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Supervisory Efficiency and Collusion in a Multiple-Agent Hierarchy

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  • Che, X.
  • Huang, Y.
  • Zhang, L.

Abstract

We analyze a principal-supervisor-two-agent hierarchy with inefficient supervision. The su-pervisor may collects a wrong signal on each agent’s unobservable effort level. When reportingto the principal, the supervisor can collude with one or both agents to manipulate the signalin exchange for a bribe. In contract design, we identify a new trade-off between the loss fromsupervisor-agent collusion and the risk from inefficient supervision: Although allowing collu-sion makes shirking more attractive to the agents, it brings in a benefit because it can “correct”an incorrect negative signal when the agent has exerted effort. Such collusive supervision savesrisk premiums that the principal has to pay for incentive provision. We characterize the princi-pal’s optimal contract choice among no-supervision, collusion-proof, and collusive-supervisioncontracts. We show that the collusive-supervision contract dominates when the supervisory ef-ficiency is at an intermediate level.

Suggested Citation

  • Che, X. & Huang, Y. & Zhang, L., 2020. "Supervisory Efficiency and Collusion in a Multiple-Agent Hierarchy," Working Papers 20/01, Department of Economics, City University London.
    • Handle: RePEc:cty:dpaper:20/01
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    File URL: https://openaccess.city.ac.uk/id/eprint/23485/1/Dept_Econ_WP2001.pdf
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    Cited by:

    1. Rotundo, G. & Ausloos, M., 2007. "Microeconomic co-evolution model for financial technical analysis signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 569-585.
    2. Alderremy, A.A. & Saad, Khaled M. & Agarwal, Praveen & Aly, Shaban & Jain, Shilpi, 2020. "Certain new models of the multi space-fractional Gardner equation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    3. Coma, Julià & Chàfer, Marta & Pérez, Gabriel & Cabeza, Luisa F., 2020. "How internal heat loads of buildings affect the effectiveness of vertical greenery systems? An experimental study," Renewable Energy, Elsevier, vol. 151(C), pages 919-930.
    4. Zhu, H.B. & Zhou, Y.J. & Wu, W.J., 2020. "Modeling traffic flow mixed with automated vehicles considering drivers ’ character difference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    5. Nie, Binjian & Zou, Boyang & She, Xiaohui & Zhang, Tongtong & Li, Yongliang & Ding, Yulong, 2020. "Development of a heat transfer coefficient based design method of a thermal energy storage device for transport air-conditioning applications," Energy, Elsevier, vol. 196(C).
    6. Li, Zhiyu & Xu, Xiwei & Jiang, Enchen & Han, Ping & Sun, Yan & Zhou, Ling & Zhong, Peidong & Fan, Xudong, 2020. "Alkane from hydrodeoxygenation (HDO) combined with in-situ multistage condensation of biomass continuous pyrolysis bio-oil via mixed supports catalyst Ni/HZSM-5-γ-Al2O3," Renewable Energy, Elsevier, vol. 149(C), pages 535-548.
    7. Shi, Xin & Jiang, Haizhou & Li, Huan & Xu, Dong, 2020. "Maritime cluster research: Evolutionary classification and future development," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 237-254.
    8. Yoo, Yeawon & Escobedo, Adolfo R. & Skolfield, J. Kyle, 2020. "A new correlation coefficient for comparing and aggregating non-strict and incomplete rankings," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1025-1041.

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