Those Who Tan and Those Who Don t: A Natural Experiment of Employment Discrimination Ronen Avraham, Tamar Kricheli Katz, Shay Lavie, Haggai Porat, Tali Regev Abstract: Are Black workers discriminated against in the labor market? Studies based on survey data have found a correlation between being Black and employment outcomes, such as hiring and salaries. Other, related studies suggest that a within-race darker skin tone is also associated with labor force disadvantages. However, it is hard to refute the possibility that other factors correlated with skin color might affect the employment outcomes for people with darker skin tone. In order to overcome this inherent limitation, we use a natural experiment to explore the effects of skin tone on employment we use a within-person research design on data from the NLSY97 longitudinal dataset and utilize changes in one s own skin tone generated by exposure to the sun, to explore the effects of skin tone on the tendency to be hired and fired. We use the average UV radiation in one s metropolitan area in a given week as an exogenous variable generating a darker skin tone for some people (those with medium, moderate brown, and dark brown skin) but not for others (those with white, pale white, and very dark brown to dark skin). We find that indeed, those people whose skin tone becomes darker by exposure to the sun (but not others) are less likely to be employed when the UV radiation in the previous four weeks in the area in which they reside is greater. These withinperson findings hold even when controlling for the week, the year, the region, demographic characteristics and the industry one is employed in. A separate analysis for women and men reveals that it is the effect of UV radiation on men s employment, but not on women s, that drive the results we present. Keywords: ***. JEL Codes: **. Introduction Studies using survey data document persistent disparities in earnings and other employment outcomes for Blacks and whites in the US (Lang and Manove 2011; Eckstein & Wolpin 1999; Bowlus & Eckstein 2002). Other, related studies suggest that a withinrace darker skin tone is also associated with labor force disadvantages. Although survey data provide an opportunity to observe patterns of employment outcomes by race in the entire labor force, it is very difficult to use them to observe a causal effect between the stigma associated with being Black and employment outcomes: it is nearly impossible to rule out the possibility that unmeasured differences between those Blacks and whites generate the observed differences in employment outcomes. [Affiliations]. We are grateful to ***. We also thank the U.S. Bureau of Labor Statistics for providing the data necessary for the completion of this research.
To deal with this inherent limitation of using survey data, we use a natural experiment to explore the effects of skin tone on employment: we use a within-person research design on data from the NLSY97 longitudinal dataset and utilize changes in one s own skin tone generated by exposure to the sun, to explore the effects of skin tone on the tendency to be hired and fired. We use the UV radiation in one s metropolitan area in the preceding four weeks as an exogenous variable generating a darker skin tone for some people (those with medium, moderate brown, and dark brown skin) but not for others (those with white, pale white, and very dark brown to dark skin). We find that indeed, those people whose skin tone becomes darker by exposure to the sun (but not all others) are less likely to be employed when exposed to greater UV radiation. These within-person findings hold even when controlling for the week, the year, the region, demographic characteristics and the industry one is employed in. A separate analysis for women and men reveals that it is the effect of UV radiation on men s employment, but not on women s, that drive the results we present. Whereas UV radiation can darken one s complexion, it does not influence her skills, her commitment or other related characteristics that might affect her employability. Moreover, exposure to UV radiation affects people with different skin tones differently: Some people those with medium, moderate brown, and dark brown skin look darker when exposed to the sun. People with moderate brown skin for example may look like they have a dark brown skin-tone after tanning. People with white and pale white complexion look different after spending time in the sun, however they do not look darker, but burnt. People with very dark brown to dark skin do not look different after spending time in the sun. If indeed people discriminate on the basis of skin tone, we would expect the people who are prone to tanning to be discriminated against more when UV radiation is greater and tanning occurs. We do not expect people who cannot tan, however, to be affected by being exposed to greater UV radiation. To test our hypotheses we utilize the 1997 National Longitudinal Survey of Youth (NLSY97). The NLSY97 is a longitudinal