How skin color, class status, and gender intersect in the labor market: Evidence from a field experiment

Journal Pre-proof How Skin Color, Class Status, and Gender Intersect in the Labor Market: Evidence from a Field Experiment Felipe A. Dias

PII:

S0276-5624(20)30006-8

DOI:

https://doi.org/10.1016/j.rssm.2020.100477

Reference:

RSSM 100477

To appear in:

Research in Social Stratification and Mobility

Received Date:

6 February 2019

Revised Date:

6 January 2020

Accepted Date:

8 January 2020

Please cite this article as: Dias FA, How Skin Color, Class Status, and Gender Intersect in the Labor Market: Evidence from a Field Experiment, Research in Social Stratification and Mobility (2020), doi: https://doi.org/10.1016/j.rssm.2020.100477

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How Skin Color, Class Status, and Gender Intersect in the Labor Market: Evidence from a Field Experiment

Author: Felipe A. Dias, Ph.D*. Eaton Hall 115 5 The Green Medford, MA 02155

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Affiliation: Assistant Professor Tufts University Department of Sociology

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Corresponding author:, Dr Felipe A. Dias, Tufts University, Department of Sociology, Eaton Hall, 215, 5 The Green, Medford, MA 02155, United States, E-mail: [email protected]

Abstract

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This article examines skin color discrimination in two Brazilian labor markets using a field experimental approach. Fictitious resumes including photographs of job candidates were randomly assigned one skin color category via photo manipulation and submitted to entry-level job openings. In addition to assessing the extent of skin color discrimination, this article adopts an intersectional framework to examine how the effect of skin color in employment is moderated by class status and varies by gender. I found mixed results about the role of skin color in predicting the employment outcomes at the initial stages of the hiring process. Results from logistic regression and Linear Probability Models show that skin color is a weak predictor of hiring outcomes (e.g. receiving a callback from employers) among male applicants and for female applicants with brown skin. However, I find strong evidence that having dark skin is causally associated with hiring outcomes among female applicants. I also found that having a higher-class status erases skin color differences, thus identifying a potential mechanism that mitigates the effects of skin color in hiring.

Keywords: Social stratification; skin color; hiring discrimination; field experiment; Intersectionality.

INTRODUCTION

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Scholars interested in how skin color shapes economic outcomes in the United States and in Latin America have shown that, across different countries, darker skin is associated with lower wages, lower occupational status, lower educational achievement, poorer health outcomes, and higher rates of arrest (Bailey, Penner and Fialho 2016; Goldsmith et al. 2007; Monk 2013, 2015; Telles 2014; Viglione, Hannon, and DeFina 2011; Villarreal 2010). Yet, in spite of ample evidence of the association between skin color and stratification outcomes, the root causes of this inequality are not well understood. We know very little, for instance, about whether the observed differences associated with skin color are due to contemporary and direct discrimination or some other factor, such as the intergenerational transmission of advantage or the legacies of past discrimination (Branigan et al. 2013; Dixon and Telles 2017: 408).

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This article makes an empirical contribution to the study of colorism by testing whether there is direct discrimination on the basis of skin color at the initial stages of the hiring process. Past scholarship on employment discrimination has focused on other axes of variation, such as race (through race-specific names), gender, employment histories, sexual orientation, class status, and parental status (e.g., Correll, Benard, and Paik 2007; Jackson 2009; Mishel 2016; Pager 2004; Pedulla 2016, 2018; Tilcsik 2011). To demonstrate whether employers directly discriminate on the basis of skin tone, I undertake, to the best of my knowledge, the first field experimental analysis of this phenomenon. To test the direct effects, I focus my analysis on entry-level, white-collar occupations in Brazil, extending my analysis by introducing a multidimensional, intersectional approach to the study of skin color inequality and discrimination. Because prior research has shown that skin color perceptions are shaped by contextual factors (Branigan et al. 2013; Dixon and Telles 2017; Villarreal 2010), I experimentally manipulate class background cues on job applications. In doing so, I expand on a recent body of research that considers the complex nature of discrimination across multiple statuses (Mishel 2016; Rivera and Tilcsik 2016; Tilcsik 2011).

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I use Brazil as a case study to test whether skin color discrimination is causally linked to hiring outcomes for several reasons. First, it would be very difficult to signal skin color on resumes and job applications in the United States, because it is not common practice to attach a photograph to job applications. Similar to other countries in Latin America, as well as some countries in Europe, it is common practice to attach a photograph on resumes and job applications in Brazil, thus allowing us to experimentally manipulate skin color within a field experimental design. Using a visual method to signal skin color within the experiment is appropriate because “color” and “race” are used interchangeably in Brazil and because skin color is a primary basis for categorization in that country (Monk 2016; Telles 2004, 2014), thus providing external validity for the experiment. By using photographs to signal “race” on job applications, this study makes an additional methodological contribution to audit studies by introducing a new research technique for the study of discrimination beyond race-specific names. The second reason for site choice owes to the fact that Brazil has the largest population of African ancestry outside of Africa and is the largest economy in Latin America. Thus, examining the consequences of skin color in Brazil’s labor market would potentially shed light on an important mechanism affecting the employment prospects of a large segment of the population. Overall, I found that skin color is a strong predictor in hiring at the initial stages of the hiring process in sales and administrative assistant positions only for female applicants with dark skin. I

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also found that having a higher-class status erases skin color differences, thus identifying a potential mechanism that mitigates the effects of skin color in hiring. BACKGROUND

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In many societies, as is evidenced by a growing and important literature, lighter skin is associated with higher social status and privilege, while darker skin is associated with lower social status and stigma. Broadly, this effect is traced to the idea that light skin embodies the physical characteristics of groups with more socioeconomic and status honor (e.g., Europeans), while dark skin signals membership in groups with less socioeconomic power and status (Afrodescendants and Indigenous peoples). The social scientific evidence provides support for this colorism framework by showing a strong association between skin tone and social outcomes. Studies show that dark skin is associated with lower wages, lower educational attainment, and lower occupational status (Goldsmith et al. 2007; Hunter 2005; Keith and Herring 1991; Monk 2016; Murguia and Telles 1996; Telles and PERLA 2014; Viglione, Hannon, and DeFina 2011).

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Although an association between skin color and a range of socioeconomic outcomes exists, scholars are unclear about the root causes of skin color inequality. Prior research has suggested that skin color inequality can be explained by two factors: the legacies of past discrimination and contemporary discrimination. The first perspective asserts that individuals with light skin experienced privilege (usually measured by higher levels of education, income, and wealth) due to their proximity to whiteness,1 and that this advantage is transmitted, intergenerationally, to their offspring (Branigan 2013; Dixon and Telles 2017; Hill 2002). Thus, the process by which light skin advantage exists in contemporary society is through the transmission of advantage from past, not contemporary discrimination. To investigate the relative weight of past and contemporary discrimination, other studies have used parental education and occupational status as controls for past advantage/disadvantage based on skin color and observed differences in socioeconomic status of the offspring. Using a longitudinal sample of African-Americans, Hill (2002: 1454) found that more advantageous social origins of mixed-race (black and white) people account only for 10 to 20 percent of the skin color gap in educational attainment. Branigan et al. (2013) also found that family background accounts for only a small part of the gap in educational achievement by skin color. In terms of wages, scholars have found that after controlling for parental education, wage differences by skin color remain (Bailey, Fialho, and Penner 2015; Goldsmith et al. 2007).2

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Although existing evidence shows that contemporary discrimination may play a larger role in explaining skin color inequality, such conclusions should be taken cautiously because of omitted variable biases (Branigan et al. 2013: 1671). Specifically, existing methods are unable to In the United States, social practices such as the “blue vein societies” and “paper bag” tests were used to restrict membership and access to social networks on the basis of lighter skin. Such practices helped perpetuate social mobility among lighter skin African-Americans (Dixon and Telles 2017). 2 Bailey, Fialho, and Penner (2015: 10) found some variation regarding the effect of maternal education. In some countries, such as Argentina, the change in income by skin color was only 2 percent after accounting for mother’s education, while in Brazil, the United States, Nicaragua, Uruguay, and the Dominican Republic was around 4 percent. In spite of these differences, the main finding was that the effect of race was large and significant, even after controlling for maternal education. 1

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rule out the impact of unobserved characteristics stemming from parental background as well as individual characteristics. Consider that scholars typically use parental education and occupational status as proxies for parental background (Branigan et al. 2013; Goldsmith et al. 2007; Hill 2002; Monk 2016), but rarely consider unobserved characteristics, such as the type of parental education (e.g., more selective colleges vs. less selective colleges), wealth, and even the race/skin color of the parents, that might be driving the association between skin color and inequality.

