Matthew A. Masten

How Should the Graduate Economics Core be Changed

The authors present suggestions by graduate students from a range of economics departments for improving the first-year core sequence in economics. The students identified a number of elements that should be added to the core: more training in building microeconomic models, a discussion of the methodological foundations of model-building, more emphasis on institutions to motivate and contextualize macroeconomic models, and greater focus on econometric practice rather than theory. The authors hope that these suggestions will encourage departments to take a fresh look at the content of the first-year core.

Identification of Instrumental Variable Correlated Random Coefficients Models

We study identification and estimation of the average partial effect in an instrumental variable correlated random coefficients model with continuously distributed endogenous regressors. This model allows treatment effects to be correlated with the level of treatment. The main result shows that the average partial effect is identified by averaging coefficients obtained from a collection of ordinary linear regressions that condition on different realizations of a control function. These control functions can be constructed from binary or discrete instruments, which may affect the endogenous variables heterogeneously. Our results suggest a simple estimator that can be implemented with a companion Stata module.

Identification of Treatment Effects Under Conditional Partial Independence

Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect parameters under nonparametric deviations from this conditional independence assumption. These deviations are defined via a conditional treatment assignment probability, which makes it straightforward to interpret. Our results can be used to assess the robustness of empirical conclusions obtained under the baseline conditional independence assumption.

Identification of Treatment Effects under Conditional Partial Independence

Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect parameters under nonparametric deviations from this conditional independence assumption. These deviations are defined via a conditional treatment assignment probability, which makes it straightforward to interpret. Our results can be used to assess the robustness of empirical conclusions obtained under the baseline conditional independence assumption.