FinTech and macroeconomics: Dataset from the US peer-to-peer lending platform

被引:1
作者
Nigmonov, Asror [1 ]
Shams, Syed
Alam, Khorshed
机构
[1] Univ New South Wales, Business Sch, Sydney, NSW, Australia
关键词
Crowdfunding; Default; Marketplace lending; Peer-to-peer lending; United States; DETERMINANTS;
D O I
10.1016/j.dib.2021.107666
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
We aggregate the United States (US) state-level data with LendingClub's loan book covering the period from 2008 to 2019. LendingClub is a FinTech lending company that provides loans through a technology-driven platform. It was one of the pioneering and leading US peer-to-peer (P2P) lending platforms. Our dataset consists of over two million observations (N=2,703,430) with diverse loan, borrowers and state-specific features. We provide the description of variables, descriptive statistics, and STATA code with the full dataset. The US possesses significant cross-state variation in terms of economic and demographic characteristics while having risk-sharing policies at the federal level to protect states' credit-worthiness. This unique feature of our combined database creates an ideal opportunity to explore the P2P lending market within the context of macroeconomic variables. As the dataset covers a 12-year period for all US states, it enables further cross-sectional and longitudinal analyses of the FinTech lending market. (C) 2021 The Author(s). Published by Elsevier Inc.
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页数:10
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