IDEAS home Printed from https://ideas.repec.org/a/aea/aejapp/v9y2017i2p60-92.html
   My bibliography  Save this article

Expertise versus Bias in Evaluation: Evidence from the NIH

Author

Listed:
  • Danielle Li

Abstract

Evaluators with expertise in a particular field may have an informational advantage in separating good projects from bad. At the same time, they may also have personal preferences that impact their objectivity. This paper examines these issues in the context of peer review at the US National Institutes of Health. I show that evaluators are both better informed and more biased about the quality of projects in their own area. On net, the benefits of expertise weakly dominate the costs of bias. As such, policies designed to limit bias by seeking impartial evaluators may reduce the quality of funding decisions.

Suggested Citation

  • Danielle Li, 2017. "Expertise versus Bias in Evaluation: Evidence from the NIH," American Economic Journal: Applied Economics, American Economic Association, vol. 9(2), pages 60-92, April.
    • Handle: RePEc:aea:aejapp:v:9:y:2017:i:2:p:60-92
    Note: DOI: 10.1257/app.20150421
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/articles?id=10.1257/app.20150421
    Download Restriction: no
    File URL: https://www.aeaweb.org/articles/attachments?retrieve=_jGuHbkqc887it0ybX_g1Pk377DxDc8C
    Download Restriction: no
    File URL: https://www.aeaweb.org/articles/attachments?retrieve=KHfSpqkGvndRlQLdi85dRzSebGBhGJCa
    Download Restriction: no
    File URL: https://www.aeaweb.org/articles/attachments?retrieve=Dd6e8o9vBVp0RtqubsZWIV8j7UQuxeqE
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pierre Azoulay & Joshua S. Graff Zivin & Gustavo Manso, 2011. "Incentives and creativity: evidence from the academic life sciences," RAND Journal of Economics, RAND Corporation, vol. 42(3), pages 527-554, September.
    2. Jacob, Brian A. & Lefgren, Lars, 2011. "The impact of research grant funding on scientific productivity," Journal of Public Economics, Elsevier, vol. 95(9), pages 1168-1177.
    3. Raymond Fisman & Daniel Paravisini & Vikrant Vig, 2017. "Cultural Proximity and Loan Outcomes," American Economic Review, American Economic Association, vol. 107(2), pages 457-492, February.
    4. Laband, David N & Piette, Michael J, 1994. "Favoritism versus Search for Good Papers: Empirical Evidence Regarding the Behavior of Journal Editors," Journal of Political Economy, University of Chicago Press, vol. 102(1), pages 194-203, February.
    5. Deepak Hegde, 2009. "Political Influence behind the Veil of Peer Review: An Analysis of Public Biomedical Research Funding in the United States," Journal of Law and Economics, University of Chicago Press, vol. 52(4), pages 665-690, November.
    6. Jordi Blanes i Vidal & Mirko Draca & Christian Fons-Rosen, 2012. "Revolving Door Lobbyists," American Economic Review, American Economic Association, vol. 102(7), pages 3731-3748, December.
    7. Kevin J. Boudreau & Eva C. Guinan & Karim R. Lakhani & Christoph Riedl, 2016. "Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance, Novelty, and Resource Allocation in Science," Management Science, INFORMS, vol. 62(10), pages 2765-2783, October.
    8. Hansen, Stephen & McMahon, Michael & Velasco Rivera, Carlos, 2014. "Preferences or private assessments on a monetary policy committee?," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 16-32.
    9. Garfagnini, Umberto & Ottaviani, Marco & Sørensen, Peter Norman, 2014. "Accept or reject? An organizational perspective," International Journal of Industrial Organization, Elsevier, vol. 34(C), pages 66-74.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Banal-Estañol, Albert & Macho-Stadler, Inés & Pérez-Castrillo, David, 2019. "Evaluation in research funding agencies: Are structurally diverse teams biased against?," Research Policy, Elsevier, vol. 48(7), pages 1823-1840.
