostered the development of strong group identities that ended up being crucial for the struggle against and eventual breakdown of those segregationist social systems. Brazil has not experienced legal forms of racial discrimination or segregation since the end of slavery in the late nineteenth and has celebrated race-mixing as a core element of its national identity, which

ii resulted in permeable group boundaries and in the lack of consistent racial group identities.

To test this hypothesis empirically, I analyze data from cross-national surveys as the World Values Survey, the International Social Survey Programme, and the Social Hubble to assess group differences in perceptions of discrimination, political trust, and participation in political acts and voluntary organizations. Findings indicate the persistence of robust differences in institutional trust and participation between race groups in South Africa and the United States but not in Brazil. Results on perceived discrimination show that non-Whites do report higher levels of perceived discrimination compared to Whites yet the group gap is conditional on the context the type of discrimination. Importantly, results from Brazil and South Africa, when analyzed jointly are suggestive that group-levels of perceived discrimination cannot account for the the lack of group differences in political attitudes and behavior in Brazil and the political salience of race in South Africa. Prior literature is corroborated by ﬁndings for the United States.

Overall, results support the theoretical claim that the politicization of racial identities is dependent on contextual conditions such as the existence of strong social cleavages and the enforcement of group boundaries by the state. Once politicized, those identities have important political consequences.

iii The dissertation of Fabr´ıcio Mendes Fialho is approved.

Stanley R. Bailey

Lorrie Frasure-Yokley

Daniel N. Posner

David O. Sears, Committee Chair

University of California, Los Angeles 2017

iv To Mark Q. Sawyer

v TABLEOF CONTENTS

1 Introduction ...... 1

1.1 Setting the Puzzle...... 1

1.2 Why Brazil, South Africa, and the United States? A Brief Historic Digression...3

1.3 Group boundaries and racial identity...... 5

1.3.1 Nation-building and racial groups...... 5

1.3.2 Social identity and the permeability of boundaries...... 10

1.4 Forging and Mobilizing Racial Identities...... 12

1.4.1 The argument...... 15

1.5 Outline of chapters...... 16

2 Perceived Discrimination ...... 19

2.1 Perceiving discrimination...... 21

2.1.1 Some consequences of perceived discrimination...... 22

2.1.2 Who perceives discrimination...... 23

2.2 Data and methods...... 26

2.2.1 Samples...... 26

2.2.2 Measures of perceived discrimination...... 27

2.2.3 Racial classiﬁcation...... 28

2.2.4 Folk categories and cultural identities...... 30

2.2.5 Sociodemographics...... 31

2.2.6 Identity salience...... 31

2.2.7 Intergroup contact...... 32

vi 2.2.8 Computational procedures...... 32

2.3 Findings...... 33

2.3.1 Racial discrimination...... 33

2.3.2 Everyday Discrimination...... 43

2.3.3 Are everyday and racial discrimination related?...... 54

2.4 Discussion...... 57

3 Institutional Trust ...... 63

3.1 Race and political trust...... 68

3.2 Measuring trust in public institutions...... 72

3.3 Testing for measurement invariance...... 74

3.3.1 Conﬁrmatory factor analytic models for ordinal variables...... 75

3.3.2 Levels of measurement invariance...... 77

3.3.3 Model ﬁt...... 80

3.4 Data and methods...... 82

3.4.1 World Values Survey datasets...... 82

3.4.2 Measures of trust in institutions...... 84

3.4.3 Racial classiﬁcation...... 85

3.4.4 Validation datasets...... 86

3.4.5 Missing data imputation...... 87

3.4.6 Computational procedures...... 88

3.5 Results...... 88

3.5.1 Correlation analysis, variables selection, and measurement model...... 88

3.5.2 Descriptive analysis...... 96

vii 3.5.3 Tests of measurement invariance using the WVS5...... 99

3.5.4 Latent mean differences among groups, WVS5...... 105

3.5.5 Validation analysis...... 107

3.6 Discussion...... 112

3.7 Appendix: Polychoric correlations...... 117

4 Social and Political Participation ...... 126

4.1 Measuring Participation...... 131

4.1.1 The political and the non-political...... 131

4.1.2 Modes of participation...... 133

4.2 Data and Methods...... 136

4.2.1 Datasets...... 136

4.2.2 Measures of political participation...... 137

4.2.3 Race classiﬁcation...... 139

4.2.4 Sociodemographics and interest in politics...... 140

4.2.5 Missing data imputation...... 140

4.2.6 Computational procedures...... 141

4.3 Findings...... 142

4.3.1 Group-level differences in political participation...... 142

4.3.2 Sociodemographic variables and race...... 147

4.3.3 Regression analysis...... 157

4.4 Discussion...... 163

5 Conclusion ...... 165

Bibliography ...... 171

viii LISTOF FIGURES

1.1 Legal racial segregation and racial classiﬁcation...... 14

2.1 Average perceived racial discrimination and race...... 34

2.2 Folk categories, cultural identities, and perceived racial discrimination...... 38

2.3 Average perceived everyday discrimination by race...... 45

2.4 Folk categories, cultural identities, and perceived everyday discrimination...... 48

2.5 Predicted probabilities of perceiving discrimination...... 56

3.1 Two-domain model of trust in institutions...... 95

3.2 Trust in Order and in Political institutions...... 97

3.3 Latent means, WVS5...... 106

3.4 Latent factor means, validation datasets...... 113

4.1 Participation in political action...... 143

4.2 Participation in voluntary organizations...... 145

4.3 Education and political activism, ISSP...... 148

4.4 Education and political activism, WVS5...... 149

4.5 Education and organizational membership, WVS5...... 150

4.6 Age and political activism, ISSP...... 152

4.7 Age and political activism, WVS5...... 153

4.8 Age and organizational membership, WVS5...... 154

4.9 Income and political activism, ISSP...... 156

4.10 Race as a predictor of political activism (ISSP)...... 158

4.11 Race as a predictor of political activism (WVS5)...... 159

ix 4.12 Race as a predictor of organizational membership (WVS5)...... 162

x LISTOF TABLES

2.1 Bivariate statistics for perceived racial discrimination in Belo Horizonte, Cape Town, and Detroit...... 36

2.2 Group differences in perceived racial discrimination with and without controls... 40

2.3 Within-group predictors of perceived racial discrimination in Belo Horizonte, Cape Town, and Detroit...... 41

2.4 Bivariate statistics for perceived everyday discrimination in Belo Horizonte, Cape Town, and Detroit...... 46

2.5 Group differences in perceived everyday discrimination with and without controls. 49

2.6 Within-group predictors of perceived everyday discrimination in Belo Horizonte, Cape Town, and Detroit...... 52

3.1 Sample size and group size...... 83

3.2 Polychoric correlation among selected indicators of institutional trust, Brazil.... 90

3.3 Polychoric correlation among selected indicators of institutional trust, South Africa 91

3.4 Polychoric correlation among selected indicators of institutional trust, United States 92

3.5 Tests for measurement invariance across race groups, World Values Survey Wave 5. 101

3.6 Measurement and structural model parameters, WVS5...... 104

3.7 Tests for measurement invariance across race groups, validation datasets...... 109

3.8 Measurement and structural model parameters, Brazil and South Africa: replication 110

3.9 Measurement and structural model parameters, United States: replication..... 111

4.1 Survey Items on participation in associations and political actions...... 135

4.2 Sample size and group size...... 138

xi ACKNOWLEDGMENTS

This dissertation is the result of a long process that began seventeen years ago when I, then a business student, ﬁgured out that my passion was in the social sciences. I am indebted to many persons who supported and guided me throughout this way.

First of all, I thank David Sears for his support and patience over the last six years. He has been there for me when I looked for advice, suggestions, letters of recommendation, or just needed someone to listen to my ideas. Being a member of his Political Psychology Lab is surely one of the most important moments of my education at UCLA. His scholarship and dedication to the discipline are unmatched and set an impeccable role model for those who, like me, wish to pursue an academic career. Having David approving this dissertation is such a honor for me.

Lorrie Frasure-Yokley has been supportive to me since we met for the very ﬁrst time. Her encouragement to keep up with my research is priceless. Lorrie’s feedback on my qualifying papers helped me to reﬁne and improve ideas that later brought me to this dissertation. I would also like to thank Daniel Posner and former dissertation committee member Phil Goff for their comments that helped to reshape this project.

I also register my gratitude to Stanley Bailey. He has proved to be a great mentor and friend. I ﬁrst met Stan in 2009 and, since then, he has been as open and supportive as one can be. To this point, I just cannot count how many times he wrote letters on my behalf, including one of those who brought me to UCLA. His research on race relations in Brazil has dramatically inﬂuenced my own, and I can’t help but hope to continue our collaborative work.

My academic trajectory started at the Federal University of Minas Gerais in 2001. I found there an incredibly active and collegial academic environment that marked me at the personal and professional levels. Bruno Reis was my supervisor for good six years. He is one of the best mentors one may wish to have and responsible not only for my engagement with empirical research but also for how I understand and practice the social sciences. Mario Fuks introduced me to the study of public opinion. Jorge Alexandre Neves taught my ﬁrst course in quantitative methods. I met Bruno, Mario, and Jorge as professors and I am glad for now count them as friends.

xii I also want to express my gratitude to Mark Q. Sawyer. I ﬁrst met him when I was still won- dering about pursuing a doctoral degree in the United States. Mark encouraged me to apply for graduate school and, generously, recommended me to the UCLA Graduate Program in Political Science. If I ever wrote this dissertation, I owed it to Mark, to whom I humbly dedicate it.

During my graduate school adventure in the United States, I was supported and encouraged by many friends in Brazil and in the US. At UCLA, I was fortunate to meet some of those who become dear friends. I thank George Ofosu, Sarah Brierley, Imil Nurutdinov, Andy Vilan,´ Mauricio Velasques,´ Lucio´ Oliveira, Antonio Pedro Ramos, and Felipe Lima for making Bunche Hall a pleasant place to be. Galen Murray was a great roommate, friend, and English instructor. Many friends from Brazil made themselves always present despite of being thousands of miles away. I thank my friends from Balu Magico´ not only for the comradeship but also for our successful collective effort to make contemporary football the “beautiful game” again. Dialetica´ has proved to be a honorable nemesis in the ﬁeld and a great group of friends out of it. O Sindicato has always been an important sponsor of ludic enterprises.

I am forever grateful to my family. My mother, Neli, and my father, Paulo, provided me with everything, in special their never-ending love, to pursue my dreams even when it meant to make sacriﬁces. My sisters, Fab´ıola and Maria Paula, are the joy of my life. Fatinha, Marcao,˜ Mateus, Meggie, and Breithiner, became my extended family by choice. I am so grateful to Breithiner and Carlanne for making me godfather of our lovely Clara.

Finally, thank you to Alexandra. My years in Los Angeles would have been less cheerful without her love and patience.

xiii VITA

Education:

2014 Candidate of Philosophy (Political Science) University of California, Los Angeles

2008 M.A. (Political Science) Federal University of Minas Gerais

2006 B.A. (Social Sciences) Federal University of Minas Gerais

Fellowships:

2017 Graduate Student Researcher Fellowship University of California, Los Angeles

2016–2017 Dissertation Year Fellowship University of California, Los Angeles

2013–2015 Jorge Paulo Lemann Fellowship University of California, Los Angeles

2012 UCLA Fellowship in Political Psychology University of California, Los Angeles

2011–2012 Fellowship for Doctoral Studies University of California, Los Angeles

2006–2008 Capes Fellowship for Master’s Studies Coordination for the Improvement of Higher Education Personnel

xiv PUBLICATIONS

“Support for Race-Targeted Afﬁrmative Action in Brazil” (with S. R. Bailey and M. Peria). Eth- nicities. Advance online publication. doi: doi.org/10.1177/1468796814567787

“Interrogating Race: Color, Racial Categories, and Class across the Americas” (with S. R. Bailey and A. M. Penner). American Behavioral Scientist 60 (4): 538-555, 2016.

“Brazil: Public Assistance Programs” (with S. R. Bailey). Encyclopedia of Public Administration and Public Policy, Third Edition. London: CRC Press, 2015.

“Race, Resources, and Political Participation in a Brazilian City” (with N. S. Bueno). Latin Amer- ican Research Review 44 (2): 59-83, 2009.

“The Private Motivations of Public Action: Women’s Associational Lives and Political Activism in Brazil” (with Solange Simoes,˜ Bruno P. W. Reis, Daniel Biagioni, and Natalia´ S. Bueno). Advances in Gender Research 13: 203-239, 2009.

“Mudanc¸a Institucional e Atitudes Pol´ıticas: A Imagem Publica´ da Assembleia´ Legislativa de Minas Gerais (1993-2006)” (with Mario Fuks). Opiniao˜ Publica´ 15 (1): 82-106, 2009.

“As Multiplas´ Definiçoes˜ do Conceito de Capital Social.” BIB: Revista Brasileira de Informaçao˜ Bibliografica´ em Cienciasˆ Sociais 65: 71-87, 2008.

“Identidade Racial na Formac¸ao˜ de Atores Coletivos: Alguns Problemas Teoricos´ com (Poss´ıveis) Implicac¸oes˜ Praticas”´ (with Andre´ Drumond Mello Silva). Revista Tresˆ Pontos 2(2): 15-22.

xv CHAPTER 1

Introduction

1.1 Setting the Puzzle

On the eve of the 21th century, race (and ethnicity) remains as a major source of tensions, conﬂicts, and inequality. It is nowadays a well-established consensus that race is a social construct with no biological correspondence (Williams, Lavizzo-Mourey and Warren, 1994; Witzig, 1996). Genetic differences among human groups across the globe are considerably smaller than any threshold to identify and differentiate subspecies in non-human species and genetic evidence also rejects the hypothesis of distinct evolutionary lineage within humans as well. Human history is better char- acterized as coexisting populations presenting differentiated phenotypical traits due to geographic constraints, but with sufﬁcient contact and inbreeding to make it a single evolutionary lineage (Loomis, 1967; Race, Ethnicity, and Genetics Working Group, 2005; Templeton, 1998).

Being a social construction, however, does not minimize the actual effect of race in everyday lives (Kinder, 2013; Smedley and Smedley, 2005). Race and related concepts as color groups as well are arbitrary forms of group classiﬁcation supposedly based on phenotypical marks and referring to an idea of a communal origin for individual sharing such traits (Guimaraes˜ , 2005). Those constructed folk group concepts and labels serve as cognitive tools for social organization (Brubaker, 2009) and eventually as the foundation of social hierarchy (Blumer, 1958; Bobo, 1999; Sidanius and Pratto, 2004). How those categories “work” and the different meanings attached to them are contextual and a product of a racial project (Omi and Winant, 1994).

As a consequence of the centrality of race as a major structuring dimension of social life around the world, a proliﬁc research agenda on comparative racial studies emerged especially in the second

1 half of the 20th century. Either focusing on speciﬁc countries (e.g. Andrews, 1980, 1991, 2004, 2010; Bailey, 2009a; Louw, 2004; Sawyer, 2006; Seekings and Nattrass, 2005; Telles, 2004; Wade, 1993; Wagley, 1952; Wright, 1990) or explicitly comparing cases (e.g. Fredrickson, 1981, 2008; Lieberman, 2003; Marx, 1998; Nobles, 2000; Wade, 1997; Winant, 2001), scholars have been devoting considerable resources and talent to analyze and understand racial dynamics in different societies – not to mentioned the almost uncountable volume of social scientiﬁc research on race relations in the American context.

This cross-national awakening of social and academic interest in race relations has led scholars to claim that a “globalization of race” is on its way (Winant, 1994). Despite of a growing international research agenda on race, some authors have argued that it has been accompanied by an exportation of US folk-concept of race (Bourdieu and Wacquant, 1999). Different racial projects have emerged in different contexts – being sometimes the result of explicit action by local and national elites (Marx, 1998; see also Posner, 2004, 2005) – to structure race relations and address political needs and objectives of the time. Different racial projects therefore result in speciﬁc social conﬁgurations with different categories, hierarchies, and meanings (Wagley, 1965).

This dissertation aims to contribute to this scholarly debate examining how racial identiﬁca- tion and identity “work” in different racialized societies and what are their political consequences. Being race a social construct, its cognitive meanings and processes are expected to vary across societies (Markus and Kitayama, 1991; Betancourt and Lopez, 1993; Fry, 1995/1996). It borrows elements from social and political psychology, historical sociology, and comparative politics to explore under what condition is race turned into an important political force. Differences in racial projects are expected to correlate with different racial cleavages and, consequently, with political attitudes and behaviors. This leads to the following research questions:

Why is race a salient political cleavage in countries as South Africa and the United States but not in others like Brazil? What are the expected political consequences of politicized racial identities?

2 The link between the historical processes of the social construction of race in different countries and how these differences are expressed in politics is the main puzzle to be assessed here. This dissertation proposes a theoretical explanation for the cross-national difference in the salience of race in Brazil, South Africa, and the United States and offers empirical evidence to illustrate the political consequences of different racial dynamics.

1.2 Why Brazil, South Africa, and the United States? A Brief Historic Di- gression

Brazil, South Africa, and the United States might be pointed as the three “classic” case studies for racial studies in the modern times. Their histories in the past 500 years are in large amount the result of the Western European expansion and the slave trade existing until the 19th century (Fredrickson, 2008). About five hundred thousand slaves were brought to mainland North America five and a half million were sent to Brazil, making these two countries major destinations of African slave traffic in the New World.1

Despite of the end of slave trade in the US in 1808, slavery existed in the country until the ratiﬁcation of the Thirteenth Amendment in December 1865. Brazil received slaves from 1561 until 1850 and abolished slavery only in 1888, being the last country in the world to do so. The American and Brazilian social and economic systems were so dependent of slave labor in some regions that the end of slavery resulted in major political watershed events in both countries: While the abolition of slavery in the United States led to a bloody civil war which lasted for two years, threating the territorial unity of the country, the end of slavery in Brazil was one of the major events triggering political reactions leading to the and of the Brazilian empire and the establishment of an oligarchy ruled in a large extent by landowners just one year after. Despite of the differences in their slavery systems and their alleged consequences for intergroup attitudes (Degler, 1971; see also Marx, 1998), the enslavement of Africans brought to the Americas is the origin of the

1Despite the hardship of getting precise numbers, estimates suggest that more than ten million Africans were traded as slaves and brought to the Americas from 1514 to 1866 (http://www.slavevoyages.org).

3 Black-White relations in both countries.

Europeans and subsaharian Africans did not come into conﬂictive contact only in the Amer- icas as a result of the slave trade. In the areas in Africa in which European settlers established colonies and outposts, tensions eventually also emerged. The initial contact between the Dutch settlers and the Hottentots living in the Cape peninsula in mid-Seventeenth century was initially friendly. Commercial trade was established between European settles and native Africans in the area, and the Dutch East India Company recommended courteous and respectful interactions with the Hottentots. As the Dutch started building permanent settlements in the Cape, Hottentots started developing negative attitudes toward those Europeans and intergroup tensions escalated. As the Dutch settlements started demanding cheap labor force, the impossibility of enslavement of the Hottentots – who moved inland and offered tough resistance – resulted in the importation of slaves from other areas in Africa as well as from Dutch colonies in Indonesia and East Asia (Crijns, 1959; Fredrickson, 1981).

This brief historic digression suggests that conﬂictive relations between color or racial groups have a long history in these countries and such contentious interactions served as the foundation for still existing social hierarchies and intergroup relations.

However, the speciﬁc pathways in the histories of Brazil, South Africa, and the United States culminated in different process of intergroup contact as well as of attitudes, beliefs, and identities – and also what conceptions of race and racial boundaries emerged in this different contexts. In common, these different racial dynamics shared the presence of African-descent groups in the bottom of social and racial hierarchies as well as similar patterns of racial inequality (Fredrickson, 2008; Marx, 1997).

4 1.3 Group boundaries and racial identity

1.3.1 Nation-building and racial groups

Racial categories as well as their use in ofﬁcial and everyday interactions are products of different racial projects (Omi and Winant, 1994; Saperstein, Penner and Light, 2013). The emergence and transformations of the American, Brazilian, and South African racial projects have been ex- tensively analyzed (Daniel, 2010; Fredrickson, 1981, 2008; Hochschild, Weaver and Burch, 2012; MacDonald, 2008; Marx, 1998; Omi and Winant, 1994; Telles, 2004; Winant, 2001) and a comprehensive review falls out of the scope of this piece. The discussion that follows focuses on how race has been differently built and the existence of legal racial segregation in Brazil, South Africa, and the United States.

The United States, arguably the most studied case regarding race and ethnicity (and the country of origin of most of theorizing about race), experienced numerous changes in its racial dynamics, from Southern slavery, to Jim Crow segregation in the South and informal exclusionary practices in other parts of the country in the ﬁrst half of the 20th century, to the Civil Rights Movement in the 1950s and 1960s, to the current demographic changes resulting from new waves of immigration and increasing racial mixing (McAdam, 1982; Woodward, 1955; Sugrue, 2008; Jones and Bullock, 2012; Hochschild, Weaver and Burch, 2012). Although a plurality of states enacted anti- miscegenation laws since the antebellum and Southern laws did not recognize a separate “mulatto” group to be treated as different from “Negros”, the United States census instrument included categories as quadroon, octoroon, and mulatto until the ﬁrst quarter of the 20th century (Nobles, 2000). Following the consolidation of legal segregation after Plessy v. Ferguson in 1896, the category mulatto was removed from the census in 1930 and the use of the one-drop rule “brought the census in line with Jim Crow and anti-miscegenation laws” (Nobles, 2000, p. 68; see also Davis, 1991).

The enactment of a segregation apparatus in the post-Civil War period and the ofﬁcial recogni- tion of Black and White as non-overlapping categories by the federal government in 1930 census made the group boundaries legally clear, with implications on the rights and life chances of individuals conditional on their socially perceived group membership. After the Civil Rights movement,

5 this strict group categorization remained to be then used to determine the eligibility to the newly- implemented afﬁrmative action programs.

Regardless of changes in how the dark-skinned group has been (self-identiﬁed), from “Negro” to “Black” to “African American”, either to redeﬁne its group identity or as a result of changes in majority-group attitudes toward minorities (Smith, 1992), the Black and White groups (as well as other not discussed here) have been conceived as discrete entities with “bright” boundaries (Alba, 2005), although socio-demographic changes and immigration has nevertheless challenged a racial structure founded mostly on a Black-White divide (Masuoka and Junn, 2013; Hochschild, Weaver and Burch, 2012; Bonilla-Silva, 2004).

South Africa experienced a race-based exclusionary system through most of the 20th century being it officially implemented after the 1948 National Party electoral victory which consolidated already existing policies and instituted one of the most extremes segregationist racial systems ever known, the apartheid (Louw, 2004). Either because Afrikaners and English-speakers concluded that both groups could not survive if political conflicts between them had persisted (Marx, 1998) or for the link between group identity and the organization of economic production (Winant, 2001), the South African racial formation in the early 20th century “bundled” Afrikaners and English- speakers as part of a single White, dominant racial group and the numerous native ethnc groups as the Xhosa, Zulu, Tswana, and others became classified as part of an encompassing Native later renamed to Bantu then to Black African group (Khalfani and Zuberi, 2001).

The Population Registration Act of 1950 required each inhabitant of South Africa to be clas- siﬁed and registered according to their “racial features” as Native, White, or Coloured – the latter being often taken as a mixed but actually serving as a residual category embracing people of all sorts of mixed ancestry including the offspring of European settlers and slave women from the Dutch East Indies in the beginning of the Dutch presence in the Cape Area (James and Lever, 2001; Jeffreys, 1953). An Indian category, originally a subgroup of the Coloureds, was added in a subsequent amendment to the Population Registration Act at a later state (Posel, 2001b).

Despite its undeniable racialist nature and its aim to preserve “racial purity” (Posel, 2001a) there was no uncontroversial criteria for racial classiﬁcation at that time. Ofﬁcials responsible for

6 registration had none to loose guidelines to proceed with the classification of individuals. Classi- fication was mostly based on “common sense” using street-level interactions excluding skin color and body features but also socioeconomic status, eating habits, language, and area of residence (Posel, 2001a). Individuals were so classified depending on their “general acceptance” as member of one or other group. Per this lack of straightforward classificatory criteria, judicial requests for reclassification were not uncommon (Erasmus and Ellison, 2008) with Blacks and Coloureds appealing to be registered as members of lighter-skinned groups as well as Coloureds and Whites being reclassified as part of darker-skinned groups after third-party denounces of misclassification (Posel, 2001a,b).

Once individuals were classified and registered as member of a group, their social and political rights, economic status, educational opportunities, and employability for certain jobs were determined by such group memberships. In addition to the Population Registration Act of 1950, the Prohibition of Mixed Marriages Act of 1949 banned intergroup marriage (Posel, 2001b). Alto- gether those two laws created rigid racial boundaries that remained enforced for approximately four decades. The Population Registration Act was repealed by the Parliament of South Africa in 1991. The Identification Act of 1997 makes no mention to race but racial classification are still present in official statistics (e.g. the decennial census) and guides policies aiming at redressing past inequalities from the apartheid era (Alexander, 2007; Khalfani and Zuberi, 2001). After almost half- century of legal segregation, discrimination, and political mobilization along group boundaries, race categories remain salient in post-apartheid South African society, with minimum occurrence of intergroup marriage (Alexander, 2007; Jacobson, Amoateng and Heaton, 2004; Muyeba, and Seekings, 2011; Seekings, 2008; Whitehead, 2012).

Brazil stands as an importantly different case in comparison to South Africa and the United States. Although race has also been a salient social and political issue in the country’s history, it has been constructed and mobilized in peculiar ways. In contrast to South Africa and the United States where whiteness had been used to quell political conﬂicts and unify groups of European ancestry against other populations in nation-building processes portraying Whites as the dominant social force, Brazilian elites did not face similar intra-White political clashes. The Portuguese

7 Crown ruled mostly unchallenged from the 17th century on, and the move of the Portuguese Court to Rio de Janeiro in 1808 consolidated its political control. Political elites acted to defuse racial cleavages which were believed to potentially jeopardize national territorial integrity in the context of a small, mostly white ruling elite amongst a much larger and highly mixed population (Marx, 1998). Numerous separatist movements and large slave revolts did happen but were triumphantly contained (Bethell and Carvalho, 1985). Moreover, slavery was one of the few institutions of nationwide range and conﬂict over it were dutifully avoided. When slave resistance grew during periods of political crisis, the national government gradually carried out measures to carefully and peacefully abolish slavery during the second half of the 19th century (Reis and Klein, 2011).

Conversely to efforts to protect “racial purity” and the denunciation of miscegenation as a major source of degeneration of the national human stock (Nobles, 2000; Posel, 2001a) race mixing was promoted to one of the most prominent civilizational features of the Brazilian society and the foundation of its national formation (Freyre, 1946 [1933], 1945; Ribeiro, 1995; Skidmore, 1993 [1974]; Telles, 2004). Distinctive to the Brazilian racial formation in comparison to South Africa and the United States (but not unique compared to other South and Central America countries; see Andrews, 2004) is that, at the same time that local elites imported European and North American scientific racism of the late-nineteenth and early-twentieth centuries, they were selective regarding its elements. Brazilian elites excluded from their racial project two core elements of scientific racism, that is, the innate nature of racial differences and the degeneracy resulting from miscegenation, developing their proper solution for the country’s “negro problem” by race mixing and the whitening of the Black population (Skidmore, 1993 [1974]). Widespread race mixing and the official promotion of the idea of Brazil as a “racial democracy” became the cornerstone for national integration. The outcome of relatively high levels of race mixing has been a multitude of phenotypes such that the discourses about skin color instead of race group became predominant, being attitudes toward “mark” rather than “origin” more prevalent (Harris, 1964; Nogueira, 1985 [1955], 1998).

Since the end the end of slavery in 1888, no ofﬁcial form of racial discrimination or segregation has taken place in Brazil and spatial segregation, although not absent, is regarded moderate

8 in comparison to other racialized societies (Charles, 2003; Christopher, 2001; Durrheim, 2005; Massey and Denton, 1993; Telles, 1995, 2004). In the absence of the enforcement of clearly defined groups, a complex classification system championed by the category moreno – an ambiguous term encompassing almost all other categories (Bailey, 2009a; Bailey and Telles, 2006; Hutchin- son, 1963 [1952]; Valle Silva, 1996) – emerged and assignment to folk categories is the function of an intricate calculus taking into account a combination of skin color, hair type, eye color, lip thickness, format of nose, and other phenotypical features, also presenting broad contextual and regional variations (Degler, 1971; Harris, 1964, 1970; Telles, 2002). Given the large range of phenotypical variation covered by the folk classification system (Harris, 1964) it is unsurprising that most of them fail to match the census classification and its five official categories – black (preto), brown (pardo), white (branco), yellow (amarelo, for East Asians), and indigenous (ind´ıgena).

Since the late 1970s, however, the most successful Brazilian racial movement since abolition, the Unified Negro Movement (Movimento Negro Unificado) has strongly advocated to stop the use of popular unofficial categories as moreno and mulatto as well as the termination of the official use of pardo, and has promoted the use of the omnibus category negro as a collective black identity (Bailey and Telles, 2006). The term had originally a negative connotation yet in recent decades it has turned into a signal of ethnic identification beyond the mere reference to skin tone (Sansone, 2003) with growing acceptance among young and educated individuals dark-skinned (Bailey and Telles, 2006). Activists and researchers who sponsor the adoption of the term have argued that racial inequality studies that Blacks and Browns have similar socioeconomic profiles (Hasenbalg, 1979; Valle Silva, 1985), and emphasize its important for mobilization against racial discrimination (Guimaraes˜ , 2005). Although in the popular racial classification system negro is usually used only by individuals in darkest end of the skin color continuum, in its revitalized version championed by the Black movement, the term has gained a strong political connotation linked to fight against discrimination and the adherence to a racialized identity.

Miscegenation and phenotypical diversity have nevertheless neither eliminated socioeconomic disparities between White and non-White populations nor necessarily promoted upward social mobility for most of race-mixed dark-skinned people (e.g. morenos, pardos, mulattoes) in Brazil

9 (Hasenbalg, 1979; Lovell and Wood, 1998; Telles, 2004; Valle Silva, 1985). “Whitening” emerged as a form of integration of race-mixed individuals: The lighter the skin color the lesser the discrimination against, with individuals with skin color closest to the darker end of the color continuum being more frequent targets of the prejudice and stigmatization previously directed to African slaves (Guimaraes˜ , 2005; Bailey, 2008a; Degler, 1971).

1.3.2 Social identity and the permeability of boundaries

A central assumption of social identity theory is that individuals strive to achieve a positive self- image and identity (Tajfel, 1974, 1981; Tajfel and Turner, 1986). An individual’s social identity consists of aspects one’s self-image derive from the knowledge of belonging to social groups and categories with emotional signiﬁcance attached to such memberships (Huddy, 2013; Tajfel, 1974, 1981; Tajfel and Turner, 1986). Consequently, an individual seeks to be associated and retain membership of groups that contribute positively to her or his social identity; if no positive payoff results from a membership, the individual will tend to opt for leaving the group (Tajfel, 1974, 1975).

However, quitting a group membership is not always a feasible option either because there are features attached to that category making membership not optional in a given social setting (Ellemers, 1993) or because leaving the group is itself an act that conﬂicts with other relevant parts of the social identity. If one cannot leave the group, there will be an effort to make membership more positively evaluated. This latter step might be reached through a creative re-interpretation of group attributes portraying them as desirable in comparison to other groups, or engagement in social action aiming to either set new parameters for intergroup comparison or to promote an improvement of the relative group status as a whole (Ellemers et al., 1988; Tajfel, 1974, 1981).

For the argument carried through this piece, mechanisms of boundary enforcement and boundary crossing (Lamont and Molnar´ , 2002; Telles and Sue, 2009; Wimmer, 2008) are important to assess the cross-national differences in the role of racial categories in perceptions. An individual afﬁliated to a group which makes no contribution to one’s self-esteem faces the option to increase

10 identity gains from group membership pursuing membership to a higher-status category or act- ing to improve conditions in the current group (Tajfel, 1974, 1981). Choosing between those two approaches, however, is not only the result of one’s individual will but also is constrained by the social structure of intergroup relations (Ellemers, 1993; Lamont and Molnar´ , 2002; Telles and Sue, 2009; Wimmer, 2008; Wagley, 1965). Quitting the current membership to join another group is an individual mobility strategy in which the individual pursues an improvement in her or his own welfare only. This strategy is feasible in circumstances allowing individual-level boundary crossing from one group to another (for instance, moving from one social class to another due to the achievement of further education). However, in other occasions, boundary crossing may not be an option either because it affects other components of one’s self-concept or because the social structure imposes and signiﬁes (at least certain) group boundaries as strict and non-negotiable. In this case, the sole available path to extract more “membership utility” from such a group is to pursue strategies of social change that beneﬁt all group members (Tajfel, 1974, 1975). A group-level attribute constraining courses of action in group membership mobility and social identity choices is the permeability of group boundaries (Ellemers, 1993; Ellemers et al., 1988).

Group formation processes differ not only regarding their genesis mechanism (Wimmer, 2008) yet also with respect to how “blurred” or “bright” (Alba, 2005) are their borders. Boundary permeability affects the odds of moving from one category to another and thereupon individual’s attachment to the group. When an individual is unsatisfied with the contribution that a relatively low status group makes to one’s self-esteem and borders are permeable, he or she will develop low levels of group identification and try to leave the group. Rejection of affiliation with the group will be more pronounced if the individual has abilities to pursue individual social mobility (Tajfel, 1975). If the individual has no option other than to stay in the group either due to the lack of resources for mobility of because group boundaries are not permeable, he or she will foster higher levels of group identification. For members of groups of relative higher status, the opposite occurs: If borders are permeable, they will adhere more strictly to their group identities than when they are not (Ellemers, 1993; Ellemers et al., 1988).

Having no opportunity to migrate to a higher status category, members of low status groups

11 with impermeable boundaries would pursue strategies promoting social change to eitherboth attribute more positive meaning to group membership andor alter the dynamics of group hierarchy and comparison (Tajfel, 1975). The “Black is Beautiful” movement in the United States (Camp, 2015) is one example of the former being the Civil Rights Movement one of the latter (McAdam, 1982).

How does the social identity theory contribute to understand why race is a politically salient identity in some countries but not in others? The three countries analyzed here differ in how permeable racial boundaries are and how they have been, or used to be, historically imposed. Importantly, they differ regarding the existence of a legal system enforcing the observance of group segregation in social, economic, cultural, and political realms. In forbidding boundary crossing, segregation in the United States and South Africa involuntarily fostered group identiﬁcation among members of political minority groups, made group membership salient, and provided a politically unifying force to overcome collective action problems in the struggle against state-enforced discrimination (Klandermans, 2002; Marx, 2002; Tajfel, 1975).

1.4 Forging and Mobilizing Racial Identities

Two dimensions are of special interest for comparison and historic contextualization of these racial orders, the experience of legal forms of racial segregation in the 20th century and the presence of a binary racial classification system (see Figure 1.1). South Africa and the United States experienced legal segregation at different points of the 20th century. The Jim Crow segregation system emerged in the American south in the aftermath of the Civil War and was dismantled only in the 1960s. South Africa has experienced racial tensions and forms of preferential treatment to Whites since the 19th century. The apartheid system, a consolidation of previous forms of racial segregation now enforced by the Union of South Africa, officially started in 1948 under the leadership of the National Party (Marx, 1998; Louw, 2004; Beinart, 2001). Both systems, the American and the South African, were based on differential treatment of members of different groups according to official forms of racial classification. Legal, state-enforced separation and segregation ended

12 up crystallizing racial categories and their mobilization as identities – even when the initial classification in one of the groups were initially based in not-so-clear criteria as “common sense” in South Africa (Posel, 2001a,b). Brazil, on the other hand, has not experienced any form of official segregation since the end of slavery in the country back in 1888. The country has nevertheless experienced non-official forms of racial discrimination resulting in blatant social inequality between color groups (Azevedo, 1966; Fernandes, 1965; Hasenbalg, 1979; Marx, 1997; Pinto, 1953). The absence of legal segregation based on race, however, precluded the formation of well-defined racial groups to be politically mobilized (Telles, 1996, 2004; Bailey, 2009a).

A second dimension is the configuration of the racial classification system. It has been shown that interracial intercourse has occurred in the three countries since the beginning of European presence (Fredrickson, 2008). However, different forms to classify individuals of mixed ancestry were developed during the establishment of racial orders. From the first US census in 1850 until the 1920 census (except in 1900), categories for people of mixed Black and White ancestry existed. After that time, the so-called one-drop rule has been used to define who should be identified as a Black person and the mixed categories for people of mixed black and white ancestry were removed from the Census since then. The new Black-White system was initially enforced by the Jim Crow system and after it by affirmative action programs and advocacy groups (Nobles, 2000). South Africa experienced enforced legal segregation under the apartheid and other forms of official discrimination before it. However, despite of the enforcement of racial boundaries, people of mixed ancestry were not classified as “native Africans.” In addition to the African and White groups, a residual category of Coloureds were also officially included as an official racial category (Posel, 2001a,b). Brazil has been widely known for its high levels of race mixing and for the plurality of folk racial categories used in everyday interactions (Harris, 1970; Valle Silva, 1996; Telles, 2004; Bailey, 2009a). Information about people with mixed phenotype has been collected since the first Brazilian census in 1872 except in the 1970 Census, the only one in the Brazilian history bringing no question about race (Nobles, 2000; Telles, 2004).

13 Figure 1.1: Legal racial segregation and racial classiﬁcation

Has the country experienced legal segregation?

