On the blurring of the color line: Wages and employment for Black males of different skin tones

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1 On the blurring of the color line: Wages and employment for Black males of different skin tones Daniel Kreisman and Marcos A. Rangel Gerald R. Ford School of Public Policy, University of Michigan Department of Economics, University of São Paulo, Brazil Population Research Center, NORC, University of Chicago Research Program in Development Studies, Princeton University Revised March, 2013 Abstract We evaluate the role skin color plays in earnings and employment for early career Black males in the NLSY97. By applying a novel, scaled measure of skin tone to a nationally representative sample, and by estimating the evolution labor market differentials over time, we bridge a burgeoning literature on skin color with more established literatures on Black White wage differentials and labor market discrimination. Our results suggest that a blurring of the color line elicits subtile yet meaningful variation in the evolution of inter and intra racial wage gaps over time. Keywords: skin color, wages, employment, race, discrimination. JEL: J150, J710, J240, J310. kreisman@umich.edu; mrangel@princeton.edu This research project and the data collection attached to it would not have been possible without the support from Dan Black and Robert T. Michael. We are also grateful to a number of the University of Chicago s administrative staff who were kind enough to share their views on the role of complexion within the African American community in South-Chicago. We wish to thank Cynthia Cook-Conley in particular. Thanks are due to Dan Black, Kerwin Charles, William Darity Jr., Jeffrey Grogger, Ricardo Madeira, Ann Morning and Heleno Pioner for comments on earlier drafts. Comments from participants of the Population Association of America Meeting (2011), and workshops at the Harris School and the University of Sao Paulo are gratefully acknowledged. The usual caveat applies. 1

2 1 Introduction Nearly a half century since the passage of Civil Rights legislation in America it remains clear that Black workers earn less on average than their White peers with similar observable skills, education, background characteristics and experience. While numerous contributions to the economics literature benchmark these labor market differentials across race, significantly fewer studies address disparities that emerge within the African American community itself. 1 Some recent evidence implies associations between perceived Whiteness for Black Americans and labor market outcomes, including racially distinct first names (Bertrand & Mullainathan, 2004; Fryer & Levitt, 2004), facial features (Belleti et al., 2008), speech patterns (Grogger, 2011) or simply acting White (Austen-Smith & Fryer Jr., 2005; Fryer & Torelli, 2009). We add to this literature by estimating the relationship between labor market outcomes and the most salient indicator of race in the US: skin tone. To accomplish this, we employ novel data on skin tone collected as part of this project in the 2008 wave of the National Longitudinal Study of Youth (NLSY97). While this is not the first study to estimate the relationship between skin color and labor market outcomes (see Hersch, 2006, 2008; Hughes & Hertel 1990; Goldsmith et al., 2007) we present three significant advantages over previous efforts: first, we make use of a standardized visual measure of skin tone used by all interviewers; second, we apply this measure to a nationally representative sample of early career Black and White males with detailed records of skill accumulation and labor market outcomes; third, we exploit the panel nature of our data to observe the evolution of wage gaps over the early careers of these respondents. We begin, following a tradition initiated with Neal and Johnson (1996), by including measures of skills accumulated before individuals become active in the labor market and observing the residual differences in employment and wages that emerge during the interaction between suppliers and demanders of labor. We build on these results, following Oettinger 1 See Carneiro, Heckman & Masterov, 2005; Lang & Lehmann, 2012; Neal & Johnson, 1996; O Neil, 1990; Ritter & Taylor,

3 (1996) and Altonji and Pierret (2001), by interacting race and skin color with measures of labor market experience, allowing us to view the evolution of wage disparities as workers accumulate skill and match quality is revealed. We find that while the lightest third of the Black complexion distribution earn more than the median group, there is no skin color differential between the median and darkest terciles, suggesting a positive, non-linear relationship between skin color and wages for Black males. While controlling for a host of background characteristics, childhood circumstances, education and a measure of skill reduces the Black-White gap by nearly half, these controls have smaller effects on the intra-racial light-dark gap. Turning our analysis to employment, we find virtually no difference in the share of weeks employed across skin color, despite significant differences across race. Lastly, longitudinal exercises indicate that the skin color gap widens as respondents accrue labor market experience. These results suggest that viewing racial disparities in stark Black and White ignores subtitle differences that emerge within race from what Alba (2009) calls a blurring of the color line. The remainder of the paper is organized as follows. Section 2 briefly summarizes theories of labor market discrimination and previous research on skin color. Section 3 describes the NLSY97 sample, our measure of skin color, and the relationship between skin color ratings and interviewer characteristics. Section 4 describes our empirical strategy and relates it to previous work. In Section 5 we discuss results and assess the robustness of our findings. Lastly, conclusions and suggestions for future research are laid out in Section 6. 2 Background 2.1 Theories of Black-White labor market differentials Two prominent economic theories have been advanced for the existence of racial gaps in wage offers. 2 The first, termed taste based prejudice, is attributable to Becker (1971). In this 2 see Lang & Lehmann, 2012 for a full discussion 3

4 view, prejudiced employers gain disutility from interacting with minority workers and thus offer lower wages for comparable work. In this scenario, prejudiced employers and minority workers sort away from one another in the labor market. Thus, wage differentials between minority and non-minority workers of the same skill and experience arise when minority workers are unable to find employment with non-prejudiced employers. The likelihood of this occurring is increasing both in the minority share of the labor force, and the share of employers that are prejudiced. In the case that sorting is not possible, the level of prejudice of the marginal discriminator determines the wage gap. Charles and Guryan (2008) test Becker s theory by combining regional measures of racial attitudes with estimates of the size of the Black labor force, finding that Black-White wage inequality increases with both Black labor supply and White racial distaste as the theory predicts. A second hypothesis suggests that employers are faced with incomplete information about potential employees. In this case, employers base decisions on the average characteristics of the group to which potential employees belong. Of course, this reasoning is not restricted to race and can be applied equally to gender, age, sexual orientation or any other feature. Such ignorance or limited information generate what is referred to as statistical discrimination in the literature (Aigner & Cain, 1977; Phelps, 1972). Empirical research on this version is more robust and largely relies on the analysis of employers learning about actual productivity as workers accumulate experience or tenure in combination with labor market search models In this spirit, Oettinger (1996) proposes a two-period model where Black and White workers have identical average productivity, but employers and employees only observe a noisy ex ante signal of match quality. After the first period, true productivity is realized and workers either choose to stay, and earn the wage associated with their realized match, or to leave and draw again from the distribution of employers. The key assumption here, a hallmark of the statistical discrimination literature, is that the signal of match quality is less precise for Black workers. Thus, sorting is more difficult and unproductive job changes are more likely. From this, the author s model yields four key predictions. First, that the Black-White 4

