Credit Markets, Evaluative Technologies, and Social Stratification

SES-1628477Marion Fourcade Kieran Healy 加州大学伯克利分校该项目研究信贷市场如何衡量和分类个人和家庭。过去三十年来，美国获得信贷的机会大大增加。中产阶级家庭借贷更多，低收入家庭和少数族裔家庭已被纳入银行体系。与此同时，可用信贷产品的种类不断增加，用于衡量和评估信用度的工具也变得更加复杂，并且这些工具越来越多地以远远超出其最初目的的新方式使用。了解这些工具及其应用如何影响人们经济繁荣、社会流动性和成功融入美国社会的机会非常重要。为了研究信贷市场如何对个人进行分类，研究人员需要将人口统计信息与人们的信贷市场行为相结合的数据。该项目将利用具有这些独特特征的原始数据集，其规模将超出以前的研究范围。该项目将利用个人信用记录的详细匿名数据与人口普查数据相结合，研究信贷市场中的评分技术如何与市场中的人口模式和过程相关。它将帮助我们更好地理解信用评分方法、债务违约、破产和丧失抵押品赎回权等不良信用事件以及就业和家庭财富等其他社会经济因素之间的关系。它还将有助于解释为什么即使信用报告机构无法收集或使用有关个人的人口统计数据，信用分类中的人口统计不平等仍然持续存在。信用评分旨在衡量个人花钱时的行为。早期研究表明，除了人们的偏好和需求之外，这种行为还受到各种环境因素的影响，例如当地的教育和就业供应、替代金融服务的密度、人们通过网络可获得的金融资源以及同龄人的信贷市场行为。例如，如果一个人的社区出现一波取消抵押品赎回权的浪潮，这可能会以一种独立于个人财务选择的方式减少自己的房屋净值。这可能反过来影响一个人为抵押贷款再融资或做出其他财务决策的能力。该项目将调查影响个人信用和金融安全的背景力量的存在和范围，并为当前有关消费信贷在经济中的作用的社会科学和政策辩论贡献新的结果。该项目还将探索信用市场和信用评分的其他社会特征。特别是，关于监控信用和分配信用评分的过程是否仅仅反映市场中的事件或其本身在该市场中产生某种独立影响一直存在争论。该项目拥有丰富的长期信用行为数据，这将使我们能够比以前更详细地调查这个问题。

SES-1628477Marion Fourcade Kieran HealyUniversity of California-BerkeleyThis project investigates how credit markets measure and classify people and households. Over the past thirty years, access to credit has expanded greatly in the United States. Middle-class families borrow more, and lower income and minority families have become incorporated into the banking system. At the same time, the variety of available credit products has increased, the tools used to measure and assess creditworthiness have become more sophisticated, and these tools are increasingly used in new ways that go well beyond their original purposes. It is important to understand how these tools and their application affect people's chances for economic prosperity, social mobility, and successful incorporation into American society. To study how credit markets classify individuals, researchers need data that combines demographic information with people's credit market behavior. This project will make use of an original dataset with these unique features on a scale that goes beyond previous research. Using detailed anonymous data on individual credit histories in conjunction with Census data, the project will investigate how scoring techniques in the credit market are related to demographic patterns and processes in markets. It will help us better understand the relationship between credit scoring methods, adverse credit events like debt default, bankruptcy, and foreclosure, and other socioeconomic factors like employment and household wealth. It will also help explain why demographic inequality in credit classification persists even though credit reporting agencies cannot collect or use demographic data about individuals. Credit scores are meant to measure the behavior of individuals as they spend their money. Earlier research suggests that, in addition to people?s preferences and wants, this behavior is shaped by various environmental factors such as the local supply of education and employment, the density of alternative financial services, the financial resources available to people through their networks, and the credit market behavior of one's peers. For example, if there is a wave of foreclosures in one's neighborhood, that may decrease one's own home equity in a way that is independent of one's individual financial choices. This may in turn affect one's ability to refinance one's mortgage or make other financial decisions. The project will investigate the existence and scope of the contextual forces that affect individual credit and financial security, and contribute new results to current social-scientific and policy debates about the role of consumer credit in the economy. The project will also explore other social features of credit markets and credit scoring. In particular, there is an ongoing debate about whether the process of monitoring credit and assigning credit scores simply reflects events in the market or itself exerts some independent effect in that market. The project?s rich data on credit behavior over long periods of time will allow the investigation of this question in more detail than has been possible before now.