Discussion Paper No. 353
The Determinants of Non-performing Loans:
Dynamic Panel Evidence from South AsianCountries
Md. Shahidul ISLAM and Shin-Ichi NISHIYAMA

September 2016

TOHOKU ECONOMICS RESEARCH GROUPGRADUATE SCHOOL OF ECONOMICS AND

MANAGEMENT TOHOKU UNIVERSITY

27-1 KAWAUCHI, AOBA-KU, SENDAI,
980-8576 JAPANThe determinants of non-performing loans: dynamic
panel evidence from South Asian countries
Md. Shahidul Islam a, b,1 Shin-Ichi Nishiyama aaGraduate School of Economics and Management, Tohoku University, Japanb
Department of Banking and Insurance, University of Dhaka, Bangladesh

Abstract:
Using the GMM estimator, this paper empirically studies the bank-specific, industry specific andmacroeconomics specific determinants of non-performing loans of banks in the South Asian countries(Bangladesh, India, Nepal and Pakistan) for the period of 1997-2012. We found that moral hazard problems
between the bank management and the depositors in addition to that between the bank management and theshareholders; and the adverse selection of borrowers by the bank significantly affect the bank credit risk. We
also found evidence that bad management, cost inefficiency, income diversification, bank size, industry
concentration ratio, inflation and GDP growth rate all significantly explain the levels of bank NPLs. Empiricalresults show a moderate degree of persistence of NPLs and a late-hit of the global financial crisis in thebanking sector of the region.

Key words: NPL, cost inefficiency, moral hazard, adverse selection
JEL classification: G21, C23

1. Introduction

Prolonged existence of non-performing loans (NPL) in the bank balance sheet causes thesevere bank management problems as it not only deteriorate the asset quality of the bank but also
reduce its earning and moral hazard problems between the bank and its funding channels mostly
attributed to the depositors. On the other hand, high levels of NPL in the economy also signal the1

Corresponding author, E-mail: sizahid2000@gmail.com. 27-1 Kawauchi, Aoba-ku, Sendai 980-8576, Japan1

bankruptcy possibilities of the borrowers resulting the worsening of business environment andeconomic conditions of a country. Due to rapid globalization and financial liberalization, the roleof banks’ in the economic development of a country increased in many folds. But the problemsof non-performing loans saddle a bank and cause the prolonged economic stagnation of a country
or even the global financial crisis. Reinhart and Rogoff (2010) pointed that NPL can be
instrumented as the signal of banking crisis. Hence, this area of research of the problems and thedeterminants of non-performing loans of banks is equally and in increased importance to theacademics and the researchers in present and as it was in the past. Understanding the factors
determining the NPLs and solving for those is important to the bank management and theregulatory bodies in order to apposite functioning of the bank and to rehabilitate the economy.
Over the past decades, majority of the research on the determinants of non-performing
loans viewed NPLs as the functions of macroeconomic and bank specific determinants.
Macroeconomic views of the determinants of NPL focus primarily on the macro-prudential
factors. Fundamentals of the theoretical relationships between the NPL and the macroeconomic
factors can be found in the Bernanke, Gertler and Gilchrist (BGG, 1998) framework of ‘financial
acceleration’ and later expansion by Gertler and Kiyotaki (2011) where credit market is procyclical and the role of asymmetric information and frictions affect the credit market. Rinaldi
and Sanchis-Arellano (2006) found household disposable income; unemployment and monetaryconditions have strong relationship with non-performing loans in the European Union banking.
Literature of the microeconomic determinants of non-performing loans emphasizes on the
bank-specific factors. Berger and DeYoung (1997) analyzed the Granger-causality relationshipbetween the NPLs and his so called ‘bad luck’, ‘bad management’, ‘skimping’ and ‘moral hazard,
where the empirical variables were cost efficiency and the capital adequacy. Among others,
2Williams (2004) followed the Berger and DeYoung hypotheses and came out the same kind of
relationship in the European banking.
Salas and Saurina (2002) studies of Spanish banking during 1985 to 1997, found thatalong with macroeconomic factors the bank specific variables also explain the future changes in
NPLs of banks.

Similar strand of literature include Louzis et al. (2012), who studies thedeterminants of NPL in Greek banking. However, no single study was out of criticism due toinsufficiently selection of variables or failure to implement the appropriate econometricmethodology (Athanasoglu, 2008).
We study the determinants of bank non-performing loans as a function of macroeconomic
factors, industry specific factors and the bank specific factors using the Generalized Methods ofMoments (GMM) estimator for the panel data of 259 banks in the South Asian countries(Bangladesh, India, Nepal and Pakistan) for 1997 to 2012.
This paper will be interesting to the concern researchers, readers, management personnel
and the policy makers as we showed that ‘adverse selection’ of borrowers by the bank in case ofcredit selection causes higher non-performing loans. We found the individual bank’s lending rate
positively affect the bad loan ratio not the macroeconomic policy rates. Charging higher rates,bank set aside the safe side borrowers as they cannot afford and create rooms for the borrowers
whose business type is high-risk high-return; that could result the higher non-performing loans.
This study empirically showed that in addition to the Berger and DeYoung’s (1997)‘moral hazard’ hypothesis between the shareholders and the bank management; another ‘moral
hazard II’ between the depositors and the bank management also affect the credit quality of abank. We showed that when a bank having excess liquidity, its management feels the cost of3

liquidity burden and disburse loans and advances without proper screening and ultimately
mounting the higher ratio of non-performing loans. In other words, depositors, as a third party
cannot monitor the cost of excess liquidity but facing the moral hazard problems as a result of
bad loans contracts.
The sample selection for this study was also notable on the ground that most of oursample countries (Bangladesh, India and Pakistan) were under the rule of British colony foraround two hundred years. We got the opportunity to study those countries’ banking systems all–together considering likely regulatory, social and economic environments. In the near past we
found similar studies on developed and developing countries of America, Europe, and Asia but
in case of South Asia, this study is a unique addition to the literature of the determinants of banknon-performing loans.
For the rest of the paper, we organized as follows: in section 2, we presented relevant
literature on the determinants of bank non-performing loans. In section 3 the empirical approachof our study and in section 4 the sample description has been outlined. In section 5 the result andfinally in section 6 we presented the conclusion and policy implications of our study.