5 wage gap will be zero at entry; second, that a gap emerges at high levels of experience; third, that Blacks have smaller returns to experience; and fourth, that Blacks have larger returns to tenure. 3 Testing these predictions using the NLSY79, the author confirms hypotheses 1-3, but fails to find evidence of hypothesis 4. We conduct similar tests with respect to both race and skin color in our empirical exercises below. Similarly, Altonji and Pierret (2001) argue that observable measures of skill should decrease in importance as employers learn about employees productivity over time, while the importance of productivity measures observable only to the econometrician should increase over time. Accordingly, if employers use race as a source of information, adding interactions between experience and unobservable skill measures (such as AFQT) should lead to a more positive coefficient on the interaction term between race and experience, and should increase the main effect of race. Conversely, if employers do not, or only partially, use race as a source of information, then the race gap should be small when experience is zero, should widen over time if race is negatively correlated with the unobserved productivity measure, and controlling for unobservable productivity should decrease the race gap in experience. The authors confirm that the importance of productivity measures observable at time of hire decrease over time, and that the importance of measures unobservable at the time of hire increase in importance. Apposite to our case, they conclude that there is little evidence that firms statistically discriminate on the basis of race. Additionally, several studies look at specific traits associated with race for Blacks and Whites that employers might use to elicit information about potential productivity. For example, Grogger (2011) find that Black NLSY97 respondents who were more consistently identified as Black using a minimal amount of speech by a group of listeners earned less than those not consistently identified as Black. In an audit study, Bertrand and Mullainathan (2004) find that fictional resumes with White names received nearly fifty percent more callbacks than those with Black sounding names. In response, Fryer and Levitt (2004) find 3 Two other predictions are contingent on assumptions about wage contracts, which are not necessarily relevant here. 5

6 that for Blacks born in the 1980 s and 1990 s, distinctively Black names are not negatively correlated with later life outcomes once childhood circumstances are accounted for. Lastly, a separate literature estimates labor market differentials between Black and White workers by observing residual differences in employment or earnings after accounting for measures of education, skill, experience and background characteristics. We draw specific attention to seminal work by Neal and Johnson (1996), who conduct these exercises with respect to wages, and to Ritter and Taylor (2011), who conduct similar exercises with employment. While our data do not allow us to clearly distinguish between the theories described above, we conduct similar empirical exercises allowing us to frame our results within the existing literature while bridging this work with a burgeoning literature on differentials within race and across skin color as described below. 2.2 Skin color in previous literature While few data sets contain information on the skin color of Americans, a small number of specialized studies have sustained research on the topic with mixed results. The National Survey of Black Americans (NSBA) collected information on three generations of 2,107 adults between 1978 and Skin color in the sample was collected from non-standardized interviewer perceptions on a Likert scale with five categories: 1. Very Dark Brown; 2. Dark Brown; 3. Medium Brown; 4. Light Brown/Light Skinned; 5. Very Light Brown/Very Light Skinned. Using these surveys, Hughes and Hertel (1990) indicate that, conditional on parents socioeconomic status, lighter Blacks were more likely to hold more prestigious occupations than their darker peers. Goldsmith et al. (2007) revisit the same data comparing two definitions of race - the first coined the one-drop rule in which only race matters, and the second the rainbow rule in which complexion matters. They find that even after controlling for demographic characteristics, education and workplace characteristics, the average African American earns over 16 percent less per hour than a lighter complexioned peer. Similarly, using the Multi-City Study of Urban Inequality ( ), administered to 6

7 approximately 8,500 households in Atlanta, Boston, Detroit and Los Angeles, also using a likert scale to classify skin color (light, medium and dark), the same authors find that wages decline with darkness of complexion. Hersch (2006) also explores these data to discuss differences in labor market outcomes according to skin color, but her findings indicate that among males, wage gaps are seemingly unrelated to variation in complexion. More recently, Akee and Yuksel (2010) use the Coronary Artery Risk in Young Adults study (CARDIA), which is the richest example regarding the objective collection of skin color measures based on readings from a reflectance spectrometer, finding that the salience of skin tone has decreased for women over time, but not for men. Finally, the New Immigrant Survey (NIS, 2003) collected information on the skin color of recent legal immigrants to the U.S. using the same eleven-point scale applied to respondents of the NLSY97 who are the focus of this paper, as described below. Hersch (2008) analyzed these data finding significant labor market differentials across skin color for immigrants of all races. We note that skin color for immigrants may play a different role in labor market outcomes than it does for African Americans. 3 Data: The NLSY97 The National Longitudinal Study of Youth 1997 cohort is administered annually to a nationally representative sample born between 1981 and Initial rounds were designed to provide a detailed description of each respondent s home environment and behaviors with a strong focus on human capital accumulation, school-to-work transitions and subsequent labor market outcomes. In 2006 a proposal for the inclusion of skin color was submitted by one of the authors to the NLSY s Principal Investigators and to the Bureau of Labor Statistics (BLS), the government agency responsible for the NLSY surveys. The scale was adopted for the 2008 wave of data collection (Round 13); field work was facilitated by the fact that NORC also 7

8 conducted interviews for the New Immigrant Survey, for which the original skin color scale was drawn five years earlier. The scale ranges from 0 (lightest) to 10 (darkest), and presents color images of human hands with identical forms but different skin tonalities; a facsimile version of the scale can be seen in Figure A.1 in the Appendix. 4 Interviewers are instructed to review and/or memorize the scale pre-interview in order to rate respondents without presenting the card directly. Of the 3,455 Black and White male respondents in the NLSY survey, 2,837 were interviewed in ,417 of these respondents received a skin color rating in 2008; the majority of those not rated were interviewed by phone. Although 95 non-rated respondents received skin color ratings in 2009 or 2010, we omit these respondents from our primary analysis as interviewer effects are arguably different across waves. 5 Figure 1 shows a histogram of skin color ratings for all 2,417 Black and White males who received a skin color rating in Although the scale is different, the distribution of skin tone across and within race is strikingly similar to that found from using a reflectance spectrometer in the CARDIA study. 6 [Figure 1 about here.] 3.1 Interviewer characteristics and measured skin color In general, information in the NLSY is collected in person. NORC interviewers were largely matched to the demographic majority of the surveyed area in an attempt to maximize response rates. Regarding racial composition, 63 percent of interviewers are White and 19 percent are Black. Most are female, have some college education and are above fifty years of age. Column 1 of Table 1 shows means of interviewer characteristics for the 167 interviewers who completed skin color ratings for Black and White respondents in Columns 2-4 show 4 See Massey et al. (2003). 5 We do include them in one of our robustness checks with no change to results. 6 Figure 1, pp.35, in Akee and Yuksel (2010) 8

