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On the Economic Consequences of Mass Shootings

The Review of Economics and Statistics 2025 107(1), 109-124 open access
In this paper, we investigate the economic consequences of mass shootings. We find that shootings have negative effects on targeted counties’ economies. Estimates using three different comparison groups yield similar results. Examining the mechanisms, we find that residents of targeted areas: (i) develop pessimistic views of financial and local business conditions; and (ii) are more likely to report poor mental health, which hinders usual activities such as work, suggesting that shootings lead to decreases in productivity. Further, we find that greater national media coverage of shootings exacerbates their local economic consequences.

Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics: Reply

American Economic Review 2022 112(9), 3137-3139 open access
In Brodeur, Cook, and Heyes (2020) we present evidence that instrumental variable (and to a lesser extent difference-in-difference) articles are more p-hacked than randomized controlled trial and regression discontinuity design articles. We also find no evidence that (i) articles published in the top five journals are different; (ii) the “revise and resubmit” process mitigates the problem; (iii) things are improving through time. Kranz and Pütz (2022) apply a novel adjustment to address rounding errors. They successfully replicate our results with the exception of our shakiest finding: after adjusting for rounding errors, bunching of test statistics for difference-in-difference articles is now smaller around the 5 percent level (and coincidentally larger at the 10 percent level).

Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics

American Economic Review 2020 110(11), 3634-3660 open access
The credibility revolution in economics has promoted causal identification using randomized control trials (RCT), difference-in-differences (DID), instrumental variables (IV) and regression discontinuity design (RDD). Applying multiple approaches to over 21,000 hypothesis tests published in 25 leading economics journals, we find that the extent of p-hacking and publication bias varies greatly by method. IV (and to a lesser extent DID) are particularly problematic. We find no evidence that (i) papers published in the Top 5 journals are different to others; (ii) the journal “revise and resubmit” process mitigates the problem; (iii) things are improving through time.