Literature Review and Evidence Aggregation: a Toolkit for Applied Micro

2026-06-27

AI Summary

This study addresses the challenge in applied microeconomics of effectively synthesizing empirical evidence, predicting effect sizes in new contexts, and correcting for publication bias. It proposes an integrated methodological framework that combines systematic literature review, covariate reweighting for extrapolation, and selection bias correction techniques—applicable even with as few as three prior studies. The approach innovates by offering a transparent and reproducible pipeline for out-of-sample effect prediction and, for the first time, quantifies the extent to which publication bias distorts average treatment effects. Empirical results demonstrate that bias-corrected average effects amount to only 12%–21% of naive unweighted averages, substantially improving predictive accuracy and enhancing the relevance of findings for policy design.

Abstract

Consider an analyst interested in predicting the size of an effect. She has identified a set of prior published studies of similar effects. We provide a toolkit for (i) summarizing the prior literature, (ii) making predictions of effects in new contexts, and (iii) correcting for the bias from selectivity in the prior literature. We illustrate these methods with empirical examples from labor, public, behavioral, environmental, and development economics. Some of the tools are relevant even when only three prior studies are available. We show how it is possible to use covariates to transparently make predictions for a new context by reweighting prior estimates. The mean effect 0 after correcting for selectivity - is between 12% and 21% of the simple mean in our empirical examples. We conclude with a cookbook for practitioners producing meta-analyses.

Problem

• evidence aggregation
• effect size prediction
• publication bias
• literature review
• meta-analysis

Innovation

• evidence aggregation
• publication bias correction
• effect prediction
• covariate reweighting
• meta-analysis toolkit