Monetary Policy and Borrowers’ Loan Defaults: Research Based on Data from Renrendai
| Published date | 01 January 2020 |
| DOI | http://doi.org/10.1111/cwe.12313 |
| Author | Haiyang Zhang,Wenda Song |
| Date | 01 January 2020 |
©2020 Institute of World Economics and Politics, Chinese Academy of Social Sciences
China & World Economy / 94–121, Vol. 28, No. 1, 2020
94
Monetary Policy and Borrowers’ Loan Defaults:
Research Based on Data from Renrendai
Wenda Song, Haiyang Zhang*
Abstract
This paper uses Renrendai data to study the relationship between monetary policy and
the default behavior of borrowers, and analyzes the transmission channels. The research
shows that tight monetary policy will lead to a significant increase in a borrower’s
probability to default, and this effect will continue for several months. There may be
two transmission channels: (i) monetary policy changes a debtor’s liquidity through
credit and balance sheet channels, which directly affects their current repayment
behavior; and (ii) monetary policy may affect a borrower’s investment, production
and protability, thus changing their long-term solvency. The paper also nds that the
repayment behavior of productive borrowers is more susceptible to monetary policy than
consumptive borrowers, and that the default behavior of borrowers in coastal provinces
is more susceptible to monetary policy than of borrowers in inland provinces. These
ndings provide new evidence for understanding how monetary policy affects individual
behavior and its transmission mechanisms.
Key words: default, liquidity, monetary policy, online lending
JEL codes: C24, E51, E52
I. Introduction
The central bank’s monetary policy is dynamically adjusted according to macroeconomic
situations and regulatory objectives, but how these adjustments will affect micro-level
family behavior has rarely been considered by existing empirical literature. This is
mainly because of the lack of available data, as there is rare household survey with as
*Wenda Song, Postgraduate Student, School of Banking and Finance, University of International Business
and Economics. Email:winda_song@163.com; Haiyang Zhang (corresponding author), Professor, School of
Banking and Finance, University of International Business and Economics, and Institute of Digital Finance,
Peking University. Email: hyang_zhang@163.com. We are grateful for support from the National Natural
Science Fund of China for their project: Big Data Analysis Based Research on Risk Taking Behavior of
P2P Lending Platforms (No. 71573040), and the Beijing Social Science Fund Key Project: Research on the
Impact of Internet Lending Industry Development on Financing of Small and Medium Enterprises in Beijing
(No. 17YJA005).
©2020 Institute of World Economics and Politics, Chinese Academy of Social Sciences
Monetary Policy and Borrowers’ Loan Defaults 95
high frequency as macro variables, such as money supply. The emergence of online
lending has made this research possible. In online lending platforms (such as Renrendai),
a loan is generally repaid in the form of monthly equal principal and interest. Debtors
without sufcient cash ow face greater pressure to make payments, and this pressure
changes dynamically because their cash ow is subject to changes to monetary policies.
Therefore, by studying the default behavior of online borrowers, higher frequency data
can be used to analyze and understand how the central bank’s monetary policy impacts
family behavior.
With the increasing uncertainty of the domestic and international economic
environment in recent years, preventing and resolving financial risk has gradually
become the main tone of China’s macroeconomic policies. Since the end of 2015,
“deleveraging” has become an important task in China’s supply-side structural reform
(Lin, et al., 2017). China began to adjust the leverage ratio of various domestic
economic sectors. The adjustment of monetary policy, deleveraging and financial
industry rectication after October 2016 reduced the Chinese M2 growth rate from 13.7
percent at the end of 2015 to 8.0 percent at the end of 2018. Information asymmetry in
nancial markets can hinder their effectiveness, causing adverse selection, moral hazard
and free-riding behavior. When adverse impacts occur, banks will reduce debt supply
and household cash ow will be affected (Mishkin, 1997).
What is the impact of monetary policy on the online lending market? The peer to
peer (P2P) lending model originated in the UK and was introduced in China in 2007 (Xu,
2017), but it did not ourish until 2013, which has become known as “the rst year of
internet nance in China.” With the regulation and maturity of this industry after 2015,
some of the platforms that are subject to regulations have become an important nancing
channel for households and small and micro enterprises. According to data from
Wangdaitianyan,1 as of December 2018, the online lending industry had a loan balance
of RMB11.14bn and a monthly turnover of RMB14.58bn. There were approximately
9.35mn loans and 41.5mn borrowers. Similar to the traditional nancial industry, how to
manage borrowers’ credit risk is also an important challenge in the P2P lending industry.
Borrowers on P2P platforms are mainly individuals and small and micro enterprises.
Although these groups have signicant nancing demands, according to Mishkin (1997),
because of information asymmetry and a lack of capital or guarantee, they are not favored
by traditional nancial institutions, especially in periods of tightening monetary policy.
In regard to default behavior in monthly repayments, we found a significant negative
correlation between M2 year-on-year growth and default rates, as shown in Figure 1.
1This website is also known as P2Peye: https://www.p2peye.com/ (online; cited 31 December 2018).
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