Abstract
This paper estimates the discrepancy in university mathematics and science course grades across races. Although there are significant Black–White and Hispanic–White grade discrepancies, or gaps, Black and Hispanic students who are equally prepared for university as White students do as well as White students. The grade gaps are explained after accounting for important factors such as a student’s academic capabilities and socioeconomic status. Varying behaviors of university students relative to high school across races are ruled out as a possible source of the grade gaps.
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Notes
The University of North Carolina system includes 16 colleges across a wide range of selectivity, including the state’s flagship university, University of North Carolina at Chapel Hill, as well as several regional and historically black colleges.
The ACT tests are a standardized test for high school achievement and college admissions in the USA produced by ACT, Inc. The ACT consists of multiple choice subject tests in English, mathematics, reading, and science reasoning with scores ranging from 1 to 36.
Also see Armor (1992), Fryer (2003), Hanushek et al. (2009), Jensen (1998), Krueger and Whitmore (2001), Tienda and Mitchell (2006), Wilson et al. (2006). Rouse et al. (2005) report that children below the poverty threshold are 1.3 times more likely to experience learning disabilities and developmental delays.
In 1999 (2009), the US Census Bureau reports that the median income of Whites, Blacks, and Hispanics was $39,915 ($62,545), $21,423 ($38,409), and $23,431 ($39,730), respectively. All figures are reported in 2009 dollars. See Table 697 of http://www.census.gov/compendia/statab/cats/income_expenditures_poverty_wealth.html.
Cook and Evans (2000) uses National Assessment of Educational Progress data.
Orr (2003) use National Longitudinal Survey of Youth (NLSY79) data. The proportion of the gap explained by income was derived by finding the percentage change in the grade gap estimates from the regression models that did (1.82) and did not (1.54) contain the income variable from columns 2 and 3 of table 2 (p. 293).
Source: US Department of Education (https://nces.ed.gov/collegenavigator/?s=AL&l=93&tc=18&xc=20&id=101879#admsns) and collegesimplify.com. There are at least 45 other universities in the databases with the same first and third quartile ACT scores as CSU-Pueblo. The list presented in this paper reflects a handful we believe to be the recognizable to a broad audience. University of Wisconsin Colleges include thirteen smaller campuses in Wisconsin’s state university system, some of which offer only two-year degrees; UW-Madison is not included (for a list, see https://www.uwc.edu/about/campuses).
See the following link for CSU-Pueblo race composition: http://www.csupueblo.edu/Grants/currentuniversitydata/Pages/default.aspx.
See the following link for CSU-Pueblo admission criteria: http://www.gocsupueblo.com/SiteCollectionDocuments/CCHEIndexChart.pdf.
All students must pass at least one college-level mathematics course and complete two Natural and Physical science courses with laboratories to obtain their degrees. See p. 62–63 of the 2011/2012 CSU-Pueblo catalog, at http://www.csupueblo.edu/catalog/Pages/default.aspx.
Schooldigger.com provides state-specific high school percentile ranks-based mathematics and reading test score data from their respective states’ Departments of Education. We have each student’s hometown zip code, and the Web site provides the distance of each high school from a zip code. The specific percentile ranks we use in our analysis are the five- year average percentile ranks of the two schools nearest to the student’s hometown zip code.
We use data from 1999 US Census, which is the most recent available.
Previous literature suggests that zip-code income measures a variety of different factors such as peer effects, community factors, observable family characteristics, and parental education levels (e.g., Corcoran et al. 1992; Ginther et al. 2000; Jenks and Mayer 1990; Solon et al. 2000). Manski (1993) posits that the family and neighborhood factors are not separately identifiable, so we take zip-code income as a proxy for socioeconomic status.
The data on university course grades were obtained after all courses were complete. The analysis assumes that retests did not vary across races and that the sample is sufficiently large that transcript grades are an accurate representation of student performance. An example of high school GPA class percentile rank calculation is as follows: a student whose high school GPA ranked 67 out of a graduating class consisting of 250 students has a high school GPA percentile rank of \((67/250) \times 100 = 26.8\). High school percentile ranks range from 1 to 100 with 1 being the best and 100 being the worst rank possible. A first-generation student is defined as a student with neither parent having education past high school. See page iii of http://nces.ed.gov/pubs2001/2001153.
There were a small number of students (159) in the data whose racial status is classified as “Other.” These include Asian (53), Indian (13), multi-ethnic (42), and unknown (51). While we use this information in the forthcoming regression analysis, we exclude Other race from the reported summary statistics.
See the following link for NCES ACT score statistics: http://nces.ed.gov/programs/digest/d10/tables/dt10_155.asp. The NCES publishes summary statistics to one decimal place.
These courses have been identified by CSU-Pueblo as high-risk courses (i.e., a 30 % or greater “DFW” rate, where D, F, and W, stand for the letter grades D, F, and withdraw, respectively. CSU-Pueblo offers individual and group tutoring as well as supplemental instruction academic support programs to help students with these historically difficult courses).
All grade gap percent changes presented in this paragraph are relative to the unconditional gap listed in column 1.
The grade gap analysis holds constant gender and parental education level as factors that can potential influence student performance. The gender and first-generation indicator variable-specific grade gap results are similar to the results presented in Table 3. The grade gaps decreased in magnitude and were not significant when key covariates were included in the specification.
Note that lower high school GPA percentile ranks indicate better academic performance than higher ranks.
First-generation student also has no significant effect on university grades in models that did not include additional covariates.
This approach is similar to that in Chapter 10 of Gelman and Hill (2006) figure 10.7.
We repeated the Black–White analysis using a stricter tolerance on the overlap assumption by removing propensity scores within 0.025 and 0.10 of zero or one. Trimming with these tolerances removes 110 and 693 observations, respectively, and results in even smaller Black–White differences than with no trimming (though on different subsets of the population).
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Acknowledgments
The authors would like to thank Armando Rodriguez, Robert Rosenman, Hajime Hadeishi, Seth Sacher, Luke Olson, and Christopher Lutz for helpful suggestions.
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Mongeon, K.P., Ulrick, S.W. & Giannetto, M.P. Explaining university course grade gaps. Empir Econ 52, 411–446 (2017). https://doi.org/10.1007/s00181-016-1078-4
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DOI: https://doi.org/10.1007/s00181-016-1078-4