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If employers are actively discriminating against brown and dark skin applicants, this form of discrimination can be motivated by racial animus or by imperfect information about an applicant’s perceived productivity. The taste-based model assumes that employers have a ‘taste’ for discrimination and are less likely to hire minority workers of identical productivity as majority workers (Becker 1971). A common feature of the taste-based discrimination model in employment is that individuals desire distance from those whom they dislike. According to this view, employers might prefer job applicants from the majority group and actively discriminate against applicants from the minority groups based on distaste or animus.

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Negative stereotypes may also impact hiring outcomes for brown and dark skin workers via consumer and/or discrimination by co-workers. Employers may not hire brown and dark skin workers if they think that consumers who are prejudiced might not want to buy products or services from companies with high minority representation. Employment discrimination may also be motivated by prejudice of co-workers. If employees at a firm are prejudiced against brown and dark skin coworkers, they might demand a premium to work alongside them (Becker 1971). Whether the source of animus is rooted in taste-based discrimination from employers, customers, or co-workers, the taste-based model predicts less employment opportunities and/or lower wages for brown and dark skin applicants.

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A second possible mechanism that might lead employers to discriminate against brown and dark-skin applicants is based on “statistical discrimination.” The statistical discrimination model assumes that employers are not acting on animus or a “taste” for discrimination, but rather that incomplete information during the screening process drives discriminatory behavior (Aigner and Cain 1977). The assumption in the original theory is that employers have a signal extraction problem from job applicants. With limited information, employers tend to use ascribed characteristics, such as gender or race, as proxies to infer the expected productivity of job applicants. Employers use group-specific averages related to productivity to evaluate individual characteristics, which leads to employers to systematically prefer majority applicants over minority applicants. Although we know very little whether there are differences in the productivity between light skin, brown skin, and dark skin individuals, evidence from a nationally representative survey suggests that Brazilians generally perceive whites as being more intelligent, as having more education, and being more honest than non-whites (blacks and browns) (Pesquisa Social Brasileira, PESB 2002). Employers might hold these views and use skin color as a proxy for expected productivity. A key assumption in the original theory of statistical discrimination is that employers are perfectly informed about a group’s productivity. This means that the original theory can only

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explain biases in the labor market on an individual basis, but it cannot explain discrimination on a group basis, since the different treatment by employers toward minorities (e.g. hiring discrimination, differences in wages) reflect actual differences in productivity. Thus, the original theory assumes that the employer’s lower estimate of the minority group’s productivity is based on the actual averages of a group’s productivity, and, importantly, that employers are perfectly informed about the group’s averages. If employers’ lower estimate of the minority group’s productivity is the result of ignorance about a group’s mean productivity or prejudice (e.g. animus), then differences in treatment by employers are discriminatory on a group basis. Overall, the statistical discrimination perspective asserts that discrimination can occur without assuming that employers are prejudiced (Aigner and Cain 1977)

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Although several mechanisms could be driving skin color discrimination in the labor market, we have not yet established empirically that skin color is used as a criterion during the hiring interface.3 We lack such evidence because the existing research on skin color stratification suffers from an omitted variable problem on the supply-side of the labor market. Existing studies estimate the extent of skin color discrimination from the “unexplained” gap in wages after controlling for individual characteristics (e.g., age, marital status, gender, education, and years of experience) (e.g. Bailey, Fialho, and Penner 2015; Goldsmith et al. 2007; Monk 2016). However, this approach is limited because researchers have only “crude proxies” to measure skills and abilities (Altonji and Blank 1999: 3191).4 A range of human capital-related variables are invisible to researchers, but visible to employers making employment decisions. These include applicants’ education, experience, and additional skills (e.g., computer software skills), all of which are correlated with skin color or race and to hiring outcomes.

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Taken together, if we want to examine the existence of direct, contemporary discrimination by employers on the basis of skin color, we must both address the omitted variable issues described above and fully control for human capital characteristics. Moreover, to assess discrimination empirically, we need to shift our attention from the supply-side of the labor market to the demand-side, thus allowing us to examine the behavior of employers as they make hiring decisions. While I do not clearly test the taste-based discrimination and statistical discrimination models, I focus my empirical analysis on evaluating whether employers discriminate on the basis of skin color and frame the findings within existing theories of labor market discrimination.

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Skin Color and Class/Social Status

In a recent review of the literature on colorism, Dixon and Telles (2017) asserted that “the appearance of skin color is both contextual and variable. Good scientific measures should reflect this complexity” (page 418). Indeed, studies of skin color categorization and inequality show that contextual factors, such as perceived class status (Villarreal 2010), bodily or phenotypical 3

A recent work by Derous et al. (2017) used photographs on resumes to examine how Human Resources managers rated the resumes on perceived suitability for certain jobs on the basis of several characteristics, including skin color. In my study, I use a correspondence test in which fictitious resumes were sent to actual job openings, thus allowing use to directly measure discrimination at the hiring interface. 4 For instance, age to measure work “experience.”

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characteristics (e.g., type of hair and size and shape of the nose and lips) (Flores and Telles 2012), political affiliation (Caruso et al. 2009), and even ethnic-sounding names (Garcia and Abascal 2016) can influence skin color categorization. In the present study, I build on this literature and introduce a multidimensional analysis of skin color discrimination by examining how class background might moderate the effects of skin color in employment (Alba, Insolera, and Lindeman 2016; Freeman et al. 2011; Kramer, DeFina, and Hannon 2016; Saperstein and Penner 2012). Studying the intersection of perceived class background and skin color deepens our understanding of the complex and contextual processes related to hiring discrimination.

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The idea that socioeconomic status influences racial perceptions is not new. One of the first scholars to capture this dynamic was Charles Wagley, who argued in 1965 that “the social racial types are defined on the basis of physical appearance as modified in their perception by the total social status of the individual” (emphasis added, page 542). Recent scholarly work in this area provides conflicting evidence, however, about whether perceived class background shapes racial perceptions. Using randomized class status cues, computer simulations, and photomorphing techniques, Freeman et al. (2011) found that subtle changes in social cues, such as attire (e.g., “white collar” suit and tie vs. a “blue-collar” t-shirt) shape racial categorization. For their part, Saperstein and Penner (2012) used longitudinal data to find that racial classification changes in relation to changes in social position. These studies show that having a higher-class status could “whiten” racial perceptions, while lower class status could “darken” racial perceptions.

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Researchers have recently examined the impact of class status or class background on employment outcomes using field experiments and correspondence tests (Jackson 2009; Rivera and Tilcsik 2016; Thomas 2018). Much of this research builds on Bourdieu’s work (Bourdieu 1984; Bourdieu and Passeron 1977) on social class as a mechanism for the reproduction of inequality. The reproduction of inequality occurs because individuals from higher socioeconomic backgrounds possess “embodies dispositions” that are recognized by social institutions (e.g. schools, labor markets) because they “speak the same language.” Such knowledge and abilities are valued and rewarded by schools and other institutions, and, in the case of the labor market, are highly valued as a form of social and technical competency, which can influence screening practices in employment (Thomas 2018).

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The perceived technical competency of higher-status applicants can mediate the hypothesized effects of skin color via two possible mechanisms. If having a higher-class status “whitens” racial perceptions, and brown and dark skin applicants are perceived as “lighter”, employers who engage in “taste-based” discrimination may be more likely to offer them an interview at the hiring interface. Alternatively, if employers discriminate against brown and dark skin applicants because they have incomplete information about their productivity, as the statistical discrimination model suggests, having a perceived higher status may provide additional information about the applicant’s technical abilities and competency and mitigate the hypothesized negative effects of brown and dark skin. If this is the case, we would predict that

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applicants with light, brown, and dark skin with higher class status would be treated equally at the point of hire. Skin Color and Gender

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A growing body of work shows that colorism is a gendered process; discrimination on the basis of skin color has more bearing on the lives of women compared to men (Drake and Cayton 1945; Hunter 2005; Russell et al. 1992). Lighter skinned black women are even perceived as more sexually attractive, at least by African American men (Clark and Clark 1980), and so, amid a dominant U.S. discourse of physical attractiveness as tied to whiteness, many African American women feel compelled to change their physical appearance to emulate whiteness, using skin bleach, hair dye, and straightening combs (Russell et al. 1992). Yet, while respondents’ scores for women’s physical attractiveness increase as the skin color of the cue images lightens, this correlation is weaker and statistically insignificant for men (Hill 2002). Since physical attractiveness may be a more important factor for women compared to men (Greitemeyter 2010; Grabe and Hyde 2006) and light skin is highly associated with beauty standards (Clark and Clark 1980; Hunter 2005; Russell et al. 1992), it is hypothesized that colorism, defined in terms of preferential treatment based on skin color, has different effects on socioeconomic outcomes for men and women.

