SELECTIVITY BIAS CORRECTION METHODS IN POLYCHOTOMOUS SAMPLE SELECTION MODELS

Polychotomous sample selection models include multinomial choice models and models with multiple rules for sample inclusion. The author analyzes two standard parametric approaches to selectivity bias correction in such models - the Lee and Generalized Heckman (GH) methods. The paper's main point is that Lee's approach, unlike GH, requires strong implicit restrictions on covariances between outcomes and selection indices. A Monte Carlo study demonstrates (1) that the Lee estimator exhibits significant bias when the data-generating process does not conform to its implicit covariance assumptions, and (2) that the GH estimator may have high variance due to multicollinearity between regressors.