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Identification of treatment effects under imperfect matching with an application to Chinese elite schools

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  • Zhang, Hongliang

Abstract

This paper extends the treatment effect framework for causal inference to contexts in which the instrument appears in a data set that can only be linked imperfectly to the treatment and outcome variables contained in another data set. To overcome this problem, I form all pairwise links between information on the instrument and information on the treatment and outcome matched by the commonly recorded personal characteristics in both data sets. I show how these imperfect conditional matches can be used to identify both the average and distributional treatment effects for compliers of the common units of the two data sets. This multiple data source approach is then applied to analyze the effect of attending an elite middle school in a Chinese city where schools' admissions lottery records can only be linked imperfectly to the administrative student records.

Suggested Citation

  • Zhang, Hongliang, 2016. "Identification of treatment effects under imperfect matching with an application to Chinese elite schools," Journal of Public Economics, Elsevier, vol. 142(C), pages 56-82.
    • Handle: RePEc:eee:pubeco:v:142:y:2016:i:c:p:56-82
    DOI: 10.1016/j.jpubeco.2016.03.004
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    Cited by:

    1. Jere R. Behrman & C. Simon Fan & Xiangdong Wei & Hongliang Zhang & Junsen Zhang, 2020. "After-School Tutoring, Household Substitution and Student Achievement: Experimental Evidence from Rural China," PIER Working Paper Archive 20-004, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. Song, Yang, 2019. "Sorting, school performance and quality: Evidence from China," Journal of Comparative Economics, Elsevier, vol. 47(1), pages 238-261.
    3. Ha, Wei & Kang, Le & Song, Yang, 2020. "College matching mechanisms and matching stability: Evidence from a natural experiment in China," Journal of Economic Behavior & Organization, Elsevier, vol. 175(C), pages 206-226.
    4. Jere R. Behrman & C. Simon Fan & Naijia Guo & Xiangdong Wei & Hongliang Zhang & Junsen Zhang, 2024. "Tutoring Efficacy, Household Substitution, And Student Achievement: Experimental Evidence From An After‐School Tutoring Program In Rural China," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 65(1), pages 149-189, February.
    5. Huang, Bin & Li, Bo & Walker, Ian & Zhu, Yu, 2022. "Does It Pay to Attend More Selective High Schools? Regression Discontinuity Evidence from China," IZA Discussion Papers 15756, Institute of Labor Economics (IZA).
    6. Dustan, Andrew, 2018. "Family networks and school choice," Journal of Development Economics, Elsevier, vol. 134(C), pages 372-391.
    7. Maurício Benegas & Márcio Veras Corrêa, 2020. "Educational supply policies: distortions and labor market performance," Journal of Economics, Springer, vol. 129(3), pages 203-239, April.
    8. Wu, Jia & Wei, Xiangdong & Zhang, Hongliang & Zhou, Xiang, 2019. "Elite schools, magnet classes, and academic performances: Regression-discontinuity evidence from China," China Economic Review, Elsevier, vol. 55(C), pages 143-167.
    9. Hu, Feng, 2018. "Migrant peers in the classroom: Is the academic performance of local students negatively affected?," Journal of Comparative Economics, Elsevier, vol. 46(2), pages 582-597.
    10. Shi, Ying, 2020. "Who benefits from selective education? Evidence from elite boarding school admissions," Economics of Education Review, Elsevier, vol. 74(C).

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    More about this item

    Keywords

    Local average treatment effect; Distributional treatment effects; Imperfect matching; Elite schools; Student achievement;
    All these keywords.

    JEL classification:

    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

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