Working Paper

Sample Selection Models Without Exclusion Restrictions: Parameter Heterogeneity and Partial Identification

Abstract: This paper studies semiparametric versions of the classical sample selection model (Heckman (1976, 1979)) without exclusion restrictions. We extend the analysis in Honoré and Hu (2020) by allowing for parameter heterogeneity and derive implications of this model. We also consider models that allow for heteroskedasticity and briefly discuss other extensions. The key ideas are illustrated in a simple wage regression for females. We find that the derived implications of a semiparametric version of Heckman's classical sample selection model are consistent with the data for women with no college education, but strongly rejected for women with a college degree or more.

Keywords: Selection; heterogeneity; heteroskedasticity; exclusion Restrictions; identification;

JEL Classification: C01; C14; C21; C24;

Bibliographic Information

Provider: Federal Reserve Bank of Chicago

Part of Series: Working Paper Series

Publication Date: 2021-07

Number: WP 2022-33