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Expertise versus Bias in Evaluation: Evidence from the NIH

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
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    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

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