Danielle Li

Faculty

Danielle Li

Support Staff

Get in Touch

Title

About

Academic Area

Danielle Li is the Class of 1922 Career Development Associate Professor, and an Associate Professor at the MIT Sloan School of Management, as well as a Faculty Research Fellow at the National Bureau of Economic Research. Her research interests are in economics of innovation and labor economics, with a focus on how organizations evaluate ideas, projects, and people. 

Danielle's work has been published in leading academic journals across a range of fields, including the Quarterly Journal of Economics, Science, and Management Science. In addition, her work has been regularly featured in  media outlets such as the EconomistNew York Times, and Wall Street Journal.

She has previously taught at the Harvard Business School and the Kellogg School of Management. She holds an AB in mathematics and the history of science from Harvard College and a PhD in economics from MIT.  

Honors

Publications

"Insurance Design and Pharmaceutical Innovation."

Agha, Leila, Soomi Kim, and Danielle Li. American Economic Review: Insights. Forthcoming. Download Preprint.

"Missing Novelty in Drug Development."

Joshua Krieger, Danielle Li, and Dimitris Papanikolaou. Review of Financial Studies. Forthcoming. Download Preprint.

"Scientific Grant Funding."

Azoulay, Pierre, and Danielle Li. In Innovation and Public Policy, edited by Austan Goolsbee and Ben Jones, 1-34. Chicago, IL: University of Chicago Press, 2022. NBER Working Paper #26889.

"'Potential' and the Gender Promotion Gap."

Benson, Alan, Danielle Li, and Kelly Shue, MIT Sloan Working Paper 6484-21. Cambridge, MA: MIT Sloan School of Management, October 2021.

Load More

Recent Insights

Ideas Made to Matter

Women are less likely than men to be promoted. Here’s one reason why

Whether you use the Nine Box system or another assessment tool, new research suggests it’s time to rethink how you rate and track potential.

Read Article
Press

Hiring algorithm impacts quality and diversity of candidates

“There is a growing body of work on the potential gains from following algorithmic recommendations, but this is the first paper to highlight the role of algorithm design on the hiring process.”

Read Article
Load More

Media Highlights