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Revealed Preference Analysis of School Choice Models

Author

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  • Nikhil Agarwal
  • Paulo Somaini
Abstract
Preferences for schools are important determinants of equitable access to high-quality education, effects of expanded choice on school improvement, and school choice mechanism design. Standard methods for estimating consumer preferences are not applicable in education markets because students do not always get their first-choice school. This review describes recently developed methods for using rich data from a school choice mechanism to estimate student preferences. Our objectives are to present a unifying framework for these methods and to help applied researchers decide which techniques to use. After laying out methodological issues, we provide an overview of empirical results obtained using these models and discuss some open questions.

Suggested Citation

  • Nikhil Agarwal & Paulo Somaini, 2020. "Revealed Preference Analysis of School Choice Models," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 471-501, August.
    • Handle: RePEc:anr:reveco:v:12:y:2020:p:471-501
    DOI: 10.1146/annurev-economics-082019-112339
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    Citations

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    Cited by:

    1. Matteo Bobba & Tim Ederer & Gianmarco León-Ciliotta & Christopher A. Neilson & Marco Nieddu, 2021. "Teacher compensation and structural inequality: Evidence from centralized teacher school choice in Perú," Economics Working Papers 1788, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Peng Shi, 2022. "Optimal Priority-Based Allocation Mechanisms," Management Science, INFORMS, vol. 68(1), pages 171-188, January.
    3. Amel Awadelkarim & Arjun Seshadri & Itai Ashlagi & Irene Lo & Johan Ugander, 2023. "Rank-heterogeneous Preference Models for School Choice," Papers 2306.01801, arXiv.org.
    4. Delaney, Judith M. & Devereux, Paul J., 2021. "High School Rank in Math and English and the Gender Gap in STEM," Labour Economics, Elsevier, vol. 69(C).
    5. Schwartz, Jacob & Song, Kyungchul, 2024. "The law of large numbers for large stable matchings," Journal of Econometrics, Elsevier, vol. 241(1).
    6. Nikhil Agarwal & Itai Ashlagi & Michael A. Rees & Paulo Somaini & Daniel Waldinger, 2021. "Equilibrium Allocations Under Alternative Waitlist Designs: Evidence From Deceased Donor Kidneys," Econometrica, Econometric Society, vol. 89(1), pages 37-76, January.
    7. Alfred Galichon & Bernard Salani'e, 2021. "Cupid's Invisible Hand: Social Surplus and Identification in Matching Models," Papers 2106.02371, arXiv.org, revised Jan 2023.
    8. Derek Neal & Joseph Root, 2024. "The Provision of Information and Incentives in School Assignment Mechanisms," NBER Chapters, in: New Directions in Market Design, National Bureau of Economic Research, Inc.
    9. Jessie Bruhn & Christopher Campos & Eric Chen, 2023. "Who Benefits from Remote Schooling? Self-Selection and Match Effects," Working Papers 2023-004, Brown University, Department of Economics.

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