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Impact Evaluation in Matching Markets with General Tie-Breaking

Author

Listed:
  • Atila Abdulkadiroglu
  • Joshua D. Angrist
  • Yusuke Narita
  • Parag A. Pathak

Abstract

Many centralized matching schemes incorporate a mix of random lottery and non-lottery tie-breaking. A leading example is the New York City public school district, which uses criteria like test scores and interviews to generate applicant rankings for some schools, combined with lottery tie-breaking at other schools. We develop methods that identify causal effects of assignment in such settings. Our approach generalizes the standard regression discontinuity design to allow for many running variables and treatments, some of which are randomly assigned. We show that lottery variation generates assignment risk at non-lottery programs for applicants away from non-lottery cutoffs, while non-lottery variation randomizes applicants near cutoffs regardless of lottery risk. These methods are applied to evaluate New York City’s school progress assessments, which give schools letter grades as a summary measure of quality. Our estimates reveal that although Grade A schools boost achievement, these gains emerge only for students who attend lottery schools. Attendance at a coveted Grade A screened school, including some of the highest performing in the district, generates no measurable effects. Evaluation methods that fail to take advantage of both lottery and non-lottery variation miss this difference in impact.

Suggested Citation

  • Atila Abdulkadiroglu & Joshua D. Angrist & Yusuke Narita & Parag A. Pathak, 2017. "Impact Evaluation in Matching Markets with General Tie-Breaking," NBER Working Papers 24172, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24172
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    References listed on IDEAS

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    1. Adrienne M. Lucas & Isaac M. Mbiti, 2014. "Effects of School Quality on Student Achievement: Discontinuity Evidence from Kenya," American Economic Journal: Applied Economics, American Economic Association, vol. 6(3), pages 234-263, July.
    2. Atila Abdulkadiro?lu & Yeon-Koo Che & Yosuke Yasuda, 2015. "Expanding "Choice" in School Choice," American Economic Journal: Microeconomics, American Economic Association, vol. 7(1), pages 1-42, February.
    3. Barrow, Lisa & Sartain , Lauren & de la Torre, Marisa, 2016. "The Role of Selective High Schools in Equalizing Educational Outcomes: Heterogeneous Effects by Neighborhood Socioeconomic Status," Working Paper Series WP-2016-17, Federal Reserve Bank of Chicago.
    4. Jonah Rockoff & Lesley J. Turner, 2010. "Short-Run Impacts of Accountability on School Quality," American Economic Journal: Economic Policy, American Economic Association, vol. 2(4), pages 119-147, November.
    5. Justine S. Hastings & Christopher A. Neilson & Seth D. Zimmerman, 2013. "Are Some Degrees Worth More than Others? Evidence from college admission cutoffs in Chile," NBER Working Papers 19241, National Bureau of Economic Research, Inc.
    6. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504.
    7. Beuermann, Diether & Jackson, C. Kirabo & Sierra, Ricardo, 2015. "Privately Managed Public Secondary Schools and Academic Achievement in Trinidad and Tobago: Evidence from Rule-Based Student Assignments," IDB Publications (Working Papers) 7308, Inter-American Development Bank.
    8. Lars Kirkebøen & Edwin Leuven & Magne Mogstad, 2014. "Field of Study, Earnings, and Self-Selection," NBER Working Papers 20816, National Bureau of Economic Research, Inc.
    9. 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.
    10. Elliott Peranson & Alvin E. Roth, 1999. "The Redesign of the Matching Market for American Physicians: Some Engineering Aspects of Economic Design," American Economic Review, American Economic Association, vol. 89(4), pages 748-780, September.
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    More about this item

    JEL classification:

    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • I20 - Health, Education, and Welfare - - Education - - - General

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