Information Design in Allocation with Costly Verification

40 Pages Posted: 20 Oct 2022 Last revised: 25 Oct 2023

See all articles by Yi-Chun Chen

Yi-Chun Chen

National University of Singapore (NUS) - Department of Economics

Gaoji Hu

Shanghai University of Finance and Economics - School of Economics

Xiangqian Yang

Hunan University - School of Economics and Trade

Date Written: October 12, 2022

Abstract

We study optimal information design on top of a single-object allocation problem with costly verification à la Ben-Porath et al. (2014). Agents learn private signals about the allocation payoff to the principal from signal distributions which are influenced by an information designer. The principal designs a mechanism to maximize her payoff based upon the designed information. We identify the agent-optimal information and the principal-optimal information. When there is only one agent, any agent-optimal information is principal-worst. When there are two or more agents, some agent-optimal information remains principal-worst; moreover, we characterize when agent-optimal information design strictly increases the probability of allocation. Finally, through identifying the principal-worst information, we obtain an optimal robust allocation mechanism for the principal.

Keywords: information design, mechanism design, costly verification, robust mechanism design

JEL Classification: D61, D82, D83

Suggested Citation

Chen, Yi-Chun and Hu, Gaoji and Yang, Xiangqian, Information Design in Allocation with Costly Verification (October 12, 2022). Available at SSRN: https://ssrn.com/abstract=4245445 or http://dx.doi.org/10.2139/ssrn.4245445

Yi-Chun Chen

National University of Singapore (NUS) - Department of Economics ( email )

1 Arts Link AS2 #06-02
Singapore 117570, Singapore 119077
Singapore

Gaoji Hu (Contact Author)

Shanghai University of Finance and Economics - School of Economics ( email )

777 Guoding Road
Shanghai, 200433
China

Xiangqian Yang

Hunan University - School of Economics and Trade ( email )

China

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