Skip to main content
×
×
Home

Improving the Interpretation of Fixed Effects Regression Results

  • Jonathan Mummolo and Erik Peterson
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.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Improving the Interpretation of Fixed Effects Regression Results
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Improving the Interpretation of Fixed Effects Regression Results
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Improving the Interpretation of Fixed Effects Regression Results
      Available formats
      ×
Copyright
Footnotes
Hide All
*

Jonathan Mummolo is an Assistant Professor of Politics and Public Affairs, Princeton University, Robertson Hall, Room 411, Princeton, NJ 08544 (jmummolo@princeton.edu). Erik Peterson is a Post-Doctoral Fellow at Dartmouth College, 026 Silsby Hall, 3 Tuck Mall, Hanover, NH, 03755 (erik.j.peterson@dartmouth.edu). The authors are grateful to Justin Grimmer, Dorothy Kronick, Jens Hainmueller, Brandon Stewart, Jonathan Wand and anonymous reviewers for helpful feedback on this project. To view supplementary material for this article, please visit https://doi.org/10.1017/psrm.2017.44

Footnotes
References
Hide All
Allison, Paul D. 2009. Fixed Effects Regression Models. London: Sage.
Baltagi, Badi. 2005. Econometric Analysis of Panel Data. New York: Wiley & Sons.
Bell, Andrew, and Jones, Kelvyn. 2015. ‘Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data’. Political Science Research and Methods 3(1):133153.
Berrebi, Claude, and Klor, Esteban F.. 2008. ‘Are Voters Sensitive to Terrorism? Direct Evidence from the Israeli Electorate’. American Political Science Review 102(3):279301.
Cameron, A. Colin, and Trivedi, Pravin K.. 2005. Microeconometrics. Cambridge: Cambridge University Press.
Earle, John S., and Gehlbach, Scott. 2015. ‘The Productivity Consequences of Political Turnover: Firm? Level Evidence from Ukraine’s Orange Revolution’. American Journal of Political Science 59(3):708723.
Gasper, John T., and Reeves, Andrew. 2011. ‘Make it Rain? Retrospection and the Attentive Electorate in the Context of Natural Disasters’. American Journal of Political Science 55(92):340355.
Greene, William H. 2008. Econometric Analysis 6th ed. Upper Saddle River, NJ: Pearson Education, Inc.
Ichino, Nahomi, and Nathan, Noah L.. 2013. ‘Crossing the Line: Local Ethnic Geography and Voting in Ghana’. American Political Science Review 107(2):344361.
Kim, In Song, and Imai, Kosuke. n.d. ‘When Should We Use Linear Fixed Effects Regression Models for Causal Inference With Longitudinal Data?’. Working Paper, ifundefinedselectfont https://imai.princeton.edu/research/FEmatch.html, accessed 9 January 2017.
King, Gary, and Zeng, Langche. 2006. ‘The Dangers of Extreme Counterfactuals’. Political Analysis 14:131159.
Plümper, Thomas, and Troeger, Vera E.. 2007. ‘Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses With Unit Fixed Effects’. Political Analysis. 15(2):124139.
Lovell, Michael C. 1963. ‘Seasonal Adjustment of Economic Time Series and Multiple Regression Analysis’. Journal of the American Statistical Association 58(204):9931010.
Nepal, Mani, Bohara, Alok K., and Gawande, Kishore. 2011. ‘More Inequality, More Killings: The Maoist Insurgency in Nepal’. American Journal of Political Science 55(4):886906.
Scheve, Kenneth, and Stasavage, David. 2012. ‘Democracy, War, and Wealth: Lessons from Two Centuries of Inheritance Taxation’. American Political Science Review 106(1):81102.
Snyder, James M., and Strömberg, David. 2010. ‘Press Coverage and Political Accountability’. Journal of Political Economy 118(2):355408.
Wooldridge, Jeffrey M. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge, London: MIT Press.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Political Science Research and Methods
  • ISSN: 2049-8470
  • EISSN: 2049-8489
  • URL: /core/journals/political-science-research-and-methods
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×
Type Description Title
UNKNOWN
Supplementary materials

Mummolo and Peterson Dataset
Dataset

 Unknown
PDF
Supplementary materials

Mummolo and Peterson supplementary material
Mummolo and Peterson supplementary material 1

 PDF (276 KB)
276 KB

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed