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Christopher Avery

Researcher at Harvard University

Publications -  78
Citations -  5697

Christopher Avery is an academic researcher from Harvard University. The author has contributed to research in topics: Matriculation & Common value auction. The author has an hindex of 31, co-authored 77 publications receiving 5260 citations. Previous affiliations of Christopher Avery include Georgia State University & University of Chicago.

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Journal Article

Multidimensional Uncertainty and Herd Behavior in Financial Markets

TL;DR: The authors studied the relationship between asset prices and herd behavior, which occurs when traders follow the trend in past trades, and found that when traders have private information on only a single dimension of uncertainty (the effect of a shock to the asset value), price adjustments prevent herd behavior.
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The Missing "One-Offs": The Hidden Supply of High-Achieving, Low-Income Students

TL;DR: This paper showed that the vast majority of very high-achieving students who are low-income do not apply to any selective college or university, despite the fact that selective institutions would often cost them less, owing to generous financial aid, than the resource-poor two-year and non-selective four-year institutions to which they actually apply.
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Student Loans: Do College Students Borrow Too Much--Or Not Enough?

TL;DR: In this paper, the authors move the discussion of student loans away from anecdote by establishing a framework for considering the use of student loan in the optimal financing of collegiate investments and show that enrolling in college is equivalent to signing up for a lottery with large expected gains, but it is also a better investment today than a generation ago.
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The Market for Evaluations

TL;DR: In this paper, the authors present pricing and subsidy mechanisms that operate through a computerized market and induce the efficient provision of product evaluations, and overcome three major challenges: evaluations, which are public goods, are likely to be underprovided; an inefficient ordering of evaluators may arise; and the optimal quantity of evaluations depends on what is learned from the initial evaluations.