About

Danielle Li is the David Sarnoff Professor of Management of Technology and a Professor at the MIT Sloan School of Management. She is also a Faculty Research Fellow at the National Bureau of Economic Research. Her research interests include the economics of innovation and labor economics, with a focus on how organizations evaluate ideas, projects, and people. Her work has been published in leading academic journals such as the Quarterly Journal of Economics, Science, and Management Science, and has been featured in media outlets including The Economist, New York Times, and Wall Street Journal. She has previously taught at Harvard Business School and the Kellogg School of Management. Dr. Li holds an AB in mathematics and the history of science from Harvard College and a PhD in economics from MIT.

Research topics

• Political Science
• Economics
• Law
• Actuarial science
• Pharmacology
• Public economics
• Medicine
• Business
• Demographic economics
• Family medicine
• Psychology

Selected publications

• What if NIH had been 40% smaller?

  Science · 2025-09-25 · 8 citations

  article

  Replaying history with less NIH funding shows widespread impacts on drug-linked research.

  PublisherDOI

• Hiring as Exploration

  arXiv (Cornell University) · 2024-11-06

  preprintOpen access1st authorCorresponding

  This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance exploitation (selecting from groups with proven track records) with exploration (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on supervised learning approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm's existing practices. The same is not true for traditional supervised learning based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.

  PublisherOA PDFDOI

• Potential and the Gender Promotions Gap

  SSRN Electronic Journal · 2024-01-01 · 25 citations

  articleOpen access
  PublisherDOI

• Potential and the Gender Promotion Gap

  Academy of Management Proceedings · 2023 · 55 citations

  • Political Science
  • Psychology
  • Political Science

  We show that widely-used subjective assessments of employee ``potential'' contribute to gender gaps in promotion and pay. Using data on 29,809 management-track employees from a large North American retail chain, we find that women receive substantially lower potential ratings despite receiving higher job performance ratings. Differences in potential ratings account for approximately half of the gender promotion gap. Women's lower potential ratings do not appear to be based on accurate forecasts of future performance or attrition: women subsequently outperform male colleagues with the same potential ratings, both on average and on the margin of promotion, and women are less likely to exit the firm. Despite this, women's subsequent potential ratings remain low, suggesting that firms persistently underestimate the potential of their female employees.

  PublisherDOI

• Evaluation and Learning in R&D Investment

  SSRN Electronic Journal · 2023-01-01

  articleOpen access
  PublisherDOI

• Evaluation and Learning in R&D Investment

  National Bureau of Economic Research · 2023-05-01 · 7 citations

  reportOpen access

  We examine the role of spillover learning in shaping the value of exploratory versus incremental R&D.Using data from drug development, we show that novel drug candidates generate more knowledge spillovers than incremental ones.Despite being less likely to reach regulatory approval, they are more likely to inspire subsequent successful drugs.We introduce a model where firms are better able to evaluate the viability of incremental drugs, but where investing in novel drugs helps firms learn about future projects.Firms appear to put more value on evaluation versus learning, and those patterns are in-part driven by the appropriability of spillovers.

  PublisherDOI

• Hiring as Exploration

  Academy of Management Proceedings · 2023-07-24

  article

  This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance “exploitation'' (selecting from groups with proven track records) with “exploration'' (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on “supervised learning

  PublisherDOI

• Evaluation and Learning in R&D Investment

  SSRN Electronic Journal · 2022-01-01

  articleOpen access
  PublisherDOI

• Insurance Design and Pharmaceutical Innovation

  American Economic Review Insights · 2022 · 37 citations

  Senior authorCorresponding
  • Business
  • Actuarial science
  • Public economics

  This paper studies how insurance coverage policies impact pharmaceutical innovation. In the United States, most patients obtain prescription drugs through insurance plans administered by Pharmacy Benefit Managers (PBMs). Beginning in 2012, PBMs began refusing to provide coverage for many newly approved drugs when cheaper alternatives were available. We document a shift in pharmaceutical R&D strategies after this policy took effect: therapeutic classes at greater risk of exclusion experienced a relative reduction in investments. This shift reduced development of drug candidates that appear more incremental: that is, those in drug classes with more preexisting therapies and less scientifically novel research. (JEL G22, I13, L65, O31)

  PublisherDOI

• Chapter 4. Scientific Grant Funding

  2022-01-01 · 7 citations

  book-chapterSenior author
  PublisherDOI

Frequent coauthors

• Pierre Azoulay

  44 shared

• Bhaven N. Sampat

  Arizona State University

  40 shared

• Joshua Graff Zivin

  National Bureau of Economic Research

  39 shared

• Dimitris Papanikolaou

  Kellogg's (Canada)

  12 shared

• Kelly Shue

  National Bureau of Economic Research

  10 shared

• Alan Benson

  8 shared

• Joshua Krieger

  Harvard University

  6 shared

• Mitchell Hoffman

  6 shared

Awards & honors

• 2019 American Economic Journal Best Paper Award