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Transparency in empirical economic research

Author

Listed:
  • Cristina Blanco-Perez

    (University of Ottawa, Canada)

  • Abel Brodeur

    (University of Ottawa, Canada)

Abstract
The open science and research transparency movement aims to make the research process more visible and to strengthen the credibility of results. Examples of open research practices include open data, pre-registration, and replication. Open science proponents argue that making data and codes publicly available enables researchers to evaluate the truth of a claim and improve its credibility. Opponents often counter that replications are costly and that open science efforts are not always rewarded with publication of results.

Suggested Citation

  • Cristina Blanco-Perez & Abel Brodeur, 2019. "Transparency in empirical economic research," IZA World of Labor, Institute of Labor Economics (IZA), pages 467-467, November.
    • Handle: RePEc:iza:izawol:journl:2019:n:467
    as

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    References listed on IDEAS

    as
    1. Brodeur, Abel & Cook, Nikolai & Heyes, Anthony, 2018. "Methods Matter: P-Hacking and Causal Inference in Economics," IZA Discussion Papers 11796, Institute of Labor Economics (IZA).
    2. Garret Christensen & Edward Miguel, 2018. "Transparency, Reproducibility, and the Credibility of Economics Research," Journal of Economic Literature, American Economic Association, vol. 56(3), pages 920-980, September.
    3. Mueller-Langer, Frank & Fecher, Benedikt & Harhoff, Dietmar & Wagner, Gert G., 2019. "Replication studies in economics—How many and which papers are chosen for replication, and why?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 48(1), pages 62-83.
    4. Benjamin A. Olken, 2015. "Promises and Perils of Pre-analysis Plans," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 61-80, Summer.
    5. 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.
    6. 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.
    7. 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.
    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. Camerer, Colin & Dreber, Anna & Forsell, Eskil & Ho, Teck-Hua & Huber, Jurgen & Johannesson, Magnus & Kirchler, Michael & Almenberg, Johan & Altmejd, Adam & Chan, Taizan & Heikensten, Emma & Holzmeist, 2016. "Evaluating replicability of laboratory experiments in Economics," MPRA Paper 75461, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

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    as


    Cited by:

    1. Abel Brodeur & Nikolai Cook & Carina Neisser, 2024. "p-Hacking, Data type and Data-Sharing Policy," The Economic Journal, Royal Economic Society, vol. 134(659), pages 985-1018.
    2. 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.
    3. 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.

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    More about this item

    Keywords

    research transparency; open science; data sharing; p-hacking and replication;
    All these keywords.

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • 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
    • I10 - Health, Education, and Welfare - - Health - - - General

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