2. The literature of the determinants of non-performing loans

The literature in the past viewed the determinants of bank non-performing loans (NPL) as
a function of bank specific and macroeconomic factors. The bank specific factors may be termed
as the microeconomic variables and can be directly found in the financial statements of a bank.
On the other hand, the macroeconomic variables are the overall economic, regulatory and legal
environment within which a bank operates its business. Extensive literature review found the4three streams of literature on the determinants of non-performing loans are macroeconomic
stream, microeconomic stream and the synthesis of macro and microeconomic stream.
Studies on the macroeconomic determinants of non-performing loans focus on theexternal events such as the overall macroeconomic conditions, which are likely to affect theborrowers’ capacity to repay their loans. The classical literature studying the interactions
between the macroeconomic environment and financial fundamentals are the models developedby Bernanke and Gertler (1989), Kiyotaki and Moore (KM, 1997), Bernanke, Gertler andGilchrist (BGG, 1998) and Gertler and Kiyotaki (GK, 2011). BGG (1998) developed theframework of ‘financial accelerator’ where credit markets are pro-cyclical and the role ofasymmetric information between the borrowers and lenders affect the credit market shocks
whereas KM (1997) and GK (2011) models allow the business cycle fluctuations and the role offrictions in case of credit market imperfections, respectively.
Rinaldi and Sanchis-Arellano (2006) studied the macroeconomic determinants of the
non-performing loans of 7 European Union countries and found strong relationship between thecredit risk and the household disposable income, rate of unemployment and the monetaryconditions of a country. Ali and Daly (2010) investigated that the same set of macroeconomic
conditions affect the default rates of Australia and the US banking differently but the later one
found to be more sensitive. The problem of NPL found to be positive to the increase in thenominal interest rates and the number of bankruptcies and negative to the higher CPI inflation,
economic growth and property price inflation in Hong Kong, according to the studies of Shu(2002).

5

Bohachova (2008) found the pro-cyclical nature of default risk where the banks
accumulate risks more rapidly in economically good times and some of these risks materialize asasset quality deterioration during recessions in the OECD countries. In the same line, Nkusu(2011) empirically studied the panel of 26 advanced economies and found that macroeconomic
vulnerabilities deteriorate the credit portfolios of banks. Espinoza and Prasad (2010), in theirdynamic panel estimates over 1995-2008 period of GCC countries showed the persistence of
non-performing loans and the inverse relationship with economic growth and the interest rates.
Castro (2013) employed dynamic panel data approach to study the macroeconomic determinantsof credit risk in Greece, Ireland, Portugal, Spain and Italy (GIPSI) and found that credit risk isnegatively affected by the GDP growth rate and the share and housing price indices but
positively affected by the unemployment rate, interest rate, credit growth and an appreciation of
the real exchange rate. Skarica (2014) found the primary cause of high levels of NPL is the
economic slowdown and deterioration in the state of employment and inflation in the sevencentral and Eastern European countries in the period of Q:3 2007 and Q:3 2012.
Group of literature on microeconomic determinants of non-performing loans wereinterested more at the variability of NPL across banks attributes to the bank level factors. Berger
and DeYoung (1997) evidenced the four mutually non-exclusive two-way causalities over thedefault risk in their studies of the US commercial banks during the period of 1985-94. They
found that ‘bad luck’, ‘bad management’, ‘skimping’ and ‘moral hazard’ could affect the samebank at the same time. They argued that, bad luck could befall a poorly managed bank that also
happens to be skimping on loan monitoring costs and for any loss of capital due to these factors,
banks respond to moral hazard incentives and take increased risks. Williams (2004), Podpieraand Weil (2008) and Louzis, Vouldis and Metaxas (2010) studies follow the Berger and6

DeYoung (1997) hypotheses and came out with same kind of results who studied the Europeansavings banks, Czech banks and Greek banking sectors for different time periods, respectively.
Keeton and Morris (1987) found that banks taking higher risk including in the form ofexcess lending eventually incur greater losses. In a comparative study of the credit risk
determinants of banks in the developed and the emerging economies, Ahmad and Ariff (2007)
found that regulatory capital and the management quality significantly determine banks credit
portfolios.
As the synthesis of macroeconomic and the microeconomic determinants of nonperforming loans, Salas and Saurina (2002), using the GMM estimation technique for the panel
data from 1985-1997 of Spanish commercial and savings banks found that GDP growth rate,
firm and family indebtedness, rapid past credit or branch expansion, inefficiency, portfolio
composition, size, net interest margins, capital ratio and the market power significantly affect thecredit risk of a bank. In a similar kind of study, Louzis et al. (2012) found that along with GDP
growth rate and unemployment rate, inefficiency and performance indicators also greatly explain
the credit problems of Greek banking sector. Rajan and Dahl (2003) found the terms of credit
and macroeconomic and business conditions affect the NPLs in the Indian banking. Festic andRepina (2011) studied the five EU countries (Bulgaria, Romania, Estonia, Latvia and Lithuania)
and found that macroeconomic slowdown and skimping on loan supervision are important forthe deterioration of credit quality.
Among others Zribi and Boujelbene (2011) concluded that the macro prudential policies
and the regulatory capital and ownership structure significantly affect the NPL of Tunisian banks.
Chaibi and Ftiti (2015) studied the French and German banking sector and found a set of7macroeconomic variables like GDP growth, interest rate, unemployment and exchange rate alongwith the bank specific variables like loan loss provisions, inefficiency and the firm size affect the
non-performing loans ratio of the banks.
This study focuses on the synthesis of the macroeconomic and the bank specificdeterminants of non-performing loans of banks in the South Asian countries. We extended theliterature of the determinants of non-performing loans by empirically showing that instead ofmacroeconomic policy rates, the individual bank’s lending rate and the information asymmetry
between the depositors and the bank is prominently determine the NPL. In this study, the panel
data of 259 commercial banks for the period of 1997-2012 which is relatively large that westudied empirically will allow the better insight into the factors determining the banking creditrisk.

