Determinants of non-performing loans in Chinese banks
| DOI | https://doi.org/10.1108/JABS-01-2016-0005 |
| Pages | 273-289 |
| Published date | 06 August 2018 |
| Date | 06 August 2018 |
| Author | Muhammad Umar,Gang Sun |
| Subject Matter | Strategy,International business |
Determinants of non-performing loans in
Chinese banks
Muhammad Umar and Gang Sun
Abstract
Purpose –The study aims to explore macroeconomic and banking industry-specific determinants of
non-performingloans (NPLs) for Chinese banks,spanning from 2005 to 2014.
Design/methodology/approach –It uses three different models to explore the determinants. The first
model has only macroeconomic variables as regressors; the second model has only banking industry-
specific variables as independent variables; and the third modelhas macroeconomic and banking industry-
specific variables as explanatory variables. Furthermore, system generalized method of moments
estimation technique has been used to measure the coefficients of independentvariables.
Findings –Gross domestic product (GDP) growth rate, effective interest rate, inflation rate, foreign
exchange rate, type of bank, bank risk-taking behavior, ownership concentration, leverage and credit
quality are significantdeterminants of NPLs in Chinese banks. Furthermore,the determinants of NPLs for
listed and unlisted banks differ. Determinants of NPLs of listed banks include GDP, bank risk-taking
behavior and credit quality. However, variation inNPLs of unlisted banks is explained by GDP, inflation
rate, foreignexchange rate, bank risk-takingbehavior, leverage and credit quality.
Originality/value –This study also finds that using only macroeconomic or banking industry-specific
variablesas regressors is not a right approachbecause it may lead to erroneous conclusions.
Keywords China, Banks, Non-performing loans
Paper type Research paper
1. Introduction
In its FSI compilation Guide of March 2006, International Monetary Fund defines non-
performing loans (NPLs) as:
[...] loans (and other assets) should be classified as the NPL when payments of principal and
interest are past due by three months (90 days) or more, or interest payments equal to three
months (90 days) interest or more have been capitalized (re-invested into the principal amount,
refinanced, or rolled over (i.e. payment has been delayed byarrangements).
Similarly, Bank for InternationalSettlements defines NPLs as:
[...]a default is considered to have occurred with regard to a particular obligor when the obligor
is past due more than 90 days on any material credit obligation to the banking group”.
So, there is consensus amongst International Financial Institutions on 90-day limit to
consider a loan as problem loan.
NPLs are unwanted byproduct of performing loans and are considered as “financial
pollution” because of their adverse effect on economic growth (Barseghyan, 2010;
Gonzales-Hermosillo, 1999;Zeng, 2012). High levels of NPLs can cause banking crisis by
rendering the banks insolvent (Barr et al.,1994), which negatively impacts the economic
growth. NPLs induce uncertainty, which results in lower lending by the banks that ultimately
affect aggregate demand and investment. The financial crisis of 2007-2008 has revealed
Muhammad Umar is
Assistant Professor at the
AIR University School of
Management (AUSOM),
AIR University, Islamabad,
Pakistan.
Gang Sun is Professor at
the School of Finance,
Dongbei University of
Finance and Economics,
Dalian, China.
Received 9 January 2016
Revised 10 October 2016
Accepted 21 December 2016
DOI 10.1108/JABS-01-2016-0005 VOL. 12 NO. 3 2018,pp. 273-289, ©Emerald Publishing Limited, ISSN 1558-7894 jJOURNAL OF ASIA BUSINESS STUDIES jPAGE 273
the importance of the relationship between the performance of the banking sector and
overall economy. It is because of this importance of NPLs, many stress test models of
central banks include NPLs (Buncicand Melecky, 2012;Marcelo et al.,2008).
As a result of the importance of NPLs, lot of research has been conducted to analyze them
from different perspectives. Many studies explore the determinants of NPLs in different
countries or regions (Ghosh, 2015;Louzis et al.,2012;Espinoza and Prasad, 2010;Dhar
and Bakshi, 2015). However, many of them either explore macroeconomic or banking
industry-specific determinants,but the studies exploring both macroeconomic and banking
industry-specific determinants are rare. Second, to the best of our knowledge, none of the
existing studies explores the determinants of NPLs for Chinese banks. So, to bridge the
above-mentioned gaps, this study explores the macroeconomic and banking industry-
specific determinants of NPLs in Chinesebanks.
The study focuses on Chinese banks because of number of reasons. First of all, China
follows socialist ideology with Chinese characteristics, which makes it unique as compared
to the other democratic countries. So, it is important to know what determines NPLs of
banks in a country like China. Second, according to the existing studies, lending by
Chinese banks is biased to the state-owned enterprises, so it is important to know what
determines the variation in the NPLs in presence of moral hazard problem and soft budget
constraints (Lu et al.,2007;Zhang et al., 2015). Third, China is undergoing a rigorous
economic reforms process, so it is important to know what determines NPLs in a country
undergoing through openingup process.
This study explores the determinants of NPLs in Chinese banks by using the data of all the
listed and unlisted banks, ranging from the year 2005 to 2014. The study explores the
determinants by using three different models and two different techniques. In the first
model, all the independent variables are macroeconomic variables. The second model
considers banking industry-specific variables as independent variables. The independent
variables in third model are macroeconomic and banking industry-specific factors. The
study calculates the coefficients of the independent variables by using panel data
techniques and generalized methodof moments (GMM) system estimation.
The study contributes to existing literature on NPLs in a number of ways. First of all, it
reveals the factors which are responsible for determining NPLs in China –a country with
distinct characteristics. Second, most of the findings of the study match the findings of the
studies conducted for other emerging economies but differ from the studies conducted for
advanced economies, providing an evidence that the determinants of NPLs differ for
economies at different stages of development. Third, it suggests that while exploring
determinants, we should not consider only macroeconomic or banking industry-specific
variables as regressors, rather we should use both type of variables simultaneously. Use of
either macroeconomic or banking industry-specific variables alone may lead to erroneous
conclusions.
The rest of the paper is as follows. Section 2 provides a glimpse of Chinese banking sector.
Section 3 describes review of existing studies. Methodology and econometric model have
been explained in Section 4. Section 5 reports the findings of the study, and Section 6
concludes.
2. Glimpse of Chinese banking sector
Like Germany and Japan, China is a country in which corporations depend more on banks
as compared to the financial markets for their financial needs. Different studies have found
that financial sector development and banking reforms have helped China to have
impressive growth (Hasan et al.,2009;Fang and Jiang, 2014;Peng et al.,2014;Lin et al.,
2015). Some of the important features of Chinese banking system have been discussed
below.
PAGE 274 jJOURNAL OF ASIA BUSINESS STUDIES jVOL. 12 NO. 3 2018
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