Danielle Li
Massachusetts Institute of Technology
29 Papers
73 Citations
Danielle Li is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Attendance & Price elasticity of demand. The author has an hindex of 14, co-authored 28 publications. Previous affiliations of Danielle Li include National Bureau of Economic Research & Harvard University.
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Papers
Public R&D Investments and Private-sector Patenting: Evidence from NIH Funding Rules
TL;DR: The impact of scientific grant funding at the National Institutes of Health on patenting by pharmaceutical and biotechnology firms is quantified and a method for flexibly linking specific grant expenditures to private-sector innovations is developed.
Big names or big ideas: Do peer-review panels select the best science proposals?
TL;DR: It is found that better peer-review scores are consistently associated with better research outcomes and that this relationship persists even when detailed controls for an investigator’s publication history, grant history, institutional affiliations, career stage, and degree types are included.
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Expertise vs. Bias in Evaluation: Evidence from the NIH
TL;DR: The authors developed a framework for separately identifying the influence of expertise and bias in project evaluation and applied it in the context of peer review at the National Institutes of Health (NIH) and found that while reviewers favor applicants whose work is related to theirs, they are also more informed about the quality of those appli- cants.
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The applied value of public investments in biomedical research
TL;DR: It is found that about 10% of NIH grants generate a patent directly but 30% generate articles that are subsequently cited by patents, and there is no systematic relationship between the “basic” versus “applied” research focus of a grant and its propensity to be cited by a patent.
Promotions and the Peter Principle
TL;DR: This paper found evidence consistent with the Peter Principle that firms prioritize current job performance in promotion decisions at the expense of other observable characteristics that better predict managerial performance, suggesting either that firms are making inefficient promotion decisions or that the benefits of promotion-based incentives are great enough to justify the costs of managerial mismatch.