发布时间：2022-05-11
MATS LAB SEMINAR (MAAS)
不对称且不确定信息下的
建设-经营-转让道路最优合同
讲座时间
2022年5月12日 10:00
讲座方式
腾讯会议ID：377-177-441
主讲人
石莎莎
01 主讲人介绍
石莎莎，中南大学商学院副教授。研究领域为PPP项目管理决策问题、信息经济学和拍卖理论等。已在Transportation Research Part B, Transportation Research Part A, Computers & Industrial Engineering等国际期刊上发表多篇文章。
02 讲座内容
交通基础设施资产的开发和运营越来越依赖于建造-运营-转让（BOT）方式，即私营企业自费建造和运营道路项目，并收取通行费，以在预定的特许期内收回其投资成本。基于BOT道路项目中普遍存在的信息问题，本讲座将针对信息不对称和不确定性条件下政府与私营企业之间的最优BOT合同进行讲解。通过将BOT合同视为道路通行能力、收费和补贴的组合，我们研究了在不对称和不确定的建设成本信息下的最优合同。我们的研究表明，我们的研究表明，相对于对称信息契约，不对称信息的最优契约是扭曲的，这是为了确保企业没有动机误报其真实信息，并且这一扭曲效应会在信息不确定环境下放大。通过研究最优合同的性质，我们发现诱使企业如实报告其信息将增加BOT道路的最优通行时间，进而降低道路服务质量、造成社会福利的损失。然而，信息不对称程度越高并不一定会造成更大的社会福利损失，当信息不对称程度服从不同的概率分布时，其对社会福利损失的影响也不同。
In recent years, the development and operations of transportation infrastructure assets have increasingly relied on the build-operate-transfer (BOT) approach, where a private firm builds and operates a road project at its own expense and charges a toll to recover its investment cost during a predefined concession period. Motivated by the prevalent informational problems in BOT road projects, we study the optimal BOT contract between government and private firm under information asymmetry and uncertainty. By viewing a BOT contract as a combination of road capacity, toll and subsidy, we investigate the optimal contract under asymmetric and uncertain information concerning construction cost. Our study indicates that the optimal BOT contract under asymmetric information is distorted relative to the symmetric-information contract, which is to ensure that the firm has no incentive to misreport its cost information. By investigating the properties of the optimal contract, we find that enticing the firm to report its information truthfully will increase the optimal volume/capacity ratio of the BOT road. It also causes a loss in social welfare. However, a higher extent of information asymmetry will not necessarily yield greater loss in social welfare, and the effect of the extent of information asymmetry on welfare loss differs when it follows different probability distributions.
