Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/324380 
Year of Publication: 
2025
Series/Report no.: 
cemmap working paper No. CWP07/25
Publisher: 
Centre for Microdata Methods and Practice (cemmap), The Institute for Fiscal Studies (IFS), London
Abstract: 
Sample selection is pervasive in applied economic studies. This paper develops semiparametric selection models that achieve point identification without relying on exclusion restrictions, an assumption long believed necessary for identification in semiparametric selection models. Our identification conditions require at least one continuously distributed covariate and certain nonlinearity in the selection process. We propose a two-step plug-in estimator that is Í n-consistent, asymptotically normal, and computationally straightforward (readily available in statistical software), allowing for heteroskedasticity. Our approach provides a middle ground between Lee (2009)'s nonparametric bounds and Honoré and Hu (2020)'s linear selection bounds, while ensuring point identification. Simulation evidence confirms its excellent finite-sample performance. We apply our method to estimate the racial and gender wage disparity using data from the US Current Population Survey. Our estimates tend to lie outside the Honoré and Hu bounds.
Subjects: 
sample selection
semiparametric identification
sieve estimation
exclusion restriction
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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