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Sniff Tests in Economics: Aggregate Distribution of Their Probability Values and Implications for Publication Bias

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  • Snyder, Christopher
  • Zhuo, Ran

Abstract

The increasing demand for rigor in empirical economics has led to the growing use of auxiliary tests (balance, specification, over-identification, placebo, etc.) supporting the credibility of a paper’s main results. We dub these “sniff tests” because standards for passing are subjective and rejection is bad news for the author. Sniff tests offer a new window into publication bias since authors prefer them to be insignificant, the reverse of standard statistical tests. Collecting a sample of nearly 30,000 sniff tests across 60 economics journals, we provide the first estimate of their aggregate probability-value (p-value) distribution. For the subsample of balance tests in randomized controlled trials (for which the distribution of p-values is known to be uniform absent publication bias, allowing reduced-form methods to be employed) estimates suggest that 45% of failed tests remain in the “file drawer” rather than being published. For the remaining sample with an unknown distribution of p-values, structural estimates suggest an even larger file-drawer problem, as high as 91%. Fewer significant sniff tests show up in top-tier journals, smaller tables, and more recent articles. We find no evidence of author manipulation other than a tendency to overly attribute significant sniff tests to bad luck.

Suggested Citation

  • Snyder, Christopher & Zhuo, Ran, 2018. "Sniff Tests in Economics: Aggregate Distribution of Their Probability Values and Implications for Publication Bias," MetaArXiv 8vdrh_v1, Center for Open Science.
    • Handle: RePEc:osf:metaar:8vdrh_v1
    DOI: 10.31219/osf.io/8vdrh_v1
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    1. Chris Doucouliagos, 2005. "Publication Bias in the Economic Freedom and Economic Growth Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 367-387, July.
    2. De Long, J Bradford & Lang, Kevin, 1992. "Are All Economic Hypotheses False?," Journal of Political Economy, University of Chicago Press, vol. 100(6), pages 1257-1272, December.
    3. 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.
    4. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
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