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Identification of and correction for publication bias

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  • Kasy, Maximilian
  • Andrews, Isaiah

Abstract

Some empirical results are more likely to be published than others. Selective publication leads to biased estimates and distorted inference. We propose two approaches for identifying the conditional probability of publication as a function of a study’s results, the first based on systematic replication studies and the second on meta-studies. For known conditional publication probabilities, we propose bias-corrected estimators and confidence sets. We apply our methods to recent replication studies in experimental economics and psychology, and to a meta-study on the effect of the minimum wage. When replication and meta-study data are available, we find similar results from both.

Suggested Citation

  • Kasy, Maximilian & Andrews, Isaiah, 2018. "Identification of and correction for publication bias," MetaArXiv 49yst, Center for Open Science.
  • Handle: RePEc:osf:metaar:49yst
    DOI: 10.31219/osf.io/49yst
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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J38 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Public Policy
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media

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