On the Specification of Propensity Scores, With Applications to the Analysis of Trade Policies

The use of propensity score methods for program evaluation with nonexperimental data typically requires that the propensity score be estimated, often with a model whose specification is unknown. Although theoretical results suggest that estimators using more flexible propensity score specifications perform better, this has not filtered into applied research. Here we provide Monte Carlo evidence indicating benefits of overspecifying the propensity score that are robust across a number of different covariate structures and estimators. We illustrate these results with two applications, one assessing the environmental effects of General Agreement on Tariffs and Trade/World Trade Organization membership and the other assessing the impact of adopting the euro on bilateral trade.