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Cited by 282

The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.

Employment Protection, Investment, and Firm Growth

Open access
Bai, John (Jianqiu), Fairhurst, Douglas, Serfling, Matthew·2020·The Review of Financial Studies

Abstract

We exploit the adoption of U.S. state-level labor protection laws to study the effect of employment protection on corporate investment rates and sales growth. We find that, following the adoption of these laws, capital expenditures as a percentage of book assets decrease, resulting in slower sales growth. Our findings are consistent with theories predicting that greater employment protection discourages investment by making projects more irreversible. Supporting this channel, following negative cash flow shocks, firms are less likely to downsize operations in states that have adopted these laws but more likely to downsize in states that have not adopted these laws. Authors have furnished code, data, and an Internet Appendix, which are available on the Oxford University Press Web site next to the link to the final published paper online.

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Crossref · 10.1093/rfs/hhz066

Cites

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Metadata

Authors
Bai, J. (., Fairhurst, D., & Serfling, M.
Venue
The Review of Financial Studies
Published
2019-06-24
Volume
33
Issue
2
Pages
644-688
ISSN
  • 0893-9454 (ISSN-L)
  • 1465-7368
OpenAlex
W3123753159
Cited by
282
References
69
FWCI
56.39
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Employment Protection, Investment, and Firm Growth. https://app.cochat.ai/papers/10.1093/rfs/hhz066
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Bai, J. (., Fairhurst, D., & Serfling, M. (2020). Employment Protection, Investment, and Firm Growth. The Review of Financial Studies. https://doi.org/10.1093/rfs/hhz066

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.