Abstract
This paper tests an explanation of the “net black advantage,” a widespread but under-theorized finding that shows among people with similar socioeconomic status, black Americans achieve higher levels of education than whites. The proposed theory hypothesizes that blacks’ superior net investment is a response to disadvantage in the labor market; simply, whites have less need for education. The hypothesis is tested using the National Longitudinal Study of Adolescent to Adult Health, when respondents were approximately 29 years old. Results show that among equally qualified individuals: (1) at equal levels of income, black workers have more education than whites (or equivalently, at equal levels of education, whites have higher income); (2) black Americans have a steeper rate of return to educational investment, thus at “some graduate school” or more, there is parity in wages; and (3) non-net rates show that 90% of black respondents have levels of education that are associated with white income advantage, even among equally qualified people.
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Notes
As of this writing, Wave IV is the most recent Add Health release. Wave V is underway, with data collection scheduled to be completed by the end of 2018. See http://www.cpc.unc.edu/projects/addhealth/design/wave-v-1.
Responses to “best guess” of income and household income questions were given in categories of dollars (e.g., less than $5000; $5000–$9999; etc.) rather than in actual dollars. In order to use the values to replace personal income’s missing values, the midpoint (in dollars) of each category was used.
The Peabody is a standardized receptive vocabulary test that has high correlations with traditional tests of the intelligence quotient. Grade point average (GPA) reflects the respondent’s grades in the four main subjects (mathematics, English language arts, social studies, and science) in the marking period just prior to the Wave I in-home interview. In the Wave IV memory tests, Add Health read a list of 15 words to each respondent. Immediately after hearing the list, respondents were asked to recall as many words as they could in 90s. Then, later in the interview, respondents were asked again to recall as many words as they could, this time in 60s. The number of words recalled correctly each time was entered into the Cronbach’s alpha calculation. The final item in the ability scale involved recall of numbers. Also from Wave IV, the interviewer would read a string of numbers, and the respondent would have to repeat them back in reverse order. Over successive trials, different strings using different numbers were given, and the strings got longer by one number each time. The test concluded when the string became so long that the respondent could not correctly restate the numbers in reverse order. The respondent’s score is the number of trials completed successfully.
After applying the above-described race categorization to the sample, but before limiting it to black and white respondents, the sample was 68% white, 16% black, 11% Hispanic, 3% Asian, and 2% “other.”
Ability for the full Wave IV sample (unweighted N = 14,799) had a mean of 0.00 with a standard deviation of 0.68. As Table 1 shows, for the estimation sample, the mean is 0.07 (SD = 0.65).
All equations reflect design-based modeling, including degrees of freedom (Chantala and Tabor 2010).
Parent income, and personal income, are both right skewed. In all models where they are used as independent variables, substituting their natural log, or alternatively their quadratic function and main effect, does produce statistically significant curvilinearity; however, using the untransformed predictors does not change the paper’s interpretations in any way. In fact, using them untransformed produces more conservative estimates of the outcome. Since the results are not substantively different, the untransformed measures are used to preserve the metrics and produce easily interpretable results.
Personal income has a sizable positive skew, and as a dependent variable (Models 5–8) it does violate the OLS assumption of normality of the error distribution; however, the skew does not change the substantive results that are presented here. In models not shown, the following diagnostic changes were made (in separate models): (1) Models were run using the natural log of personal income as the outcome. (2) An increasingly popular alternative to logging skewed-dependent variables (as long as they have only positive values) is to use Poisson regression with robust standard errors (Wooldridge 2010; and see; Gould 2011 for a summary). The technique was deployed here as a diagnostic tool. (3) The OLS analyses were rerun, except respondents with personal incomes above $80,000 were eliminated (thereby making personal income normally distributed). In all cases for these various diagnostic models, the coefficients for the key variables all retained the same signs with similar magnitudes, and none fell in or out of significance. Among covariates, there were changes in significance of the location dummy variables, but as a whole each larger concept (region and urbanicity) retained significance. Given this paper’s desire to create directly comparable models that predict education and then income, and the benefit of maintaining meaningful metrics and coefficients (rather than resorting to log dollars or changes in odds), the decision was made to use OLS regression. The results herein are robust; substantive interpretations do not change based on the type of model used.
Figure 1 black–white income comparisons, adjusted Wald tests: Edu level 1: F(1, 128) = 11.13, p = .001; Edu level 2: F(1, 128) = 13.65, p < .001; Edu level 3: F(1, 128) = 12.79, p < .001; Edu Level 4: F(1, 128) = 3.31, p = .07; Edu level 5: F(1, 128) = 0.08, ns; Edu level 6: F(1, 128) = 0.23, ns.
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Acknowledgements
This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.
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Mangino, W. Income Returns in Early Career: Why Whites Have Less Need for Education. Race Soc Probl 11, 45–59 (2019). https://doi.org/10.1007/s12552-018-9233-5
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DOI: https://doi.org/10.1007/s12552-018-9233-5