AI 中文总结
该研究针对COVID-19学校关闭的学习损失估计，采用多种元分析校正技术评估发表偏倚与p-hacking，发现核心学习损失约为-0.12 SD且统计稳健。
AI 中文摘要
我们重新审视教育经济学中的核心估计值：COVID-19学校关闭相关的人力资本损失。疫情学习损失的估计值可能受发表偏倚、p-hacking（p值操纵）以及标准化效应量与其标准误之间的机械相关性影响。我们采用多种校正技术（包括PET-PEESE、三参数选择模型（3PSM）、鲁棒贝叶斯元分析（RoBMA）、元分析工具变量估计（MAIVE）、右截断元分析（RTMA）及多偏倚敏感性分析），开展全面多方法偏倚评估。我们偏好的设定RoBMA与MAIVE基于不同假设，却收敛于约-0.12 SD的效应量，相当于约一个学年30%的学习损失。尽管部分方法揭示了发表偏倚与选择性报告的迹象，但这些发现并未消解核心结论：COVID-19学习损失在经济层面具有显著性，且统计上稳健。
英文摘要
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.
Comments56 pages, 16 figures, 9 tables. Also circulated as CEPR Discussion Paper 21630 and EconStor Preprint 341461. JEL: I21, I24, I28, C18