Yes No

Has the country Yes United States adopted a binary racial classiﬁcation system? No South Africa Brazil

14 1.4.1 The argument

The discussion above shows how these racial systems have (or had) differently enforced racial categories and how such an enforcement created conditions for the political mobilization of racial identities. Racial (and ethnic) identities have been politically salient in South Africa and the United States but not in Brazil. With regard to it, a major difference between these two sets of countries is the existence or not of past official racial segregation, not the structure of the racial classification system. The enforcement of racial classification for the implementation of legal racial discrimination resulted in “crisp” group boundaries in South Africa and the USA, making it easier for race to be perceived as the cause of being discriminated against – after all, one’s group membership was the criterion for receiving discriminatory treatment – and fostering the development of a politicized racial identity.2 It is important to emphasize this point because some influential Black activists in Brazil (e.g. Nascimento, 1977; Nascimento and Nascimento, 2001) have defended that all non-Whites should promptly abandon the use of the Census and folk categories (as morenos and mulatos) to embrace the omnibus category negro as part of the development of a Black (negro) political consciousness based on the awareness that both Browns (pardos) and Dark Blacks (pretos) are subject to the same discriminatory practices. It follows, according to this perspective, that the formation of a massive negro group could gain political momentum to demand policies to alleviate inequalities and to promote upward social mobility of not-Whites.3 This study proposes, however, neither the racial classification system itself not the acknowledgement of social inequalities are sufficient causes of group consciousness. Brazilians of different race do acknowledge that racial inequalities in the country has structural causes (Bailey, 2002) but it has been proved hard create a negro consciousness in the country (Telles, 2004). Moreover, racial groups have developed strong group consciousness under both the American binary and the South African ternary racial

2It does not mean, however, that Black South Africans and Black Americans experience the same form of “blackness,” not to mention the South African Coloureds. This discussion, however, is not central for the argument advanced here and will not be further developed.

3The argument about the negative political consequences of the fragmentation of non-Whites into multiple groups and categories mirrors the claims of American advocacy groups as the NAACP and the Japanese Citizens League, who have argued that the introduction of a multiracial option in the US census could result in the destabilization of racial categories and the weakening of civil rights policies (Lee and Bean, 2010; Moore, 2001).

15 classiﬁcation systems.

In this dissertation, I argue that the political salience of racial identities is conditional on how race has been politicized in a society. Even though patterns of social inequalities might be (and actually are) closely related to race, awareness of those inequalities is not sufficient to trigger group consciousness. I contend that race will be a politically relevant cleavage only if group memberships are first politicized. Such a process will lead to emergence of racial identification and to the consolidation of racial identities (Huddy, 2013). Politicization of a group, however, is not the result of an act of will. It requires a set of social conditions to make that dimension (in this case, race) to be perceived as politically relevant. It is claimed that the existence of state-enforced discrimination does contribute to the formation group consciousness and, once formed, its effects might then persist long after discriminatory laws are gone. This argument is derived from the So- cial Identity Theory framework presented above. Legal discrimination targets members of specific groups. State action to enforce group boundaries for the implementation of those policies make them less permeable, preventing inter-group mobility. If individual inter-group mobility is not an option, group members will develop a strong sense of group identification and will pursue political action for social transformation (Tajfel, 1974, 1975; Ellemers, 1993). Jim Crow in the American South and the apartheid in South Africa, in inscribing group membership and its consequences in the law, closed group boundaries and, involuntarily, fomented the development of strong group identities among members of discriminated groups. On the other hand, there was no similar legal discrimination system in Brazil since late-nineteenth century, when the slavery was abolished. These differences in the enforcement of group boundaries are theorized as one of the hypothesis why group consciousness and linked fate (Dawson, 1994; Miller et al., 1981) are strongly developed among non-Whites in the US and South Africa but not in Brazil.

1.5 Outline of chapters

Three studies follow this introduction. I analyze the consequences of racial identiﬁcation on both political and (possibly) non-political outcomes. In Chapter2, I analyze group-level differences

16 in perceptions of different forms of discrimination. Namely, I assess perception of being victim of racially-motivated discrimination and perceptions of discrimination that might be potentially attributed to other reasons. Dark-skinned individuals tend to report higher levels of perceived discrimination on both measures although the magnitude of group differences is conditional on the form of discrimination and on the country. Importantly, the correlation between the two forms of discrimination is stronger in South Africa and in the United States. In Brazil, they are mostly dissociated.

Chapter3 examines group-level differences in trust in public institutions, with a focus on political and law-enforcement institutions. Confirmatory factor analytic methods are employed to assess the within-country, cross-group comparability of results. Findings show how trust in public institutions are shaped by the salience of racial identities across countries. Wide group differences between race groups are found in South Africa, both regarding trust in political and trust in law- enforcement agencies. Whites and, to a lesser extent, Coloureds are distrustful in public institutions compared to Blacks, reflecting the current Black African political dominance in the country: The incontestable numerical majority of the group leaves lesser room for political influence of the other groups, which is manifested in expression of institutional distrust. In the United States, Blacks are extremely distrustful of law-enforcement institutions, what is in line with the history of police violence and mass incarceration in the country. Results are less conclusive with regard to political institutions, indicating that group differences in political trust fluctuates in time following changes in political climate. In Brazil, group differences in institutional trust pales in comparison to the other countries, being mostly negligible. Puzzling, when (modest) group gaps are detected, they use to show Blacks and Browns as more trusting in public institutions than Whites, which might the effect of poverty-alleviation policies in the country.

Chapter4 addresses race group membership and participation in voluntary organizations and political acts. Results show that, even though sociodemographic variables do affect levels of political participation, contrarily to the literature, controlling for them do not always close the group gap in participation, except for the effect of education in some cases. Again, small group-level differences are found in Brazil. In South Africa and the United States, Whites tend to be more

17 politically active except in acts that demand group mobilization and coordination such as participation in demonstrations. American Blacks also tend to be more active members in churches.

In the conclusion, I review the major ﬁndings and discuss the theoretical implications of this study as well as suggest directions for future research.

18 CHAPTER 2

Perceived Discrimination

Prejudice and discrimination are persisting vexing phenomena in modern societies. Being the target of discrimination, as extensive literature has consistently shown, has the deleterious effects on one’s quality of life and accomplishments, with negative effects on health outcomes, educational achievement, and self-esteem, to name just a few examples (Landrine and Klonoff, 1996; Major, Quinton and McCoy, 2002; Pascoe and Richman, 2009; Williams et al., 2012).

A large-and-growing literature has been devoted to the study of the causes and consequences of perceived discrimination (Allport, 1954; Blumer, 1958; Canache et al., 2014; Crijns, 1959; Crosby, 1984; Dion, 2002; Duckitt, 2001; Feagin, 1991; Forman, Williams and Jackson, 1997; Guimaraes˜ , 2005; Harrell, 2000; Layton and Smith, 2017; Monk, 2015; Nogueira, 1985 [1955]; Sigelman and Welch, 1991; Levin et al., 2002; Major, Quinton and McCoy, 2002; Sellers and Shelton, 2003; Schildkraut, 2005; Tropp, 2007; Seekings, 2008; Hunt and Wilson, 2009; Silva, 2012; Silva and Leao˜ , 2012; Kinder, 2013). Although studies have been conducted in a plurality of countries, the bulk of this research agenda remains focused on the United States and lesser is known about other countries. This study aims to contribute to the understanding of how members of racial groups perceive mistreatment against themselves in three countries experiencing dissimilar racial dynamics and historical trajectories, Brazil, South Africa, and the United States. These countries are landmark cases in the study of societies where skin color has been a marker of social status and life chances.

It has been well-established in the literature that, in racialized societies, dark-skinned individuals do experience higher levels of discrimination (Bailey, 2009a; Canache et al., 2014; Forman, Williams and Jackson, 1997; Layton and Smith, 2017; Monk, 2015; Seekings, 2008; Silva and

19 Leao˜ , 2012; Silva and Paixao˜ , 2014; Welch et al., 2001; Williams et al., 2008, 2012). We know little, however, about how and why individuals interpret hostile behavior either as unfair treatment or as racially-motivated bigotry. This study aims to contribute to the literature on perceived discrimination addressing the relevance of the porosity of group boundaries employing the Social Identity Theory framework (Ellemers, 1993; Huddy, 2013; Tajfel, 1981; Tajfel and Turner, 1986) in a comparative study.1 It is argued that levels of perceived racially-motivated discrimination are conditional on the prevalent racial dynamics in a given society. Importantly, and more instigating, is that perceptions of general forms of unfair treatment are racialized in contexts where race is a more salient identity. In societies where racial identities have been historically enforced and made “bright” (Alba, 2005) – as the United States and South Africa – perceptions of mistreatment in social settings (e.g. been treated with less courtesy than others) will be interpreted as being motivated by one’s race; in countries where racial boundaries have not been enforced and are therefore more ﬂuid – in Brazil, for instance – perceptions of unfair treatment and racial discrimination are expected to be lesser correlated or even orthogonal. Such results suggest that racialized identities affect not only political attitudes but also other perceptions (Bailey, 2009a; Klandermans, Roefs and Olivier, 2001b; Schuman et al., 1997; Sears, Sidanius and Bobo, 2000).

In this paper, I assess how members of different racial groups perceive themselves as being victims of different forms of discrimination – everyday and racial discrimination – in Belo Horizonte (Brazil), Cape Town (South Africa), and Detroit (the United States). Analyzing cross-national data, I examine whether and how group differences in perception of discrimination varies across contexts. The meaning of race as a social construct is not universal; it is rather strongly context- dependent. Racial projects (Omi and Winant, 1994) attribute different contents to racial categories as cognitive tools for social organization (Brubaker, 2009). As a consequence, race is experienced in a variety of ways in time and space (Fry, 1995/1996).

I analyze survey data collected from the three aforementioned cities in 2004-5. Data analysis will focus on racial differences in perception of discrimination within a context and on how patterns of perceptions do differ across contexts. To assess whether group differences in perceived everyday

1Social Identity Theory is discussed in the Introduction of this dissertation.

20 and racial discrimination are due to factors other than race, factors as sociodemographics and intergroup contact are also taken into account.

This chapter is structured as follows. First, I selectively review studies on perceived discrimination and its attributional processes. Next, I discuss data and methods employed in this research. Third, I present empirical ﬁndings from the survey data analysis. Following the presentation of result I discuss and interpret the general ﬁndings and their implications for the literature on perceived discrimination.

2.1 Perceiving discrimination

From subtle forms of mistreatment to openly hostile behavior, discriminatory practices against an outgroup to reinforce group boundaries and hierarchies are mechanisms of stigmatization and maintenance of group-based inequalities (Feagin, 1991; Jackman, 1994; Link and Phelan, 2001; Sidanius and Pratto, 1999). Despite of its negative effects on the life chances and well being of social minorities worldwide (e.g. Simpson and Yinger, 1985; Winant, 2001) a growing body of literature has addressed how perceptions of discrimination varies across contexts, between groups within a same context, and among members of a same group. As suggested by (Dion and Earn, 1975, p. 949), “the stressfulness of an event depends not on its intrinsic qualities but on an individual’s interpretation of its personal signiﬁcance and his evaluation of it as harmful or not,” and several factors play a role in determining such an interpretation.

Trivial group categorization may be enough, under certain circumstances, to trigger in-group favoritism and prejudice against the outgroup (Sherif and Sherif, 1953; Tajfel, 1970; Tajfel and Turner, 1986). Regarding the perception of discrimination itself – or the perceiving attitudes and behaviors as such – by the target of bigotry, different theories have been offered to address the phenomenon. The attribution ambiguity theory (Crocker and Major, 1989) poses that members of disadvantaged groups attribute negative feedback against them to discrimination rather them to a fair assessment as a defensive mechanism to protect their self-esteem. According to Crocker and Major(1989), members of stigmatized groups continually receive feedback, either positive

21 or negative) and need to evaluate feedback quality as either a non-prejudiced information about individual attributes or a discriminatory reaction based on group membership. Attributing negative feedback to discrimination allows members of stigmatized groups to engage in self-esteem protection ruling it as reﬂecting one’s preconceptions about the group rather than fair evaluation.

Some individuals, on the other hand, are neither aware nor vigilant about potential sources of discrimination, minimizing their own accounts of negative experiences (Crosby, 1984). It does not follow, however, that such individuals disregard the existence of discrimination against their groups. Disadvantaged group members generally perceive substantial discrimination against their groups as a whole at the same time they minimize discrimination against themselves personally. The denial of personal experiences of discrimination simultaneously to perceptions of group-level mistreatment has been called personal/group discrimination discrepancy (Taylor et al., 1990).

The attribution ambiguity and the personal/group discrimination discrepancy theories offer conﬂicting explanations of why individuals perceive or not discrimination. Ruggiero and Tay- lor(1995) offer a model aiming to make compatible such contrasting prediction. According to them, cognizing a negative feedback as discriminatory or not is conditional on a base-rate heuris- tics of discrimination occurrence. Based on experimental ﬁndings, Ruggiero and Taylor argue that perception of discrimination is conditional to situational ambiguity. Under circumstances in which the probability of discrimination is ambiguous, individuals tend to attribute negative feedback to their own poor task performance, minimizing the feeling of being discriminated against; however, if there is no situational ambiguity and it is interpreted as being of high-certainty, poor feedback is then attributed to discrimination. In the next section I discuss why situation ambiguity in the context of race relations may or not occur in the three countries under scrutiny.

2.1.1 Some consequences of perceived discrimination

A substantial literature has been devoted to the study of the consequences of perceived discrimination. Mental health research has shown that perceived discrimination correlates with negative health outcomes, leading to psychological distress and linked to feelings of anger, anxiety, and

22 depression (Sellers and Shelton, 2003; Brown, 2001), lowered self-esteem (Crocker and Major, 1989; Williams et al., 2012), and multiple forms of unhealthy behavior (Jackson, Knight and Raf- ferty, 2010; Pascoe and Richman, 2009; Pavao˜ et al., 2012; Penner et al., 2009; Williams and Mohammed, 2009).

When there exist expectations of being victims of discrimination, individuals and groups develop strategies to avoid its negative effects (Crosby, 1984; Sellers and Shelton, 2003). Perhaps paradoxically, social and political research has also found what might be deemed as the “unexpected positive consequences” of perceived discrimination. Under certain circumstances, perceptions of discrimination increases protest and militancy (Dion, 2002). Marx(2002) argues that formally encoded prejudice (e.g. in the form of legal segregation) reinforces social cleavages and identities along the lines of exclusion leading to the uniﬁcation of the victims of stigmatization (see also Lau, 1989). Klandermans(2002) states that collective grievances do produce a “we feeling.” As a result, group identity is strengthened and consensus on sociopolitical attitudes is built (Hunt and Wilson, 2009) favoring group mobilization, group consciousness, and political activism (Dion, 2002; Olsen, 1970; Schildkraut, 2005; Shingles, 1981). Among Blacks in the United States, perception of discrimination against one’s group is positively related to the belief that Obama’s election is an important advancement for African Americans and represents symbolic progress toward racial equality (Hunt and Wilson, 2009). Tate(2010) demonstrates that Black respondents’ perceptions that their own group still faces discrimination increases support to afﬁrmative action policies. Masuoka and Junn(2013) point out that Blacks and Latinos reporting perceptions of discrimination against their groups have increased their sense of group closeness and linked fate.

2.1.2 Who perceives discrimination

Regardless of the different attributional mechanisms developed to cope with discrimination, members of disadvantaged groups still do report higher levels of perceived mistreatment compared to other groups (e.g. Bailey, 2009a; Seekings, 2008; Silva and Paixao˜ , 2014; Welch et al., 2001; Williams et al., 2012.

23 Blacks in the United States consistently report higher levels of perceived mistreatment against themselves and their group as a whole and that discrimination is one of the main reasons for the economic disadvantages experienced by their group (Schuman et al., 1997; Sigelman and Welch, 1991; Welch et al., 2001). Recent studies on colorism (Banks, 1999; Hunter, 2013) ﬁnds that darker skin color is associated with higher skin color discrimination among African Americans (Monk, 2015). Although parcels of Whites also recognize the existence of discrimination against Blacks, there is a group gap on this regard. Black respondents are considerably more prone to report perception of discrimination against themselves and their group in several social settings and in the job and housing markets (Forman, Williams and Jackson, 1997; Schuman et al., 1997; Sigelman and Welch, 1991; Welch et al., 2001). While some 80% of Black respondents believe that the police treat their group unfairly, only half of the Whites do believe in police mistreatment against Blacks (Schuman et al., 1997; see also Tyler, 2005). Interesting enough, Whites not only tend to perceive lower levels of discrimination toward Blacks – they also report similar levels of mistreatment against both themselves and Blacks (Welch et al., 2001), a pattern consistent with the belief among Whites scoring high in racial resentment that Blacks are the main responsible for their living conditions (Sears and Henry, 2005; Schuman and Krysan, 1999; Kinder and Sanders, 1996).

Studies on prejudice and discrimination in Brazil and South Africa have mostly, but not exclu- sively, focused on having prejudiced attitudes and the socioeconomic consequences of discrimination. In the Brazilian case, most studies in race relations have paid attention on racial inequalities (e.g. Bailey, Loveman and Muniz, 2013; Fernandes, 1965; Hasenbalg, 1985, 1979; Hasenbalg, Valle Silva and Lima, 1999; Lovell and Wood, 1998; Loveman, Muniz and Bailey, 2012; Muniz, 2012; Pinto, 1953; Valle Silva, 1985) attributing its persistence to discriminatory dynamics leading to cumulative intergenerational disadvantages against non-White groups (Hasenbalg, 1979). Other studies have analyzed the persistence of prejudiced attitudes and behaviors against Blacks (and eventually Browns) in the country (see, for instance, Guimaraes˜ , 2003; Bailey, 2002; Bastide and Van den Berghe, 1957; Hanchard, 1994; Pinto, 1953, ch. 5; Telles, 2004, ch. 6; Telles and Bailey, 2013, Twine, 2001; Willems, 1949). Lesser attention has been given to perceptions of

24 discrimination notwithstanding recent work has add important contributions.

In a series of studies, Bailey(2002, 2004, 2009 a) documented a widespread acknowledgement of the enduring nature of discrimination against negros among Brazilians of different racial groups with some 60% of Whites and 70% of Blacks and Browns agreeing that there is “a lot” of racial prejudice in the country and about half of them agree that Whites have “a lot of prejudice” against negros (less than one-quarter believes that negros are very prejudiced against Whites). Neverthe- less, in what resembles a textbook example of the person/group discrimination discrepancy (Taylor et al., 1990), much lesser respondents report personal experiences of discrimination against themselves: Six percent of Whites, 18% of Browns, and 36% of Blacks expressed being discriminated against because of their skin color or race (Bailey, 2009a, p. 97-101).

Similar results are reported by Silva and Leao˜ (2012) with levels of perceived racial discrimination increasing from 7% to 15% to 40%, respectively, among Whites, Browns, and Blacks. Analyzing data on skin color collected by interviewer’s observation using a color palette (Telles, 2014), Layton and Smith(2017) found that darker skin color is positively associated with self- reported perceived discrimination.

Research on prejudiced attitudes toward out-groups is abundant in South Africa (see, among others, Burns, 2006; Crijns, 1959; Duckitt, 1991, 1993; Duckitt and Farre, 1994; Durrheim and Dixon, 2010; Foster and Nel, 1991; Heaven, 1980; MacCrone, 1937, 1949; Orpen, 1971a; Pet- tigrew, 1960; Ray, 1980; Van den Berghe, 1962) but the number of studies on perceptions of discrimination had been lesser frequent perhaps because the harshness of the apartheid system led scholar to take the prevalence of perceived discrimination among non-Whites as self-evident given the strict enforcement of legal discrimination and segregation. During the apartheid, Orpen (1971b, 1975) studied perceptions of discrimination among the Coloureds, a group that broadly shared “Western values” with Whites but had their access to socially desirable outcomes (prestigious job positions and equivalent salaries) denied because of the then-segregated job market structure. Orpen found higher levels of perceived discrimination and alienation among Coloured respondents compared to their White counterparts.

Although African workers wages have been improving since the late apartheid period (Crankshaw,

25 1997) and South Africa has been transitioning from a race-based toward a class-based stratification system (Seekings and Nattrass, 2005), the wealth gap (Burger and Woolard, 2005) and strong group identities (Gibson, 2006; Muyeba, and Seekings, 2011) persist. Even so, most South Africans believe that race relations in the country has improved since the transition to the new political dispensation (Roefs, 2006; Seekings, 2008), with survey respondents usually denying being discriminated against because of their race (Seekings, 2008). Roefs(2006) argues that, although racial and ethnic/linguistc identities remain strong, most respondents – ranging from 53% of Whites to 60% of Coloureds and 63% of Blacks – reported not having experienced racial discrimination “at all” in a 2003 national survey. Williams et al.(2008, 2012) also found a small number of respondents (less than 10%) from all groups reporting experiences of acute or chronic discrimination because of their race. A sizable share of South Africans, however, report the feeling of being discriminated against several social settings and at work (Grossberg, 2002; Roefs, 2006). For instance, some 40% of Whites and Coloureds and over 50% of Blacks reported being treated with less respect, with more than half of respondents believing that others act “as if they were better” than them. Other forms of perceived discrimination are more racialized with Blacks reporting manifold higher levels of perceived worse treatment in restaurants, of being watched in shops, and of other people reacting as they were afraid of them than Whites, with Coloureds somewhat in between (Seekings, 2008). Williams et al.(2008) found lower levels of reported discrimination for respondents of all groups but the same overall pattern – Blacks perceived more, Whites perceiving less, and Coloureds in an intermediate position – is again identified. With regard to other predictors of perceived discrimination, findings from previous research have been less consistent.

2.2 Data and methods

2.2.1 Samples

I analyze data from a comparative international survey conducted in major metropolitan areas of Brazil, South Africa, and the United States in 2004-2005 as part of the the “Social Hubble” project (Seekings, Jooste, Langer and Maughan-Brown, 2005). Face-to-face interviews were conducted in

26 representative samples of the adult population in Detroit, USA in 2004, and both Belo Horizonte, Brazil and Cape Town, South Africa, in 2005. Hereafter, the Belo Horizonte Area Survey, the Cape Area Survey, and the Detroit Area Survey will be referred, respectivelly, as BHAS, CAS, DAS.

Stratified multi-stage probability samples were drawn from the three metropolitan areas. The sampling frame consisted of adult respondents (18 years of age or older in Belo Horizonte and Cape Town, 21 years or older in Detroit) residing in those areas at the time of fieldwork. The Belo Horizonte Area sample was stratified as ‘capital’ and ‘surrounding municipalities’; the Cape Area and the Detroit Area samples were stratified according to the racial composition of enumeration areas, being settlement type (formal, informal and small-holding) also accounted for in the CAS. After enumeration areas had been selected at random from within the strata, successive random samples were taken of households and adult individuals. Datasets include post-stratitication weights to account for bias due to unit nonresponse and oversampling.

The response rate 78% in BHAS, 63% in CAS, and 60% in DAS. Total sample size for the BHAS, CAS, and DAS are, respectively, 1,122, 1,209[1,182], and 734 cases. Information on sample design and data collection procedures in BHAS, CAS, and DAS are detailed, respectively, in Suyama and Fernandes(2007), Seekings et al.(2005), and Farley, Krysan and Couper(2009).

The BHAS and CAS questionnaires fielded in 2005 were modeled after the DAS experience and bring several questions similar in content to those fielded in 2004 DAS. Item format, however, oftentimes differs across studies. Data on racial self-classification follows Census categories in each country; information on other group identities available in BHAS and CAS are coded following their theoretical relevant and also empirical occurrence in the collected data. Other predictors, as sociodemographics and interracial contact (see below) are coded in manner to equalize them across samples – even if it resulted in coarser measures than their questionnaire versions.

2.2.2 Measures of perceived discrimination

I analyze two measures of perceived discrimination as dependent variables, experiences of everyday discrimination and perceptions of racial discrimination. The perceived everyday discrimina-

27 tion index was based on three likert-type items measuring how often, if ever, has the respondent been treated with less respect or courtesy than others people, people act as if they are afraid of, and people act as they are better than him or her (Forman, Williams and Jackson, 1997; see also Harrell, 2000).2 To assess whether the items form a unidimensional scale, a measurement invariance model (Steenkamp and Baumgartner, 1998; Vandenberg and Lance, 2000) is imposed on the data, constraining factor loadings, thresholds, and item intercepts to equality across groups (Mill- sap and Tein, 2004; Wu and Estabrook, 2016). For each sample, model ﬁt measures are excellent (CFI > .93; TLI > .95; RMSEA < .04) indicating that the same measurement structure is present for different groups in a same sample and thus allowing valid between-group comparisons. Latent factor scores are then estimated and normalized in a 0-100 scale.3

Perception of racial discrimination is measured using one question asking the respondents whether have they ever been treated worse than other people because their race. Wording for this survey item and response categories vary across cities. DAS used a binary question asking whether the respondent has ever been treated badly because of their race or ethnicity. CAS asked respondents whether had they been treated differently because of their race in the last ﬁve years (focusing therefore on the post-apartheid period); BHAS asks whether the respondents have been treated differently because of their race with no time reference.4

2.2.3 Racial classiﬁcation

Data on racial classification in the BHAS are collected using the five official Census categories since the 1991 Census: Black (preto), Brown (pardo), Indigenous (Ind´ıgena), White (branco), and

2Likert-type items with three points are used in CAS, with four points in BHAS, and with ﬁve points in DAS.

3Similar results are obtained using additive scales. However, latent variable modeling is employed in this analysis due to its superior psychometric properties in special regarding multiple group comparisons. See Chen(2008), Little (1997), and Steinmetz(2013) for a discussion.

4BHAS and CAS asked whether have respondents ever been treated worse than other people or benefited because of their race offering treated worse, benefited, both treated worse and benefited, and neither as response options. Being ‘treated worse’ and ‘both worse and benefited’ are coded as being mistreated because of the respondent’s race; the latter is coded as mistreatment because it implies that oftentimes the respondent is treated worse because of his or her race even if it is not always the case.

28 Yellow (amarelo or of Asian ancestry) (Nobles, 2000). Respondents could also answer “I don’t know,” “Other,” or refuse to answer. Analysis focuses on the categories Black, Brown, and White and exclude all the other groups. According to the 2010 Brazilian Census, blacks, browns, and whites represent more than 99% of the Brazilian population and 98.7% of the Belo Horizonte Metropolitan Area (Brazilian Institute of Geography and Statistics, 2011). These three groups correspond to 999 cases and represent 89% of the respondents in the 2005 BHAS.

CAS collected data on race using an adapted version of the four official South African Census categories (Black African, Coloured, Indian or Asian, and White). The category Black African was split into two options, African and Black, resulting in a total of five racial categories. Respondents could also choose “Other,” “Refuse to define myself in racial terms,” and “I don’t know.” Black and African are offered separately to capture potential differences in racial or cultural identity; taken together, 96.5% of those who classified themselves as either African or Black also reported that they were or would be classified as Black/African under the apartheid system and are treated here as a comprehensive “Black” category. I restrict my analyses to the Black, Coloured, and White groups, excluding all the other cases. According to the 2011 South African Census, Black Africans, Coloured, and Whites sum up to 97% of the South African population and 96.7% of the Cape Metropolitan Area (Statistics South Africa, 2012a,b). These three groups comprise 1,089 respondents and represent 90% of the respondents in the 2005 CAS.5

Regarding the DAS, racial background was asked using the official federal race categories since 1997: Black or African American, American Indian or Alaska Native, Asian or Pacific Islander, White, and “other race”. Asian and Pacific Islander (with Native Hawaiian) are offered as one single category in DAS due to the low presence of such populations in Detroit Metropolitan Sta- tistical Area. As it has been the Census standard procedure since 2000, respondents are allowed to choose more than one category and are also asked whether or not they have Hispanic origin (Jones and Bullock, 2012). Analysis focuses on non-Hispanic Blacks and Whites respondents. Although other ethnic and racial groups as Asians, Hispanic/Latinos, Arab Americans, and multiracials are

5From an initial total of 1,090 Blacks, Coloureds, and Whites in the dataset, one Black respondent has been excluded from analysis due to missing data on enumeration area.

29 also of indisputable social and political prominence in the contemporary United States (Segura and Rodrigues, 2006), the limited number of cases for those groups preclude the possibility of group- level analysis. According to the 2010 American Census, Blacks and Whites represent 85% of the American population and 92.8% in the Detroit Metro Area (United States Census Bureau, 2010). Black and White Americans represent 89% of the total number of 2004 DAS respondents.

Henceforth, only the three largest groups in Belo Horizonte and Cape Town and the two largest in Detroit are included in analysis. The (weighted) racial composition of valid cases in each sample is: 19.1% of respondents are classiﬁed as Black, 43.1% as Brown, and 37.8% as White in 2005 BHAS; 39% as Black African, 41.5% as Coloured, and 19.5% as White in 2005 CAS; and 21.8% as Black or African American and 78.2% as White in 2004 DAS. All respondents are assigned to race groups based on self-classiﬁcation.

2.2.4 Folk categories and cultural identities

Other forms of social classiﬁcation, as folk categories and cultural identities, may have their own meaning independent from how individuals classify themselves in terms of Census categories. Cultural identities remain salient in South Africa and many embrace religious or ethnic identities at the same time individuals still see society through racial lenses (McLaughlin, 2007; Seek- ings, 2008). For instance, despite of being both classiﬁed as White in the Census, Afrikaners and English-speakers may diverge on attitudinal domains (Foster and Nel, 1991; Gibson, 2006). In the Brazilian context, choosing a color or racial category may not be related only to phenotype but may be linked to political orientations as use of negro championed by the Black movement (Bailey, 2008b; Bailey and Telles, 2006; Silva and Leao˜ , 2012; Telles, 2004).

CAS questionnaire offered a list of 30+ different groups and asked respondents to choose the one best describing themselves culturally.Afrikaans-speaker, Afrikaner, English-speaker, Chris- tian, Muslim, Coloured, White, Mfengu, Xhosa, and South African are most-mentioned options. An open-ended question preceded self-classiﬁcation using Census categories and asked BHAS respondents how they see themselves in racial terms. Responses were recoded and the popular

30 moreno and the Black movement-sponsored negro categories along with the ofﬁcial branco, pardo, and preto terms are mentioned by some 60% of respondents (ambivalent classiﬁcation or use of multiple terms not counted), with numerous other answers also present in the data.

2.2.5 Sociodemographics

Comparing sociodemographic indicators across societies imposes harmonization challenges. In- come is differently distributed across countries and comparative measures are subject to exchange ratio ﬂuctuations. Educational systems may establish different rules for promotion and be structured in peculiar tiers. To deal with this matter, I standardize measures from different surveys recoding them using equivalent formats. It is expected that the constructed variables assess the respondents’ relative position within their cities.

Education is measured using three categories: Lesser than complete secondary education com- pleted high school or equivalent, and some post-secondary information or higher. Household income is measured using four categories: low income, middle-low, middle-high, and high income.

Respondent’s age is measured using four categories, 18-29 years old, 30-44, 45-59, and 60 or more. Respondent’s gender is based on self-categorization as either female or male.

2.2.6 Identity salience

Current literature suggests that ranking race as an important identity is correlated with higher levels of perceived discrimination (Sellers et al., 2003; Sellers and Shelton, 2003). A dummy variable indicating whether race is perceived as a salient identity or not is included in the analyses.

In CAS, the variable captures whether race is reported as the first most important identity (rather than class or culture). In BHAS, due to the small number of cases listing race as an important identity, it assesses whether race is mentioned as either the first or second most prominent identity (rather than class or religion). In DAS, a question asking how important is for the respondent that people think of themselves as American rather than identified to ethnic or racial groups is coded as “yes” if the respondent stated that identifying as American is only slightly important

31 or not important at all, being coded “no” otherwise.6

2.2.7 Intergroup contact

Two forms of intergroup contact are measured in all surveys. Interracial friendship asks how many of the respondents’ ﬁve closest friends are of the same skin color or race as them; the same question format was used in all surveys. Respondents’ answers are recoded as none to some, most, and all of them.

BHAS, CAS, and DAS also asked the respondents about interracial contact in the workspace. BHAS and CAS used a similar question format as the one about interracial friendship, asking how many of the ﬁve people with whom the respondents work most closely are also of their same race. DAS asked how often the respondent interacts with co-workers of different race. Variables on co-workers’ race in BHAS and DAS are recoded as none or some of the co-workers are of same race as the respondent, and most or all are. DAS data is recoded as none to some contact with co- workers of a different race, and contact most or all the time. A “does not work” option is included for respondents without an occupation by the time of the survey interview. Although interpretation of ﬁnding vary depending on the survey, the variable aims to measure exposition to contact with co-workers of different races.

2.2.8 Computational procedures

Statistical package R (R Core Team, 2017) is the software for data analysis. Frequencies and regression models with design-based standard errors are conducted using the R package survey (Lumley, 2010, 2014). Conﬁrmatory factor analysis for estimation of everyday discrimination scores is performed using lavaan (Rosseel, 2012; Rosseel et al., 2016; see also semTools Con- tributors, 2016). To prevent loss of information due to missing cases (especially due to list-

6From its face value, DAS survey item used to measure racial salient reads as less appropriate them the questions used in BHAS and CAS. Due to the lack of a most appropriate measure of racial salience, this item is included as a proxy for embracing race group identity as important to be kept rather than disregarded for the adopting of a more encompassing national identity. I return to this point in the discussion.

32 wise deletion in regression analysis) a Fully Conditional Specification (FCS) multiple imputation model (Van Buuren et al., 2006; Kropko et al., 2014) using predicted mean matching (PMM) method (Morris, White and Royston, 2014) is implemented via R library mice (Van Buuren and Groothuis-Oudshoorn, 2011; Van Buuren, 2012). Multiple imputation is a state-of-art technique to address missing data issues because it improves accuracy and power of analyses relative to “traditional” methods to handle with missing data (e.g. listwise deletion or mean imputation) (Schafer and Graham, 2002; Enders, 2010). PMM is a flexible, distribution-free method that preserves the original data distribution and suitable for imputing categorical variables (Vink et al., 2014). Frequencies and regression estimates for the “complete” data sets are merged using the R library mitools (Lumley, 2012) to obtain Rubin’s (1987) coefficients and standard errors for imputed data.

2.3 Findings

2.3.1 Racial discrimination

I first examine the levels of perceived racial discrimination among race groups. Figure 2.1, PanelA reports the mean scores and 95% confidence interval of perceived racial discrimination by race. In Detroit, an expressive fifty-point gap between Blacks (77.1%) and Whites (24%) is found. In Belo Horizonte and Cape Town, the skin color continuum correlates with perceived racial discrimination, with darker skin respondents reporting higher levels of mistreatment attributed to race. In Belo Horizonte, the major gap regarding racial discrimination is between Blacks (32.1%) and non- Blacks (Browns, 10.4%; Whites, 4.1%). In Cape Town, although there is a monotonic increase in reported perceptions of racial discrimination from Whites (19.8%) to Coloureds (22.8%) to Blacks (26%), group differences are modest and their confidence intervals do heavily overlap.

Figure 2.1, PanelB shows the mean levels of perceived racial discrimination by race and centrality of racial identity. Prior literature has suggested that racial identity centrality is associated with higher levels of perceived discrimination (Sellers et al., 1998; Sellers and Shelton, 2003). No group difference is found for Blacks or Coloureds yet the drop for Whites is now in the order

33 Figure 2.1: Average perceived racial discrimination and race

A. Census categories

Belo Horizonte Cape Town Detroit

75 ●

● 25 ● ● ● ● ● ● 0 Black Brown White Black Coloured White Black White (N=191) (N=431) (N=377) (N=425) (N=452) (N=212) (N=142) (N=510)

B. Census categories and importance of racial identity

Belo Horizonte Cape Town Detroit

● 75

● ● ● 25 ● ● ● ● 0 Black Brown White Black Coloured White Black White (N=191) (N=431) (N=377) (N=425) (N=452) (N=212) (N=142) (N=510)

Is this identity important? ● No Yes

Source: 2005 BHAS, 2005 CAS, 2004 DAS. Note: Figures are group averages with 95% conﬁdence interval using design-based standard errors with Rubin’s (1987) adjustment for between-imputation variance.

34 of seventeen percent points for Whites reporting race as their most central identity compared to electing other group afﬁliations in Cape Town. In Belo Horizonte, Browns reporting race as a central identity are 7.5% more prone to report perceived racial discrimination; the corresponding difference for Blacks reach 10%. As suggestive as such results might be about identity centrality as an important predictor of perceived racial discrimination in Brazil, the number of respondents choosing race as a central identity is too small to support assertive inferences.

Table 2.1 reports the mean perceived racial discrimination by socio-demographics and intergroup contact per race group. Regarding socioeconomic predictors, both education and income are negatively correlated with perceived racial discrimination in Belo Horizonte: Respondents with higher income and education tend to report lower levels of racial discrimination. Education is positively associated with perceived discrimination among Blacks and Whites in Detroit yet income goes in opposite for each group, being positively associated with perceived discrimination for Black respondents and the opposite being found for Whites. Having secondary or further education is correlated with higher levels of perceived discrimination for Blacks and Whites in Cape Town, high income among Blacks and low income for Whites are related with lesser racial discrimination; no pattern is present for Coloureds. With regard to gender, if a noticeable gap exists, the tendency is for male respondents to report higher levels of perceived racial discrimination as for Blacks in Detroit, Browns in Belo Horizonte, and Coloureds in Cape Town. Exceptions are Blacks in Belo Horizonte, the only group in which female respondents report substantially more discrimination than their male counterparts. Data show a curvilinear, inverted U-shape relationship between perceived discrimination and age in for both groups in Detroit as well as for Coloured and Whites in Cape Town. Age is negatively related to racial discrimination for Browns in Belo Hori- zonte only, and Whites present a weak U-shape association. There is no pattern of association for Black respondents in Belo Horizonte and Cape Town.