3. Empirical Approach of the determinants of non-performing loans study
3.1. Econometric Model

We viewed the determinants of non-performing loans (NPL) of banks as the function ofbank-specific, industry specific and the macroeconomic specific variables and the general model
to be estimated is of the following linear form:
𝐉𝐉

𝐣𝐣

𝐦𝐦
𝐍𝐍𝐍𝐍𝐍𝐍𝐢𝐢𝐢𝐢 = 𝐜𝐜 + ∑𝐣𝐣=𝟎𝟎 𝛃𝛃𝐣𝐣 𝐗𝐗 𝐢𝐢𝐢𝐢 + ∑𝐋𝐋𝐥𝐥=𝟎𝟎 𝛃𝛃𝐥𝐥 𝐗𝐗 𝐥𝐥𝐢𝐢𝐢𝐢 + ∑𝐌𝐌
𝐦𝐦=𝟎𝟎 𝛃𝛃𝐦𝐦 𝐗𝐗 𝐢𝐢𝐢𝐢 + 𝛆𝛆𝐢𝐢𝐢𝐢

𝐰𝐰𝐰𝐰𝐰𝐰𝐰𝐰𝐰𝐰, 𝛆𝛆𝐢𝐢𝐢𝐢 = 𝛎𝛎𝐢𝐢 + 𝛖𝛖𝐢𝐢𝐢𝐢 ......Equation (1)8Where, 𝑁𝑁𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 is the ratio of non-performing loans of bank i at time t where i = 1,.....,N, t = 1,.....,
T and c is a constant term. The superscripts j, l and m of Xit denote the bank-specific, industry

specific and macroeconomic specific determinants respectively. εit is the disturbance with νi theunobserved bank-specific effect and υit the idiosyncratic error. The error components of theregression model also distributed as νi ~IIN (0, σ2ν ) and independent of υit ~ IIN (0, σ2υ ).

We adopted a dynamic specification of a model that includes a lagged dependent variable in theright hand side among the regressors to account for the time persistence in the NPL structure
following the recent literature in panel data studies (see, Salas and Saurina, 2002, Louzis et al.,2012). The dynamic specification model of the determinants of non-performing loan is:
𝐉𝐉

𝐣𝐣

𝐦𝐦
𝐍𝐍𝐍𝐍𝐍𝐍𝐢𝐢𝐢𝐢 = 𝐜𝐜 + 𝛅𝛅𝛅𝛅𝛅𝛅𝛅𝛅𝐢𝐢,𝐭𝐭−𝟏𝟏 + ∑𝐣𝐣=𝟎𝟎 𝛃𝛃𝐣𝐣 𝐗𝐗 𝐢𝐢𝐢𝐢 + ∑𝐋𝐋𝐥𝐥=𝟎𝟎 𝛃𝛃𝐥𝐥 𝐗𝐗 𝐥𝐥𝐢𝐢𝐢𝐢 + ∑𝐌𝐌
𝐦𝐦=𝟎𝟎 𝛃𝛃𝐦𝐦 𝐗𝐗 𝐢𝐢𝐢𝐢 + 𝛆𝛆𝐢𝐢𝐢𝐢..Equation (2)Where, NPLi,t−1 is the one-period lag of non-performing loans and δ is the speed of adjustment

to the equilibrium. A value of 0 < δ < 1 implies the persistence of NPL in the industry but tends
to return to the normality level.

Literature usually applies the fixed effects (FE) or the random effects (RE) modeling in static
type of relationships but in dynamic relationships these models produce biased (especially whentime dimension T gets smaller) and inconsistent estimates (see Baltagi, 2001). Thus we precede
the following five step issues for the econometric model of NPL determinants.
Firstly, we tested our data for non- stationarity using the Fisher test which does not require apanel to be balanced. This test is a question when the use of a relatively large T in a model of
non-performing loans may be criticized on grounds of non-stationarity.

The null of non-stationarity has been rejected at 1% level at every level.
9Secondly, we examined whether the individual effects are fixed or random. The relevant
Hausman test for equation 1 confirms the evidence in favor of a FE modeling 2 . Also theestimation result confirms the existence of individual effect since the F-statistics is significant (F(126, 687) = 15.11, Prob > F = 0.0000). However, the least square (within) estimator of the FEmodel in the presence of a lagged dependent variable among regressors is both biased andinconsistent 3.
Thirdly, we proceed with the estimation of our model using the one step generalized methods ofmoments (GMM) estimator of Arellano and Bond (1991) paradigm which suggest thatconsistency and efficiency gains can be obtained by using all available lagged values of thedependent variable along with the exogenous regressors as instruments.
Fourthly, we dealt with the problem of endogenuity with estimation of bank non-performing loan.
The question is whether capital variable (E/TA) is endogenous or not. To confirm such, we ran
the same model twice separately. First time we treated capital variable as strictly exogenous andsecond time as endogenous.

Sargan test 4 for over-identifying restrictions indicates that noendogenuity assumption is valid for capital variable in NPL modeling and we treated capitalvariable as strictly exogenous.

