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The Phantom Menace: Omitted Variable Bias in Econometric Research

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  • Kevin A. Clarke

    (Department of Political Science University of Rochester Rochester, New York, USA, kevin.clarke@rochester.edu)

Abstract

Quantitative political science is awash in control variables. The justification for these bloated specifications is usually the fear of omitted variable bias. A key underlying assumption is that the danger posed by omitted variable bias can be ameliorated by the inclusion of relevant control variables. Unfortunately, as this article demonstrates, there is nothing in the mathematics of regression analysis that supports this conclusion. The inclusion of additional control variables may increase or decrease the bias, and we cannot know for sure which is the case in any particular situation. A brief discussion of alternative strategies for achieving experimental control follows the main result.

Suggested Citation

  • Kevin A. Clarke, 2005. "The Phantom Menace: Omitted Variable Bias in Econometric Research," Conflict Management and Peace Science, Peace Science Society (International), vol. 22(4), pages 341-352, September.
    • Handle: RePEc:sae:compsc:v:22:y:2005:i:4:p:341-352
    DOI: 10.1080/07388940500339183
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    References listed on IDEAS

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    Cited by:

    1. Vance, Colin & Ritter, Nolan, 2012. "The Phantom Menace of Omitted Variables. A Comment," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 29(2), pages 233-238.
    2. repec:zbw:rwirep:0282 is not listed on IDEAS
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    5. Biørn, Erik, 2017. "Identification, Instruments, Omitted Variables, and Rudimentary Models: Fallacies in the ‘Experimental Approach’ to Econometrics," Memorandum 13/2017, Oslo University, Department of Economics.
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    8. Clarke, Damian, 2019. "A convenient omitted variable bias formula for treatment effect models," Economics Letters, Elsevier, vol. 174(C), pages 84-88.
    9. Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02181597, HAL.
    10. Baccini, Leonardo, 2014. "Cheap talk: transaction costs, quality of institutions, and trade agreements," LSE Research Online Documents on Economics 44923, London School of Economics and Political Science, LSE Library.
    11. Boris Sokolov & Eduard Ponarin, 2019. "Disillusionment And The Growth Of Mass-Level Euroscepticism In Post-Communist East-Central Europe," HSE Working papers WP BRP 89/SOC/2019, National Research University Higher School of Economics.
    12. Escañuela Romana, Ignacio, 2018. "Instability in the basic New Keynesian model under limited information," MPRA Paper 88015, University Library of Munich, Germany.
    13. Giuseppe De Luca & Jan Magnus & Franco Peracchi, 2015. "On the Ambiguous Consequences of Omitting Variables," Tinbergen Institute Discussion Papers 15-061/III, Tinbergen Institute.
    14. Helge Holtermann, 2011. "Explaining the Development-Civil War Relationship," LIS Working papers 566, LIS Cross-National Data Center in Luxembourg.
    15. Tunstall, Thomas, 2015. "Recent Economic and Community Impact of Unconventional Oil and Gas Exploration and Production on South Texas Counties in the Eagle Ford Shale Area," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 45(1).
    16. Nolan Ritter & Colin Vance, 2011. "The Phantom Menace of Omitted Variables – A Comment," Ruhr Economic Papers 0282, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    17. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2018. "Balanced Variable Addition In Linear Models," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1183-1200, September.
    18. Nielsen, Bo Bernhard & Raswant, Arpit, 2018. "The selection, use, and reporting of control variables in international business research: A review and recommendations," Journal of World Business, Elsevier, vol. 53(6), pages 958-968.
    19. Volkan Yeniaras & Tugra Nazli Akarsu, 2017. "Religiosity and Life Satisfaction: A Multi-dimensional Approach," Journal of Happiness Studies, Springer, vol. 18(6), pages 1815-1840, December.
    20. Hannah Druckenmiller & Solomon Hsiang, 2018. "Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences," Papers 1810.07216, arXiv.org, revised Aug 2019.
    21. Ilya Lokshin, 2015. "Whatever Explains Whatever: The Duhem-Quine Thesis And Conventional Quantitative Methods In Political Science," HSE Working papers WP BRP 23/PS/2015, National Research University Higher School of Economics.
    22. Yusep Suparman & Henk Folmer & Johan Oud, 2014. "Hedonic price models with omitted variables and measurement errors: a constrained autoregression–structural equation modeling approach with application to urban Indonesia," Journal of Geographical Systems, Springer, vol. 16(1), pages 49-70, January.

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