(Luận văn thạc sĩ) determinants of non performing loans evidence from southeast asian countries

692 | ICUEH2017

Determinants of non-performing loans:
Evidence from Southeast Asian countries
NGUYEN THI HONG VINH
Banking University of Hochiminh City –

NGUYEN MINH SANG
Banking University of Hochiminh City –

Abstract
The purpose of this study is to examine the bank-specific and macroeconomic determinants
of non-performing loans using an empirical framework that incorporates the related literature
and theoretical hypothesis. To account for non-performing loans persistence, the paper applies
the Generalized Method of Moments technique for dynamic panels which use bank-level data for
Southeast Asian commercial banks over the period 2010 to 2015. The empirical results provide
some evidence to affirm that both bank-level and macroeconomic factors play a role in rising the
non-performing loans of Southeast Asian banks. The findings indicate that the high non
performing loans during these years is associated with low profitability, low credit growth, low
loan to deposit, high equity and large bank size. Finally, the macroeconomic determinants have
the significant effect on loan quality in the anticipated ways. The results also find fiscal variable
has negative effect on non-performing loans and found to be significant. These findings may be
helpful for policy makers to design macro-prudential and fiscal policies.
Keywords: non-performing loans; macroeconomic determinants; bank-specific
determinants; GMM estimation.

1. Introduction
Non-performing loans (NPLs) have been a limiting factor to economic stability and
growth of economies. It is also linked with bank failure and financial crises in both
emerging markets and advanced economies. In Southeast Asian area, NPLs exceeded
4.759% in 2010 and over 3% in the period 2010 to 2015. Within the region, the average

NPLs ratios in the period are highest for Philippines and Thailand banks at 11.18% and
3.417% while Singapore banks have very low NPLs ratio, below 1% (Table 1). What are


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the key determinants of the NPLs issue in the Southeast Asian countries? This study
empirically analyses the effect of bank-specific and macroeconomic determinants of bank
NPLs for this area.
Table 1
The rate of non-performing loans for Southeast Asian countries, 2010-2015 (%)
Year

Indonesi
a

Cambodi
a

Philippine
s

Singapor
e

2010

5.700

4.713

10.574

0.900

Laos

Malaysi
a

Thailan
d

Vietna
m

4.656

2.012

2011

2.440

4.047

10.937

0.739

5.22
0

5.437

4.070

2.450

2012

2.177

2.946

11.428

0.739

1.540

2.526

2.992

3.393

2013

2.256

2.126

12.813

0.705

1.740

1.609

2.830

3.010

2014

2.442

2.284

14.474

1.120

1.996

1.523

2.892

2.522

2015

4.220

2.381

6.855

0.790

1.162

1.929

3.060

1.877

Averag
e

3.206

3.083

11.180

0.832

2.332

2.605

3.417

2.544

Source: Bankscope, authors’ own estimations

We contribute to existing empirical analyses in three ways. First, most of the existing
literature has focus on U.S. or European cases (Berger and DeYoung 1997, Salas and
Sarina, 2002, Louzis et al., 2010 and Anastasiou et al., 2016). Although Southeast Asian
has become an important economic area, the Southeast Asian topic has not earned
enough discussions. Thus, the purpose of this paper is to examine Southeast Asian banks
with the latest and a wider range of panel data that cover 204 banks from 2010 to 2015
in 8 countries. Second, most studies focus mainly on the relationship between bankspecific determinants and NPLs. This study discusses bank-specific, macroeconomic
determinants and NPLs together. Finally, dynamic panel techniques are adopted to
analyze the panel data, which are designed to check the persistence of NPLs. We thus
investigate the persistence of NPLs to eliminate any abnormal NPLs, and that the NPLs
rates of all banks tend to converge to the same long-run average value.
The rest of the paper is structured as follows. Section 2 overviews previous researches
on the determinants of NPLs. Section 3 provides the method that used in this research,
while Section 4 describes the data that are used. Empirical results are presented in Section
5. Finally, Section 6 contains concluding remarks.


