IDEAS home Printed from https://ideas.repec.org/p/qss/dqsswp/1407.html
   My bibliography  Save this paper

Does an aptitude test affect socioeconomic and gender gaps in attendance at an elite university?

Author

Listed:
  • Jake Anders

    (Department of Quantitative Social Science, Institute of Education, University of London)

Abstract

The increasing use of aptitude tests as part of the admissions processes at elite English universities potentially has significant implications for fair access to these institutions. I attempt to isolate the impact of the introduction of one such test on the proportion of successful applicants by school type (as a proxy for socioeconomic status) and by gender using a difference in differences approach and administrative data from the University of Oxford. The introduction of the test coincided with the implementation of a guideline number of interviews per available place, significantly reducing the proportion of applicants offered an interview (by 14 percentage points) and, hence, increasing the proportion of interviewees offered places (by 3.6 percentage points). By gender, I find some evidence that these changes may be having differing effects at different stages of the admissions process, but not on each group's overall chances of securing an offer. I do not find any evidence that the policy has negative side effects on the chances of applicants from less advantaged socioeconomic backgrounds at any stage of the process.

Suggested Citation

  • Jake Anders, 2014. "Does an aptitude test affect socioeconomic and gender gaps in attendance at an elite university?," DoQSS Working Papers 14-07, Quantitative Social Science - UCL Social Research Institute, University College London.
  • Handle: RePEc:qss:dqsswp:1407
    as

    Download full text from publisher

    File URL: https://repec.ucl.ac.uk/REPEc/pdf/qsswp1407.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    2. Jake Anders, 2012. "Using the Longitudinal Study of Young People in England for research into Higher Education access," DoQSS Working Papers 12-13, Quantitative Social Science - UCL Social Research Institute, University College London.
    3. Jake Anders, 2012. "The Link between Household Income, University Applications and University Attendance," Fiscal Studies, Institute for Fiscal Studies, vol. 33(2), pages 185-210, June.
    4. Parente Paulo M.D.C. & Santos Silva João M.C., 2016. "Quantile Regression with Clustered Data," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 1-15, January.
    5. David Card, 1992. "Using Regional Variation in Wages to Measure the Effects of the Federal Minimum Wage," ILR Review, Cornell University, ILR School, vol. 46(1), pages 22-37, October.
    6. repec:esx:essedp:728 is not listed on IDEAS
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. Geiser, Saul, 2008. "Back to the Basics: In Defense of Achievement (and Achievement Tests) in College Admissions," University of California at Berkeley, Center for Studies in Higher Education qt8kd4q096, Center for Studies in Higher Education, UC Berkeley.
    9. Rothstein, J.M.Jesse M., 2004. "College performance predictions and the SAT," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 297-317.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Claire Crawford, 2014. "Socio-economic differences in university outcomes in the UK: drop-out, degree completion and degree class," IFS Working Papers W14/31, Institute for Fiscal Studies.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Valentine Fays & Benoît Mahy & François Rycx, 2023. "Wage differences according to workers' origin: The role of working more upstream in GVCs," LABOUR, CEIS, vol. 37(2), pages 319-342, June.
    2. Nicola Gagliardi & Benoît Mahy & François Rycx, 2021. "Upstreamness, Wages and Gender: Equal Benefits for All?," British Journal of Industrial Relations, London School of Economics, vol. 59(1), pages 52-83, March.
    3. Andreas Hagemann, 2017. "Cluster-Robust Bootstrap Inference in Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 446-456, January.
    4. Besstremyannaya, Galina & Dasher, Richard & Golovan, Sergei, 2022. "Quantifying heterogeneity in the relationship between R&D intensity and growth at innovative Japanese firms: A quantile regression approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 67, pages 27-45.
    5. Katarzyna Burzynska & Olle Berggren, 2015. "The Impact of Social Beliefs on Microfinance Performance," Journal of International Development, John Wiley & Sons, Ltd., vol. 27(7), pages 1074-1097, October.
    6. Aysa Ipek Erdogan, 2023. "Drivers of SME Growth: Quantile Regression Evidence From Developing Countries," SAGE Open, , vol. 13(1), pages 21582440231, March.
    7. Pérez Pérez, Jorge, 2020. "The minimum wage in formal and informal sectors: Evidence from an inflation shock," World Development, Elsevier, vol. 133(C).
    8. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    9. E. Mark Curtis & Barry T. Hirsch & Mary C. Schroeder, 2016. "Evaluating Workplace Mandates with Flows Versus Stocks: An Application to California Paid Family Leave," Southern Economic Journal, John Wiley & Sons, vol. 83(2), pages 501-526, October.
    10. Cummins, Joseph R., 2017. "Heterogeneous treatment effects in the low track: Revisiting the Kenyan primary school experiment," Economics of Education Review, Elsevier, vol. 56(C), pages 40-51.
    11. Markus Grillitsch & Magnus Nilsson, 2019. "Knowledge externalities and firm heterogeneity: Effects on high and low growth firms," Papers in Regional Science, Wiley Blackwell, vol. 98(1), pages 93-114, February.
    12. Pau Insa-Sánchez, 2021. "Inequality of Opportunity in Access to Secondary Education in 19th Century," Documentos de Trabajo (DT-AEHE) 2106, Asociación Española de Historia Económica.
    13. Michael Haylock, 2022. "Distributional differences in the time horizon of executive compensation," Empirical Economics, Springer, vol. 62(1), pages 157-186, January.
    14. Opoku, Eric Evans Osei & Aluko, Olufemi Adewale, 2021. "Heterogeneous effects of industrialization on the environment: Evidence from panel quantile regression," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 174-184.
    15. Opoku, Eric Evans Osei & Dogah, Kingsley E. & Aluko, Olufemi Adewale, 2022. "The contribution of human development towards environmental sustainability," Energy Economics, Elsevier, vol. 106(C).
    16. Evangelia Papapetrou & Pinelopi Tsalaporta, 2016. "Inter-industry wage differentials in Greece: rent-sharing and unobserved heterogeneity hypotheses," Working Papers 213, Bank of Greece.
    17. Amal Jmaii & Damien Rousselière & Christophe Daniel, 2017. "Semi†parametric Regression†based Decomposition Methods: Evidence from Regional Inequality in Tunisia," African Development Review, African Development Bank, vol. 29(4), pages 660-673, December.
    18. John Deke, 2016. "Design and Analysis Considerations for Cluster Randomized Controlled Trials That Have a Small Number of Clusters," Evaluation Review, , vol. 40(5), pages 444-486, October.
    19. Luigi Aldieri & Concetto Paolo Vinci, 2017. "Quantile Regression for Panel Data: An Empirical Approach for Knowledge Spillovers Endogeneity," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(7), pages 106-113, July.
    20. Jaepil Han, 2020. "Identifying the effects of technology transfer policy using a quantile regression: the case of South Korea," The Journal of Technology Transfer, Springer, vol. 45(6), pages 1690-1717, December.

    More about this item

    Keywords

    Higher Education; Aptitude Test; Gender; Socioeconomic Gradient; Difference in Differences.;
    All these keywords.

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:qss:dqsswp:1407. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr Neus Bover Fonts (email available below). General contact details of provider: https://edirc.repec.org/data/dqioeuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.