In most cases, having more friends of the same race as the respondent is correlated with higher levels of perceived racial discrimination, exceptions being White respondents in Belo Horizonte and Detroit. There is a curvilinear relationship for Blacks in Cape Town and Detroit, and those with “some” but not all friends of the same race report higher levels of perceived racial discrimi-

35 Table 2.1: Bivariate statistics for perceived racial discrimination in Belo Horizonte, Cape Town, and Detroit.

Belo Horizonte Cape Town Detroit Black Brown White Black Coloured White Black White Education Less than HS 32.2 12.0 5.8 24.1 22.2 5.7 62.9 11.5 HS 39.3 7.4 3.1 29.8 25.8 26.7 70.2 24.5 Post-HS 8.9 8.1 2.9 29.6 22.6 22.2 83.5 25.4

Income Low 36.2 15.0 5.8 26.0 24.0 11.0 63.4 28.3 Mid-Low 39.0 11.4 13.2 27.0 22.1 26.0 78.7 23.5 Mid-High 34.3 10.9 3.8 27.4 23.4 18.3 78.6 24.3 High 11.3 7.9 2.1 20.0 22.1 19.7 98.1 23.1

Gender Female 35.8 8.4 4.9 25.2 20.7 20.9 72.2 23.3 Male 29.2 12.5 3.2 26.8 25.1 18.6 87.5 24.8

Age 18-29 33.6 12.1 4.6 25.5 25.0 17.4 59.5 24.6 30-44 38.4 10.4 4.2 27.5 27.0 23.1 74.2 27.9 45-59 18.6 8.8 2.2 21.0 21.1 24.9 83.8 23.5 60+ 28.7 8.1 6.1 36.2 9.2 12.6 79.1 20.7

Race of closest friends None/some 30.3 7.0 6.9 24.4 14.6 8.8 78.5 37.2 Most 33.5 12.8 3.4 30.8 15.2 24.5 82.4 24.8 All 33.7 13.9 1.6 23.3 33.9 25.0 67.1 16.2

Race of coworkers None/Some 34.0 9.5 3.4 26.7 19.1 18.3 78.5 12.6 Most/All 39.4 15.6 2.6 22.2 22.9 29.1 79.1 31.1 Does not work 24.2 6.1 7.6 27.1 24.0 13.7 73.5 21.7

Source: 2005 BHAS, 2005 CAS, 2004 DAS.

36 nation. Regarding the race of respondent’s co-workers, there are a complex patterns. Non-White respondents in Belo Horizonte report higher levels of perceived racial discrimination when most or all close co-workers are of the same race as them. In Cape Town, Black respondents express lesser racial discrimination having more co-workers of the same race yet the opposite is found for Coloureds and especially for Whites. In Detroit, interactions in the workspace with more or less individuals of different groups has no inﬂuence on the already high levels (close to 80%) of perception of racial discrimination among Black respondents; among Whites, interacting most or all the time with co-workers of a different race leads to a 20% increase in perceiving racial discrimination against themselves compared to those with lesser interaction.

Figure 2.2 breaks group means by self-classification in the open-ended question in Belo Hor- izonte and by cultural identities in Cape Town. Two findings are noteworthy in Belo Horizonte. First is the relatively small (and non-significant) differences between Black respondents identifying themselves as such (preto, or dark black, 37%) and as the politically charged negro category (30.2%). In addition, and contrarily to results for Blacks, Brown respondents identified as negro did report higher levels of perceived racial discrimination (16.7%) than those self-identified using the pardo (brown) census category in the open-ended question (9.8%).

In the Cape Town sample, a noticeable finding for Blacks is the 15% higher level of perceived racial discrimination for self-identified members of the Mfengu ethnic minority (41.6%) compared to other Black identities as the Xhosa (25.2%). Among Coloureds, Christians (29%) and Afrikaans-speakers (23%) reported levels of perceived racial discrimination almost twice as high as those respondents self-identified, respectively, as Muslim (16.6%) and Coloured (12.4%). White respondents identified as Afrikaner (24.7%) or South African (26%) reported perceived racial discrimination more often than other identity groups as the English-speakers (17.2%) or Christians (11.7%).

Univariate and bivariate statistics discussed in this section points out a Black/non-Black cleavage in perceptions of racial discrimination in Belo Horizonte and Detroit while no similar pattern is present in the Cape Town sample. Those differences remain after accounting for other predictors. Blacks in Detroit reported higher perceptions of racial discrimination for every level of those

37 Figure 2.2: Folk categories, cultural identities, and perceived racial discrimination.

Belo Horizonte Cape Town

Preto ● Mfengu ● (N=13) (N=26)

Moreno ● Xhosa ● (N=19) Black (N=297) Black

Negro ● South African ● (N=90) (N=13)

Other ● Other ● (N=69) (N=88)

Afrikaans ● Pardo ● (N=47) (N=85) Christian ● (N=87)

Moreno Coloured

● Brown (N=72) Coloured ● (N=111)

Muslim ● Negro ● (N=63) (N=69) South African ● (N=95) Other ● (N=205) Other ● (N=49)

Afrikaner ● (N=30) Branco ● (N=215) Christian ● (N=47)

White English ● White (N=34) Moreno ● (N=21) White ● (N=14)

South African ● Other ● (N=66) (N=141) Other ● (N=22)

0 20 40 60 80 0 20 40 60 80

Source: 2005 BHAS, 2005 CAS. Note: Figures are group averages with 95% conﬁdence interval using design-based standard errors with Rubin’s (1987) adjustment for between-imputation variance.

38 predictors.

To examine whether group differences (or its absence) in perceived racial discrimination might be account by other predictors, I ﬁt a series of logistic regression models for each city. I start with models including only race as a predictor then I run regressions controlling for socio-demographics, intergroup contact, folk categories and cultural identities, centrality of race identity, then models controlling race for all other predictors.

Results are presented in Table 2.2. Differences between Blacks and Whites in Detroit are large and statistically signiﬁcant across all models; Whites do consistently report lower levels of perceived racial discrimination and controlling for other predictors does not close the group gap. In Belo Horizonte, no class of predictors accounts for the group differences between Blacks and the other groups. In Cape Town, difference between groups is consistently small and not signiﬁcant.

From Table 2.2, four findings are worth noticing. First, group differences in Detroit, as discussed above, are large and significant; no other predictor accounts for it. Similar results are found for the Black/non-Black divide in Belo Horizonte. In Cape Town, the darker the skin tone, the higher the level of perceived discrimination but group differences are minimal and non-significant.

39 Table 2.2: Group differences in perceived racial discrimination with and without controls

Belo Horizonte (1) (2) (3) (4) (5) (6) Intercept −0.75∗∗∗ −0.24 −0.80∗∗∗ −0.83∗∗∗ −0.81∗∗∗ −0.58 (0.17) (0.31) (0.23) (0.23) (0.18) (0.48) Brown −1.41∗∗∗ −1.29∗∗∗ −1.44∗∗∗ −1.32∗∗∗ −1.37∗∗∗ −1.35∗∗∗ (0.25) (0.26) (0.25) (0.29) (0.25) (0.30) White −2.40∗∗∗ −2.04∗∗∗ −2.51∗∗∗ −2.41∗∗∗ −2.37∗∗∗ −2.18∗∗∗ (0.35) (0.36) (0.39) (0.65) (0.35) (0.68) Controls: included? Sociodemographics No Yes No No No Yes Contact No No Yes No No Yes Folk identity No No No Yes No Yes Race salience No No No No Yes Yes

Cape Town (1) (2) (3) (4) (5) (6) Intercept −1.05∗∗∗ −1.08∗∗∗ −1.38∗∗∗ −1.14∗∗∗ −1.08∗∗∗ −1.68∗∗∗ (0.11) (0.25) (0.26) (0.40) (0.18) (0.53) Coloured −0.18 −0.01 −0.22 0.33 −0.17 0.35 (0.21) (0.22) (0.21) (0.47) (0.21) (0.48) White −0.35 −0.15 −0.31 −0.19 −0.34 −0.06 (0.27) (0.33) (0.27) (0.52) (0.27) (0.55) Controls: included? Sociodemographics No Yes No No No Yes Contact No No Yes No No Yes Cultural identity No No No Yes No Yes Race salience No No No No Yes Yes

Detroit (1) (2) (3) (4) (5) Intercept 1.22∗∗∗ 0.45 1.12∗∗ 1.25∗∗∗ 0.22 (0.19) (0.60) (0.56) (0.22) (0.93) White −2.37∗∗∗ −2.51∗∗∗ −2.32∗∗∗ −2.38∗∗∗ −2.49∗∗∗ (0.27) (0.32) (0.29) (0.27) (0.34) Controls: included? Sociodemographics No Yes No No Yes Contact No No Yes No Yes Race salience No No No Yes Yes ∗p < .1; ∗∗p < .05; ∗∗∗p < .01

Source: 2005 BHAS, 2005 CAS, 2004 DAS. Note: Design effect-based estimates with 95% conﬁdence interval using standard errors with Rubin’s (1987) adjustment for between-imputation variance.

40 Table 2.3: Within-group predictors of perceived racial discrimination in Belo Horizonte, Cape Town, and Detroit.

Belo Horizonte, Brazil Cape Town, South Africa Detroit, United States Black Brown White Black Coloured White Black White Intercept −0.95 −1.68∗∗ −3.32∗∗ −1.38∗∗∗ −1.36∗ −3.16∗∗ 0.74 −2.07∗ (0.65) (0.80) (1.56) (0.50) (0.71) (1.45) (0.69) (1.09) Education: HS 0.53 −0.56 −0.10 0.49 0.22 1.75∗ 0.58 1.15 (0.46) (0.64) (0.90) (0.32) (0.45) (0.98) (0.51) (0.76) Education: Post-HS −0.52 −0.27 0.50 0.47 0.08 1.50 1.10∗∗ 1.22∗ (0.93) (0.69) (1.14) (0.45) (0.42) (0.96) (0.44) (0.73) Income: Mid-Low 0.09 −0.20 1.17 −0.10 0.07 −0.90 0.36 −0.18 (0.55) (0.76) (1.19) (0.30) (0.66) (1.04) (0.34) (0.57) Income: Mid-High −0.12 −0.43 −0.20 −0.09 0.01 −1.47 0.18 −0.43 (0.55) (0.74) (1.23) (0.32) (0.59) (1.05) (0.65) (0.70) Income: High −1.51∗ −0.60 −0.86 −0.80 −0.15 −0.90 3.19∗∗∗ −0.55 (0.83) (0.91) (1.55) (0.64) (0.61) (1.00) (0.95) (0.63) Age: 30-44 0.46 −0.27 −0.12 0.14 0.24 0.66 0.15 0.30 (0.42) (0.38) (0.86) (0.24) (0.36) (0.66) (0.51) (0.55) 41 Age: 45-59 −0.54 −0.48 −0.61 −0.14 −0.06 0.90 0.89∗ 0.05 (0.59) (0.51) (0.91) (0.33) (0.40) (0.66) (0.48) (0.59) Age: 60+ 0.22 −0.22 −0.17 0.64 −1.32∗∗ −0.24 0.96∗ 0.13 (0.71) (0.68) (0.97) (0.51) (0.53) (0.78) (0.57) (0.51) Gender: Female 0.39 −0.20 0.26 −0.23 −0.27 0.13 −1.05∗ −0.12 (0.38) (0.36) (0.70) (0.26) (0.26) (0.44) (0.55) (0.39) Friends: Most 0.01 0.46 −0.40 0.43∗ 0.05 1.32∗ 0.19 −0.57 (0.40) (0.40) (0.58) (0.25) (0.48) (0.69) (0.57) (0.46) Friends: All 0.25 0.67 −1.27 −0.15 1.03∗∗ 1.47∗ −0.58 −1.05∗ (0.54) (0.59) (1.08) (0.29) (0.52) (0.76) (0.53) (0.57) Coworkers: Most/All 0.31 0.34 0.14 −0.28 −0.06 0.46 −0.41 0.99∗ (0.47) (0.41) (0.81) (0.29) (0.45) (0.69) (0.60) (0.51) Coworkers: No work −0.52 −0.62 0.80 0.07 0.32 −0.26 −0.41 0.64 (0.45) (0.49) (0.89) (0.33) (0.41) (0.71) (0.62) (0.56) Salience: Yes 0.45 0.70 −0.25 0.33 0.12 −0.68 0.06 −0.32 (0.46) (0.81) (1.26) (0.27) (0.33) (0.50) (0.33) (0.45) Preto 0.26 (0.78)

Continued on next page Belo Horizonte, Brazil Cape Town, South Africa Detroit, United States Black Brown White Black Coloured White Black White Pardo 0.06 (0.53) Branco 0.42 (0.57) Moreno 0.39 −0.50 −0.59 (0.73) (0.53) (1.30) Negro −0.12 0.44 (0.37) (0.44) South African 0.43 −0.11 −0.08 (0.81) (0.54) (0.90) Xhosa −0.03 (0.34) Mfengu 0.72 (0.55) Coloured −1.14∗∗ (0.51) 42 Afrikaans-speaker −0.32 (0.53) Christian −0.12 −0.32 (0.57) (1.13) Muslim −0.73 (0.56) White −0.67 (1.30) Afrikaner 0.17 (0.98) English-speaker −0.26 (1.30) ∗p < .1; ∗∗p < .05; ∗∗∗p < .01

Source: 2005 BHAS, 2005 CAS, 2004 DAS. Note: Design effect-based estimates with 95% confidence interval using standard errors with Rubin’s (1987) adjustment for between-imputation variance. Multivariate regression models are fit for each group to examine how predictors explain within- group variance in perceptions of racial discrimination. Results are presented in Table 2.3. Regres- sion coefficients are additive, linear estimated differences from a hypothetical respondent who is a male young adult with less then complete high school, low income, with no friends and no coworkers of the same race as himself, and who does not believe that his race is an important identity; in Belo Horizonte and Cape Town, he is coded as part of a residual “other” folk/cultural identity category different not including the ones entered as predictors in the model.

A broad assessment of Table 2.3 points out that sociodemographics, race of friends and coworkers, salience of racial identity and folk/cultural identities are weak within-group predictors of perceived racial discrimination. Being member of a race group is itself the major predictor of the odds of being discriminated against because of one’s race. In Belo Horizonte, high income Blacks report lower levels of perceived racial discrimination although such a result should be taken with caution due to the small number of cases in that subgroup (2% out of 191 cases). Identifying with the Black movement-championed category negro has a small negative effect for Black respondents; its effect is large (odds ratio = 1.55) in comparison to being classiﬁed either as the census category pardo or the residual category other but it is associated with an equally large standard error for Brown respondents. In Cape Town, having all close friends of one’s same race is associated with higher levels of perceived racial discrimination among Coloureds and Whites in Cape Town. In Detroit, high education and income increases perception of being racially discriminated against among Black respondents; age has a positive effect yet estimates lack precision.

2.3.2 Everyday Discrimination

Figure 2.3, PanelA shows the mean scores and 95% conﬁdence interval of perceived everyday discrimination by race. Results remarkably depart from those presented in Figure 2.1A. In all samples, Whites report lower levels of perceived everyday discrimination on average although patterns differ among cities. In Detroit, Whites report lower average scores (44.5 points) than Blacks (47.6) the difference is relatively small. Means for Blacks and Whites differ in both Belo Horizonte and Cape Town. The intermediate race categories, respectively Brown and Coloured,

43 fall in between Blacks and Whites but cities differ on how close the middle category is from the other. In Cape Town, Coloureds (28.9) are between Blacks (35.7) and Whites (22.7). In Belo Horizonte, Browns (37.6) are closer to Blacks (43.2) than to Whites (27.9).

Figure 2.3, PanelB reports the average levels of perceived everyday discrimination by race and importance of racial identiﬁcation. No within-group difference conditional on identity centrality is found for either Blacks or Whites in Detroit. No difference is found for Blacks or Coloured but a ten-point drop is detected for Whites reporting race as the most central identity compared to those mentioning other group afﬁliations in Cape Town. In Belo Horizonte, there is a seven-point increase for Browns and a six-point decrease for Whites referring to race as their most central group identity, no difference is found for Blacks. Although those trends may be indicative of the importance of accounting for identity centrality as a predictor of perceived everyday discrimination, for the groups where there are divergences in means such point estimates are too imprecise to permit inferences on its effect. I will return to this point in the discussion.

Table 2.4 brings the mean perceived everyday discrimination by race conditional on sociodemographics and intergroup contact. Data show an overall mild curvilinear relationship between education and perceived everyday discrimination for non-Whites in Belo Horizonte. Education has no effect for Blacks in Detroit but has a positive linear association for Whites.

Income has an inverted U-shape relationship, with higher levels of perceived everyday discrimination for individuals with low and high income levels, for Blacks in Belo Horizonte and Detroit and for Coloured in Cape Town. Higher incomes are associated with lesser perceived everyday discrimination for non-Blacks in Belo Horizonte. Wealthier Whites in Detroit and Cape Town report a ten-point higher level of perceived discrimination compared to their low-income counterparts. No relationship is present for Black Capetonians.

Males report higher levels of everyday discrimination than females for non-Blacks in Belo Horizonte and non-Whites in Cape Town, no differences are found in Detroit. Age is negatively related to perceived discrimination, with older respondents reporting lower levels than younger ones.

44 Figure 2.3: Average perceived everyday discrimination by race

A. Census categories

Belo Horizonte Cape Town Detroit 60

● ● ● 40 ● ●

● ● ● 20

0 Black Brown White Black Coloured White Black White (N=191) (N=431) (N=377) (N=425) (N=452) (N=212) (N=142) (N=510)

B. Census categories and importance of racial identity

Belo Horizonte Cape Town Detroit 60

● ● ● 40 ● ●

● ● ● 20

0 Black Brown White Black Coloured White Black White (N=191) (N=431) (N=377) (N=425) (N=452) (N=212) (N=142) (N=510)

Is this identity important? ● No Yes

45 Table 2.4: Bivariate statistics for perceived everyday discrimination in Belo Horizonte, Cape Town, and Detroit.

Belo Horizonte Cape Town Detroit Black Brown White Black Coloured White Black White Education Less than HS 42.0 36.7 26.9 34.4 28.4 22.9 47.1 39.1 HS 48.0 40.4 23.2 38.4 32.4 21.9 46.5 43.0 Post-HS 45.9 37.1 32.7 37.7 28.2 22.9 48.2 45.8

Income Low 47.0 41.9 38.7 35.7 32.5 13.0 49.0 38.1 Mid-Low 39.9 41.7 30.2 34.7 27.4 21.8 45.3 43.2 Mid-High 41.8 37.6 28.3 37.1 26.5 24.9 43.9 44.4 High 50.1 33.9 26.7 36.3 30.9 22.6 54.3 48.2

Gender Female 43.3 34.6 25.0 33.9 26.2 22.8 47.5 43.9 Male 43.2 40.8 31.4 37.5 31.9 22.6 47.9 45.2

Age 18-29 43.4 42.6 33.2 35.5 32.7 25.0 53.2 49.9 30-44 44.8 38.1 29.9 35.9 28.8 23.3 49.6 50.3 45-59 43.3 33.3 23.9 36.8 25.7 19.6 51.4 46.9 60+ 39.4 29.5 21.3 31.5 24.6 22.7 36.2 34.9

Race of closest friends None/Some 41.4 36.6 30.9 37.5 24.9 16.2 48.0 49.3 Most 43.4 39.6 28.2 38.0 26.4 23.5 48.4 44.5 All 49.0 34.1 23.2 32.3 33.1 29.8 46.0 42.2

Race of coworkers None/Some 43.9 38.6 30.8 34.9 26.0 23.9 46.7 44.8 Most/All 48.0 39.3 28.9 35.3 28.1 22.2 50.2 48.3 Does not work 38.8 34.5 23.3 36.1 30.7 21.9 43.9 39.2

Source: 2005 BHAS, 2005 CAS, 2004 DAS.

46 With regard to respondents being of the same race as their closest friends, patterns differ among cities. In Detroit, a more homogeneous friendship network is associated with lesser perceived everyday discrimination but the association is stronger for Whites. In Cape Town, having all friends of the same race as the respondent is associated with lesser perceived discrimination for Blacks but the opposite is found for Coloureds; for White Capetonians, there is a linear trend associating a more homogeneous group of closest friends and more perceived discrimination. In Belo Horizonte, more friends of the same race is correlated with higher perceived everyday discrimination for Blacks and the opposite is found for Browns and Whites.

Examining the race of respondent’s co-workers, I ﬁnd differences of three of more points only for Blacks in Belo Horizonte, and for Blacks and Whites in Detroit. Blacks in Belo Horizonte do report higher levels of everyday discrimination when respondents have more co-workers of the same race as them. In Detroit, on the other hand, frequent interaction with individuals of other races do increase perceptions of everyday mistreatment. It is remarkable that being unemployed in Belo Horizonte and in Detroit is associated with lesser perceived everyday discrimination but the reverse happens for non-Whites in Cape Town.

In addition to the Census race groups, differences between respondents self-identified using folk categories in Belo Horizonte and as members of cultural and religious groups in Cape Town are also examined (Figure 2.4). By and large, no substantial differences for categories expressed in the open-ended question on color/race in Belo Horizonte are found. Exceptions are the ten-point decline in perceived everyday discrimination for Blacks who spontaneously classified themselves using the official category preto (dark black) and for Whites self-classified as morenos.

Among Capetonian Black respondents, there are no differences among cultural groups. Re- garding Coloureds, perceived everyday discrimination for self-identified Afrikaner-speakers and Muslims are about eight points higher than for respondents identified as Coloureds or Christians. The most expressive difference occurs between Whites identified as Afrikaners or South Africans and those identified as either English-speakers and Christians, with the former two groups scoring ten to fifteen points higher than the latter ones.

Descriptive statistics presented above suggest mean differences between race groups are found

47 Figure 2.4: Folk categories, cultural identities, and perceived everyday discrimination.

Belo Horizonte Cape Town

Preto ● Mfengu ● (N=13) (N=26)

Moreno Xhosa

● Black ● Black (N=19) (N=297)

Negro ● South African ● (N=90) (N=13)

Other ● Other ● (N=69) (N=88)

Afrikaans ● Pardo ● (N=47) (N=85) Christian ● (N=87) Moreno Coloured ● Brown (N=72) Coloured ● (N=111) Muslim ● Negro ● (N=63) (N=69) South African ● (N=95) Other ● (N=205) Other ● (N=49)

Afrikaner ● (N=30) Branco ● (N=215) Christian ● (N=47)

White English ● White (N=34) Moreno ● (N=21) White ● (N=14) South African ● Other ● (N=66) (N=141) Other ● (N=22) 0 20 40 60 80 0 20 40 60 80

Source: 2005 BHAS, 2005 CAS. Note: Figures are group averages with 95% conﬁdence interval using design-based standard errors with Rubin’s (1987) adjustment for between-imputation variance.

48 Table 2.5: Group differences in perceived everyday discrimination with and without controls.

Belo Horizonte, Brazil (1) (2) (3) (4) (5) (6) Intercept 43.22∗∗∗ 54.45∗∗∗ 44.95∗∗∗ 44.52∗∗∗ 43.26∗∗∗ 56.44∗∗∗ (2.17) (3.87) (2.43) (2.86) (2.25) (4.51) Brown −5.61∗∗ −4.90 −5.90∗∗ −6.44∗∗ −5.64∗∗ −6.13∗ (2.54) (2.82) (2.61) (2.89) (2.59) (3.44) White −15.24∗∗∗ −13.87∗∗∗ −15.59∗∗∗ −19.44∗∗∗ −15.27∗∗∗ −17.26∗∗∗ (2.45) (2.96) (2.66) (4.31) (2.48) (4.65) Controls: included? Sociodemographics No Yes No No No Yes Contact No No Yes No No Yes Folk identity No No No Yes No Yes Race salience No No No No Yes Yes

Cape Town, South Africa (1) (2) (3) (4) (5) (6) Intercept 35.66∗∗∗ 39.76∗∗∗ 33.39∗∗∗ 36.61∗∗∗ 36.97∗∗∗ 38.75∗∗∗ (0.71) (1.88) (1.95) (2.01) (1.39) (3.49) Coloured −6.76∗∗∗ −5.86∗∗ −6.84∗∗∗ −5.09 −6.74∗∗∗ −4.84 (2.41) (2.78) (2.34) (4.43) (2.42) (4.30) White −12.99∗∗∗ −12.24∗∗∗ −12.53∗∗∗ −11.76∗∗∗ −13.62∗∗∗ −11.51∗∗ (3.08) (3.75) (3.09) (4.36) (3.10) (4.52) Controls: included? Sociodemographics No Yes No No No Yes Contact No No Yes No No Yes Cultural identity No No No Yes No Yes Race salience No No No No Yes Yes

Detroit, United States (1) (2) (3) (4) (5) Intercept 47.63∗∗∗ 51.46∗∗∗ 51.06∗∗∗ 47.92∗∗∗ 55.89∗∗∗ (1.59) (4.47) (3.45) (1.72) (5.42) White −3.11 −2.58 −2.30 −3.20 −2.45 (2.00) (1.95) (1.91) (2.06) (1.96) Controls: included? Sociodemographics No Yes No No Yes Contact No No Yes No Yes Race salience No No No Yes Yes ∗p < .1; ∗∗p < .05; ∗∗∗p < .01

Source: 2005 BHAS, 2005 CAS, 2004 DAS. Note: Design effect-based estimates with 95% conﬁdence interval using standard errors with Rubin’s (1987) adjustment for between-imputation variance.

49 in Belo Horizonte and Cape Town but not in Detroit. Patterns of differences remain after condi- tioning on socio-demographic predictors, with darker-skinned groups reporting higher means of perceived everyday discrimination. Between-group gaps are more pronounced in Belo Horizonte and Cape Town; except for low socioeconomic status respondents (less than high school education and low income) group differences for this sample are tighter in Detroit. I find similar results for intergroup contact. For some groups, there is an effect for centrality of race identity but estimates are noisy. Regarding spontaneous color/race classification in Belo Horizonte and cultural identities in Cape Town, few yet important departures emerge. Curiously, Black respondents in Belo Hor- izonte who spontaneously classify themselves as such report lower levels of perceived everyday discrimination; Whites calling themselves moreno report a similar trend. The picture is complex for non-Black respondents in Cape Town yet one figure is worth noticing: Afrikaans-speakers Coloureds and Whites identified as Afrikaners report levels of perceived everyday discrimination almost as high as levels reported by Blacks–an explanation is to feel being discriminated against for speaking a language associated with the apartheid regime.

To disentangle the effect of race from potential confounder variables, I run a series of models regressing perceived everyday discrimination on race in each city. I start with models including race as the only predictor then run models controlling for socio-demographics, intergroup contact, folk categories and cultural identities, centrality of race identity, then models controlling race for all other predictors.

Results are presented in Table 2.5. Differences between Blacks and Whites in Detroit are small and not statistically significant across all models. In Belo Horizonte, no class of predictors explains the group differences between Blacks and Whites; however, once sociodemographics are accounted for, the difference between Blacks and Browns are not statistically significant. In Cape Town, difference between Blacks and Whites is also consistent across models, with no set of controls explaining group differences. The gap between Blacks and Coloureds becomes statistically non-significant once controlled for cultural identity.

Four important ﬁndings reported in Table 2.5 are worth highlighting. First, group differences in Detroit, as discussed previously, are small, not statistically signiﬁcant, and not explained by other

50 predictors. Socio-demographics variables account for differences between Blacks and Browns in Belo Horizonte. Cultural identity explains differences between Blacks and Coloureds in Cape Town. Finally, Intergroup contact and identity salience have minimal power in explaining group differences in all three samples.

51 Table 2.6: Within-group predictors of perceived everyday discrimination in Belo Horizonte, Cape Town, and Detroit.

Belo Horizonte, Brazil Cape Town, South Africa Detroit, United States Black Brown White Black Coloured White Black White Intercept 47.27∗∗∗ 48.31∗∗∗ 49.69∗∗∗ 36.55∗∗∗ 34.52∗∗∗ 26.24∗∗ 59.81∗∗∗ 50.01∗∗∗ (6.87) (5.32) (6.22) (3.45) (6.60) (9.43) (6.83) (6.09) Education: HS 4.27 3.33 −0.22 5.59∗∗∗ 2.18 −8.60∗ −3.26 4.19 (4.65) (3.07) (3.24) (1.99) (4.60) (4.75) (4.34) (4.76) Education: Post-HS 3.23 3.39 10.72∗∗∗ 3.05 −2.31 −7.28 −1.81 3.16 (9.98) (3.34) (3.29) (2.40) (3.91) (4.63) (3.92) (3.73) Income: Mid-Low −8.49 −1.45 −9.13 −1.81 −3.06 5.53 −5.19∗ 3.32 (6.58) (5.23) (6.18) (1.79) (3.69) (7.31) (3.01) (3.34) Income: Mid-High −7.68 −6.18∗ −13.80∗∗ 0.11 −4.74 3.95 −7.56∗∗ 1.40 (5.81) (4.98) (5.82) (2.02) (3.38) (7.85) (3.31) (3.73) Income: High 0.43 −10.26∗ −20.56∗∗∗ −3.30 1.31 3.12 1.50 5.08 (8.96) (5.34) (5.71) (3.38) (4.50) (9.20) (3.67) (3.75) Age: 30-44 1.65 −3.29 −1.67 0.85 −3.15 −2.71 −4.05 −0.54 (4.23) (2.57) (2.54) (1.63) (2.86) (5.56) (4.20) (2.72) 52 Age: 45-59 0.04 −7.98∗∗ −4.28 2.19 −6.73 −8.73∗ −1.80 −4.36 (6.09) (3.23) (3.82) (2.13) (4.24) (4.63) (4.71) (2.83) Age: 60+ −2.54 −9.47∗∗ −6.40∗∗ −2.05 −10.04∗∗ −4.69 −17.94∗∗∗ −14.76∗∗∗ (7.29) (4.01) (3.19) (3.08) (4.26) (4.92) (4.86) (2.96) Gender: Female 1.71 −5.71∗∗ −5.72∗∗∗ −3.99∗∗ −6.43∗ 2.52 −2.94 −1.03 (4.17) (2.23) (1.86) (1.56) (3.33) (2.96) (2.65) (2.30) Friends: Most 1.87 2.45 −2.44 1.09 0.22 9.32∗∗ −2.14 −3.69 (3.85) (2.31) (3.08) (1.76) (3.32) (3.89) (2.70) (2.50) Friends: All 8.55 0.08 −7.58∗∗ −5.68∗∗∗ 6.83∗ 16.06∗∗∗ −3.17 −4.44∗∗ (6.15) (4.21) (3.14) (1.71) (3.90) (5.33) (3.40) (2.26) Coworkers: Most/All 3.42 −0.25 0.37 0.06 0.03 −8.63∗ 2.90 0.17 (3.87) (2.80) (2.94) (1.99) (4.38) (4.56) (3.54) (2.23) Coworkers: No work −6.76 −1.41 −2.74 3.49∗ 6.12∗ −6.13 1.79 −2.20 (4.64) (3.16) (2.99) (1.85) (3.68) (5.61) (3.73) (3.41) Salience: Yes −4.25 8.07 −4.20 1.10 −1.90 −7.46∗∗ 0.61 −6.09∗ (6.33) (6.44) (4.54) (1.42) (2.74) (3.55) (2.47) (3.22) Preto −12.51 (8.19)

Continued on next page Belo Horizonte, Brazil Cape Town, South Africa Detroit, United States Black Brown White Black Coloured White Black White Pardo −0.17 (2.86) Branco 1.35 (2.66) Moreno 0.18 −0.42 −9.54∗∗ (8.46) (2.43) (4.87) Negro 1.57 0.55 (3.93) (3.10) South African −0.46 −0.92 5.72 (5.28) (4.55) (5.04) Xhosa −0.88 (2.23) Mfengu −0.80 (2.66) Coloured −3.84 (5.71) 53 Afrikaans-speaker 5.43 (6.07) Christian −5.70 −8.32∗ (5.23) (4.56) Muslim 4.91 (4.99) White 2.60 (8.87) Afrikaner 7.02 (5.22) English-speaker 0.53 (5.84) ∗p < .1; ∗∗p < .05; ∗∗∗p < .01

Source: 2005 BHAS, 2005 CAS, 2004 DAS. Note: Design effect-based estimates with 95% conﬁdence interval using standard errors with Rubin’s (1987) adjustment for between-imputation variance. To assess within-group predictors of perceived everyday discrimination, multivariate regression models for each group are presented in Table 2.6. Regression coefﬁcients are additive, linear estimated differences from a hypothetical respondent who is, again, a male young adult with less then complete high school, low income, with no friends and no coworkers of the same race as himself, and who does not believe that his race is an important identity; in Belo Horizonte and Cape Town, he is coded as part of a residual “other” folk/cultural identity category different not including the ones entered as predictors in the model.

Results from regression analysis in Table 2.6 strongly reverberate trends presented in Table 2.4. For most groups, age has a negative effect, especially for elders. When a signiﬁcant effect for gender is present, it suggests that females perceive lesser discrimination than males when controlling for other factors. Income is a consistent predictor for non-Blacks in Belo Horizonte only. Having a homogeneous same-race friendship network is associated with lesser perceived everyday discrimination for Whites in Detroit and Belo Horizonte but a strong positive effect is found Cape Town. As also present in Figure 2.3B, a salient racial identity is associated with lower perceived discrimination in Cape Town; after controlling for other variables, a similar yet weak effect is also present for Whites in Detroit. Folk categories and cultural identities have modest-to-weak effects in general; after taking other predictors into account, being a White moreno in Belo Horizonte and a White Christian in Cape Town are the only identities with signiﬁcant, negative effects compared to respondents choosing other categories than the ones included in the models.

2.3.3 Are everyday and racial discrimination related?

Heretofore, everyday and racial discrimination have been examined as separate phenomena. Yet, is perceiving one type of discrimination related to also perceiving the other? Although the cross- sectional nature of the survey data here analyzed precludes any assessment of whether perceiving one leads to the perception of the other, it permits the assessment of simultaneous reporting them or not. Predicted scores for everyday discrimination and predicted probabilities of racial discrimination from regression models presented in Tables 2.3 and 2.6 are plotted against each other in Figure 2.5. A LOESS curve is ﬁtted to illustrate the relationship between abscissas and ordinates

54 in each panel.

Predicted values for Detroit corroborate descriptive ﬁndings presented above. Blacks and Whites do not signiﬁcantly differ regarding levels of perceived everyday discrimination but dramatically diverge on perceived racial discrimination: Virtually all White respondents (98%) are below 50% predicted chance of being racially discriminated against while the vast majority of Black respondents (90%) are above it. Although perceiving everyday discrimination be positively associated with the probability of perceived racial discrimination among Whites, a Black individual in Detroit will still be most likely perceive being discriminated against because of his or her race while the opposite is true for Whites. Such trends are clear in Figure 2.5 in spite of non-linearities.

Results for Belo Horizonte are also congruent with descriptive statistics. Black and Brown respondents are at a similar range of scores in the perceived everyday discrimination scale, none of them with predicted scores below twenty points while one-quarter of Whites are in the lower end of the scale. At the same time, 16% of Black respondents, 3% of Browns, and only 0.3% of Whites have a predicted score on that scale above ﬁfty points. The bulk of respondents are placed in the middle of the predicted range of scores. Predicted probabilities of perceiving racial discrimination are underlated to everyday discrimination among Black and Whites – the LOESS curve escapes from being mostly ﬂat among Whites due to one outlier. Among Brown respondents, there is some evidence that perceiving everyday discrimination is correlated to higher odds of reporting racial discrimination against at upper end of the perceived everyday discrimination predicted scores. A post-hoc interpretation of such a result proposes that Brown individuals perceiving higher levels of everyday discrimination might understand such events as mistreatment against because of their race.