2

3

The relevant Hausman test chi-squared statistics was 𝜒𝜒 2 (11 ) = 3905.20 with p-value is 0.0000

The Monte Carlo studies that measured the corresponding bias in the coefficients of the lagged
dependent variables have found that the bias is significant for small values of T but goes to zero as T
increases (see Judson and Owen, 1999).
4
In both the cases ρ = 0.00 which means the question of endogenuity is irrelevant for capital variable in
NPL modeling.
10

Finally, we addressed the unobserved time effects in the error components of our model asfollows:
𝐣𝐣

𝐉𝐉

𝐦𝐦
𝐍𝐍𝐍𝐍𝐍𝐍𝐢𝐢𝐢𝐢 = 𝐜𝐜 + 𝛅𝛅𝛅𝛅𝛅𝛅𝛅𝛅𝐢𝐢,𝐭𝐭−𝟏𝟏 + ∑𝐣𝐣=𝟎𝟎 𝛃𝛃𝐣𝐣 𝐗𝐗 𝐢𝐢𝐢𝐢 + ∑𝐋𝐋𝐥𝐥=𝟎𝟎 𝛃𝛃𝐥𝐥 𝐗𝐗 𝐥𝐥𝐢𝐢𝐢𝐢 + ∑𝐌𝐌
𝐦𝐦=𝟎𝟎 𝛃𝛃𝐦𝐦 𝐗𝐗 𝐢𝐢𝐢𝐢 + 𝛆𝛆𝐢𝐢𝐢𝐢

𝐰𝐰𝐰𝐰𝐰𝐰𝐰𝐰𝐰𝐰, 𝛆𝛆𝐢𝐢𝐢𝐢 = 𝛎𝛎𝐢𝐢 + 𝛌𝛌𝐭𝐭 + 𝛖𝛖𝐢𝐢𝐢𝐢 ......Equation (3)Where λt is the unobservable time effect and we tested the joint significance of time effects as

𝑯𝑯𝟎𝟎 = 𝝀𝝀𝟐𝟐 = 𝝀𝝀𝟑𝟑 = 𝝀𝝀𝑻𝑻 = 𝟎𝟎. The relevant LM test 5 approves the inclusion of time dummies. We
experimented for time dummies for all years jointly and separately but found the year dummies
2003, 2009 and 2010 are significant. Considering all these, we estimated the determinants of
non-performing loans by the following dynamic equation:
𝐉𝐉

𝐣𝐣

𝐦𝐦
𝐍𝐍𝐍𝐍𝐍𝐍𝐢𝐢𝐢𝐢 = 𝐜𝐜 + 𝛅𝛅𝛅𝛅𝛅𝛅𝛅𝛅𝐢𝐢,𝐭𝐭−𝟏𝟏 + ∑𝐣𝐣=𝟎𝟎 𝛃𝛃𝐣𝐣 𝐗𝐗 𝐢𝐢𝐢𝐢 + ∑𝐋𝐋𝐥𝐥=𝟎𝟎 𝛃𝛃𝐥𝐥 𝐗𝐗 𝐥𝐥𝐢𝐢𝐢𝐢 + ∑𝐌𝐌
𝐦𝐦=𝟎𝟎 𝛃𝛃𝐦𝐦 𝐗𝐗 𝐢𝐢𝐢𝐢 + 𝛄𝛄𝛄𝛄𝟎𝟎𝟎𝟎 + 𝛄𝛄𝛄𝛄𝟎𝟎𝟎𝟎 + 𝛄𝛄𝛄𝛄𝟏𝟏𝟏𝟏 + 𝛆𝛆𝐢𝐢𝐢𝐢

𝐰𝐰𝐰𝐰𝐰𝐰𝐰𝐰𝐰𝐰, 𝛆𝛆𝐢𝐢𝐢𝐢 = 𝛎𝛎𝐢𝐢 + 𝛖𝛖𝐢𝐢𝐢𝐢 ......Equation (4)5The relevant LM test chi-squared statistics was 𝜒𝜒 2 (12) = 85.73 with p-value = 0.0000.

113.2. Empirical determinants of non-performing loans of banks

For this empirical study of the econometric model of the determinants of non-performing
loan developed in section 3.1, we have used 3 categories of proxy variables namely (a) firm
specific, (b) industry specific and (c) macroeconomic specific (see table-1 for a summary ofthese variables).
3.2.1. The dependent variables
Non-performing loan ratio

We used the ratio of nonperforming loan to total loan (NPL/TL) as the dependent
variable in our model. By non-performing loans, we mean the volume of impaired loans and bydefinition, a loan is impaired when, based on current information and events, it is probable that acreditor will be unable to collect all amounts due according to the contractual terms of the loan
agreement (see FASB, 2008). Alternatively, this ratio has been used to proxy the credit risk
measures in many of the past literature (see V. Castro, 2013, B. Imbierowicz et. al., 2014).

3.2.2. The explanatory variables(a) Bank-specific explanatory variables(i)

Return on assets

We used return on average assets (ROA) as the proxy for the bank performance which
negatively influences the levels of non-performing loans of a bank. Past performance can reflect
the high quality of management (Louzis et. al., 2012), leading to a low level of NPL.

12

<Table-1>(ii)

Equity to Total Assets ratio

The ratio of equity to total assets measures the capitalization strength of a bank considering
the regulatory requirements regarding the minimum equity holdings. Anticipating impact of thisvariable on bank non-performing loan is negative as low financial capital will cause high nonperforming loan (Berger and DeYoung, 1997). To address the ‘moral hazard’ hypothesis, whenthird party is bearing the risk of the excessive risk taking cost of another party but cannot easily
be charge for or prevent that risk taking behavior of the bank, we expect equity to total assets
ratio will significantly affect bank non-performing loan ratio.

(iii)

Cost to income ratio

Cost to total income ratio is the proxy for inefficiency variable and we expect statisticallysignificant and negative relationship to the non-performing loan. Berger and DeYoung (1997)
examined different intertemporal relationships of this measured cost efficiency/inefficiency andthose subsequently affect the non-performing loan of a bank. In their so called ‘bad luck’hypothesis, increases in problem loans due to exogenous effects precede the decreases inmeasured cost efficiency whereas in ‘bad management’ hypothesis have the opposite temporalorder where low measured cost efficiency occur before causing higher problem loans. On theother hand, according to their ‘skimping’ hypothesis, when management chose short run cost
efficiency in exchange of long run profitability will experience the mounting of non-performing
loans in their balance sheet.

