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Cited by 3,174

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Agency Problems and Dividend Policies around the World

Open access
La Porta, Rafael, Lopez‐de‐Silanes, Florencio, Shleifer, Andrei, Vishny, Robert W.·2000·The Journal of Finance
DOI 10.1111/0022-1082.00199
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Abstract

This paper outlines and tests two agency models of dividends. According to the “outcome model,” dividends are paid because minority shareholders pressure corporate insiders to disgorge cash. According to the “substitute model,” insiders interested in issuing equity in the future pay dividends to establish a reputation for decent treatment of minority shareholders. The first model predicts that stronger minority shareholder rights should be associated with higher dividend payouts; the second model predicts the opposite. Tests on a cross section of 4,000 companies from 33 countries with different levels of minority shareholder rights support the outcome agency model of dividends.

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Citations

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Metadata

Authors
La Porta, R., Lopez‐de‐Silanes, F., Shleifer, A., & Vishny, R. W.
Venue
The Journal of Finance
Publisher
Wiley
Published
2000-02
Volume
55
Issue
1
Pages
1-33
ISSN
  • 0022-1082 (ISSN-L)
  • 1540-6261
DOI
10.1111/0022-1082.00199
OpenAlex
W2891389313
Cited by
3,174
References
3
FWCI
105.81
Type
journal article
Language
en
Open access
green
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 1050 d)

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CoChat. (2026). Citation check: Agency Problems and Dividend Policies around the World. https://app.cochat.ai/papers/10.1111/0022-1082.00199
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La Porta, R., Lopez‐de‐Silanes, F., Shleifer, A., & Vishny, R. W. (2000). Agency Problems and Dividend Policies around the World. The Journal of Finance. https://doi.org/10.1111/0022-1082.00199

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.