9 mean interviewer characteristics weighted by the 2,417 NLSY respondents who received skin color ratings in [Table 1 about here.] Table 2 shows means of skin color ratings by interviewer characteristics for Black and White male respondents. While differences across interviewer age and gender are trivial, we find that the average skin color rating of Black respondents interviewed by Black interviewers (5.9) is more than one unit lower (lighter) than it is for those interviewed by non-black interviewers (6.7). The former also report skin color with higher variance among Blacks than the latter. These simple correlations raise an important issue: despite the use of a standardized skin color measure, skin color gradations may still depend on subjective evaluations correlated with interviewer race or the racial composition of the respondent group interviewers contact in the field. We address this concern by evaluating the reliability of interviewer skin color ratings. [Table 2 about here.] 3.2 Assessing the reliability of interviewer ratings Ideally we would have several interviewers rate each respondent and conduct inter-rater reliability tests. In lieu of this, we approximately test the reliability of skin color ratings using siblings. As a thought experiment, imagine that each respondent has only one twin sibling. Now imagine that all siblings who live apart are interviewed by different interviewers while all siblings living together are interviewed by the same interviewer, and that living apart is an idiosyncratic phenomenon. We could then examine the correlation between ratings by these different interviewers as if we had two measures for the same individual. That is, we 9

10 would estimate (γ 1 ) in a dyadic linear regression between the skin color of sibling i, as rated by interviewer r, and the skin color of sibling j, as rated by interviewer s: color r i = γ 0 + γ 1 color s j + ω i (1) plim ˆγ 1 = cov(colorr i, color s j) var(color s j ) (2) While this quantity would be ideally suited for an evaluation of classical measurement error, this is unlikely to be our case. If observed skin color is equal to the true color plus some error, and skin color measures are discrete and bounded from above and below, 0-10 in our case, then the measurement error is necessarily negatively correlated with the true color value and should be considered non-classical. Moreover, the truncation of the distribution of errors generates a non-zero correlation between errors of different raters. 7 In other words, for respondent i rated by interviewer r, we observe: colori r = color + ε r i, and can rewrite the slope coefficient from equation (2) above as: plim ˆγ 1 = cov(color i, color j ) + cov(ε r i, color j ) + cov(ε s j, color i ) + cov(ε r i, ε s j) var(color j ) + 2cov(εs j, color j ) + var(εs j ) (3) Assuming identical covariance between the error and the true measure, independent of rater, Equation 3 simplifies to: plim ˆγ 1 = cov(color i, color j ) + 2cov(ε s j, color j ) + cov(ε r i, ε s j) var(color j ) + 2cov(εs j, color j ) + var(εs j ) (4) Equation 4 reveals opposing forces. On one hand, a positive relationship between errors by different raters, cov(ε r i, ε s j) > 0, would overestimate the reliability of our measure. On the other, legitimate variation in phenotypes expected within a given family, as predicted by the genetics of skin color determination, cov(colori, colorj ) < var(colorj ), would move the estimated value of the slope parameter away from unity, even if correlated measurement 7 See Black et al. (2000), section 2.2 in particular, for a thorough discussion. 10

11 errors were not an issue. 8 Therefore, we cannot consider the estimated quantity as an exact indicator of the relevance of the measurement error, but only as a raw approximation of it. Despite differences between the ideal experiment and the variation available in our data, we estimate the slope coefficient from a dyadic regression of White, Black or Hispanic pairs of individuals (N=1,082) and find point estimates between 0.71 and 0.73 that are statistically significant (and different than one) at conventional levels. These results are robust to taking into consideration that discreteness in the skin color scale generates a non-trivial amount of correct classification between siblings simply by chance. In addition, we computed several traditional measures of reliability using the same sample. 9 Despite the absence of consensus regarding benchmark values, the overall impression is that these numbers indicate a low, yet not extremely low, reliability of our skin color measure. Finally, we investigate whether interviewers are affected by relative reference groups when rating skin color by examining whether the correlation is higher between siblings who co-reside and are interviewed by the same interviewer than it is for siblings that live apart and who are rated by different interviewers. Correlations are indeed stronger among the former, but we are still able to reproduce equivalent reliability measures between those same-rater and different-rater pairs of siblings once we account for interviewer characteristics using a fixed-effects specification. Therefore, if this type of subjectivity is a factor in our estimates, it should be eliminated when interviewer characteristics are controlled for. 3.3 Analysis sample [Table 3 about here.] Table 3 describes the sequence of restrictions used in order to reach our working sample. We restrict our analysis to non-hispanic Black and White males, avoiding the modeling 8 See Banerjee et al (1999) on inter-rater reliability indicators, and Barsh (2003), Diamond (1994), Parra et al. (2004) and Strum et al. (1998) on the genetics of skin color. 9 The resulting Cohen κ-statistic is 0.24, Scott s π-statistic is 0.24, and Holley and Guilford s G-index is

12 of female labor force decisions which are often substantively different for Black and White women. 10 We limit analysis to respondents who have AFQT scores, a measure of highest grade completed, and who have entered the labor market, defined as the first of four consecutive semesters of non-enrollment. The employment sample is restricted to respondents with labor force participation measures, and the wage sample is restricted to non-self employed, nonmilitary, 30-plus hour per week jobs. We divide Black respondents into terciles of the Black skin color distribution, where skin tones 1-5 comprising the lightest third, and 8-10 the darkest. Table 4 shows summary statistics across race and skin tone for Black and White respondents in our sample; results for the employment sample are similar. Although not statistically different at conventional levels, Table 4 reveals subtile differences in AFQT, maternal education, region of residence and family wealth measured in 1997 across skin tone within the Black community. [Table 4 about here.] 4 Empirical specification Our interest is in wage and employment differentials that emerge within race and across skin color that cannot be accounted for by differences in background, experience or skill. We model this relationship first on the pooled sample and then add interactions with experience to analyze the evolution of inter- and intra-racial wage gaps as productivity and match quality are observed. In all specifications, we assume that skin color has no significance for White respondents in the labor market and accordingly set color equal to zero for Whites. 10 See Neal,