13

(iv)

Bank size

Bank size has been measured in terms of natural logarithm of a bank’s total assets andassumed the predictive capacity of this variable is negative over NPL under the ‘diversification’hypothesis where the theoretical argument that larger banks are mostly diversified to manage
their loan portfolio and ‘bad’ borrowers (Zribi and Boujelbene, 2011).
(v)

Off-balance sheet income
Banking business has been diversified many folds now a day. Following Louzis et. al. (2012),

we examined the effect of off-balance sheet income on NPL and expect a statistically significant
negative relationship. The net non-interest income (non interest expense less non-interest
income) over total assets (NNII/TA) has been taken as the proxy for the off-balance sheet
income and expects positive impact of this variable on non-performing loans.
(vi)

Liquidity ratio

Higher liquidity ratio reduces the liquidity risk of a bank and fosters the ability of the
management for loan servicing and monitoring that result the lower levels of non-performing
loan. On the other hand, excess liquidity is a good proxy of moral hazard problems between thebank management and the depositors because they cannot monitor and make the managementbound for effective utilization of the fund. We calculated the liquidity ratio (LA/D&STF) as theliquid assets of a bank over the deposits and short term funding in percentage form and expect astatistically significant and inverse relationship with non-performing loan.

14

(vii)

Growth rate of total loan

One of the fundamentals of bank management is loan management which is directly related
to the quality control of the credit disburse. By increasing the volume of loans, management may
try to offset or reduce the ratio of non-performing loans to total loans. We expect the growth of
total loan will significantly reduce the non-performing loan ratio of a bank.

(viii)

Interest income to total loan ratio

We introduced the ratio of interest income to total loan as the proxy for the lending rate of abank and how this affects the credit quality of its balance sheet. The intuition of such
introduction was to compare the predictive nature of the individual lending rate and themacroeconomic policy rates. Again, higher individual bank lending rate will invite the risky
borrowers whose business type is high risk- high return type because the safe side customers
may not afford the higher cost of loans. In that sense, we may assume that due to the adverse
selection of borrowers by the bank is one of the main reasons of higher non-performing loans.
Our hypothesis is the higher the lending rate; the higher will be the default risk as such rate
deteriorates the borrowers’ ability and also the willingness to repay the loan.

(b). Industry-specific variables(ix)

Concentration ratio

Concentration ratio (CR3) is the proxy variable for the market concentration and its impact
on bank non-performing loan in our empirical study. This is a common and widely used measure15

of market concentration where higher concentration means lower competition and vice versa andcalculated as the ratio of the sum of total assets of largest three (3) banks to that of the industry.
According to the structure-conduct-performance (SCP) hypothesis, banks in highly concentrated
markets earn monopoly rents, because they tend to collude (Gilbert, 1984) and thus deteriorate
the market competition. We expect the negative influence of higher concentration on NPL
because in absence of perfectly competitive market scenario, borrowers may feel reluctant to
repay the loans.

(c). Macroeconomic-specific variables(x)Standard deviation of short term interest rate

As the proxy for the short term lending rate, we used the annualized standard deviation ofmonthly average of daily call money rates (SDint) and expect the positive relationship with thenon-performing loan ratio of a bank.
(xi)

Term spread of interest rate

Term spread of interest rate is the proxy variable for the long term interest rate in our present
study. We used the difference of yield spread of 10 year and 5 year treasury bonds as the proxyof term structure of interest rate (R) and its impact on the bank non-performing loan ratio.
Previous literature (see Castro, 2013, Nkusu, 2011) emphasized the impact of long term interest
rate on the problem loan of a bank. An increase in the lending rate simultaneously weakens thedebt servicing capabilities of a borrower hence we expect positive relationship with NPL of a
bank.

16

(xii)GDP growth rate

Stable Gross domestic product (GDP) growth rate represents the stability of an economy andthus affect the demand and supply of loans and deposits directly. We assume that as GDP growth
ensures the stability of the economy and in that stable economic environment a borrowers’
business risk reduces significantly and increases the ability of debt servicing (Salas and Saurina,
2002).

We expect statistically significant inverse relationship with GDP growth and non-performing loan of a bank.
(xiii)

Rate of inflation

The effect of inflation on the non-performing loan ratio of a bank is not deterministic in thepast literature. Theoretically, inflation should reduce the real value of debt and hence make debt
servicing easier but also high inflation may pass through to nominal interest rates, reducing
borrowers’ loan servicing capacity (B. Skarika, 2014). Although Rinaldi and Sanchis-Arellano(2006) reported positive relationship, following Shu (2002), we expect inflation and NPL will
have statistically significant inverse relationship.

4. Sample and data

In this research of the determinants of the non-performing loans of banks, we studied theunbalanced panel of 259 6 South Asian banks over the period of 1997-2012. By banks we mean
the financial intermediary who takes deposits and provide loans and advances in the ordinary
courses of business. We excluded the data of Islamic banks from our sample as in India and6

By countries, India represents 60% banks in our total sample while Bangladesh, Nepal and Pakistan represent
12%, 10% and 18% respectively.

