Data and codes for meta-analysis
This site collects meta-analysis methods and applications by Tomas Havranek, Zuzana Irsova, and their co-authors at Charles University, Prague. Meta-analysis pools what a literature has estimated and, done properly, corrects it for publication bias and p-hacking. It also relates the differences between studies to the methods and settings behind them. Nearly every paper here comes with its data and estimation code. The papers have appeared in journals such as Nature Communications, Review of Economics and Statistics, Journal of the European Economic Association, Journal of Labor Economics, and Journal of Political Economy Microeconomics.
A new meta-analysis approach robust to p-hacking:
Meta-Analysis Instrumental Variable Estimator (MAIVE) — Irsova, Bom, Havranek & Rachinger, Nature Communications, 2025
One-click meta-analysis in your browser, with corrections for p-hacking:
EasyMeta.org, which runs MAIVE, RTMA, and standard models
Nontechnical, step-by-step guidelines on how to do a meta-analysis:
The Practitioner’s Guide to Modern Meta-Analysis
Two 2026 notes in the Journal of Economic Surveys extend it: reporting guidelines updated for AI and a floor on the use of AI in meta-analysis.
Three principles, as of 2026:
- Correct for publication bias. RoBMA, by Bartoš, Maier, and Wagenmakers.
- Correct for p-hacking. Our MAIVE for spurious precision, and Mathur’s RTMA.
- Estimates are not independent. Cluster by study, with CR2 standard errors following Pustejovsky and Tipton.
Browse by field: Macroeconomics · Micro and experimental · Energy and environmental · International · Labor and education · Financial · Meta-research methods