Migrants and default: Evidence from China

• Published date: 01 September 2022
• Author: Jianwen Li,Jinyan Hu
• Date: 01 September 2022
• DOI: http://doi.org/10.1111/irfi.12371

ORIGINAL ARTICLE

Migrants and default: Evidence from China

Jianwen Li

1

| Jinyan Hu

1,2

1

School of Economics, Shandong University,

Jinan, China

2

School of Economics, Qingdao University,

Qingdao, China

Correspondence

Jinyan Hu, School of Economics, Shandong

University, 27 Shanda Nanlu, Jinan, Shandong

250100, China.

Email: hwx@sdu.edu.cn

Funding information

China Postdoctoral Science Foundation,

Grant/Award Number: 2021M691958;

National Natural Science Foundation of China,

Grant/Award Number: 71873079; Natural

Science Foundation of Shandong Province,

Grant/Award Number: ZR2021QG057

Abstract

Reform and opening up have stimulated internal migration;

at the same time, home bias and regional discrimination

have become pressing problems in China, even though the

internet has reduced distance-related informational fric-

tions. Evidence from an emerging peer-to-peer lending plat-

form shows that migration statuses can reveal additional

information about borrowers' credit risk. Ceteris paribus,

migrants, interprovincial migrants, upward-moving migrants,

and long-distance migrants are less likely to default; this is

especially true for migrants with stronger educational back-

grounds and work experience. Further analyses show that

migrants tend to have higher credit quality, lower financing

costs, and better economic attributes and assimilate them-

selves to a higher trusted culture, allowing them to default

less than nonmigrants. This means that lenders and trading

platforms may rely on the informative content of migration

to adjust their lending policies in a fashion that attracts

more participation from creditworthy migrants.

KEYWORDS

default risk, migrant, peer-to-peer lending

JEL CLASSIFICATION

G14, G23, J15, O15

1|INTRODUCTION

Globalization facilitates migration across countries, and international migration can influence remittances and con-

sumption (Barham & Boucher, 1998; Dustmann et al., 2017). At the same time, internal migration has become a

Received: 9 January 2021 Revised: 20 October 2021 Accepted: 29 November 2021

DOI: 10.1111/irfi.12371

© 2021 International Review of Finance Ltd.

472 International Review of Finance. 2022;22:472–505.

wileyonlinelibrary.com/journal/irfi

global phenomenon, especially in Africa, Asia, and Latin America (Gui et al., 2012). Regional differences in surplus

labor, living conditions, income disparity, policies, and housing costs drive individual mobility (Knight et al., 2011;

Lee, 1966; Plantinga et al., 2013). In the late 1970s, reform and opening up in China stimulated internal migration.

Then, migration restrictions have declined over time, resulting in an increasing number of migrants leaving their

native places to find jobs (Poncet, 2006). The seventh national population census of the People's Republic of China

in 2020 showed that approximately 376 million individuals left their native places to work, a 69.73% increase relative

to the sixth national population census in 2010. To date, a rich set of literature has focused on the effects of migra-

tion on remittances (Barham & Boucher, 1998), wage gaps (Zhang & Xie, 2013; Zhu, 2002), consumption (Dustmann

et al., 2017), savings (Bauer & Sinning, 2011), and investment (Quisumbing & McNiven, 2010; Vaaler, 2011).

Information asymmetry between market participants can lead to market inefficiency (Akerlof, 1970), and infor-

mational frictions are associated with geographic distance (Senney, 2019). Consequently, the local information

advantage is beneficial to breaking down informational barriers and providing guidance to market participants

(Knyazeva & Knyazeva, 2012). Therefore, geographic proximity or geographic distance plays a vital role in the finan-

cial market. Moreover, investors are more likely to hold and buy stocks of local firms than of nonlocal firms (Coval &

Moskowitz, 1999). Banks are more likely to charge remote borrowers more than local borrowers (Degryse &

Ongena, 2005). Even though the internet has reduced distance-related informational frictions, home bias and

regional discrimination also exist in the emerging online lending market. Lenders prefer to lend money to local or

nearby borrowers (Burtch et al., 2014; Jiang et al., 2020; Lin & Viswanathan, 2016; Wu et al., 2020). Moreover,

regional discrimination is a pressing problem in the Chinese online lending market (Wang et al., 2021).

The previous literature has shown that informational frictions lead to home bias. However, ability, skill, or educa-

tion can affect the migration decision. For example, skilled workers are more likely to move upward than are non-

skilled workers (Sorek, 2009). Furthermore, migrants are more monetarily motivated than are comparable natives

(Knight et al., 2011) and are more likely to have sufficient savings for money transferring activities (Barham &

Boucher, 1998). Alternatively, migrants leaving their comfort zones and adapting to a new environment are more

resilient, responsible, and hard working. Here, geographic distance is the moving distance, which is different from

the proxy of information asymmetry commonly used in the prior literature (Coval & Moskowitz, 1999; Knyazeva &

Knyazeva, 2012; Lin & Viswanathan, 2016; Senney, 2019). Interprovincial migrants, upward-moving migrants, and

long-distance migrants tend to have more economic motivations and thus incorporate themselves into cultures that

are vastly different than their own compared to their local peers. Therefore, migration status might be a proxy for

creditworthiness and reveal additional information about borrowers' credit risk.