17

Nepal there is no or very limited Islamic banking operation. For our analyses, we collected datafrom various sources. The dependent variable and the bank specific explanatory variables, wecollected data from the Bureau Van Dijk’s Bank Scope database (Bank Scope 2013) using theuniversal model of banking database. We took the primary data set from the Bank Scope but
calculated by our own to get the concentration ratio (CR3) which we used as the industry specificexplanatory variables. For macroeconomics specific variables, we collected data from twosources. We collected the data of short term money market rates 7 and the term spread of interest
rate 8 from the central banks websites of the respective countries. From International Financial
Statistics (IFS) database (IFS 2014), we collected yearly data of rate of inflation and the growthrate of gross domestic product (GDP).
In table-2 in the following the descriptive statistics of the empirical variables used in thepresent study has been presented. As we see in South Asia, the non-performing loan to total loan
ratio was quiet high (more than 8%) in the studied period.
Among other key indicators, ROA was around 1% and the cost to income ratio was morethan 61%. Also the average equity to capital ratio was around 10% and the liquid assets to thedeposit and short term funding 19.71% indicates the quiet level of solvency of the South Asianbanks. Average rate of inflation was slightly higher than 7% and the South Asian nation’s GDP
grew more than 6% on an average during the period.
<Table-2>

7

We used the annualized standard deviation of monthly average of daily call money rates as the proxy of shortterm interest rates.
8
Term structure of interest rate is proxied as the difference between the yields spread of 10 year and 5 year
Treasury bonds (T-bond). Also for Nepal, we sampled the development bond yield as the equivalent to 5 year
T-bond and the national savings certificates yield as equivalent to 10 year T-bond as they have no suchclassified maturity bonds.

18

5. Empirical results
5.1. The baseline result

Table-3 in the following presents the GMM regression output of equation 4 of thedeterminants of non-performing loans (NPL) of the banks in South Asia for the total sample
period of 1997-2012. The first column of the table presents the name of the dependent and thedeterministic variables including bank specific, industry specific and macroeconomic specificwhile each column of model 1, model 2 and model 3 presents the coefficient and standard error
respectively.
The Wald-test confirms the fine goodness of fit of our panel data set and the Sargan-testshows no evidence of over-identifying restrictions. According to the results of AB (AR1) test anegative first order autocorrelation exists but does not imply the inconsistency of the estimates.
Inconsistency would imply if there is the second-order autocorrelation (Arellano and Bond,
1991) but is rejected by AB (AR2) test subsequently.
Empirical results show a moderate degree of persistence of non-performing loans inSouth Asian banking as the one-period lagged dependent variable (δ (one period lagged
NPL/TL) = 0.38604) is statistically significant also justify the use of GMM dynamic panel
estimation of our model. This level of persistence of NPL in the South Asian region seems
similar to the French and German banking market (Chaibi et. al., 2015).

<Table-3>

19

The statistically significant negative coefficient of the performance variable (proxied byreturn on asset (ROA) implies the ‘bad management’ hypothesis where performance serves as aproxy for the quality of management and lowering the levels of non-performing loans.

Results support the ‘moral hazard’ hypothesis that low capitalized banks leads to anincrease in non-performing loans. Statistically significant coefficient of capital variable (E/TA)
indicates that for every 1 percent decrease in equity to total asset ratio would increase NPL byapproximately 23 basis points.
Empirical evidence found that ‘skimping hypothesis’ is also persists in the South Asianbanking region. Short-run cost efficiency burgeon the future level of NPL. Our empirical results
support the so called ‘diversification hypothesis’ in both proxied variables size (lnTA) and net
non-interest income ratio (NNII/TA). Statistically significant negative coefficients of both thedeterministic variables predict that banks having the capabilities of income diversification interms of size and other non-interest income will have low levels of non-performing loans.
We found empirical evidence in favor of the ‘liquidity hypothesis’ that banks with noliquidity shortage become able to expand their loans and advances and can reduce the nonperforming loans ratio significantly. Numerically speaking, if a bank can increase the liquid
assets to deposit and short term funding and subsequently increase the total loans by 1
percentage point then the NPL is supposed to be reduced by 4 basis point.
Also we empirically proved that in the South Asian banking, rather than macroeconomic
policy rates, individual bank’s lending rate significantly determines its non-performing loan ratio.
In model 1 of table-3, we included the individual bank lending rate as the determinant of NPL.
20

Whereas, in model 2 and model 3, we included the short term lending rate (proxy of which is theannualized standard deviation of monthly average of daily call money rates, SDint) and the longterm interest rate/term spread of interest rate (proxied by the difference between the yields spread of
10 year and 5 year Treasury bonds, R) as the determinants of NPL. Models outcome show, if thebank increases its lending rate by 1 percent then its NPL ratio goes up by 36 basis points which
are statistically significant whereas the coefficients of the SDint and R are statisticallyinsignificant.
Industry factor is also a significant determinant of non-performing loans in South Asia.
Our proxy variable concentration ratio (CR3) is statistically significant and having negative
coefficient means the higher ratio of non-performing loan is significantly due to the lack of farecompetition in the banking industry.
As our hypothetical expectation, the systematic factors affect the non-performing loan
ratio of individual bank inversely. GDP growth rate significantly reduces the NPL. Also thesignificant negative coefficient of inflation rate implies that higher inflation weakens theborrowers’ ability to debt service by reducing their real income and these findings are consistent
to the previous literature (see Chaibi et al. 2015).
Our empirical results also evidence that in the year of 2003, 2009 and 2010, the nonperforming loan was significantly high may be due to the hit of the global recession which was
confirmed by the positive coefficients of the year dummies in the baseline model.

215.2. Robustness check
In table-4 we presented the result of our empirical model of the determinants of nonperforming loans in South Asia using the fixed effect 9 panel estimator as an alternative to the
GMM estimator.
However, we found no change in signs and no significant change in values of thecoefficients of the explanatory variables in the alternative estimator which confirm that ourbaseline GMM estimation output is robust and the coefficients of the estimation can beinterpreted with confidence.

<Table-4>

6. Conclusion and implications of the study
We studied the cross-country panel data set with micro and macro level variables andpresent the empirical results on how bank specific, industry specific and macroeconomics
specific factors affect the bank non-performing loans. We followed the single stage model of
NPLs determinants for the empirical study that included four South Asian countries’ that is
Bangladesh, India, Nepal and Pakistan banking sector data covering the period of 1997-2012.
Our empirical findings are consistent with our theoretical analysis and significantly positive
persistence of NPLs behavior in the sample region justify the use of GMM estimator, an up-to
date econometric methodology that we effectively addressed the issues that profits show atendency to persist over time, reflecting impediments to market competition, informational
opacity and/or sensitivity to regional/macroeconomic shocks.
9

The Hausman test allows the null hypothesis that the individual effect and the explanatory variables areuncorrelated, rejected in all the cases.