Black Capetonians are clustered at the higher end of predicted everyday discrimination scores with less within-group variance compared to Coloureds and Whites, with 36% of the former and 59% of the latter with predicted scores under twenty-ﬁve points. Regarding racial discrimination, more variance is once more found among Coloureds and Whites than among Blacks – more Coloureds and Whites score either below 25% and above 50% chance of reporting racial discrimination. In fact, ﬁve percent of Coloureds and six percent of Whites are above probability of 0.5 of

55 Figure 2.5: Predicted probabilities of perceiving discrimination.

Belo Horizonte : Black Belo Horizonte : Brown Belo Horizonte : White 100

75 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ●●● ● ●●● ● ●●● ● 50 ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ●●●●●●● ● ● ● ●● ●●●●●●● ● ● ● ●● ●●●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ● ●●● ● ● ● ●● ●● ●● ●● ● ●● ●● ●● ●● ● ●● ●● ●● ●● ● ● ●● ●●●● ● ●● ● ● ●● ●●●● ● ●● ● ● ●● ●●●● ● ●● ●●● ●● ● ●● ● ●●● ●● ● ●● ● ●●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●● ● ● ●●● ● ● ●●● ● ● ●●● ● ● ●●● ● ● ●●● ●●●● ● ●●● ● ●● ● ● ●●●● ● ●●● ● ●● ● ● ●●●● ● ●●● ● ●● ● ● ● ●● ●●●● ●● ● ● ●● ●●●● ●● ● ● ●● ●●●● ●● ● 25 ● ● ● ● ●● ●●● ●●●●● ● ● ● ● ●● ●●● ●●●●● ● ● ● ● ●● ●●● ●●●●● ●●● ● ●● ●●●● ●●●●●● ● ●●● ● ●● ●●●● ●●●●●● ● ●●● ● ●● ●●●● ●●●●●● ● ●●●●●●●● ●●● ● ●●● ●●●●●●●● ●●● ● ●●● ●●●●●●●● ●●● ● ●●● ●● ● ●● ●● ●●●●●●●●● ● ●● ●● ● ●● ●● ●●●●●●●●● ● ●● ●● ● ●● ●● ●●●●●●●●● ● ●● ● ●●●●●● ●●●●●●●●●●●●●●● ●● ● ● ● ● ●●●●●● ●●●●●●●●●●●●●●● ●● ● ● ● ● ●●●●●● ●●●●●●●●●●●●●●● ●● ● ● ● ●●● ●●●●●●●●●●●● ●●●●● ● ● ●●● ●●●●●●●●●●●● ●●●●● ● ● ●●● ●●●●●●●●●●●● ●●●●● ● ● ● ●●●● ●●●●●●●●●●●●●●●●●●●●●●● ●● ● ● ●●●● ●●●●●●●●●●●●●●●●●●●●●●● ●● ● ● ●●●● ●●●●●●●●●●●●●●●●●●●●●●● ●● ● ●● ● ●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●● ●● ● ●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●● ●● ● ●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●● ● ● ● ●●●●●●●●●●●●●●● ●●●●●● ●●●●●●● ● ● ● ● ●●●●●●●●●●●●●●● ●●●●●● ●●●●●●● ● ● ● ● ●●●●●●●●●●●●●●● ●●●●●● ●●●●●●● ● ● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●● ● ● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●● ● ● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●● ● ● ● ●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ● ● ● ●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ● ● ● ●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ● ●●●●●●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● 0 ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

Cape Town : Black Cape Town : Coloured Cape Town : White 100

75 ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ●● ● ● ● ●●● ●● ●● ● ● ● ●●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ●● ● ● ● 50 ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ●●●● ● ● ●● ●●●● ● ● ●● ●●●● ● ● ● ●● ●●● ●● ●●●●● ● ● ●● ●●● ●● ●●●●● ● ● ●● ●●● ●● ●●●●● ●●●● ●●●●● ● ● ●● ●● ●●●● ●●●●● ● ● ●● ●● ●●●● ●●●●● ● ● ●● ●● ●●●●● ●●●● ●●●● ● ●●●●●● ●●●●● ●●●● ●●●● ● ●●●●●● ●●●●● ●●●● ●●●● ● ●●●●●● ● ● ●●● ● ● ●● ●● ●●●●●●● ● ● ●●● ● ● ●● ●● ●●●●●●● ● ● ●●● ● ● ●● ●● ●●●●●●● ● ●● ● ● ●●● ● ● ● ●●●●●● ●●●● ● ●● ● ● ●●● ● ● ● ●●●●●● ●●●● ● ●● ● ● ●●● ● ● ● ●●●●●● ●●●● ● ● ● ●●●●●●●●●●●●●●● ●● ● ● ● ●●●●●●●●●●●●●●● ●● ● ● ● ●●●●●●●●●●●●●●● ●● ● ● ●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●● ● ●● ● ● ● ● ●●●●●●●●●●●● ● ● ●● ● ● ● ● ●●●●●●●●●●●● ● ● ●● ● ● ● ● ●●●●●●●●●●●● ● ●● ●●● ●● ●●●●●●●●●●●●●●●●●●● ●● ●● ●●● ●● ●●●●●●●●●●●●●●●●●●● ●● ●● ●●● ●● ●●●●●●●●●●●●●●●●●●● ●● ● ● ● ● ●●●●●●●●●●●●●●●●●● ●●● ● ● ● ● ●●●●●●●●●●●●●●●●●● ●●● ● ● ● ● ●●●●●●●●●●●●●●●●●● ●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●●●●●●●●●● ●● ● ● ●●●●●●●●●●●●●●●●●●●●●● ●● ● ● ●●●●●●●●●●●●●●●●●●●●●● ●● 25 ●● ●●● ●●●●●●●●●●●●●●●●●●● ● ● ● ●● ●●● ●●●●●●●●●●●●●●●●●●● ● ● ● ●● ●●● ●●●●●●●●●●●●●●●●●●● ● ● ● ● ●● ●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ● ●● ●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ● ●● ●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ●● ● ● ● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ● ● ● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ● ● ● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●● ● ●● ●●●●●●●●●●●●●●●●●●●● ●●●● ● ● ● ●● ● ●● ●●●●●●●●●●●●●●●●●●●● ●●●● ● ● ● ●● ● ●● ●●●●●●●●●●●●●●●●●●●● ●●●● ● ●●●●●●●●●● ●●●●●●●●●●●●●●●●●●● ●● ● ●●●●●●●●●● ●●●●●●●●●●●●●●●●●●● ●● ● ●●●●●●●●●● ●●●●●●●●●●●●●●●●●●● ●● ● ● ● ● ● ●● ●●●●●●●●●●●●●●●●● ●●●●●● ● ● ● ● ●● ●●●●●●●●●●●●●●●●● ●●●●●● ● ● ● ● ●● ●●●●●●●●●●●●●●●●● ●●●●●● ● ● ●● ●● ●●●●●●●●●●●●●●● ●●●●●●● ●●●●● ●● ● ● ●● ●● ●●●●●●●●●●●●●●● ●●●●●●● ●●●●● ●● ● ● ●● ●● ●●●●●●●●●●●●●●● ●●●●●●● ●●●●● ●● ● ● ● ●● ●●●●●●●● ●●●●●●●●●●●●●● ● ● ● ● ● ●● ●●●●●●●● ●●●●●●●●●●●●●● ● ● ● ● ● ●● ●●●●●●●● ●●●●●●●●●●●●●● ● ● ●● ● ●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ● ●● ● ●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ● ●● ● ●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ● ● ● ● ●●●●●●●●●●●●●●● ●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●●● ●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●●● ●●●●●●●●●●●● ● ● ●●●●●●●●●●●●● ●● ● ●● ● ● ● ● ●●●●●●●●●●●●● ●● ● ●● ● ● ● ● ●●●●●●●●●●●●● ●● ● ●● ● ● ● ●● ●● ●● ●●●● ●●● ●● ● ● ● ● ●● ●● ●● ●●●● ●●● ●● ● ● ● ● ●● ●● ●● ●●●● ●●● ●● ● ● ● 0 ● ●●●●●● ●●● ● ● ● ● ● ● ●●●●●● ●●● ● ● ● ● ● ● ●●●●●● ●●● ● ● ● ● ● 0 20 40 60

Predicted racial discriminationPredicted racial Detroit : Black Detroit : White

● ● ●●● ●●● ● ●●●●●●●●●●● ●●● ●●● ● ●●●●●●●●●●● 100 ●●●●●● ●●●●●● ● ●● ● ●● ●●● ●● ●●● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ●● ●●● ● ● ●●● ● ●● ● ●●● ● ●● ● ●● ● ● ●● ●● ● ●● ● ● ●● ●● ● ●● ●●● ● ●● ● ●● ●●● ● ●● ●●● ● ● ● ●●●● ●● ● ● ●●● ● ● ● ●●●● ●● ● ● ● ●● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ●● ●●● ● ● 75 ● ● ● ● ●●● ● ● ● ● ●●● ● ●● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ●●●● ● ●● ● ●● ●●●● ●●● ● ●● ● ●● ●●●● ●●● ● ● ● ●● ● ● ●● ● ●● ● ●● ●● ● ●● ● ●● ●● ●●●● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ●●●● ● ● ●●●● ● ●●●● ●●● ● ●●●● ●●● ● ● ●● ● ● ●● ●●● ● ●● ● ●●● ● ●● ● 50 ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ●●● ● ● ● ●● ● ●●● ● ● ● ●●● ● ● ●●● ● ●● ● ●●● ● ●● ● ●●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ●●●●●● ● ● ● ●●●●●● ●● ● ● ● ●● ●● ● ● ● ●● ● ● ●●● ● ● ●● ● ●● ● ● ● ●●● ● ● ●● ● ●● ● ● ●● ●●●● ● ● ●● ●●●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ●● 25 ●● ● ●● ● ● ● ●● ●● ●● ● ●● ● ● ● ●● ●● ● ●● ● ● ●● ●●● ● ●● ● ●● ● ● ●● ●●● ● ●● ● ●● ● ● ● ● ●● ● ● ● ●● ●● ● ●● ●● ● ●● ●● ● ●● ●● ● ● ● ●● ●● ● ● ● ●● ●● ● ● ● ●●● ● ●● ● ● ● ●●● ● ●● ● ● ● ●●●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ●● ● ● ● ● ●● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● 0 0 20 40 60 0 20 40 60 Predicted everyday discrimination

Source: 2005 BHAS, 2005 CAS, 2004 DAS. Note: Predicted everyday discrimination treatment is calculated from models in Table 2.6. Predicted probability of racial discrimination is calculated from models in Table 2.3. Dot size is proportional to the number of respondents from the same city and self-identiﬁed to the same Census category reporting the same predicted values in both dimensions. For the sake of city-wise comparison, data for all groups are plotted, being the target group in a panel are represented by dark gray dots and other observation are in light gray.

56 reporting racial discrimination against less than 2% of Blacks. Although such a finding might be at odds with data presented in Figure 2.1A, results in Figure 2.5 are predicted values that take into consideration a set of other predictors and some of them, as the composition of friendship network, that might influence perceptions of racial discrimination above expectations given group membership itself. Levels of perceived racial discrimination among White respondents are nonetheless minimally related to everyday discrimination while a strong, positive association is present for Blacks and Coloureds. Such results may be conjectured as reflecting the feeling among parcels of Whites of loss of sociopolitical dominance and resentment against affirmative actions for the empowerment of groups of color in post-apartheid South Africa. Among Blacks and Coloureds, perceptions of everyday and racial discrimination are strongly associated, suggesting perhaps that feelings of mistreatment remain racialized.

2.4 Discussion

This study’s goal is to examine group differences in perceived mistreatment in three societies where race has been an important structural dimension: Brazil, South Africa, and the United States. More speciﬁcally, it aims to test whether being member of a race group is a consistent predictor of perceiving racial and non-racial forms of discrimination and how group differences compare across contexts. A major contribution this research makes is to demonstrate how the perception of non-racial forms of discrimination becomes racialized in contexts where group racial identities are strong even if perception of salience of racial identity at the individual level is not a strong predictor of discrimination itself.

The literature on perceived discrimination has been consistent in pointing out dark-skinned groups as experiencing higher levels of perceived mistreatment against compared to their light- skinned counterparts (Bailey, 2009a; Canache et al., 2014; Forman, Williams and Jackson, 1997; Layton and Smith, 2017; Seekings, 2008; Silva and Paixao˜ , 2014; Welch et al., 2001; Williams et al., 2008, 2012). I ﬁnd, in line with previous research, that darker skin color is positively associated with perceived discrimination of different sorts. In all samples, White respondents

57 report lower levels of perceived everyday and racial discrimination than Black respondents; in Brazil and South Africa, middle groups as Browns and Coloured falling in between the lighter and the darker groups. These differences in reported discrimination between Browns/Coloureds and Blacks also supports the colorism approach to race relations (Hunter, 2013; Monk, 2015). However, both the distance among groups in general, and particularly the position of Browns and Coloureds, do vary depending on the type of discrimination and the contexts. Simply put, my ﬁndings suggest that group differences in perceived discrimination are context-dependent.

In Detroit, for instance, Blacks do score higher, on average, than Whites in everyday discrimination but the difference is small and non-signiﬁcant (Welch et al., 2001); on the other hand, there is a considerable racial discrimination gap between groups, with African Americans being dramatically more prone to denounce mistreatment because of their race (in special high education, high income respondents; see Figure 2.3 and Table 2.5) than Whites if they have an overall similar score regarding everyday discrimination.

A rather more complex picture emerge in ternary classification systems. Both in Belo Hori- zonte and Cape Town, the parallelism of skin color and perceived everyday discrimination suggests that the darker the skin color in either city the higher the probability of perceiving mistreatment in social settings. Differences between Browns/Coloureds and Blacks, however, vanishes when certain controls are included in regression models (Table 2.5). Namely, once sociodemographics are accounted for, the difference between Browns and Blacks in Belo Horizonte loses its statistical significance, at least in part due to the strong negative effects of income and age on perceived everyday discrimination among Browns (Table 2.6, column 2). In Cape Town, the difference between Blacks and Coloureds turns into statistical non-significance after controlling for cultural (ethnic, linguistic, religious) identities – although no cultural identity reaches statistical significance as a predictor of everyday discrimination, possibly the negative effect of identification as either Coloured or Christian contributes to such a result (Table 2.6, column 5; see also Figure 2.4).

Regarding perceived racial discrimination in Belo Horizonte and Cape Town, important contrasts emerge, both within and between cities. Although group differences in perceptions of racial discrimination remain statistical signiﬁcant controlling for other predictors (Table 2.2), Browns

58 and Whites differ only modestly in relative terms: About 5% of Whites and 10% of Browns report perceptions of mistreatment because of their race. This 5% gap pales in comparison to the one- third of Blacks reporting being victims of racial bigotry (Figure 2.3A). A Black respondent is three times more prone to denounce racial discrimination than a Brown. Such a finding helps to shed light on the difficulties of the Brazilian Black Movement. In addition to the hurdles to create grass- roots due to a “disjuncture between the attitudes of elites and of the general population” (Bailey, 2004, p. 741; see also Burdick, 1998; Hasenbalg and Valle Silva, 1999), evidence presented here suggests that Blacks and Browns profoundly disagree regarding attributing their own misfortunes and mistreatments to racial discrimination even though they acknowledge the existence of discrimination against non-Whites (Bailey, 2009a). While the largest group difference in Belo Horizonte is a Black/non-Black gap regarding perceived racial discrimination, groups do not differ in that dimension in Cape Town. As presented in Table 2.2, group differences in perceptions of racial mistreatment reach statistical significance in no regression model and are small in comparison to Belo Horizonte and Detroit.

Figure 2.5 presents an important contribution advanced by this study, that is, how social structure inﬂuences the impermeability of group boundaries (Ellemers et al., 1988; Ellemers, 1993) and, consequently, the perception of discrimination and the attribution of its causes. As argued above, one of the legacies of past legal discrimination systems in South Africa and in the United States is the persistence of racialized group identities, especially amongst those who suffered the harsh effects of such political arrangement. One anticipated consequence of this enforcement of group boundaries is attribution of hostile acts as a racial discriminatory practice. Although the cross-natural character of the data does not permit to test for the causal inﬂuence of one type of perceived discrimination on the other, evidence in Figure 2.5 provides initial support to this claim at least regarding the simultaneity of them.

This attribution is clear in Detroit. Although Black and White respondents are overall similar in their perceptions of everyday discrimination, any level of perceived everyday discrimination is associated to a high probability of reporting perceived racial discrimination – less than 10% of Black respondents have a probability under .50 of perceiving racial discrimination.

59 In Cape Town, a strong correlation between predicted everyday discrimination and predicted racial discrimination is present among Blacks and Coloureds. In both groups, higher predicted everyday discrimination scores associated with higher chances of reporting being victim of racial discrimination. Even though it is not possible to test the direction of causality between them, data suggest that Blacks and Coloureds in Cape Town see discrimination in social settings through racialized lenses. The two forms of discrimination are, however, not correlated among White respondents. Being less often mistreated in social settings, why are White respondents similar to Blacks and Coloureds regarding overall perception of racial discrimination? An explanation may come from the post-apartheid policies aiming to redress socioeconomic inequalities in the country. Whites in South Africa “have no qualms” in “expressing opposition to race-based policies, such as afﬁrmative action and black economic empowerment” and perceive such policies are unfair treatment against White individuals as such (Seekings, 2008, p. 18; see also Alexander, 2007 and Roberts, 2014). In other words, in a context where discourses and practices remain highly racialized (Muyeba, and Seekings, 2011), White South Africans may perceive race-based afﬁrmative action policies as a form of racial discrimination against them.

Arguing for racialized perceptions of discrimination in South Africa and the United States is anything but counter-intuitive. What about the country where racial boundaries have been historically diffused until very recently? The absence of correlation between the two forms perceived discrimination under examination are easily detected among Black and White respondents in Belo Horizonte. Among Whites, the trend line is mostly flat except for the influence of one single outlier. However the level of perceived everyday discrimination among Whites, it is not correlated with substantial variation regarding racial discrimination. Among Blacks, a different data pattern fits the same story. Although reporting the higher levels of both everyday and racial discrimination in Belo Horizonte, the predicted scores in both dimensions are uncorrelated: The dispersion of data points form a quasi-perfect circle with all possible combination of results – for instance, there are Black respondents scoring high in predicted everyday discrimination but low in racial discrimination, respondents scoring low in everyday and high in racial discrimination, and respondents scoring high in both axes. Putting it shortly, there is no clear pattern of association between

60 everyday and racial discrimination among Black respondents to authorize the suggestion that mistreatment in social setting are being perceived as caused because of their race. Among Browns, however, a somewhat different picture is found. For most of its range, predicted everyday discrimination is only a mildly correlated with racial discrimination yet the association spikes at the highest value of everyday discrimination due to the present of one inﬂuential point. Excluding the outlier (not shown), the trend line still indicated a positive correlation between axes but it never above the .25 probability of perceived racial discrimination. These results accredit no strong claim of a racialized perception of eveyday discrimination as found for Blacks and Coloureds in South Africa. Nevertheless, it might suggest that a burgeoning attribution of everyday discrimination to racial discrimination among Browns who experience intense mistreatment in social settings, which might eventually lead to the awakening of racial consciousness and the self-identiﬁcation using the Black Movement-endorsed negro category (Silva and Leao˜ , 2012).

An unexpected finding is the lack of explanatory power of the racial identity salience measure, contradicting the previous results in the literature (Sellers et al., 1998; Sellers and Shelton, 2003). Some timid results suggest that salience of racial identity correlated with higher chances of perceiving racial discrimination among Blacks and of perceiving both everyday and racial discrimination among Browns in Belo Horizonte, in line with results presented by Silva and Leao˜ (2012); yet it reduces perception of discrimination among White Capetonians. In no case, however, does the coefficient of racial identity salience reach statistical significance. No effect is found either for Black or White respondents in Detroit, perhaps due to the indirect way the survey item is used to assess the importance of group identity. In all samples, the lack of explanatory power for this variables might eventually be attributed to the coarseness of the binary variable. Future research employing more refined measures of racial salience are encouraged.

To conclude, this study proposes the use of the social identity theory framework to explain why perceptions of discrimination are associated with racial hostility. Although the data analyzed here do not allow to test the causal mechanisms underlying association between everyday and racial discrimination under certain conditions, ﬁndings presented above suggest that, in contexts where racial groups boundaries are salient, even forms of mistreatment not directly related to prejudice

61 and bigotry may be associated with perceptions of racial discrimination.

62 CHAPTER 3

Institutional Trust

Trust is a fundamental building block of social and political life. German social theorist Georg Simmel once put it, “conﬁdence, evidently, is one of the most important synthetic forces within society” (Simmel, 1950, p.318). Social science research has shown the relevance of different forms of trust on a range of political outcomes as democratic governance and institutional performance (Boix and Posner, 1998; Gamson, 1968; Mishler and Rose, 2001; Putnam, 1993; Tyler, 1998), corruption (Rothstein and Uslaner, 2005), and policy preferences as attitudes on taxation (Rudolph, 2009; Scholz, 1998; Yamamura, 2014), redistribution and government spending (Bergh and Bjørnskov, 2011; Edlund, 1999; Rudolph and Evans, 2005; Svallfors, 1999, 2013), foreign policy (Hetherington and Husser, 2012), and environmental policies (Konisky, Milyo and Richard- son, 2008). Regardless of the political importance of trust, “no government yet established has had the loyalty and trust of all its citizens” (Aberbach and Walker, 1970, p.1199). There existing distrust in the government, individuals are more prone to not comply with the law or even avoid paying service charges (Fjeldstad, 2004; Jackson et al., 2012; Marien and Hooghe, 2011).

Social cleavages do affect political attitudes and behaviors (Bartolini and Mair, 1990; Lipset and Rokkan, 1967). In some modern societies, race is an crucial sociopolitical structuring dimension and a source of group differentiation with implications for attitudes and preferences, trust in institutions amongst them. As argued by Uslaner in his authoritative study on race and trust in the United States, a country where race is a main political cleavage, “Race is the life experience that has the biggest impact on trust” (2002, p.91). If race is a politically relevant dimension, a group may distrust governments and policies perceived as beneﬁting other groups at their expense, undermining the governmental capacity for action (Durrheim, 2010; Hetherington and Globetti,

63 2002).

Race should not, however, be taken for granted as an important political dimension across multiracial societies. The salience of group boundaries is neither automatic nor universal, it is conditional on the social context because some ingroup/outgroup categorizations may be more meaningful and consequential in some circumstances than in others (Posner, 2004; Simon and Klandermans, 2001). Under appropriate conditions, group membership evolve into group iden- tiﬁcation and consciousness, having political consequences and being related to attitudes toward the government (Hutchings and Valentino, 2004; Lau, 1989; McClain, Carew, Walton and Watts, 2009; Miller, Gurin, Gurin and Malanchuk, 1981). One anticipated outcome is the emergence of differences in political trust as a consequence of perceptions of how fair is the state and its agencies in treating citizens who are members of one group or another. To test such a hypothesis, this study compares levels of political trust across racial groups in Brazil, South Africa, and the United States, three countries where race constitutes an importance socioeconomic dimension despite of experiencing dramatically distinct race formation processes (Fredrickson, 1981, 2008; Omi and Winant, 1994; Marx, 2002; Ribeiro, 1995).

Marx(1998) argues that the nation-building processes in those countries emphasized or neu- tralized race conditional on the elites’ needs to keep territorial integrity and warrant their political survival. To heal the wounds of civil wars that threatened breaking their countries apart, political elites – Northern and Southern in the United States, Afrikaner and British in South Africa – forged common, overarching identities to foster White unity at the expense of other groups. Both the U.S. Northern and British winners in such wars, to respectively appease the Southern and the Afrikaners to set up the conditions for an intra-White alliance, accepted the segregation of Black as a condition for such a coalition (Marx, 2002). The imposition of Blacks’ rights by the U.S. federal government after the Reconstruction was abandoned, diminishing Southern Whites objections to national unity and culminating in the Jim Crow laws in the South (McAdam, 1982; Woodward, 1955). To placate the strong Afrikaner nationalism, increasing exclusion of Coloureds and “na- tives” (i.e., Black Africans) and uniﬁcation of Whites against the cultural and demographic “black threat” took place, resulting in the apartheid system after the 1948 election (Louw, 2004). The

64 fate of race in Brazil followed a dramatically distinct path. Since the mid-17th century the Por- tuguese colonial rule remained mostly unchallenged; the arrival of the Portuguese court in 1808 consolidated the central government without intra-White disputes for political control. Separatist movements and large slave rebellions in the provinces were triumphantly contained (Bethell and Carvalho, 1985). Slavery was a national institution and efforts were made to avoid conflicts over it; as slave resistance thrived during political crisis, the government implemented measures which slowly and peacefully abolished slavery during the 19th century (Reis and Klein, 2011). Without major violent conflicts being fought on race or slavery, there was no impetus to purposely develop a racial ideology to unify regional White elites (Marx, 1996). After the end of slavery in 1888, the Brazilian elites, fearing the resurgence of the slave revolts of the past and amidst a largely non-White population, were eager to defuse any potential of racial conflict. No system of legal segregation were ever enforced in the country since abolition and, by the first half of the 20th century, race-mixing was praised, promoted, and incorporated to the very definition of “Brazilianness” (Freyre, 1946 [1933]; Pierson, 1939; Skidmore, 1993 [1974]).

Racial formation projects in Brazil, South Africa, and the United States resulted in different mechanisms of boundary enforcement which are relevant to understand cross-national variations in the political salience of race (Lamont and Molnar´ , 2002; Omi and Winant, 1994; Telles and Sue, 2009; Wimmer, 2008). Both in South Africa and in the U.S. South, the development of an overarching White identity to overshadow separatist forces culminated in legal discrimination systems where the state apparatus was in charge of enforcing and overseeing group boundaries as means to implement segregationist policies, which were absent in Brazil (Andrews, 1991; Marx, 2001, 2002). Such a legal enforcement of racial boundaries had (and has had) critical effects on the permeability of group boundaries (Ellemers, 1993; Ellemers et al., 1988). Regardless of how boundaries are deﬁned in a society (Morning, 2008; Nobles, 2000; Posel, 2001a; Telles and Paschel, 2014), they lose their “porosity” once enforced, group membership become clearly deﬁned, and boundary crossing for intergroup mobility becomes largely restricted.

Closure of group boundaries has a critical effect on the salience of group identities and their potential for political mobilization. According to the Social Identity Theory (e.g., Tajfel, 1981;

65 Tajfel and Turner, 1986), individuals pursue membership to groups providing them with some utility (e.g., prestige, self-esteem, power). If membership to a group makes no positive utility contribution, individuals will leave the group whenever it is possible (i.e., when group boundaries are permeable); if leaving the group is not a feasible or realistic option, group members will either pursue the re-signiﬁcation of group membership to turn it into a positive reference or engage in strategies of social change to provide more utility to the group as whole via transformation of group hierarchies (Tajfel, 1974, 1975). This latter point implies that group membership turns into a reference for cognitive engagement and mobilization.

Brazil, South Africa, and the United States differ in terms of the permeability of racial boundaries as a consequence of how race groups have been historically constructed as part of their corresponding nation-building processes. Importantly, state-enforced racial segregation contributed to make race a politically salience identity. In contexts where one’s life chances are perceived as determined by group membership and the state is responsible for policing group boundaries to exert discriminatory policies, members of those groups being discriminated against would perceive the state as a hostile agent and develop antagonistic attitudes toward it; on the other hand, groups beneﬁting from government actions will champion the state and aim preserve it. Legal segregation in South Africa and in the United States, in precluding race boundary crossing and using group membership as the basis for implementation of policies, involuntarily fomented strong group identities among members of discriminated groups and served as a unifying political force to overcome collective action problems in their struggle against state-sponsored discrimination (Huddy, 2013; Klandermans, 2002; Marx, 2002).

From the discussion above, I argue that trust in public institutions, among other attitudes, might be affected by race group membership conditional on whether such a membership is politicized. Trust in public, state-related institutions is deﬁned as the perception that state action leads to desirable outcomes.1 In societies where race is an important cleavage, preference formation is expected to occur along race lines. In effect, individuals will hold different expectations about the capacity

1Similarly, Hetherington(2005, p.9) deﬁnes political trust as “the degree to which people perceive that government is producing outcomes consistent with their expectations.”

66 of the state to deliver policy outputs benefiting their own race groups (Aberbach and Walker, 1970; Durrheim, 2010; Hetherington and Globetti, 2002; Sears, 1993). When race is not a salient cleavage, such an effect should be absent. Among the three societies analyzed here, racial differences in institutional trust are expected to be more pronounced in South Africa and in the United States but not in Brazil as a consequence of their nation-building and racial formation processes (Marx, 1998). More specifically, group-level differences in trust are expected to be found in the first two societies but not in the latter. Such anticipated results are consistent with the current record on race and attitudes in those countries (e.g., Bailey, 2009a; Bobo, Charles, Krysan and Simmons, 2012; Kinder and Sanders, 1996; Klandermans, Roefs and Olivier, 2001b; Roberts, Kivilu and Davids, 2010; Schuman, Steeh, Bobo and Krysan, 1997; Sears, Sidanius and Bobo, 2000).

To test this conjecture, I analyze comparative data on confidence in institutions from the World Values Survey using an innovative approach in studies on race and trust. Applying confirmatory factor analysis and measurement invariance techniques, I develop a measurement model of institutional trust and assess the comparability of the hypothesized construct across groups within a country. To overcome limitations in the WVS data and minimize capitalization on chance, analyses are replicated using other nationally representative datasets. Relative country-specific results are then compared cross-nationally. As expected, group differences in political trust are found between race groups in the United States and South Africa, not in Brazil. Notably, between-group variation in trust is also conditional on the functions carried by the state institutions.

This paper is structured as follows. First, I selectively review the literature on race and political trust. Next, I discuss how trust has been measured in the existing literature and the advantages of developing a multidimensional model. Third, I review the measurement invariance approach in the context of confirmatory factor analysis, followed by an overview of the data and methods employed in the analysis. Results from multiple-group confirmatory factor analyses using the World Values Survey and other datasets are presented. Finally, I discuss the general findings and their implications for the literature on race and political trust.

67 3.1 Race and political trust

Comparative studies on race and political trust are scarce at best, being the lack of appropriate cross-national data one of the major causes. (Dowley and Silver, 2005). Definitions and categories of race do not travel easily across contexts, making it complex to develop valid comparative measures of group membership. Nonetheless, group differences in institutional trust in societies where race (and/or ethnicity) is regarded as an salient social cleavage have been found using available cross-national data even when only coarse measures of racial classification are available (Silver and Dowley, 2000). Most of research on race and trust (either social or political) have been restricted to single-country studies. Evidence from country-specific analysis has nevertheless suggested the worthiness of cross-national comparisons that might reveal distinct patterns racial gaps across societies, reflecting how different racial projects (Omi and Winant, 1994) politicize – or not – racial boundaries.

Public opinion studies in the US have consistently reported differences in attitudes and preferences between race groups (e.g, Kinder and Winter, 2001; Kluegel and Smith, 1986; Schuman, Steeh, Bobo and Krysan, 1997; Sigelman and Welch, 1991). When it comes to research on race and political trust, the racial gap is conditional on the object of trust. Trust in the police exhibits one of the most pronounced and persistent racial gaps. Race has been a strong and consistent predictor of attitudes toward the police (Hindelang, 1974; Weitzer and Tuch, 1999, 2004). Schu- man et al.(1997), for instance, report a consistent 35%+ difference between Blacks and Whites in perceptions of police misconduct against the former group through 1980s and 1990s, with Blacks overwhelmingly reporting perceptions of unfair treatment against their group by the police. Group differences are not just remarkable, they also rise among Blacks in the period from 69% of survey interviewees to 88% in 1992 reporting perceptions of police unfair treatment. Other studies also point to the suspicion of Blacks toward the police (e.g., Gabbidon and Higgins, 2009; Sears, 1969; Tuch and Weitzer, 1997; Tyler, 2005). Mistrust and perceptions of police violence against Blacks have served as a catalyst and justiﬁcation for riots as the ones in Watts in 1965, Los Angeles in 1992, and Ferguson in 2014 (Fogelson, 1968; Moran and Waddington, 2016; Sears and Tomlinson,

68 1968; Sears, 1969, 2000). In the aftermath of the 1965 Watts Riots, Sears(1969) found that 54% of Black respondents in Los Angeles county reported they could not trust in the police, compared to only 6% of Whites. Blacks are also less trusting in the criminal justice system (Weitzer and Tuch, 1999; Tyler, 2005) and in Supreme Court leaders (Richardson, Houston and Hadjiharalambous, 2001).

Racial differences in trust in political institutions and in the government are nevertheless in- consistent, with the Black-White gap and its direction changing over time as short-term responses to the political environment (Abramson, 1983; Howell and Fagan, 1988; Wilkes, 2015). Analyzing survey evidence from the post-Civil Rights era, Aberbach and Walker(1970) and Miller(1974) note that Blacks were at least as trusting as Whites in the mid-1960 but lesser in 1970, and attribute the loss of confidence among Blacks as a cynical reaction to the frustrations with slow social progress. More recent research also support the claim that the correlation between race and trust varies in time. Miller and Borrelli(1991) show levels of trust (and distrust) among Blacks and Whites varied at different rates from the mid-1960 to late-1980s, with the racial gap waxing and waning in time. Hetherington(1998) finds no differences between Blacks and Whites in 1988 but reports that Blacks were less trusting in the government than Whites in 1996 (see also Uslaner, 2002); Cook and Gronke(2005) point to no group difference in 2000; Avery(2007) shows that political trust among Blacks declined after the Supreme Court’s decision in Bush versus Gore in 2000; Abrajano and Alvarez(2010) argues that Whites were more trusting than Blacks in 2004; and Wilkes(2015) points that Blacks trusted more in the federal government than Whites during the Obama’s administration. Put together, such findings shows that the racial gap in institutional trust, if any, ebbs and flows as a response to political events. Blacks and Whites also generally converge regarding confidence in Legislative and Executive leaders (Richardson, Houston and Hadjiharalambous, 2001). Alford(2001) shows that Blacks and Whites present similar pattern of decline of trust in the government since the 1960s. The only instance in which a more consistent group gap might persist is in the Black’s preference for the federal government while whites tend to be more supportive of their local governments (Nunnally, 2012; Uslaner, 2001; but see Hether- ington and Nugent, 2001). Even here, evidence shows that the presence of Black mayors lead to a

69 decline of trust in the local government among Whites and an increase among Blacks, suggesting that the dynamics of trust in subnational governments might also result from perceptions of repre- sentativeness in local political institutions (Abney and Hutcheson, 1981; Bobo and Gilliam, 1990; Howell and Fagan, 1988; Howell and Perry, 2004; Rahn and Rudolph, 2005).

Studies on political attitudes in Brazil have been unanimous to portrait the country as one of the world’s leaders in distrust in political institutions (e.g., Baquero, 2001; Lopes, 2004; Moises´ and Carneiro, 2008; Moises´ and Meneguello, 2013; Power and Jamison, 2005; Ribeiro, 2011). Such a lack of conﬁdence also affect interpersonal trust, only about 7% of Brazilians believing that “most people” could be trusted, putting Brazil as the country with the lowest level of generalized trust among 43 studied societies (Inglehart, 1997; Silver and Dowley, 2000). Importantly, despite of the high levels of economic inequalities among race groups (e.g., Bailey, Loveman and Muniz, 2013; Hasenbalg, 1979; Lovell and Wood, 1998; Telles, 2004; Valle Silva, 1985) and the widespread belief that racial inequalities are mostly due to structural factors (Bailey, 2002, 2009a; Telles and Bailey, 2013), the potential impact of such group inequalities on support to public institutions has been largely neglected, with no systematic study focusing on race differences in conﬁdence in the government and public agencies.

Notwithstanding the Brazilian unfortunate record of police violence against minorities (Barros, 2008; Machado and Noronha, 2002; Pinheiro, 1997; Soares, 2006), few studies have assessed its impact on confidence in law-enforcement agencies. Oliveira(2011) finds no difference in trust in the police between Whites and non-Whites in a nationally representative sample; on the other hand, the author shows that non-Whites have a lesser positive evaluation of their interactions with the police and that confidence in the institution is related to quality of the citizen-police contact, indirectly suggesting that non-Whites might be less trusting in the police forces. Other studies have shown only small-to-moderate, non-significant group differences in trust in the police (Lopes, 2013; Silva and Beato, 2013), being disparagement toward the police and the justice system in general widespread across race groups (Noronha, Machado, Tapparelli, Cordeiro, Laranjeira and Santos, 1999).

South Africa experienced one of the most drastic political transitions in the 20th century, from

70 the apartheid racial state to a non-racialist democratic dispensation (Louw, 2004; MacDonald, 2008). Such a regime change impacted on how different groups perceive the legitimacy and trustworthiness of the national government. Before the first South African universal election in April 1994, Blacks were less trusting in the government than Coloureds and Whites, the latter two groups expressing similar overall levels of confidence. After the African National Congress victory by an overwhelming majority, the trend in trust reversed. The Black majority, satisfied with the new democratic dispensation, exhibit increased levels of confidence in the government, being the opposite observed among Coloureds and Whites, with a particularly strong and persistent decline among the latter (Klandermans, Roefs and Olivier, 2001b; Garcia-Rivero, Kotze´ and Du Toit, 2002). In other words, in post-apartheid South Africa, all non-Black groups became suspicious of the country’s government now dominate by the Black constituency and politicians (Askvik, 2008; Southall and Daniel, 2005; Davids, 2010). In spite of the “increasingly pessimistic” development of trust in the government since the democratization (Mattes, 2002, p.31; but see Askvik, 2010), the post- apartheid racial gap in public confidence remains. Blacks trust the government a lot more than the other groups. In 2003, 62% of Blacks expressed trust in the national government, compared to 36% of Coloureds and 25% of Whites, similar numbers been also found for trust in the parliament (Daniel, Southall and Dippenaar, 2006). In 2005, the gap persisted: 74% of Black South Africans expressed trust in the national government, compared to 49% of Coloureds and 27% of Whites (Rule and Langa, 2010), being groups differences in 2007 alike (Roberts, 2008).

Absence of data disallows pre/post-transition comparisons to assess how the end of the apartheid system affected trust in law-enforcement and order-maintenance institutions. Episodes of violence against anti-apartheid protests, the most notorious being the police repression of the 1976 Soweto uprising which resulting in more than 700 deaths (Baines, 2006), nevertheless provides historical evidence that Blacks were at least suspicious of, if not averse to, the police and defense forces. In the immediate aftermath of the apartheid, Whites were more trusting in the police yet believed in worsening of service; Blacks were more likely to express mistrust but also to expect improvement in the police service in the new regime (Gastrow and Shaw, 2001). In effect, some recent evidence suggests that Blacks are now the most trusting group in the police (Fry, 2013) even though it re-

71 mains the least trusted national institution by this group, with Blacks depositing more conﬁdence in the armed forces and in the Court system (Roberts, 2008). Coloureds and Whites express less trust in order-maintenance institutions than Blacks, being Whites the least trusting group overall (Daniel, Southall and Dippenaar, 2006; Pickel, 1997; Rule and Langa, 2010).

3.2 Measuring trust in public institutions

Since the Miller-Citrin debate (Citrin, 1974; Miller, 1974), scholars have contended on what objects do citizens evaluate when asked about political trust, whether institutions or incumbents (Feldman, 1983; Hibbing and Theiss-Morse, 2001; Poznyak et al., 2014; Williams, 1985), with efforts being made to distinguish incumbent- and regime-based components of political trust (Abram- son and Finifter, 1981; Citrin and Muste, 1999; Craig, Niemi and Silver, 1990; Shingles, 1987). Evidence from studies conducted in the United States tend to support that respondents evaluate incumbents’ performance rather than institutions per se (Citrin, 1974; Citrin and Luks, 2001).

Regardless of disagreements on what is the target object of the conﬁdence being assessed, lesser attention has been paid to the dimensionality of trust. Trust in political institutions has usually been operationalized, implicitly or explicitly, in the United States and abroad, as a unidimensional construct (Feldman, 1983; Marien, 2011; Newton and Zmerli, 2011; Zmerli and Newton, 2011). Multi-item scales on political trust reviewed by Citrin and Muste(1999), most from studies using American samples, invariably measure trust as unidimensional even when tapping on a variety of objects (e.g, incumbents, the Congress, the press, the armed forces). Most of the those scales, however, focus on attitudes toward political ﬁgures and authorities, with considerably lesser attention paid to institutions.

Recent comparative research have focused on trust in public institutions. Some studies by Newton and Zmerli analyze data from the World Values Survey and the European Social Survey (Newton and Zmerli, 2011; Zmerli and Newton, 2008, 2011) and propose unidimensional scales including institutions as varied as the civil service, the parliament, and the police to be used in comparative survey analysis.