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The empirical evidence in support of the gendered aspects of skin color inequality and discrimination is unclear. Monk (2015: 423) examined perceptions of discrimination among black Americans and did not find significant gender differences in discrimination based on skin color among black respondents. However, Assari and Caldwell (2017) found that male youth with dark skin reported higher levels of perceived discrimination compared to male youth with lighter skin, though the differences in perception of discrimination were not statistically different between female youth with darker and lighter skin tones.

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In terms of observed wage differences and occupational status by skin color, research shows that lighter skin is associated with higher wages and higher occupational status among males, but not females (Hersh 2006; Monk 2013). These findings suggest that males with darker skin are more discriminated against compared to females with the same skin color. Contradicting this evidence, a recent study finds that the effects of skin color on a range of socioeconomic outcomes are stronger for darker skinned women than for darker skinned men (Ryabov 2018). Such differences in empirical evidence could be due to the different ways that scholars are measuring inequality and discrimination, with some studies focusing on perceived discrimination while others focus on wage decomposition analysis or differences in occupational status. In the present study, I adjudicate between different perspectives about the relative weight that skin color has for men and women by examining biases in employment. A BACKGROUND ON BRAZIL’S RACIAL CONTEXT The empirical focus of the present study is Brazil. Scholars have long recognized the two elements of racial commonsense in Brazil, namely the denial of racial prejudice and integration through racial miscegenation (Hanchard 1994; Winant 2001; Telles 2004; Twine 1998). A 7

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positive articulation of race mixture in Brazil is the product of a long history of sociopolitical, economic, and transnational factors (Telles 2004), and, while there is a discourse around racial egalitarianism in Brazil, negative stereotypes are still attached to Afro-descendants. According to a nationally representative survey, 47 percent of Brazilians showed some form of agreement with the statement “Good Blacks [Negros] Are Blacks with White Soul” (DATAFOLHA 1995: 129). This belief was shared by whites (46 percent), browns (47 percent), and blacks (48 percent). A full 43 percent of all respondents agreed, to some degree, with the statement “Blacks [Negros] are only good in music and sports.”5 When asked about their level of agreement about the statement, “Negros who don’t screw upon entering, do so on leaving,” 23 percent of respondents agreed or strongly agreed.6 Brazil’s racial stereotypes are also gender specific. For instance, nonwhite men (blacks and browns)7 are often associated with criminality and are mistrusted, while non-white women are associated with a lack of willpower and with being both domestic and sex workers (DATAFOLHA 1995: 121-128).

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Understanding the root causes of racial inequality in Brazil and assessing the role that discrimination may play in society, is, of course, a complex process. The relative absence of institutionalized racism, combined with a dominant discourse around race mixture (mestizaje), has made racial discrimination difficult to measure in the Latin American context (Telles 2004). Within this framework, the rigid class-structure that characterizes many Latin American countries could be expected to exert great influence in shaping racial inequality.8 i Given the significant levels of class inequality in Brazil, for example, it becomes difficult to empirically disentangle the effects of race-based discrimination from class-based inequality (Bailey 2009; Telles 2004).

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Evidence from public opinion surveys captures the complexities of the roles of race and class-based factors in shaping racial inequalities. For instance, although 27 percent of Brazilians with dark skin claim to have experienced discrimination due to their skin color, around 30 percent also claim to have experienced discrimination due to their socioeconomic status (Telles 2014: 212).9 Moreover, while 82 percent of Brazilians believe that “racial discrimination impedes negros [Afro-Brazilians] from getting a job” (Bailey 2009: 98), they point to class position as a more important explanation for racial inequality. For instance, nearly 60 percent of Brazilians agree that, “In Brazil, negros [Afro-descendants] are not discriminated against because of their color, but because they are poor” (Bailey 2009: 102),10 Thus, although ordinary Brazilians recognize that racial discrimination exists, most also believe that class discrimination

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40 percent of whites, 45 percent of browns, and 42 percent of blacks agreed or strongly agreed with the statement (DATAFOLHA 1995: 129). 6 25 percent of whites, 24 percent of browns, and 22 percent of blacks agreed or strongly agreed with the statement (DATAFOLHA 1995: 129). 7 The DATAFOLHA survey did not ask specifically about blacks (pretos) or browns (pardos), but rather “negros,” which combines blacks and browns in the same “negro” category. 8 Brazil, along with other countries in Latin America, is among the most unequal countries in the world. For instance, while the wealthiest 20 percent in Brazil share 60 percent of the total income, the poorest 20 percent share only 3 percent of the total income (World Bank 2009). 9 Among Brazilians with brown skin color, however, only 8 percent claim to have experienced discrimination due to their skin color, and about 21 percent claim to have experienced class-based discrimination (Telles 2014: 212). 10 Among Afro-Brazilians, 62 percent showed agreement to the statement compared to 57 percent of whites (Bailey 2009: 103).

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factors into racial inequalities. Whether the observed differences by race (and skin color) in the labor market are rooted in class-based inequalities and barriers (e.g., access to education) or active discrimination by employers is an unsettled question for most Brazilians. The present study seeks to deepen our understanding about the role that skin color plays in shaping stratification outcomes in that country. Data and Methods

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This article adopts a field experiment approach to test whether skin color is causally linked to hiring outcomes at the initial stage of the hiring process in Brazil. Such experiments combine experimental methods with real-life contexts to better isolate and understand causal mechanisms. The key component of experimental research is random assignment of people to treatment and control groups, which allows the researcher to isolate certain variables while manipulating others so that causal inferences can be more effectively drawn (Pager 2007; Riach and Rich 2002). In a labor market study such as this, the randomization of job applications to each job opening removes concerns over applicants’ omitted variable biases, such as unobserved worker characteristics (e.g., human capital), and employers’ omitted biases (e.g., employer preferences or organizational policies related to hiring). Randomization also removes concerns regarding differences in social network activation and mobilization (Fernandez and Fernandes-Mateo 2006; Smith 2005), both of which might be correlated with race and employment opportunities (that is, none of the job applicants relied on network ties to find jobs). For these reasons, field experimental methods allow us to draw unbiased causal estimates of the average treatment effects of skin color in employment and tease out how skin color interacts with class status.

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Each employer in the field experiment received one resume, for which I randomly assigned one skin color category, one social class category, and one gender, for job openings in sales and administrative assistant.11 Additionally, I randomized by city and occupation to ensure that similar numbers of resumes for each skin color, gender, and social class categories were submitted for each occupation and city.12 Measuring Skin Color Empirically

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Previous studies of skin color inequality have used interviewer-rates categorization when assessing patterns of skin color inequality and discrimination, yet, as mentioned above, this method has the potential to introduce measurement bias. Confounding factors that may shape observers’ skin color perceptions include other bodily/phenotypical characteristics (e.g., the size of lips, shape of nose, and type of hair) (Dixon and Telles 2017), social class (Villarreal 2010), political affiliation (Caruso et al. 2009), and even names (Garcia and Abascal 2016). The same person might be classified as “darker” or “lighter” depending on these characteristics, and,

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Initially I also included job openings for real estate agents, but excluded this job category from the analysis because of the low number of job openings over the course of the data collection (n=67) and the high callback rates (57 percent). I suspect that the resume for real estate agent was not a good representation of the average job seeker in this area, and the low number of openings suggests that employers likely use other means for recruitment (e.g. professional boards and word of mouth). 12 The project was approved by the Institutional Review Board at the University of California at Berkeley.

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consequently, studies relying on such measures may under- or overestimate patterns of inequality and discrimination.

