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Glenn Ellison
Researcher at Massachusetts Institute of Technology
Publications - 112
Citations - 25201
Glenn Ellison is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Population & Mutual fund. The author has an hindex of 50, co-authored 112 publications receiving 23895 citations. Previous affiliations of Glenn Ellison include National Bureau of Economic Research & Microsoft.
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Risk taking by mutual funds as a response to incentives
TL;DR: In this paper, the authors examine a potential agency conflict between mutual fund investors and mutual fund companies, where investors would like the fund company to use its judgment to maximize risk-adjusted fund retraction.
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Risk Taking by Mutual Funds as a Response to Incentives
TL;DR: In this article, the authors examine the agency conflict between mutual fund investors and mutual fund companies, and show that a fund company in its desire to maximize its value as a concern has an incentive to take actions which increase the flow of investment.
Posted Content
Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach
Glenn Ellison,Edward L. Glaeser +1 more
TL;DR: The authors discusses the prevalence of Silicon Valley-style localizations of individual manufacturing industries in the United States Several models in which firms choose locations by throwing darts at a map are used to test whether the degree of localization is greater than would be expected to arise randomly and to motivate a new index of geographic concentration.
ReportDOI
Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach
Glenn Ellison,Edward L. Glaeser +1 more
TL;DR: In this article, the authors discuss the prevalence of Silicon Valley-style localizations of individual manufacturing industries in the United States and explore the nature of agglomerative forces in describing patterns of concentration.
Journal ArticleDOI
Learning, local interaction, and coordination
TL;DR: In this article, the authors discuss the dynamic implications of learning in a large population coordination game, focusing on the structure of the matching process which describes how players meet, and consider the rates at which the dynamic systems converge.