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Race to College: The “Reverse Gap”

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Abstract

This article uses the National Longitudinal Study of Adolescent Health to establish that once socioeconomic status is considered, black Americans go to college at higher rates than whites. The outcome replicates numerous other studies that use different datasets and varying methods. Combining statistics and literature, I propose that blacks’ superior educational investment is an “empirical generalization.” This leads to discussions of the black-white “gap” in education and the “attitude-achievement paradox.” The latter claims that black people have high educational aspirations but fail to act on those attitudes. But when considering the choice to invest in education, the “attitude-achievement paradox” evaporates. Black Americans have high educational aspirations and, when there are enough resources, act on those aspirations by going to college at higher rates than whites. The paper concludes with a theoretical explanation of why black people, more than whites, efficiently translate resources into educational investment. I use literature to show that in the United States, the bearers of light skin are afforded numerous informal opportunities that allow them to get higher returns out of a given level of human capital. Non-whites, on the other hand, have fewer informal opportunities, and they therefore deploy “supra-normal efforts” of skill acquisition as a strategy to overcome their informal disadvantage.

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Notes

  1. Quotation from the Add Health website (http://www.cpc.unc.edu/projects/addhealth). Add Health uses a nested sampling design. All analyses presented here use the appropriate weight and cluster variables to assure proper statistics. See the website for a complete description of the study.

  2. In all analyses that follow, college attendance is predicted without reference to high school graduation status. However, the conclusions of this paper apply regardless of whether or not high school dropouts are eliminated from the college attendance models. Further, the findings also hold when applied to high school graduation.

  3. N’s do not match proportions because Add Health oversampled certain subpopulations. All calculations and statistics use the appropriate design-based sampling weights.

  4. There are moderate correlations among the three class variables, family income, neighborhood income, and parents’ education; Pearson’s correlations (r 2) range from 0.36 to 0.39. There is also a correlation of 0.40 between parents’ education and child’s vocabulary. To be sure multi-collinearity is not driving the results in Table 6, I ran numerous diagnostic models (not shown). First, I ran Models 6.1, 6.2, and 6.3 as OLS equations despite that college attendance is dichotomous and therefore violates the OLS assumption of a continuous and unbounded outcome (Berry 1993). Such an approach has been used successfully to diagnose models of non-continuous dependent variables (Brueckner 1995). After confirming that in the OLS equations all predictors retained the same direction of effect and statistical significance as their logistic counterparts, I calculated the variance inflation factor (VIF) and the tolerance for each OLS model (Fox 1991). By any standard, multi-collinearity is not problematic. The mean VIF for each model is, respectively, 1.13, 1.14, and 1.19. Further, none of the VIFs for individual variables approached the generally accepted “rule of thumb” of 10.00, nor the more conservative guideline of 4.00 (O’Brien 2007). In the OLS version of Model 6.1, the highest individual VIF was 1.28 (tolerance = 0.78) for neighborhood income followed by family income with a VIF of 1.20 (tolerance = 0.84). The addition of the variables in Model 6.2 changed things only minimally. In OLS Model 6.3, parents’ education had the highest VIF (1.42; tolerance = 0.70), followed by child’s vocabulary and neighborhood income, each with a VIF of 1.38 (tolerance = 0.72). As my own rule, I investigate multi-collinearity when the tolerance of any variable is less than 0.70. Thus, I returned to the logistic version of Model 6.3 and repeatedly respecified it with every possible combination of the vocabulary and three class variables. Even when each of these variables was included alone, without the other three, statistically significant superior black achievement remained. Next, I used Ender’s (n.d.) “collin” command that allows collinearity diagnostics to be computed directly on logistic regression. There were no substantive differences from the OLS analogs just described. Finally, log transformations of the income variables only strengthened the results. The finding that blacks invest in college more than whites is highly robust.

  5. More accurately, when blacks did suburbanize, they were channeled into “black” suburbs (Jackson 1985).

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Acknowledgments

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 to Ronald R. Rindfuss and Barbara Entwisle for their 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. The author thanks Tad Krauze, Marc Silver, Grace Johnson, and the RASP reviewers for useful comments on earlier drafts of this paper. Any mistakes that remain are the author’s.

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  1. Department of Sociology, Hofstra University, 202B Davison Hall, Hempstead, NY, 11549, USA

    William Mangino

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Mangino, W. Race to College: The “Reverse Gap”. Race Soc Probl 2, 164–178 (2010). https://doi.org/10.1007/s12552-010-9037-8

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