A growing number of school districts use centralized assignment mechanisms to allocate school seats in a manner that reflects student preferences and school priorities. Many of these assignment schemes use lotteries to ration seats when schools are oversubscribed. The resulting random assignment...

The widely used Oaxaca decomposition applies to linear models. Extending it to commonly used nonlinear models such as duration models is not straightforward. This paper shows that the original decomposition that uses a linear model can also be obtained by an application of the mean value...

Centralized school assignment algorithms must distinguish between applicants with the same preferences and priorities. This is done with randomly assigned lottery numbers, nonlottery tie-breakers like test scores, or both. The New York City public high school match illustrates the latter, using...

We provide a number of contributions of policy, practical and methodological interest to the study of the returns to educational qualifications in the presence of misreporting. First, we provide the first reliable estimates of a highly policy relevant parameter for the UK, namely the return from...

We provide a number of contributions of policy, practical and methodological interest to the study of the returns to educational qualifications in the presence of misreporting. First, we provide the first reliable estimates of a highly policy relevant parameter for the UK, namely the return from...

Knowledge of treatment effect heterogeneity or "essential heterogeneity" plays an important role in our understanding of how programs work and in the design of systems to allocate them among the eligible. This paper provides a relatively non-technical survey of the current state of the treatment...

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using...

Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require the conditional independence assumption (i.e.,...

We use a linear probability model with interactions and a switching probit model (SPM) to estimate heterogeneous effects of Medicaid expansions on Medicaid take-up, private insurance coverage and crowd-out. Specifically, we estimate: i) LATEs; ii) ATETs for the currently eligible; and iii) ATETs...

This paper discusses the nonparametric identification of causal direct and indirect effects of a binary treatment based on instrumental variables. We identify the indirect effect, which operates through a mediator (i.e. intermediate variable) that is situated on the causal path between the...