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Methods Matter: P-Hacking and Causal Inference in Economics

Author

Listed:
  • Abel Brodeur

    (Department of Economics, University of Ottawa, Ottawa, ON)

  • Nikolai Cook

    (Department of Economics, University of Ottawa, Ottawa, ON)

  • Anthony Heyes

    (Department of Economics, University of Ottawa, Ottawa, ON, and University of Sussex)

Abstract
The economics 'credibility revolution' has promoted the identification of causal relationships using difference-in-differences (DID), instrumental variables (IV), randomized control trials (RCT) and regression discontinuity design (RDD) methods. The extent to which a reader should trust claims about the statistical significance of results proves very sensitive to method. Applying multiple methods to 13,440 hypothesis tests reported in 25 top economics journals in 2015, we show that selective publication and p-hacking is a substantial problem in research employing DID and (in particular) IV. RCT and RDD are much less problematic. Almost 25% of claims of marginally significant results in IV papers are misleading.

Suggested Citation

  • Abel Brodeur & Nikolai Cook & Anthony Heyes, 2018. "Methods Matter: P-Hacking and Causal Inference in Economics," Working Papers 1809E, University of Ottawa, Department of Economics.
  • Handle: RePEc:ott:wpaper:1809e
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    File URL: https://socialsciences.uottawa.ca/economics/sites/socialsciences.uottawa.ca.economics/files/1809e.pdf
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    References listed on IDEAS

    as
    1. Joshua D. Angrist & Jörn-Steffen Pischke, 2010. "The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 24(2), pages 3-30, Spring.
    2. Alberto Abadie, 2020. "Statistical Nonsignificance in Empirical Economics," American Economic Review: Insights, American Economic Association, vol. 2(2), pages 193-208, June.
    3. Emeric Henry, 2009. "Strategic Disclosure of Research Results: The Cost of Proving Your Honesty," Economic Journal, Royal Economic Society, vol. 119(539), pages 1036-1064, July.
    4. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    5. Tomas Havranek & Anna Sokolova, 2016. "Do Consumers Really Follow a Rule of Thumb? Three Thousand Estimates from 130 Studies Say "Probably Not"," Working Papers 2016/08, Czech National Bank.
    6. Cristina Blanco-Perez & Abel Brodeur, 2020. "Publication Bias and Editorial Statement on Negative Findings," The Economic Journal, Royal Economic Society, vol. 130(629), pages 1226-1247.
    7. Katherine Casey & Rachel Glennerster & Edward Miguel, 2012. "Reshaping Institutions: Evidence on Aid Impacts Using a Preanalysis Plan," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(4), pages 1755-1812.
    8. Leamer, Edward E, 1983. "Let's Take the Con Out of Econometrics," American Economic Review, American Economic Association, vol. 73(1), pages 31-43, March.
    9. T. D. Stanley, 2008. "Meta‐Regression Methods for Detecting and Estimating Empirical Effects in the Presence of Publication Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(1), pages 103-127, February.
    10. Abel Brodeur & Mathias Lé & Marc Sangnier & Yanos Zylberberg, 2016. "Star Wars: The Empirics Strike Back," American Economic Journal: Applied Economics, American Economic Association, vol. 8(1), pages 1-32, January.
    11. Gerber, Alan & Malhotra, Neil, 2008. "Do Statistical Reporting Standards Affect What Is Published? Publication Bias in Two Leading Political Science Journals," Quarterly Journal of Political Science, now publishers, vol. 3(3), pages 313-326, October.
    12. T. D. Stanley, 2005. "Beyond Publication Bias," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 309-345, July.
    13. Isaiah Andrews & Maximilian Kasy, 2019. "Identification of and Correction for Publication Bias," American Economic Review, American Economic Association, vol. 109(8), pages 2766-2794, August.
    14. repec:hal:wpspec:info:hdl:2441/eu4vqp9ompqllr09iatr74eao is not listed on IDEAS
    15. repec:hal:spmain:info:hdl:2441/eu4vqp9ompqllr09iatr74eao is not listed on IDEAS
    16. Leamer, Edward E & Leonard, Herman B, 1983. "Reporting the Fragility of Regression Estimates," The Review of Economics and Statistics, MIT Press, vol. 65(2), pages 306-317, May.
    17. Tomáš Havránek, 2015. "Measuring Intertemporal Substitution: The Importance Of Method Choices And Selective Reporting," Journal of the European Economic Association, European Economic Association, vol. 13(6), pages 1180-1204, December.
    18. Emeric Henry, 2009. "Disclosure of research results: the cost of proving your honesty," Post-Print hal-01023670, HAL.
    19. Chris Doucouliagos & T.D. Stanley, 2013. "Are All Economic Facts Greatly Exaggerated? Theory Competition And Selectivity," Journal of Economic Surveys, Wiley Blackwell, vol. 27(2), pages 316-339, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Research methods; causal inference; p-curves; p-hacking; publication bias.;
    All these keywords.

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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