Knowledge that Transforms

Little is known about the costs of crime to victims. We use unique and detailed register data on victimizations and monthly labor market outcomes from the Netherlands and estimate event study designs to assess short- and long-term effects of criminal victimization. Across offenses, both males and females experience significant decreases in earnings (up to −12.9%) and increases in benefit receipt (up to +6%) after victimization. The negative labor market responses are lasting (up to 4 years) and accompanied by shorter-lived responses in health expenditure. Heterogeneity results suggest that most groups of victims, including the noninjured, suffer nontrivial losses.

The utility of machine learning (ML) for regression-based causal inference is illustrated by using lasso to select control variables for estimates of college characteristics’ wage effects. Post-double-selection lasso offers a path to data-driven sensitivity analysis. ML also seems useful for an instrumental variables (IV) first stage, since two-stage least squares (2SLS) bias reflects overfitting. While ML-based instrument selection can improve on 2SLS, split-sample IV and limited information maximum likelihood do better. Finally, we use ML to choose IV controls. Here, ML creates artificial exclusion restrictions, generating spurious findings. On balance, ML seems ill-suited to IV applications in labor economics.

Parents spend considerable time and resources investing in their children’s development. Given evidence that the Earned Income Tax Credit (EITC) affects maternal labor supply, we investigate how the maximum available EITC amount affects a broad array of time use activities, focusing on the amount and nature of time spent with children. Using 2003–18 time use data, we find that federal and state EITC expansions increase maternal work time, reducing time devoted to home production, leisure, and time with children. However, almost none of the reduction comes from time devoted to “investment” activities, such as active learning and development activities.

This paper examines the evidence from randomized evaluations of sector-focused training programs that target low-wage workers and combine up-front screening, occupational and soft-skills training, and wraparound services. The programs generate substantial and persistent earnings gains (12%–34%) following training. Theoretical mechanisms for program impacts are explored for the WorkAdvance demonstration. Earnings gains are generated by getting participants into higher-wage jobs in higher-earning industries and occupations, not just by raising employment. Training in transferable and certifiable skills (likely underprovided from poaching concerns) and reductions of employment barriers to high-wage sectors for nontraditional workers appear to play key roles.

Female workers earn $0.89 for each male-worker dollar even in a unionized workplace, where tasks, wages, and promotion schedules are identical for men and women by design. Using administrative time-card data on bus and train operators, we show that this earnings gap can be explained by female operators taking fewer hours of overtime and more hours of unpaid time off than male operators. Female operators, especially those with dependents, pursue schedule conventionality, predictability, and controllability more than male operators. While reducing schedule controllability can limit the earnings gap, it can also hurt female workers and their productivity.

New parenthood causes large decreases in labor market incomes for mothers but not fathers, a stylized fact known as the “child penalty.” We combine a simple household model with estimates of child penalties in heterosexual nonadopting, adopting, and same-sex couples to better understand what causes the child penalty in heterosexual nonadopting couples. Our results largely rule out giving birth and the father’s advantage in the labor market as mechanisms, leaving preferences, gender norms, and discrimination as the main explanations. In addition, our paper provides novel evidence on the impact of children on labor market outcomes of adopting and same-sex couples.

We study the impact of artificial intelligence (AI) on labor markets using establishment-level data on the near universe of online vacancies in the United States from 2010 onward. There is rapid growth in AI-related vacancies over 2010–18 that is driven by establishments whose workers engage in tasks compatible with AI’s current capabilities. As these AI-exposed establishments adopt AI, they simultaneously reduce hiring in non-AI positions and change the skill requirements of remaining postings. While visible at the establishment level, the aggregate impacts of AI-labor substitution on employment and wage growth in more exposed occupations and industries is currently too small to be detectable.