Selection bias corrections based on the multinomial logit model: Monte Carlo comparisons

François Bourguignon, Martin Fournier, Marc Gurgand

Research output: Contribution to journalArticlepeer-review


This survey presents the set of methods available in the literature on selection bias correction, when selection is specified as a multinomial logit model. It contrasts the underlying assumptions made by the different methods and shows results from a set of Monte Carlo experiments. We find that, in many cases, the approach initiated by Dubin and MacFadden (1984) as well as the semi-parametric alternative recently proposed by Dahl (2002) are to be preferred to the most commonly used Lee (1983) method. We also find that a restriction imposed in the original Dubin and MacFadden paper can be waived to achieve more robust estimators. Monte Carlo experiments also show that selection bias correction based on the multinomial logit model can provide fairly good correction for the outcome equation, even when the IIA hypothesis is violated.

Original languageEnglish (US)
Pages (from-to)174-205
Number of pages32
JournalJournal of Economic Surveys
Issue number1
StatePublished - Feb 2007


  • Monte Carlo
  • Multinomial logit
  • Selection bias

ASJC Scopus subject areas

  • Economics and Econometrics


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