To address this issue, we collect highly granular data from a noninstitutional lending setting, the peer-to-peer

lending market. Our dataset covers all unsecured credit loans from 2010 to 2016, and we trace the repayment

records up to February 2020, which is 16 months after the last repayment observed in our dataset. Our final sample

is restricted to having loan-level and borrower-level information, which leaves us with 28,324 funded loans from

413,014 loan applications from 352 prefectural- or county-level cities across all 31 provinces and municipalities of

Mainland China. We also collect the moving distance from Google Maps services, benchmarked lending rate from

the People's Bank of China, macro variables from the CEIC and Chinese Research Data Services Platform (CNRDS),

and the trust index from a national survey of Chinese enterprises in 2000 (Zhang & Ke, 2003).

The comprehensive coverage of our dataset provides us with reliable power to explore the impact of migration

on default risk. More fine-tuned migration measures can reveal multidimensional information. Therefore, based on

their destinations, we categorize migrants into interprovincial, intraprovincial, upward-moving, or downward-moving

migrants. We also calculate the geographic distance (i.e., the moving distance) between administrative centers of the

working location and the native place. Specifically, we also examine the heterogeneity of migration statuses on the

default outcomes across educational backgrounds or work experience since education and working years may be

influential factors of migration and residence. We further discuss what factors set migrants apart from their local

peers and allow them to have different degrees of repayment performance.

LI AND HU 473

Our empirical results show that migrants are less likely to default than are their local peers after controlling for

loan- and borrower-level information and a rich set of fixed effects. Remarkably, interprovincial, upward-moving, and

long-distance migrants are more likely to repay their loans. At the same time, the impact is more pronounced for

migrants with stronger educational backgrounds and work experience. Further analyses show that migrants tend to

have higher credit quality, lower financing costs, and better economic attributes and assimilate better to a more

highly trusted culture, allowing them to default less. We employ a battery of robustness tests, including an alterna-

tive probability model, alternative default definition, alternative sample specification, Heckman selection model, pro-

pensity score matching, and instrumental variable analysis, to address selection bias and the potential endogeneity.

Our results show that migration statuses can reveal additional information about borrowers' credit risk, and our find-

ings are unlikely to be an unintentional byproduct of mobile groups. Therefore, lenders and trading platforms may

adjust their lending policies to attract more creditworthy migrants based on the informative content of migration.

To the best of our knowledge, we are among the first to document that migration statuses can significantly influ-

ence the repayment behavior of individual borrowers in the noninstitutional lending market. The closest paper to

ours is that of Aguilar et al. (2018), which explored the relationship between Chinese internal migration and credit

risk using data from a commercial bank located in seven cities. Both studies aim to use trading information to build

up the micro foundation of how migration status affects repayment behavior. The present study significantly

extends, if not contrast, the existing literature that focuses on the relationships between migration and remittances,

wage gaps, consumption, savings, and investment. Our study further differs from that of Aguilar et al. (2018) because

it focuses on the influence of more fine-tuned migration measures on default risk in the noninstitutional lending mar-

ket and what allows migrants to default less. The broader coverage of our dataset allows us to carry out Heckman

selection model, propensity score matching, and instrumental variable approach to tackle the issues of selection bias

and potential endogeneity.

In doing so, we also extend the literature on home bias and regional discrimination. In our paper, geographic dis-

tance is a migrant's moving distance, one measure of migration status, which is different from the proxy of informa-

tion asymmetry commonly used in the prior literature (Coval & Moskowitz, 1999; Knyazeva & Knyazeva, 2012; Lin &

Viswanathan, 2016; Senney, 2019). Specifically, our results indicate that migrants are responsible groups and that

migration status can reveal additional information about borrowers' credit risk. Regional discrimination and home

bias are pressing problems that widely exist in China (Jiang et al., 2020; Wang et al., 2021). Therefore, our goal is to

establish apparent causal effects of migration on default risk, which can help lenders and trading platforms adjust

their policies to increase the participation of creditworthy migrants.

1

Finally, our paper also contributes to the emerging literature on understanding the predictable factors of default

risk in the online lending market. Our results suggest that migration can reveal additional credit information after

controlling for loan- and borrower-level characteristics as well as a set of fixed effects. This finding has critical and

normative implications because more credit risk measurements can help reduce lenders' losses in the credit market.

The rest of this paper is organized as follows. The related literature and testable hypotheses are presented in

the following section. Section 3describes the dataset and models. Section 4reports the baseline results, heterogene-

ity tests, and various robustness tests. Section 5discusses the reasons behind why migrants default less. The last

section concludes the paper.

2|LITERATURE REVIEW AND TESTABLE HYPOTHESES

2.1 |Migration studies

Geographic differences in the supply and demand of the labor market drive migrants to move from low- to high-

wage regions to find work (Knight et al., 2011). Ability, skill, or education can affect migration decisions; for example,

474 LI AND HU