13 4.1 Cross-sectional analysis We begin our analysis by pooling observations across waves, estimating versions of: y itj = α + β f(black i, Color i ) + γ 1X 1,it + γ 2X 2,i + δ S it + τ t + η j + ε it (5) Where y itj is either the natural log of real wages or the share of weeks employed 30+ hours in the past interview year for individual (i) in year (t) rated by interviewer (j) in S it includes highest grade completed at time t and AFQT standardized over Black and White males with skin color ratings. The vector X 1,it includes age, MSA status and region at time t. X 2,i includes age at labor market entry and contains time invariant measures (as of 1997) of childhood circumstances, including: household poverty ratio, if the family ever received government aid, if respondent lived with both parents at age 6, lived in the South or outside an MSA at age 12, mother s highest grade completed, height and weight, and proxies for behavioral problems constructed using principal component analysis (school suspension, use of alcohol or drugs, characteristics of peers, and participation in illicit acts). 11 In each exercise we present two parameterizations of f(black i, Color i ). The first, which we call the blurred color line, divides the Black population into terciles of the Black skin color distribution. The second, termed the sharp color line, excludes skin tone and compares Blacks and Whites in the traditional sense. Blurred Color Line: f 1 = β 1 LightBlack i + β 2 MedBlack i + β 3 DarkBlack i Sharp Color Line: f 2 = ˇβ 1 Black f 1 = β 1 LightBlack i + β 2 Black i + β 3 DarkBlack i f 1 re-estimates the blurred color line replacing the median tercile with a main effect for Black, allowing the reader to interpret coefficients on the first and third terciles as relative 11 See the Data Appendix for definitions and a list of all constructs. 13

14 to the median Black respondent. In this case, β 1 = β 1 β 2, β 3 = β 3 β 2, and β 2 is identical in f 1 and f 1. We also show outcomes from a non-parametric specification with dummy indicators for each separate skin tone in the Appendix. In our preferred specification, we include interviewer fixed effects (η j ) to account for subjective variation in skin color ratings across interviewers. Otherwise, we include controls for interviewer characteristics listed in Tables 1 and 2 above. We follow with series of robustness checks to test for sensitivity to alternative sample definitions. Since employment shares are bounded by 0 [ de t d t ] 1, we estimate a generalized linear model (GLM) using a quasi-maximum likelihood estimator based on a logistic transformation, as described in Papke and Wooldridge (1996), allowing us to abstract the share of weeks employed between interviews from the number of weeks with valid labor market outcomes during that time. 12 To facilitate interpretation of these coefficients, marginal effects are reported for employment. 4.2 Longitudinal analysis We extend this analysis to investigate how intra-racial wage differentials evolve by including interactions between skin tone and experience as described in Equation 6 below: y itj = α + β f(black i, Color i ) + γ 1X 1,it + γ 2X 2,i + δ S it + ρexperience it + ω 1f(Black i, Color i ) Experience it + ω 2S it Experience it + τ t + η j + ε it (6) This strategy builds on previous work by Altonji and Pierret (2001). The authors argue that at time of hire wages will be strongly correlated with observable measures, such as education, and the relationship between unobservable skill measures, such as AFQT, will be low. Over time, the importance of the former should decrease and the value of latter should increase, 12 We test several other specifications, including a dummy indicating more than 75% of weeks employed, the total number of weeks employed, share of weeks unemployed and an alternate estimator accounting for mass points at 0 and 1 with little substantive change to results. Results are available upon request. 14

15 evidenced by negative coefficients on interactions between education and experience, and positive coefficients on interactions between experience and AFQT. They proceed to test whether employers elicit information from race until true productivity is revealed. In this case, interactions between Black and experience, or dark skin and experience for intra-racial comparisons, should be positive despite a negative main effect. Analogously, these specifications approximate exercises in Oettinger (1996). While Altonji and Pierret use potential experience to avoid the endogenous nature of tenure or actual experience, Oettinger specifically compares interactions between race and both experience and tenure, predicting a higher returns to tenure for Blacks and to experience for Whites, as described in Section 2 above. Translating this to skin tone differentials, we would expect little difference in wages at labor market entry and higher returns to tenure for dark skinned, relative to lighter skinned, Black respondents, and higher returns to experience for light skinned Blacks, compared with their darker skinned peers if skin color is used as a source of information by employers. Accordingly, we model two versions of interactions between skin color and experience. The first interacts race and skin color with potential experience, defined by the number of years since labor market entry. The second adds to this main effects and interactions with job-specific tenure, allowing us to interpret results with respect to the existing literature. 5 Results 5.1 Wages Our main specification, Table 5, shows the impact of various controls on inter and intra racial wage gaps, as described in Equation 5 above. We begin with raw differences in Column 1, controlling only for year effects, and find that accounting for educational attainment and AFQT in Column 2 reduces the sharp Black-White gap (Panel B) by roughly one-third, from 21.5 to 13.5 percentage points, and reduces the intra-race light-median gap by one- 15

16 quarter, from 8.1 to 6.1 percentage points (Panel C), and has no discernible effect on the medium-dark gap, which remains statistically indistinguishable from zero. The effects for each complexion group can be observed in Panel A. Column 3 replaces S it (AFQT and education) with a host of background controls accounting for individual characteristics, geography and childhood circumstances. Comparatively, these controls account for a larger share of the Black-White wage gap, nearly one-half, than do controls for education and AFQT. The effect of controlling for background characteristics on the light-dark gap is relatively similar to controlling for only education and pre-market skills. This implies larger differences in Black-White background characteristics correlated with wages than skill accumulation, particularly in the case of lighter Blacks. Column 4 includes both sets of controls which explain a larger share of the inter-racial gap in wages. Intra-racial gaps remain less sensitive to the addition of covariates. Columns 5 and 6 add interviewer characteristics and interviewer fixed effects respectively. Our preferred specification, in Column 6, reduces the Black-White wage gap by approximately half, from 21.5% to 11.8%. These estimates are in line with recent literature, highlighting the fact that differences in pre-market skills and background characteristics explain a significant share of observed wage disparities. 13 In contrast with the effect on inter-racial wage gaps (Panel B), the inclusion of subsequent controls on intra-racial wage gaps (Panel C) has little impact, leading us to conclude that controls for background, demographics, skill and education, explain a larger share of the inter-race wage gap (roughly one-half) than they do the intra-race gap (one-third) in the most conservative estimate. 14 Importantly, we are not in a position to rule out: i) that the accumulation of skill is itself a response to expected labor market discrimination (see Coate & Loury, 1993); or ii) that our skill measures are racially/color-biased or incomplete in nature (see Goldsmith et al, 1997; Darity & Mason, 1998). 13 see Black et al. (2008), Carneiro, et al. (2005) and Lang & Manove (2011). 14 In an earlier version we also explored an alternative estimation based on heteroskedasticity of measurement errors following contributions by Lewbel (2012). The results confirmed findings presented here. 16

