Publication bias and p-hacking in the effect of COVID-19 on learning

Martina Luskova, Nino Buliskeria, Ali Elminejad, Tomas Havranek, Zuzana Irsova, Stepan Jurajda and Marek Kapicka

Papers from arXiv.org

Abstract: We revisit a central estimate in the economics of education: the human-capital loss associated with COVID-19 school closures. Estimates of pandemic learning loss may be affected by publication bias, p-hacking, and the mechanical correlation between standardized effect sizes and their standard errors. We conduct a comprehensive multi-method assessment of bias by applying a wide range of correction techniques - including PET-PEESE, three-parameter selection models (3PSM), Robust Bayesian Meta-Analysis (RoBMA), Meta-Analysis Instrumental Variable Estimation (MAIVE), Right-Truncated Meta-Analysis (RTMA), and multi-bias sensitivity analysis. Our preferred specifications, RoBMA and MAIVE, rely on different assumptions yet converge on an effect size of approximately -0.12 SD, equivalent to a learning loss of about 30% of a school year. Although some methods reveal signs of publication bias and selective reporting, these findings do not explain away the central finding: the COVID-19 learning deficit is economically meaningful and statistically robust.

Date: 2026-08
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Related works:
Working Paper: Publication Bias and P-Hacking in the Effect of COVID-19 on Learning (2026)
Working Paper: Publication bias and p-hacking in the effect of COVID-19 on learning (2026)
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