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Margaret C. Levenstein

Researcher at University of Michigan

Publications -  53
Citations -  2470

Margaret C. Levenstein is an academic researcher from University of Michigan. The author has contributed to research in topics: Cartel & Collusion. The author has an hindex of 20, co-authored 53 publications receiving 2301 citations. Previous affiliations of Margaret C. Levenstein include United States Census Bureau & Inter-university Consortium for Political and Social Research.

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What Determines Cartel Success

TL;DR: In this paper, the authors compare the findings of these studies of cartel activity from the 1800s through the 1980s with the characteristics of a sample of international cartels from the 1990s, and present preliminary evidence on whether the stylized facts regarding industry characteristics for historical cartels also hold for contemporary cartels.
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What determines cartel success

TL;DR: The authors examined a wide variety of empirical studies of cartels to answer the following questions: (1) Can cartels succeed? (2) If so, for how long? (3) What impact do cartels have? (4) What causes cartels to break up?
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Breaking Up Is Hard to Do: Determinants of Cartel Duration

TL;DR: In this article, the authors estimate the impact of cartel organizational features, as well as macroeconomic fluctuations and industry structure, on cartel duration using a data set of contemporary international cartels and estimate a proportional hazards model with competing risks, distinguishing factors that increase the risk of "death by antitrust" from those that affect natural death, including defection, dissension and entry.
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Proximity effects on the dynamics and outcomes of scientific collaborations

TL;DR: This paper uses path overlap, an innovative measure of functional proximity, to examine how physical space shaped the formation and success of scientific collaborations among the occupants of two academic research buildings.
Posted Content

Using Social Media to Measure Labor Market Flows

TL;DR: In this article, the authors use data from Twitter to create indexes of job loss, job search, and job posting, which are derived by counting job-related phrases in Tweets such as "lost my job."