Abstract
A rich literature has documented the negative association between dark skin tone and many dimensions of U.S.-born Americans’ life chances. Despite the importance of both skin tone and immigration in the American experience, few studies have explored the effect of skin tone on immigrant assimilation longitudinally. I analyze data from the New Immigrant Survey (NIS) 2003 to examine how skin tone is associated with occupational achievement at three time points: the last job held abroad, the first job held in the United States, and the current job. Dark-skinned immigrants experience steeper downward mobility at arrival in the United States and slower subsequent upward mobility relative to light-skinned immigrants, net of human and social capital, race/ethnicity, country of origin, visa type, and demographics. These findings shed light on multiple current literatures, including segmented assimilation theory, the multidimensionality of race, and the U.S. racial hierarchy.
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Notes
On the other hand, some scholars point out that the United States and Latin America are not much different in terms of the construction and understanding of race because complexion, rather than lineage, centers in racial identification processes in both regions (Goldsmith et al. 2006).
The National Survey of Black Americans 1979–1980, the 1979 Chicano National Survey, the 1990 Latino National Political Survey, and the National Survey of American Life 2001–2003 are the major national-level surveys that measure respondents’ skin color and race. The Multi-City Study of Urban Inequality 1992 and the Detroit Area Study 1995 data are the commonly used regional studies. Data on skin tone are also collected in health-focused surveys, such as the National Heart, Lung, and Blood Institute’s Coronary Artery Risk Development in Young Adults.
The National Longitudinal Survey of Youth 1997 and the Add Health survey are among the few longitudinal data sets that measure respondents’ skin tone. However, they contain limited immigration-related information.
The NIS second wave data were publicly released but are not included in this analysis. Because of high attrition rates of the sample in the second wave, inclusion of the second wave data in the analysis reduces the sample size to approximately one-half of the original sample. A thorough analysis of sample selection in the second wave is underway.
The missing observations in the last job held before immigration were imputed using their first job held before immigration.
One limitation of this analytic frame is that time spans between T1, T2, and T3 are inconsistent across the sample. However, controlling for age, U.S. labor market experience, and whether they achieved LPR while residing in the United States mitigates this problem.
Despite the strength of the ISEI, using an occupational index as a proxy measure of labor market status in the United States may also have limitations: both within-occupation and between-occupation wage inequality constitute a considerable portion of total wage variance (Avent-Holt and Tomaskovic-Devey 2014; Kim and Sakamoto 2008), and within-occupation bias in job sorting can result.
Although some may suggest a subgroup analysis by sending regions, such an analysis is beyond the focus of the current study on the destination reception. Rosenblum et al. (2016) have discussed this issue, although cross-sectionally.
For the sample construction, see Table A2, online appendix.
As a robustness check of the GLS random-effects model, I also analyze the effects of skin tone at each time point separately using OLS regression and find that it yields results similar to the current analysis.
Here I assume that skin tone is time-constant. However, it also should be acknowledged that skin tone may change. For example, construction workers who tend to work outdoors may have darker skin tone than their original tone (Hersch 2008), and some people intentionally bleach their skin (Glen 2009).
A trade-off exists between random-effects models and fixed-effects models: random-effects models risk omitted variable bias by assuming that unobserved attributes are independent of observed variables, but they have a higher efficiency than fixed-effect models. In addition, fixed effects include only estimates for measures that vary over time, excluding time-invariant cases and variables (Allison 2009). Although the result of the Hausman test indicates that the coefficients in the two models are different at p < .05, results from a fixed-effects specification show patterns that are quite similar to the current random-effects model. The fixed-effect result is available upon request.
Treating all nonworking states as unemployment can cause bias. Of the full sample of 8,573 observations (including those without a skin tone measure), 58.3% are employed, and 16.4% are unemployed, constituting 39.3% of total nonworking individuals at T3. The majority (60.7%) of the nonworking sample are retirees, homemakers, disabled, other, on leave, or temporally laid off.
The results from the balanced panel (sample members with a job at all three time points only) are broadly same as those from the current sample in the magnitude of the coefficients. The only difference is that skin tone × T3 interaction effects become marginally statistically significant at p < .10, which is likely due to the reduced sample size.
R2 in models that fit race is larger than in models fitting skin tone. R2 is .18 in Model 6, where race/ethnicity and its interaction with time are included, compared with .06 in Model 2, where skin tone and its interaction with time are included. This suggests that the explanatory power of race and ethnicity is larger than that of skin tone. However, the difference in R2 may be due to interval versus categorical variable differences in explanatory power. R2 is not different between Model 3, where skin tone is fitted with additional control for covariates (.43), and in Model 7, where race/ethnicity is fitted instead (.43). An additional control for race/ethnicity in Model 4 does not change R2 from Model 3, where only skin tone and its time interaction are fitted. These results suggest that self-identified race and skin tone are two dimensions of race, as discussed earlier.
Similarly, the model can be stratified by gender given that skin tone influences may be different for men and women. However, gendered immigration assimilation processes are complex because skin tone effects are compounded with other factors, such as visa type (e.g., a spouse of U.S. citizen visa would be granted more to females), and thus deserve a separate study.
Skin tone has a curvilinear effect on occupational mobility: the negative skin tone × Time2 and skin tone × Time3 interaction effects are stronger among darker-skinned immigrants.
One limitation of this study is that undocumented immigrants are not included in the analyses. The majority of undocumented immigrants are from Mexico and Latin America, having emerged as a racialized class in the United States (Massey and Pren 2012). I speculate that including them in the analyses would not change the results significantly. They are likely to have held lower occupational status even before immigration because of their relatively low human capital, and the dark skin tone penalty in the United States relative to pre-immigration is less salient. Nevertheless, the skin tone effect for undocumented immigrants’ assimilation process is worth further research.
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Acknowledgments
The author thanks Jennifer Lundquist, Donald Tomaskovic-Devey, and David Cort for their support and generous comments on this article. Thanks also to the anonymous reviewers and the editors for thoughtful comments and suggestions.
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Han, J. Does Skin Tone Matter? Immigrant Mobility in the U.S. Labor Market. Demography 57, 705–726 (2020). https://doi.org/10.1007/s13524-020-00867-7
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DOI: https://doi.org/10.1007/s13524-020-00867-7