Danielle Li


Danielle Li

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Danielle Li is 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.  



"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.

"Generative AI at Work."

Brynjolfsson, Erik, Danielle Li, and Lindsey R. Raymond, MIT Sloan Working Paper 6848-23. Cambridge, MA: MIT Sloan School of Management, April 2023. NBER Working Paper 31161.

"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.

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Recent Insights

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Ideas Made to Matter

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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.

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Media Highlights

Executive Education

Executive Education Course

Making AI Work: Machine Intelligence for Business and Society

Over six weeks, you’ll explore the technical and strategic considerations for robust, beneficial, and responsible AI deployment. You’ll examine the various stages of a proprietary ML Deployment Framework and unlock new opportunities by investigating the key challenges and their related impact. Guided by leading experts and MIT academics, you’ll build a toolkit for addressing these challenges within your own organization and context.

  • Jun 7-Aug 1, 2023
  • Aug 16-Oct 3, 2023
  • Nov 15, 2023-Jan 23, 2024
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