| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000433071 |
| 005 | | 20200225111925 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781088309377 |
| 035 | |
▼a (MiAaPQ)AAI13809533 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 330 |
| 100 | 1 |
▼a Qiu, Yin Jia. |
| 245 | 10 |
▼a Research Productivity and the Dynamic Allocation of NIH Grants. |
| 260 | |
▼a [S.l.]:
▼b Yale University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 91 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-03, Section: A. |
| 500 | |
▼a Advisor: Berry, Steven T. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Yale University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a 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. |
| 590 | |
▼a School code: 0265. |
| 650 | 4 |
▼a Economics. |
| 690 | |
▼a 0501 |
| 710 | 20 |
▼a Yale University.
▼b Economics. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-03A. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0265 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15490599
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |