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Administrative Discretion in Scientific Funding: Evidence from a Prestigious Postdoctoral Training Program

Author

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  • Donna K. Ginther
  • Misty L. Heggeness

Abstract

The scientific community is engaged in an active debate on the value of its peer-review system. Does peer review actually serve the role we envision for it—that of helping government agencies predict what ideas have the best chance of contributing to scientific advancement? Many federal agencies use a two-step review process that includes programmatic discretion in selecting awards. This process allows us to determine whether success in a future independent scientific-research career is more accurately predicted by peer-review recommendations or discretion by program staff and institute leaders. Using data from a prestigious training program at the National Institute of Health (NIH), the Ruth L. Kirschstein National Research Service Award (NRSA), we provide evidence on the efficacy of peer review. We find that, despite all current claims to the contrary, the existing peer-review system works as intended. It more closely predicts high-quality science and future research independence than discretion. We discover also that regression discontinuity, the econometric method typically used to examine the effect of scientific funding, does not fit many scientific-funding models and should only be used with caution when studying federal awards for science.

Suggested Citation

  • Donna K. Ginther & Misty L. Heggeness, 2020. "Administrative Discretion in Scientific Funding: Evidence from a Prestigious Postdoctoral Training Program," NBER Working Papers 26841, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:26841
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    Cited by:

    1. 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).
    2. Lawson, Cornelia & Salter, Ammon, 2023. "Exploring the effect of overlapping institutional applications on panel decision-making," Research Policy, Elsevier, vol. 52(9).
    3. Bol, Thijs & de Vaan, Mathijs & van de Rijt, Arnout, 2022. "Gender-equal funding rates conceal unequal evaluations," Research Policy, Elsevier, vol. 51(1).
    4. Azoulay, Pierre & Greenblatt, Wesley H. & Heggeness, Misty L., 2021. "Long-term effects from early exposure to research: Evidence from the NIH “Yellow Berets”," Research Policy, Elsevier, vol. 50(9).
    5. Pierre Azoulay & Danielle Li, 2020. "Scientific Grant Funding," NBER Working Papers 26889, National Bureau of Economic Research, Inc.
    6. Pierre Azoulay & Danielle Li, 2020. "Scientific Grant Funding," NBER Chapters, in: Innovation and Public Policy, pages 117-150, National Bureau of Economic Research, Inc.
    7. Cruz-Castro, Laura & Sanz-Menendez, Luis, 2021. "What should be rewarded? Gender and evaluation criteria for tenure and promotion," Journal of Informetrics, Elsevier, vol. 15(3).

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    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy

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