Improving the Interpretation of Fixed Effects Regression Results

Jonathan Mummolo, Erik Peterson

Research output: Contribution to journalArticlepeer-review

157 Scopus citations

Abstract

Fixed effects estimators are frequently used to limit selection bias. For example, it is well known that with panel data, fixed effects models eliminate time-invariant confounding, estimating an independent variable's effect using only within-unit variation. When researchers interpret the results of fixed effects models, they should therefore consider hypothetical changes in the independent variable (counterfactuals) that could plausibly occur within units to avoid overstating the substantive importance of the variable's effect. In this article, we replicate several recent studies which used fixed effects estimators to show how descriptions of the substantive significance of results can be improved by precisely characterizing the variation being studied and presenting plausible counterfactuals. We provide a checklist for the interpretation of fixed effects regression results to help avoid these interpretative pitfalls.

Original languageEnglish (US)
Pages (from-to)829-835
Number of pages7
JournalPolitical Science Research and Methods
Volume6
Issue number4
DOIs
StatePublished - Oct 1 2018

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations

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