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- Creators: Hong, Yili
Description
Two-sided online platforms are typically plagued by hidden information (adverse selection) and hidden actions (moral hazard), limiting market efficiency. Under the context of the increasingly popular online labor contracting platforms, this dissertation investigates whether and how IT-enabled monitoring systems can mitigate moral hazard and reshape the labor demand and supply by providing detailed information about workers’ effort. In the first chapter, I propose and demonstrate that monitoring records can substitute for reputation signals such that they attract more qualified inexperienced workers to enter the marketplace. Specifically, only the effort-related reputation information is substituted by monitoring but the capability-related reputation information. In line with this, monitoring can lower the entry barrier for inexperienced workers on platforms. In the second chapter, I investigate if there is home bias for local workers when employers make the hiring decisions. I further show the existence of home bias from employers and it is primarily driven by statistical inference instead of personal “taste”. In the last chapter, I examine if females tend to have a stronger avoidance of monitoring than males. With the combination of the observational data and experimental data, I find that there is a gender difference in avoidance of monitoring and the introduction of the monitoring system increases the gender wage gap due to genders differences in such willingness-to-pay for the avoidance of monitoring. These three studies jointly contribute to the literature on the online platforms, gig economy and agency theory by elucidating the critical role of IT-enabled monitoring.
ContributorsLiang, Chen, Ph.D (Author) / Gu, Bin (Thesis advisor) / Hong, Yili (Thesis advisor) / Chen, Peiyu (Committee member) / Arizona State University (Publisher)
Created2019
Description随着中国居民消费占GDP比例的提升，人均GDP的增长，银行等贷款机构对个人和零售业务的长期发展，中国金融机构的个人贷款不良资产规模发生了很大的变化。居民个人对外负债主要是以债权方式体现。基于债权的一致性，对于借贷人的个人外部负债缺少特定的强制性的偿贷顺序安排，偿贷行为也不会受到法律框架的强制约束，导致借贷人在偿贷能力不足时，个体的主观意愿对于偿贷行为结果的影响显著。
既往的个人贷款不良资产管理和服务模式，均参照企业贷款不良资产管理和服务模式，体现出在标的资产的产品特点、信贷主体差异、法律完备性、对公共基础服务支撑要求等方面存在显著的不同。原有针对企业贷款不良资产的管理和服务模式在适应个人贷款不良资产管理和服务时，也就需要采用不同的方式和策略，所以，优化、提升对于借贷人的管理和服务模式就存在必要的调整和优化空间。
由于借贷人的自然人属性，区别于企业的法人属性，其生命周期自然存续期间，偿贷能力存在修复的可能，外部征信环境的改善，也会对个人贷款不良资产的产生影响。现有的个人贷款不良资产的管理和服务模式也需要做出必要的调整过和安排。
21世纪前20年，互联网/通讯/IT技术发展迅速，AI、BigData、Blockchain等技术逐渐成熟，对厘清个人贷款不良资产偿贷机制提供了必要的基础数据。在此基础上，运用日趋完备的信息不对称和行为决策等理论工具，对既有对个人贷款不良资产管理和服务模式做出优化和调整就存在可能性。
本文基于P2P个人贷款不良资产管理和服务过程中形成的数据，选取金额回款率和失联事件发生率来计量借款人的行为决策结果，通过对这两个指标在个人贷款不良资产管理和服务中呈现的规律进行分析，初步厘清了个人贷款不良资产的偿贷过程中的行为决策机制，在既有框架基础上，对个人贷款不良资产管理和服务中的资产定价优化、资产交易模式、管理和服务机构评价、不良资产策略管理策略、催收服务策略等提供提供了有益的补充。
ContributorsGuo, Dagang (Author) / Huang, Xiaochuan (Thesis advisor) / Chang, Chun (Thesis advisor) / Zhu, Ning (Committee member) / Hong, Yili (Committee member) / Arizona State University (Publisher)
Created2021