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2. Literature review
In the literature, NPLs are affected by both bank-specific and macroeconomic
determinants. The bank-specific determinants are the direct result of managerial
decisions, included profitability, capitalization, asset quality, and size. The
macroeconomic environment relates to the economic growth, inflation, unemployment,
income tax, and fiscal policies.
2.1.

The impact of bank-specific determinants on non-performing loans

Recent studies dealing with bank-specific determinants employ variables such as
profitability, capital, credit growth, and size. The relation between asset quality and
profitability is one of central topics in banking studies. Bad management hypothesis
proposed by Berger and DeYoung (1997) suggest that the efficient banks are better at
managing their credit risk. This hypothesis also argues that low cost efficiency is a signal
of poor management practices, thus implying that as a result of poor loan underwriting,
monitoring and control, NPLs are likely to increase. Berger và DeYoung (1997) find
empirical evidence for the bad management hypothesis, suggesting that low-efficiency
causes lead to bad debt. This study examine the hypothesis of US commercial banks for
the period 1985-1994 and concluded that, in general, the down efficiency led to increase
problem loans in the future. Podpiera and Weill (2008) test the relationship between cost
efficiency and NPLs in the Czech banking sector for the period 1994-2005. Beside that,
Salas and Saurina (2002) and Klein (2013) examine the relationship between the lagged
of NPLs to current NPLs. These findings support the bad management hypothesis that the
rising of NPLs in the past indicated bad credit risk management of banks. This causes the
higher NPLs in the future.
According to moral hazard hypothesis, Keeton and Morris (1987) find that low
capitalization of banks leads to an increase in NPLs by examining the US commercial
banks for the period 1979-1985. In order to test this hypothesis, the research variables

are ROE, bank size and risk-taking of the bank represented by the variables of ROE,
total assets, gross loan on total assets. The study results show that NPLs are rising
for banks with relatively low equity on assets. This is explained by with thinly
capitalized banks, their managers increase the riskiness of their loan portfolio in the
moral hazard incentives. The negative relationship between NPLs and capital ratios
are also found by Salas and Saurina (2002), Louzis et al., (2010) and Stolz and
Wedow (2005). Salas and Saurina (2002) investigate determinants of NPLs of


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Spanish Commercial and Savings Banks for the period 1985-1997. They find a
negative impact of lagged solvency ratio to NPLs which is consistent with the moral
hazard hypothesis. Louzis et al. (2010) also mention that there is a negative influence
of capitalization to NPLs when they examine empirically this relation for Greek
banking sector.Hellmann et al. (2000) and Stolz and Wedow (2005) indicate NPLs
have a positive coefficient of CAR and they explain that bank raised capital to keep
up their buffer when portfolio risk risen.
Procyclical credit policy hypothesis refers to the relation between loan growth and
NPLs. Accordingly, banks adopt a liberal credit during the boom of the cycle, and a tight
policy in the contraction phase. A number of studies find a negative relationship between
loan growth and NPLs (Louzis et al. 2010; Le 2016; Jimenez, Salas, and Saurina 2006). A
number of other studies find that loan growth have a positive relationship to NPLs (Clair,
1992; Keeton 1999; Demirguc-Kunt and Detragiache 1997; and Foo et al. 2010).
Size effect hypothesis mentions that there is the relationship between bank size
and asset quality. Bank size is negatively related to NPLs. For economies of scale,
larger banks can have lower costs and undertake more screening and monitoring.
This helps banks to reduce credit risk arising from asymmetric information between
lenders and borrowers. Some studies are consistent with a positive relationship
between NPLs and bank size (e.g. Louzis et al, 2010; Das and Gosh 2007, Le 2016);

while other studies find bank size is negatively related to NPLs (e.g. Salas and
Saurina 2002).
2.2.

