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Semiparametric Estimator for Binary‐outcome Sample Selection: Prejudice Matters in Election

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  • Jin‐Young Choi

Abstract

a semiparametric estimator for binary‐outcome sample‐selection models is proposed that imposes only single index assumptions on the selection and outcome equations without specifying the error term distribution. I adopt the idea in Lewbel (2000) using a ‘special regressor’ to transform the binary response Y so that the transformed Y becomes linear in the latent index, which then makes it possible to remove the selection correction term by differencing the transformed Y equation. There are various versions of the estimator, which perform differently trading off bias and variance. A simulation study is conducted, and then I apply the estimators to US presidential election data in 2008 and 2012 to assess the impact of racial prejudice on the elections, as a black candidate was involved for the first time ever in the US history.

Suggested Citation

  • Jin‐Young Choi, 2018. "Semiparametric Estimator for Binary‐outcome Sample Selection: Prejudice Matters in Election," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(3), pages 536-553, June.
    • Handle: RePEc:bla:obuest:v:80:y:2018:i:3:p:536-553
    DOI: 10.1111/obes.12207
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    Cited by:

    1. Myoung‐jae Lee & Jin‐young Choi, 2022. "Finding mover–stayer quantile difference due to unobservables using quantile selection corrections," Bulletin of Economic Research, Wiley Blackwell, vol. 74(3), pages 704-721, July.

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