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Cited by 253

The DOI matches its Crossref record; 141 of the 141 references it cites were found in a registry or an index.

Institutional investors' horizons and corporate employment decisions

Open access
Ghaly, Mohamed, Dang, Viet Anh, Stathopoulos, Konstantinos·2020·Journal of Corporate Finance

Abstract

Monitoring by long-term investors should reduce agency conflicts in firms’ labor investment choices. Consistent with this argument, we find that abnormal net hiring, measured as the absolute deviation from optimal net hiring predicted by economic fundamentals, decreases in the presence of institutional investors with longer investment horizons. Firms dominated by long-term shareholders reduce both over-investment (over-hiring and under-firing) and under-investment (under-hiring) in employees. The monitoring role of long-term investors is stronger for firms facing higher labor adjustment costs both in absolute terms and relative to capital adjustment costs, and those for which human capital is regarded as more important. The effect is also more pronounced for firms that have stronger incentives and/or more opportunities to deviate from expected net hiring. We address endogeneity concerns by exploiting exogenous changes to long-term institutional ownership resulting from annual reconstitutions of the Russell indexes.

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Metadata

Authors
Ghaly, M., Dang, V. A., & Stathopoulos, K.
Venue
Journal of Corporate Finance
Publisher
Elsevier BV
Published
2020-10
Volume
64
Pages
101634
ISSN
0929-1199 (ISSN-L)
OpenAlex
W3123632618
Cited by
253
References
141
FWCI
38.44
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Institutional investors' horizons and corporate employment decisions. https://app.cochat.ai/papers/10.1016/j.jcorpfin.2020.101634
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Ghaly, M., Dang, V. A., & Stathopoulos, K. (2020). Institutional investors' horizons and corporate employment decisions. Journal of Corporate Finance. https://doi.org/10.1016/j.jcorpfin.2020.101634

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

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