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, forthcomming.

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%.