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My approach circumvents these methodological and measurement limitations in several regards. First, the field experimental design allows us to control physical features and manipulate skin tone in order to examine its effects. If these physical features influence external perceptions of skin color, they will have the same effects for all skin color categories. For the purposes of methodological clarity and consistency of measurement, I divide the photographs used in this study into three categories: light skin, brown skin, and dark skin. To control for racialization (Bailey 2009) on the basis of phenotype, I purposefully use the same photographs, with the skin tone manipulated. To do so, I first selected a wide range of photographs (on the basis of age, apparent physical fitness, physical attractiveness, dress, body position, and facial expression) from an online depository and secured full photography releases. A focus group then helped select four photographs (two male, two female) and a Photoshop professional was hired to manipulate the skin tone of each photograph so that it would look “natural.” With three skin tone variants for each of the four photographs, I ended up with 12 photographs: four photographs for the light-skin category, four photographs for the medium-skin category, and four photographs for the dark-skin category. These photos are shown in Figure 1.

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One possible concern over holding constant other phenotypical characteristics associated with race, such as hair type and the size and shape of the nose and lips, is that doing so may generate an unnatural look of the photographs. This is unlikely the case because of the history of racial miscegenation in Brazil, and it is not uncommon to see a range of phenotypes, including brancos (whites) who have loose curly hair and tanned skin, or pardos (browns) and pretos (blacks) who have straight hair and tanned or dark skin. This is particularly salient at the margins, as boundaries around race or color blur (Butler 1998). Nonetheless, I recognize that not including other phenotypical characteristics may bias my estimates of discrimination since my photographs did not have characteristics often associated with Afro-centric phenotype, such as broad nose and full lips, which could interact with skin color to further limit economic opportunities to those with brown and dark skin. “Insert Figure 1 here”

Signaling Class Status on Resumes

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The second axis of variation in this experiment is class status. Rather than using a social scientific definition of class status, such as those tied to factors such as educational background, wealth, and income, I employ a lay definition of class status as either “middle class” or “lower class” (see Jackson 2009; Thomas 2018). Correspondence audit studies to evaluate class-based discrimination in the labor market have used several signals of social class, such as last names, undergraduate extracurricular activities, undergraduate athletic awards, and personal interests (e.g. Jackson 2009; Rivera and Tilcsik 2016; Thomas 2018). Although prior research has used a combination of class-based cues to consistently signal social class, it is not common to include undergraduate extracurricular activities or personal interests on resumes and/or job applications in Brazil. For this reason, I use only the fictitious applicants’ names (first and last) as a signal of social class in the present study. Scholars have long used names to signal ethnic and racial background on job applications (Bertrand and Mullainathan 2004; Brown and Gay 1985;

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Hubbuck and Carter 1980; McIntosh and Smith 1974). According to Broad (1996) and Clark (2014), one’s last name is an important indicator of social class. Recent correspondence tests in the United Kingdom and in the United States have used names to signal class background and test for discrimination (Jackson 2009; Rivera and Tilcsik 2016). In this article, I build on this existing literature and use first and last names to signal social class on the resumes.

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I draw on a list of first and last names from one compiled by Scottini (2012) in his analysis of typical names of public and private school students.13 I selected ten of the most typical public school forenames in public schools and randomly matched these with ten of the most common public school surnames. Because a few first and last names hold a strong ethnoracial signal as well as a class signal, owing to intentional migration from Italy, Germany, Lebanon, and Japan to Brazil, I selected only Portuguese sounding names (with clear Portuguese heritage) that would avoid the potential confounding variable of race signaling. I conducted a pre-test to examine the validity of the association between the first and last names and class status. The two first and last names with the highest composite scores (based on two questions) were selected to represent the “middle class” sounding names. The two first and last names with the lowest scores were selected to represent the “lower-class” status on the resume (Table 1).14

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“Insert Table 1 here”

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Creating the Bank of Resumes

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Aiming to create resumes that closely approximate those of real job seekers in São Paulo and Rio de Janeiro to increase the external validity of the experiment, I began by accessing resumes of actual job applicants posted online at www.monster.com.br and at www.linkedin.com. I removed the name, address, and contact information from these resumes, then altered the educational information and previous employers. In selecting the templates, I sorted through dozens of resumes to select those that looked “competitive,” judging by the qualifications, work experience (e.g., consistent work history with little or no employment gaps), and educational background (e.g., college, some college, courses, specialization, and seminars attended). To increase the chances of callbacks, resumes with little or no relevant experience and training were eliminated. After a preliminary review of over one hundred resumes, and, after consulting with a human resources manager in Brazil, I selected one resume for administrative assistant/clerical jobs and one resule for sales. The proportions of individuals working in these four occupations in Sao Paulo and in Rio de Janeiro are presented in Table 2. “Insert Table 2 here”

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An illustrative example, though far from academic, is an online parody in which a woman from a lower class background was trying to find her name on a can of Coca-Cola. The grocery store worker mocked her in presuming that her “lower class” name would be on a “quality” can of soda, and that she should instead look for her name on a “low quality” soda. https://www.youtube.com/watch?v=NZb0XKHgtjo 14 A complete list of first and last names used on the survey and a sample survey can be found in Appendices A and B.

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Responding to Job Ads

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I selected Brazil’s two largest metropolitan areas, São Paulo and Rio de Janeiro, to maximize the greatest number of job listings for the study. For São Paulo, I used the online and print versions of Folha de São Paulo and the Estado de São Paulo, in addition to online job bank websites empregasampa, vivaanuncios, and zap. For Rio de Janeiro, I used the print and online versions of O Globo, as well as the online job bank websites empregosrj, vivalocal, O Dia Classificados/O Globo, empregacarioca, and riovagas.15 With the help of research assistants, I logged each advertised position’s occupation, the name and contact information for each employer (email address and phone number if available), and any information on the specific requirements of the position provided in the ad.

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For each resume sent, I used a between-subject factorial design by randomly assigning a skin color category (light, medium, dark), gender (male, female), and social status (“poor,” “middleclass”) for sales and administrative assistant/clerical occupations. I block randomized for each city and for each occupation.16 In order to record the callbacks from employers, I set up 24 email accounts and 24 virtual phone lines with only active voice mail, one for each skin color, gender, class status, and city cell. Only invitations to appear in person for interview were recorded as a positive response (e.g., a “yes” or callback). In total, I responded to 820 job openings from September 2014 to July 2015.

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“Insert Table 3 here”

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Table 3 presents the distribution of the fictitious resumes sent. Each of the three skin color categories made up one third of resumes sent. Half of the sample was resumes with lower-class sounding names and half were resumes with middle class sounding names. About 60 percent of all resumes sent were in Rio de Janeiro and 40 percent were in Sao Paulo.17 About 58 percent of resumes were in sales and 42.4 percent in administrative assistant/clerical jobs.

Analytic Plan

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To answer the question concerning the effects of skin color on the probability of receiving a callback from employers, I used logistic regression and Linear Probability Models (LPMs). For the logistic regressions, I report estimates as average marginal effects (AME) of each predictor holding other predictors in the model at their actual value for each observation as well as the 15

Although I could not find the exact number of job seekers that find jobs through online and print job ads, such as the ones I used in this project, one research estimated that 44 percent of job seekers obtain jobs through social networks. The remaining 56 percent of job seekers rely on a variety means, such as online and newspaper (print) job postings, as well as through private companies that “match” resumes to employers (e.g. CATHO online) (Source: http://noticias.r7.com/educacao/noticias/pesquisa-revela-que-44-das-pessoas-conseguem-emprego-por-indicacao20100618.html). 16 Block randomization refers to a procedure when subjects are divided into subgroups or blocks, and complete random assignment occurs within each block (Gerber and Green 2012). 17 There were more job openings in Rio de Janeiro compared to Sao Paulo using the online and print sources, which resulted in a larger sample for Rio de Janeiro.

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average predicted probabilities. Data were split by gender for all regression models because the effects of skin color vary between male and female applicants. Reporting the average marginal effects and predicted probabilities, as well as the coefficients from Linear Probability Models allows me to make comparisons across models and between groups (Mood 2010).

