A
Andrei Shleifer
Researcher at Harvard University
Publications - 519
Citations - 286543
Andrei Shleifer is an academic researcher from Harvard University. The author has contributed to research in topics: Government & Shareholder. The author has an hindex of 171, co-authored 514 publications receiving 271880 citations. Previous affiliations of Andrei Shleifer include National Bureau of Economic Research & University of Chicago.
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Salience and Asset Prices
TL;DR: Bordalo, Gennaioli, and Shleifer as discussed by the authors proposed a salience-based probability weighting approach to choice under risk, which accounts for several time series and cross-sectional puzzles in finance.
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Debt Enforcement Around the World
TL;DR: In this paper, the authors describe how debt enforcement will proceed against an identical hotel about to default on its debt and use the data on time, cost and the likely disposition of the assets (preservation as a going concern vs. piecemeal sale) to construct a measure of the efficiency of debt enforcement in each country.
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Disclosure by Politicians
TL;DR: In this article, the authors collected data on the rules and practices of financial and conflict disclosure by politicians in 175 countries and found that disclosure is correlated with lower perceived corruption when it is public, when it identifies sources of income and conflicts of interest, and when a country is a democracy.
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A Reason for Quantity Regulation
TL;DR: Glaeser et al. as discussed by the authors present an alternative reason for quantity regulations: one from the perspective of a benevolent government. But this argument is not general: it does not explain such quantity restrictions as blue laws and hunting and fishing regulations, which affect all market participants equally.
Posted Content
Building Blocks of Market Clearing Business Cycle Models
TL;DR: The authors compare real business cycle and increasing returns models of economic fluctuations and present new evidence on co-movement of both outputs sand labor inputs across sectors and show that the increasing returns model is easier to reconcile with the data than the real cycle model.