Designing Incentives for Audit Effort: Dynamic Signalling under Asymmetric Information

Chun-Hung Chen

International Game Theory Review2026https://doi.org/10.1142/s0219198926500040article
AJG 1ABDC B
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0.50

Abstract

We develop a dynamic signaling model to examine how audit fee structures shape accountants’ effort choices and reveal their intrinsic integrity. A firm hires an accountant whose type-rigorous or fraudulent-is private information and offers a contract combining fixed salary and performance-based pay. Each accountant selects an observable effort level that serves as a type signal. We derive the perfect Bayesian equilibria and identify conditions under which rigorous types distinguish themselves by exerting higher effort and when imitation occurs. When performance-based pay sufficiently differentiates between types, fraudulent types are deterred from mimicry, preserving audit integrity. Conversely, when base salary differences fall within critical bounds, pooling equilibria emerge-either fraudulent types mimic rigorous effort or rigorous types reduce effort to match fraudulent behavior. These results demonstrate that high audit fees alone do not ensure quality and that poorly calibrated incentives may encourage collusion. By highlighting the dual function of audit fees as both incentives and signals, this study advances contract theory under asymmetric information and offers practical guidance for designing remuneration schemes that foster audit quality and integrity.

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https://doi.org/https://doi.org/10.1142/s0219198926500040

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@article{chun-hung2026,
  title        = {{Designing Incentives for Audit Effort: Dynamic Signalling under Asymmetric Information}},
  author       = {Chun-Hung Chen},
  journal      = {International Game Theory Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1142/s0219198926500040},
}

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Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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