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I also report the AMEs, the average predicted probabilities, and coefficients from Linear Probability Models when examining the interaction effects of skin color and class status, also split by gender. Although the main effects from linear probability models are generally quite similar to estimates from logistic regression, this is not the case for the interaction effects for nominal or continuous predictors (Ganzach et al. 2000). Prior research has found that the interaction effects from logistic regression and linear probability models differ in terms of the size of the effect, statistical significance, and direction of the relationship (e.g. Huselid and Day 1991; Landerman et al. 1989).18 In interpreting the interaction effects, I give priority to the estimates from logistic regressions (AME and average predicted probabilities). As Ganzach et al. (2000) explains, “on a priori basis, [logistic regression] is more likely to represent the specific features of a probability scale” (248). This is because logistic regression is constrained by a ceiling of 1 and a floor of 0, it is based on a S-shaped curvature, and it is often more stable under widely changing conditions (e.g. the interaction or the multiplication of any column or any row in the frequency table by a constant).

Sample Characteristics

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Results

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Table 4 displays the characteristics for the field experiment. Overall, the 820 resumes generated a total of 88 callbacks (a 10.7 percent callback rate). These callbacks were not equally distributed across males and females. Female applicants were about twice as likely to receive a callback compared to male applicants (14.4 percent vs 7 percent, respectively). Thus, even though roughly half of individuals working in sales are male and half are female, and, a slight majority of women work in administrative assistant positions in Brazil (38 percent male and 62 percent female), male applicants were much less likely to receive a callback for these occupations compared to female applicants in the field experiment. These patterns indicate that employers favor female applicants in sales and in administrative assistant positions in Sao Paulo and in Rio de Janeiro. Importantly, the descriptive findings also show small skin color differences in the callback rates for male applicants, but larger differences by skin color among female applicants. Female applicants with light skin were roughly twice as likely to receive a callback (18.7 percent) than female applicants with dark skin (9.4 percent). The differences in callback rates between female applicants with light skin and brown skin were smaller (18.7 percent vs 15.7 percent, respectively). However, among male applicants, the callback rates for light skin (6.7 This is because in logistic regression, “small changes in the probability of the outcome near the endpoints of the empirical range of the independent variables are associated with large changes of the independent variables” (Ganzach et al. 2000: 247). In linear probability models, on the other hand, “changes in probability represent a similar effect of the independent variables across the entire probability scale” (Ganzach et al. 2000: 248). 18

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percent), brown skin (6.0 percent), and dark skin (8.2 percent) were very similar. These preliminary findings only partially support the “direct discrimination” hypothesis, which predicted that skin color discrimination exists at the initial stage in the hiring process in semiskilled, white-collar occupations (sales and administrative assistant occupations). From a cursory glance at the callback rates, it seems that skin color discrimination only affects female applicants with dark skin, but not male applicants (of any skin color ) or female applicants with brown skin.

“Insert Table 4 here”

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Logistic Regressions, Predicted Probabilities, and Linear Probability Models

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“Insert Table 5 here”

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Table 5 presents average marginal effects obtained from logistic regression models and the coefficients from Linear Probability Models predicting callback rates using the sample of female applicants (Model 1 and Model 2) as well as the sample of male applicants (Model 3 and Model 4), adjusting for class status, city, job posting requirements, and occupation. According to Models 1 and 2, female applicants with dark skin had a 9.2 percentage-point lower probability of receiving a callback from employers compared to female applicants with light skin. Female applicants with brown skin had a 3 percentage-point lower predicted probability of receiving a callback compared to female applicants with light skin. As shown in Model 3 and Model 4 of Table 5, male applicants with dark skin had a 1.4 percentage-point higher probability of receiving a callback compared to male applicants with light skin. Male applicants with brown skin had only a 1 percentage-point lower probability of receiving a callback from employers.

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To compare the results of the models for male and female applicants, Figure 2 presents average predicted probabilities derived from Model 1 and Model 3 (Table 5), which adjust for class status, city, job posting requirements, and occupation. Female applicants with dark skin had a far lower predicted probability of receiving a callback from employers (0.094) compared to female applicants with light skin (0.186). On average, having dark skin among female applicants was associated with a 9.2 percentage-point decrease in the probability of receiving a callback (p < .05). The average predicted probability of receiving a callback was lower among female applicants with brown skin (0.157) compared to female applicants with light skin (0.186), but this difference was not statistically significant (p > .05). Male applicants with dark skin actually had a slightly higher probability of receiving a callback from employers (0.082) compared to male applicants with light skin (0.068), but this small difference was not statistically significant (p > .05). Male applicants with brown skin had a lower probability of receiving a callback (0.059) compared to male applicants with light skin (0.068), but this difference was also not statistically significant (p > .05). These findings only partially support the “direct discrimination” hypothesis, which predicted that hiring outcomes at the initial stages of the hiring process in sales and administrative assistant positions are causally associated with discrimination by 14

employers on the basis of skin color. These models suggest that skin color discrimination is associated only with hiring outcomes for female applicants with dark skin, but not causally associated with hiring outcomes for female applicants with brown skin and for male applicants (of any skin color).

“Insert Figure 2 here”

Is Skin Color Discrimination Mitigated by Class Status?

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Building on the existing research on skin color categorization (Caruso et al. 2009; Dixon and Telles 2017; Garcia and Abascal 2016), I hypothesized that perceived class status might mitigate the effects of skin color at the initial stages of the hiring process. Table 6 presents average marginal effects obtained from logistic regression models and coefficients from Linear Probability Models for the interaction of skin color and perceived class status using the sample of female applicants (Model 5 and Model 6) as well as the sample of male applicants (Model 7 and Model 8), adjusting for city, job posting requirements, and occupation.19 As shown in Model 5, the probability of receiving a callback from employers is 13.8 percentage-points lower among female applicants with dark skin and lower class status compared to female applicants with light skin and lower class status. Female applicants with brown skin and lower class status had a 7.9 percentage-point lower probability of receiving a callback compared to female applicants with light skin and lower class status. However, the average marginal effects are much smaller in magnitude among female applicants with middle class status. Female applicants with brown skin and middle class status had a 1.9 percentage-point higher probability of receiving a callback compared to female applicants with light skin. Female applicants with dark skin and middle class status had a lower probability of receiving a callback (around 4.5 percentage points) compared to female applicants with light skin and middle class status.

“Insert Table 6 Here”

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To compare the results for lower class and middle class female applicants, Figure 3 presents average predicted probabilities derived from Model 5 (Table 6), which adjust for class status, city, job posting requirements, and occupation. Although female applicants with middle 19

The results presented here show that the interaction effect in the Linear Probability Model is larger in magnitude for female applicants with brown skin (Model 6) compared to the average marginal effects (Model 5), but it falls short of statistical significance (p > .05). The coefficient for the interaction of dark skin and lower class status in the Linear Probability Model (Model 6) is smaller in magnitude compared to the AME for dark skin females and lower class status in Model 5. Also, the AME is statistically significant at p < .05 in Model 5, but the coefficient for the interaction of dark skin and lower class status in Model 6 falls short of statistical significance at p < .05. These patterns are consistent with prior studies that found that the results for the interaction effects often differ in logistic regressions and in Linear Probability Models (e.g. Huselid and Day 1991; Landerman et al. 1989). For the reasons described in the “Analytic Plan” section of the paper, I will base my conclusions regarding the interaction effects of skin color and class status from the average marginal effects and average predicted probabilities.

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class status and light skin had a higher probability of receiving a callback (0.194) than did female applicants with middle class status and brown skin (0.175) and females with middle class status and dark skin (0.130), these differences in predicted probabilities are not statistically significant (p > .05). These results contrast sharply with the patterns for female applicants with lower class status. Female applicants with dark skin and lower class status had a far lower probability of receiving a callback (0.058) compared to female applicants with light skin and lower class status (0.197). On average, having dark skin and lower class status is associated with a 13.8 percentage-point decrease in the probability of receiving a callback (p < .05). Female applicants with brown skin and lower class status had a lower predicted probability (0.118) than female applicants with light skin and middle class status (0.197), but this difference was not statistically significant. These patterns reveal just some of the barriers that face job applicants with dark skin and lower-class status in the initial stages of the hiring process. These findings are striking when considering the fact that the resumes used in the study had identical educational and work-related experiences. Importantly, these patterns illustrate the contextual nature of colorism in employment. These results show that class status mitigates the hypothesized negative effects of having dark skin among female applicants.

