Abstract

Non-performing loans of commercial banks have long hampered the development of the banking sector, and directly reflect the credit risk and asset quality. With the continuous development of the financial industry, the introduction of financial inclusion has greatly eased the shortage of funds, and narrowed the gap between poor and rich. However, whether the promotion of financial inclusion in the financial industry could affect the non-performing loans of commercial banks has not been verified. Therefore, this paper discusses the possible associations between financial inclusion and non-performing loans of commercial banks on the regional level, constructs a panel data model by selecting the data of 31 provinces (including 4 municipalities) in China from 2005 to 2016, and uses the fixed effect model for empirical test. The empirical results (from an overall national sample) reveal a negative impact of the financial inclusion on non-performing loans. Moreover, the development of the banking sector and the regional consumption could enhance the impact of financial inclusion, while government intervention and unemployment could reduce the impact of financial inclusion. From the analysis of the regional sample, when the development of financial inclusion reaches a high level, the lagged financial inclusion promote the non-performing loans of commercial banks; however, when the financial inclusion is underdeveloped, the development of commercial banks act as a disincentive to non-performing loans. Therefore, the local governments should pay more attention to the influences of financial inclusion on the financial industry, in order to maintain the stability of banking asset quality. In addition, the negative impact of financial inclusion on non-performing loans of commercial banks is significant in China central region, while its impacts in China eastern and western regions are not significant. This indicates that the development of the financial industry and economy can hamper the effects of financial inclusion. It is necessary to adjust the financial resource allocation according to the characteristics of different regions in China, so that the financial inclusion can effectively promote the regional financial industry upgrade, improve regional capital flow efficiency, and fundamentally reduce the non-performing loans of commercial banks. According to the sample analysis by time, there is a significant negative impact relationship between inclusive finance and commercial banks’ non-performing loans after the financial crisis, while the impacts before and during the financial crisis are not significant. This demonstrates that the impact of the global financial crisis on China’s regional economy has further enhanced the inefficiency of the inclusive financial system on credit risk, which in turn, helps commercial banks better maintain asset quality stability.

Highlights

  • After the 2008 global financial crisis, many countries have begun their economic recovery, and the banking sector plays an important role in this process

  • Based on the regional level, this paper explores the impact of financial inclusion on non-performing loans of commercial banks, in order to provide corresponding solutions for alleviating the problem of banks’ asset quality, and proposes policy recommendations to help financial regulators and local governments better promote the development of financial industry

  • The size of bank assets and inflation in the western region are statistically significant at the confidence level of 1%, and the lagged variable of inflation is statistically significant at the confidence level of 5%. These results indicates that the development of banking sector and the regional consumption in western region could alleviate the increase of non-performing loans by maintaining the stability of banking asset quality

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Summary

Introduction

After the 2008 global financial crisis, many countries have begun their economic recovery, and the banking sector plays an important role in this process. TThheeccoorree ooff ffiinnaanncciiaall iinncclluussiioonn iiss ttoo pprroovviiddee mmoorree ffiinnaanncciiaall sseerrvviicceess ffoorr mmoorree ppeeooppllee iinn oorrddeerr ttoo hheellpp tthheemm ggeett rriidd ooff tthhee ffiinnaanncciiaall ddiiffffiiculties, tthhuussnnaarrrorowwininggthtehgeapgabpetwbeetewnereinchraicnhd panodor.pIonotrh.eIpnrothceessporof ceosnsstoruf cctionngstrhuecitninclgustihvee ifinnclaunsciivael sfiynsatenmci,atlhseysintetemra, cthtieoninbtertwaceteionnfibneatnwceiaelninfisntiatnuctioanl isnasntidtuutisoenrss iasntdheukserysfiascthoer okfeyprfoamctotrinogf pthroemfinotainncgiatlhienfdinuasntrcyi’asl dinedvuelsotprym’sednet.vWeloitphmmeonrte. Based on the regional level, this paper explores the impact of financial inclusion on non-performing loans of commercial banks, in order to provide corresponding solutions for alleviating the problem of banks’ asset quality, and proposes policy recommendations to help financial regulators and local governments better promote the development of financial industry. In the process of regional economic development, exploring the impact of non-performing loans of commercial banks from the regional perspective can better help financial regulators in developing countries to control the asset quality of banking sector. The structure of this paper is as follows: Section 2 introduces the measurement of financial inclusion and the logical relationship between financial inclusion and non-performing loans; Section 3 includes data sources, variable construction, descriptive statistics, and empirical model construction; Section 4 is the empirical analysis, including unit root test of variables, the discrimination of panel data model type, and regression analysis; Section 5 presents the conclusions and suggestions

Measurement of Financial Inclusion
10 Receptivity of financial services
The Logical Relationship between Financial Inclusion and Non-Performing Loans
Data Source
Non-Performing Loans Variable
Financial Inclusion Variable
Control Variables
Descriptive Statistics
Model Construction
Unit Root Test
The Discrimination of Panel Data Model Type
Test Method F test
The Regression Analysis of Overall Sample
The Regression Analysis of Different Subsamples
The Regression Analysis of Different Subperiod Samples
Conclusions
Findings
Policy Recommendations
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