TESTING FOR SELECTIVITY BIAS IN PANEL DATA MODELS

We discuss several tests to check for the presence of selectivity bias in estimators based on panel data. One approach to test for selectivity bias is to specify the selection mechanism explicitly and estimate it jointly with the model of interest. Alternatively, one can derive the asymptotically efficient LM test. Both approaches are computationally demanding. In this paper, we propose the use of simple variable addition and (quasi-) Hausman tests for selectivity bias that do not require any knowledge of the response process. We compare the power of these tests with the asymptotically efficient test using Monte Carlo methods.