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Evidence on Research Transparency in Economics

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  • Edward Miguel

Abstract

A decade ago, the term "research transparency" was not on economists' radar screen, but in a few short years a scholarly movement has emerged to bring new open science practices, tools and norms into the mainstream of our discipline. The goal of this article is to lay out the evidence on the adoption of these approaches—in three specific areas: open data, pre-registration and pre-analysis plans, and journal policies—and, more tentatively, begin to assess their impacts on the quality and credibility of economics research. The evidence to date indicates that economics (and related quantitative social science fields) are in a period of rapid transition toward new transparency-enhancing norms. While solid data on the benefits of these practices in economics is still limited, in part due to their relatively recent adoption, there is growing reason to believe that critics' worst fears regarding onerous adoption costs have not been realized. Finally, the article presents a set of frontier questions and potential innovations.

Suggested Citation

  • Edward Miguel, 2021. "Evidence on Research Transparency in Economics," Journal of Economic Perspectives, American Economic Association, vol. 35(3), pages 193-214, Summer.
  • Handle: RePEc:aea:jecper:v:35:y:2021:i:3:p:193-214
    DOI: 10.1257/jep.35.3.193
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    Citations

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    Cited by:

    1. Balafoutas, Loukas & Celse, Jeremy & Karakostas, Alexandros & Umashev, Nicholas, 2025. "Incentives and the replication crisis in social sciences: A critical review of open science practices," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 114(C).
    2. Timothy B. Armstrong & Patrick Kline & Liyang Sun, 2023. "Adapting to Misspecification," Papers 2305.14265, arXiv.org, revised Sep 2025.
    3. Libman, A., 2024. ""Zoo" of empirical results: Quantitative research and accumulation of knowledge in social sciences," Journal of the New Economic Association, New Economic Association, vol. 65(4), pages 178-194.
    4. Felix Chopra & Ingar Haaland & Christopher Roth & Andreas Stegmann, 2024. "The Null Result Penalty," The Economic Journal, Royal Economic Society, vol. 134(657), pages 193-219.
    5. repec:cdl:ucsbec:qt5b17d1dr is not listed on IDEAS
    6. Rose, Julian & Neubauer, Florian & Ankel-Peters, Jörg, 2024. "Long-term effects of the targeting the ultra-poor program: A reproducibility and replicability assessment of Banerjee et al. (2021)," Ruhr Economic Papers 1107, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    7. Thibaut Arpinon & Romain Espinosa, 2023. "A practical guide to Registered Reports for economists," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 9(1), pages 90-122, June.
    8. Maximilian Kasy & Jann Spiess, 2022. "Optimal Pre-Analysis Plans: Statistical Decisions Subject to Implementability," Papers 2208.09638, arXiv.org, revised Jul 2024.
    9. Ankel-Peters, Jörg & Fiala, Nathan & Neubauer, Florian, 2023. "Do economists replicate?," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 219-232.
    10. Brodeur, Abel & Cook, Nikolai M. & Hartley, Jonathan S. & Heyes, Anthony, 2022. "Do Pre-Registration and Pre-analysis Plans Reduce p-Hacking and Publication Bias?," GLO Discussion Paper Series 1147, Global Labor Organization (GLO).
    11. Christophe Pérignon & Olivier Akmansoy & Christophe Hurlin & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johanneson & Michael Kirchler & Albert Menkveld & Michael Razen & Utz Weitzel, 2022. "Reproducibility of Empirical Results: Evidence from 1,000 Tests in Finance," Working Papers hal-03810013, HAL.
    12. Maximilian Kasy & Jann Spiess, 2022. "Rationalizing Pre-Analysis Plans:Statistical Decisions Subject to Implementability," Economics Series Working Papers 975, University of Oxford, Department of Economics.
    13. Robert Kirkby, 2023. "Quantitative Macroeconomics: Lessons Learned from Fourteen Replications," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 875-896, February.
    14. Aldy, Joseph E., 2022. "Learning How to Build Back Better through Clean Energy Policy Evaluation," RFF Working Paper Series 22-15, Resources for the Future.
    15. Garg, Prashant & Fetzer, Thiemo, 2024. "Causal Claims in Economics," I4R Discussion Paper Series 183, The Institute for Replication (I4R).
    16. Emilio Depetris-Chauvin & Felipe González, 2023. "The Political Consequences of Vaccines: Quasi-experimental Evidence from Eligibility Rules," Documentos de Trabajo 572, Instituto de Economia. Pontificia Universidad Católica de Chile..
    17. Deer, Lachlan & Adler, Susanne J. & Datta, Hannes & Mizik, Natalie & Sarstedt, Marko, 2025. "Toward open science in marketing research," International Journal of Research in Marketing, Elsevier, vol. 42(1), pages 212-233.
    18. Ravi Jagadeesan & Davide Viviano, 2025. "Publication Design with Incentives in Mind," Papers 2504.21156, arXiv.org, revised Sep 2025.
    19. , 2023. "The Political Consequences of Vaccines: Quasi-experimental Evidence from Eligibility Rules," Working Papers 953, Queen Mary University of London, School of Economics and Finance.
    20. Tom L. Dudda & Lars Hornuf, 2025. "The Perks and Perils of Machine Learning in Business and Economic Research," CESifo Working Paper Series 11721, CESifo.
    21. Thibaut Arpinon & Marianne Lefebvre, 2024. "Registered Reports and Associated Benefits for Agricultural Economics," Post-Print hal-04635986, HAL.
    22. repec:osf:osfxxx:u4vgs_v1 is not listed on IDEAS
    23. Thibaut Arpinon & Romain Espinosa, 2023. "A Practical Guide to Registered Reports for Economists," Post-Print halshs-03897719, HAL.

    More about this item

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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