    2. Albert Banal-Estañol & Inés Macho-Stadler & David Pérez-Castrillo, 2019. "Funding Academic Research: Grant Application, Partnership, Award, and Output," Working Papers 1093, Barcelona School of Economics.
    3. Tania Babina & Alex Xi He & Sabrina T. Howell & Elisabeth Ruth Perlman & Joseph Staudt, 2020. "The Color of Money: Federal vs. Industry Funding of University Research," NBER Working Papers 28160, National Bureau of Economic Research, Inc.
    4. Zhang, Wanshu & Wang, Xuefeng & Chen, Hongshu & Liu, Jia, 2024. "The impact of early debut on scientists: Evidence from the Young Scientists Fund of the NSFC," Research Policy, Elsevier, vol. 53(2).
    5. Park, Hyunwoo & Lee, Jeongsik (Jay) & Kim, Byung-Cheol, 2015. "Project selection in NIH: A natural experiment from ARRA," Research Policy, Elsevier, vol. 44(6), pages 1145-1159.
    6. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
    7. Kok, Holmer & Faems, Dries & de Faria, Pedro, 2022. "Pork Barrel or Barrel of Gold? Examining the performance implications of earmarking in public R&D grants," Research Policy, Elsevier, vol. 51(7).
    8. James J. Heckman & Sidharth Moktan, 2020. "Publishing and promotion in economics - The tyranny of the Top Five," Vox eBook Chapters, in: Sebastian Galliani & Ugo Panizza (ed.), Publishing and Measuring Success in Economics, edition 1, volume 1, chapter 1, pages 23-32, Centre for Economic Policy Research.
    9. Wang, Jian & Lee, You-Na & Walsh, John P., 2018. "Funding model and creativity in science: Competitive versus block funding and status contingency effects," Research Policy, Elsevier, vol. 47(6), pages 1070-1083.
    10. Sotaro Shibayama & Deyun Yin & Kuniko Matsumoto, 2021. "Measuring novelty in science with word embedding," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-16, July.
    11. Corinna Ghirelli & Enkelejda Havari & Elena Meroni & Stefano Verzillo, 2023. "The long-term causal effects of winning an ERC grant," Working Papers 2313, Banco de España.
    12. Jürgen Janger & Nicole Schmidt-Padickakudy & Anna Strauss-Kollin, 2019. "International Differences in Basic Research Grant Funding. A Systematic Comparison," WIFO Studies, WIFO, number 61664, April.
    13. Julian Kolev & Yuly Fuentes-Medel & Fiona Murray, 2019. "Is Blinded Review Enough? How Gendered Outcomes Arise Even Under Anonymous Evaluation," NBER Working Papers 25759, National Bureau of Economic Research, Inc.
    14. Brogaard, Jonathan & Engelberg, Joseph & Parsons, Christopher A., 2014. "Networks and productivity: Causal evidence from editor rotations," Journal of Financial Economics, Elsevier, vol. 111(1), pages 251-270.
    15. Marco Cozzi, 2020. "Public Funding of Research and Grant Proposals in the Social Sciences: Empirical Evidence from Canada," Department Discussion Papers 1809, Department of Economics, University of Victoria.
    16. Nicolas Carayol, 2016. "The Right Job and the Job Right: Novelty, Impact and Journal Stratification in Science," Post-Print hal-02274661, HAL.
    17. Alexander Whalley & Justin Hicks, 2014. "Spending Wisely? How Resources Affect Knowledge Production In Universities," Economic Inquiry, Western Economic Association International, vol. 52(1), pages 35-55, January.
    18. Pierre Azoulay & Danielle Li, 2020. "Scientific Grant Funding," NBER Working Papers 26889, National Bureau of Economic Research, Inc.
    19. Ayoubi, Charles & Pezzoni, Michele & Visentin, Fabiana, 2019. "The important thing is not to win, it is to take part: What if scientists benefit from participating in research grant competitions?," Research Policy, Elsevier, vol. 48(1), pages 84-97.
    20. Graddy-Reed, Alexandra & Lanahan, Lauren & D'Agostino, Jesse, 2021. "Training across the academy: The impact of R&D funding on graduate students," Research Policy, Elsevier, vol. 50(5).

    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aea:aejapp:v:9:y:2017:i:2:p:60-92. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.