dataset that follows the lives of a sample of several thousand Americans who were born between 1980-84. It provides rich information on personal characteristics, including the employment status for each week through the surveyed year. The NLSY97 also includes data on respondents skin tone that was collected once in 2008. The date in which the skin tone was recorded by the interviewer is also available. We matched the NLSY97 with data on the average weekly UV radiation in the metropolitan areas in which respondents reside, for those respondents residing in a metropolitan area where a weather station exists (leaving out respondents residing in remote areas). Thus, for each of those respondents we now have weekly data on her employment status as well as average UV radiation in the area in which she resides, together with other demographic and employment characteristics (like her occupation and industry). We use person fixed effects models to predict the effects of the average UV radiation in the previous four weeks on the employment status of respondents in the current Page2
week. By doing so, we control for all the unobserved and time-invariant characteristics of the respondents in our data. We find that indeed, those people whose skin tone becomes darker by exposure to the sun (but not others) are less likely to be employed when the UV radiation in the previous four weeks in the area in which they reside is greater. These within-person findings hold even when controlling for the week, the year, the region, demographic characteristics and the industry one is employed in. A separate analysis for women and men reveals that it is the effect of UV radiation on men s employment, but not on women s, that drive the results we present. This effect is also non-trivial in size, and it is most prominent in low-skilled jobs. We further show that our results are robust to various checks. Skin Tone and Employment Outcomes Studies using survey data observe persistent disparities in earnings and other employment outcomes for Black workers compared to white workers and for people with darker skin tones compared to lighter skin tones (Lang and Manove 2011; Eckstein & Wolpin 1999; Bowlus & Eckstein 2002; Goldsmith, Hamilton, and Darity 2006; Hughes & Hertel). Altogether, greater effects of skin tones were found on employment when compared to wages, and for men when compared to women (Hersch 2006). In one study, Mill and Stein (2016) used population censuses from the beginning of the 20 th century, in which children s skin tones were classified as White, Black or Mulatto. They traced the within-family differences in economic outcomes for African American siblings who were classified as having different skin colors (one was classified as Black and the other as Mulatto ). By doing so they control for the unobserved characteristics of African American families. They find that Mulatto children (of 1910) earned similar wages as adults (in 1940) as their Black siblings and therefore conclude that skin color within African Americans families in itself played only a minor role in the earnings gap between Blacks and Mulattos in 1940. Nonetheless, in a recent study using data on recent employment outcomes, Kreisman and Rangel (2015) document earning differences by skin tone for Blacks in the US (within race). Although survey data provide an opportunity to observe patterns of employment outcomes by race (or skin tone) in the entire labor force, it is very difficult to use them to observe a causal effect between the stigma associated with being Black (or having a darker skin tone) and employment outcomes: it is nearly impossible to rule out the possibility that unmeasured differences between those of lighter and darker skin (as well as people of different races) generate the observed differences in employment outcomes. To deal with this inherent limitation of using survey data, we use a natural experiment to explore the effects of skin tone on employment. We use a within-person research design on data from the NLSY97 longitudinal dataset and utilize changes in one s own skin tone generated by exposure to the sun, to explore the effects of skin tone on the tendency to be employed. Page3
Data and Methods We use the 1997 National Longitudinal Survey of Youth (NLSY97). The NLSY97 is a longitudinal dataset that follows the lives of a sample of several thousand Americans who were born between 1980-84. The initial sample included 9,022 respondents who were first interviewed in 1997. The last round was administered in 2015-16. The NLSY97 provides rich information on respondents personal characteristics, including the employment status for each week through the surveyed year. Respondents skin tones ware assessed once, by the interviewer in 2008-2009 on a scale of 0-10 ranging from the lightest to the darkest (see the Appendix for the scale used). The date in which the skin tone was determined by the interviewer is also available. We matched the NLSY97 employment data with data on the average weekly