This paper examines the impact of male casualties due to World War II on fertility and female employment in the United States. We rely on the number of casualties at the county level and use a difference-in-differences strategy. While most counties in the United States experienced a baby boom following the war, we find that the increase in fertility was lower in high-casualty-rate counties than in low-casualty-rate counties. Analyzing the channels through which male casualties could have decreased fertility, we provide evidence that county male casualties are positively related to 1950s female employment and household income.

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.

We use unique data from journal submissions to identify and unpack publication bias and p-hacking. We find initial submissions display significant bunching, suggesting the distribution among published statistics cannot be fully attributed to a publication bias in peer review. Desk-rejected manuscripts display greater heaping than those sent for review; i.e., marginally significant results are more likely to be desk rejected. Reviewer recommendations, in contrast, are positively associated with statistical significance. Overall, the peer review process has little effect on the distribution of test statistics. Lastly, we track rejected papers and present evidence that the prevalence of publication biases is perhaps not as prominent as feared.

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).

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.