Peter Ganong, Avik Garg, Maximilian Kasy
arXiv 27 Jun 2026 · Econometrics
arXiv:2606.28848 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by appendix_command · 82% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Andrews, I. and Kasy, M (2019) Identification of and correction for publication bias self | 1.000 | 16 | 3 | 100% |
| 2 | Card, D., Kluve, J., and Weber, A (2018) What works? a meta analysis of recent active labor market program evaluations | 1.000 | 15 | 5 | 100% |
| 3 | Crosta, T., Karlan, D., Ong, F., Rüschenpöhler, J., and Udry, C. R (2024) Unconditional cash transfers: A bayesian meta-analysis of randomized evaluations in low and middle income countries | 1.000 | 14 | 3 | 100% |
| 4 | Egger, M., Smith, G. D., Schneider, M., and Minder, C (1997) Bias in meta-analysis detected by a simple, graphical test | 1.000 | 12 | 3 | 100% |
| 5 | Stanley, T., Doucouliagos, H., and Ioannidis, J (2017) Finding the power to reduce publication bias | 1.000 | 8 | 3 | 100% |
| 6 | Ioannidis, J., Stanley, T. D., and Doucouliagos, H (2017) The power of bias in economics research | 1.000 | 6 | 3 | 100% |
| 7 | DellaVigna, S. and Linos, E (2022) Rcts to scale: Comprehensive evidence from two nudge units | 0.977 | 15 | 6 | 93% |
| 8 | Cohen, J. P. and Ganong, P (2026) Disemployment effects of unemployment insurance: A meta-analysis self | 0.965 | 20 | 7 | 90% |
| 9 | Elliott, G., Kudrin, N., and Wüthrich, K (2022) Detecting p-hacking | 0.950 | 7 | 4 | 86% |
| 10 | Sager, L. and Singer, G (2025) Clean identification? the effects of the clean air act on air pollution, exposure disparities, and house prices | 0.928 | 5 | 3 | 80% |
Showing the top 10 of 71 scored citations.