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Cited by 283

The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.

CEOs' hometown connections and access to trade credit: Evidence from China

Kong, Dongmin, Pan, Yue, Tian, Gary Gang, Zhang, Pengdong·2020·Journal of Corporate Finance

Abstract

In this study, we investigate how informal institutions, namely, chief executive officers' hometown connections with suppliers, impact firms' access to trade credit. Using unique data manually collected from China, we find that hometown connections significantly increase access to trade credit. The hometown effect is more pronounced for non-state-owned firms, firms in provinces with poorly developed financial institutions, and firms whose chief executive officers come from hometowns with a strong merchant guild culture or hold an important position in the hometown's chamber of commerce. We suggest two plausible channels for the hometown effect: information and social trust. Overall, this study contributes to the literature by documenting how hometown connections help firms to obtain external financing in emerging markets.

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Metadata

Authors
Kong, D., Pan, Y., Tian, G. G., & Zhang, P.
Venue
Journal of Corporate Finance
Publisher
Elsevier BV
Published
2020-06
Volume
62
Pages
101574
ISSN
0929-1199 (ISSN-L)
OpenAlex
W2999273691
Cited by
283
References
64
FWCI
48.75
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: CEOs' hometown connections and access to trade credit: Evidence from China. https://app.cochat.ai/papers/10.1016/j.jcorpfin.2020.101574
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Kong, D., Pan, Y., Tian, G. G., & Zhang, P. (2020). CEOs' hometown connections and access to trade credit: Evidence from China. Journal of Corporate Finance. https://doi.org/10.1016/j.jcorpfin.2020.101574

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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.