A frequent challenge in program impact estimation, and causal modeling more generally, is estimation of the effect of a binary endogenous variable on a binary outcome of interest. We report results from Monte Carlo experiments designed to assess the performance of estimators frequently applied in this circumstance. Many rely on an instrumental variables identification strategy and in those instances our central interest is the overidentified case. Even when identification is technically achieved by functional form, it is widely perceived that instruments generate more credible identification. Our focus is on widely used models available in the popular STATA statistical software package, but we also evaluate a semi-parametric instrumental variables random effects model not yet available in STATA. The parameters of interest in these experiments are program impact, test statistics assessing endogeneity and overidentification tests. We consider performance under alternative behavioral circumstances by varying distributional assumptions for unobservables, instrument strength levels, sample sizes, and impact magnitudes. Some models turn in a somewhat disappointing performance. Those that rely on joint normality for identification are not particularly robust to error misspecification, raising questions about whether they should be preferred to the semi-parametric estimator (regardless of comparative ease of estimation) or even to simple single equation models that ignore endogeneity. We provide examples of the methods using data from Bangladesh and Tanzania.
Error Correlation Average Treatment Effect Mean Absolute Deviation Monte Carlo Experiment Linear Probability Model
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