Source Themes

Tighter money-laundering regulations in offshore financial havens may inadvertently spur incentives to launder money domestically. Our study exploits regulations targeting financially based money laundering in Caribbean jurisdictions to uncover the creation of front companies in the United States. We find that counties exposed via offshore financial links to these jurisdictions experienced an increase in business activities after the tightening of anti-money-laundering regulations. The effect is more pronounced among small firms, in sectors at high risk of money laundering, and in regions with high intensities of drug trafficking. Our work provides the first empirical evidence of the real effects of policy-induced money-laundering leakage.

Analyses of spatial or network data are now very common. Nevertheless, statistical inference is challenging since unobserved heterogeneity can be correlated across neighboring observational units. We develop an estimator for the variance-covariance matrix (VCV) of OLS and 2SLS that allows for arbitrary dependence of the errors across observations in space or network structure and across time periods. As a proof of concept, we conduct Monte Carlo simulations in a geospatial setting based on U.S. metropolitan areas. Tests based on our estimator of the VCV asymptotically correctly reject the null hypothesis, whereas conventional inference methods, e.g., those without clusters or with clusters based on administrative units, reject the null hypothesis too often. We also provide simulations in a network setting based on the IDEAS structure of coauthorship and real-life data on scientific performance. The Monte Carlo results again show that our estimator yields inference at the correct significance level even in moderately sized samples and that it dominates other commonly used approaches to inference in networks. We provide guidance to the applied researcher with respect to (i) whether or not to include potentially correlated regressors and (ii) the choice of cluster bandwidth. Finally, we provide a companion statistical package (acreg) enabling users to adjust the OLS and 2SLS coefficient’s standard errors to account for arbitrary dependence.

The effects of robotics and artificial intelligence (AI) on the job market are matters of great social concern. Economists and technology experts are debating at what rate, and to what extent, technology could be used to replace humans in occupations, and what actions could mitigate the unemployment that would result. To this end, it is important to predict which jobs could be automated in the future and what workers could do to move to occupations at lower risk of automation. Here, we calculate the automation risk of almost 1000 existing occupations by quantitatively assessing to what extent robotics and AI abilities can replace human abilities required for those jobs. Furthermore, we introduce a method to find, for any occupation, alternatives that maximize the reduction in automation risk while minimizing the retraining effort. We apply the method to the U.S. workforce composition and show that it could substantially reduce the workers’ automation risk, while the associated retraining effort would be moderate. Governments could use the proposed method to evaluate the unemployment risk of their populations and to adjust educational policies. Robotics companies could use it as a tool to better understand market needs, and members of the public could use it to identify the easiest route to reposition themselves on the job market.

This paper investigates the effect of supporters on the performance of soccer players by skin color using objective player performance data and an automated skin color recognition algorithm. Identification comes from an exceptional change in access to stadiums: due to the COVID-19 restrictions, one third of the games of the highest Italian soccer league 2019/2020 season were played in closed stadiums. I identify a significant increase in the performance of non-white players, relative to white players, when supporters are banned from the stadium. The effect does not differ between home and away games, and players playing in top versus minor teams, while weaker players are impacted more than others. These results suggest that discrimination faced by non-white individuals affects performance.