72 It is not surprising that most studies develop and implement unidimensional measures of trust in public institutions. Either focusing on institutions or incumbents, the objects of trust do share a common reference: the state. Incumbents and politicians hold public ofﬁces. Institutions are responsible for carrying state-related activities as policing, road maintenance, elaboration and enforcement of laws, and national defense. It is therefore conceivable that their shared root in the state explains the covariance among multiple items measuring institutional trust. Moreover, survey respondents may not hold distinct cognitions for vague and distant objects (Citrin and Muste, 1999). It is worth to notice that items on trust are generally asked in batteries, which may have methodological implications for the assessment of the dimensionality of trust due to response effects and other sources of measurement error (Krosnick, 1991; Saris and Gallhofer, 2014).

Some scholars, however, have argued that trust in different institutions may be predicted by different determinants (Listhaug and Wiberg, 1995). Importantly, evidence support the separation of politics-related and order maintenance institutions as distinct dimensions of political trust. Zmerli and Newton(2017), for instance, present results supporting the unidimensionality of their political trust scale but note that trust in justice-related and law-enforcement agencies may follow a different logic than institutions of government and politics. Listhaug(1984) andD oring¨ (1992), in discussing results from exploratory factor analyses, also suggest to treat political and government institutions and law enforcement agencies as two separate domains for they are related to different state functions and evoke speciﬁc symbolisms (Doring¨ , 1992; Sunshine and Tyler, 2003).

A multidimensional model is advantageous for the study of trust in multiracial societies: Its ﬂexibility to measure two distinct yet correlated dimensions allows for a more ﬁne-grained examination of the effect of political and racial dynamics on each dimension. For instance, as discussed above, Black Americans are strongly suspicious of the police and the justice system but express a quite similar level trust in the government. Distrust in law-enforcement maintenance is certainly a consequence of the history of police violence against non-Whites in the country; on the other hand, the federal government played an important role in overruling legal racial discrimination. In South Africa, Whites report high levels of distrust in the post-apartheid government yet the lack of police violence against the now-ethnic minority might result in lesser harsh feelings toward the police.

73 A two-domain model of trust permits to accommodate possible differences in conﬁdence toward different components of the government which would be masked if they were collapsed into one single dimension. In the analysis of trust in political institutions carried below, a two-dimension model of political trust will be developed and tested using multiple datasets from three countries.

3.3 Testing for measurement invariance

As deﬁned by Horn and McArdle(1992, p.117), the aim of measurement invariance (or measurement equivalence) analysis is to test “whether or not, under different conditions of observing and studying phenomena, measurement operations yield measures of the same attribute.” Tests of measurement equivalence address whether the same construct has been measured and whether measurement parameters are the same across populations (Steinmetz et al., 2009). In other words, measurement invariance assesses whether the same latent construct(s) underlie(s) a set of observed variables and explain covariance among them across multiple group of interest in an equivalent manner. Measurement invariance (or equivalence) is a critical step in studies comparing constructs across different populations. If invariance does not hold, it might not even be claimed that the same construct has been measured in different group (Little, 1997; Steinmetz et al., 2009). Prior to empirical cross-group analyses of interest (e.g., means comparison), it is therefore important to assess whether the theoretical structure underlying the observed indicators is equivalent across population to warrant comparability of results (Billiet, 2003; Hui and Triandis, 1985; Meredith, 1993). If invariance is assumed yet not tested for and supported, between-group comparison are questionable. In the absence of invariance, it is not possible to rule whether group differences are be caused by true differences on the latent construct between groups or by systematic measurement biases, rendering conclusions about differences in means or other statistics dubious or inconclusive (Davidov et al., 2014; Kankarasˇ and Moors, 2010; Little, 1997; Meredith, 1993; Millsap, 2011; Poortinga, 1989; Steenkamp and Baumgartner, 1998; Steinmetz et al., 2009; Vandenberg and Lance, 2000).

Multiple-group conﬁrmatory factor analysis (MGCFA) is a widely used technique to assess measurement invariance (Billiet, 2003; Bollen, 1989;J oreskog¨ , 1971; Little, 1997; Millsap, 2011;

74 Vandenberg and Lance, 2000). MGCFA is a powerful, suitable tool for measurement invariance analysis that permits sequential tests for different levels of invariance (section 3.3.2) and for goodness-of-ﬁt comparisons between models with less and more invariance constraints (section 3.3.3).

Measurement invariance in the context of common-factor model for ordinal variables is brieﬂy discussed in the next sections (see Millsap, 2011; Millsap and Tein, 2004; Muthen´ , 1984; Temme, 2006 for a review).2 It is also possible to test for measurement of structural invariance, i.e. invariance of latent parameters (factor means, variances, and covariances). Tests of structural invariance are nevertheless not performed in this study and reviewing them falls short of its scope (for a discussion of structural invariance tests, see Vandenberg and Lance, 2000).

Section 3.3.1 briefly presents how confirmatory factor analysis for ordinal variables, the statistical technique employed in this study, departs from the confirmatory factor analytic models for continuous measures as well as the differences between single-group and multiple-group models. Section 3.3.2 discusses the steps to evaluate measurement invariance in multiple-group model. Section 3.3.3 discusses measures of model fit in the context of measurement invariance analysis. Sections 3.3.1 and 3.3.3 cover materials which the non-technical reader might skip without prejudice to the understanding of substantive results; the reader is nevertheless encouraged to skim Section 3.3.3 for an appreciation of the multiple stages in a measurement equivalence analysis.

3.3.1 Conﬁrmatory factor analytic models for ordinal variables

Conﬁrmatory factor analysis (CFA) is a measurement model where one or more latent constructs are hypothesized as explaining the covariance within a set of measured, observed variables (Bollen, 1989). In CFA models for continuous variables, manifest indicators y are assumed to be directly inﬂuenced by a latent factor (with some measurement error) and enter the analysis as inputs for

2Measurement equivalence can be tested for categorical variables models with observed variables measured with two or more levels. Constraints for model identiﬁcation, however, differ and some invariance tests (e.g. invariance of threshold) cannot be performed for dichotomous variables (Millsap and Tein, 2004; Millsap, 2011; Wu and Estabrook, 2016).

75 model estimation estimation.

In CFA models for ordered-categorical variables (also referred to as item factor analysis; Wirth and Edwards, 2007) observed categorical variables y are assumed to be the empirical, discretized realization of latent continuous variates y∗ which are caused by a latent factor. Respondents are assumed to have latent scores on a latent variate y∗ and the translation of latent scores into observed categories is determined by item-specific τ latent threshold parameters that partition the continuous distribution of y∗ into ordinal categories (for an ilustration, see Wirth and Edwards, 2007, p.59). Latent variates are generally assumed to be normally distributed and threshold parameters are estimated from the univariate distribution of the observed variables as percentiles of the standard normal distribution; the number of estimated thresholds is the number of observed categories minus one (e.g., in the case of a four-point likert scale item, three thresholds are estimated). Because the latent variates y∗ are not directly observed, further constraints for model identification are placed on either the thresholds or the latent variates distribution in addition to usual CFA model constraints for identification of the latent factor metric and location (Millsap and Tein, 2004; Temme, 2006; Wirth and Edwards, 2007). After the necessary identification constraints are imposed, the model can be estimated and results be mostly interpreted following the same procedures for as a CFA for continuous observed variables.

Single-group CFA models for continuous variable are generally identified either by fixing the mean and variance of the latent factor at zero and one, respectively, and allowing factor loadings to be freely estimated, or by fixing at least one factor loading per construct to an arbitrary value (often fixing the loading for marker variable at one) and allowing the latent factor mean and variance to be freely estimated by the model. In single-group categorical CFA models, additional constraints are imposed on the distribution of each latent variate y∗ for identification purposes. Latent variate intercept is fixed (generally at zero), then either its scale is fixed to arbitrary value (usually at one) with thresholds being estimated by the model or the thresholds are fixed to predetermined values and the latent variate scale is estimated.

For measurement invariance tests, factor structure and parameters are compared across populations using multiple-group models. If model identiﬁcation in MGCFA for continuous variables

76 is a straightforward extension of the single-group case, a more complex identification strategy is necessary for categorical variables model for, in addition to the multiple-group identification of the latent factors, thresholds, and latent variate parameters also need to be identified. Millsap and Tein(2004) proposed the following set of identification constraints for congeneric (i.e., observed variables are caused by one factor only) multiple-group CFA models for ordinal variables. Most identification constraints are imposed on the reference group, being parameters for the other groups estimated in comparison to the reference. In all groups, the intercepts of all latent variates y∗ are fixed to zero.3 In the reference group only, a standard normal distribution is assigned to the latent variates y∗, with mean and variance fixed to zero and one, respectively; this sets the metric and location of the latent variates and identifies all thresholds for this group as well. One threshold per variate is constrained to equality across groups; this anchors the location of the latent variates in the non-reference groups relative to the reference, allowing the identifying all other thresholds in such groups. One variable per factor is set as the marker variable and its factor loading is fixed to one, setting the metric of latent factors in all groups; a second threshold also being constrained to cross-group equality in the marker variable in each factor, thus identifying the factor means in the non-reference groups. Latent factor means are set to zero in the reference group but freely estimated in the others.4 All other model parameters are estimated by the model.

Once this baseline model is identiﬁed, additional constraints are placed to test for different levels of measurement equivalence.

3.3.2 Levels of measurement invariance

Conﬁgural invariance is the ﬁrst level of invariance to be tested and must be established for the other tests to be meaningful. It requires the same number of latent factors to be measured and the same indicators to be used in the measurement of each factor in different groups. In other words,

3The latent variate intercept is the value assumed by y∗ when the latent factor has a value of zero. It therefore is interpreted similarly to the intercept in a regression model.

4If the factor model is non-congeneric (i.e. some variables are allowed to load in more than one factor), a second threshold must be constrained to equality across all groups.

77 it implies that the same baseline model is appropriate for all groups. If the baseline model has a good ﬁt to the multiple-group data, it is concluded that the same theoretical constructs underlies the data for each group (Horn and McArdle, 1992; Steinmetz et al., 2009).

Conﬁgural invariance is however not sufﬁcient to warrant that observed items and latent factors have the same relationships across groups. To test for equal factor-item calibration, the equality of factor loadings, or metric invariance, is assessed. In models for continuous observed variables, it indicates that their scales can be compared across groups (Meredith, 1993). In the context of ordered-categorical indicators, metric invariance implies that a change in the latent factor causes the same change in an item’s latent variate across groups (Temme, 2006). Invariance of factor loadings indicate that the causal effect of the latent factor on its manifest indicators is the same and that the latent construct has the same meaning across groups (Bollen, 1989; Steinmetz et al., 2009).

In MGCFA invariance test for continuous observed variables, a next, more stringent test is for scalar invariance, which implies that the item intercepts – e.g. their values when the latent factor value itself is zero – are invariant across groups. Scalar invariance implies that group differences in the means of the manifest variables are due to differences in the means of the latent construct (Steenkamp and Baumgartner, 1998). If observed variables means are allowed to vary across groups, they “absorb” the group differences in latent means as upward or downward additive bias (Meredith, 1995) rendering comparison of latent factor means worthless. This way, if item intercepts are invariant across groups, it is then possible to meaningfully compare cross- group differences in latent means. Because comparison of means should be conducted between constructs with the same metric, testing for scalar invariance is only consequential if metric invariance holds (Steenkamp and Baumgartner, 1998; Steinmetz et al., 2009). As discussed above, latent variates intercepts are ﬁxed at zero for model identiﬁcation purposes in MGCFA models for ordinal variables, making scalar invariance tests untenable. An alternative strategy in the context of MGCFA for ordered-categorical to assess the cross-group comparability of latent means is to test for the invariance of thresholds (Millsap and Tein, 2004; Millsap, 2011). Although not formulate by Millsap and Tein(2004) in these terms, a rationale for the pertinence of threshold invariance

78 for comparison of latent is as follows: If group A has a higher latent mean score in a construct than group B, it would be expected for group A members to exhibit, on average, higher scores in the latent variates caused by that construct than group B members. Because the latent thresholds τ that convert a normally distributed continuous variate into categories are estimated from the univariate distribution of the observed variable, it can be demonstrated that the larger the proportion of responses in an observed category the lower the minimum threshold of that category (Wirth and Edwards, 2007, p.59). Hence, if threshold parameters are allowed to differ across groups, they would soak up group differences in the distribution of the latent variates scores y∗ and render the latent factor means comparison worthless, just as for item intercept in models for continuous variables. According to the identiﬁcation strategy proposed by Millsap and Tein(2004), invariance of thresholds are tested after metric invariance is tested and held.5

It is also possible to test for the invariance of unique variances (and covariances) in the measurement model. It implies that the residual variances (i.e. part of the item variances not explained by the latent factor) are the same across groups (Vandenberg and Lance, 2000). Measurement invariance of residuals, however, is not generally tested for conﬁrmatory factor analytic models explicitly account for residuals. Therefore, model comparisons can be securely performed even in the absence of residuals invariance.6

The discussion on measurement equivalence presented above might imply that invariance is fully achieved at on level before proceeding to the next. Cross-group equality of all measurement parameters, although desirable in multiple-group analysis, is regarded as an unrealistic goal in many empirical situations. When full invariance of parameters across groups does not hold, partial invariance should be attained to allow for valid groups comparisons. Partial invariance may be achieved even if only a subset of parameters pass the invariance test in each stage (Byrne, Shavel- son and Muthen´ , 1989). Once conﬁgural invariance is held as a prerequisite for other invariance

5See Wu and Estabrook(2016) for alternative model identiﬁcation strategies and sequences for measurement equivalence tests.

6Residual invariance is required to assess equality of construct reliability across groups. For such a reliability test, equivalence of factor variances across groups must also be established (Vandenberg and Lance, 2000).

79 tests, partial metric invariance is achieved if at least two indicators per latent factor have invariant loadings across groups; partial scalar (for continuous variables) or threshold invariance (for ordinal variables) is attained if the intercepts or thresholds for two or more items are invariant across groups (Byrne, Shavelson and Muthen´ , 1989; Steenkamp and Baumgartner, 1998; but see Vanden- berg and Lance, 2000). Per the partial invariance measurement approach, at least two items per factor must hold metric and threshold (or scalar) invariance to grant comparisons of latent means meaningful.

3.3.3 Model ﬁt

Model fit indexes provide information to evaluate whether the proposed measurement model ade- quately accounts for relationships between variables in the observed data. An array of fit indexes has been developed to assess model fit in confirmatory factor analysis, even though there is no consensus on which is the single best indicator or whether there are cutoff values defining what is an appropriate model fit (e.g., Barrett, 2007; Bentler, 2007; Brosseau-Liard and Savalei, 2014; Browne and Cudeck, 1992; Fan and Sivo, 2005; Hu and Bentler, 1999; Kenny and McCoach, 2003; MacCallum, Browne and Sugawara, 1996; Markland, 2007; Marsh, Hau and Wen, 2004; Mulaik, 2007; Rigdon, 1996; Sivo et al., 2006; Steiger, 2007; Fan, Trompson and Wang, 1999).

Good fit in a CFA model indicates that the model satisfactorily accounts for the covariance among observed variables and might be a plausible approximation for the underlying structure in the data. A well-fitted model in a MGCFA (e.g., CFI ≥ 0.90) would suggest that the same latent factors exist across the populations under study, therefore supporting the presence of configural invariance. Model restriction at the configural invariance stage are minimal, aiming on identification only. How are goodness of fit indexes expected to behave when additional constraints are added to the initial model? If model parameters are approximately cross-group invariant, model fit is expect to present minimal degrees of change although perfect equality of fit in the less and the more restricted models may be unrealistic.

Previous research has shown that some small departures in the goodness-of-ﬁt index are toler-

80 able and should not be interpreted as lack of invariance (Chen, 2007; Cheung and Rensvold, 2002; Meade, Johnson and Braddy, 2008; Steenkamp and Baumgartner, 1998). A simulation study by Cheung and Rensvold(2002) has proposed that the Bentler’s Comparative Fit Index (CFI; Bentler, 1990), the McDonald’s Goodness-of-Fit Index (MFI; McDonald, 1989), and the γˆ (gamma-hat; Steiger, 1989; West, Taylor and Wu, 2012) are robust statistics to test for measurement invariance. When comparing goodness-of-ﬁt indexes for less and more constrained models testing for different levels of invariance (for instance, models with and without equality constraints on factor loading to test for metric invariance), small model ﬁt degeneration such as ∆CFI ≤ −0.01, ∆MFI ≤ −0.02, and ∆ˆγ ≤ −0.001 should be interpreted as evidence suggesting the presence of measurement invariance (Cheung and Rensvold, 2002). In the discussion that follows, because ∆CFI and ∆ˆγ are mostly redundant (Chen, 2007; Meade, Johnson and Braddy, 2008), both indexes are reported but only ∆CFI is interpreted.7

Data analyzed in this study were not originally collected for the study of race group differences in social and political attitudes; consequently, groups accounting for relatively small shares of a country’s population are not oversample. This results in significant sample differences in group size. Unequal sample sizes are known to influence the sensitivity of goodness-of-fit indexes in measurement invariance tests (Chen, 2007). Moreover, fit indexes are dependent on the chi-square statistic, which is heavily influenced by the sample size (Bollen and Long, 1992;J oreskog¨ , 1969; Mulaik, 2009). In the case of multiple group analysis, the chi-square statistic may be somewhat dominated by the model fit to the largest group, making measurement invariance tests insensitive to group differences in model fit Chen(2007). To avoid assuming cross-group measurement equivalence in cases it may not hold (e.g., due to good model fit in the larger but not in the smaller groups) additional steps as examination of the Expected Parameter Change (EPC; Saris, Satorra and Van der Veld, 2009; Oberski, 2014; Oberski, Vermunt and Moors, 2015) and model re-fit releasing constraints suspected to be noninvariant are performed in the specification search (Mac-

7As it will be seen below, there are instances in which ∆CFI and ∆MFI present some support to the presence of invariance but ∆ˆγ does not. Because of the redundancy between ∆CFI and ∆ˆγ, measurement invariance will be assumed to exist when ∆CFI and ∆MFI concurrently support it.

81 Callum, 1986) to accept or reject model invariance.8

The Root Mean Square Error of Approximation (RMSEA; Browne and Cudeck, 1992; Steiger, 1998; Steiger and Lind, 1980) and the Tucker-Lewis Index (Bentler and Bonett, 1980; Tucker and Lewis, 1973), goodness-of-ﬁt indexes commonly used to assess overall model ﬁt, are also reported.

3.4 Data and methods

3.4.1 World Values Survey datasets

The World Values Survey Wave 5 (henceforth, WVS5; Kittilson, 2007; World Values Survey, 2014) is the primary comparative survey data source for this study. Its core questionnaire includes an extensive battery of items on trust in public institutions to be replicated in the participant countries.9 Surveys were carried out in Brazil, South Africa, and the United States in 2006. WVS5 collected data from nationally representative samples of adults using face-to-face interviews in Brazil (18 years of age or older) and South Africa (age 16 years and over), and a online research panel was used the United States (age 18 years and over). The questionnaire was translated by specialists into the national languages – Portuguese in Brazil; Afrikaner, Sotho, Tswana, and Zulu in South Africa – from the English master questionnaire and pre-tested before ﬁeldwork; the original English instrument was used in the United States and also South Africa.10 Original and effective sample sizes are reported in Table 3.1.

Cross-national variation in data collection method is assumed to have no signiﬁcant inﬂuence

8Model re-ﬁtting is performed, for instance, in situation where a parameter considerably differs across groups before constrained to equality. In such cases, it is reasonable to presume that the parameter may not be invariant across groups then models with and without equality constraints are compared to assess constraint impact on parameter change and model ﬁt.

9All countries participating in a World Values Survey wave are expected to replicate the comparative questionnaire. Oftentimes, items may not be included in the local questionnaire for several reasons, ranging from cultural inadequacy to political censorship. Fortunately, none of the three countries under scrutiny had excluded items of interest for this research.

10Datasets and questionnaires for all waves and countries participating in the World Values Survey are publicly available at www.worldvaluessurvey.com free of charge.

82 Table 3.1: Sample size and group size.

(a) World Values Survey Wave 5

Brazil South Africa United States

N%N%N% Full sample 1,500 2,988 1,249 Effective sample size 1,457 2,882 1,049 Black 136 9.3 2,073 71.9 127 12.1 Brown 562 38.6 Coloured 288 10.0 White 759 52.1 521 18.1 922 87.9

(b) Auxiliary datasets

Brazil South Africa United States United States LAPOP 2008 SASAS 2008 LAPOP 2008 GSS 2008

N%N%%%N% Full sample 1,497 3,321 1,500 1,354 Effective sample size 1,413 2,971 1,249 1,722 Black 149 10.5 1,994 67.1 153 12.2 187 15.1 Brown 544 38.5 Coloured 561 18.9 White 720 51.0 416 14.0 1,096 87.8 1,056 84.9

83 on the results; between-group comparisons are ﬁrst performed within country then patterns in group differences are compared among countries. Therefore, data for groups being compared directly were gathered using the same data collection method. As a cautionary note, it should be noted that although the psychology and sources of errors in survey research are now well under- stood (Tourangeau, Rips and Rasinski, 2000; Weisberg, 2005), there is an ongoing debate on data collection methods effect in web surveys (Couper et al., 2004; Peytchev et al., 2006; Sax, Gilmartin and Bryant, 2003) and their effects on data quality (Couper, 2000; Heerwegh, 2009; Heerwegh and Loosveldt, 2008). Understanding the sources of measurement error in web surveys remains a chal- lenge (Couper, 2000) and evidence suggests that certain web survey designs (e.g. paging) may induce higher levels of measurement error (Peytchev et al., 2006).11 On the other hand, web survey participants demonstrate less social desirability response bias and extreme response styles than face-to-face respondents (Heerwegh, 2009; Liu, Conrad and Lee, 2017). This research does not address such methodological debates yet it is important to notice potential web survey design effects on ﬁndings discussed. Most relevant for the aims of this study is that data collection mode effect is similar for respondents from different racial and ethnic background within a country.

3.4.2 Measures of trust in institutions

The World Values Survey Wave 5 core questionnaire includes a rich battery of items on conﬁdence in seventeen different social and political institutions (listed here in the same order as in the survey instrument): The churches, the armed forces, the press, television, labor unions, the police, the courts, the government (in the respondent’s nation’s capital), political parties, parliament, the

11Web survey design effect is of most interest here. Even though the web survey instrument used to collect the WVS5 data in the United States is not publicly available – survey questions are but not the layout design used to present them to respondents (e.g., using paging or scrolling; Couper et al., 2004) nor the response formats used to collect their responses (e.g., drop boxes or radio button Couper et al., 2004; Peytchev et al., 2006) – it should not come as unexpected if at least all items in a same battery were presented to the respondent on a same page, what could result in measurement error due to paging: Having access to all items on trust in institutions at once, respondents could be tempted to gauge and adjust their responses to one item vis-a-vis answers delivered to other questions on the screen. It will be discussed later in this piece that substantial residual correlations (i.e. correlations between pairs of items not explained by the statistical model) were more often found in the US data than in the Brazilian or South African samples.

84 civil service, major companies, environmental organizations, women’s organizations, charitable or humanitarian organizations, the regional organization (NAFTA in the United States, Mercosul in Brazil, the African Union in South Africa), and the United Nations. Although not all items are used in the analysis presented below, this wide range of institutions provide a unique background for preliminary analysis and subsequent selection of variables for descriptive and multivariate analysis. Items on institutional trust are measured on a four-point Likert scale: None at all, not very much, quite a lot, or a great deal of conﬁdence.

3.4.3 Racial classiﬁcation

Data on respondents’ race were collected using different strategies in face-to-face and web surveys. In face-to-face surveys (Brazil and South Africa), the interviewer coded the respondent’s race or ethnicity by observation using the country’s official census categories. Racial categories in the Brazilian sample were assigned using the five official Census categories since the 1991 Census: Black (preto), Brown (pardo), Indigenous (Ind´ıgena), White (branco), and Yellow (amarelo or of Asian ancestry), with no information provided for 1.6% of respondents. Analysis focuses on the categories Black, Brown, and White and exclude all the other groups. According to the 2010 Brazilian Census, blacks, browns, and whites represent about 99% of the Brazilian population (Brazilian Institute of Geography and Statistics, 2011). The South African survey collected data on race using the four official South African population categories (Black African, Coloured, Indian or Asian, and White). I restrict my analyses to the Black, Coloured, and White groups, excluding all the other cases. According to the 2011 South African Census, such groups encompass 97% of the South African population (Statistics South Africa, 2012a). In the web survey data collection mode (United States), race was informed by the respondent in a recruitment survey. Respondents in the sample are classified as White (non-Hispanic), Black (non-Hispanic), other (non-Hispanic), and Hispanic. Only Blacks and Whites are included in the analysis; they represent 85% of the American population according to the 2010 Census (United States Census Bureau, 2010). Number of observations per group is reported in Table 3.1.

Using data on racial classiﬁcation other than self-assignment might eventually raise questions

85 about the results. If race and ethnicity are important identities in the political arena as it is assumed for the American and South African cases (Ferree, 2006; Hutchings and Valentino, 2004; Lee, 2008; McLaughlin, 2007), it might be expected that self-classification as a group member has more powerful political implications. Nevertheless, in the United States and in South Africa, because racial and ethnic identities are salient and part of the everyday lexicon, divergences in racial classification by the self and by others are expected to be small (Alexander, 2007; Blustein, 1994; Jacobson, Amoateng and Heaton, 2004; Saperstein, 2006, 2012). Consequently, racial classification by the interviewer is expected to have no significant effect on the findings presented in this piece. Larger discrepancies would be more likely in the Brazilian case, where an intricate calculus may take place in the racial classification process and its outcome is conditional not only on who is classified but also on who classifies (Bailey and Telles, 2006; Harris, 1970; Telles, 2004). Lack of racial and ethnic self-identification is a limitation of the WVS5 data. An alternative to evaluate the robustness of results from the WVS5 data is to replicate the analysis using other datasets.

3.4.4 Validation datasets

Due to the exploratory nature of this study, latent variable modeling is replicated using four nationally representative surveys containing items on institutional trust that allow for a re-evaluation and validation of the measurement model developed using the WVS5 data. Data on institutional trust from four surveys conducted in 2008 – the American and the Brazilian components of Amer- icasBarometer (LAPOP), the South African Social Attitudes Study (SASAS), the General Social Survey (GSS)12 – are analyzed. All studies are face-to-face surveys except the US LAPOP, which is a web survey. Their instruments differ on how information about race is collect, the number of questions on trust, and the measurement scale employed. SASAS uses ﬁve-point Likert scale questions to measure institutional trust and race is assigned by interviewer’s observation. A seven- point scale is used in both LAPOP surveys and race is self-declared – race is provided by the respondent during the recruitment stage in the US survey and is self-assigned during the interview

12Items on conﬁdence in institutions were asked to two out of four split ballots in 2008 GSS.

86 in the Brazilian survey. GSS also inquires respondents about their race and uses a three-point scale is implemented in question about trust. Validation analysis focuses on surveys questions with face validity to proxies for the WVS5 questions. Only items asking about trust in the same institutions as the ones included in the measurement model are used in the validation analysis.

3.4.5 Missing data imputation

Missing data is a recurrent issue in quantitative data analysis and also affects this one. Prior to performing the analyses, I employed a Fully Conditional Speciﬁcation multiple imputation model using Predictive Mean Matching (Morris, White and Royston, 2014; Van Buuren, 2007, 2012; Van Buuren et al., 2006) to deal with the missing data. Multiple imputation has been regarded as a state-of-art technique to address missing data issues because it improves accuracy and power of analyses relative to “traditional” methods to handle with missing data (e.g. listwise deletion or mean imputation) (Enders, 2010; Schafer and Graham, 2002). Simulation studies have shown that Fully Conditional Speciﬁcation is more accurate than other approaches for categorical data and the Predictive Mean Matching method preserves the original data distribution (Kropko et al., 2014; Vink et al., 2014).

Separate group imputation (Enders and Gottschall, 2011) is used to preserve the multiple group data structure during the imputation phase. Multiple imputation is therefore performed separately for each race group within each country. For each group, sixty “complete” datasets are imputed.13 Gender, education, perceptions about one’ ﬁnancial situation, perceptions on ethnic relations in the country, participation in associations, and attitudes toward political regimes are used as auxiliary variables in the imputation.14 Auxiliary variables are included aiming only to reduce bias and improve power in the FCS estimation. The algorithm used for implementation of multiple imputa-

13Graham, Olchowski and Gilreath(2007) suggests as a rule-of-thumb that, for each one percent of missing data, one imputed dataset must be generated – i.e., if there are 30% of cases presenting missing data, at least thirty imputed datasets should be generated.

14Language spoken at home is also included as an auxiliary variable in multiple imputation in the South African samples.

87 tion reaches convergence quickly (Van Buuren and Groothuis-Oudshoorn, 2011); after monitoring model diagnostics, it was set for each imputed dataset to be generated after 30 iterations. Analyses are repeated for all imputed datasets and results (including model ﬁx indexes) are pooled according to Rubin’s (1987) methods.15

3.4.6 Computational procedures

All statistical procedures are performed using the statistical package R 3.4.1 (R Core Team, 2017). Frequencies for each “complete” dataset are calculated using the R package survey (Lumley, 2014) and merged using the R library mitools (Lumley, 2012). Polychoric correlations and conﬁrmatory factor analysis for estimation of measurement models and latent factor scores are performed using lavaan (Rosseel, 2012; Rosseel et al., 2016). Fully Conditional Speciﬁca- tion (FCS) multiple imputation models by Gibbs sampling are implemented via R library mice (Kropko, Goodrich, Gelman and Hill, 2014; Van Buuren, 2012; Van Buuren, Brand, Groothuis- Oudshoorn and Rubin, 2006; Van Buuren and Groothuis-Oudshoorn, 2011).

3.5 Results

3.5.1 Correlation analysis, variables selection, and measurement model

I start analyzing the polychoric correlations among the variables on trust in institutions present in the WVS5 questionnaire to inform variable selection for further investigation. Institutional trust – and political trust as usually deﬁned (e.g, Newton and Norris, 2000; Newton and Zmerli, 2011)– has been measured as a unidimensional construct encompassing agencies responsible for an array of tasks attributed to the state in modern societies. As argued earlier, some previous research has suggested that order-maintenance and government-related institutions would be more appropriately

15Missing data imputation is not performed using the US LAPOP for the survey has a negligible amount of missing in the variables of interest for this study: There is no missing case with regards to institutional trust among Blacks and only four cases among Whites.

88 analyzed as constituting separate dimensions (Doring¨ , 1992; Listhaug, 1984). Consequently, analysis will focus on institutions responsible for administrative/political and punitive/law-enforcement state-related activities as two distinct yet correlated domains of trust. A second relevant criterion is to choose items that are at least moderately correlated to each other; measurement redundancy serves as evidence that selected variables for each domain are actually measuring a same dimension (Blalock, 1982). Finally, because of the cross-group, cross-national comparative approach of this study, a desirable property of a set of items is its recurrence across groups and contexts to warrant minimum comparability. Due to differences between the political systems in the three societies analyzed here, and because we are firstly interested in how groups in a same country may differ in their levels of trust in political and order-maintenance, cross-national face validity in configural equivalence of the measurement models might suffice but at least partial measurement equivalence (Byrne, Shavelson and Muthen´ , 1989; Davidov et al., 2014; Steenkamp and Baumgartner, 1998) is expected to be reached for within-country comparisons.16

Analysis of correlation tables including items on conﬁdence in fourteen institutions (all institutions referred to in Section 3.4.2 but trust in the TV,in regional organizations, and in the UN; see the Appendix for the correlation tables) suggests that three items related to political and administrative state duties present moderate-to-strong inter-item correlations, forming a similar cluster across all groups and countries: Trust in the parliament/congress, in the political parties, and in the national government. Correlation between pairs of items are mostly equal or higher than 0.45. Correlation between trust in parties and trust in congress are usually in the 0.60-0.70 range, reaching 0.80 in one group. Such high correlations between those variables might be, in addition to be referring to related objects, at least in part also due to questionnaire effects. They are sequentially asked during the questionnaire administration.17 Per the face value of variables in this cluster, they are assumed

16Statistical tests for cross-national conﬁgural invariance are not performed because it falls outside the scope of this study. Preliminary results from ongoing research nevertheless suggests that conﬁgural equivalence of cross-national multidimensional measurement models of trust is more often held than not (Fialho, 2017).

17A potential consequence of highly correlated pairs of variables administered in batteries is the presence of large residual covariance – i.e., strong association between two variables not accounted by the statistical model (Gerbing and Anderson, 1984).

89 Table 3.2: Polychoric correlation among selected indicators of institutional trust, Brazil

(a) Blacks

Institutions (1) (2) (3) (4) (5) (6)

(1) Army 1.00 (2) Police 0.29 1.00 (3) Courts 0.36 0.64 1.00 (4) Government 0.36 0.63 0.66 1.00 (5) Parties 0.27 0.54 0.43 0.60 1.00 (6) Parliament 0.25 0.49 0.45 0.62 0.75 1.00

(b) Browns

Institutions (1) (2) (3) (4) (5) (6)

(1) Army 1.00 (2) Police 0.40 1.00 (3) Courts 0.34 0.59 1.00 (4) Government 0.36 0.53 0.62 1.00 (5) Parties 0.21 0.47 0.52 0.61 1.00 (6) Parliament 0.24 0.47 0.49 0.59 0.76 1.00

Institutions (1) (2) (3) (4) (5) (6)

(1) Army 1.00 (2) Police 0.43 1.00 (3) Courts 0.36 0.61 1.00 (4) Government 0.42 0.48 0.65 1.00 (5) Parties 0.27 0.45 0.48 0.62 1.00 (6) Parliament 0.33 0.38 0.50 0.60 0.75 1.00

Source: World Values Survey Wave 5. Note: Full correlation tables are presented in the Appendix.

90 Table 3.3: Polychoric correlation among selected indicators of institutional trust, South Africa

(a) Blacks

Institutions (1) (2) (3) (4) (5) (6)

(1) Army 1.00 (2) Police 0.36 1.00 (3) Courts 0.37 0.68 1.00 (4) Government 0.34 0.46 0.53 1.00 (5) Parties 0.28 0.38 0.39 0.46 1.00 (6) Parliament 0.31 0.43 0.54 0.63 0.53 1.00

(b) Coloureds

Institutions (1) (2) (3) (4) (5) (6)

(1) Army 1.00 (2) Police 0.34 1.00 (3) Courts 0.35 0.68 1.00 (4) Government 0.27 0.49 0.56 1.00 (5) Parties 0.29 0.38 0.36 0.64 1.00 (6) Parliament 0.21 0.42 0.48 0.82 0.72 1.00

Institutions (1) (2) (3) (4) (5) (6)

(1) Army 1.00 (2) Police 0.52 1.00 (3) Courts 0.50 0.64 1.00 (4) Government 0.46 0.51 0.61 1.00 (5) Parties 0.43 0.39 0.41 0.73 1.00 (6) Parliament 0.45 0.50 0.58 0.83 0.70 1.00

Source: World Values Survey Wave 5. Note: Full correlation tables are presented in the Appendix.

91 Table 3.4: Polychoric correlation among selected indicators of institutional trust, United States

(a) Blacks

Institutions (1) (2) (3) (4) (5) (6)

(1) Army 1.00 (2) Police 0.64 1.00 (3) Courts 0.55 0.90 1.00 (4) Government 0.57 0.79 0.86 1.00 (5) Parties 0.39 0.61 0.68 0.72 1.00 (6) Parliament 0.40 0.57 0.63 0.67 0.81 1.00

(b) Whites

Institutions (1) (2) (3) (4) (5) (6)

(1) Army 1.00 (2) Police 0.52 1.00 (3) Courts 0.32 0.69 1.00 (4) Government 0.46 0.49 0.53 1.00 (5) Parties 0.28 0.36 0.42 0.63 1.00 (6) Parliament 0.22 0.35 0.46 0.59 0.76 1.00

Source: World Values Survey Wave 5. Note: Full correlation tables are presented in the Appendix.

92 to measure conﬁdence in agencies performing political-administrative duties related to state and government in a broad sense. This dimension will henceforth be referred to as “Political.”

A second cluster of moderately-to-strongly correlated variables, related to law enforcement and order maintenance and hereafter to be called “Order”, includes trust in the police, in the courts, and in the armed forces. Correlation between trust in the police and in the courts is about 0.6 or larger for all groups, being larger than 0.9 among Black Americans. Correlations between those two variables and trust in the army are somewhat smaller but still substantial, being about 0.3-0.5 across groups.

The remaining eight variables, including usual suspects for the measurement of political trust, are not retained for posterior analysis. Religious institutions have historically played important roles in the political life in many countries (Norris and Inglehart, 2011), as in the Black political mobilization and the Civil Rights movement in the United States (McAdam, 1982; McClerking and McDaniel, 2005; McDaniel, 2008), in both justifying and ﬁghting the apartheid in South Africa (De Gruchy and De Gruchy, 2004; Rasool, 2004), as well as supporting and resisting against the military rule in Brazil (Azevedo, 2004; Bruneau, 1982; Simoes˜ , 1985). However, trust in the churches is mostly orthogonal to trust in other institutions, being correlated with them usually at 0.3 or lesser (only exception being its correlation with trust in the army in the US). Trust in environmental, women’s, and humanitarian organizations do form a well-correlated cluster of variables with inter-item correlations in the 0.50-0.70 range, possibly indicating cognitive engagement with post-materialist politics (Inglehart, 1997), but only modest correlated to variables loading in the Political and Order clusters, falling short of focus of this study. Trust in major companies is strongly associated to variables in the Political cluster in the US, ranging from 0.45 to 0.80; however, it is moderately-to-weakly correlated to either the Political or Order clusters in Brazil and South Africa.18 Trust in labor unions is moderately correlated to both Politics- and Order-related institutions in Brazil and South Africa, perhaps reﬂecting the importance of unions in those countries’s politics (Coradini, 2007; Hunter, 2010; Seekings, 2000; Webster, 1998) but such a pattern

18It is worth notice that trust in major companies is more strongly correlated with political institutions and weakly associated with civil society organizations in the US, being the opposite observed in Brazil and South Africa.

