This paper develops and applies a method for decomposing cross section variability of earnings into components that are forecastable at the time students decide to go to college (heterogeneity) and components that are unforecastable. About 60% of variability in returns to schooling is...

This paper develops and applies a method for decomposing cross section variability of earnings into components that are forecastable at the time students decide to go to college (heterogeneity) and components that are unforecastable. About 60% of variability in returns to schooling is...

This paper extends the widely used ordered choice model by introducing stochastic thresholds and interval-specific outcomes. The model can be interpreted as a generalization of the GAFT (MPH) framework for discrete duration data that jointly models durations and outcomes associated with...

This paper contributes to a growing literature that attempts to determine whether disparities in police stops and searches of potential criminals of different races stem from taste-based discrimination. The key challenge in making this evaluation is that police officers have more information...

This paper provides a general description of the relationship between individual decision problems and aggregate crime regressions. The analysis is designed to elucidate the behavioral and statistical assumptions that are implicit in the use of aggregate crime regressions for both the analysis...

This paper examines how estimates of the deterrent effect of capital punishment depend on alternate choices of assumptions concerning the homicide process. Specific models of the homicide process represent bundles of these assumptions, which involve the unobserved heterogeneity, the relevant...

This paper considers semiparametric identification of structural dynamic discrete choice models and models for dynamic treatment effects. Time to treatment and counterfactual outcomes associated with treatment times are jointly analyzed. We examine the implicit assumptions of the dynamic...