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“Insert Figure 3 Here”

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According to Model 7 in Table 6, as well as Figure 4, male applicants with dark skin and lower class status, and those with brown skin and lower class status had negligibly higher probabilities of receiving a callback than male applicants with light skin and middle class status (0.1 percentage-point and 1.8 percentage-point, respectively). Among males with middle class status, brown skin applicants had a 3.8 percentage-point lower probability of receiving a callback compared to light skin applicants. Male applicants with dark skin and middle class status had a 2.7 percentage-point higher probability compared to male applicants with light skin and middle class status. However, these small differences in the predicted probabilities among male applicants with light, brown, and dark skin with lower class status and with middle class status are not statistically significant (p > .05). These patterns continue to show that skin color discrimination is not causally associated with hiring outcomes at the initial stages of the hiring process and that class status does not mitigate the effects of skin color in predicting a callback among male applicants, at least for the occupations included in this study.

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“Insert Figure 4 Here”

Discussion and Conclusion A growing body of research shows that dark skin is associated with lower wages, lower educational attainment, and lower occupational status. Scholars suggest that two possible factors are associated with the socioeconomic disadvantage of individuals with darker skin tone: the legacies of past discrimination and contemporary discrimination (direct discrimination). Using a field experiment approach in two labor markets in Brazil, I tested whether employers

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discriminate on the basis of skin color. Overall, I found mixed results concerning the role of skin color in predicting the hiring outcomes at the initial stages of the hiring process in sales and administrative assistant positions. Skin color is not causally linked with hiring outcomes among male applicants and among female applicants with brown skin. However, the results presented here show clear and convincing evidence that having dark skin is causally associated with poorer hiring outcomes among female applicants.

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Among male applicants, the effects of skin color were weaker and statistically insignificant, suggesting that employers do not discriminate against male job seekers with dark skin and brown skin in administrative assistant and sales jobs. One plausible explanation for the positive effect, albeit small in size, of dark skin for male applicants is that employers used stereotypical expectations of productivity for job-related tasks. Employers typically use stereotypes about the characteristics that workers need to perform certain job-related tasks and such stereotypes influence hiring decisions. Administrative and clerical work are characterized by specialized work, such as filling, shipping, labeling, and billing. The preference for female applicants in clerical and administrative assistant jobs could be based on gender ideologies that characterize women as being “uniquely suited for boring, menial tasks where qualities of leadership or independence were totally unnecessary” (Davies 1982: 174). For non-technical sales positions (e.g. retail sales), such as those included in the present study, women may experience an advantage in comparison to male applicants because employers may see women as a better fit for selling to female customers.

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When employers are making hiring decisions, they are not, however, exposed to one single group-specific stereotype (e.g. gender, race, sexual orientation, immigrant status), but rather a constellation of stereotypical characteristics. These stereotypes can be amplified, such as being both black and female in occupations where being black and being female are associated with negative stereotypes (e.g. in managerial positions) (Collins 1999). Recent work by Pedulla (2018) provides an alternative possibility, whereby congruent stereotypes that are based on race and gender may not be amplified in certain contexts, but rather “muted.” For male applicants in the present study, employers may anticipate lower productivity based on gender stereotypes, but having the additional information about skin color is redundant, and, consequently, has an insignificant effect. In other words, the hypothesized effects of brown and dark skin were “muted” by the stereotype-consistent information provided by gender, which is consistent with the null effects of skin color for male applicants. Being female, however, was consistent with the productivity that employers expected from applicants, but skin color was used by employers to make hiring determinations. Another key finding from the field experiment was that, in situations where skin color is causally associated with hiring decisions, having a higher-class status mitigates these effects. Results from regression models showed that female applicants with light skin, brown skin, and dark skin were equally likely to receive a callback from employers when higher class status was signaled on their resumes. Among female applicants with lower class names, however, the differences in employment outcomes by skin color varied significantly. Female applicants with light skin and lower-class names were more likely to receive a callback compared to applicants

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with dark skin and lower-class names. Two possible mechanisms explain these patterns. If employers’ decisions at the hiring interface were motivated by animus, as the taste-based model predicts, having a higher-class status “whitened” racial perceptions, potentially reducing the amount of animus toward brown and dark skin applicants. If skin color discrimination in employment is motivated by imperfect information related to anticipated productivity, as the statistical discrimination models suggest, and higher-class status is associated with greater levels of social and technical competency (Thomas 2018), signaling a higher class status on the resumes may have given employers additional information that may have helped reduce bias on the basis of skin color.

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By experimentally manipulating class status and skin color, this study goes beyond previous research on skin color inequality by demonstrating empirically the multidimensionality of skin color discrimination. Previous research has theorized about the processes by which class status shapes racial categorization, while the present study is the first to demonstrate empirically how skin color combines with class background to shape patterns of hiring discrimination. I show that skin color interacts with class status in predicting whether applicants succeed or struggle in attaining entry-level occupations.

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My findings contribute to a growing literature on the association between skin color and socioeconomic outcomes by highlighting the existence of contemporary discrimination at the initial stage of the hiring process. Prior research in this area has been unable to effectively adjudicate between competing explanations for the observed patterns of skin color inequality. On the one hand, the observed patterns of skin color inequality could be attributed to past discrimination and the intergenerational transmission of advantages. On the other hand, scholars argue that skin color discrimination continues to shape life outcomes differently for lighter and darker skin individuals. Although quantitative studies have shown a correlation between skin color and socioeconomic outcomes, this study is the first to demonstrate empirically the conditions under which employers discriminate on the basis of skin color when evaluating job candidates at the initial stages of the hiring process. The results presented here indicate that direct discrimination in hiring only affects female applicants with dark skin. If the legacy of past discrimination is transmitted intergenerationally, this process is exacerbated by contemporary and direct discrimination by employers against females with dark skin. For male applicants in sales and administrative assistant positions, skin color is not causally associated with hiring outcomes during the initial screening for jobs.

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This article also contributes to the literature on racial stratification in Brazil specifically. For most of the twentieth century, Brazil’s official discourse of “racial democracy” has been based on notions of racial egalitarianism and racial harmony, in spite of overwhelming evidence of a persistent pattern of racial inequality. Scholars have proposed competing explanations for the persistence of this form of inequality, from the legacies of slavery and the historical transmission of disadvantage to social origins (e.g., parental education) and Brazil’s (indeed, Latin America’s) rigid class structure which prevents non-whites from experiencing social mobility, and, therefore, achieving parity with whites. Others still point to active discrimination in contemporary society as having an independent, possibly magnifying, effect on racial

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inequality (Telles 2004). These diverging perspectives have not been limited to scholarly work, but extend into the policy arena as some suggest that race-targeted affirmative action policies are necessary to undo the effects of past and present racial discrimination, while others promote class-targeted policies to increase equality in Brazil (Telles and Paixão 2013; Peria and Bailey 2014). The findings suggest that race-based discrimination, at least measured in terms of skin color, is a multidimensional phenomenon shaped by a combination of factors, such as gender and class status, not merely one’s perceived race.

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These barriers could be overcome with the implementation of affirmative action policies in the labor market, especially in entry-level occupations, that take into account the intersection of applicants’ skin color, gender, and class status. The bulk of the existing affirmative action policies are concentrated in four-year universities (Telles 2004; Telles and Paixão 2013), which affects a small percentage of the Afro-Brazilian population, in particular black women, who have lower levels of education. A more robust set of labor market policies targeting all women in entry-level occupations could potentially lead to a significant reduction in racial inequality in Brazil, especially the overall disadvantage women with dark skin face at the initial stages of the hiring process.

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In spite of the empirical, conceptual, and methodological contributions, this study is not without limitation. Although field experiments are better equipped to establish causal relationships between predictors and outcomes of interest compared to lab experiments, they are still limited in their ability to achieve a high degree of external validity because of the costs associated with conducting correspondence audit tests across a wide range of occupations. In this project, I focused my empirical tests on sales and clerical and administrative assistant jobs, which often require organizational and communication skills, all of which potentially favor female applicants. I also did not include unskilled and male-dominated occupations (e.g. drivers, loaders, industrial labor, and other types of labor-intensive occupations) or skilled technical occupations (e.g. software developers, engineers, attorneys, skilled accountants, and medical professionals). In male-dominated unskilled occupations (e.g. loader, construction laborer, drivers), it would be plausible to assume that skin color would not play a major role given the overrepresentation of Afro-descendent men in such occupations. However, it would be plausible to expect skin color to be more salient in skilled and professional occupations, where employers may engage in taste-based discrimination or statistical discrimination.

