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Worker-firm relational contracts in the time of shutdowns: experimental evidence

Published online by Cambridge University Press:  14 March 2025

Sera Linardi Open the ORCID record for Sera Linardi [Opens in a new window]  and
Colin Camerer
Sera Linardi*
Affiliation:
University of Pittsburgh, Pittsburgh, USA
Colin Camerer*
Affiliation:
California Institute of Technology, Pasadena, USA
*
linardi@pitt.edu
camerer@caltech.edu
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Abstract

Exogeneous disruptions in labor demand have become more frequent in recent times. The COVID-19 pandemic has resulted in millions of workers being repeatedly laid off and rehired according to local public health conditions. This may be bad news for market efficiency. Typical employment relations—which resemble non-enforceable (implicit) contracts—rely on reciprocity (Brown et al. in Econometrica 72:747–780, 2004), and hence could be harmed when workers’ efforts no longer guarantee reemployment in the next period. In this paper we extend the BFF paradigm to include a per-period probability (0%, 10%, 50%) of publicly observable “shutdown”, where a specific firm cannot contract with any workers for several periods. A Perfect Bayesian Equilibrium exists in which these shutdowns destabilize relationships, but do not harm efficiency. Our experiment shows that, remarkably, market efficiency can be maintained even with very frequent stochastic shutdowns. However, the dynamic of relational contracts changes from one where a worker finds stable employment to one where she juggles multiple employers, laying the burden of maintaining productivity upon workers and worsening worker-side inequality.

Keywords

Relational contractsGift exchangeLabor marketsUnemploymentShocksLayoffsRecessionRepeated game

JEL classification

J31: Wage Level and Structure • Wage Differentials J64: Unemployment: Models, Duration, Incidence, and Job Search J23: Labor Demand C92: Laboratory, Group Behavior
Type
Original Paper
Information
Experimental Economics , Volume 24 , Issue 4 , December 2021 , pp. 1267 - 1293
Copyright
Copyright © 2021 Economic Science Association

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Footnotes

Supplementary information The online version contains supplementary material available at (https://doi.org/10.1007/s10683-020-09697-1).

The authors would like to thank the following individuals for their excellent research assistance: Erin Carbone, Xiaohong Wang, Jinyong Jeong, Taisuke Imai, Devdeepta Bose. We thank the referees, editors, and participants at seminars for their very useful feedback. We acknowledge the financial support from Behavioral and Neuroeconomics Discovery Fund (via MacArthur Foundation) and grant administration assistance of Tiffany Kim and Alisha Cunniff.

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