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Student-Athletes? The Impact of Intercollegiate Sports Participation on Academic Outcomes

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Abstract

As of 2011, there were over 444,000 college athletes playing for over 18,000 teams in the United States. Using data from a longitudinal survey of college students at over 400 institutions, we estimate the impact of participation in intercollegiate athletics on academic outcomes. We focus on whether the effects differ across sports and student demographics including gender, race, and pre-college academic ability. Among our results, we find participation in college sports has a small, negative effect on GPA. This effect is stronger among football and basketball players, stronger among males, weaker among top students, but does not differ across race.

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Notes

  1. Athletes transferring from another university during the pursuit of their education.

  2. For a full listing of the variables included in the PSM process, they are shown in Tables A1, A2, and in the Appendix.

  3. For a more detailed discussion of additional covariate adjustment, we direct the reader to Hullsiek and Louis [2002], Bang and Robins [2005], and Ridgeway et al. [2012].

  4. For the few students in the data who took the ACT but not the SAT, we converted their ACT score into the equivalent SAT score using guidelines described on the official ACT website [ACT, Inc. 2008].

  5. This variable has 10 categories: none (not degree-seeking); associate’s degree; bachelor’s degree; master’s degree; Ph.D. or Ed.D.; MD, DO, DDS, or DVM; JD (law); BD or MDIV (divinity); other degree; and undecided.

  6. The 16 aggregated freshman majors are: agriculture, biological sciences, business, education, engineering, English, health professional, history or political science, humanities, fine arts, mathematics or statistics, physical sciences, social sciences, other technical field, other non-technical field, and undecided.

  7. At the time students in our sample took these exams, the GRE was scored on a 400–1,600 scale, the LSAT on a 120–180 scale, the MCAT on a 3–45 scale, and the GMAT on a 200–800 scale.

  8. Appendix Tables A4 and A5 contain OLS estimates of the impact of intercollegiate sports participation. These results generally show smaller impacts from sports participation on academic outcomes than our PSM estimates. Table A6 contains additional coefficient estimates for our key outcome variables for evaluation of individual control efficacy.

  9. Relative influence refers to the contribution of the variable for predicting treatment, as measured by the percentage increase in the logistic log-likelihood attributable to the variable [Friedman 2001]. Here, pre-college exercise and sports behavior is found to have the highest relative influence in all eight sets of propensity scores.

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Acknowledgements

The authors would like to thank David McClough, Michael Jetter, participants at the 77th Annual Meeting of the Midwest Economics Association, and two anonymous referees for their helpful comments and suggestions. Any remaining errors are the authors’ own.

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Authors and Affiliations

  1. School of Business, Georgia Gwinnett College, 1000 University Center Lane, Lawrenceville, 30043, GA, USA

    P Wesley Routon

  2. Department of Economics and Finance, 5795 Lewiston Rd., Niagara University, 14109, NY, USA

    Jay K Walker

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Appendix

Appendix

Table A1

Table A1 Means and balance measures of individual and family demographics
Full size table

Table A2

Table A2 Means and balance measures of student and college type variables
Full size table

Table A3

Table A3 Means and balance measures of degree-related variables
Full size table

Table A4

Table A4 Effects of college sports participation (OLS results)
Full size table

Table A5

Table A5 Effects of college sports participation on graduate school admittance exam scores (OLS results)
Full size table

Table A6

Table A6 Additional coefficient estimates for select full sample OLS models
Full size table

Here, we present three tables of summary statistics and balance measures as a sample of our propensity score weighting process. The variables in these three tables collectively represent the matching covariates used in each of the eight matching procedures. For brevity, these tables only relate the effectiveness of the matching procedure for one of the eight sets of weights — football and basketball student-athletes. We present this set since these students were the most different from their control sample and thus the most difficult to balance. Table A1 displays sample means for the individual and family demographic variables, Table A2 means of the student and college type variables, and Table A3 the two degree-related variables. Recall that school fixed effects were also used in the outcome analysis to control for any remaining differences at this level. As shown by the lack of significance asterisks in the weighted columns in all three tables, there are no differences between the treatment and control groups when the weights are used — the balance condition is satisfied. This was the case for all eight sets of weights.

In addition, we present three tables (Tables A4, A5 and A6) that display the results of OLS estimation on the same outcome variables and sub-samples using our matching covariates as controls. Table A5 shows the effects of intercollegiate sports participation on graduate school admission test scores while Table A4 presents the effects on the remaining outcomes. Table A6 relates the efficacy of our controls by showing additional coefficient estimates for our primary academic outcomes.

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Routon, P., Walker, J. Student-Athletes? The Impact of Intercollegiate Sports Participation on Academic Outcomes. Eastern Econ J 41, 592–611 (2015). https://doi.org/10.1057/eej.2014.32

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