The impact of macroeconomic determinants on non-performing loans

The determinants of NPLs should not be sought exclusively in bank-specific factors but
also are viewed in macroeconomic factors. The financial accelerator theory, discussed in
Bernanke and Gertler (1989), Bernanke and Gilchrist (1999), and Kiyotaki and Moore
(1997), is the most prominent theoretical framework for macro-financial linkages and
credit risk. This theory explains credit risk and its relationship with the cyclical
fluctuations in the economy. During business upturn, NPLs ratios tend to be low because
high borrowers’ net worth which improve the debt servicing capacity of borrowers and
the lenders assume less risk when lending to high net worth agents. This leads to a
loosening of lending standards and strong credit growth derived from competitive
pressure and optimistic macroeconomic outlook. In downturns, NPLs ratios is high
because borrowers’ net worth is reduced. This coupled with the decline in the value of


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collaterals, engenders great caution among lenders, and lead to tightening of credit
extension.
Empirical studies tend to examine the macro-financial linkages and NPLs. Salas and
Saurina (2002) estimate a significant negative effect of GDP growth on the NPLs ratio
from Spanish bank sector. They conclude a quick transmission of macroeconomic
developments to the ability of economic agents to service their loans. Beck et al. (2015)
estimate that the most significant factors affecting NPLs are GDP growth, share prices,
interest rates and the exchange rate. Nkusu (2011) finds that a deterioration in the
macroeconomic environment—proxied by slower economic growth, higher

unemployment or falling asset prices—is associated with rising NPLs. On the contrary,
improving macroeconomic conditions reduce NPLs. Ghosh (2006) conclude that the
variables related to NPLs increases are unemployment, inflation, and public debt. Fofack
(2005) also notes that the NPLs can be determined by different factors e.g. GDP, interest
rate, exchange rate, net interest margins, interbank loans. Espinoza and Prasad (2010)
show that NPLs decline with growth and rise with interest rates and fiscal and external
deficits by introducing macro variables. Louzis et al. (2010) notes that NPLs are
significantly related to macro variables and the quality of management. Messai (2013)
finds that unemployment and the real interest rate influence NPLs positively.
3. Methodology
Following the earlier literature discussion (e.g. Salas and Sarina, 2002, Merkl and
Stolz 2005, Louzis et al., 2010 and Anastasiou et al., 2016 on banking and
macroeconomic related studies), a dynamic approach is adopted in order to account for
the time persistence in the NPLs structure. The relationships between determinants and
NPLs can be specified as follows:
𝑁𝑃𝐿$% = 𝛼𝑁𝑃𝐿$%() + 𝛽𝑀$% + 𝜋) 𝐹$% + 𝜀),$% , 𝛼 ≤ 1

(3.1)

where t and i denote time period and banks, respectively, 𝜀),4,5,6,$% = 𝜂% + 𝜐$% and
𝜂$% is an unobserved bank-specific effect, 𝜐$% is the idiosyncratic error term. To test for
the persistence of NPLs, we use lagged NPLs (i.e., NPL t -1 ) as an explanatory variable
and we expect a positive and significant sign. The vector of explanatory variables includes
bank-specific variables (F), included the profitability proxied by the ratio of equity on total
assets, the capital and solvency presented by the ratio of equity on total assets and the


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ratio of loan to deposit, loan proxied by percentage change in gross loan, and

