Nonlinear Incentives and Advisor Bias

Jun Honda and Roman Inderst

EconStor Preprints from ZBW - Leibniz Information Centre for Economics

Abstract: We analyze firms' competition to steer an advisor's recommendations through potentially non-linear incentives. Even when firms are symmetric, so that the overall size of compensation would not distort advice when incentives were linear, advice is biased when firms are allowed to make compensation nonlinear, which they optimally do. Policies that target an advisor's liability are largely ineffective, as firms react to such increased liability by making incentives even steeper, increasing bonus payments while reducing the linear (commission) part at the same time. This observation may justify policymakers' direct interference with firms' compensation practice, as frequently observed notably in consumer finance.

JEL-codes: L51 M52 (search for similar items in EconPapers)
Date: 2017
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https://www.econstor.eu/bitstream/10419/253657/1/SSRN-id3088484.pdf (application/pdf)

Related works:
Working Paper: Nonlinear Incentives and Advisor Bias (2018)
Working Paper: Nonlinear incentives and advisor bias (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:esprep:253657

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