UV radiation in the metropolitan areas where respondents reside. Thus, for each respondent we have weekly data on her employment status and average UV radiation in the area in which she resides, together with other demographic and employment characteristics (like her occupation and industry). We drew the UV data from a dataset collected by the National Oceanic and Atmospheric Administration (NOAA), which gauges UV radiation in 58 metropolitans in the US according to the physical location of the 58 stations. The NOAA uses the common index of ground UV (largely UV-A) radiation, which is primarily related to the elevation of the sun in the sky, the amount of ozone in the stratosphere, and the amount of clouds present. Hence, UV levels are typically greater in the summer. The UV index ranges from 0 (lowest) to 15-16 (highest), where higher levels of UV render a greater effect on one s skin. (For more information on UV radiation collection see the following link). The effects of UV radiation on people s skin tone vary by skin type and initial skin tone. In general, UV radiation tends to generate a darker skin tone for those with medium, moderate brown, and dark brown skin, but not for those with white, pale white, and very dark brown to dark skin. Those with white or pale skin burn but do not tan. Those with very dark brown to dark skin absorb UV radiation, with only minimal change to their complexion. The Fitzpatrick scale classifies human skin-colors according to the following categories: Skin type Emoji Sun-burning behavior Tanning behavior Very light or pale Often Occasionally Light or lightskinned Often Sometimes Light intermediate Rarely Usually Page4
Dark intermediate Rarely Often Dark or brown type No Sometimes darkens Very dark or black type No Naturally blackbrown skin Altogether our dataset comprises 9.58 million respondent-by-week observations of 9,022 individuals that were followed from 1994 to 2016. Building on what we know about the tendency to tan, we created three skin tone categories, two categories for the people who tend not to tan, and one category for the people who do tend to tan: lightest (skin tones 0-2), darkest (skin tones 6-10) and intermediate (skin tones 3-5). We use separate person fixed effects models for each skin tone category to predict the effects of the average UV radiation in the previous four weeks on the employment status of respondents. By doing so, we control for all the unobserved and time-invariant characteristics of the respondents in our data. Note that we cannot estimate the effects of skin tone and of skin tone interacted with the UV on employment, because the skin tone of respondents was determined only once and therefore has only one value per respondent, i.e. not varying across time. Our general logistic regression equations (for each skin-category) are the following: 1 p(y it ) = 1 + e z it z it = α i + δ t + β UV it + B X it + ε it Where the dependent variable indicates whether person i was employed during week t; α i is the time-invariant individual effect; δ t is the time fixed effect which we model using year and week dummies; UV it is our explanatory variable of interest, measured as the average UV radiation in one s metropolitan area in the previous 4 weeks; X it represents a host of time-varying control variables, specifically education, age, marital status, number of children, location and industry Overall, we expect the UV coefficient to be negative for the regression models for the intermediate skin category but not for the lightest and darkest skin categories. Results Table 1 presents the descriptive statistics for the variables we use in the analysis. Page5
Table 1: Descriptive Statistics Skin Level 1 Skin Level 2 Skin Level 3 All Mean S.D. N Mean S.D. N Mean S.D. N Min Max UV (past 4 weeks) 4.983708 2.80819 902729 5.186 2.789469 662723 5.399857 2.761 272496 0.066071 12.618 Employed 0.9404904 0.23658 902729 0.893713 0.308205 662723 0.871114 0.335 272496 Female 0.490023 0.4999 902729 0.505383 0.499971 662723 0.455027 0.498 272496 Year 2008.024 4.36167 902729 2008.107 4.370678 662723 2008.058 4.413 272496 1998 2016 Age 26.11776 4.3431 902729 26.208 4.380833 662723 26.17432 4.412 272496 18 36.333 Married 0.2816801 0.44982 900493 0.226 0.418325 661455 0.171349 0.377 272164 Children in HH 0.4821466 0.88096 901678 0.734 1.077095 661679 0.755447 1.145 271986 0 12 Job Zone 1 Job Zone 2 0.4779042 0.49951 485227 0.601 0.489775 356214 0.670505 0.470 140682 Job Zone 3 0.1893238 0.39177 485227 0.177 0.381841 356214 0.130628 0.337 140682 Job Zone 4 0.175691 0.38056 485227 0.110 0.312923 356214 0.068616 0.253 140682 Job Zone 5 0.0527238 0.22348 485227 0.025 0.155 356214 0.013086 0.114 140682 Midwest South 0.2764593 0.44725 892059 0.372652 0.483511 661638 0.649999 0.476971 271625 Northeast 0.2391512 0.42657 892059 0.197 0.398 661638 0.156779 0.364 271625 West 0.2994936 0.45804 892059 0.213 0.410 661638 0.07344 0.261 271625 Education: In Table 2, we present the results of logistic regression models predicting the employment