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Tractable Compensation Plan under Asymmetric Information

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  • Ruina Yang
  • Yinhua Mai
  • Chung‐Yee Lee
  • Chung‐Piaw Teo

Abstract

In an elegant study on salesforce incentive design, Steenburgh and Ahearne (2012) have argued that a multi‐faceted portfolio approach, based on the classification of workers into laggards, core, and star performers, can induce better result from the salesforce, compared to traditional approaches used in companies. In this paper, we construct a portfolio of incentive contracts with a three‐piece “linear–quadratic–linear” structure and study their incentive effects. It is well known that such contracts can extract 100% of the incremental benefits (i.e., optimal) when the performance levels of the agents are exponentially or uniformly distributed, but its effect is still unknown in the more natural case when performance levels are normally distributed. We show that the proposed three‐piece contract can capture more than 95.32% of the incremental benefits, obtained from the optimal incentive contract over fixed salary contract, when the cost functions of efforts are quadratic, and the performance levels are normally distributed. In this case, the more traditional three‐piece linear contract has a corresponding tight lower bound of 82.64%. Interestingly this is the same as the tight lower bound from a two‐piece linear contract, and thus adding more linear pieces to the contract does not help to improve the lower bound when the performance levels follow the normal distributions. This provides a partial theoretical explanation for the superiority of the portfolio approach advocated by Steenburgh and Ahearne (2012).

Suggested Citation

  • Ruina Yang & Yinhua Mai & Chung‐Yee Lee & Chung‐Piaw Teo, 2020. "Tractable Compensation Plan under Asymmetric Information," Production and Operations Management, Production and Operations Management Society, vol. 29(5), pages 1212-1218, May.
  • Handle: RePEc:bla:popmgt:v:29:y:2020:i:5:p:1212-1218
    DOI: 10.1111/poms.13151
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    References listed on IDEAS

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    1. Jean-Jacques Laffont & Jean Tirole, 1993. "A Theory of Incentives in Procurement and Regulation," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262121743, April.
    2. Laffont, Jean-Jacques & Tirole, Jean, 1986. "Using Cost Observation to Regulate Firms," Journal of Political Economy, University of Chicago Press, vol. 94(3), pages 614-641, June.
    3. Taylor, Frederick Winslow, 1911. "The Principles of Scientific Management," History of Economic Thought Books, McMaster University Archive for the History of Economic Thought, number taylor1911.
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    Cited by:

    1. Xu, Su Xiu & Guo, Ren-Yong & Zhai, Yue & Feng, Jianghong & Ning, Yu, 2024. "Toward a positive compensation policy for rail transport via mechanism design: The case of China Railway Express," Transport Policy, Elsevier, vol. 146(C), pages 322-342.
    2. Huibing Cheng & Shanshui Zheng, 2022. "Incentive Compensation Mechanism for the Infrastructure Construction of Electric Vehicle Battery Swapping Station under Asymmetric Information," Sustainability, MDPI, vol. 14(12), pages 1-18, June.
    3. Yang, Xiaolin & Gou, Qinglong & Wang, Xin & Zhang, Juzhi, 2022. "Does bonus motivate streamers to perform better? An analysis of compensation mechanisms for live streaming platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    4. Peng, Weicai & Tian, Zhongjun, 2022. "Information acquisition, selling effort and pre-order strategy," International Journal of Production Economics, Elsevier, vol. 249(C).
    5. Artur Mitsel & Aleksandr Shilnikov & Pavel Senchenko & Anatoly Sidorov, 2021. "Enterprise Compensation System Statistical Modeling for Decision Support System Development," Mathematics, MDPI, vol. 9(23), pages 1-19, December.

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