Faculty
Danielle Li is the Class of 1922 Career Development 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 Economist, New 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.
Hoffman, Mitchell, Lisa Kahn, and Danielle Li. Quarterly Journal of Economics Vol. 133, No. 2 (2018): 765-800. Download paper.
Azoulay, Pierre, Joshua S. Graff Zivin, Danielle Li, and Bhaven N. Sampat. Review of Economic Studies Vol. 86, No. 1 (2019): 117-152.
Agha, Leila, Soomi Kim, and Danielle Li. American Economic Review: Insights. Forthcoming. Download Preprint.
Joshua Krieger, Danielle Li, and Dimitris Papanikolaou. Review of Financial Studies. Forthcoming. Download Preprint.
Azoulay, Pierre, and Danielle Li. In Innovation and Public Policy, edited by Austan Goolsbee and Ben Jones, Innovation and Public Policy: University of Chicago Press, Forthcoming.
Benson, Alan, Danielle Li, and Kelly Shue, MIT Sloan Working Paper 6484-21. Cambridge, MA: MIT Sloan School of Management, October 2021.
“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.”
A new approach analyzed resumes for a Fortune 500 firm — and bested traditional screening practices.
"The point is to find someone who will be creative and seek solutions for this particular position."
"...an ongoing concern is the potential for automated approaches to codify existing human biases..."
Using AI to recruit isn't inherently bad, says Danielle Li...It's about using the right kind of algorithm.
End-of-year approvals are later associated with more hospitalizations, life-threatening events and deaths, according to a new study.