93 of overall moderate correlations complicated assigning it as an indicator of one or other dimension.19 In the US samples, it correlates moderately with Political institutions among Blacks but only weakly among White respondents. Indeterminacy in the status of trust in labor unions precludes its inclusion in subsequent analysis. The press, and the media in general, have indisputable importance in the political world (Habermas, 1989 [1962]; McCombs and Shaw, 1972; Stomberg¨ , 2015; Valentino and Nardis, 2013), with potential implications for trust in the government (Miller, Goldenberg and Erbring, 1979; Rothman, 1996; but see Bennett et al., 1999; Chong and Druck- man, 2007). On theoretical grounds, television and the press, although related to political trust, would be more properly conceived as an external variable expected to be related – eventually influencing – level of political trust but not as part of the construct itself. Empirically, the correlation between trust in the press and in political institutions is only moderate in most of the groups and correlation patterns are context-dependent. Finally, confidence in the civil service is a clear candidate to be included as a measure of political trust (Catterberg and Moreno, 2006) as it is related to administrative and service-providing state functions. Moreover, it holds consistent correlations (0.35-0.65) with most items in both Order and Political dimensions. Nevertheless, measures of confidence in the civil service are included in none of the auxiliary datasets used for validation of WVS5 findings. For this inability to include it in follow-up analyses, it is not retained.

Tables 3.2-3.4 report the polychoric correlations among items on trust in the parliament/congress, political parties, the national government (Political dimension of institutional trust), the police, the courts, and the armed forces (Order dimension).

Based on the previous discussion on the measurement of trust and on items retained for statistical analysis, a two-factor measurement model is proposed. Figure 3.1 presents the general conﬁgural structure of the model (residual covariances are not shown but added when appropriate in the statistical analyses). Each factor is measured by three indicators, and the Order and Political factors are allowed to correlate. Even in case of highly correlated factors, a two-factor model is

19Results from exploratory factor analysis presented inD oring¨ (1992) are also ambivalent regarding what dimension of trust is conﬁdence in labor unions referring to, being it oftentimes loading in factors dominated by political or by civil society institutions, conditional on the country’s political history.

94 Figure 3.1: Two-domain model of trust in institutions

∗ [τ11, τ12, ..., τ1(C−1)] y1 y1 1 λ1 ε1

λ2 ∗ [τ21, τ22, ..., τ2(C−1)] ξ1 y2 y2 1 ε1 λ3

∗ [τ31, τ32, ..., τ3(C−1)] y3 y3 1 ε1 φ1,2

∗ [τ41, τ42, ..., τ4(C−1)] y4 y4 1 λ4 ε1

λ5 ∗ [τ51, τ52, ..., τ5(C−1)] ξ2 y5 y5 1 ε1 λ6

∗ [τ61, τ62, ..., τ6(C−1)] y6 y6 1 ε1

Note: This is a general representation of the proposed two-factor model for the measurement of conﬁdence in institutions. For presentation purposed and to retain its cross-national generality, it does not include residual covariances (nonetheless they are included in the statistical modeling). In this representation, let ξ represent the latent factors, λ represent the factor loadings, y∗ represent the continuous latent variates measuring trust in each institution and y to be its empirical observation measured using a C-categories item, ε be the residual variance of the observed variable (not explained by the latent factors ξ), and let τ be the C − 1 item-speciﬁc latent threshold parameters partitioning the continuous distribution of y∗ into the observed C categories in y.

95 preferable to a more parsimonious, unidimensional model because it allows for the means levels of trust in each dimension to be separately estimated – the two dimensions may covary but trust in on dimension may remain at lower levels than in the other even if both increase.

3.5.2 Descriptive analysis

The first step to assess group differences in political trust between groups is to examine response patterns to the six items trust in Order and in Political institutions. Item frequencies are reported in Figure 3.2. A first, striking finding is the dramatic difference in the distribution of responses across countries. Within-country race group differences are quite modest for all items in Brazil, in contrast to what is found in South Africa and the United States. Brazilians of all skin tones are highly suspicious of the country’s congress and of the political parties: About 75% percent of Blacks, Browns, and Whites report none or not very much confidence in those institutions. These figures corroborate previous findings on the generalized distrust in political institution in Brazil (e.g, Lopes, 2004; Moises´ and Carneiro, 2008). In sharp contrast, about half of respondents of all races in Brazil report “quite a lot” or “a great deal” of confidence in the national government, with only one-fourth declaring no confidence in it. Trust in the police and in the courts are close to a fifty-fifty trustful v. suspicious split. Interestingly, the army is a most prestigious institution among the institutions analyzed in Figure 3.2: 75% of respondents reported at least quite a lot of trust in the armed forces.

In South Africa, Black respondents are substantially more conﬁdent in political institutions than the other groups. Distrust is widespread among non-Blacks, with Whites being the less trustful group and Coloureds being closer to the latter than to the former in most cases (Rule and Langa, 2010). This is hypothesized to represent the post-apartheid political balance in South Africa, where Blacks are by a considerable margin the most numerous group and with enormous electoral and political leverage to control the state and its agencies and resources. Some 60% of Blacks respondents report quite a lot or a great deal of conﬁdence in all institutions – except political parties, when “just” 50% of Blacks report such a level of trust in those institutions. Coloured and White respondents report an expressively lower level of trust in the parliament and in political parties,

96 Figure 3.2: Trust in Order and in Political institutions

Brazil South Africa United States

Black Army White

Brown/ Coloured

Black Police White

Brown/ Coloured

Black Courts

White

Brown/ Coloured

Black Government

White

Brown/ Coloured

Black Parties

White

Brown/ Coloured

Black Congress

White

Brown/ Coloured

0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 %

Confidence level: None at all Not very much Quite a lot A great deal

Source: World Values Survey Wave 5.

97 what is again hypothesized as reflecting the relative lack of political power in the new democratic dispensation. Whites, the dominant political group during the apartheid system, systematically report the lowest percentage of “a great deal” of confidence and the highest share of distrustful respondents (responding none or not very much confidence) toward all six institutions. Coloured respondents, a political minority during the apartheid, remain suspicious of public agencies in the democratic system, where they also lack substantive political impact in most areas of the country.

US data show an important difference in trust in Political and in Order institutions. Regarding Political institutions, Black respondents are only slightly more suspicious than Whites (Alford, 2001). Among Order institutions, the Army enjoys considerable prestige among both Blacks and Whites, with more than 60% or more of respondents reporting quite a lot or a great deal of con- ﬁdence in the institution and being the most respected government institution (Leal, 2005). The courts and the police, however, are highly mistrusted by Blacks (see also Richardson, Houston and Hadjiharalambous, 2001; Schuman, Steeh, Bobo and Krysan, 1997): At least half of them report none of not very much trust in both institutions, suggesting lack of belief in the fairness of the procedures followed by the police in the exercise of its authority (Sunshine and Tyler, 2003; Tyler, 2005).

In summary, the analysis of the frequency distribution of items on trust in Order and Political institutions suggests strong differences in the country-specific dynamics of confidence in public institutions. There is no substantial differences between racial groups in Brazil, being perhaps surprising the unexpected finding that Blacks are slightly less distrustful than other respondents. In South Africa and in the United States, group differences in political trust are salient but in somewhat distinct directions: In the US, there are negligible group discrepancies in reported levels of trust in Political organizations but a trust gap in trust in Order agencies, with Black being considerably less trustful. In South Africa, Coloureds and Whites are more mistrustful than Blacks toward both types of institutions, being especially wary of the Political-related ones, probably reflecting the political and electoral dominance of Blacks in the democratic South Africa.

98 3.5.3 Tests of measurement invariance using the WVS5

Table 3.5 displays the ﬁt indices for the models testing for measurement invariance. Starting with the US sample, it is important to state that (1) the difference in group sizes (7:1 ratio) and, more important, (2) the small number of Black respondents (127 cases) imposes challenges to model estimation due to potential instability and variability of parameters for the Black group, which could also lead to improper solutions.20

Starting with the US sample, the CFI value for the configural model (Model A1, equivalent to Figure 3.1) suggests that the two-factor model has a good fit to the data for both groups, an evidence of configural invariance; TLI also indicates acceptable fit. RMSEA (> 0.08), however, indicate otherwise. Examination of model residual matrices showed large inter-item correlations not accounted by the model, and modification indexes (Chou and Bentler, 1990;S orbom¨ , 1989) indicated their substantial impact on the model chi-square. After allowing two pairs of residuals to covary (Models B1 and C1) in both groups, model fit improved dramatically in comparison to the baseline model (Model C: CFI > 0.99; TLI > 0.98; RMSEA ≤ 0.06; ∆χ2(4) = 185.73, p < 0.01) and is then retained. The second step is to test for metric invariance, i.e. equality of factor loadings between groups (Model D1). Constraining factor loadings to equality across groups did not signif- icantly deteriorate model fit (∆ CFI = 0, MFI = -0.001; γˆ = -0.001), and RMSEA improves due to the increase in the degrees of freedom. Such results suggest that full metric invariance holds in the model.21 Thresholds for all variables are then constrained to equality between groups to test for

20Small sample size makes parameter estimates more vulnerable to sampling variability and eventually to out-of- bounds results. Imputing missing data helps to avoid loss of cases – even a loss of ten cases would means a negative impact of about 8% in the number of cases given the small number of Black respondents – yet it also adds between- imputation variability to the estimates (Enders, 2010). Inspection of results across the sixty “complete,” imputed datasets indicated the sporadic occurrence of negative residual variances for the variables on trust in the courts and in the government. To address this issue, a parameter constraint restricting residual variance to be larger than 0.01 is imposed on the estimation of residual variance in the Black subsample – residual variances for the reference group, Whites, is constrained to be equal to one for model identification (Millsap and Tein, 2004). Eyeball inspection of confirmatory factor analysis results indicate that, in most cases of negative variance, the negative estimate is not statistically significant, which suggests that improper solutions are most probably a consequence of sampling variance due to small sample size (Chen et al., 2001). A computational consequence of imposing inequality constraints on model estimation is the impossibility of analyzing the Expected Parameter Change to test for parameter invariance. Search for invariance violation in the US sample is performed by model re-specification and parameter comparison.

21A search for “hidden” sources of metric non-invariance due to the potential insensitivity of ﬁt measures to detect

99 threshold invariance (Model E1). CFI, MFI, and γˆ indicates no downgrade in model ﬁt, supporting the claim of invariance of thresholds (∆ CFI = +0.004, MFI = +0.009; γˆ = +0.006).22 Full metric and threshold invariance is achieved in the US WVS5 sample.

Analysis of configural invariance in the Brazil data (Model A2) received strong support by most of relative fit indexes (CFI > 0.95; TLI > 0.90). RMSEA > 0.1 nevertheless indicates some lack of absolute model fit. Examination of model residuals shown the presence of substantial correlation between trust in the parliament and in political parties not accounted by the model; modification index indicates significant improvement in model fit if residuals for this pair of items is allowed to correlate. After this model modification (Model B2), goodness-of-fit indexes improved: CFI > 0.99, TLI > 0.98, and RMSEA ≤ 0.06, usually taken as indicative of good model fit (Browne and Cudeck, 1992; Hu and Bentler, 1999). In testing for full metric invariance (Model C2), all fit indexes sensitive to model invariance do improve rather than deteriorate (∆ CFI = + 0.004; ∆ MFI = +0.009; ∆γ ˆ = +0.004). There is a reduction in the χ2 for overall model fit (∆χ2 = -12.03), indicating no loss of fit. These results support full metric invariance in the Brazilian sample. Due to the considerable differences in group size (see Table 3.1) and its consequences to sensitivity to violations of invariance (Chen, 2007), EPC tests were performed and results support the full metric invariance model. Next, a full threshold invariance model (Model D2) is fitted to the data. The χ2 test suggest deterioration in overall model fit (∆χ2(20) = 42.93, p < 0.01). Nonetheless, two of the indexes used for the assessment of measurement invariance, CFI and MFI, experienced minor deteriorations only (∆ CFI = -0.005; ∆ MFI = 0.01) and support the establishment of model invariance (Cheung and Rensvold, 2002).

Test for configural invariance in South Africa (Model A3) suggests that the two-domain factor model fits well to the multigroup data (CFI = 0.99; TLI = 0.98; RMSEA ≈ 0.06). Fitting a full metric model to the South African sample (Model B3) resulted in small worsening in model fit (∆ lack of invariance when samples sizes are unequal (Chen, 2007) is performed. Analysis of parameter change when equality constraints are released nevertheless suggest that full metric invariance holds for the model.

22Search for “hidden” sources of threshold non-invariance is also performed. Analysis of parameter change when equality constraints of thresholds are released does not provide evidence of lack of invariance.

100 Table 3.5: Tests for measurement invariance across race groups, World Values Survey Wave 5.

United States

Model χ2 df CFI TLI MFI γˆ RMSEA

(A1) Conﬁgural 220.713 16 0.947 0.900 0.907 0.939 0.156 (B1) + Parliament ↔ Parties 103.536 14 0.977 0.950 0.958 0.972 0.110 (C1) + Army ↔ Courts 34.983 12 0.994 0.985 0.989 0.993 0.060 (D1) Full metric 40.470 16 0.994 0.988 0.988 0.992 0.054 (E1) Full threshold 32.092 26 0.998 0.998 0.997 0.998 0.021

Brazil

Model χ2 df CFI TLI MFI γˆ RMSEA

(A2) Conﬁgural 192.101 24 0.966 0.937 0.944 0.963 0.120 (B2) + Parliament ↔ Parties 58.315 21 0.993 0.984 0.987 0.992 0.060 (C2) Full metric 46.283 29 0.997 0.995 0.996 0.996 0.035 (D2) Full threshold 89.216 49 0.992 0.993 0.986 0.991 0.041

South Africa

Model χ2 df CFI TLI MFI γˆ RMSEA

(A3) Conﬁgural 112.056 24 0.991 0.983 0.985 0.990 0.062 (B3) Full metric 157.344 32 0.987 0.981 0.978 0.986 0.064 Partial metric (C3) + Order 6→ Army 106.916 30 0.992 0.988 0.987 0.991 0.052 Partial threshold (D3) + Army - T2, T3 260.394 46 0.977 0.978 0.963 0.976 0.070 (E3) + Government - T2 211.530 44 0.982 0.982 0.971 0.981 0.063

Source: World Values Survey Wave 5. Note: + indicates a model modiﬁcation in relation to the previous model: ↔ is the addition of residual covariance between a pair of observed variables (for instance, Parliament ↔ Parties means that the residual for those two variables are allowed to covary); 6→ is release of between-groups equality constraint on factor loadings (here, Order 6→ Army means that the loading of the factor Order on the variable Army is not constrained to equality between groups); - is the release of between-groups equality constraint on thresholds (Army - T1,T2 means that the ﬁrst and the second thresholds in the variable Army are not constrained to equality between groups).

101 CFI = -0.004; ∆ MFI = -0.007; γˆ = -0.004) yet not enough to reject full metric invariance (Cheung and Rensvold, 2002). Once again, due to differences in group size (see Table 3.1), EPC test was conducted to assess non-invariance in factor loadings and detected a potential non-invariance in the factor loading of Order on trust in the army. Equality constraints on the loading for trust in the army were released (Model C3), leading to a loading 60% larger for the White group in comparison to Blacks and Coloureds. Model fit measures modestly improved relative to the full metric invariance condition (∆ CFI = +0.005; ∆ MFI = +0.009; γˆ = +0.005) and partial metric invariance was established. Partial threshold invariance (Model D3), imposing between-group equality constraints for all thresholds (except for the thresholds of trust in the army)23, resulted in loss of model fit (∆ CFI = -0.015; ∆ MFI = -0.024; γˆ = -0.015) beyond acceptable levels for measurement invariance (Cheung and Rensvold, 2002). χ2 test also shows considerable decline in overall model fit (∆χ2(16) = 153.48). Based on results from EPC test, equality constraints for the second threshold for trust in the government were released (Model E3). This model modification resulted in substantial overall model improvement (∆χ2(2) = 48.86, p < 0.01; RMSEA = 0.063) and two of the fit indexes used to test for model invariance achieved, relative to the configural and the partial metric invariance model, a deterioration in fit acceptable (∆ CFI = -0.01; ∆ MFI ≈ -0.015) for the establishment of partial invariance (Byrne, Shavelson and Muthen´ , 1989).

Results for invariance tests presented above are now summarized. Tests reported above assessed the invariance of the proposed measurement model of trust across racial groups within each country. Those analyses aimed to examine whether the same latent constructs, Order and Politi- cal, were being measured when one compares institutional trust among Blacks and Whites in the United States; among Blacks, Browns, and Whites in Brazil; and among Blacks, Coloureds, and Whites in South Africa. Evidence from the analysis of WVS5 data support the claim that, in each country, the same constructs have been measured across groups. In the American and Brazilian cases, full invariance across groups is achieved, which suggesting that the three indicator variables for each factor are equality caused by the latent factors among members of different groups. Less perfect invariance is reached in South Africa, with some level of non-invariance across groups been

23One threshold only – the ﬁrst threshold – remains constrained to equality for identiﬁcation purposes.

102 detected; nevertheless, the partial invariance reached is sufﬁcient to allow for further analysis. In other words, results from the measurement invariance analysis suggests that the Order and Polit- ical latent factors are equivalent across race groups within each country, rendering latent means (i.e., the average latent group score in a factor) comparison between groups meaningful. Such a comparative analysis is performed in the next section.

Parameter estimates for the full (Brazil and US) and partial (South Africa) invariance models are presented in Table 3.6.

103 Table 3.6: Measurement and structural model parameters, WVS5

Brazil South Africa United States White Black Brown Black White Coloured White Black

Measurement model Order: Factor loadings (unstandardized) Courts 1.00 1.00 1.00 Police 0.73 (0.08) 0.59 (0.05) 0.91 (0.14) Army 0.37 (0.04) 0.31 (0.03) 0.48 (0.04) 0.29 (0.05) 0.51 (0.06) Political: Factor loadings (unstandardized) Parliament 1.00 1.00 1.00 Parties 0.95 (0.08) 0.55 (0.03) 1.10 (0.10) Government 1.59 (0.19) 0.90 (0.06) 2.43 (0.46)

Correlated residuals (unstandardized) Parliament ↔ Parties 0.48 (0.04) 0.72 (0.21) 0.77 (0.14) 0.55 (0.07) 0.70 (0.20)

104 Courts ↔ Army -0.49 (0.14) -0.39 (0.31)

Structural model Factor variance (unstandardized) Order 2.32 (0.40) 2.15 (0.52) 2.31 (0.40) 3.23 (0.43) 2.21 (0.34) 2.21 (0.38) 2.46 (0.58) 3.94 (0.86) Political 1.09 (0.16) 1.04 (0.27) 1.12 (0.20) 2.01 (0.19) 2.50 (0.30) 2.34 (0.33) 0.77 (0.11) 0.82 (0.13)

Factor covariance (unstandardized) Order ↔ Political 1.33 (0.15) 1.37 (0.27) 1.41 (0.20) 1.98 (0.17) 1.74 (0.21) 1.47 (0.22) 0.96 (0.12) 1.63 (0.22)

Factor correlation (standardized) Order ↔ Political 0.84 (0.02) 0.91 (0.05) 0.87 (0.03) 0.78 (0.02) 0.74 (0.03) 0.65 (0.05) 0.69 (0.03) 0.91 (0.06)

Factor means (unstandardized) Order 0.00 0.23 (0.17) 0.11 (0.10) 0.00 -1.08 (0.11) -0.58 (0.12) 0.00 -1.33 (0.16) Political 0.00 0.31 (0.11) 0.19 (0.07) 0.00 -1.68 (0.11) -1.11 (0.12) 0.00 -0.34 (0.05)

Source: World Values Survey Wave 5. Note: Standard errors with Rubin’s (1987) adjustment for between-imputation variance are presented in parentheses. Factor loadings are the only measurement parameters reported in this table. When factor loadings are invariant across groups, it is reported for the reference group only. 3.5.4 Latent mean differences among groups, WVS5

If threshold invariance, full or partial, is achieved, there is supporting evidence that the measurement model is equivalent across groups and group latent means can be then compared (Byrne, Shavelson and Muthen´ , 1989; Meredith, 1993; Steenkamp and Baumgartner, 1998). Partial invariance of thresholds in South Africa and in the United States and full invariance in Brazil are achieved, allowing for meaningful between-group comparison of latent means within each country.

It is critical to emphasize that, in a multidimensional model, the latent means for all factors are set to arbitrary values in the reference group. For model identification, latent means in the reference group are usually fixed to zero and the means for other groups are estimated relative to the reference group. For instance, Whites are the reference group in the US sample, latent means for this groups are fixed to zero for identification purposes, and latent means for Blacks are freely estimated relative to the (fixed) means for the White group. Consequently, it would be incorrect to infer that Whites express the same level of trust in Order and in Political institutions but it is possible to conclude that Blacks are more (or less) trustful than Whites in a same factor.

Figure 3.3 shows the latent means for the Order and Political factors per race groups in each of the three countries (Estimates are also reported at the bottom two lines in Table 3.6). Findings from the US sample are consistent with the literature on institutional trust in the country. Blacks are considerably less trusting in law-enforcement agencies as the Police and the Court system than Whites (Hindelang, 1974; Richardson, Houston and Hadjiharalambous, 2001; Schuman et al., 1997; Tyler, 2005). Regarding trust in Political institutions, Blacks are less trustful than Whites but only marginally, and latent mean difference is not of lesser substantial signiﬁcance especially in comparison to the racial gap found between group means in the Order factor (Alford, 2001; Richardson, Houston and Hadjiharalambous, 2001).

Results from South Africa support previous studies highlighting the discredit of non-Black South Africans toward public institutions in the post-apartheid era (e.g, Askvik, 2008; Davids, 2010; Garcia-Rivero, Kotze´ and Du Toit, 2002; Rule and Langa, 2010). Distrust in political insti-

105 Figure 3.3: Latent means, WVS5

Brazil South Africa United States

● ● 0 ● ● ● Order ●

−1 ● ●

−2

Mean ● ● 0 ● ● ●

● Political

−1 ●

● −2

Black Brown White Black Coloured White Black White

Source: World Values Survey Wave 5. Note: 95% conﬁdence intervals are calculated using standard errors with Rubin’s (1987) adjustment for between-imputation variance.

106 tutions is remarkably widespread among non-Blacks. The latent mean for Whites in the Political factor has the largest relative distance from the reference group mean, of about 1.5 standard de- viations in the negative direction, across all countries. White South Africans’ lack of trust in the government is hypothesized as reﬂecting the group’s loss of political power in the country under the post-apartheid democratic dispensation. Coloureds, a group which remains a political minority in the new regime, also trust less in Political institutions than their Black compatriots whose dominate national political institutions (Southall and Daniel, 2005). Whites and Coloureds are less trusting in Order institutions than Blacks as well, reﬂecting a surge of concerns about personal safety after transition especially among Whites (Fry, 2013; Pickel, 1997; Roberts, 2010).

A different pattern is found in Brazil. Non-Whites in Brazil are not less trustful in Order institutions (Noronha et al., 1999; Silva and Beato, 2013). Latent means for the Order factor are somewhat higher among Blacks and Browns in comparison to the White reference group yet differences are not statistically significant. Perhaps surprisingly, Blacks and Browns are more trusting in Political institution and the latent means for these groups are statistically different from the mean for reference group. No previous study on institutional trust in Brazil has ever reported a similar finding. A post-hoc hypothesis to explain such a result is that Blacks and Browns, whom are less economic privileged than Whites on average (Telles, 2004), might be more positively affected by recent anti-poverty and affirmative actions policies (Bailey, Fialho and Peria, OnlineFirst; Fialho and Bailey, 2015).

3.5.5 Validation analysis

For, to the best of the author’s knowledge, no previous study on trust and race employed a similar measurement invariance approach to test for group latent differences in confidence in public institutions, it is imperative to replicate the findings discussed above using other sources of data in order to assess whether they are robust across studies or due to chance. Confirmatory factor analytic models with the same two-factor structure as the ones tested using the World Values Sur- vey (although eventually varying with regards to the number of items) were fitted to other publicly available datasets containing similar variables, at least in terms of face validity, on institutional

107 trust. Table 3.7 reports fit indexes for the measurement invariance tests performed on each study. Partial threshold invariance is achieved in the 2008 South African Social Attitudes Survey, the 2008 US AmericasBarometer, and in the 2008 Brazilian AmericasBarometer. An invariant structure is imposed – not tested – on the 2008 US General Social Survey data because the restricted number of indicators and of thresholds in the ordinal observed variables do not permit to test for partial measurement invariance. Consequently, full measurement invariance can only be either accepted or rejected in the 2008 GSS data; goodness-of-fit measures suggest that measurement invariance is a feasible assumption for the measurement of trust using the 2008 GSS. Because the 2008 US LAPOP also used a web survey to collect data alike the US WVS5, it remains of interest to compared results from those studies to findings from face-to-face survey as the 2008 GSS, ac- knowledging the more limited measurement options in the later. Model parameters are presented in Tables 3.8-3.9.

108 Table 3.7: Tests for measurement invariance across race groups, validation datasets.

Brazil, LAPOP 2008

Model χ2 df CFI TLI MFI γˆ RMSEA

Conﬁgural 128.905 22 0.975 0.949 0.963 0.975 0.102 + Courts ↔ Federal Govt 73.131 19 0.987 0.969 0.980 0.987 0.080 Full metric 68.652 27 0.990 0.984 0.985 0.990 0.057 Full threshold 154.848 79 0.982 0.990 0.974 0.982 0.045

South Africa, SASAS 2008

χ2 df CFI TLI MFI γˆ RMSEA

Conﬁgural 402.899 24 0.965 0.935 0.938 0.959 0.126 + Police ↔ Army 96.543 21 0.993 0.985 0.987 0.992 0.060 Full metric 132.337 29 0.991 0.985 0.983 0.989 0.060 Full threshold 299.684 61 0.978 0.984 0.961 0.974 0.063 Partial threshold + Army - T3,T4; Government - T3 213.475 55 0.986 0.988 0.974 0.983 0.054

United States, LAPOP 2008

χ2 df CFI TLI MFI γˆ RMSEA

Conﬁgural 250.052 14 0.950 0.894 0.910 0.941 0.165 + Army ↔ Police 141.337 12 0.973 0.932 0.949 0.967 0.132 + Army ↔ Congress 98.730 10 0.981 0.944 0.965 0.977 0.119 + Army ↔ Parties 55.318 8 0.990 0.963 0.981 0.987 0.098 Full metric 58.234 12 0.990 0.976 0.982 0.988 0.079 Partial metric + State 6→ Government 50.189 11 0.992 0.978 0.984 0.990 0.076 Partial threshold + Government - T1,T2,T3,T5,T6,T7 67.684 30 0.992 0.992 0.985 0.990 0.045

United States, GSS 2006

χ2 df CFI TLI MFI γˆ RMSEA

Conﬁgural 11.579 2 0.991 0.949 0.996 0.996 0.087 Full metric 18.499 4 0.987 0.961 0.994 0.994 0.076 Full threshold 26.011 6 0.982 0.964 0.992 0.992 0.073

Source: 2008 AmericasBarometer (LAPOP), 2008 General Social Survey (GSS), 2008 South African Social Attitudes Study (SASAS).

109 Table 3.8: Measurement and structural model parameters, Brazil and South Africa: replication

Brazil, LAPOP 2008 South Africa, SASAS 2008 White Black Brown Black White Coloured

Measurement model Order: Factor loadings (unstandardized) Courts 1.00 1.00 Police 0.92 (0.13) 0.62 (0.03) Army 0.59 (0.08) 0.60 (0.04) State: Factor loadings (unstandardized) Parliament 1.00 1.00 Parties 0.62 (0.07) 0.48 (0.03) Government 0.66 (0.08) 1.01 (0.06)

Correlated residuals (unstandardized) Court ↔ Government 0.32 (0.09) 0.35 (0.17) 0.15 (0.10) Army ↔ Police 0.31 (0.02) 0.35 (0.15) 0.27 (0.04) 110 Structural model Factor variance (unstandardized) Order 1.27 (0.26) 1.03 (0.28) 0.92 (0.23) 1.31 (0.12) 1.37 (0.17) 0.94 (0.11) State 2.00 (0.40) 1.22 (0.28) 2.09 (0.39) 1.90 (0.16) 1.95 (0.23) 1.39 (0.16)

Factor covariance (unstandardized) Order ↔ State 1.25 (0.16) 0.95 (0.16) 1.36 (0.20) 1.37 (0.09) 1.54 (0.16) 0.96 (0.09)

Factor correlation (standardized) Order ↔ State 0.78 (0.04) 0.85 (0.11) 0.98 (0.04) 0.87 (0.02) 0.94 (0.02) 0.84 (0.03)

Factor means (unstandardized) Order 0.00 -0.14 (0.04) 0.00 (0.03) 0.00 -0.69 (0.08) -0.16 (0.06) State 0.00 0.09 (0.05) 0.24 (0.04) 0.00 -1.09 (0.10) -0.52 (0.07)

Source: 2008 AmericasBarometer (LAPOP), 2008 South African Social Attitudes Study (SASAS). Note: Factor correlation for Browns in the 2008 Brazil LAPOP analysis is constrained to be lesser than 1 to prevent improper solutions, which would have otherwise happened in a number of occasions. In those cases, factor correlation is truncated and ﬁxed at 0.99 and no standard error is computed. Reported factor correlation is the average correlation across sixty imputed datasets; standard error of the correlation is the average across the solutions in which factor correlation was freely estimated. Table 3.9: Measurement and structural model parameters, United States: replication

United States, LAPOP 2008 United States, GSS 2006 White Black White Black Measurement model Order: Factor loadings (unstandardized) Courts 1.00 1.00 Police 0.62 (0.07) Army 0.47 (0.06) 0.71 (0.09) State: Factor loadings (unstandardized) Parliament 1.00 1.00 Parties 0.84 (0.05) Government 1.70 (0.18) 1.09 (0.17) 1.43 (0.20)

Correlated residuals (unstandardized) Army ↔ Congress -0.30 (0.05) -0.01 (0.13) Army ↔ Parties -0.22 (0.05) -0.19 (0.13) Army ↔ Police 0.36 (0.03) 0.05 (0.11) 111

Structural model Factor variance (unstandardized) Order 1.98 (0.31) 1.59 (0.50) 0.82 (0.17) 1.13 (0.29) State 2.25 (0.23) 2.65 (0.53) 0.71 (0.12) 0.92 (0.21)

Factor covariance (unstandardized) Order ↔ State 1.50 (0.14) 1.39 (0.33) 0.69 (0.09) 1.00 (0.19)

Factor correlation (standardized) Order ↔ State 0.71 (0.02) 0.68 (0.05) 0.91 (0.05) 0.99 (0.03)

Factor means (unstandardized) Order 0.00 -0.97 (0.18) 0.00 -0.42 (0.12) State 0.00 0.19 (0.16) 0.00 0.11 (0.09)

Source: 2008 AmericasBarometer (LAPOP), 2008 General Social Survey (GSS). Note: Factor correlations for Blacks and Whites in the 2008 GSS analysis are constrained to be lesser than 1 to prevent improper solutions, which would have otherwise happened in all sixty imputed datasets. Consequently, factor correlation is truncated and fixed at 0.99 and no standard error is computed. Latent means from Tables 3.8 and 3.9 are plotted in Figure 3.4. Replication results support findings presented in Table 3.3. No substantive differences are found between race groups in the Brazilian 2008 LAPOP replication data with regards to either factors. Mean group differences are yet statistically non-significant between Browns and Whites in the Order factor, with Blacks slightly less trusting than Browns and Whites. In the Political factor, both Browns and Blacks have higher latent means than Whites but it is only statistically different from the White reference group for Browns.

Results from the South African 2008 SASAS are consistent with the WVS5 and also indicate Coloureds and Whites less trusting in both Order and Political institutions than Blacks. White respondents are again the most distrusting group in both factors, being Coloureds in an intermediate position between Blacks and Whites. Relative distances of Coloureds and Whites to the Black reference group are smaller than in the WVS5 yet remain statistically signiﬁcant. Compared to the WVS5, the racial gap in trust in the 2008 SASAS data is nevertheless relatively tighter.

Both US replication datasets, the 2008 GSS and the 2008 LAPOP, corroborate the finding that Blacks are less confident in Order, law-enforcement agencies. Blacks’s disposition to distrust Or- der institutions is captured in measures including trust in police (WVS5, LAPOP 2008) or not (2008 GSS). With regards to trust in Political institutions, replication datasets show Blacks scoring higher than Whites in 2008, even tough differences are small and not statistically significant. Changes from 2006 to 2008 might be a response to the emergence of Barack Obama as a viable Black presidential candidate which ultimately won the 2008 national election that year. This result lines up with the political reality model and other previous studies that predicted fluctuations in political trust among Black Americans fluctuates in response to the political environment.

3.6 Discussion

Institutional trust is an important asset for democratic governance. Trusting that the government will implement appropriate policies is especially relevant in society deeply cut along group lines – and it is by no means less pertinent when race is the relevant social divide. This study analyzed dif-

112 Figure 3.4: Latent factor means, validation datasets

Brazil South Africa United States United States LAPOP 2008 SASAS 2008 LAPOP 2008 GSS 2008

0 ● ● ● ● ● ● ●

● Order ● −1 ●

−2 Mean ● ● ● ● 0 ● ● ● ● Political ●

−1 ●

−2

Black Brown White Black Coloured White Black White Black White

Source: 2008 AmericasBarometer (LAPOP), 2008 General Social Survey (GSS), 2008 South African Social Attitudes Study (SASAS). Note: 95% conﬁdence intervals are calculated using standard errors with Rubin’s (1987) adjustment for between-imputation variance.

113 ferences in political trust across three societies where race is an important socioeconomic division but diverge with regards to its political salience. Data from multiple sources were analyzed using – to the author’s best knowledge – an innovative approach in research on race and trust. Results show that race is an important predictor of trust in institutions only where the boundaries of race are politicized.

Tests for measurement equivalence using multiple-group conﬁrmatory factor analysis suggests than the same latent constructs of institutional trust have been measured across race groups within each country. This result, obtained from the analysis of the World Values Survey and the replication datasets as well, indicates that institutional trust can be securely compared across race groups and that differences in trust levels are not mere methodological artifacts. Although similar tests of measurement invariance are absent in previous studies on the topic, results presented provide indirect support to ﬁndings from other studies on racial differences in trust: Racial gaps in institutional trust, if any and in whichever direction, represent true differences in that dimension.

Results from this study show that, in certain contexts, race is an important predictor of confidence in two types of public institutions, law enforcement and politics-related governmental agencies. In the United States, Blacks are consistently more suspicious of order-maintenance institution. Such results corroborate previous studies indicating a lack of trust toward the police and the courts (e.g, Gabbidon and Higgins, 2009; Schuman et al., 1997; Sears, 1969; Tyler, 2005; Weitzer and Tuch, 2004). Racial differences in confidence in political institutions as parties, the congress, and the national government itself, are more variable, consistent with previous studies on race and political trust in the US. In 2006, Blacks are found to be less trusting in political institutions; in 2008, however, Blacks were more trusting than Whites by a small, non-significant margin perhaps as a reaction to the ascension of Barack Obama as a national political figure. Moreover, the Black- White difference in trust in political institutions is substantially tighter than for law enforcement agencies and never statistically significant; lack of statistical significance might nonetheless be due to small Black samples. Such ebbs and flows in political trust have been also reported in other studies (e.g., Alford, 2001; Miller and Borrelli, 1991). It is nevertheless worth to note that Blacks scored slightly higher than White respondents in political trust in 2008, when Barack Obama was a

114 competitive candidate for the presidency and eventually being the national election victor that year (Wilkes, 2015). This adds evidence in support to the political reality model which predicts that political trust among Blacks oscillating according to events (Abramson, 1983; Howell and Fagan, 1988; Wilkes, 2015).

Finding from South Africa show non-Blacks as more distrusting in political institutions than the Black majority. Coloureds and Whites are suspicious of both order maintenance and politics- related institutions yet the lack of trust in public institution is even more remarkable among Whites with regards to political agencies. Such results might reflect the loss of political power among Whites in the post-apartheid regime and the perceived impossibility to regain control over the government (Askvik, 2008; Davids, 2010; Klandermans, Roefs and Olivier, 2001b). A political minority during and after the apartheid, Coloureds remain somewhat mistrustful toward the national government at the same time that they might also benefit from state-sponsored affirmative action programs (Adam, 1997; Pickel, 1997).

Data from Brazil challenges expectations from the conventional wisdom that racial minority are discontent and distrusting in political institutions. No remarkable racial differences in trust were found in the country. Lack of confidence toward law enforcement agencies is evenly widespread (Lopes, 2013; Noronha et al., 1999) with no substantive differences across race groups. Regarding trust in government-related institutions, some small group differences are found yet oftentimes in the opposite direction to what would be otherwise expected. Blacks and Browns are not more mis- trusting toward government-related institutions; when differences are present, non-White respondents are somewhat more trust in the state-related political institutions, perhaps for their perception of being positively benefited by national anti-poverty and affirmative actions policies (Bailey, Fi- alho and Peria, OnlineFirst; Fialho and Bailey, 2015).

This study adds one important caveat to the literature on race and trust about the role of racial membership as a determinant of conﬁdence in public institutions. Contrary to the argument on a worldwide growing salience of race (see, for instance, Winant, 1994), it is argued here that race is an important political divide that might have an impact on conﬁdence in public institutions only if race is constructed as a critical political cleavage as in the American and South African cases. If

115 a racial project “defuses” race as a political dimension, its importance in the political realm will be limited, if any, even if race has dramatic effects on one’s life chances. Findings discussed in this piece calls attention for the limits of the overgeneralization of theories on race and ethnicity and how they might have political effects.