17 [Table 5 (Reg 1) here.] 5.2 Employment In Table 6 we estimate the same set of regressions on the share of weeks employed in the past year. As above, the inclusion of individual characteristics reduces the Black-White gap by nearly half, from 17% to 8%, comparable to findings in Ritter and Taylor (2011). Yet, we find little evidence of employment differentials across skin color. Moreover, the sequential inclusion of control variables leaves the intra-racial employment gap virtually unchanged. We conclude that while inter-racial differences in employment correspond with Black-White differences in wages, the same is not true for differences within race, across skin color. Therefore, a unifying theory explaining skin color differentials will have to be distinct, at least in this dimension, from one that applies to racial differences in labor market outcomes. [Table 6 (Reg 2) here.] 5.3 Sensitivity analysis In Table A.1 in the Appendix we replicate our preferred specification, Column 6 of Table 5, over various sample restrictions. Results are largely robust to these changes with two exceptions. First, restricting the sample to observations after 2006 increases the light-dark wage gap indicating a widening of the intra-racial gap either in later years or among more experienced workers. Second, in Columns 7 9 we address possible effects of mixed-race ancestry, defined as an identifiable non-white (for Whites) or non-black (for Blacks) biological parent. A recent working paper by Arcidiacono et al. (2012) finds that mixed-race children born to White mothers are advantaged in several academic and labor market outcomes. In Column 7, which includes a mixed-race dummy and an interaction with Black, the inter-racial gap 17

18 in wages increases, particularly for lighter Blacks. Excluding respondents with identifiable mixed-race parentage in Column 8 reduces the intra-racial advantage for the lightest tercile relative to median Black from to 0.058; moreover, although still positive, the intraracial coefficient on Light is no longer statistically different from zero. Nonetheless, it is still the case that differences are statistically significant between lightest and darkest terciles. We are reluctant to classify respondents by their parent s race on two accounts. First, our sample consists of respondents classified by the NLSY as non-hispanic Black or White, which explicitly excludes a small sample of mixed individuals. Thus, by our definition here, several mixed respondents were omitted when we restricted to non-hispanics. Also, since most of our presumed mixed-race respondents here have an Hispanic parent, the mixed classification should be interpreted with caution. Second, the NLSY was not able to locate both biological parents for all respondents, raising concerns about selection. If respondents for whom both parents can be contacted are advantaged over those for whom they cannot, all else equal, those respondents should be more likely to find a mixed race parent. We in fact find that having two identifiable biological parents increases the likelihood of finding a mixed race set for Blacks. Omitting presumed mixed-race respondents does not affect our longitudinal results, although we do not show all of these robustness checks for brevity. 5.4 Longitudinal analysis In Table 7 we interact skin tone with both potential experience (Columns 1-2) and tenure (Columns 3-4) to show the evolution of inter and intra-racial wage gaps, as described in Equation 6 above. Both potential experience and tenure are mean centered in all specifications such that level differences in wages correspond to those from our non-interacted models. Interactions between experience and education (HGC, mean centered) and AFQT are also included to address conditional differences in returns to experience by skin color and race. Each model also includes our full set of controls We model experience linearly in all cases as these data contain only early career experience - mean potential experience is 3.9 and mean tenure is 2.1 in the wage sample - average marginal effects using higher 18

19 Results in Table 7 indicate that as workers accrue experience two phenomena occur. First, the wage gap between Black and White workers widens as potential experience increases. Second, as workers accrue job specific tenure, the composite Black-White gap narrows. Yet, a blurring of the color line reveals that these results are driven by a divergence in hourly wages within the Black population. Column 2 indicates that the entire Black-White gap in potential experience is driven by the darkest tercile, for whom the Black-White gap widens by 2 percentage points per year of potential experience, while wages for the lighter twothirds of the color distribution remain roughly constant over time. Columns 3 and 4, which combine potential experience and tenure, indicate that the lightest Black respondents gain 3.6 percentage points on Whites with each year of tenure, while the darkest skinned Blacks do not advance significantly relative to Whites as tenure accumulates. Intra-racial differences in returns to tenure exist, although they are not statistically different from zero. In Table A.2 in the Appendix we repeat our exercises using a non-parametric specification of skin color by including dummies for each value of skin tone, with skin tones 1-3 and 9-10 combined for sufficient sample size. Column 1 re-estimates static intra-racial gaps, confirming larger average differentials for the darkest two terciles compared with the lightest third. Although Blacks with skin colors 1-3 have worse labor market outcomes, these outliers account for a small number of observations (47 individuals in total). While some of these exceedingly light ratings may be attributable to albinism, it is unlikely that most of them are. Columns 2 and 3 repeat exercises for potential experience and tenure. In both cases it is clear that the darkest tercile of Blacks earn less as experience accrues compared to their lighter peers. [Table 7 (Reg 3) about here.] order polynomials are similar and are omitted for ease of interpretation and brevity. 19

20 5.5 Discussion Static versions of statistical discrimination models seem at odds with our findings. We discover little evidence of convergence in wages across skin tone for Blacks as labor market experience accrues. In fact, our evidence suggests that both Black-White and dark-light wage gaps increase over time. These findings add to the evidence of growth in racial differences in wages across the life course presented by Tomaskovic-Devey et al. (2005), indicating that a widening happens within the African American population as their careers progresses and improved wage offers are obtained (or not). Dynamic models of statistical discrimination provide a better explanation for these findings. Our evidence suggests that experience accumulation may generate smaller gains for darker Blacks if they are representative unproductive job changes. Therefore, if Oettinger s (1996) argument can be extended from White-Black to Light-Dark differences in the quality of prospective job-market match signal, the wage differences we uncover within the Black population are result of mismatches and job instability. In an additional empirical exercise, not reported here, using the same specification as the wage regressions above, we find significant differences between dark and light Blacks in the probability of holding a job for one year or more. Combined with our results showing no differential in employment probabilities, this indicates that the effects of potential experience accrue differentially for lighter and darker skinned Blacks, representing differential patterns of job mobility. Thus, despite having similar employment likelihoods in a given year, darker skinned Blacks face a higher rate of job switches than lighter Blacks, resulting in differential trajectories of wage accumulation. It is important to keep in mind that these effects may be related to time changes in labor market churning associated with a boom-bust pattern of the U.S. business cycle during the time period studied here; thus, we make these claims with caution. 20

