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Research Productivity and the Dynamic Allocation of NIH Grants
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Research Productivity and the Dynamic Allocation of NIH Grants. |
| 개인저자 | Qiu, Yin Jia. |
| 단체저자명 | Yale University. Economics. |
| 발행사항 | [S.l.]: Yale University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 91 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03A. Dissertation Abstract International |
| ISBN | 9781088309377 |
| 학위논문주기 | Thesis (Ph.D.)--Yale University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: A.
Advisor: Berry, Steven T. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | This dissertation studies optimal funding allocation for Research and Development (R&D) on academic scientific research.Chapter 1 introduces institutional settings of academic scientific research and discusses previous progress in the literature of the economics of science.Chapter 2 introduces data from the National Institutes of Health (NIH) and uses this data to analyze the impact of funding on research output. I construct a panel dataset at the principal investigator (PI) and year level and estimate a research production function. Extending the previous literature, I explicitly incorporate funding dynamics into research production functions. I show that funding has dynamic effects on research output through the learning-by-doing channel and that the unobserved total factor productivity (TFP) at the PI level is persistent.Chapter 3 develops an empirical framework to study how the NIH could allocate research funding in a dynamically optimal manner, especially in terms of balancing funds between young and veteran PIs. Using estimates from Chapter 2, I formulate the planner (the NIH)'s funding allocation problem as a dynamic programming problem in which the planner maximizes the discounted sum of research output subject to a budget constraint. Because the planner's dynamic programming problem suffers from the curse of dimensionality, I adopt approximate dynamic programming methods from the operations research literature to allow computation. I provide three main results. First, a forward-looking policy with a discount factor of 0.9 funds 30% more young PIs than a myopic policy does, which translates to 5% more research output per year in the long run. Second, the NIH appears to be accounting for some intertemporal tradeoffs, but may still be underfunding young PIs: the discount factor that rationalizes the NIH's funding behavior is about 0.75. Finally, a temporary funding cut, similar to the one proposed by the current administration, would have a long-lasting effect on overall research output through its adverse impact on investment in young PIs. |
| 일반주제명 | Economics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
