Extensive and intensive margin effects in sample selection models: racial effects on wages

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8 Citations (Scopus)


In sample selection models, a treatment can influence the observed outcome in two ways: by affecting the binary selection or participation decision and by affecting the latent outcome. The former is called the ‘extensive margin effect’, and the latter is called the ‘intensive margin effect’. Despite the popularity of these effects, however, the intensive margin effect does not have the traditional causal parameter interpretation because it is conditioned on the selecting or participating decision, which is a post-treatment variable possibly affected by the treatment. The paper presents a causal framework for sample selection models and introduces various subpopulation effects. It is difficult to separate such effects in general; however, in certain popular models (nearly parametric sample selection models, semiparametric ‘independence models’, semiparametric zero-censored models and ‘polynomial approximation’ models) with linear latent equations, they are separately identified and easily estimable with probit and least squares estimators. An empirical analysis is provided to illustrate these causal effects in sample selection models.

Original languageEnglish
Pages (from-to)817-839
Number of pages23
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
Issue number3
Publication statusPublished - 2017 Jun

Bibliographical note

Funding Information:
The author is grateful to the Associate Editor and the two reviewers for their detailed and helpful comments. This research has been supported by a National Research Foundation of Korea grant funded by the Korean Government (NRF-2015S1A5A2A01009718).

Publisher Copyright:
© 2016 Royal Statistical Society


  • Censored model
  • Extensive margin effect
  • Intensive margin effect
  • Participation effect
  • Sample selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty


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