21 6 Conclusion We present evidence of the role skin color plays in earnings and employment for African American males based on a novel and representative sample of American youth. By differentiating skin tone within race, we add to a robust literature on racial differentials in labor market outcomes in the tradition of Neal and Johnson (1996), Ritter and Taylor (2011) and others. Moreover, by employing a uniform measure of skin tone to estimate skin color differentials in a representative sample, and by estimating the evolution of these differentials over time, we add to a nascent literature on the relationship between skin color and earnings building on earlier work by Hersch (2006, 2008), Goldsmith et al. (2007) and others. While we find no impact of complexion on employment, we show that measured differences in earnings within race, across skin color cannot be explained away by skills accumulated before labor market entry. In fact, we show that accounting for pre-market skill accumulation increases the empirical importance of intra-racial differences relative to inter-racial differentials. In particular, while close to two-thirds of racial wage gaps are explained by pre-market factors, this is true only for at most one-third of the color gap between light and medium skinned African Americans. The color gap in wages not explained by skills brought to the market amounts to 5.4 percentage points (or half the racial gap) based on our most conservative estimates. Additional analyses comparing the evolution of wage profiles as respondents accumulate labor market experience are equally revealing. We find that as Black workers accumulate experience, the gap between light and darker skinned Black respondents widens, while the gap between the lightest skinned Black workers and their White counterparts remains constant. In addition, we provide a novel description of the distribution of skin tone among Black and White American youth and investigate the relationship between skin color ratings and interviewer and respondent characteristics. We find that Black NLSY respondents were rated with both lighter skin tones and higher variance by Black interviewers by White interviewers. 21

22 To account for potential effects from interviewer heterogeneity, we include interviewer fixed effects in our preferred specification, finding little meaningful effect on results. Our results are subject to limitations. It is important to consider that when examining the accumulation of labor market experience we only observe early careers (the average age among respondents is 28 in 2010). Moreover, although our skin color measure is a significant improvement on previous work, it is not entirely objective, and cannot be treated as cardinal. We highlight the importance of perception in racial classifications and underscore the inherent subjectivity of racial classifications both in surveys and in day-to-day interactions. Finally, we are not in a position to rule out the possibilities that the accumulation of such skills is in itself a response to expected labor market discrimination; or that our skill measures are racially/color-biased or incomplete in nature. Nonetheless, by presenting evidence on the relevance of skin tone in labor market outcomes beyond traditional dichotomous racial classifications, we challenge the notion of race as Black and White as it is traditionally employed in economics. 22

23 References Aigner, D. and Cain, G. G. (1977). Statistical theories of discrimination in labor markets. Industrial and Labor Relations Review, 30(2). Akee, R. and Yuskel, M. (2010). Skin tone s decreasing importance on employment: Evidence from a longitudinal dataset, IZA Discussion Paper Number Alba, R. (2009). Blurring the Color Line: The New Chance for a More Integrated America. Boston: Harvard University Press. Altonji, J. G. and Pierret, C. R. (2001). Employer learning and statistical discrimination. Quarterly Journal of Economics, 464(2). Arcidiacono, P., Beauchamp, A., Hull, M., and Sanders, S. (2012). Isolating mechanisms for the racial divide in education and the labor market: Evidence from interracial families. Unpublished manuscript, Duke University. Arrow, K. (1998). What has economics to say about racial discrimination? Journal of Economic Perspectives, 12(2). Austen-Smith, D. and RolandFryer Jr. (2005). An economic analysis of acting white. The Quarterly Journal of Economcis, May. Banerjee, M., Capozzoli, M., McSweeney, L., and Sinha, D. (1999). Beyond kappa: A review of interrater agreement measures. Canadian Journal of Statistics, 27:3 23. Barsh, G. (2003). What controls variation in human skin color? PLoS Biol, 1(1). Becker, G. (1971). The Economics of Discrimination, 2nd ed. Chicago: The University of Chicago Press. Belleti, N. and Wade, T. (2008). Racial characteristics and female racial attractiveness perception among united states university students. In Kauffman, K. D., editor, Racism in the 21st Century. Lansing: Springer. Bertrand, M. and Mullainathan, S. (2004). Are emily and greg more employable than lakisha and jamal? a field experiment on labor market discrimination. The American Economic Review, 94(4). Black, D., Haviland, A., Sanders, S., and Taylor, L. (2008). Why do minority men earn less? a study of wage differential among the highly educated. Review of Economics and Statistics, 88(2): Black, D. A. (1995). Discrimination in an economic search model. Journal of Labor Economics, 13(2): Black, D. A., Berger, M., and Scott, F. (2000). Bounding parameter estimates with nonclassical measurement error. Journal of the American Statistical Association, 95(451):

24 Black, D. A., Kolesnikova, N., and Taylor, L. J. (2009). The role of location in evaluating racial wage disparity. Federal Reserve of St. Louis Working paper B. Carneiro, P., Heckman, J., and Masterov, D. (2005). Labor market discrimination and racial differences in premarket factors. Journal of Law and Economics, 48(1):1 40. Charles, K. and Guryan, J. (2008). Prejudice and wages: An empirical assessment of becker s the economics of discrimination. Journal of Political Economy, 116(5): Coate, S. and Loury, G. (1993). Will affirmative-action policies eliminate negative stereotypes. The American Economic Review, 83(5). Darity Jr., W. and Mason, P. (1998). Evidence on discrimination in employment: Codes of color, codes of gender. Journal of Economic Perspectives, 12(2). Diamond, J. (1994). Race without color. Discover, 15(11). Faldon, J. (2009). Statistical discrimination and the implications of employer-employee racial matches. Unpiblished manuscript, Vanderbilt University. Fryer Jr, R. and Levitt, S. (2004). Causes and consequences of distinctive black names. Quarterly Journal of Economics, 119(3): Fryer Jr, R. and Torelli, P. (2009). An empirical analysis of acting white. NBER Working Paper Number Goldsmith, A., Hamilton, D., and Darity Jr., W. (2007). From dark to light: Skin color and wages among african-americans. Journal of Human Resources, 42(4): Grogger, J. (2011). Speech patterns and racial wage inequality. Journal of Human Resources, 46(1):1 25. Gyimah-Brempong, K. and Price, G. (2006). Crime and punishment: And skin hue too? The American Economic Review, 96(2): Helmreich, J. (2010). Richard alba, blurring the color line. Society, 47(4). Herrig, C., Keith, V. M., and Horton, H. D. (2004). Skin Deep: How Race and Complexion Matter in the Color-Blind Era. University of Illinois Press: Chicago, IL. Hersch, J. (2006). Effects among african americans: perceptions and reality. American Economic Review Papers and Proceedings, 96(2): Hersch, J. (2008). Profiling the new immigrant worker: The effects of skin color and height. Journal of Labor Economics, 26(2). Hughes, M. and Hertel, B. R. (1990). The significance of color remains: A study of life chances, mate selection, and ethnic consciousness among black americans. Social Forces, 64(4). 24

