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Specification Searching and Significance Inflation Across Time, Methods and Disciplines

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  • Eva Vivalt

Abstract

This paper examines how significance inflation has varied across time, methods and disciplines. Leveraging a unique data set of impact evaluations on 20 kinds of development programmes, I find that results from randomized controlled trials exhibit less significance inflation than results from studies using other methods. Further, randomized controlled trials have exhibited less significance inflation over time, but quasi‐experimental studies have not. There is no robust difference between results from researchers affiliated with economics departments and those from researchers affiliated with other predominantly health‐related departments. Overall, the biases found appear much smaller than those previously observed in other social sciences.

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  • Eva Vivalt, 2019. "Specification Searching and Significance Inflation Across Time, Methods and Disciplines," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(4), pages 797-816, August.
    • Handle: RePEc:bla:obuest:v:81:y:2019:i:4:p:797-816
    DOI: 10.1111/obes.12289
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    Cited by:

    1. Peter Pütz & Stephan B. Bruns, 2021. "The (Non‐)Significance Of Reporting Errors In Economics: Evidence From Three Top Journals," Journal of Economic Surveys, Wiley Blackwell, vol. 35(1), pages 348-373, February.
    2. Abel Brodeur, Nikolai M. Cook, Anthony Heyes, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell Us about Publication Bias and p-Hacking in Online Experiments," LCERPA Working Papers am0133, Laurier Centre for Economic Research and Policy Analysis.
    3. Brodeur, Abel & Cook, Nikolai & Heyes, Anthony, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell us about p-Hacking and Publication Bias in Online Experiments," GLO Discussion Paper Series 1157, Global Labor Organization (GLO).
    4. Abel Brodeur, Nikolai M. Cook, Jonathan S. Hartley, Anthony Heyes, 2022. "Do Pre-Registration and Pre-analysis Plans Reduce p-Hacking and Publication Bias?," LCERPA Working Papers am0132, Laurier Centre for Economic Research and Policy Analysis.
    5. 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.
    6. Graham Elliott & Nikolay Kudrin & Kaspar Wüthrich, 2022. "Detecting p‐Hacking," Econometrica, Econometric Society, vol. 90(2), pages 887-906, March.
    7. Brodeur, Abel & Cook, Nikolai M. & Hartley, Jonathan S. & Heyes, Anthony, 2023. "Do Pre-Registration and Pre-Analysis Plans Reduce p-Hacking and Publication Bias?: Evidence from 15,992 Test Statistics and Suggestions for Improvement," GLO Discussion Paper Series 1147 [pre.], Global Labor Organization (GLO).
    8. Bruns, Stephan B. & Ioannidis, John P.A., 2020. "Determinants of economic growth: Different time different answer?," Journal of Macroeconomics, Elsevier, vol. 63(C).
    9. Andrew C. Chang & Trace J. Levinson, 2020. "Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting," Finance and Economics Discussion Series 2020-090, Board of Governors of the Federal Reserve System (U.S.).
    10. Abel Brodeur & Nikolai Cook & Carina Neisser, 2022. "P-Hacking, Data Type and Data-Sharing Policy," ECONtribute Discussion Papers Series 200, University of Bonn and University of Cologne, Germany.
    11. Abel Brodeur & Nikolai Cook & Anthony Heyes, 2020. "Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics," American Economic Review, American Economic Association, vol. 110(11), pages 3634-3660, November.
    12. Nathan Fiala & Ana Garcia-Hernandez & Kritika Narula & Nishith Prakash, 2022. "Wheels of Change: Transforming Girls' Lives with Bicycles," CESifo Working Paper Series 9865, CESifo.
    13. Nathan Fiala & Ana Garcia-Hernandez & Kritika Narula & Nishith Prakash, 2022. "Wheels of Change: Transforming Girls’ Lives with Bicycles," Working papers 2022-04, University of Connecticut, Department of Economics.
    14. Graham Elliott & Nikolay Kudrin & Kaspar Wuthrich, 2022. "The Power of Tests for Detecting $p$-Hacking," Papers 2205.07950, arXiv.org, revised Jun 2023.
    15. Bruns, Stephan B. & Asanov, Igor & Bode, Rasmus & Dunger, Melanie & Funk, Christoph & Hassan, Sherif M. & Hauschildt, Julia & Heinisch, Dominik & Kempa, Karol & König, Johannes & Lips, Johannes & Verb, 2019. "Reporting errors and biases in published empirical findings: Evidence from innovation research," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    16. Eliot Abrams & Jonathan Libgober & John List, 2020. "Research Registries: Facts, Myths, and Possible Improvements," Artefactual Field Experiments 00703, The Field Experiments Website.
    17. Doucouliagos, Hristos & Hinz, Thomas & Zigova, Katarina, 2022. "Bias and careers: Evidence from the aid effectiveness literature," European Journal of Political Economy, Elsevier, vol. 71(C).

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