Publications

Financial Incentives and Performance: A Meta-Analysis of Experiments in Economics (2026)

Cala Petr, Havranek Tomas, Irsova Zuzana, Luskova Martina, Matousek Jindrich, and Novak Jiri 

Journal of Political Economy Microeconomics, forthcoming.

Abstract:

Economists typically model financial incentives as enhancing performance, whereas psychologists emphasize that incentives can backfire. Experimental findings are mixed. We collect 2,193 estimates from 88 economics experiments and account for 48 contextual factors. Using recent advances in correcting for publication bias and p-hacking, we find that the corrected mean effect of financial incentives on performance is close to zero across most field contexts. Laboratory settings and loss framing yield statistically significant but modest positive effects even after bias correction. Our results suggest that increasing financial rewards rarely produces large performance gains in the experimental settings most studied by economists.

PDF, code, and data are available here

The Effect of Face Masks on Covid Transmission: A Meta-analysis (2026)

Luskova Martina

Economics of Disasters and Climate Change 10, 7 (2026).

Abstract:

The effect of face masks on COVID-19 transmission is crucial for the health of populations. Quantifying this effect has direct economic relevance, as it supports better-informed decisions on managing health risks while minimising economic burdens. However, the effect varies substantially across primary studies. To address this, we perform a quantitative meta-analysis based on 258 estimates from 44 primary studies. In addition, we collect more than 30 variables capturing methodological and contextual differences. We examine publication bias by implementing various statistical tests, revealing mild evidence for the phenomenon. After controlling for publication bias, wearing a face mask is associated with a reduced risk of COVID-19 infection by 11.8% to 28.5%. Our contribution to other meta-analyses on this topic involves the use of Bayesian and Frequentist model averaging to identify the drivers behind the heterogeneity of the estimates. The results indicate that for variables temperature, geographical latitude, control group masked by lower grade protection, and panel data structure, the estimated protection of masks is lower. After controlling for the heterogeneity of the primary studies, the implied estimate suggests that masks reduce the risk of COVID-19 infection by 13.1% to 19.7%.

Guidance for the Use of AI in the Meta-Analysis of Economics Research (2026)

Nikolai Cook, František Bartoš, Pedro RD Bom, Sebastian Gechert, Klára Kantová, Jerome Geyer-Klingeberg, Tomáš Havránek, Zuzana Irsova, 

Martina Luskova, Matěj Opatrný, Franz Prante, Heiko J Rachinger, TD Stanley

Journal of Economic Surveys, forthcoming.

Abstract:

Meta-analysis is widely accepted to be the most rigorous and objective approach to the synthesis, interpretation, and understanding of findings from specific areas of empirical economics research. With the advent of increasingly capable generative artificial intelligence and AI’s potential to transform the practice of meta-analysis, the Meta-Analysis of Economics Research Network (MAER-Net) has adopted this set of principles. These principles are meant to provide guidance to meta-researchers, as well as editors and reviewers, in the use of AI in meta-analysis of economics research. Future meta-analyses that employ AI are expected to embody these guiding principles and to follow the associated Reporting Guidelines for Meta-Analysis in Economics - Updated for AI (Cook et al., 2025).

Reporting Guidelines for Meta-Analysis in Economics - Updated for AI (2026)

Nikolai Cook, František Bartoš, Pedro RD Bom, Sebastian Gechert, Klára Kantová, Jerome Geyer-Klingeberg, Tomáš Havránek, Zuzana Irsova, 

Martina Luskova, Matěj Opatrný, Franz Prante, Heiko J Rachinger, TD Stanley

Journal of Economic Surveys, forthcoming.

Abstract:

Meta-analysis is how science takes stock of its vast research output. The advent of increasingly capable artificial intelligence (AI) promises an unprecedented ability to identify and synthesize relevant research and its findings. In this document, the Meta-Analysis of Economics Research Network (MAER-Net) updates existing Reporting Guidelines to be consistent with community-driven best practices for the responsible use and disclosure of AI-assistance in meta-analysis research. This update is meant to further improve the transparency, replicability, and quality of meta-analyses by building upon the 2020 and 2013 Reporting Guidelines published by this Journal. Cook et al. (2025) describe the guiding principles behind the update. Future meta-analyses, whether or not they use AI, are expected to follow these updated guidelines, or to be prepared to give reasons if they deviate from them.