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Cited by 1,065

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

A Theory of Collective Reputations (with Applications to the Persistence of Corruption and to Firm Quality)

Tirole, Jean·1996·The Review of Economic Studies

Abstract

The paper is a first attempt at modelling the idea of group reputation as an aggregate of individual reputations. A member's current incentives are affected by his past behaviour and, because his track record is observed only with noise, by the group's past behaviour as well. The paper thus studies the joint dynamics of individual and collective reputations and derives the existence of stereotypes from history dependence rather than from a multiplicity of equilibria or from the existence of a common trait as is usually done in the literature. It shows that new members of an organization may suffer from an original sin of their elders long after the latter are gone, and it derives necessary and sufficient conditions under which group reputations can be rebuilt. Last, the paper applies the theory to analyse when a large firm can maintain a reputation for quality.

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Metadata

Authors
Tirole, J.
Venue
The Review of Economic Studies
Published
1996-01
Volume
63
Issue
1
Pages
1
ISSN
0034-6527 (ISSN-L)
OpenAlex
W1590632185
Cited by
1,065
References
4
FWCI
45.90
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Theory of Collective Reputations (with Applications to the Persistence of Corruption and to Firm Quality). https://app.cochat.ai/papers/10.2307/2298112
The paper
Tirole, J. (1996). A Theory of Collective Reputations (with Applications to the Persistence of Corruption and to Firm Quality). The Review of Economic Studies. https://doi.org/10.2307/2298112

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.