The top 10 MIT Sloan news stories of 2022
From “smart skills” to digital marketing trends, here are the stories readers were drawn to this year.
Faculty
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 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.
Azoulay, Pierre, Joshua S. Graff Zivin, Danielle Li, and Bhaven N. Sampat. Review of Economic Studies Vol. 86, No. 1 (2019): 117-152.
Hoffman, Mitchell, Lisa Kahn, and Danielle Li. Quarterly Journal of Economics Vol. 133, No. 2 (2018): 765-800. Download paper.
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.
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.
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.
From “smart skills” to digital marketing trends, here are the stories readers were drawn to this year.
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.
“Now data can be used to solve other people's problems ... I think it's really important to find a way to measure and compensate that.”
Workers who were given access to generative artificial intelligence tools became 14% more productive on average than those who were not.
Danielle Li and Pierre Azoulay note that the DARPA model does best when its directors have an understanding of needed breakthroughs.
"The point is to find someone who will be creative and seek solutions for this particular position."
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