25 Krieger, N., Sidney, S., and Coakley, E. (1998). Racial discrimination and skin color in the cardia study: Implications for public health research. American Journal of Public Health, 88(9). Lang, K. and Lehmann, J. K. (Forthcoming). Racial discrimination in the labor market: Theory and empirics. Journal of Economic Literature. Lang, K. and Manove, M. (2011). Education and labor market discrimination. American Economic Review, 100. Massey, D. and Martin, J. A. (2003). The nis skin color scale. NIS. Neal, D. (2006). Why has black-white skill convergence stopped? In Hanushek, E. and Welch, F., editors, Handbook of the economics of education, Vol. 1, chapter 9. Amsterdam: Elseiver Science. Neal, D. and Johnson, W. R. (1996). The role of premarket factors in black-white wage differences. Journal of Political Economy, 104(5): Oettinger, G. (1996). Statistical discrimination and the early career evolution of the blackwhite wage gap. Journal of Labor Economics, 14(1): O Neil, G. (1990). The role of human capital in earnings differences between black and white men. Journal of Economic Perspectives, 4(Fall): Papke, L. and Wooldridge, J. (1996). Panel data methods for fractional response variables with an application to test pass rates. Journal of Econometrics, 145(1). Parra, E. J., Kittles, R. A., and Shriver, M. D. (2004). Implications of correlations betweeen skin color and genetic ancestry for biomedical reserach. Nature Genetics Supplement, 36(11). Phelps, E. (1972). The statistical theory of racism and sexism. American Economic Review, 62(4): Ritter, J. and Taylor, L. J. (2011). Racial disparity in unemployment. Review of economics and statistics, 93(1). Strum, R., Box, N., and Ramsay, M. (1998). Human pigmentation genetics: the difference is only skin deep. BioEssay, 20: Telles, E. and Lim, N. (1998). Does who classify race matter? self vs. social classification of race and racial income inequality in brazil. Demography, 5(4). Tomaskovic-Devey, D., Thomas, M., and Johnson, K. (2005). Race and the accumulation of human capital across the career: A theoretical model and fixed effects application. American Journal of Sociology,

26 Table 1: Interviewer characteristics, means weighted by interviewers and respondents. Weighted by Interviewers Weighted by Respondents Black White All Interviewer gender Male Female Missing gender Interviewer race White Black Other Missing race Interviewer age Over Under Missing age Interviewer education HS or less Some college College More than college Missing education Respondents per interviewer N ,583 2,417 Notes: Column 1 shows interviewer characteristics for the 167 interviewers who interviewed 834 Black and 1,583 White male respondents in Columns 2-4 show mean interviewer characteristics by respondent race; i.e. interviewer means weighted by respondents. 26

27 Table 2: Skin color ratings for Black and White males by interviewer characteristics. Skin Color, White Skin Color, Black mean sd mean sd Interviewer gender Male Female Missing gender Interviewer race White Black Other Missing race Interviewer age Over Under Missing age Interviewer education HS or less Some college College More than college Missing education Observations 1, Notes: Statistics are means and standard deviations of skin tones by interviewer characteristics (rows) and respondent race (columns). Sample is 2,417 Black and White Males with skin color ratings in

28 Table 3: Sample restrictions. Individuals Observations All Respondents 8, ,776 Black and White Males 3,455 48,370 With Color Rating in ,417 33,838 With AFQT 2,007 28,098 Entered Labor Market 1,937 27,118 In Employment Sample 1,855 12,953 In Wage Sample 1,694 8,436 White 1,148 5,715 Black 546 2,721 Notes: Entered labor market indicates if respondent was ever observed after two consecutive non-enrolled terms. Employment sample refers to respondents with nonmissing HGC and employment history. Wage sample refers to respondents with wages in 30+ hour per week jobs. 28

29 Table 4: Summary statistics - means and standard deviations by race and skin color for wage regression sample. White Black, 1-5 Black, 6-7 Black, 8-10 AFQT (z-score) (0.94) (0.84) (0.73) (0.71) HGC Ever (2.72) (2.51) (2.40) (2.21) Entry Age (2.63) (2.42) (2.33) (2.20) South (0.44) (0.48) (0.47) (0.42) Non-MSA (0.25) (0.22) (0.22) (0.24) Poverty ratio (1997) (275.8) (223.2) (226.6) (161.6) Mom HGC < HS (0.47) (0.50) (0.49) (0.50) Mom HGC > HS (0.42) (0.35) (0.34) (0.29) Potential experience (1.86) (1.76) (1.73) (1.76) Tenure (1.97) (1.92) (1.41) (1.42) Wage observations (2.55) (2.60) (2.43) (2.31) Observations 1, Notes: AFQT is a z-score normalized to the working color sample of 2,417. HGC is respondents highest grade completed. Entry Age is age at which respondents first enter the labor market, defined by two consecutive nonenrolled years. South and Non-MSA are averaged over individuals. Poverty ratio is for the family, measured in Wage observations counts the number of observations respondents contribute to the wage regressions. 29

