Research

This paper examines how differences in risk aversion influence occupational choices and the distribution of earnings. I first show that more risk-averse workers earn significantly less, and that this earnings gap widens over time. A key driver of this pattern is that risk-averse individuals tend to select occupations with more stable, but lower, earnings and slower earnings growth. To quantify the importance of this channel and distinguish it from sorting based on unobserved traits, I develop and estimate a structural model of occupation choice that accounts for heterogeneity in risk aversion and human capital accumulation. In the model, risk aversion is correlated with both observed and unobserved initial skills, and it influences skill accumulation through occupational choices. Using the estimated model on the non-college sample, I perform a decomposition analysis showing that 30 percent of the earnings gap between the most and least risk-averse workers (14 log points) can be explained by occupational choices. Of this, approximately 55 percent is due to lower pay in safer occupations, while the remaining 45 percent is attributable to slower human capital accumulation. In a counterfactual analysis, I find that social insurance providing an earnings floor enables risk-averse workers to select into relatively higher-return occupations, reducing the earnings gap between risk-tolerant and risk-averse workers by around 29 percent (4 log points). Similar patterns are observed in the college sample.

In this paper we estimate major specific earnings profiles using matched American Community Survey (ACS) and Longitudinal Employer-Household Dynamics (LEHD) data. The advantage of the matched data relative to the ACS alone is that it provides a long panel of worker earnings, thus avoiding estimating life cycle profiles using cross- cohort variation. Once we allow the returns to major to vary by cohort, we find that engineering, computer science, and business majors experience faster earnings growth relative to humanities majors. For example, the gap in earnings between technical majors like engineering and computer science and humanities grows by 5-6% between ages 23 and 50. Our estimates also indicate that more recent graduates in these fields earn a larger premium relative to humanities than earlier cohorts.

This paper studies how labor input regulations influence firms' technology adoption decisions. Technological progress is a key driver of productivity growth, and its interaction with labor inputs is of great interest in both economic theory and policy discussions. While much of the literature focuses on how technological change affects labor markets, our study investigates how labor market regulations, in turn, influence firms' adoption of new technologies. We analyze the nurse staffing regulations implemented in California in the early 2000s, which mandated minimum nurse-to-patient ratios in hospitals. Using a difference-in-differences approach with propensity score matching, we assess how hospitals with varying pre-regulation staffing levels responded to the policy. Our preliminary findings suggest that Californian hospitals with fewer nurses than required levels before the policy were more likely to adopt technologies aimed at improving labor productivity, such as staffing management systems, compared to matched non-Californian hospitals. Conversely, those who already had enough nurses were less inclined to invest in health information technology like Clinical Decision Support (CDS).