Sorting the good guys from bad: on the optimal audit structure with ex-ante information acquisition

In a costly state verification model under commitment, the principal may acquire a costly public and imperfectly revealing signal before or after contracting. If the project remains profitable after all signal realizations, optimally the signal is collected, if at all, only after contracting, and it...

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Bibliographic Details
Published in:Economic theory 2014-10, Vol.57 (2), p.339-376
Main Authors: Menichini, Anna Maria C., Simmons, Peter J.
Format: Article
Language:English
Subjects:
Acquisition costs
Analysis
Auditing
Auditing policies
Audits
Comparative analysis
Contracts
Cost efficiency
Costs
Credit risk
Debt contracts
Determinism
Economic theory
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Expected returns
Game Theory
Information acquisition
Information management
Liability
Methods
Microeconomics
Minimization of cost
Policyholders
Public Finance
Repayments
Research Article
Revenue
Risk management
Social and Behav. Sciences
Stochastic processes
Studies
Tax audits
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Summary:In a costly state verification model under commitment, the principal may acquire a costly public and imperfectly revealing signal before or after contracting. If the project remains profitable after all signal realizations, optimally the signal is collected, if at all, only after contracting, and it may be acquired randomly or deterministically. Moreover, audit is deterministic and targeted on some signal-state combinations. The paper provides a detailed characterization of the optimal contract and performs a comparative static analysis of signal acquisition strategy and payoffs with respect to enforcement costs and informativeness of the signal. We explore the robustness of the results, including commitment issues. The results are interpreted in light of the observed features of financial contracts, showing that the optimality of the standard debt contract with deterministic audit extends to a setting with random audit.
ISSN:0938-2259
1432-0479
DOI:10.1007/s00199-014-0818-y