CAREER: Information Frictions in Consumer Credit Markets: Evidence on Policy, Practice, and Beliefs

职业：消费信贷市场中的信息摩擦：政策、实践和信念的证据

• 批准号: 1944138
• 负责人: Christopher Palmer
• 金额: $ 40万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944138&HistoricalAwards=false
• 关键词: CAREER; Information; Frictions; Consumer; Credit

AbstractThis project develops data and empirical methods to quantify how information frictions distort the functioning of consumer credit markets. The research advances understanding of the unintended consequences of the U.S. bankruptcy system, how loan screening can be enhanced to improve credit access across disadvantaged groups, and how households form expectations about future prices. These three topic areas are critical to the fundamental economics of credit markets, as well as to several credit products, policy questions, and consumer decisions. This project provides tools to measure the extent, causes and impact of market failures in three domains: (1) testing for moral hazard in debt accumulation among personal bankruptcy filers, (2) explaining the prevalence of credit rationing, (3) understanding extrapolative expectation formation. Using newly collected data detailing the composition and timing of liabilities incurred by bankruptcy filers, this project develops an identification strategy based on wage garnishing policies to test for strategic borrowing effects. To study loan rejections, this project assembles rich data on hundreds of lenders and millions of loan applicants in a setting where applications are frequently rejected with credit not offered on any terms. Combining plausibly exogenous variation in firm and loan-officer lending policies with a semi-parametric selection model allows the characterization of information frictions that lead to such a high percentage of loans being rejected in equilibrium. The project further explores whether, conditional on those frictions, current loan screening practices are optimal. Finally, the project uses survey micro data of house price expectations and quantile-regression techniques to study heterogeneity in extrapolative expectations and the linkage between optimistic beliefs and behavior.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

摘要该项目开发了数据和经验方法，以量化信息摩擦如何扭曲消费信贷市场的功能。该研究对了解美国破产系统的意外后果的理解，如何增强贷款筛查，以改善弱势群体的信贷获取以及家庭如何形成对未来价格的期望。这三个主题领域对于信贷市场的基本经济学以及几种信贷产品，政策问题和消费者决策至关重要。该项目提供了衡量三个领域市场失败的程度，原因和影响的工具：（1）测试个人破产申请者债务积累的道德危害，（2）解释信用配给的流行，（3）理解外在期望值形成。使用新收集的数据详细介绍破产申报人产生的负债的组成和时机，该项目根据工资装饰政策制定了识别策略，以测试战略借贷效应。为了学习贷款拒绝，该项目在申请中经常拒绝任何条款中未提供信用的申请中，汇编了数百名贷方和数百万贷款申请人的丰富数据。结合公司和借贷商贷款策略的合理外源变化与半参数选择模型相结合，可以表征信息摩擦，从而导致如此高的贷款在平衡中被拒绝。该项目进一步探讨了，以这些摩擦为条件，当前的贷款筛查实践是最佳的。最后，该项目使用房屋价格期望和分位数回归技术的调查微数据来研究外推期望的异质性，以及乐观的信念和行为之间的联系。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的评估来支持的。智力优点和更广泛的影响审查标准。