116 3.7 Appendix: Polychoric correlations

117 Brazil: Blacks

Institutions (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

(1) Churches (2) Army 0.19 (3) Police 0.25 0.29 (4) Courts 0.30 0.36 0.64 (5) Government 0.34 0.36 0.63 0.66 (6) Parties 0.19 0.27 0.54 0.43 0.60 118 (7) Parliament 0.18 0.25 0.49 0.45 0.62 0.75 (8) Civil service 0.23 0.41 0.64 0.58 0.63 0.53 0.51 (9) Major companies 0.32 0.43 0.49 0.58 0.62 0.40 0.39 0.75 (10) Labor unions 0.31 0.33 0.68 0.58 0.62 0.41 0.36 0.51 0.50 (11) Press 0.15 0.49 0.52 0.33 0.53 0.45 0.20 0.35 0.48 0.50 (12) TV -0.08 0.27 0.32 0.23 0.39 0.41 0.19 0.24 0.34 0.38 0.76 (13) Environmental 0.13 0.36 0.50 0.50 0.42 0.14 0.28 0.55 0.70 0.54 0.32 0.21 (14) Women’s 0.19 0.30 0.29 0.33 0.36 0.09 0.04 0.54 0.56 0.45 0.30 0.28 0.54 (15) Humanitarian 0.34 0.20 0.28 0.41 0.37 0.18 0.24 0.30 0.36 0.36 0.14 0.12 0.39 0.58

Source: World Values Survey Wave 5. Brazil: Browns

Institutions (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

1) Churches (2) Army 0.31 (3) Police 0.28 0.40 (4) Courts 0.22 0.34 0.59 (5) Government 0.24 0.36 0.53 0.62 (6) Parties 0.22 0.21 0.47 0.52 0.61 119 (7) Parliament 0.16 0.24 0.47 0.49 0.59 0.76 (8) Civil service 0.20 0.24 0.34 0.35 0.45 0.35 0.43 (9) Major companies 0.18 0.34 0.38 0.39 0.43 0.37 0.44 0.41 (10) Labor unions 0.18 0.24 0.41 0.34 0.30 0.43 0.39 0.30 0.35 (11) Press 0.14 0.41 0.40 0.38 0.41 0.43 0.36 0.28 0.33 0.44 (12) TV 0.13 0.24 0.41 0.34 0.37 0.41 0.37 0.28 0.31 0.49 0.69 (13) Environmental 0.24 0.39 0.37 0.38 0.32 0.31 0.28 0.38 0.52 0.40 0.23 0.23 (14) Women’s 0.25 0.35 0.26 0.26 0.32 0.34 0.28 0.31 0.48 0.26 0.23 0.21 0.68 (15) Humanitarian 0.35 0.34 0.35 0.41 0.31 0.29 0.26 0.33 0.45 0.36 0.21 0.20 0.59 0.62

Source: World Values Survey Wave 5. Brazil: Whites

Institutions (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

(1) Churches (2) Army 0.32 (3) Police 0.30 0.43 (4) Courts 0.29 0.36 0.61 (5) Government 0.34 0.42 0.48 0.65 (6) Parties 0.28 0.27 0.45 0.48 0.62 120 (7) Parliament 0.25 0.33 0.38 0.50 0.60 0.75 (8) Civil service 0.30 0.30 0.44 0.44 0.47 0.45 0.47 (9) Major companies 0.22 0.31 0.34 0.34 0.33 0.35 0.34 0.54 (10) Labor unions 0.21 0.35 0.45 0.41 0.43 0.43 0.43 0.39 0.33 (11) Press 0.23 0.41 0.33 0.34 0.44 0.40 0.37 0.28 0.28 0.43 (12) TV 0.20 0.30 0.31 0.31 0.36 0.40 0.37 0.33 0.29 0.42 0.70 (13) Environmental 0.28 0.36 0.32 0.32 0.34 0.30 0.30 0.39 0.53 0.38 0.24 0.21 (14) Women’s 0.22 0.28 0.28 0.27 0.26 0.26 0.24 0.34 0.45 0.34 0.20 0.18 0.64 (15) Humanitarian 0.25 0.32 0.27 0.26 0.29 0.26 0.29 0.34 0.40 0.37 0.28 0.20 0.55 0.62

Source: World Values Survey Wave 5. South Africa: Blacks

Institutions (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

(1) Churches (2) Army 0.19 (3) Police 0.28 0.36 (4) Courts 0.24 0.37 0.68 (5) Government 0.20 0.34 0.46 0.53 (6) Parties 0.18 0.28 0.38 0.39 0.46 121 (7) Parliament 0.20 0.31 0.43 0.54 0.63 0.53 (8) Civil service 0.29 0.30 0.46 0.47 0.40 0.42 0.53 (9) Major companies 0.20 0.31 0.32 0.38 0.31 0.32 0.40 0.49 (10) Labor unions 0.11 0.35 0.38 0.41 0.33 0.39 0.34 0.44 0.31 (11) Press 0.20 0.38 0.30 0.28 0.29 0.37 0.30 0.28 0.27 0.32 (12) TV 0.19 0.30 0.34 0.33 0.33 0.33 0.34 0.32 0.37 0.34 0.66 (13) Environmental 0.22 0.27 0.32 0.32 0.30 0.32 0.31 0.44 0.53 0.36 0.29 0.33 (14) Women’s 0.29 0.23 0.29 0.31 0.28 0.30 0.31 0.39 0.42 0.31 0.26 0.29 0.59 (15) Humanitarian 0.22 0.25 0.23 0.29 0.26 0.24 0.29 0.39 0.45 0.32 0.27 0.30 0.59 0.60

Source: World Values Survey Wave 5. South Africa: Coloureds

Institutions (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

(1) Churches (2) Army 0.23 (3) Police 0.18 0.34 (4) Courts 0.25 0.35 0.68 (5) Government 0.07 0.27 0.49 0.56 (6) Parties 0.07 0.29 0.38 0.36 0.64 122 (7) Parliament 0.12 0.21 0.42 0.48 0.82 0.72 (8) Civil service 0.23 0.38 0.51 0.45 0.49 0.48 0.53 (9) Major companies 0.16 0.27 0.24 0.27 0.29 0.31 0.25 0.39 (10) Labor unions 0.06 0.33 0.40 0.36 0.33 0.33 0.29 0.43 0.30 (11) Press 0.34 0.40 0.35 0.20 0.40 0.36 0.29 0.35 0.40 0.41 (12) TV 0.28 0.29 0.38 0.28 0.38 0.32 0.31 0.32 0.36 0.39 0.69 (13) Environmental 0.18 0.20 0.33 0.30 0.18 0.22 0.23 0.46 0.53 0.38 0.38 0.24 (14) Women’s 0.29 0.27 0.28 0.29 0.14 0.18 0.18 0.40 0.43 0.31 0.36 0.33 0.67 (15) Humanitarian 0.33 0.19 0.33 0.33 0.17 0.22 0.15 0.39 0.45 0.35 0.35 0.23 0.66 0.70

Source: World Values Survey Wave 5. South Africa: Whites

Institutions (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

(1) Churches (2) Army 0.31 (3) Police 0.19 0.52 (4) Courts 0.23 0.50 0.64 (5) Government 0.26 0.46 0.51 0.61 (6) Parties 0.28 0.43 0.39 0.41 0.73 123 (7) Parliament 0.30 0.45 0.50 0.58 0.83 0.70 (8) Civil service 0.18 0.49 0.51 0.44 0.58 0.59 0.66 (9) Major companies 0.18 0.30 0.24 0.26 0.34 0.41 0.36 0.44 (10) Labor unions 0.11 0.39 0.41 0.22 0.30 0.38 0.37 0.41 0.29 (11) Press 0.16 0.36 0.31 0.21 0.31 0.35 0.33 0.38 0.39 0.39 (12) TV 0.17 0.38 0.43 0.29 0.30 0.35 0.36 0.45 0.40 0.47 0.79 (13) Environmental 0.17 0.28 0.26 0.23 0.32 0.31 0.29 0.40 0.69 0.24 0.40 0.38 (14) Women’s 0.21 0.24 0.25 0.22 0.29 0.30 0.33 0.35 0.50 0.24 0.36 0.34 0.73 (15) Humanitarian 0.24 0.28 0.29 0.31 0.28 0.29 0.31 0.35 0.54 0.24 0.25 0.28 0.67 0.75

Source: World Values Survey Wave 5. United States: Blacks

Institutions (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

(1) Churches (2) Army 0.56 (3) Police 0.39 0.64 (4) Courts 0.30 0.55 0.90 (5) Government 0.46 0.57 0.79 0.86 (6) Parties 0.34 0.39 0.61 0.68 0.72 124 (7) Parliament 0.25 0.40 0.57 0.63 0.67 0.81 (8) Civil service 0.17 0.43 0.57 0.60 0.56 0.67 0.68 (9) Major companies 0.32 0.45 0.67 0.58 0.73 0.60 0.60 0.56 (10) Labor unions 0.08 0.22 0.28 0.25 0.34 0.36 0.53 0.39 0.23 (11) Press 0.27 0.49 0.53 0.52 0.54 0.48 0.55 0.43 0.53 0.43 (12) TV 0.23 0.51 0.54 0.53 0.52 0.49 0.52 0.46 0.58 0.43 0.86 (13) Environmental 0.20 0.50 0.54 0.49 0.59 0.57 0.65 0.63 0.69 0.41 0.37 0.48 (14) Women’s 0.16 0.40 0.44 0.40 0.45 0.41 0.52 0.66 0.53 0.43 0.55 0.57 0.73 (15) Humanitarian 0.25 0.53 0.51 0.52 0.67 0.59 0.55 0.69 0.59 0.46 0.50 0.52 0.70 0.80

Source: World Values Survey Wave 5. United States: Whites

Institutions (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

(1) Churches (2) Army 0.53 (3) Police 0.35 0.52 (4) Courts 0.28 0.32 0.69 (5) Government 0.40 0.46 0.49 0.53 (6) Parties 0.31 0.28 0.36 0.42 0.63 125 (7) Parliament 0.24 0.22 0.35 0.46 0.59 0.76 (8) Civil service 0.23 0.33 0.47 0.52 0.55 0.58 0.68 (9) Major companies 0.27 0.37 0.39 0.43 0.45 0.57 0.49 0.58 (10) Labor unions 0.08 0.07 0.27 0.31 0.11 0.29 0.27 0.26 0.12 (11) Press 0.09 0.11 0.24 0.40 0.23 0.34 0.32 0.32 0.30 0.48 (12) TV 0.11 0.21 0.27 0.34 0.26 0.38 0.30 0.29 0.38 0.43 0.77 (13) Environmental 0.02 0.03 0.27 0.39 0.12 0.24 0.35 0.46 0.26 0.39 0.41 0.30 (14) Women’s 0.07 0.08 0.34 0.45 0.20 0.29 0.40 0.45 0.26 0.40 0.44 0.39 0.78 (15) Humanitarian 0.22 0.15 0.39 0.41 0.29 0.32 0.36 0.46 0.30 0.24 0.28 0.22 0.63 0.59

Source: World Values Survey Wave 5. CHAPTER 4

Social and Political Participation

The study of political participation is one of the most recurrent topics in political science. Group differences in political participation has been largely studied (Campbell, Converse, Miller and Stokes, 1960; Milbrath, 1965; Nie and Verba, 1975; Pizzorno, 1970; Rosenstone and Hansen, 1993; Sanchez, 2006; Verba, Ahmed and Bhatt, 1971; Verba and Nie, 1972; Verba, Nie and Kim, 1978; Verba, Schlozman and Brady, 1995; Wolﬁnger and Rosenstone, 1980), being the political participation of racial groups one of the most explored topics (e.g, Guterbock and London, 1983; Leighley, 2001; Leighley and Vedlitz, 1999; Lien, 1994; Matthews and Prothro, 1966; Olsen, 1970; Schildkraut, 2005; Shingles, 1981; Uhlaner, Cain and Kiewiet, 1989; Verba et al., 1993; Wrinkle et al., 1996).

One of the most proliﬁc approaches to political participation focuses on the importance of resources for electoral and non-electoral forms of political participation (Anderson, 2007; Leighley, 1995; Milbrath, 1965; Verba and Nie, 1972; Wolﬁnger and Rosenstone, 1980). According to this approach, a larger endowment of resources, either material or cognitive, increases the probability of political involvement, engagement, and participation (Verba, Schlozman and Brady, 1995). In other words, individuals with higher socioeconomic status, political information, political interest, and connected to recruitment networks tend to be more politically active. If political participation is conceived as a form of input to the political system (Dahl, 1963; Easton, 1957), severe inequalities in politically relevant resources might lead to biased outputs from the system in favor of those individuals and groups more capable to exert pressure on the system (Dahl, 1996, 2006; Verba, 2003).

The resource-based model of political participation has been widely employed to explore the

126 links between race and political participation in the United States. Scholars have argued that racial minorities have overall lower levels of participation in most political activities – voting, contacting politicians, membership to political groups, and so on – as a consequence of their less plentiful stocks of resources: Blacks and Latinos would participate less than Whites because they have less access to politically relevant resources (Verba, Schlozman and Brady, 1995; Verba et al., 1993). In this scenario, the group differences in participation would disappear after properly controlling for the effect of different resources. According to this approach, the impact of race and ethnicity on political participation would result from the strong inﬂuence on the acquisition or development of resources.

A second, complementary approach to political participation, also developed in the American context, proposes that psychological factors do play a signiﬁcant role in explaining the political participation of racial groups. In response to a lack of attention to the role of racially-speciﬁc factors as consciousness in the mainstream resource model, authors have argued that race has a distinctive role other than just access to resources (Leighley and Vedlitz, 1999). Namely, extensive research agenda has explored how group consciousness might also be, using the resource model jargon, an important force leading to political participation (Bobo and Gilliam, 1990; Matthews and Prothro, 1966; Miller, Gurin, Gurin and Malanchuk, 1981; Shingles, 1981). Some studies analyzing data collected during and in the aftermath of the Civil Rights movement in the United States even claimed that, once the effect of resources is controlled, Blacks would equally or more politically active than Whites because of the effect of psychological factors leading to action, group consciousness being one of the most salient (Olsen, 1970; Shingles, 1981; Verba and Nie, 1972).

In effect, research has shown that Blacks are less prone than White to engage in political acts as voting, contacting politicians, donating money, talking about politics, and participating in political organizations(Olsen, 1970; Uhlaner, Cain and Kiewiet, 1989; Verba et al., 1993). However, once controls for resources are taken into account, group differences in political participation vanish (Verba et al., 1993) or even flip (Olsen, 1970; Stoll, 2001). Moreover, some studies have also found that Blacks do participate more in some acts as protests and community activities (Verba et al., 1993) and that a sense of group identification has significant influence on a range of political

127 activities (Chong and Rogers, 2005).

There is, however, a lack of studies on race and political participation in other societies, including Brazil and South Africa, countries of interest in this piece. Race has not been pointed as an effective dimension for forging a sense of group identity or consciousness in Brazil. Factors usually identiﬁed as part of an Afro-descendant identity – as religion and skin color – or structural factor as residential segregation are not exclusive for those Afro-descendants and are not effective for mobilization (Telles, 1996). Some authors have pointed that even though race identiﬁcation might lead to differential vote choice in the country despite the absence of a strong racial consciousness among non-Whites in Brazil (Bailey, 2009b; Souza, 1971; Castro, 1993; Soares and Valle Silva, 1985, 1987), it is effect would not be a major determinant of voting behavior (Prandi, 1996). Some recent studies have also suggested that the major cause of unequal levels of political participation between Whites and non-Whites is the difference in resource between groups, and the effect of race on participation dissipates when controlled by resources (Bueno, 2012; Bueno and Fialho, 2009).

Perhaps even more surprising is the contrast between the paucity of quantitative research on race and non-electoral political participation in South Africa and abundance of studies on social and political attitudes (e.g., Burns, 2006; Foster and Nel, 1991; Gibson, 2006; Klandermans, Roefs and Olivier, 2001a; MacCrone, 1937; Orpen, 1971a; Pettigrew, 1960; Ray, 1980; Roberts, Kivilu and Davids, 2010; Seekings, 2008; Van den Berghe, 1962), social movements (e.g., Ballard, Habib and Valodia, 2006; Piombo, 2005; Seekings, 2000), and voting behavior (De Kadt, 2017; Fer- ree, 2006; Johnson and Schlemmer, 1996; Lijphart, 1980; Mattes, 2012; Mattes and Thiel, 1998). Even though research on party preference and voting choice are marked by strong racial divides (De Kadt, 2017; Ferree, 2006; Naidu, 2006), studies on political activism have oftentimes presented aggregate ﬁgures only, not comparing them ﬁgures across groups (e.g., Davids, 2010; Mat- tes, 2002). In two rare publications of the kind, it has been shown that Black South Africans are more active in community participation and in joining demonstrations than Whites and, to a certain extent, than Coloureds (Klandermans, Roefs and Olivier, 2001b; Mattes, 2008). However, a more systematic look at non-electoral political participation in South Africa is still lacking. Some

128 comparative analysis including Brazil and South Africa has shown that race would have opposite effects on participation in these countries. In Brazil, race loses its impact on political participation when controlled by resources effect; in South Africa, resources lose their effect on participation when controlled by race, being Black South Africans the most active group (Reis, Fialho, Bueno, Candian and Drumond, 2007). Such preliminary ﬁndings suggest that racial identity would still be a major driving force behind political participation in the post-Apartheid South Africa, and the lack of strong racial identities in Brazil would be responsible for the non-effect of race.

As stated above, literature on race and politics in the United States has argued that, in addition to the inﬂuence of socioeconomic resources on political behavior, psychological dimensions as linked fate (Dawson, 1994) and group consciousness (Miller et al., 1981), in fostering a sense of strong group attachment and need of collective action for social change, are important predictors of political participation among members of minorities, eventually offsetting the consequences of socioeconomic inequalities. To extend this argument to the analysis of race and political participation to South Africa and Brazil, I aim to generalize it from a Social Identity Theory perspective to examine under what conditions is race an important predictor of political attitudes and behavior.

According to the Social Identity Theory, individuals aim to be members of groups providing them some positive utility as prestige, power, and self-esteem. If membership to a certain group provides no such rewards, individuals will leave the group whenever possible and aspire membership to other groups. If leaving the group is not a possible, group members will either pursue to change the meaning of group membership (Camp, 2015) or will engage in collective strategies of social change (McAdam, 1982) to alter group hierarchies in order to turn it into a more positive and rewarding afﬁliation (Tajfel, 1974, 1975; Tajfel and Turner, 1986).

Importantly, the feasibility of changes in group afﬁliation might not be only a function of individual will. It is also conditional to structural features deﬁning group membership, being the permeability of group boundaries of crucial importance (Ellemers, 1993; Ellemers et al., 1988). If groups are, in a given society, conceived as discrete entities with “bright” and impermeable boundaries (Alba, 2005), boundary crossing will be unlikely and collective action to pressure for changes in the group hierarchies becomes the last resort against stigmatization of group membership (Elle-

129 mers, 1993; Tajfel, 1975).

Racial formation projects in the three countries examined in this piece, Brazil, South Africa, and the United States, remarkably differ in how race boundaries have been enforcement. Cross- national differences in how race has been socially constructed account for differences in the permeability of group boundaries across societies and, consequently, for the political salience of race (Lamont and Molnar´ , 2002; Omi and Winant, 1994; Telles and Sue, 2009; Wimmer, 2008).

Marx(1998) argues that the differences in nation-building processes in those countries have attenuated or emphasized the salience of race as a response to elites? needs to survive politically and to keep national territorial integrity. In South Africa and in the American South, the development and strengthening of an encompassing White identity to refrain separatist forces – from the Afrikaners and the Southerners, respectively – in the aftermath of bloody civil wars during the second half of the nineteenth century resulted in the institutionalization of legal discrimination systems and the use of the state apparatus to enforce group boundaries for the implementation as means to implement segregationist policies; in Brazil, legal discrimination has been absent since the end of slavery in 1888 (Andrews, 1991; Marx, 2001, 2002). The state-backed enforcement of racial boundaries has been proven to have critical long-lasting effects on group boundaries permeability even after legal discrimination is abolished.

Building upon this discussion, I contend that the salience of race for political mobilization will be conditional on how race is experienced in a society. The presence of racial inequalities is not itself sufﬁcient for the politicization of race. As predicted by the Social Identity Theory, an identity will be politicized if, in addition to group stigmatization, group boundaries are impermeable and prevent individual mobility from one group to another. As the result of their racial formation processes and legal enforcement of group boundaries, in societies as South Africa and the United States, race is expected to be an important political force with potential to counterbalance the lack of other determinants of political participation. Such “race effect” should be absent in Brazil.

To examine this hypothesis, I analyze data from cross-national surveys collected in the mid- 2000s in Brazil, South Africa, and the United States. In order to also address an important gap in the political participation literature in Brazil and in South Africa, this analysis focuses on politi-

130 cal activism and on participation in voluntary organizations of political and non-political nature – analysis of non-political organizations is of relevance here to examine whether the politicization identities also affect behaviors outside the political realm. The analysis presented below examines group-level differences in political participation across race groups and whether they are a consequence of socioeconomic inequalities and interest in politics. Findings suggest that Blacks in the United States and Blacks and Coloureds in South Africa do tend to participate at higher levels than expected by their socioeconomic statuses. With regards to resource-demanding political acts and to membership to voluntary organizations, better-off groups (Whites, in both contexts) tend to display higher levels of engagement. In Brazil, substantive group differences in participation and membership are found neither for mobilization- nor for resource-demanding activities.

Next section discusses the measurement of political (and nonpolitical) participation. Datasets and methodological aspects of this study are outlined next. Statistical results are then presented and discussed. The piece concludes with some remarks and potential implications of the ﬁndings.

4.1 Measuring Participation

4.1.1 The political and the non-political

Even though political participation has been an important research topic in the social sciences, its very deﬁnition is not always straightforward. At the conceptual level, what is the difference between a political and a nonpolitical act?

Political scientists do make, implicitly or explicitly, references to either the electoral process or state action when defining political participation. Lester Milbrath, for instance, defines political behavior, broadly conceived, as “behavior which affects or intended to affect the decisional outcomes of government” (Milbrath, 1965, p.1). At the same token, Verba and Nie(1972, p.2) argued that political participation “refers to those activities by private citizens that are more or less directly aimed at influencing the selection of governmental personnel and/or the actions they take.” Huntington and Nelson(1976, p.3) define it as “activity by private citizens designed to in-

131 ﬂuence government decision-making.” This perspective has inﬂuenced most large-scale studies on political participation (Teorell, Torcal and Montero, 2007).

Despite of the undeniable importance of the state in modern societies, this focus on state actions and on how strategic position in it are filled (i.e., elections) may be too restrictive to accommodate contemporary forms of public action as, for instance, boycotting and other forms of political consumerism (Follesdal, Micheletti and Stolle, 2004; Stolle and Hooghe, 2004). Some scholars have criticized this electoral- and state-centered definition of political participation. It has been argued that “the political” concerns about conflicts and their regulation, which obviously includes the state but is not restrained to it (Dahrendorf, 1968; Reis, 2000; Warren, 1999, 2001). In addition, political participation is an attempt to influence the distribution of social goods and values, which might included the distribution of political power through the allocation of strategic positions in state institutions but might also refer to other goods outside the scope of the state, where it might act, at best, as a mediator (Rosenstone and Hansen, 1993; Warren, 2001).

This alternative approach to political participation encompasses both strictly political acts voting, campaigning, and contacting politicians as well as activities like petitioning, participating in demonstrations, and boycotts. All these acts, among others, aim to inﬂuence the distribution of goods and values (power being one of them) and include the state as a potential target of action although not the only one.

Moreover, this enlarged definition of “political” – and consequently of political participation – makes room to establish a distinction between membership to different types of voluntary organizations (for a discussion of the typology of associaitions employed here see Van der Meer, Grotenhuis and Scheepers, 2009). Being a member of activist organizations that have the state as their primary focus of action, as advocacy and lobbying groups, is a form of political participation as it aims to influence the allocation of social goods. Interest groups are organizations focusing primarily on market relations and might be either professional or consumer organizations. They potentially encompass political consumerism groups – therefore being related to politics – but would be excluded under the more conventional definitions of political participation. Other associations as leisure and religious organizations fall short of being “political” even though they are important

132 spaces for mobilization and for the development of political skills (Erickson and Nosanchuk, 1990; Rosenstone and Hansen, 1993; Verba, Schlozman and Brady, 1995).

4.1.2 Modes of participation

Political participation is a multidimensional construct, where components are driven by different “logics” – causes and intended consequences (Claggett and Pollock, 2006; Verba and Nie, 1972; Verba et al., 1973; Verba, Nie and Kim, 1978). Each component or “mode” of participation is a cluster of activities that usually go together – i.e., individuals performing one act in one cluster is likely to perform other acts from the same modes but not necessarily from another cluster (Dalton, 2002; Teorell, Torcal and Montero, 2007; Verba, Nie and Kim, 1978). For the existing literature has pointed not only to group-level differences in participation but also that some groups may be more active in speciﬁc types of participation, it is important to account for the variety of forms of political action in this analysis.

The original typology of modes of participation proposed by Verba and Nie(1972) encom- passed four dimensions: voting, campaigning, communal activity, and personalized contacts. Those four dimensions eventually also emerged in cross-national research (Verba, Nie and Kim, 1978). Voting referred to participating (i.e., casting a vote) in elections for different political offices; campaigning included acts as attending rallies, contributing money to a candidate, and persuading others how to vote; communal activity invokes acts related to the solution of local problems such as joining or forming a local group or contacting a local official on a social problem; and personalized contacts involved contacting local, state, or national officials on a personal problem. Further research suggested the addition of other modes of participation and, interestingly, suggested that money contribution have become a dimension itself, detached from campaigning, in more recent times (Claggett and Pollock, 2006).

Verba and Nie’s and Claggett and Pollock’s typologies cover only “conventional” forms of participation, which might be attributed to the time of development of the original model. Nev- ertheless, its dimensions – in either original or expanded version – do not accommodate forms of

133 non-electoral participation such as protest activity and emerging forms of political consumerism (Inglehart, 1990; Kaase and Marsh, 1979; Norris, 2002; Stolle and Hooghe, 2004). Teorell, Torcal and Montero(2007) propose a typology of the modes of political participation that includes engagement in non-electoral forms of participation. In addition to representational (i.e., that take part in or directly target the electoral process) modes of participation as voting, party activity (which resembles Verba and Nie’s “campaign activity”), and contacting politicians and ofﬁcials, Teorell, Torcal and Montero propose two forms of extra-representational political participation: Consumer participation and protest activity. Consumer participation includes acts as boycotts, “buycotts,” petitioning, and also donating money for political purposes. This mode is described as using market mechanisms to send (sometimes anonymous and vague) political messages.1 Protest activity includes acts as taking part in demonstrations and eventually illegal protest acts.

Table 4.1 lists the political acts addressed in this study. Political actions as signing petitions and joining boycotts represent forms of political consumerism. Donating money might be either a form of political consumerism or campaign/party activity (conditional to the beneﬁciary). At- tending political rallies are also, according to Verba and Nie, a form of campaign/party activity. Having contacted politicians and the media are evident examples of contacting. Participation in demonstrations is an example of protest activity.

None of the typologies discussed above, however, discusses at length the participation in voluntary organizations. As discussed in the previous section, Van der Meer, Grotenhuis and Scheepers (2009) classifies organizations as activist, interest, and leisure. To their threefold classification, I add religious groups as a separate category of associations. Leisure and religious organizations are, in principle, non-political associations strictu sensu per their intended missions are not the regulation of conflicts and the allocation of goods and values in society. However, these organizations are important spaces for mobilization and the development of politically-relevant skills (Baumgart- ner and Walker, 1988; Erickson and Nosanchuk, 1990; McDaniel, 2008; Rosenstone and Hansen, 1993; Verba, Schlozman and Brady, 1995). Activist and interest organizations do have salient

1Although some scholars have interpreted it as an emerging form of political activism, boycotting was an usual strategy of Black resistance in the United States and South Africa (Beinart, 2001; McAdam, 1982).

134 Table 4.1: Survey Items on participation in associations and political actions

2004 International Social Survey Programme Political action Here are some different forms of political and social action that people can take. Please indicate, for each one, whether you have done any of these things in the past year, whether you have done it in the more distant past, whether you have not done it but might do it, or have not done it and would never, under any circumstances, do it: - Signed a petition - Boycotted, or deliberately bought, certain products for political, ethical or environmental reasons - Took part in a demonstration - Attended a political meeting or rally - Contacted, or attempted to contact, a politician or a civil servant to express your views - Donated money or raised funds for a social or political activity - Contacted or appeared in the media to express your views

World Values Survey 5 Political action Now I’d like you to look at this card. I’m going to read out some forms of political action that people can take, and I’d like you to tell me, for each one, whether you have done any of these things, whether you might do it or would never under any circumstances do it: - Signing a petition - Joining in boycotts - Attending peaceful demonstrations

Associations Associational type Now I am going to read off a list of voluntary organizations. For each one, could you tell me whether you are an active member, an inactive member or not a member of that type of organization? - Church or religious organization Church - Sport or recreational organization Leisure - Art, music or educational organization Leisure - Labor Union Interest - Political party Activist - Environmental organization Activist - Professional association Interest - Humanitarian or charitable organization Activist - Consumer organization Interest

Source: 2004 International Social Survey Programme and World Values Survey Wave 5.

135 political aims. Activist associations do address the state advocating the provision of goods and policies. Interest groups are market-oriented yet they are also considered political organizations per they might demand state intervention and regulation (trade unions, for instance, advocating for labor protections) or might be directly related to political consumerism acts.

In the subsequent analysis, political acts and membership to voluntary organizations will be assessed separately rather than as clusters or dimensions. The classiﬁcation of political acts and organizations above is nevertheless of interest for the comparison of group-level political participation as it helps to map differences in participatory levels across different types of behavior. For this matter,the inclusion of nonpolitical organizations is also pertinent to disentangle what might be speciﬁc about membership to different groups.

4.2 Data and Methods

4.2.1 Datasets

In this analysis, I analyze data from two comparative social surveys in order to examine the role of race as a predictor of political participation in Brazil, South Africa, and the United States: the 2004 International Social Survey Programme (ISPP), and the World Values Survey Wave 5 (WVS5) conducted in 2006 (Kittilson, 2007; ISSP Research Group, 2012; World Values Survey, 2014).2 ISSP and WVS5 collected data from nationally representative samples of adults (18+ years old in Brazil and the United States, 16+ in South Africa) using face-to-face survey interviews, with exception of the American WVS5 survey which is an online research panel.3

ISSP and WVS5 core questionnaires were translated into the countries’s national languages

2Datasets and questionnaires are publicly available at www.worldvaluessurvey.com and www.issp.org free of charge.

3The World Values Survey is administered by local researchers afﬁliated to the World Values Survey Association; national surveys are expected to replicate the core questionnaire and follow standard sampling procedures. The Amer- ican ISSP survey is conducted as a module of the General Social Social. The South African ISSP survey is a module of the South African Social Attitudes Survey. ISSP questionnaire is randomly administered to a share of the GSS and SASAS samples using a split-ballot. The Brazilian ISSP is a stand-alone survey. National ISSP surveys are expected to replicate the year’s comparative module.

136 – Portuguese in Brazil; Afrikaner, Sotho, Tswana, Venda, and Zulu in South Africa – from the master questionnaire in English. The original English instrument was used in the United States and also in South Africa.4 Original and effective sample sizes are reported in Table 4.2.

4.2.2 Measures of political participation

ISSP questionnaire includes a six-items battery on political action asking respondents whether they have ever taken part in each of the following acts: Signing a petition; boycotting or deliberately buying products for political, ethical, or environmental reasons; participation in demonstrations; attending political rallies; contacting or attempting to contact a politician or a civil servant; donating money or fundraising; and contacting or appearing in the media to express their views.5 WVS5 questionnaire asks whether the respondent has ever participated in three political action items: Signing a petition; joining in boycotts; attending peaceful demonstrations. These measures of political actions have been long used to assess conventional participation (e.g. contact politicians) and protest potential (e.g. boycotting) (e.g, Marsh and Kaase, 1979; Norris, 2002; Rucht, 2007). ISSP and WVS5 political action items are coded as binary variables based on whether the respondent has ever performed that action, recently or in a more distant past, or not.

WVS5 also inquiries about participation in voluntary organizations. Respondents are asked whether they are members of nine different types of organizations and, in case of positive answer, whether they are active or passive members. Item format permits respondents only to report whether they are members of organizations of a kind but does not gauge whether he or she is a member of several organizations within a category, which might artificially deflate levels of engagement in associations (Norris, 2002, p.147). Non-religious organizations are classified into three groups according to their purpose: Leisure associations with socializing and recreational purposes, interest organizations that represent the socio-economic interests of their members in the

4GSS surveys conducted before 2006 were only administered in English. Respondents without enough English ﬂuency to do the interview were excluded as out-of-scope (Smith et al., 2013).

5A seventh item, “joined an internet political forum or discussion group” was included in the American and Brazil- ian questionnaires but not in the South African and is therefore excluded from this analysis.

137 Table 4.2: Sample size and group size.

(a) World Values Survey Wave 5

Brazil South Africa United States

N%N%N% Full sample 1,500 2,988 1,249 Effective sample size 1,457 2,882 1,049 Black 136 9.3 2,073 71.9 127 12.1 Brown 562 38.6 Coloured 288 10.0 White 759 52.1 521 18.1 922 87.9

(b) 2004 International Social Survey Programme

Brazil South Africa United States

N%N%N% Full sample 2,000 2,784 1,472 Effective sample size 1,903 2,553 1,368 Black 253 13.3 1,778 69.7 216 15.8 Brown 812 42.7 Coloured 427 16.7 White 838 44.0 348 13.6 1,152 84.2

138 market, and activist associations which target the state and advocate broad societal interests with no direct socio-economic beneﬁts (Van der Meer, Grotenhuis and Scheepers, 2009). Churches and religious organizations are classiﬁed as a separate associational type due to the multiple roles – including political (e.g., Bruneau, 1982; McClerking and McDaniel, 2005; Rasool, 2004) – they might perform in additional to their spiritual missions.

Table 4.1 reports question wording for all political participation questions as well as the classi- ﬁcation of organizations into different types.

4.2.3 Race classiﬁcation

Data collection mode for racial classification varies across surveys. All questionnaires use country- specific census categories although they differ in how information on racial classification is collected. ISSP employed self-classification in Brazil and the United States; in South Africa, respondent’s race was assigned by interviewer observation. WVS5 respondents are classified by the interviewer in all countries.

Racial classification items in the Brazilian ISSP and the WVS5 questionnaires requests respondents’ self-classification using one of the five official Census categories since the 1991 Census: Black (preto), Brown (pardo), Indigenous (Ind´ıgena), White (branco), and Yellow (amarelo or of Asian ancestry). Analysis focuses on the Black, Brown, and White categories, and exclude all the other groups. According to the 2010 Brazilian Census, blacks, browns, and whites represent about 99% of the Brazilian population (Brazilian Institute of Geography and Statistics, 2011). The American ISSP questionnaire asks the respondent to express her or his race (more than one category allowed) and is classified as Black, White, and other.6 In the American WVS5 survey, respondent’s race was informed in a recruitment survey; respondents in the sample are classified as White (non-Hispanic), Black (non-Hispanic), other (non-Hispanic), and Hispanic. Only non- Hispanic Blacks and Whites are included in the analysis; they represent 85% of the American

6GSS permits respondents to self-classify using more than one category and a race category is imputed based on a set of questions on race and ethnicity.

139 population according to the 2010 Census (United States Census Bureau, 2010). In South Africa, both the ISSP and the WVS surveys collected data on race using the four ofﬁcial South African population categories (Black African, Coloured, Indian or Asian, and White). Analysis includes the Black, Coloured, and White groups only. According to the 2011 South African Census, these three groups correspond to 97% of the South African population (Statistics South Africa, 2012a). Number of observations per group is reported in Table 4.2.

4.2.4 Sociodemographics and interest in politics

Sociodemographic variables are taken into account in descriptive statistics and also as controls in a series of logistic regressions predicting participation in different political activities. Due to the large differences in socioeconomic levels across countries, some compromise in the operational- ization of the variables is necessary. Education is coded as a three-level variable, less than high school education, complete high school, and some college of more. Household income, present only in the ISSP data, is coded in a four-point scale as low, middle-low, middle-high, and high income. Respondents are also classiﬁed into four age groups, up to 24 years old (youth), from 25 to 44 (adults), 45-59 (mature adults), and 60 years old or more (elders). In regression models, gender (female or male) and a four-point political interest scale, ranging form “not interested at all” to “very interested” are included as controls.

4.2.5 Missing data imputation

To prevent loss of data due to item nonresponse, I perform a Fully Conditional Speciﬁcation multiple imputation model using Predictive Mean Matching (Morris, White and Royston, 2014; Van Buuren, 2007, 2012; Van Buuren, Brand, Groothuis-Oudshoorn and Rubin, 2006). Multiple imputation is as a state-of-art approach to address data missingness that improves accuracy and power of analyses in comparison to “traditional” methods to handle with missing data such as mean imputation or listwise deletion (Enders, 2010; Schafer and Graham, 2002). Simulation studies have shown that Fully Conditional Speciﬁcation models perform best than other techniques to

140 multiply impute categorical data and that the Predictive Mean Matching method has the property of preserving the distribution of the original data (Kropko et al., 2014; Vink et al., 2014).

