Asymptotic bias reduction for a conditional marginal effects estimator in sample selection models

In this paper we discuss the differences between the average marginal effect and the marginal effect of the average individual in sample selection models, estimated by the Heckman procedure. We show that the bias that emerges as a consequence of interchanging them, could be very significant, even in the limit. We suggest a computationally cheap approximation method, which corrects the bias to a large extent. We illustrate the implications of our method with an empirical application of earnings assimilation and a small Monte Carlo simulation.