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Article . 2020 . Peer-reviewed
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Risks to Human Capital

Authors: Mehran Ebrahimian; Jessica A. Wachter;

Risks to Human Capital

Abstract

We build a model with inalienable human capital, in which investors finance individuals who can potentially become skilled. Although investment in skill is always optimal, it does not take place in some states of the world, due to moral hazard. In intermediate states of the world, individuals acquire skill; however outside investors and individuals inefficiently share risk. We show that this simple moral hazard problem, combined with risk aversion of individuals and outside investors, amplifies the equity premium, lowers the risk-free rate, and leads to disaster states that fall especially heavily on some agents but not on others. We show that the possibility of disaster states distorts risk prices and affects wealth inequality, even under calibrations in which they never occur in equilibrium. This paper was accepted by Lukas Schmid, finance. Funding: M. Ebrahimian acknowledges funding from the Swedish House of Finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03068 .

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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