status of respondents by skin level categories. Table 2: Logistic Regression Models Predicting Employment Skin Level 1 Skin Level 2 Skin Level 3 (1) (2) (3) (4) (5) (6) (7) (8) (9) UV 0.002-0.010-0.014-0.011*** -0.028*** -0.036*** -0.008** 0.005 0.008 (-0.002) (0.007) -0.008 (0.002) (0.008) (0.008) (0.002) -0.011 (0.012) Age 0.123-0.068 0.469 (-0.320) (0.318) (0.108) Married 0.258*** 0.522*** 0.440*** (0.019) (0.023) (0.034) Children 0.038*** -0.101*** -0.154*** (0.011) (0.010) (0.013) Week and Year Dummies Y Y Y Y Y Y Region Dummies Y Y Y Education Dummies Y Y Y Industry Dummies Y Y Y Adjusted R² N 902,729 902,729 845,717 662,723 662,723 613,437 272,496 272,496 251,103 ***= 0.001, **=0.01, *=0.05 Standard errors are in parentheses. As expected, we find that for respondents who belong to the intermediate skin category (those who tan) an increase in the average UV radiation in the previous four weeks results in reduction of the likelihood of being employed, under all specifications. This effect is not trivial in size: An increase of one unit in the average UV radiation in the previous four weeks results in a decline of 0.035 percentage points in the likelihood of being employed. This means that a single s.d. increase in the UV radiation results in a 0.3 s.d's decline of the likelihood of being employed. For respondents who do not tan (those who belong to the light and dark skin categories), changes in the average UV radiation in the previous four weeks do not affect the likelihood of employment. Page6
In the models presented in table 3, we explore differences in the effects of the average UV radiation in the previous four weeks by job characteristics. To do so, we use the Occupational Information Network (O*NET) job-zones (link). The O*NET groups occupations into five job zones according to the education, experience, and training needed to do the work. The lowest skilled job zone is zone 1, which includes, for example, counter and rental clerks and logging equipment operators. The highest skilled job zone is zone 5 that includes, for example, lawyers and surgeons. Note that because of the nature and the number of observations, we could not estimate person fixed effects logistic regressions in which occupations are controlled for. Table 3: Logistic Regression Models Predicting Employment (for Skin2), by Job Zones Skin Level 2 (1) UV -0.055*** (0.014) UV X Job Zone 2 0.045*** (0.008) UV X Job Zone 3 0.009 (0.011) UV X Job Zone 4 0.040** (0.013) UV X Job Zone 5 0.090*** (0.021) Job Zone 2 0.002 (0.050) Job Zone 3 0.942*** (0.066) Job Zone 4 0.789*** (0.085) Job Zone 5 0.116 (0.153) Adjusted R² N 351,381 ***= 0.001, **=0.01, *=0.05 ;Standard errors are in parentheses. All regressions include Age, Married, Children, and dummies for week, year, region, and education. Page7
We find that the strongest effect of the UV radiation in the previous four weeks is for job zone 1 (the low-skilled occupations, the omitted job zone in the table). This is not surprising. Low skilled jobs typically have high turnover rate so that chances of becoming unemployed and reemployed are greater, translating to a greater effect for the average UV radiation in the previous four weeks. For this reason, it does not necessarily suggest that discrimination is greater in job zone 1 compared to other job zones. In table 4, we present the results of separate person fixed effects regression models predicting the employment of the people who belong to the intermediate skin category by gender. Table 4: Logistic Regression Models Predicting Employment (for Skin2), by Gender Skin Level 2 Men Women (1) (2) UV -0.075*** 0.040*** (0.013) 0.012 Age -0.291 0.163 (0.488) 0.442 Married 0.034*** 0.595*** (0.355) (0.034) Children 0.081*** -0.227*** (0.016) (0.014) Adjusted R² N 301,968 311,469 ***= 0.001, **=0.01, *=0.05 ;Standard errors are in parentheses. All regressions include dummies for week, year, region, education and industry. We find that the effect of the average UV radiation in the previous four weeks is negative for men but positive for women. One possible interpretation of the gender differences in the effects of UV radiation is that women are less vulnerable to such discrimination. This hypothesis is consistent with previous studies (Hersch 2006). Page8
Finally, we did not find any significant differences between the effects of average UV radiation in the previous four weeks on unemployment when estimating separately the South, Northeast, West and Midwest U.S. Robustness Checks Our findings suggest a causal link between skin-tone and employment status. We now turn to present our attempts to challenge these findings against several alternative hypotheses. The Season in which skin tone was assessed The skin tones of respondents were assessed on different dates in 2008. Whereas the skin tone of many respondents was assessed in the winter, the skin tone of some was assessed in May or in October when UV radiation tends to be greater (none were interviewed during the June-September period). Because some skin tones are affected by UV radiation, we worry that the recorded skin tone of respondents assessed in May or October is darker than their skin tone in the winter, generating inaccurate classification of the 3 categories which we estimated separately. In the models presented in table 5, we therefore separate those respondents (who belong to the intermediate skin category) whose skin tone was assessed in the winter from those assessed in May or October. Page9
