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Do Schools Matter for High Math Achievement? Evidence from the American Mathematics Competitions

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  • Glenn Ellison
  • Ashley Swanson

Abstract

This paper uses data from the American Mathematics Competitions to examine the rates at which different high schools produce high-achieving math students. There are large differences in the frequency with which students from seemingly similar schools reach high achievement levels. The distribution of unexplained school effects includes a thick tail of schools that produce many more high-achieving students than is typical. Several additional analyses suggest that the differences are not primarily due to unobserved differences in student characteristics. The differences are persistent across time, suggesting that differences in the effectiveness of educational programs are not primarily due to direct peer effects.

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  • Glenn Ellison & Ashley Swanson, 2016. "Do Schools Matter for High Math Achievement? Evidence from the American Mathematics Competitions," American Economic Review, American Economic Association, vol. 106(6), pages 1244-1277, June.
    • Handle: RePEc:aea:aecrev:v:106:y:2016:i:6:p:1244-77
    Note: DOI: 10.1257/aer.20140308
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    2. Faia, Ester, 2020. "Educational Inequality, Assortative Mating and Women Empowerement," CEPR Discussion Papers 14547, C.E.P.R. Discussion Papers.
    3. Ainoa Aparicio Fenoll & Sarah Zaccagni, 2021. "Gender Mix and Team Performance: Differences between Exogenously and Endogenously Formed Teams," CEBI working paper series 21-03, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    4. Nagore Iriberri & Pedro Rey-Biel, 2019. "Competitive Pressure Widens the Gender Gap in Performance: Evidence from a Two-stage Competition in Mathematics," The Economic Journal, Royal Economic Society, vol. 129(620), pages 1863-1893.
    5. Kiss David, 2017. "A Model about the Impact of Ability Grouping on Student Achievement," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 17(3), pages 1-10, July.
    6. Paulo Bastos & Julian Cristia & Beomsoo Kim, 2016. "Good schools or good students? Evidence on school effects from universal random assignment of students to high schools," Discussion Paper Series 1607, Institute of Economic Research, Korea University.
    7. Vishaal Baulkaran & Pawan Jain, 2023. "Who uses robo‐advising and how?," The Financial Review, Eastern Finance Association, vol. 58(1), pages 65-89, February.
    8. Hemelt, Steven W. & Lenard, Matthew A., 2020. "Math acceleration in elementary school: Access and effects on student outcomes," Economics of Education Review, Elsevier, vol. 74(C).
    9. Fenoll, Ainoa Aparicio & Moscarola, Flavia Coda & Zaccagni, Sarah, 2021. "Mathematics camps: A gift for gifted students?," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 738-751.

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    More about this item

    JEL classification:

    • H75 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Government: Health, Education, and Welfare
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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    1. Do Schools Matter for High Math Achievement? Evidence from the American Mathematics Competitions (AER 2016) in ReplicationWiki

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