Collaborative Research: Fingerprinting to Reduce Risky Borrowing

合作研究：减少借贷风险的指纹识别

• 批准号: 1459864
• 负责人: Dean Yang
• 金额: $ 9.27万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1459864&HistoricalAwards=false
• 关键词: Collaborative; Research; Fingerprinting; Reduce; Risky

The research will quantify the impacts of improved personal identification of borrowers through a randomized controlled trial on technology to collect digital fingerprints of microloan borrowers. This alternative means of establishing personal identity, will allow for testing dynamic repayment incentives, which involve conditioning future credit access on a borrower?s past repayment performance. This study is designed to measure how changes in borrowers? behavior due to increased incentives to repay loans affect their profits, consumption, investment, and household wellbeing. In addition, this study will examine how lenders respond to the new technology, by measuring how loan officers interact with clients, how microfinance institutions adjust their borrowing requirements, and the local availability of credit. Impacts will be measured using administrative data from microlenders in combination with two years of survey data on borrowers and loan officers. Our results could lead private and non-profit lenders to adopt fingerprint identification technology of their own accord. In addition, our findings could help shape the policies of governments and of development institutions such as the World Bank, which could (for example) provide technical assistance to help foster the establishment of fingerprint-based credit reference bureaus that could further enhance the social benefits of the technology.We implement a randomized controlled trial involving microfinance customers and loan officers at five different microfinance instiutions. Credit officers play important roles in screening customers and enforcing dynamic incentives for loan repayment, and rely on their personal assessments and knowledge of customers in the absence of reliable ways to identify new or repeat borrowers. We randomize access to fingerprinting technology for screening borrowers and checking credit histories at the credit officer level, stratified by MFI. This strategy also generates variation in the fraction of credit officers in a given geographic area who are using the new technology. We use this variation to estimate the effect of fingerprinting on behaviors of three important sets of actors: borrowers themselves, credit officers, and MFIs. Our main estimates will come from OLS regressions, which are unbiased because of the random assignment of credit officers to the fingerprinting technology, but will include district and MFI (strata) fixed effects to improve precision. At the borrower level, we focus on repayment outcomes and on measures of household welfare. On the supply side, we measure the time allocation and decision-making of credit officers to see whether they reallocate their effort to monitoring borrowers who were not fingerprinted and are therefore not subject to increased repayment incentives, and to measure the evolution of decisions about access to credit at the extensive and intensive margins. We use the regional variation in the intensity of fingerprinting to see whether MFIs reallocate staff time or loanable funds to areas in which they have comparative advantages (more access to fingerprint technology than their competitors within the market, and more access than they themselves have in other markets). Ultimately, this project seeks to deepen our understanding of the impacts of a pervasive form of information asymmetry in many developing countries by studying dynamic behavior of borrowers and lenders, and measuring supply side responses to improved borrower identification. Answers to these questions will provide a fuller picture of the economic impacts of reducing a common but so far understudied type of information asymmetry.

该研究将通过一项随机对照试验，量化改善借款人个人身份识别的影响，该试验采用了收集小额贷款借款人数字指纹的技术。这一确立个人身份的替代手段将允许测试动态还款激励措施，其中涉及将借款人未来获得信贷的条件？过去的还款表现。本研究旨在衡量借款人如何变化？由于偿还贷款的动机增加而产生的行为影响了他们的利润、消费、投资和家庭福利。 此外，本研究还将通过衡量信贷人员如何与客户互动、小额金融机构如何调整其借款要求以及当地信贷供应情况，研究贷款人如何应对新技术。 将利用小额贷款机构的行政数据，结合两年来对借款人和信贷人员的调查数据，对影响进行衡量。 我们的研究结果可能会导致私人和非营利贷款机构采用指纹识别技术自己的雅阁。此外，我们的研究结果可以帮助制定政府和世界银行等发展机构的政策，这些机构可以（例如）提供技术援助，帮助建立基于指纹的信用参考局，从而进一步增强该技术的社会效益。我们实施了一项随机对照试验，涉及五个不同小额信贷机构的小额信贷客户和贷款官员。 信贷干事在甄别客户和强制执行偿还贷款的积极激励措施方面发挥着重要作用，在没有可靠办法确定新的或重复的借款人的情况下，他们依靠个人评估和对客户的了解。 我们随机访问指纹技术，以筛选借款人，并在信贷官员级别检查信用记录，按MFI分层。 这一战略还产生了特定地理区域内使用新技术的信贷人员比例的变化。 我们使用这种变化来估计指纹识别对三组重要行为者行为的影响：借款人本身，信贷人员和小额信贷机构。 我们的主要估计将来自OLS回归，由于信贷人员对指纹技术的随机分配，这些回归是无偏的，但将包括地区和MFI（分层）固定效应以提高精度。 在借款人层面，我们关注的是还款结果和家庭福利的衡量标准。 在供应方面，我们衡量信贷人员的时间分配和决策，看看他们是否重新分配他们的努力，以监测借款人谁没有指纹，因此不受增加还款激励，并衡量在广泛和密集的利润率获得信贷的决策的演变。 我们利用指纹识别强度的区域差异来观察小额金融机构是否将工作人员的时间或可贷款资金重新分配到它们具有比较优势的领域（比市场上的竞争对手更多地获得指纹识别技术，比它们自己在其他市场上更多地获得指纹识别技术）。 最后，本项目旨在通过研究借款人和贷款人的动态行为，并衡量供应方对改善借款人身份的反应，加深我们对许多发展中国家普遍存在的信息不对称形式的影响的理解。 这些问题的答案将提供一个更全面的图片的经济影响，减少一个常见的，但迄今为止研究不足的信息不对称类型。