Danielle Li

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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.  

Honors

Publications

"Potential and the Gender Promotion Gap."

Benson, Alan M., Danielle Li, and Kelly Shue. Academy of Management Proceedings Vol. 2023, No. 1 (2023).

"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.

"Insurance Design and Pharmaceutical Innovation."

Agha, Leila, Soomi Kim, and Danielle Li. American Economic Review: Insights Vol. 4, No. 2 (2022): 191-208. Download Preprint.

"Missing Novelty in Drug Development."

Joshua Krieger, Danielle Li, and Dimitris Papanikolaou. The Review of Financial Studies Vol. 35, No. 2 (2022): 636–679. Download Preprint.

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Recent Insights

Ideas Made to Matter

Generative AI research from MIT Sloan

Ideas about how to best deploy generative artificial intelligence, how it will affect the workforce, and how it should be regulated.

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Ideas Made to Matter

The top 10 MIT Sloan articles of 2023

Artificial intelligence is everywhere. But it’s humble leadership that leads our list.

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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.

  • Nov 15, 2023-Jan 23, 2024
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  • Nov 20, 2024-Jan 28, 2025
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