Cultural diversity and borrowers’ behavior: evidence from peer-to-peer lending

Abstract

We study cultural diversity and borrowers’ behavior using data from peer-to-peer lending platform Renrendai. We proxy cultural diversity with the Linguistic Diversity Index, measured by the population-weighted number of dialects spoken in a region, and we show that it has a negative (positive) effect on the loan amount (default rate) of the borrowers. We address endogeneity using two novel instruments, the river length and land slope of Chinese cities, a Heckman two-stage model, and an IV-Heckit model. We also study areas where financial institutions’ loan balances are higher (lower) than average. In areas with low (high) loan balances, the amount borrowed (the default rate) is affected more (less). We argue that lenders’ behavior is a reason that borrowers in diverse cultures apply for smaller loans. Our results pass a number of robustness tests. Finally, we offer suggestions for improving risk management and inclusive financial development.

KEYWORDS:

• Culture
• cultural diversity
• dialect diversity
• peer-to-peer lending

JEL CLASSIFICATIONS:

• G21
• G41
• G20

Acknowledgements

We wish to thank the participants of the internal seminar series at University of Hull and Prof. Mahbub Zaman for their valuable comments. The usual disclaimer applies.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Another strand of literature studies the impact of cultural differences between lenders and borrowers on loan spreads and interest rates (Chui, Kwok, and Zhou 2016 and the references therein).

2 See Figure 1 and Table A2 describing the number of dialects in each city.

3 They are the first who calculated and used the concept of dialect diversity.

4 More details are given in Table A3 in the appendix.

5 By ‘borrower’s location', we mean their permanent residence.

6 210,841 loans were issued during the period 2013–2018. Someone may have applied several times during the observed period.

7 Following the literature (Chen, Huang, and Ye 2020; Liu, Jiao, and Xu 2020), two special administrative regions (i.e. Hong Kong and Macao) are not included due to their special status and restricted P2P loan markets for the residents there. We have also not considered Tibet and Taiwan since the data is missing for these two regions. There are 23 provinces, 4 municipalities, 5 autonomous regions and 2 special administrative regions in China. Following the literature (Chen, Huang, and Ye 2020; Liu, Jiao, and Xu 2020), we denote them as provinces for short.

8 According to Renrendai, investors can choose to bid and lend money to borrowers; the bid provides the information about loans and the borrowers.

9 This ID number only includes the first and the last three digits for privacy reasons. The first two digits determine the province where a borrower is from. We cannot determine the borrower’s city as that requires four digits.

10 We have also replaced GDP with the one-year lag of GDP. The empirical results remain unchanged. We wish to thank an anonymous reviewer for mentioning this robustness test.

11 With Mandarin being the official language used in the classroom.

Additional information

Notes on contributors

Zhongfei Chen

Dr Zhongfei Chen is an Associate Professor in School of Economics at Jinan University. His research interests include Energy, Environmental Economics and Macroeconomics. He has published articles in more than 20 peer-reviewed journals, including Global Environmental Change, Energy Economics, and International Review of Financial Analysis among others.

Ming Jin

Mrs. Ming Jin is a PhD candidate in Economics. Her research interests are in the areas of applied econometrics, Corporate Finance and Empirical Finance.

Athanasios Andrikopoulos

Dr Athanasios Andrikopoulos is a Lecturer in Finance and Director of BSc Financial Management at University of Hull, UK. His research interests are in the areas of international finance, applied econometrics, industrial organisation, and alternative investments. He has published in high profile peer review journals including the European Journal of Operational Research, Journal of Banking and Finance, Journal of Business Research, and Annals of Operations Research, among others. His research articles can be found on his ORCID website (https://orcid.org/0000-0003-3998-5751).

Youwei Li

Dr Youwei Li is a Professor of Finance at Hull University Business School, UK, previously he worked at the Queen's University of Belfast. Professor Li holds a PhD in Financial Econometrics from Tilburg University, the Netherlands. He has published articles in international journals in areas of asset pricing, investment, longevity risk, market microstructure, and quantitative finance. His research articles can be found on his Google Scholar Profile (https://scholar.google.co.uk/citations?hl=en&user=ePhWkK0AAAAJ), his SSRN Profile (https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=539212), and the ORCID website (https://orcid.org/0000-0002-2142-7607).