22

Among the bank specific determinants, statistically significant coefficients of the proxyvariables support the Berger and DeYoung’s (1997) ‘bad management’, ‘skimping’ and ‘moral
hazard’ hypotheses and the Louzis et al. (2012) ‘diversification’ and ‘too big to fail’ hypotheses.
The findings also support that ‘moral hazard II’ where the depositors are mainly affected and the‘adverse selection’ of borrowers by the banks are also significantly affect the level of nonperforming loans of a bank. Significantly negative coefficients of inflation and GDP growth rate
reveal the ‘bad luck’ hypotheses of Berger and DeYoung (1997) where the external elements
affect the mounting of NPLs. In case of South Asia, concentration ratio of banks still has somedeterministic capacity of non-performing loans along with some adverse effect of the global
financial crisis.
For the policy implications, we suggest the banks to take appropriate actions to achieve
cost efficiency and the excellence of management so to address the moral hazard and adverse
selection issues to minimize the non-performing loans ratio. The policy makers should focus themacro prudential policies so that the bank level lending rate remains within justified range toreduce the credit risk problems of each bank. Hoping these initiatives will benefit the society as awhole.
In this paper, we comprehensively addressed the question of how microeconomic andmacroeconomic forces affect the non-performing loans of a bank. But studies on a number of
additional explanatory variables like corporate tax rates, ownership structure, deposit insurance,
rate of unemployment, and portfolio effect, those could not be tested due to limitation of data andthe degrees of freedom or for the potential multicollinearty problem, would be tested as theextension of the model.

23

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Table-1: Description of variables used in the study of non-performing loans of banks
Variables
Dependent variables

Notation

Description

Non-performing loan ratio
Independent variables(a). Bank-specific variables
Return on averageassetsi.
Equity to total
assets ratioii.
Cost to income ratioiii.
Bank sizeiv.
Off-balance sheet
income ratio

NPL/TLNon-performing loan (%) over total loan is a proxyvariable for credit quality or credit risk exposed to a bank

v.
vi.
vii.

Liquidity ratio
Growth of total loan
Interest income toTotal loan ratio(b). Industry-specific variables

Expected
effect

ROA
E/TA
C/TI
ln(TA)
NNII/TA
LA/D&S
TF
GTL

i

viii.
Concentration ratio
CR3(c). Macroeconomic-specific variablesix.
Standard deviation ofshort term interest rate SDintx.
Term spread ofinterest rate
Rxi.
Rate of inflation
%Inf
xii.
Economic growth rate %GDP

Net income over average total assets (%)
Equity to total assets ratio (%) is a measure ofcapital adequacy of respective bankTotal cost over total revenue (%)
Natural logarithm of total assets of a bank-

Net non-interest income over total assets (%)
Liquid asset to total deposits and short term funding ratio (%) express the liquidity position of a bank
Annual growth rate (%) of loan+
Total interest income over total loan (%)
Sum of total assets of largest three banks over that of theindustry-

Annualized standard deviation of monthly average of daily
call money rates
Difference between the yields spread of 10 year and 5 year
Treasury bonds, R (%)
Annual rate of inflation (%)
Real economic growth rate as a % change in GDP

+
+
-

30

Table-2: Descriptive statistics of the variables of non-performing loans model
Variables
Dependent VariableNon-performing loan ratio (NPL/TL)
Independent variables
Bank specific
Return on Average Assets (ROA)
Equity to Total Asset ratio (E/TA)
Cost to income ratio (C/TI)
Size (lnTA)
Net non-interest income ratio (NNII/TA)
Liquidity ratio (LA/D&STF)
Growth rate of total loan (GTL)
Interest income to total loan ratio (i)Industry specificConcentration ratio (CR3)
Macroeconomics specific
Short term interest rate (Sdint)
Term spread of interest rate (R)GDP growth rate
Rate of inflation

MeanMedianStandard Deviation

8.34324.280010.4056

0.99509.988361.2300
7.18700.878819.712826.7537
12.45421.14507.090048.5100
7.12530.694514.4250
19.7450
10.5900

3.374612.383153.54452.05473.314828.851246.5776
19.9496

0.55980.54120.17981.34301.0741
6.13657.02610.99920.60006.18006.37001.1464
1.22372.25923.411631

Table-3: Determinants of non-performing loan (NPL) of banks in South Asia, 1997-2012, total sample

Variables
Dependent variable: Nonperforming loan ratio (NPL/TL)
Independent variables
Bank specific

Model1Model2Model3

Coeff.

S.E.

Coeff.

S.E.

Coeff.

S.E.

One period lagged NPL/TL.38604***0.03275.38268***0.03312.2555***

0.04235Return on Asset (ROAA)-.89498***

0.08190-.89981***0.08228-.85744***0.08573

Equity to Total Asset ratio (E/TA)-.22691***0.04090-.22004***

0.04128-.25058***

0.04735

Cost to income ratio (C/TI)-.00558*

0.00300-.00548*0.00301-0.00445

0.00310Size (lnTA)
Net non-interest income ratio(NNII/TA)-2.588***0.27484-2.6939***

0.27950-2.2267***

0.37601-.22295*

0.13306-.23793*0.133960.04667

0.16507

Liquidity ratio (LA/D&STF)-.04484***

0.01200-.04534***

0.01216-.03005**

0.01320Growth rate of total loan (GTL)-.04396***0.00597-.04433***

0.00631-.04023***

0.00690

Interest income to total loan ratio (i).36009***0.05069.36556***

0.05105.29436***

0.05846-2.4719***0.80702-2.4921***0.82473-2.604**

1.14280-0.04647

0.07552-0.02061

0.08061-0.05310.14793

Industry specificConcentration ratio (CR3)
Macroeconomics specific
Short term interest rate (Sdint)
Term spread of interest rate (R)GDP growth rate-.35605***

