IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp13838.html
   My bibliography  Save this paper

What Is at Stake without High-Stakes Exams? Students' Evaluation and Admission to College at the Time of COVID-19

Author

Listed:
  • Arenas, Andreu

    (University of Barcelona)

  • Calsamiglia, Caterina

    (IPEG)

  • Loviglio, Annalisa

    (University of Bologna)

Abstract

The outbreak of COVID-19 in 2020 inhibited face-to-face education and constrained exam taking. In many countries worldwide, high-stakes exams happening at the end of the school year determine college admissions. This paper investigates the impact of using historical data of school and high-stakes exams results to train a model to predict high-stakes exams given the available data in the Spring. The most transparent and accurate model turns out to be a linear regression model with high school GPA as the main predictor. Further analysis of the predictions reflect how high-stakes exams relate to GPA in high school for different subgroups in the population. Predicted scores slightly advantage females and low SES individuals, who perform relatively worse in high-stakes exams than in high school. Our preferred model accounts for about 50% of the out-of- sample variation in the high-stakes exam. On average, the student rank using predicted scores differs from the actual rank by almost 17 percentiles. This suggests that either high-stakes exams capture individual skills that are not measured by high school grades or that high-stakes exams are a noisy measure of the same skill.

Suggested Citation

  • Arenas, Andreu & Calsamiglia, Caterina & Loviglio, Annalisa, 2020. "What Is at Stake without High-Stakes Exams? Students' Evaluation and Admission to College at the Time of COVID-19," IZA Discussion Papers 13838, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp13838
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp13838.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Terrier, Camille, 2020. "Boys lag behind: How teachers’ gender biases affect student achievement," Economics of Education Review, Elsevier, vol. 77(C).
    2. Fernanda Estevan & Thomas Gall & Louis-Philippe Morin, 2019. "Redistribution Without Distortion: Evidence from an Affirmative Action Programme at a Large Brazilian University," The Economic Journal, Royal Economic Society, vol. 129(619), pages 1182-1220.
    3. Lavy, Victor & Sand, Edith, 2018. "On the origins of gender gaps in human capital: Short- and long-term consequences of teachers' biases," Journal of Public Economics, Elsevier, vol. 167(C), pages 263-279.
    4. Calsamiglia, Caterina & Loviglio, Annalisa, 2019. "Grading on a curve: When having good peers is not good," Economics of Education Review, Elsevier, vol. 73(C).
    5. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    6. Ghazala Azmat & Caterina Calsamiglia & Nagore Iriberri, 2016. "Gender Differences In Response To Big Stakes," Journal of the European Economic Association, European Economic Association, vol. 14(6), pages 1372-1400, December.
    7. Xiqian Cai & Yi Lu & Jessica Pan & Songfa Zhong, 2019. "Gender Gap under Pressure: Evidence from China's National College Entrance Examination," The Review of Economics and Statistics, MIT Press, vol. 101(2), pages 249-263, May.
    8. Analia Schlosser & Zvika Neeman & Yigal Attali, 2019. "Differential Performance in High Versus Low Stakes Tests: Evidence from the Gre Test," The Economic Journal, Royal Economic Society, vol. 129(623), pages 2916-2948.
    9. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    10. Schlosser, Analia & Neeman, Zvika & Attali, Yigal, 2018. "Differential Performance in High vs. Low Stakes Tests: Evidence from the GRE Test," CEPR Discussion Papers 13360, C.E.P.R. Discussion Papers.
    11. repec:oup:econjl:v:129:y:2019:i:10:p:2916-2948. is not listed on IDEAS
    12. Evren Ors & Frédéric Palomino & Eloïc Peyrache, 2013. "Performance Gender Gap: Does Competition Matter?," Journal of Labor Economics, University of Chicago Press, vol. 31(3), pages 443-499.
    13. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    14. Eduardo M. Azevedo & Jacob D. Leshno, 2016. "A Supply and Demand Framework for Two-Sided Matching Markets," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1235-1268.
    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. Luiz Brotherhood & Bernard Herskovic & Joao Ramos, 2022. "Income-based affirmative action in college admissions," UB School of Economics Working Papers 2022/425, University of Barcelona School of Economics.
    2. Arenas, Andreu & Calsamiglia, Caterina, 2022. "Gender Differences in High-Stakes Performance and College Admission Policies," IZA Discussion Papers 15550, Institute of Labor Economics (IZA).

    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. Arenas, Andreu & Calsamiglia, Caterina, 2022. "Gender Differences in High-Stakes Performance and College Admission Policies," IZA Discussion Papers 15550, Institute of Labor Economics (IZA).
    2. Delaney, Judith M. & Devereux, Paul J., 2021. "Gender and Educational Achievement: Stylized Facts and Causal Evidence," IZA Discussion Papers 14074, Institute of Labor Economics (IZA).
    3. Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," MPRA Paper 110703, University Library of Munich, Germany.
    4. Blankenship, Brian & Aklin, Michaël & Urpelainen, Johannes & Nandan, Vagisha, 2022. "Jobs for a just transition: Evidence on coal job preferences from India," Energy Policy, Elsevier, vol. 165(C).
    5. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
    6. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    7. Mona Aghdaee & Bonny Parkinson & Kompal Sinha & Yuanyuan Gu & Rajan Sharma & Emma Olin & Henry Cutler, 2022. "An examination of machine learning to map non‐preference based patient reported outcome measures to health state utility values," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1525-1557, August.
    8. Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
    9. Mehmet Güney Celbiş & Pui-Hang Wong & Karima Kourtit & Peter Nijkamp, 2021. "Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach," Sustainability, MDPI, vol. 13(23), pages 1-29, December.
    10. Amitabh Chandra & Courtney Coile & Corina Mommaerts, 2023. "What Can Economics Say about Alzheimer's Disease?," Journal of Economic Literature, American Economic Association, vol. 61(2), pages 428-470, June.
    11. Ahlfeldt, Gabriel M. & Heblich, Stephan & Seidel, Tobias, 2023. "Micro-geographic property price and rent indices," Regional Science and Urban Economics, Elsevier, vol. 98(C).
    12. Nir Chemaya & Daniel Martin, 2023. "Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals," Papers 2311.14720, arXiv.org, revised Jan 2024.
    13. Etienne Dagorn & David Masclet & Thierry Penard, 2022. "The Behavioral Determinants of School Achievement: A Lab in the Field Experiment in Middle School," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 2022-05, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
    14. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2022. "Multiway Cluster Robust Double/Debiased Machine Learning," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1046-1056, June.
    15. Dario Sansone & Anna Zhu, 2023. "Using Machine Learning to Create an Early Warning System for Welfare Recipients," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(5), pages 959-992, October.
    16. Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022. "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, vol. 94(C).
    17. Yongtong Shao & Tao Xiong & Minghao Li & Dermot Hayes & Wendong Zhang & Wei Xie, 2021. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1082-1098, May.
    18. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
    19. Sonan Memon, 2021. "Machine Learning for Economists: An Introduction," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 60(2), pages 201-211.
    20. John Aoga & Juhee Bae & Stefanija Veljanoska & Siegfried Nijssen & Pierre Schaus, 2020. "Impact of weather factors on migration intention using machine learning algorithms," Papers 2012.02794, arXiv.org.

    More about this item

    Keywords

    performance prediction; high-stakes exams; college allocation; COVID-19;
    All these keywords.

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

    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:iza:izadps:dp13838. 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: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.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.