A separate group imputation strategy (Enders and Gottschall, 2011) is employed in order to preserve the multiple group data structure during the missing data imputation process. Multiple data is imputed for each race group within each country separately. Sixty “complete” datasets are generate for each group. 7 Gender, education, age, interest in politics, perceptions about one’ ﬁnancial situation, perceptions on ethnic relations in the country, participation in associations, and attitudes toward political regimes are used as auxiliary variables in the imputation.8 Auxiliary variables are included aiming only to reduce bias and improve power in the FCS estimation. The algorithm used for implementation of multiple imputation reaches convergence quickly (Van Bu- uren and Groothuis-Oudshoorn, 2011); after monitoring model diagnostics, it was set for each imputed dataset to be generated after 30 iterations. Analyses are repeated for all imputed datasets and results (including model ﬁx indexes) are pooled according to Rubin’s (1987) methods.

4.2.6 Computational procedures

R is the statistical package used for data analysis (R Core Team, 2017). Descriptive statistics and regression models incorporating post-stratiﬁcation weights are performed using the R package survey (Lumley, 2010, 2014).9 Frequencies and regression estimates from the “complete” data sets are merged using mitools (Lumley, 2012) to obtain Rubin’s (1987) coefﬁcients and standard errors for imputed data.

7A rule of thumb to determine the number of “complete” imputed datasets is suggested by Graham, Olchowski and Gilreath(2007): For each one percent of missing data, one “complete” dataset should be generated. In this study, the number of imputations is conservatively set to sixty for all groups to accommodate differences in the level of missing data across races and countries.

8Language spoken at home is also included as an auxiliary variable in multiple imputation in the South African samples.

9All datasets expect the Brazilian ISSP survey include post-stratiﬁcation weights; the Brazilian ISSP dataset is self-weighted.

141 4.3 Findings

4.3.1 Group-level differences in political participation

Figure 4.1 reports the frequency of participation in political acts for members of different racial groups across countries. It is worth noting the overall similarity of results for items presents both in the WVS and the ISSP surveys. Even though sheer percentages eventually vary across surveys for signing petitions, joining boycotts, and participation in demonstrations, the relative group differences are consistent across surveys with boycotting the only one exception.

Results for the United States are largely in line with what the previous research has found in the country, namely, that Blacks are overall lesser politically active whites when other predictors of political participation are not taken into account (Olsen, 1970; Verba et al., 1993; Verba, Schlozman and Brady, 1995) although difference is not always substantive. White respondents sign petitions, boycott, attend rallies, donate money or raise funds to social or political activities, and contact politicians or the media to express their views and demands more often than Blacks. Group differences range from 10% in petitioning and rallying to some 20% with regard to boycotting (in the ISSP sample only) and for resource-demanding acts as fund-raising and contacting politicians. A 5% difference in favor of Whites is found for contacting the media, the least frequent activity for both groups. Black respondents nevertheless report higher participation in demonstration by a 5% margin. Interestingly, American Blacks tend to be more participating in an activity that usually does not ﬁt into the realm of “conventional activities” (Milbrath, 1965; Verba and Nie, 1972). I will return to this point in the discussion. The only instance of no group difference in participation is boycotting in the WVS5 data. 19.7% of respondents of both races reported to have ever participated in boycotts, in shape contrasts with the 20% gap found in ISSP where 42.8% of Whites and 23.5% of Blacks declared to have joined boycotts. 10

Brazilian data strongly points race as a weak predictor of political action. The overall pattern

10Even though this analysis is not posited to assess the cause of response differences across surveys, it might be due to questionnaire effects. Right before the battery on political activism, ISSP respondents were inquired whether “should people prejudiced against any racial or ethnic group be allowed to hold public meetings?”, while WVS5 respondents were answered a generic question about interest in politics.

142 Figure 4.1: Participation in political action

Brazil South Africa United States 80 ● 60 ● ● Petition

40 ● 20 ● ● 0

40 Boycott 30 ● 20 ● ● 10 ● ● ● 0

30 Demonstration ● 20 ● ● ● ● ● 10

40 ● ● 30 Rally ●

% 20 10 0 50 Politicians 40

30 ● 20 ● 10 ● 0 Donate 40 ●

20 ● ● 0

15 Media 10 ● 5 ● ● 0 ISSP WVS ISSP WVS ISSP WVS 2004 2006 2004 2006 2004 2006

Race ● Black Brown Coloured White

Source: World Values Survey Wave 5 and 2004 International Social Survey Programme. Note: 95% conﬁdence intervals are calculated using standard errors with Rubin’s (1987) adjustment for between-imputation variance.

143 suggests an absence of signiﬁcant differences among groups. Results from the WVS5 show Blacks as the most politically active group by small, non-substantive margins and Browns as less active in petitioning, boycotting, and joining demonstrations than Blacks and Whites: Browns are 8% less prone to sign petitions, some 5% less to boycott, and only 2% less to take part in demonstrations. ISSP data show Whites as more participant in demonstrations but less in rallies, but difference is never larger than 7%; group differences are minimal to none for petitioning, contacting politicians or the media, or fund-raising. These results corroborate ﬁndings in previous studies pointing out the lack of power of race for political mobilization (Hasenbalg, 1979; Lamounier, 1968; Telles, 1996).

Findings from South Africa, on the other hand, point out that political activity for different groups is strongly conditional on the type of action being performed. White South Africans are about twice (38%) as prone as Coloureds (20%) to sign petitions and ﬁvefold more than Blacks. Coloureds and Blacks are nevertheless more engaged in contentious (yet not necessarily violent) activities that demand organization and mobilization. Blacks (34%) and Coloureds (27%) have taken part in political rallies more often than Whites (20%), and are also two times more active in demonstrations. Higher levels of participation in peaceful protests by Coloreds and especially for Blacks are also found by (Klandermans, Roefs and Olivier, 2001b). No substantive differences are found for donating money or contacting politicians and the media.

Participation in voluntary associations and organizations is presented in Figure 4.2. Over again, no signiﬁcant group differences are found in the Brazilian data. Whites reports higher levels of associative behavior yet by modest margins only. Group differences exceed 4% only with regard to active participation in churches, where there is a ten-point distance between Browns (45%) and Whites (55%) with Blacks falling in between (49.7%).

Blacks in South Africa are less active in all kinds of voluntary organizations. With regard to Leisure associations and Churches, Blacks participate some 10-15% less than Coloreds and Whites, which report similar associative levels. White respondents are nevertheless more active in Activist and Interest advocacy groups. Nineteen percent of Whites do participate in Activist groups relative to 6% of Blacks and 9% of Coloureds. Sixteen percent of Whites are engaged in Interest

144 Figure 4.2: Participation in voluntary organizations

Brazil South Africa United States

20 Activist 15 ● ● 10 5 ● 0

20 ● Interest ●

10 ●

% 40

30 ● Leisure

20 ● ● 10 0

60 Church ● ● ● 40

0 WVS WVS WVS 2006 2006 2006

Race ● Black Brown Coloured White

Source: World Values Survey Wave 5. Note: 95% conﬁdence intervals are calculated using standard errors with Rubin’s (1987) adjustment for between-imputation variance.

145 groups compared to 12% of Coloureds and 8.5% of Blacks. Results suggest that participation in groups that represent one’s interests either in the market (Interest groups) or in the political space (Activist groups) are more resource-demanding in South Africa, tilting active membership in these associations toward the better-off individuals which happen, on average, to be Whites.

A different pattern is found in the United States. Blacks and Whites report similar levels of participation in Leisure and Interest groups yet diverge regarding others organizational types. While Whites are about 8% more active than Blacks in Activist associations, Blacks are some 10% more prone to be active church members. Higher levels of Black participation in churches reﬂects the historic importance of such institutions for this population (McAdam, 1982).

Results form the descriptive analysis of group-level participation in political acts and voluntary organizations can be summarized as such: In Brazil, group differences in participation in political activism and in associations tend to be small despite of the average socio-economic inequalities in the group-level which would otherwise imply that Whites, the better-off group (on average), would be substantively more politically engaged (Milbrath, 1965; Verba, Schlozman and Brady, 1995; Wolﬁnger and Rosenstone, 1980). Patterns of political activism in South Africa are mixed, with Blacks and Coloureds participating more in protests acts that demand organization and coordination as political rallies and demonstrations (Klandermans, Roefs and Olivier, 2001b), being possibly a legacy of the organized struggle against the apartheid regime, especially among Blacks. On the other hand, regarding participation in associations, Blacks are consistently lesser active than other groups, with Coloureds being as engaged as Whites in recreational and religious organizations but also lesser participating in advocacy groups. In the United States, political activism and engagement with politically-oriented organization is strongly related to the possession of resources (Brady, Verba and Schlozman, 1995; Milbrath, 1965; Verba, Schlozman and Brady, 1995; Schloz- man, Verba and Brady, 2012), leading to overall higher levels of participation among Whites. There are, however, two noticeable exceptions: Blacks are more active members of churches and in taking part in demonstrations and protests (McAdam, 1982; McClerking and McDaniel, 2005; McDaniel, 2008).

146 4.3.2 Sociodemographic variables and race

The evidence presented thus far suggest that race may be a more important predictor of political participation in some contexts (South Africa and the United States) but not in others (Brazil). Other demographic factors as education, income, and age, however, are also politically relevant dimensions (Milbrath, 1965; Schlozman, Verba and Brady, 2012; Verba, Schlozman and Brady, 1995; Wolﬁnger and Rosenstone, 1980); examining these sociodemographic variables contributes to understand both intra-group variance as well as how social inequalities may contribute to group- level differences in political participation. Before presenting results from multivariate regression models where the predictive power of race on participation in voluntary organizations and political acts is controlled for socioeconomic background, it is useful to inspect how education, age, and income do correlate with the outcomes of interest.

Education. Education is an overall important predictor of political activism and organizations attachment yet its effect vary across race, country, and outcome (Figures 4.3, 4.4, and 4.5). In the United States, there is a strong, positive correlation between education and participation in most acts and associations. Higher levels of education are related to petitioning, boycotting (in the ISSP survey), contacting politicians and the media, and ﬁnancial donations. Whites are more engaged in these activities regardless of the educational level; in other words, Black educational achievement is not sufﬁcient to close the participation gap in these political acts. More educated respondents are active members in Interest and Leisure associations. There is no membership substantive gap across levels of education regarding participation in Leisure and Interest organizations, which happen to also be those with small to none group-level difference (see Figure 4.2). Some remarkable exceptions are noted. Education seems to have no effect for Blacks on participation in rallies and on Activist organizations membership; indeed, the membership gap widens in higher educational levels. Blacks are more active members in churches and more engaged in demonstrations for all levels of education, even though the group-level differences vanish for respondents with college education.

In Brazil, more education is associated with more active participation in all association types

147 Figure 4.3: Education and political activism, ISSP

Brazil South Africa United States 80 ● ●

● Petition 60 ● ● 40 ● ● ● 20 ● ● ● ● 0 60

● Boycott ● 40 ● ● ● ● 20 ● ● ● ● ● ● 0

● Demonstration 60

● 40 ● ● ● ● ● 20 ● ● ● ● ● 0 60 ●

● Rally 40 ● ● ● ● ● ● ● % ● ● 20 ●

●

60 Politicians

40 ● ● 20 ● ● ● ● ● ● ● ● ● 0

60 ● Donate 40 ● ● ● ● ● 20 ● ● ● ● ● ● 0

20 Media 15 ● ● ● 10 ● ● ● 5 ● ● ● ● 0 ● ● Less than High Some College Less than High Some College Less than High Some College HS school college HS school college HS school college

Race ● Black Brown Coloured White

Source: 2004 International Social Survey Programme.

148 Figure 4.4: Education and political activism, WVS5

Brazil South Africa United States ● 80 ● ● ● Petition 60 ● ● 40 ● 20 ● ● 0 ●

30 ● Boycott ● ● 20 ● % ● ● 10 ● ● 0

● Demonstration 50

40 ● 30 ● ● 20 ● ● ● ● 10 ● 0 Less than High Some college Less than High Some college Less than High Some college HS school or more HS school or more HS school or more

Race ● Black Brown Coloured White

Source: World Values Survey Wave 5.

149 Figure 4.5: Education and organizational membership, WVS5

Brazil South Africa United States

● 3000 ● Activist

2000 ● ● ● 1000 ● ● ● ● 0 ● 4000 ● ● 3000 Interest ● 2000 ● ● ● 1000 ● ● 0

% 6000 ●

● Leisure 4000 ● ● ● ● ● 2000 ● ● 0 8000

6000 ● Church ● ● ● ● ● ● ● 4000 ● 2000 0 Less than High Some college Less than High Some college Less than High Some college HS school or more HS school or more HS school or more

Race ● Black Brown Coloured White

Source: World Values Survey Wave 5.

150 except churches. Education is also correlated with petitioning, boycotting, contacting politicians and the media, and participation in demonstrations. Among Whites, education is only weakly correlated with rallying but more noticeable for Blacks and Browns. Money donation and fund- raising, however, are not inﬂuenced by education.

Figures for South Africa show that, similarly to Brazil, higher levels of education are associated with membership in Leisure, Activist, and Interest groups; education is also associated with active membership in churches for Coloureds and Blacks but the inverse is found among Whites. Respondents with more education are also more engaged in petitioning and boycotting. Education also has a modest, positive effect for Coloureds to contact politicians and for Blacks to contact the media and to make donations. Education has no effect on rallying among Blacks but increases the odds of participation for Coloureds and Whites. Even though educational achievement closes the group gap between Coloureds and Whites with regard to petitioning, its effect among Blacks is close to none. Perhaps most importantly, education increases the probability of taking part in demonstrations for all groups but its effect is not strong enough among Whites to catch up with the high levels of demonstrations among Coloureds and Blacks.

Age. Levels of participation through age cohorts are also conditional on groups and context (Figures 4.6, 4.7, and 4.8). The effect of age on political activism in Brazil is minimal for almost all acts; exceptions are rallying and contacting the media, which are less frequent among elders than young adults. Elders are also less active in Leisure associations but more engaged with Activist groups. Education has small effects on membership to churches and to Interest groups – mature Blacks adults are particularly engaged in the latter yet the anomalous ﬁgure might be the result of the small sample size for this age group.

In South Africa, Whites of all ages are more prone to sign petitions than Blacks and Coloured yet the reverse is true for rallying. Age has modest effects on contacting politicians and donating money; its effect is also small on contacting the media among Blacks and Whites but Coloured elders do it relatively more often. Regarding boycotting, results are conditional on the data source: Age is inconsequential for Blacks yet has a positive effect for the other groups in the ISSP data, Black and Coloured adults tend to be prone to boycotting than White respondents in the WVS5.

151 Figure 4.6: Age and political activism, ISSP

Brazil South Africa United States 80 ● ● 60 Petition

40 ● ● ● ● ● ● 20 ● ● ● ● 0 50

40 Boycott

30 ● ● 20 ● ● ● ● ● ● ● ● ● 10 ● 0

40 ● Demonstration 30 ● ● ● 20 ● ● ● ● ● ● 10 ● ● 0 ● ● ● 40 ● ● ● ● Rally 30 ● ● ● ● % 20 10 0 ● 60 Politicians 40 ● ● ● 20 ● ● ● ● ● ● ● ● ● 0 60 Donate 40 ● ● ● ● 20 ● ● ● ● ● ● ● ● 0 20

15 Media ● 10 ● ● ● 5 ● ● ● ● ● ● ● 0 ● Up to 24 25−44 45−59 60+ Up to 24 25−44 45−59 60+ Up to 24 25−44 45−59 60+

Race ● Black Brown Coloured White

Source: 2004 International Social Survey Programme.

152 Figure 4.7: Age and political activism, WVS5

Brazil South Africa United States

80 ● ● ● ● ● Petition 60 ● ● 40 ● 20 ● ● ● ● 0 ● 30 Boycott 20 ● ●

% ● ● ● ● 10 ● ● ● ● ● 0

● Demonstration ● ● 20 ● ● ● ●

10 ● ● ● ● ● 0 Up to 24 25−44 45−59 60+ Up to 24 25−44 45−59 60+ Up to 24 25−44 45−59 60+

Race ● Black Brown Coloured White

Source: World Values Survey Wave 5.

153 Figure 4.8: Age and organizational membership, WVS5

Brazil South Africa United States

● 20 ● ● Activist 15 ● ● 10 ● ● ● ● 5 ● ● 0 ● 40 ●

30 Interest ● ● 20 ● ● ● 10 ● ● ● ● ● ● 0 % ● ● 40 Leisure ● ● ● ● 20 ● ● ● ● ● ● 0

75 ●

● Church ● ● ● ● ● 50 ● ● ● ● ● 25

0 Up to 24 25−44 45−59 60+ Up to 24 25−44 45−59 60+ Up to 24 25−44 45−59 60+

Race ● Black Brown Coloured White

Source: World Values Survey Wave 5.

154 On demonstrations, although results also are somewhat conditional on the survey being analyzed, Blacks and Coloured are more likely to participate in demonstrations than Whites of all ages yet this group difference is absent among elders in the WVS5 data. Participatory peak in demonstrations and rallies for Blacks and Coloureds is found among mature adults respondents (45-59 years old in 2004-2006) followed by adults (25-44 years old), many of whom were in their youth and young adults years during the transition from apartheid to the new dispensation and their higher levels of participation in demonstrations and rallies might reﬂect their political engagement at the time. Age is negatively associated to participation in Leisure groups and has moderate effects on Interest organizations. It is weakly correlated to active membership in Churches among Whites and Blacks but Colored elders are strongly active. Age is however a strong predictor of active membership to Activist groups among White South Africans yet its effect for Coloureds and Blacks is either weak or even negative.

In the United States, Blacks of all ages participate lesser than their White counterparts in all political acts with exception of demonstrations. For all ages, Blacks are lesser active in their membership to Interest and Activist groups. Although age has a negative effect on membership to Leisure organizations among Whites and also among young and adult Blacks, Black elders make the most active group in this associational type. Whites are lesser active in Churches than Black throughout their life cycle.

Income. For measures of household income are absent in the WVS5 questionnaire, its relationship is examined only in the ISSP data; in other words, the relationship between income and political participation is restricted to activism (Figure 4.9).

The correlation between income and political activism is surprisingly weak in Brazil. No clear pattern, either positive or negative, is found for contacting politicians, rallying, or even donating money, an act directly related to disposable income (Verba, Schlozman and Brady, 1995). High income respondents are modestly more engaged with boycotting, participation in demonstrations, and contacting the media. A stronger trend is detected for signing petitions only, being high income respondents 20% more prone to petitioning the low income ones.

Errand trends are also present in the South Africa data, especially regarding donations and con-

155 Figure 4.9: Income and political activism, ISSP

Brazil South Africa United States

● ● ●

60 Petition ● 40 ● ● ● ● 20 ● ● ● ● 0 50

40 ● Boycott ● 30 ● 20 ● ● ● ● 10 ● ● ● ● ● 0

50 Demonstration

40 ● ● 30 ● ● ● ● ● 20 ● ● ● ● 10 ● 0

● 40 ● ● ● ● ● ● ● ● Rally 30 ● ●

% 20 ● 10 0 Politicians

40 ● ● ● 20 ● ● ● ● ● ● ● ● ● 0 60 Donate ● 40 ● ● ● 20 ● ● ● ● ● ● ● ● 0 20 ●

15 Media

10 ● ● ● ● 5 ● ● ● ● ● ● 0 ● Low Mid−lowMid−high High Low Mid−lowMid−high High Low Mid−lowMid−high High

Race ● Black Brown Coloured White

Source: 2004 International Social Survey Programme.

156 tacting politicians and the media. Once again, it is interesting to point that group-level differences are persistent across levels of income for certain political acts. Whites engage lesser than other respondents in boycotting, demonstrations, and rallying at all income levels. Conversely, high and low income Black respondents are less like to sign petitions than Whites and Coloureds.

4.3.3 Regression analysis

The evidence presented thus far reports the proportion of respondents in each race group who have been engaged in different political activities and organizations across contexts. It has also been show how sociodemographic variables may assess intra-group variance in political engagement. Those analyses, however, cannot account for the simultaneous effect of all those dimensions at once and how they can jointly explain group differences in participation. This section assesses how powerful is race group identiﬁcation as a predictor of participation in voluntary organizations and political actions when controlled for sociodemographic variables introduced in the previous section.

A series of logistic regression models are fitted to ISSP and WVS5 data to examine whether group differences (or lack of) in selected political acts – signing petitions, joining boycotts, participation in demonstrations, and participation in rallies – and active membership in voluntary organizations presented in Figures 4.1 and 4.2 remain after controlling for other potential predictors of political participation.11 In all regression models, the predictor of interest is the respondent’s race. Analysis of ISSP data include controls for sociodemographic variables as education, household income, age, and also control for interest in politics. Analysis of WVS5 data controls for education, age, gender, and interest in politics. Figures 4.10 to 4.12 report coefficients and significance (reported as 95% intervals) for Blacks, Browns, and Coloureds as appropriate for the country.

Political activism. Findings for the United States are somewhat conditional on the data source, particularly with regard to boycotting and signing petitions. ISSP data suggest that Blacks are

11These four political acts are the most often performed across the three countries. As shown in Figure 4.1, money donation, contacting politicians, and contacting the media are oft-performed acts in the United States but less fre- quently in Brazil and South Africa.

157 Figure 4.10: Race as a predictor of political activism (ISSP)

Brazil South Africa United States

0.2 Petition

0.0 ● ●

● ● −0.2 ● −0.4

0.2 Boycott ● ● ● 0.0 ●

−0.2 ●

−0.4 Demonstration Estimate 0.2 ● ● ● 0.0 ● ●

−0.2

−0.4

0.2 ●

● ● Rally ● 0.0 ● −0.2

−0.4 Black Brown Black Coloured Black

Race ● Race Race+SES Race+Interest Race+All

Source: International Social Survey Programme, 2004. Note: Dependent variables are binary: If the respondent has ever taken part in that political act, they are coded as one, otherwise as zero. Figures report logistic regression coefﬁcients and 95% conﬁdence intervals. ‘Race’ indicates race categories as the only predictor in the model; ‘Race+SES’ indicates controlling for sociodemographics; ‘Race+Interest’ indicates controlling for political interest; ‘Race+All’ indicates controlling for both sociodemographics and political interest.

158 Figure 4.11: Race as a predictor of political activism (WVS5)

Brazil South Africa United States

0.2 Petition

0.0 ● ● ● ● −0.2 ●

−0.4

0.2 Boycott ● ● ● ● 0.0 ●

Estimate −0.2

−0.4 Demonstration 0.2

● ● ● 0.0 ● ●

−0.2

−0.4 Black Brown Black Coloured Black

Race ● Race Race+SES Race+Interest Race+All

Source: World Values Survey Wave 5. Note: Dependent variables are binary: If the respondent has ever taken part in that political act, they are coded as one, otherwise as zero. Figures report logistic regression coefﬁcients and 95% conﬁdence intervals. ‘Race’ indicates race categories as the only predictor in the model; ‘Race+SES’ indicates controlling for sociodemographics; ‘Race+Interest’ indicates controlling for political interest; ‘Race+All’ indicates controlling for both sociodemographics and political interest.

159 less prone to sign petitions, join boycotts, and rallying. Controlling for sociodemographics close the group gap in participation for rallying and bring group differences to non-signiﬁcance for petitioning, but Whites remain more prone to boycotting after controls are included in the model. Controlling for interest in politics has no noticeable effect on propensity to joining these acts (as well as for taking part in demonstrations). It is arguable that group differences in rallying are a result of differences in sociodemographics (more notably age and college education). Controlling for sociodemographics do reduce group differences in petitioning but do not close it, and has minimal effects on differences in boycotting – and suggests that propensity to boycotting is related to predictors absent in the model. With regards to taking part in demonstrations, group differences do slightly increase when sociodemographic variables are accounted for. Such a result points out that there are (modest) group differences – not accounted by the model – and that group identiﬁca- tion is strong enough to offset resource inequalities in propensity to join demonstrations. Findings from the WVS5 corroborate the Black (modest) higher propensity to join demonstrations. Group differences in petitioning are less robust and vanish once controls are included in the models, and no difference in boycotting is found.

Results for Brazil and South Africa are less subject to data source effects. Findings from Brazil suggest that group differences in political activism are minimal, if any, and are not robust to controlling for sociodemographics. Rallying is an exception, with Blacks and Browns consistently having positive estimates (indicating that these groups would be more active in attending rallies) but statistically signiﬁcant for Browns only and being modest in size.

South African ﬁndings indicate important differences in how racial groups are politically mobilized in the country. White South African respondents are more prone to sign petitions relative to Blacks and the difference is robust after controls are included in the model. Differences in the propensity to petitioning are also presented in comparison to Coloureds but results suggest that they are considerably attenuated after controlling for sociodemographic variables. Blacks and Coloureds are somewhat more willing to join boycotts but differences are small when no controls are included in the model. However, after controlling for sociodemographic variables, Coloureds and Blacks are more inclined to boycotting in comparison to Whites. Blacks and Coloureds are

160 more active with regards to joining demonstrations and attending rallies and, again, differences in group propensity to act widens in comparison to Whites after sociodemographics are accounted for. Results for South Africa suggest not only that Blacks and Coloureds tend to be more politically engaged than Whites in acts that might demand mobilization but also that, in the absence of sociodemographic differences between groups, the participation gap could be even larger. In other words, racial identiﬁcation has been a powerful force pushing toward political activism in spite for sociodemographic differences. Worth noting is the lack of statistical relevance of interest in politics to explain group differences in political activism.

Voluntary organizations. Results for Brazil and the United States reveal straightforward patterns. Blacks, Browns, and Whites in Brazil do not differ in their average propensity to be active members in Leisure, Interest, and Activist organizations; with regard to being active Church members, Blacks and Browns are lesser participant in churches by a small margin.

No group-level differences in membership to Leisure and Interest organizations is detected between Blacks and Whites in the United States. Consistent with Figure 4.2, Blacks are somewhat less engaged regarding their membership to Activist groups and more active members in Churches. Group differences in membership to Activist organizations and Churches are robust to the inclusion of controls in the regression models.

Results for South Africa indicate that Blacks are lesser engaged in organizations and associations compared to respondents of other races (Figure 4.2); however, once sociodemographic variables are controlled for, membership gaps for Church and Activism are reduced, and vanish for Interest groups. Even controlling for sociodemographics and political interest, Blacks remain less engaged in Leisure groups. A similar picture is found for Coloureds regarding Interest and Activist groups, with their lesser engagement relative to Whites being shortened for Activist and also vanishing for Interest groups. No group difference in participation in Leisure organizations is found. Finally, Coloureds are more active in churches than Whites (and, consequently, than Blacks) and differences in propensity to active membership in churches do increase after controlling for sociodemographic variables.

161 Figure 4.12: Race as a predictor of organizational membership (WVS5)

Brazil South Africa United States

0.2 Activist

0.0 ● ● ● ● ● −0.2

−0.4

0.2 Interest

0.0 ● ● ● ● ● −0.2

−0.4

Estimate 0.2 Leisure

● 0.0 ● ● ● ● −0.2

−0.4

0.2 ● Church ● 0.0 ● ● ● −0.2

−0.4 Black Brown Black Coloured Black

Race ● Race Race+SES Race+Interest Race+All

162 4.4 Discussion

This study examined the salience of race as a predictor of political participation in three societies marked, at the same time, by large racial inequalities and different racial dynamics. It is argued that race is itself a strong force pushing to participation in some political acts and associations conditional on whether race has been politicized. Namely, it is hypothesized that, in societies where race has been politicized, with group membership been enforced by state action for the implementation of discriminatory policies, race became a salient political force for group mobilization capable of offsetting the lack of other factors that foster political engagement.

Results discussed above indicate that, even though socioeconomic factors are important predictors of political participation, in societies as the United States and South Africa, where race has been a major politicized dimension, race offsets the differences in socioeconomic resources with regards to participation in acts requesting group mobilization and organization. In the United States, Blacks tend to be somewhat more active in demonstrations (regardless of socioeconomic resources) and eventual gaps in participation in rallies vanish after sociodemographics are controlled for. Black Americans are also more active in churches, a historically important class of institutions in their struggle against inequality and stigmatization in the country. However, Blacks in the US are less participant in more individualized or resource-demanding acts as signing petitions, donating, contacting politicians and the media, and joining politically activist groups. In South Africa, non-Whites (Coloureds and Blacks) are more prone to join demonstrations and political rallies than Whites. What is more, once sociodemographics are accounted for, the expected level of participation of Blacks and Coloureds in those acts increases in comparison to the expected level of engagement for Whites. However, Whites are more actively participants in Interest and Activist organizations and in individualized political acts as petitioning. Blacks and Coloureds are in participatory disadvantage in such actions even after controlling for resources. Substantive group-level differences in political participation are absent in Brazil, with resources tending to have similar effects on participation across groups. Although this ﬁnding is not new in the literature on race and politics in Brazil, it is still puzzling given the remarkable overall racial inequalities in the country:

163 For Whites are, on average, better-off than Blacks and Browns, one might expected to ﬁnd Whites to be more political active at least in sheer numbers even if the participatory gap would eventually disappear after controlling for sociodemographic variables.

The general patterns of ﬁndings presented in this paper provides some corroboration to the hypothesis that, when belonging to a race group is a politicized experience, group membership might trump the negative effect of lack of political assets to exert power to mobilize member to take part in certain types of political acts. This effect of race would nevertheless be absent if the political action in question does not demand group coordination and mobilization. In the absence of this group consciousness, race will lack such a mobilizing power and political participation by individuals of different groups will be conditional on their political resources.

164 CHAPTER 5

Conclusion

This dissertation examined cross-national variance in the political salience of race in three important cases in comparative racial studies, Brazil, South Africa, and the United States. Despite of the thriving interest in racial inequalities and discrimination (Winant, 1994, 2001), that political implications of race identiﬁcation have perhaps been taken for granted. Employing a social identity theory framework (see Tajfel, 1981; Tajfel and Turner, 1986), I argue that race can be a powerful political force under certain circumstances. Namely, I contend that race is a salient political dimension when racially-targeted discriminatory practices are inscribed in the law and the state apparatus is used of implement those policies, which implies that the state is also responsible for the enforcement of group boundaries as a mean to deﬁne racial group membership.

Jim Crow laws in the Southern United States and the apartheid system in South Africa provide exemplary support to this argument. In both systems, membership to a race group used to be ﬁxed and had tremendous consequences on one’s life chances. The state-enforced closure of group boundaries involuntarily contributed to the formation of strong group identities and set up the state as the main target for political action (Marx, 2002). In Brazil, non-Whites have experienced pervasive forms of discrimination and inequality but no type of legal discrimination or enforcement of group boundaries. This resulted in the lack of deﬁnition who is the political constituency and what political concessions to to be demanded from the state.

To test the explanatory power of this argument, I performed extensive analysis of cross-national datasets to assess how the relationship between race and a series of outcomes are context-dependent.

In Chapter 2, I examined group-level differences in two perceptions of discrimination. More speciﬁcally, I analyzed perceptions of racial discrimination (or at least perceived as such by the

165 survey respondents) and of everyday forms of mistreatment that might not be racially-motivated. Non-Whites (Blacks, Browns, and Coloureds) tend to report higher levels of perceived discrimination of both kinds than Whites but group differences are conditional on country and type of discrimination. There are small group differences in the perception of everyday discrimination in the United States but Blacks are four times more prone to denounce being target of racial discrimination than Whites. Curiously, reported levels of everyday discrimination are higher than of racial discrimination for all groups in South Africa; moreover, perceptions both types of discrimination are strongly correlated among Blacks and Coloureds but not among Whites, which might suggest a racialization of perceptions of everyday discrimination – i.e., Blacks and Coloureds in South Africa might interpret episodes of everyday discrimination as motivated by race. Data from Brazil suggests that the color continuum parallels perceptions of everyday discrimination even though Browns tend to be closer to Blacks than Whites in this dimension, but that perceptions of racial discrimination are perceived in a Black/non-Black way, with Browns being much closer to Whites.

In Chapter 3, I analyze group differences in institutional trust. I focus on political and law- enforcement institutions. Using a multiple-group confirmatory factor analytic model, I compare the group differences for latent factor means within each country. Results show, in the United States, Blacks tend to be consistently more distrustful in law-enforcement agencies than Whites but group differences in trust in political institutions is less robust, varying according to the political conditions. If in the mid-2000s Blacks were less trusting in political institutions, the gap closes in 2008, when Barack Obama runs for the presidency. In South Africa, perhaps due to the strength of group identities and the Black African dominance of most public institutions in the post-apartheid dispensation, Whites and, in a lesser extent, Coloureds are suspicious of public agencies, specially of political institutions. Such results are interpreted as reflecting, on the one hand, the existing conflicts in groups interests and, on the other hand, the Black African electoral dominance in the country which, once combined, trigger feelings of distrust against the state apparatus. Findings from Brazil indicate little to none group differences with regards to trust in public institutions. Perhaps surprisingly, when group differences are detectable, Blacks and Browns tend to be more trusting than Whites. It is suggested that this unexpected result might be a consequence of extensive

166 anti-poverty policies implemented in the country during the mid-2000s which, given the over- representation of non-Whites among the poor, might positively affect the life condition of such groups.

Chapter 4 assesses group differences in participation in political acts and membership in voluntary organizations. A complex pattern emerges but two or three major ﬁndings can be found. In Brazil, there are small group differences in both political activism and membership to organization, and other predictors have similar effects on political engagement across groups. In South Africa and in the United States, Blacks tend to be less active in most of the examined political acts but participate more in acts that demand group mobilization such as demonstrations. More interesting, however, are the difference in the effect of sociodemographic variables on participation. In the United States, controlling for socioeconomics tend to, at least modestly, reduce the participation gap across most acts and organizations except membership to churches, where Blacks are consistently more active. This is in line with prior literature (e.g. Verba et al., 1993) that suggests that the major cause of race differences in political participation is resource asymmetry. In South Africa, controlling for socioeconomic variables tend do always boost the participation of non-Whites relative to Whites: When Blacks and Coloureds participate less in an activity or group, it reduces the gap; when they do participate more than Whites, controlling for sociodemographics widens the gap. This suggests that group membership is such a strong predictor of political behavior in South Africa only the large socioeconomic disparities in the country can counterbalance its effect.

Perceptions of discrimination are not necessarily political. Perpetrators of discriminatory acts may be racially motivated or not, and being discriminated against may or may not have political implications for the victim. The analysis of perceived discrimination nevertheless contributes to clarify and support the argument made in this dissertation. Blacks are more prone than Whites to report perceived discrimination in the United States, and there are also solid group differences in political trust and participation. However, the same is not true in South Africa and Brazil. Group differences in perceived discrimination in South Africa are modest compared to results from the United States; there are nonetheless consistent group differences in political trust and participation. Data from Brazil show that, even though there is small to none group differences in political

167 trust and participation, there are modest discrepancies in perceived everyday discrimination (which however vanish when controlled for other variables) and, importantly, Blacks report much higher levels of perceived racial discrimination than the other groups; if perceptions of unfair treatment were enough to affect political behavior, one would expect Blacks to be less trusting in public institutions and perhaps more politically active (at least after accounting for socioeconomic differences).

Future research can expand these finding in two directions. A similar comparative approach can be applied to the study of other social and political dimensions such as intergroup contact and attitudes toward policies, among other outcomes of interest. Longitudinal studies assessing the robustness of findings across time are also welcome. If available data permit, it would be of interest to examine how the end of the apartheid in South Africa affected group identities and its influence on political behavior, whether changes in the official discourse on race and the implementation of affirmative action policies in Brazil has affected the the permeability of group boundaries in the country, and how recent changes in the political landscape in the United States have reinforced racial trenches. Comparisons including Brazil and other South American countries may also prove to be fruitful.

Overall, results discussed above support the hypothesis that the political salience of race is conditional on how racial groups are conceived and organized in a society. More speciﬁcally, the existence of racial inequalities is not sufﬁcient to foster group consciousness. Closure of group boundaries shown to be an important factor to create a sense of linked fate and of collective action as the only to demand social transformation to improve the life conditions of the group as a whole. When boundaries are permeable, group members might opt to pursue individualistic strategies of social mobility, hindering the chances of emergence of group consciousness.

Prior research in the United States has demonstrate the importance of racial group consciousness in the country but lesser is yet known about other contexts. This study aimed to add evidence from two other countries where race is a fundamental social dimension to the literature. At the group-level, Brazilian and South African data indicate that group differences in political behavior are more strongly associate with the societal salience of race identity than perceptions of mistreat-

168 ment or political resources, corroborating the hypothesis advanced in this work.

This argument raises important questions about the future of race relations in Brazil, South Africa, and in the United States. Brazil experienced one of the most violent chapters in the world’s history of slavery. After decades of political efforts to “defuse the racial bomb,” race has become mostly ineffective for political mobilization except among a small negro elite in the country. Since the mid-1990s, the federal government has implemented an array of affirmative action programs, some of them having race as an eligibility condition. It remains unsure whether those programs will survive to changes in the Brazilian political landscape. If so, would these race-based state actions to ameliorate inequalities result in a certain degree of closure of group boundaries? In case of positive answer, would this case of “positive discrimination” be strong enough to bring about the awake of racial consciousness? Or does Black or negro racial consciousness only flourish in cases of negative discrimination? Quarter-century after the end of the apartheid, race remains salient in South Africa as many wounds are still to be healed. What should one expect for the future? Will the upward social mobility and the political dominance of Black Africans lead to the vanishing of their racial consciousness? Or should one expect for the growth of intra-race ethnic fragmentation? Will Coloureds, a group that was a minority during the apartheid and remains one after it, develop more skeptical attitudes toward a Black-dominated South African state? White South Africans, in special the Afrikaner community, justified the build-up of the apartheid system as a form to survive politically against the overwhelmingly Black population. Now, the group has limited political power. If their lack of electoral influence turns into strong feelings of vulnerability, would their group identities become even strong? Would Afrikaners and English-speakers coalesce into only one single white minority to protect the political influence or will their cultural identities prevail? What about the United States? How will the current changes in the political landscape, as the rise of alt-right groups, influence the already delicate state of race relations in the country? Do those group represent the rebound of a White political identity? Will this reinforce Black group consciousness?

The study of social identities is a fascinating topic, and the social identity theory has been proved to be a powerful approach for the study of group dynamics. Results from this study are

169 encouraging and suggest that there is much to be gained in applying it to the study of comparative political behavior.

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