This paper uses newly available Chinese micro data to estimate the return to college education for late 20th century China when allowing for heterogeneous returns among individuals selecting into schooling based on these differences. We use recently developed semiparametric methods to identify...

Instrumental Variables (IV) methods identify internally valid causal effects for individuals whose treatment status is manipulable by the instrument at hand. Inference for other populations requires some sort of homogeneity assumption. This paper outlines a theoretical framework that nests all...

This paper proposes strategies for defining, identifying, and estimating features of treatment-effect distributions in contexts where multiple outcomes are of interest. After describing existing empirical approaches used in such settings, the paper develops a notion of treatment preference that...

A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this paper, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in...

RCTs would be more useful if there were more realistic expectations of them and if their pitfalls were better recognized. For example, and contrary to many claims in the applied literature, randomization does not equalize everything but the treatment across treatments and controls, it does not...

This paper estimates returns to education using a dynamic model of educational choice that synthesizes approaches in the structural dynamic discrete choice literature with approaches used in the reduced form treatment effect literature. It is an empirically robust middle ground between the two...

The purpose of this essay is to discuss two approaches to inference, and how "human capital" can provide a way to combine them. The first approach, ubiquitous in economics, is based upon the Rubin/Holland potential outcomes model and relies upon randomized treatment to measure the causal effect...

Empiricism in the sciences allows us to test theories, formulate optimal policies, and learn how the world works. In this manner, it is critical that our empirical work provides accurate conclusions about underlying data patterns. False positives represent an especially important problem, as...

We explore a key underlying assumption, the exclusion restriction, commonly used in interpreting IV estimates in the presence of heterogenous treatment effects as a local average treatment effect (LATE). We show through a series of simple examples that in some commonly featured cases that this...