Research

Abstract: A large literature argues that technological change since the 1980s altered the demand for workers' skills, increasing wage inequality and polarization. By estimating a model of occupational choice using panel data from the Survey of Income and Program Participation (SIPP), I find that changes in the supply of workers' skills were also major driving factors in increasing inequality and polarization. Specifically, 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, even if this does not maximize their income. These results suggest that skill formation has been a key channel through which long run changes in the nature of work have affected wage inequality. Finally, to obtain my estimates I prove a new identification result in a multi-dimensional potential outcome model and show how to robustly estimate it semiparametrically adapting results from mixture models.

Abstract: Omitted variables are one of the most important threats to the identification of causal effects. Several widely used methods, including Oster (2019), have been developed to assess the impact of omitted variables on empirical conclusions. These methods all require an exogenous controls assumption: the omitted variables must be uncorrelated with the included controls. This is often considered a strong and implausible assumption. We provide a new approach to sensitivity analysis that allows for endogenous controls, while still letting researchers calibrate sensitivity parameters by comparing the magnitude of selection on observables with the magnitude of selection on unobservables. We illustrate our results in an empirical study of the effect of historical American frontier life on modern cultural beliefs. Finally, we implement these methods in the companion Stata module regsensitivity for easy use in practice.

Abstract: In this paper, we provide new semiparametric identification results for a general class of learning model in which outcomes of interest depend on i) predictable heterogeneity, ii) initially unpredictable heterogeneity that may be revealed over time, and iii) transitory uncertainty. We consider a common environment where the researcher only has access to longitudinal data on choices and outcomes. We establish point-identification of the outcome equation parameters and the distribution of the three types of unobservables, under the standard assumption that unpredictable heterogeneity and uncertainty are normally distributed. We also show that a pure learning model remains identified without making any distributional assumption. We then derive and study the asymptotic properties of a sieve MLE estimator for the model parameters, and devise a highly tractable profile likelihood based estimation procedure. Monte Carlo simulation results indicate that our estimator exhibits good finite-sample properties.