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Literature Review and Evidence Aggregation: a Toolkit for Applied Micro

Author

Listed:
  • Peter Ganong
  • Avik Garg
  • Maximilian Kasy

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—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.

Suggested Citation

  • Peter Ganong & Avik Garg & Maximilian Kasy, 2026. "Literature Review and Evidence Aggregation: a Toolkit for Applied Micro," NBER Working Papers 35403, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:35403
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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