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Cited by 2,561

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Political Connections and Corporate Bailouts

Open access
FACCIO, MARA, MASULIS, RONALD W., McCONNELL, JOHN J.·2006·The Journal of Finance

Abstract

We analyze the likelihood of government bailouts of 450 politically connected firms from 35 countries during 1997–2002. Politically connected firms are significantly more likely to be bailed out than similar nonconnected firms. Additionally, politically connected firms are disproportionately more likely to be bailed out when the International Monetary Fund or the World Bank provides financial assistance to the firm's home government. Further, among bailed‐out firms, those that are politically connected exhibit significantly worse financial performance than their nonconnected peers at the time of and following the bailout. This evidence suggests that, at least in some countries, political connections influence the allocation of capital through the mechanism of financial assistance when connected companies confront economic distress.

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Crossref · 10.1111/j.1540-6261.2006.01000.x

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Metadata

Authors
FACCIO, M., MASULIS, R. W., & McCONNELL, J. J.
Venue
The Journal of Finance
Publisher
Wiley
Published
2006-12
Volume
61
Issue
6
Pages
2597-2635
ISSN
  • 0022-1082 (ISSN-L)
  • 1540-6261
OpenAlex
W3121335362
Cited by
2,561
References
6
FWCI
102.65
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Political Connections and Corporate Bailouts. https://app.cochat.ai/papers/10.1111/j.1540-6261.2006.01000.x
The paper
FACCIO, M., MASULIS, R. W., & McCONNELL, J. J. (2006). Political Connections and Corporate Bailouts. The Journal of Finance. https://doi.org/10.1111/j.1540-6261.2006.01000.x

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.