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Another aspect of this paper that limits my ability to achieve a high degree of external validity is that, similar to recent field experiments, which have analyzed a small number of labor markets (e.g. Gaddis 2014; Pedulla 2016; Tilcsik 2011), I focused my analysis on two metropolitan areas, thus excluding other labor markets in Brazil whereby markers of one’s race (e.g. skin color), and markers of social class (e.g. first and last names) could be context specific and have different meanings. Prior research has shown regionally-specific patterns of racialization in Brazil (e.g. Bailey and Telles 2006; Dias 2014; Telles 2004), which could affect how skin color is perceived and the extent of race-based discrimination. Different patterns of economic regional development, such as the ones observed in Brazil, could impact levels of competition between light, brown, and dark skin workers. Moreover, larger proportions of Afro-

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Brazilians and the smaller supply of light skin workers in the North and Northeastern regions of Brazil could potentially impact discrimination patterns, as employers might have to rely on available “non-white” labor (brown and dark skin applicants) to fill open positions. Alternatively, high minority concentrations have been associated with higher levels of anti-black sentiment and discriminatory behavior (see Quillian 1996; Taylor 1998). Future research would benefit from including a larger number of labor markets in different regions to empirically test whether the size of minority population is associated with high levels of employment discrimination.

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Lastly, my findings are limited to employer preferences at the initial stage of the hiring process, and cannot account for discrimination in later stages of hiring, such as at the interview, or in the promotion stages. In spite of these limitations, this article makes a significant contribution to our understanding of the role of skin color in generating inequality in hiring opportunities, and the overall reproduction of social inequality in Brazil. This article also makes inroads into the understanding of the complexities of skin color in hiring and how skin color combines with perceived class status and gender to create distinct opportunities for job seekers.

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Authorship Statement: I, Felipe Dias, confirm that I have contributed to all research, analysis, and development of the article and grant permission for the final version to be published. The manuscript is unpublished and not under consideration by another journal. The manuscript presents an original research analysis and conclusions. The manuscript is based on my dissertation research and has received funding from the National Science Foundation (Award #1203332). The manuscript has also won the Outstanding Graduate Student Paper Award from the American Sociological Association Section on Race, Class and Gender (2017).

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Wade, Peter. 1993. Blackness and Race Mixture: The Dynamics of Racial Identity in Colombia. Baltimore: Johns Hopkins University Press. Wagley, Charles. 1965. “On the Concept of Social Race in the Americas.” In Contemporary Cultures and Societies of Latin America, ed. by Dwight B. Heath and Richard N. Adams. New York: Random House.

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Winant, Howard. 2001. The World is a Ghetto: Race and Democracy since World War II. New York, NY: Basic Books.

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Figure 1. Photographs Used to Signal Skin Color Categories.

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Light Skin

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Figure 2. Average Predicted Probabilities of Receiving a Callback from Employers among Male and Female Applicants, Adjusted for Occupation, Class Status, City, and Job Requirements (Skills, Education, and Work Experience).

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Figure 3. Average Predicted Probabilities of Receiving a Callback from Employers among Female Applicants by Class Status, Adjusted for Occupation, City, and Job Requirements (Skills, Education, and Work Experience).

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of ro -p re lP ur na Jo Figure 4. Average Predicted Probabilities of Receiving a Callback from Employers among Male Applicants by Class Status, Adjusted for Occupation, City, and Job Requirements (Skills, Education, and Work Experience).

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Table 1. Selected Names for Lower-Class and Middle-Class Status Men

Women

Lower Class Status

Gleison da Conceição

Graziela da Conceição

Cleberson do Nascimento

Crislaine do Nascimento

Arthur Meirelles

Sofia Meirelles

Heitor Vasconcelos

Luísa Vasconcelos

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Middle Class Status

of

Class Status

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Table 2. Selected Occupations-Rio de Janeiro and São Paulo, 2010 Occupations Sales Clerical and Administrative Assistant

Male

Female

46.99%

53.01%

38%

62%

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Source: Instituto Brasileiro de Geografia e Estatística (2010)

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Table 3. Distribution of Applications Submitted in the Field Experiment Frequency

Percent

Skin Color Light Skin

273

33.2%

Brown Skin

274

33.4%

Dark Skin

273

33.3%

Total

820

100.0%

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Class Background Lower Class

Total

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Labor Market Rio de Janeiro

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Sao Paulo

Sales

Total

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Administrative/Clerical

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Total Occupation

49.8%

408

50.2%

820

100.0%

482

58.8%

338

41.2%

820

100.0%

472

57.6%

348

42.4%

820

100.0%

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Middle Class

412

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Source: Field Experiment Data

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Table 4. Proportions of Applicants Receiving Callbacks by Skin Color, Gender, and Occupation Percent Callbacks/Applications Callback Males

7.0%

28/402

Females

14.4%

60/418

Total

10.7%

88/820

Light Skin

18.7%

26/139

Brown Skin

15.7%

Dark Skin

9.4%

Total

14.6%

22/140

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13/139

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Males in Sales and Administrative/Clerical

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Females in Sales and Administrative/Clerical

Light Skin

6.7%

9/134

6.0%

8/134

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Brown Skin Dark Skin

8.2%

11/134

7.0%

28/402

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Total

61/418

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Table 5. Average Marginal Effects from Logistic Regressions and Linear Probability Models Predicting Callbacks among Male and Female Applicants Males

AME1 (Model 1)

LPM (Model 2)

AME1 (Model 3)

LMP (Model 4)

-0.030

-0.030

-0.010

-0.011

(0.044)

(0.045)

(0.030)

(0.030)

-0.092*

-0.092*

0.014

0.014

(0.041)

(0.042)

(0.032)

(0.032)

-0.042

-0.040

-0.014

-0.013

(0.034)

(0.035)

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(0.026)

(0.026)

0.008

0.028

0.031

(0.035)

(0.026)

(0.026)

-0.034

-0.036

-0.009

-0.009

(0.038)

(0.037)

(0.026)

(0.025)

-0.052

-0.046

0.025

0.019

(0.040)

(0.037)

(0.028)

(0.023)

0.019

0.014

0.100

0.080

(0.065)

(0.055)

(0.059)

(0.047)

0.052

0.052

0.088***

0.085***

(0.036)

(0.036)

(0.022)

(0.022)

Brown Skin

Dark Skin

Lower Class

MSA (ref. = Rio de Janeiro) 0.010

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Experience Required (Yes = 1)

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(0.035) Job Posting Requirement

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Class Status (ref. = Middle Class)

Skills Required (Yes = 1)

College Required (Yes = 1)

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Skin Color (ref. = Light Skin)

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Occupation (ref. = admin. Assistant) Sales

Constant

R-Squared

0.201*

0.004

(0.050)

(0.031)

0.033

0.035

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n (observations)

418

418

402

402

Note: 1Coefficient estimates are presented as average marginal effects calculated while holding other predictors at their actual value. Robust standard errors in parentheses.

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*p < .05, ** p < .01; ***p < .001 (two-tailed tests).

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Table 6. Average Marginal Effects from Logistic Regressions and Linear Probability Models Predicting Callbacks among Male and Female Applicants: Interaction Effects of Skin Color and Class Status Females Males AME1

LPM

AME1

LMP

(Model 5)

(Model 6)

(Model 7)

(Model 8)

-0.029

0.021

-0.009

-0.038

(0.044)

(0.066)

(0.03)

(0.042)

-0.091*

-0.045

0.013

0.024

(0.041)

(0.062)

(0.031)

(0.05)

Dark Skin

Lower Class

0.024

-0.014

-0.025

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Class Status (ref. = Middle Class) -0.042

(0.025)

(0.044)

(0.034)

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Brown Skin x Lower Class

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Dark Skin x Lower Class

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Brown Skin and Lower Class (ref. = Light Skin and Lower Class)

Dark Skin and Lower Class (ref. = Light Skin and Lower Class)

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Brown Skin and Middle Class (ref. = Light Skin and Middle Class)

Dark Skin and Middle Class (ref. = Light Skin and Middle Class)

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Brown Skin

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Skin Color (ref. = Light Skin)

(0.066) -0.101

0.055

(0.09)

(0.062)

-0.094

-0.02

(0.082)

(0.065)

-0.079

0.018

(0.06)

(0.043)

-0.138*

0.001

(0.054)

(0.039)

0.019

-0.038

(0.064)

(0.043)

-0.045

0.027

(0.061)

(0.051)

MSA (ref. = Rio de Janeiro)

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Sao Paulo

0.01

0.008

0.028

0.031

(0.035)

(0.035)

(0.026)

(0.027)

-0.029

-0.032

-0.01

-0.011

(0.038)

(0.037)

(0.026)

(0.026)

-0.05

-0.044

0.023

0.017

(0.04)

(0.036)

(0.028)

(0.024)

0.011

0.011

0.106

0.08

(0.062)

(0.055)

(0.062)

(0.048)

Skills Required (Yes = 1)

College Required (Yes = 1)

Occupation (ref. = admin. Assistant) Sales

0.053

0.053

0.089***

0.084***

(0.036)

(0.023)

(0.022)

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(0.036)

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Experience Required (Yes = 1)

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Constant

n (observations)

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R-Squared

418

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Job Posting Requirement

0.165**

0.012

(0.056)

(0.04)

418

402

402

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Note: 1Coefficient estimates are presented as average marginal effects calculated while holding other predictors at their actual value. Robust standard errors in parentheses. *p < .05, ** p < .01; ***p < .001 (two-tailed tests).

