We evaluate the impacts of adopting algorithmic predictions of future offending (risk assessments) as an aid to judicial discretion in felony sentencing. We find that judges' decisions are influenced by the risk score, leading to longer sentences for defendants with higher scores and shorter...

Currently available asymptotic results in the literature suggest that matching estimators have higher variance than reweighting estimators. The extant literature comparing the finite sample properties of matching to specific reweighting estimators, however, has concluded that reweighting...

We examine instrumental variables estimation in situations where the instrument is only observed for a sub-sample, which is fairly common in empirical research. Typically, researchers simply limit the analysis to the sub-sample where the instrument is non-missing. We show that when the...

In this paper we propose an estimator for models in which an endogenous dichotomous treatment affects a count outcome in the presence of either sample selection or endogenous participation using maximum simulated likelihood. We allow for the treatment to have an effect on both the sample...

In this paper, we propose a new approach to estimating sample selection models that combines Generalized Tukey Lambda (GTL) distributions with copulas. The GTL distribution is a versatile univariate distribution that permits a wide range of skewness and thick- or thin-tailed behavior in the data...

This paper uses the control function to develop a framework for testing for selection bias. The idea behind our framework is if the usual assumptions hold for matching or IV estimators, the control function identifies the presence and magnitude of potential selection bias. Averaging this...

This article presents identification results for the marginal treatment effect (MTE) when there is sample selection. We show that the MTE is partially identified for individuals who are always observed regardless of treatment, and derive uniformly sharp bounds on this parameter under three...

This paper assesses the performance of common estimators adjusting for differences in covariates, such as matching and regression, when faced with so-called common support problems. It also shows how different procedures suggested in the literature affect the properties of such estimators. Based...

This article presents the eqregsel command for implementing the estimation and bootstrap inference of sample selection models via extremal quantile regression. The command estimates a semiparametric sample selection model without instrument or large support regressor, and outputs the point...