Estimating Grouped Data Models with a Binary-Dependent Variable and Fixed Effects via a Logit versus a Linear Probability Model: The Impact of Dropped Units

Research output: Contribution to journalArticle

Abstract

This letter deals with a very simple question: if we have grouped data with a binary-dependent variable and want to include fixed effects in the specification, can we meaningfully compare results using a linear model to those estimated with a logit? The reason to doubt such a comparison is that the linear specification appears to keep all observations, whereas the logit drops the groups where the dependent variable is either all zeros or all ones. This letter demonstrates that a linear specification averages the estimates for all the homogeneous outcome groups (which, by definition, all have slope coefficients of zero) with the slope coefficients for the groups with a mix of zeros and ones. The correct comparison of the linear to logit form is to only look at groups with some variation in the dependent variable. Researchers using the linear specification are urged to report results for all groups and for the subset of groups where the dependent variable varies. The interpretation of the difference between these two results depends upon assumptions which cannot be empirically assessed.

Original languageEnglish (US)
Pages (from-to)139-145
Number of pages7
JournalPolitical Analysis
Volume28
Issue number1
DOIs
StatePublished - Jan 1 2020

Keywords

  • binary logit
  • clustered data
  • fixed effects
  • marginal effects
  • panel data

ASJC Scopus subject areas

  • Sociology and Political Science
  • Political Science and International Relations

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