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Beyond Weeks Closed: Reassessing Pandemic Educational Disruption with TIMSS 2003–2023

Author

Listed:
  • Marzena Binkiewicz

    (University of Warsaw, Faculty of Economic Sciences)

  • Artur Pokropek

    (Polish Academy of Sciences, Institute of Philosophy and Sociology)

Abstract

Using six TIMSS cycles (2003–2023), UNESCO school-closure data, and World Bank GDP indicators, we estimate deviation-from-trend and continuous-treatment Difference-in-Differences models for mathematics achievement, liking learning mathematics, and confidence in mathematics. Mathematics achievement declined substantially relative to pre-pandemic trends, but closure duration did not consistently explain cross-national variation in achievement losses. Unexpectedly, longer closures were associated with slightly higher liking learning mathematics, particularly among Grade 4 students in higher-GDP countries, while confidence in mathematics remained largely unchanged. These findings suggest a decoupling of cognitive and affective outcomes during the pandemic and indicate that school-closure duration alone is an insufficient measure of educational harm. Comparative evaluations of pandemic education policies should therefore incorporate indicators of instructional continuity, remote-learning quality, and recovery efforts alongside formal school-closure measures.

Suggested Citation

  • Marzena Binkiewicz & Artur Pokropek, 2026. "Beyond Weeks Closed: Reassessing Pandemic Educational Disruption with TIMSS 2003–2023," Working Papers 2026-21, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2026-21
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    File URL: https://www.wne.uw.edu.pl/download_file/f8cd9516-583e-4274-862c-ae484f0e78e8/4282
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    References listed on IDEAS

    as
    1. Noam Angrist & Peter Bergman & Moitshepi Matsheng, 2022. "Experimental evidence on learning using low-tech when school is out," Nature Human Behaviour, Nature, vol. 6(7), pages 941-950, July.
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    Keywords

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    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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