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How Does Labor Mobility Affect Corporate Leverage and Investment?

Published online by Cambridge University Press:  14 February 2024

Ali Sanati Open the ORCID record for Ali Sanati [Opens in a new window]
Ali Sanati*
Affiliation:
American University Kogod School of Business
*
asanati@american.edu (corresponding author)
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Abstract

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I develop a dynamic model to investigate how labor mobility impacts firms’ decisions. In the model, firms make investment and financing decisions, hire labor with different skill and mobility levels, and set wages through bargaining. The model predicts that, in response to an increase in labor mobility, high-skill firms operate with lower financial leverage, become less responsive to investment opportunities, and invest at lower rates, while low-skill firms remain unaffected. I confirm these predictions in the data using shocks to workers’ mobility across firms. The results are useful in understanding the effects of labor mobility changes driven by government policies or technological shocks, such as the rise of remote work.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , First View , pp. 1 - 39
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0), which permits non-commercial re-use, distribution, and reproduction in any medium, provided that no alterations are made and the original article is properly cited. The written permission of Cambridge University Press must be obtained prior to any commercial use and/or adaptation of the article.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

I am especially grateful to an anonymous referee, Hengjie Ai, Frederico Belo, Ran Duchin (the editor), Murray Frank, and Andrew Winton for detailed discussions and suggestions. I also thank Alan Benson, Anmol Bhandari, Briana Chang, Mark Egan, Bob Goldstein, Itay Goldstein, Janya Golubeva, Robert Hauswald, Loukas Karabarbounis, Lars-Alexander Kuehn, Erik Loualiche, Hamed Mahmudi, Eric McKee, Antonio Mello, Yoonsoo Nam, Anthony Rice, Juliana Salomao, Aaron Sojourner, Martin Szydlowski, Luke Taylor, Richard Thakor, Tracy Wang, Randy Wright, and seminar participants at American University, Cornerstone Research, Florida State University, University of Minnesota, University of Oklahoma, University of Wisconsin–Madison, 2018 Econometric Society (North American Summer Meetings), 2018 EFA, 2018 FMA, 2018 MFA, and 2017 AFA (PhD poster session) for helpful comments. I also thank David Porter at the Minnesota Supercomputer Institute for helpful discussions. I am responsible for all remaining errors.

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