Table 5: Month of Skin Color Estimation Logistic Regression Models Predicting Employment Skin Level 2 May & October Winter (1) (2) UV 0.014-0.064*** (0.014) (0.011) Age -0.291 0.552 0.501 0.033 Married 0.535*** -0.074*** (0.035) (0.014) Children -0.135*** -0.326*** (0.014) (0.045) Adjusted R² N 235,542 377,895 ***= 0.001, **=0.01, *=0.05 ;Standard errors are in parentheses. All regressions include dummies for week, year, region, education and industry. We find that the general effects for UV radiation we observe in the intermediate skin tone category are driven by the people whose skin tone was assessed in the winter, and not in May or October where exposure to UV radiation might have biased the assessment of skin tone and therefore the classification into the intermediate category which is hypothesized to be effected. Past and future UV Our main explanatory variable is the average UV radiation in the previous four weeks. In Table 6, we present models predicting not only the effects on unemployment of the average UV radiation in the previous four weeks, but also of the average UV radiation in the following four weeks (on people who belong to the intermediate skin category), as a falsification test: Page10
Table 6: Falsification Test - Future UV Logistic Regression Models Predicting Employment (skin 2) only future past and future (1) (2) UV -0.036*** (0.009) Future UV -0.010 (0.009) Age -0.034 (0.321) Married 0.535*** (0.023) Children -0.096*** (0.010) Adjusted R² N 600,774 ***= 0.001, **=0.01, *=0.05 ;Standard errors are in parentheses. All regressions include dummies for week, year, region, education and industry. As predicted, we find that it is only the UV radiation in the previous four weeks but not in the following four weeks that affects one s employment. Occupations that require sun exposure One possible alternative explanation to our finding is that the people who belong to the intermediate skin category tend to work in occupations in which employment is greater when UV radiation is lower. To rule out this explanation, in the model presented in table 7, we include the O*NET s index variable that captures the degree to which one s occupation typically involves exposure to the sun (ranging from 1 to 100). In a separate analysis we find that the people who belong to the intermediate skin category tend to work more in occupations that involve greater sun exposure. Page11
Table 7: Sun Exposure Logistic Regression Models Predicting Employment (skin 2) only future (1) UV -0.027* (0.011) Sun Occupation -0.006*** (0.001) UV X Sun Occupation 0.001*** 0.000 Adjusted R² N 385,988 ***= 0.001, **=0.01, *=0.05 ;Standard errors are in parentheses. All regressions include age, married, children, and dummies for week, year, region, and education. We find that the interaction between the sun exposure index variable and the average UV radiation in the previous four weeks is actually positive. This suggests that people are more likely to be employed (and not less) in occupations that involve greater exposure to the sun when UV increases. As mentioned above, people of the intermediate skin tone category tend to work in occupations with higher sun exposure (compared to the light and dark skin tones groups). The combined conclusion is that our main finding, that darker skin tone (as proxied by the increased UV for the intermediate skin tone group) decreases the likelihood of being employed is under-estimated, and in fact more severe. Conclusion In this paper we present the results of a natural experiment we use people s tendency to tan when exposed to UV radiation to test for employment discrimination on the basis of skin tone. We show that UV radiation negatively affects the likelihood of being employed for people whose skin tone becomes darker by exposure to the sun (but not others). These within-person findings hold even when controlling for the week, the year, the region, demographic characteristics and the industry one is employed in. Second, we show that the effect of UV radiation on the employment status of the people who tend to tan is not trivial. In fact, it accounts for about one-third of the gap in employment prospects between the darkest and the lightest skin categories. Our study overcomes the limitations of the previous literature by providing evidence for a causal effect of skin tone on employment, net of the effects of other factors correlated Page12
with skin tone. We thus provide evidence for labor force discrimination and not merely inequalities on the basis of skin tone. Page13
Appendix Page14