Essays in Labor Economics

Skills are central to many questions in labor economics ranging from the causes of rising wage inequality to understanding how individuals make decisions about their career. However, skills are typically measured incompletely in standard datasets, so an important empirical challenge in labor economics is to develop methods to measure or control for these unobserved variables. This dissertation builds a range of methodologies to deal with unobserved skills in models in labor economics and explores how the evolution of workers' skills has contributed to long-term trends in wage inequality.

Chapter 2 develops an approach to estimating workers’ skills from panel data and examines how changes in skills and occupational sorting patterns have contributed to rising wage inequality in the United States since the 1980s. The methodology uses repeated measurements of individuals’ labor market outcomes over time to reveal their underlying skills. Estimating a model of occupational choice using panel data from the Survey of Income and Program Participation (SIPP), I find that (1) as tasks in high-skill jobs have become increasingly complex, the distribution of workers' ability to perform those tasks has become more dispersed, (2) workers' ability to perform low-skill work tasks has become more homogeneous, and (3) workers have increasingly sorted into occupations by skill level, which has increased wage inequality.

Chapter 3 builds a related panel data methodology that can accommodate a more general class of models in which individuals can have imperfect information about their abilities. In particular, in addition to unobserved heterogeneity known to individuals, the methodology in this chapter also allows for initially unpredictable heterogeneity that may be revealed over time.

Chapter 4 develops a complementary approach to assessing the sensitivity of estimates of causal effects to omitted variable bias. In the canonical example of omitted variable bias, the presence of unobserved ability creates a bias in estimates of the causal effect of education on wages. This chapter provides an approach to quantifying the size of this bias by reasoning about the importance of unobserved ability compared to other included variables. In contrast to existing methods for sensitivity analysis in this setting, the approach also allows the research to explicitly reason about correlation between the omitted variable and observed controls.