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Student Employment and Education: A Meta-Analysis

Author

Listed:
  • Katerina Kroupova

    (Charles University, Prague, Czech Republic)

  • Tomas Havranek

    (Charles University, Prague, Czech Republic & CEPR)

  • Zuzana Irsova

    (Charles University, Prague, Czech Republic)

Abstract

Educational outcomes have many determinants, but one that most young people can readily control is choosing whether to work while in school. Sixty-nine studies have estimated the effect, but results vary from large negative to positive estimates. We show that the results are systematically driven by context, publication bias, and treatment of endogeneity. Studies ignoring endogeneity suffer from an upward bias, which is almost fully compensated by publication selection in favor of negative estimates. Net of the biases, the literature suggests a negative but economically inconsequential mean effect. The effect is more negative for high-intensity employment and educational outcomes measured as decisions to dropout, but it is positive in Germany. To derive these results we collect 861 previously reported estimates together with 32 variables reflecting estimation context, use recently developed nonlinear techniques to correct for publication bias, and employ Bayesian and frequentist model averaging to a sign a pattern to the heterogeneity in the literature.

Suggested Citation

  • Katerina Kroupova & Tomas Havranek & Zuzana Irsova, 2021. "Student Employment and Education: A Meta-Analysis," Working Papers IES 2021/28, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2021.
  • Handle: RePEc:fau:wpaper:wp2021_28
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    File URL: https://ies.fsv.cuni.cz/en/veda-vyzkum/working-papers/6469
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    Cited by:

    1. Zuzana Irsova & Hristos Doucouliagos & Tomas Havranek & T. D. Stanley, 2024. "Meta‐analysis of social science research: A practitioner's guide," Journal of Economic Surveys, Wiley Blackwell, vol. 38(5), pages 1547-1566, December.
    2. Slichter, David & Tran, Nhan, 2023. "Do better journals publish better estimates?," MPRA Paper 118433, University Library of Munich, Germany.
    3. Samuel Fiifi Eshun & Evzen Kocenda, 2025. "What Really Drives Financial Inclusion? Evidence from a Meta-Analysis of 3,817 Estimates," Working Papers IES 2025/14, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Aug 2025.
    4. Fan, Hongzhong & He, Shi & Kwan, Yum K., 2022. "FDI forward spillover effects in emerging markets: A comparative meta-analysis of China and Eastern Europe," Economic Systems, Elsevier, vol. 46(3).

    More about this item

    Keywords

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    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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