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Are University Admissions Academically Fair?

Author

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  • Debopam Bhattacharya

    (University of Cambridge)

  • Shin Kanaya

    (University of Aarhus)

  • Margaret Stevens

    (University of Oxford)

Abstract

Admission practices at high-profile universities are often criticized for undermining academic merit. Popular tests for detecting such biases suffer from omitted characteristic bias. We develop a bounds-based test to circumvent this problem. We assume that students who are better qualified on observableswould, on average, appear academically stronger to admission officers based on unobservables. This assumption reveals the sign of differences in admission standards across demographic groups that are robust to omitted characteristics. Applying our methods to admissions data from a British university, we find higher admission standards for men and slightly higher ones for private school applicants, despite equal admission success probability across gender and school background.

Suggested Citation

  • Debopam Bhattacharya & Shin Kanaya & Margaret Stevens, 2017. "Are University Admissions Academically Fair?," The Review of Economics and Statistics, MIT Press, vol. 99(3), pages 449-464, July.
  • Handle: RePEc:tpr:restat:v:99:y:2017:i:3:p:449-464
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    as
    1. Anwar Shamena & Fang Hanming, 2012. "Testing for the Role of Prejudice in Emergency Departments Using Bounceback Rates," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 13(3), pages 1-49, December.
    2. Kate Antonovics & Brian G. Knight, 2009. "A New Look at Racial Profiling: Evidence from the Boston Police Department," The Review of Economics and Statistics, MIT Press, vol. 91(1), pages 163-177, February.
    3. Pedro Carneiro & James J. Heckman & Edward Vytlacil, 2010. "Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin," Econometrica, Econometric Society, vol. 78(1), pages 377-394, January.
    4. Amitabh Chandra & Douglas O. Staiger, 2010. "Identifying Provider Prejudice in Healthcare," NBER Working Papers 16382, National Bureau of Economic Research, Inc.
    5. Nicola Persico, 2009. "Racial Profiling? Detecting Bias Using Statistical Evidence," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 229-254, May.
    6. Scott E. Carrell & Bruce I. Sacerdote & James E. West, 2011. "From Natural Variation to Optimal Policy? The Lucas Critique Meets Peer Effects," NBER Working Papers 16865, National Bureau of Economic Research, Inc.
    7. Peter Arcidiacono & Esteban M. Aucejo & Hanming Fang & Kenneth I. Spenner, 2011. "Does affirmative action lead to mismatch? A new test and evidence," Quantitative Economics, Econometric Society, vol. 2(3), pages 303-333, November.
    8. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643.
    9. John Knowles & Nicola Persico & Petra Todd, 2001. "Racial Bias in Motor Vehicle Searches: Theory and Evidence," Journal of Political Economy, University of Chicago Press, vol. 109(1), pages 203-232, February.
    10. Kristensen, Dennis, 2009. "Uniform Convergence Rates Of Kernel Estimators With Heterogeneous Dependent Data," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1433-1445, October.
    11. Shin Kanaya, 2015. "Uniform Convergence Rates of Kernel-Based Nonparametric Estimators for Continuous Time Diffusion Processes: A Damping Function Approach," CREATES Research Papers 2015-50, Department of Economics and Business Economics, Aarhus University.
    12. Bhattacharya, Debopam, 2013. "Evaluating treatment protocols using data combination," Journal of Econometrics, Elsevier, vol. 173(2), pages 160-174.
    13. Nikolay Gospodinov & Masayuki Hirukawa, 2008. "Nonparametric Estimation of Scalar Diffusion Processes of Interest Rates Using Asymmetric Kernels," Working Papers 08011, Concordia University, Department of Economics, revised Dec 2008.
    14. Bhattacharya, Debopam, 2009. "Inferring Optimal Peer Assignment From Experimental Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 486-500.
    15. Rilstone, Paul, 1996. "Nonparametric Estimation of Models with Generated Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 37(2), pages 299-313, May.
    16. Roland G. Fryer Jr. & Glenn C. Loury, 2005. "Affirmative Action and Its Mythology," Journal of Economic Perspectives, American Economic Association, vol. 19(3), pages 147-162, Summer.
    17. Bouezmarni, Taoufik & Scaillet, Olivier, 2005. "Consistency Of Asymmetric Kernel Density Estimators And Smoothed Histograms With Application To Income Data," Econometric Theory, Cambridge University Press, vol. 21(2), pages 390-412, April.
    18. Hanming Fang & Andrea Moro, 2010. "Theories of Statistical Discrimination and Affirmative Action: A Survey," NBER Working Papers 15860, National Bureau of Economic Research, Inc.
    19. Marianne Bertrand & Sendhil Mullainathan, 2004. "Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination," American Economic Review, American Economic Association, vol. 94(4), pages 991-1013, September.
    20. Hansen, Bruce E., 2008. "Uniform Convergence Rates For Kernel Estimation With Dependent Data," Econometric Theory, Cambridge University Press, vol. 24(3), pages 726-748, June.
    21. James J. Heckman, 1998. "Detecting Discrimination," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 101-116, Spring.
    22. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
    23. Caroline M. Hoxby, 2009. "The Changing Selectivity of American Colleges," Journal of Economic Perspectives, American Economic Association, vol. 23(4), pages 95-118, Fall.
    24. Greenstone, Michael & Gayer, Ted, 2009. "Quasi-experimental and experimental approaches to environmental economics," Journal of Environmental Economics and Management, Elsevier, vol. 57(1), pages 21-44, January.
    25. Bhattacharya, Debopam & Dupas, Pascaline, 2012. "Inferring welfare maximizing treatment assignment under budget constraints," Journal of Econometrics, Elsevier, vol. 167(1), pages 168-196.
    26. Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, July.
    27. Roland Fryer & Glenn C. Loury & Tolga Yuret, 2003. "Color-Blind Affirmative Action," NBER Working Papers 10103, National Bureau of Economic Research, Inc.
    28. Enno Mammen & Christoph Rothe & Melanie Schienle, 2010. "Nonparametric Regression with Nonparametrically Generated Covariates," SFB 649 Discussion Papers SFB649DP2010-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    29. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, number 8355.
    30. Margaret Stevens & Kathryn Graddy, 2003. "The Impact of School Inputs on Student Performance: An Empirical Study of Private Schools in the United Kingdom," Economics Series Working Papers 146, University of Oxford, Department of Economics.
    31. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    32. Rothstein, J.M.Jesse M., 2004. "College performance predictions and the SAT," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 297-317.
    33. Peter Arcidiacono & Esteban Aucejo & Ken Spenner, 2012. "What happens after enrollment? An analysis of the time path of racial differences in GPA and major choice," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 1(1), pages 1-24, December.
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    Cited by:

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    4. Bhattacharya, D. & Rabovic, R., 2020. "Do Elite Universities Practise Meritocratic Admissions? Evidence from Cambridge," Cambridge Working Papers in Economics 2056, Faculty of Economics, University of Cambridge.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I20 - Health, Education, and Welfare - - Education - - - General
    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination

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