0.04973-.347***

0.05090-.23719***0.06644

Rate of inflation-.08256**

0.04103-.07584*0.04128-.08165*0.04657

Year 2003 (Dummy)

1.3335***0.35420

1.287***

0.363021.20010.93165

Year 2009 (Dummy).78418***0.23774.73405***0.24331.67239**0.26626

Year 2010 (Dummy)

1.5772***0.24039

1.5915***0.24644

1.4354***0.27692

Intercept
Number of observations

29.078***2.48970

29.913***2.55090

26.69***

3.36610639
χ2(15) =1675.96
χ2 (97) =432.550.00000.00000.0000502
χ2 (17) =462.37
χ2 (84) =356.510.00000.0000632
χ2 (16) =1646.42
χ2 (97) =425.98AB test AR(1)(p-value)z = -2.2202

0.0264z = -2.2588

0.0239z = -1.8947

0.0581

AB test AR(2)(p-value)z = -2.0702

0.1257z = -2.032

0.2076z = -1.2372

0.2160Wald test (p-value)
Sargan test(p-value)

0.0000Note: The table reports the regression output from GMM estimation of the determinants of non-performing
loan of banks. Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked
with ***, **, and * respectively. Wald test shows the fine goodness of fit of the model while Sargan test is thetest for over-identifying restrictions in GMM dynamic model estimation. AB test AR(1) and AR(2) refer tothe Arellano-Bond test that average auto covariance in residuals of order 1 and order 2 is 0 (𝐻𝐻0 : no auto
correlation).

32

Table-4: Robustness check (Fixed effect estimator as an alternative to GMM estimator) of thedeterminants of non-performing loan (NPL) of banks in South Asia, 1997-2012, total sample
Variables
Dependent variable: Nonperforming loan ratio (NPL/TL)

Model1Model2Model3

Coeff.

S.E.

Coeff.

S.E.

Coeff.

S.E.

Return on Average Assets (ROA)-.29541**0.11459-.3269***0.11357-.24456***

0.0946Equity to Total Asset ratio (E/TA)-.17608***

0.04761-.17636***0.04745-.47348***0.04425

Cost to income ratio (C/TI).02456***0.00564.02502***0.00557.01126**0.0046Size (lnTA)
Net non-interest income ratio(NNII/TA)-3.5787***

0.33512-3.6271***

0.34529-1.585***0.46202-1.4154***0.17854-1.5544***0.18002.42223**0.20303

Liquidity ratio (LA/D&STF).03345*0.01843.03282*0.01852.02712*0.0162Growth rate of total loan (GTL)-.04813***0.00688-.06076***

0.00807-.03899***

0.00684

Interest income to total loan ratio (i)0.12737*0.08085.14825*0.08087

0.10460.07403-5.201***1.2007-5.0306***

1.1942-4.5399***

1.2959-.37423***0.13045-.25632**

0.11137-0.24544

0.17054

Independent variables
Bank specificIndustry specificConcentration ratio (CR3)
Macroeconomics specific
Short term interest rate (Sdint)
Term spread of interest rate (R)GDP growth rate-.40553***0.08368-.40277***0.08552-.19983**0.08981

Rate of inflation-.28727***

0.06852-.26805***

0.06887-.21166***

0.0596

Year 2003 (Dummy)

1.2868*0.675180.91775

0.67769

0.45174

1.2951

Year 2009 (Dummy)

0.62774

0.458390.309170.46391

0.394960.39086

Year 2010 (Dummy)

2.1735***0.45633

2.3779***0.455192.0481***0.41501

Intercept

42.554***2.666943.63***

2.728126.969***3.9521

Number of observations

825

811

669

R2
Wald test (p-value)

0.4713F (14, 684)
= 43.55

0.00000.4899F (15, 669)
= 42.830.00000.379
F(16, 526)
= 20.06

0.0000Hausman test, χ2 (p-value)

39.5.200.00001443.440.0000123.820.0000Note: The table reports the regression output from Fixed Effect estimation of the determinants of NPL.
Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked with ***, **,and * respectively. Wald test is the test for the goodness of fit of the model while Hausman test confirm thejustification of using fixed effect estimator.

33

Appendix

Table-A5: Correlation matrix* of the variables studied for the model of the determinants of nonperforming loan of banks in South Asia
Variables**npltl

L.npltlroa

10.1508i

0.3061eta
ladstf0.27950.5165cti-0.1570.2829lnta

0.10360.21260.001
0.0448-0.1440.04260.11920.3205gtl
nniita
cr3
gdp
inf
ltint
sdint
_cons

0.26940.03120.08320.2301roa

10.24780.24080.10370.04690.04640.06640.05630.04130.0371ieta

ladstf

cti

lnta

gtl

nniita

cr3

gdp

inf

ltint

sdint

_cons

1
0.02310.27171-0.21121

0.05620.04651

0.121

0.0078-0.00141-0.0181

0.074

0.03640.03391-0.2617

0.035-0.17250.23570.0381

1-0.05840.0402-0.02360.20430.051910.14760.0203-0.06390.0342

0.038

0.175

0.0101

1-0.0926-0.0959-0.1173-0.0452-0.047-0.1092

0.0619-0.0482-0.0282

0.04710.34030.25290.0448-0.1188-0.2015-0.0836-0.0649-0.0210.07840.036410.3027-0.20770.0501-0.0634

0.19620.07320.32331
0.36630.09470.0287

0.09410.03940.08820.04250.12130.0412-0.42390.14550.05370.11560.18080.07690.30110.1655-0.017
0.9431

0.0977-0.1779

1
0.0213* Output of Stata** Refer to the table-1 of summary statistics for elaboration of the names of the variables

34

1