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Appendix A Table A1. List of First and Last Names and the Average Scores for Class Status

Names

Class-Based Score 1

Class-Based Score 2

Average

1.142857

1.238095

1.190476

Cleberson do Nascimento

1.142857

1.285714

1.214286

Crislaine do Nascimento

1.142857

1.380952

1.261905

Graziela da Conceição

1.190476

1.52381

1.357143

Jessica das Graças

1.095238

1.619048

Cleiton Batista

1.333333

1.47619

Tamires da Conceição

1.380952

1.714286

1.547619

Leticia Carvalho

1.952381

2

1.976191

Beatriz Gonsalves

2.047619

Manuela Henriques

1.952381

Guilherme Carvalho Gabriela Martins

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Gleison da Conceição

1.357143 1.404762

2.071429

2.238095

2.095238

2.095238

2.095238

2.095238

1.952381

2.238095

2.095238

2.238095

2.047619

2.142857

2

2.333333

2.166667

2.190476

2.238095

2.214286

Luísa dos Vasconcelos

2.333333

2.52381

2.428572

Arthur Meirelles

2.285714

2.571429

2.428572

Sofia Meirelles

2.380952

2.714286

2.547619

Heitor Vasconcelos

2.619048

2.47619

2.547619

Bernardo Henriques

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Lucas Martins

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Gabriel Gonsalves

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2.095238

Notes: This table shows the names used in the pilot-study survey that I conducted in Brazil in February, 2014. The top four names (first and last names) have the lowest averages for the two questions in the survey (see Appendix B) that asked respondents (N=27) from a convenient sample to rate the names based on perceived class (Class-based Score 1) and perceived educational level (Class-based Score 2). The scores ranged from 1=lowest class status or educational status to 3=highest class status or highest educational status. I added the two scores for each name in each survey to create a composite score for the two questions. I then averaged the composite score for each name (column 3). The lowest averages represent the names with the lowest class status, and the highest averages represent the names with the highest class status. I selected four names (two males and two females) to signal the “lower class” names, and

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four names (two males and two females) to signal the “middle class” names. The names selected are in bold.

43

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Appendix C. Sample Resumes

Sample Resume 1: Administrative Assistant

Crislaine Nascimento Rua Professor Manuel Lima, 44, Freguesia (Jacarepaguá), 22760-130, RJ Telefone: (21)3500-1950

Síntese de Qualificações

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OBJETIVO: ATUAR NA AREA ADMINISTRATIVA

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Email: [email protected]

Idiomas

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Sete anos de experiência na área administrativa. Conhecimento de rotinas administrativas, armazenamento, recebimento, logística reversa, inventário cíclico estático, regras de gerenciamento dos materiais, controle de estoque, e experiência em produção. Conhecimento de sistemas SHX, AS 400 (PRD I), CP, SAP, SABA, Risk Manager, Remedy (ARS), Regulus, e Leads.

Fluência em Inglês e Espanhol Formação Acadêmica

Universidade do Estado do Rio de Janeiro (UERJ), Pós-graduação em Administração, 2007-2008.

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Universidade do Estado do Rio de Janeiro (UERJ), Completo em Administração, 2002-2005. Histórico Profissional 09/2011-08/2014-Assistente Administrativa Senior-White Martins Gases Industriais Ltda. Realizando as funções abaixo: 

Responsável pela realização dos cadastros de clientes e fornecedores, materiais diretos e indiretos no SAP: inclusões, alterações, ampliações, desbloqueio, classificações fiscais, e toda manutenção desses itens;

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  

Desenvolvimento e implementação de melhorias e aprimoramento da equipe e coordenação de práticas de conscientização departamental; Desenvolvimento de fluxograma relacionado aos processos da área; Elaboração e manutenção dos manuais de procedimentos das atividades, e emissão de relatórios diversos.

04/2009-08/2011- Assistente Administrativa, Gestão Internacional-BRQ IT Services Principais Funções: 

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Responsável por controle de receitas e despesas de filiais do Exterior, contratação e pagamento de PJ's, controle de faturamento e recebimento de Projetos Globais, controle de férias de equipe corporativa do Exterior, controle de carta fiança da empresa.  Agendamento de reuniões, organização de viagens, suporte em projetos, coleta de informações para propostas comerciais, contratação de serviços de manutenção a serem realizados no escritório 12/2006-03/2009- Assistente Administrativa de Vendas- Piraquê S/A



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Atuação em dar suporte a equipe de vendas, comprar material de escritório, enviar pedidos de pagamentos de todas as contas regionais; Responsável pelo controle de gastos fixos e variáveis, enviar informações para auxiliar na análise de resultados Acompanhamento e cobrança de todos os pagamentos de verbas aos clientes e distribuidores, controlar prazos da regional, acompanhar e resolver pendência com o RH de todos os colaboradores regionais, analisar ferramentas que possam auxiliar no resultado e desenvolver, lançar despesas da equipe regional no sistema e pedir reembolso, acompanhar o pagamento desse reembolso, enviar pedidos de amostras.

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Principais Funções:

Sample Resume 2: Sales

Cleberson Nascimento

Jo

Rua Joaquim Méier, 755, Méier, RJ, 20725-050 Telefone: (21) 35001949 Email: [email protected]

OBJETIVO: ATUAR NA AREA COMERCIAL

46

Síntese de Qualificações Experiência em gestão de equipes comerciais, por empresas conhecidas e renomadas no Mercado de bens de consumo. Buscando novas oportunidades de trabalho e aprendizado, disposto a conhecer novos mercados e a contribuir com minha experiência em treinamento e gestão de pessoas na área comercial. Treinamento e Gestão de Equipes de vendas, definição de metas e estratégias para atingimento das mesmas. Acompanhamento Diário de vendedores ao Mercado, buscando solucionar problemas e conquista de novos clientes. Aplicação das técnicas de coaching diretivo e participativo, seminal e mensal de feedback de acordo com as Necessidades da equipe.

Idiomas

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Fluência em Inglês e Espanhol

Formação Acadêmica

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PUC-RJ Bacharel, Administração Geral, 2003-2006

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Histórico Profissional 11/2010-09/2014- Vendedor na Kavo do Brasil

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Principais Realizações:

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Atuei na área de radiologia odontológica, venda de tomógrafos e panorâmicos de grande valor agregado. Realizei visitas para todo o Brasil, atuando na área de montagem de equipamentos, suporte técnico, e participei em diversos eventos e atuei na elaboração de campanhas. 02/2008-09/2010- Gerente de Contas na Lan Professional

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Principais Realizações:

Trabalhei na área de vendas de grandes projetos, atuando na área de técnica de editais, treinamento de servidores, storages e softwares do fabricante. Fui responsável pelo comprimento das metas, configurações técnicas utilizando o configurador oficial disponibilizado aos parceiros pela Sun Microsystems. 12/2006-01/2008- Vendedor Interno na Ingram Micro

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Principais Realizações:

Responsável pelo atendimento a clientes, gestão de pedidos, cotações de projetos. Atuação com carteira de clientes corporativos comercializando todo o portfólio da empresa. Diversos treinamentos em produtos de informática distribuídos pela companhia. 01/2005-12-2006- Vendedor Interno na RCA Sistemas Responsável pelo planejamento, contratação e gestão de equipe comercial, acompanhamento e treinamento de vendedores em campo e em sala. Atuei na area de definição de estratégicas comerciais

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e metas de faturamento anual, considerando curva sazonal. Responsável pela roteirização de áreas de atendimento.

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