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Decomposing Outcome Differences between HBCU and Non-HBCU Institutions

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Abstract

This paper investigates differences in outcomes between historically black colleges and universities (HBCU) and traditional college and universities (non-HBCUs) using a standard Oaxaca/Blinder decomposition. This method decomposes differences in observed educational and labor market outcomes between HBCU and non-HBCU students into differences in characteristics (both student and institutional) and differences in how those characteristics translate into differential outcomes. Efforts to control for differences in unobservables between the two types of students are undertaken through inverse-probability weighting and propensity score matching methodologies. We find that differences in student characteristics make the largest contributions to each outcome difference. However, some hope in identifying policy levers comes in the form of how characteristics translate into outcomes. For example, whereas HBCUs appear to be doing a better job helping female graduates parlay their education into higher earnings, non-HBCUs are doing a better job in helping graduates in science, technology, engineering, and mathematics translate their training into higher earnings. Patterns and importance of regressors are similar at different points of the distributions of outcomes.

Suggested Citation

  • Mels de Zeeuw & Sameera Fazili & Julie L. Hotchkiss, 2020. "Decomposing Outcome Differences between HBCU and Non-HBCU Institutions," FRB Atlanta Working Paper 2020-10, Federal Reserve Bank of Atlanta.
    • Handle: RePEc:fip:fedawp:88475
    DOI: 10.29338/wp2020-10
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    References listed on IDEAS

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    1. Nicole M. Fortin, 2008. "The Gender Wage Gap among Young Adults in the United States: The Importance of Money versus People," Journal of Human Resources, University of Wisconsin Press, vol. 43(4).
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    1. de Zeeuw, Mels & Fazili, Sameera & Hotchkiss, Julie L., 2021. "Decomposing differences in Black student graduation rates between HBCU and non-HBCU Institutions: The devil is in the details," Economics Letters, Elsevier, vol. 202(C).

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    More about this item

    Keywords

    HBCU; decomposition; student debt; returns to education; propensity score matching; inverse-probability weighting; quantile regressions;
    All these keywords.

    JEL classification:

    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I26 - Health, Education, and Welfare - - Education - - - Returns to Education
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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