30 Table 5: Conditional differences in log hourly wages by race and skin color Panel A: Blurred color line, compared with White (1) (2) (3) (4) (5) (6) Black, light *** *** ** * * ** (0.032) (0.030) (0.031) (0.031) (0.031) (0.033) Black, medium *** *** *** *** *** *** (0.026) (0.025) (0.026) (0.026) (0.027) (0.028) Black, dark *** *** *** *** *** *** (0.029) (0.030) (0.031) (0.030) (0.030) (0.030) HGC 0.058*** 0.050*** 0.050*** 0.045*** (0.007) (0.007) (0.007) (0.006) AFQT 0.040*** 0.032*** 0.032*** 0.031*** (0.011) (0.011) (0.011) (0.011) Panel B: Sharp color line (1) (2) (3) (4) (5) (6) Black *** *** *** *** *** *** (0.019) (0.020) (0.021) (0.022) (0.022) (0.023) Panel C: Blurred color line, compared with Black (main effect not shown) (1) (2) (3) (4) (5) (6) Black, light 0.081** 0.061* 0.069** 0.054* 0.054* 0.072** (0.038) (0.033) (0.034) (0.032) (0.032) (0.034) Black, dark (0.035) (0.033) (0.033) (0.032) (0.032) (0.030) Controls, All Panels Interviewer FE X Interviewer Controls X Region & MSA X X X X Background X X X X Behaviors X X X X Height & Weight X X X X Age, Age at Entry Both Age Both Both Both Year FE X X X X X X N 8,436 8,436 8,436 8,436 8,436 8,436 Individuals 1,694 1,694 1,694 1,694 1,694 1,694 R Dependent variable is natural log of real hourly wage. Sample is respondents who have entered the labor market (not enrolled and have at least two semesters out of school), with non-missing HGC and AFQT, and valid wages (30+ hrs./week). Coefficients in Panels A and B are relative to White, in Panel C coefficients are relative to medium Black. Controls are described in the Data Appendix. Standard errors clustered on individuals in parentheses. p < 0.10, p < 0.05, p <

31 Table 6: Conditional Differences in Share of Weeks Employed in the Past Interview Year for Men by Race and Skin Tone (Coefficients are Marginal Effects). Panel A: Blurred color line, compared to White (1) (2) (3) (4) (5) (6) Black, light *** *** *** *** *** *** (0.021) (0.020) (0.022) (0.021) (0.022) (0.022) Black, medium *** *** *** *** *** *** (0.018) (0.018) (0.020) (0.020) (0.021) (0.022) Black, dark *** *** *** *** *** *** (0.020) (0.020) (0.021) (0.021) (0.021) (0.023) HGC 0.028*** 0.024*** 0.024*** 0.022*** (0.006) (0.006) (0.006) (0.005) AFQT 0.036*** 0.033*** 0.031*** 0.032*** (0.009) (0.009) (0.009) (0.009) Panel B: Sharp color line (1) (2) (3) (4) (5) (6) Black *** *** *** *** *** *** (0.013) (0.014) (0.016) (0.016) (0.017) (0.018) Panel C: Blurred color line, compared with Black (main effect not shown) (1) (2) (3) (4) (5) (6) Black, light (0.026) (0.023) (0.025) (0.023) (0.023) (0.024) Black, dark * * (0.025) (0.023) (0.024) (0.023) (0.023) (0.024) Controls, All Panels Interviewer FE X Interviewer Controls X Region & MSA X X X X Background X X X X Behaviors X X X X Height & Weight X X X X Age, Age at Entry Both Age Both Both Both Year FE X X X X X X Individuals 1,855 1,855 1,855 1,855 1,855 1,855 N 12,953 12,953 12,953 12,953 12,953 12,953 Notes: Dependent variable is the share of weeks employed 30+ hours in the past interview year (weeks employed/weeks with non-missing labor force status). Coefficients are marginal effects from a GLM with logit-link specification. Sample is respondents who have entered the labor market (not enrolled and have at least two semesters out of school), with non-missing HGC and AFQT. Coefficients in Panels A and B are relative to White, in Panel C coefficients relative to medium Black. Controls are described in the Data Appendix. Standard errors clustered on individuals in parentheses. p < 0.10, p < 0.05, p <

32 Table 7: Conditional differences in wages by race and skin color, interactions with potential experience and tenure. (1) (2) (3) (4) Panel A: Blurred color line, compared with White Black, light ** ** ** ** (0.033) (0.032) (0.030) (0.030) Black, medium *** *** *** *** (0.028) (0.028) (0.027) (0.027) Black, dark *** *** *** *** (0.029) (0.029) (0.030) (0.030) Potexp (centered) 0.021*** 0.020*** 0.018*** 0.018*** (0.005) (0.005) (0.005) (0.005) Light*Potexp (0.009) (0.009) (0.008) (0.008) Medium*Potexp (0.006) (0.007) (0.007) (0.007) Dark*Potexp *** *** *** *** (0.006) (0.006) (0.006) (0.006) HGC 0.051*** 0.051*** 0.049*** 0.050*** (0.005) (0.005) (0.005) (0.005) AFQT 0.029*** 0.030*** 0.027** 0.025** (0.011) (0.011) (0.011) (0.011) HGC*potexp (0.001) AFQT*potexp 0.007** (0.003) Tenure (centered) 0.020*** 0.021*** (0.005) (0.005) Light*Tenure 0.036** 0.038*** (0.015) (0.014) Medium*Tenure 0.018* 0.022** (0.011) (0.011) Dark*Tenure (0.013) (0.013) HGC*tenure ** (0.001) AFQT*tenure (0.005) Panel B: Sharp color line (main effects not shown) Black*Potexp ** *** *** (0.005) (0.005) (0.004) (0.004) Black*Tenure 0.023*** 0.027*** (0.009) (0.009) Panel C: Blurred color line, compared with Black (main effect not shown) Light*Potexp (0.010) (0.010) (0.009) (0.009) Dark*Potexp ** ** ** ** (0.008) (0.008) (0.008) (0.008) Light*Tenure (0.017) (0.016) Dark*Tenure (0.015) (0.015) N 8,436 8,436 8,312 8,312 R Notes: Dependent variable is log of real hourly wage. Sample is respondents who have entered the labor market with non-missing HGC and AFQT, and valid wages (30+ hrs./week). Panels A and B are relative to Whites, Panel C coefficients relative to medium Black. Potential experience is (Age-HGC-6) in Columns 1-2, and is job tenure (rounded to nearest year) in columns 4-6. Standard errors clustered on individuals in parentheses. p < 0.10, p < 0.05, p <

33 Figure 1: Histogram of skin color for Black and White males, by race. Notes: Sample is 2,417 non-hispanic Black and White male respondents with skin color ratings in

34 Appendix Figure A.1: NIS/NLSY skin color rating card. Notes: NIS skin color rating scale. Users of the scale are requested to notify NIS staff by contacting Jennifer Martin at 34

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