macroeconomic factor (M) included GDP, inflation, Government budget balance (% GDP)
and income tax (% GDP). Previous researches show that bank-specific characteristic
variables are likely to be potentially endogenous (Athanasoglou et al. 2008) and some
other independent variables are not strictly exogenous.
This paper applies the two-step dynamic panel data approach suggested by Arellano
and Bover (1995) and Blundell and Bond (2000) and also uses dynamic panel GMM
technique to address potential endogeneity, heteroskedasticity, and autocorrelation
problems in the data (Doytch and Uctum, 2011). The dynamic panel data model provides
for a more flexible variance-covariance structure under the moment conditions. The
GMM approach is better than traditional OLS in examining financial variable movements.
Driffill et al. (1998) indicate that a conventional OLS analysis of the actual change in the
short rate on the relevant lagged term spread yields coefficients with some wrong signs
and wrong size. The research also follow Windmeijer’s (2005) finite-sample correction to
report standard errors of the two-step estimation, without which those standard errors
tend to be severely downward biased. By using GMM estimation, it allows for
instrumenting of the endogenous variables and provides consistent estimates. We use the
lags of right hand side variables in the equations as instruments. The two-step estimation
is used because it is asymptotically more efficient than the one-step estimation for the
presence of heteroskedasticity and serial correlation (Blundell and Bond 2000). In this
estimation, the Hansen J-test is used to test the validity of instrument sets and the
Arellano-Bond test is applied to check the absence of second-order serial correlation in
the first differenced residuals.
Table 2 lists the variables used in this study. The NPLs variable is represented by NPLs
to gross loan. The macroeconomic variables consist of the real GDP annual growth rate
(GDP); inflation calculated as the average change in the CPI (INF); Government budget
balance as % of GDP (FISCAL); Income tax as % of GDP (TAXC); and unemployment
rate.

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Table 2
Summary of explanatory variables
Classification

Variable

Descriptions

Bank-level

NPL

Ratio of non-performing loan to total loans

Variables
(Bankscope)

ROE

Ratio of net income after tax to average equity

ETA

Ratio of equity on total assets

LTD

Ratio of loan to customer deposit

LGR

Percentage change in gross loan provided to nonbank sectors

TA

Logarithm of bank’s total asset

Macroeconomic

GDP

Real GDP annual growth rate

Variables
( IMF - IFS)

INF

Inflation, average consumer price (percentage
change)

FISCAL

Government budget balance as % of GDP

TAXC

Income tax as % of GDP

UNEMP

Unemployment rate

4. Descriptions of variables and data sources
Models are estimated on an annual panel dataset of 204 commercial banks in eight
Southeast Asian countries (Singapore, Malaysia, Indonesia, Philippines, Thailand,
Vietnam, Cambodia, and Laos) from 2010 to 2015. The bank-level data are extracted from
BankScope, and it consists of 903 observations. The macroeconomic data come from IMF
– IFS website.
Table 3 reports the summary of statistics for the maximum, minimum, average and
standard deviation of the variables used to estimate determinants of non-performing
loans. The statistics are calculated from yearly data in which all variables are expressed
in percentage. From these figures, the NPLs ratio is from 0.00% to 101.22%, and the
return on equity is from 86.751% to 82.786% show the difference in profitability of
different banks. Besides that, the loan to deposit is very large with 102.8579%. This shows that
the Southeast Asian banks still depend on lending activities.


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Table 3
Descriptive statistics of variables
Variable

Obs.

Mean

SD

Min

Max

NPL

903

3.605

8.386

0

101.220

ROE

903

9.754

11.027

-86.751

82.786

ETA

903

15.001

10.988

2.787

87.588

LTD

903

102.857

72.069

0.55

682.640

LGR

903

23.158

35.919

-64.05

480.060

LNTA

903

21.829

1.909

16.737

26.533

GDP

903

5.584

1.772

0.818

15.240

INF

903

4.703

3.289

-0.895

18.677

FISCAL

903

-3.119

2.001

-12.4

1.100

UNEMP

903

3.772

2.575

0.200

7.500

TAXIN

903

14.098

3.2632

10.100

22.400

Source: Bankscope, authors’ own estimations.

Because our panel is unbalanced, we employ the unit root test by Augmented DickeyFuller (ADF) Fisher type test. The null hypothesis shows that all panels contain a unit
root. The results are presented in Table 4. All of our variables are found to be stationary.
Correlation coefficients among all our variables are found not to exceed 0.382.
Table 4
Unit root test
Fisher type ADF p-values

Fisher type ADF Statistics

NPL

0.0000

-8.8598

ROE

0.0000

-4.0812

ETA

0.0000

-3.1995

LTD

0.0000

-10.7507

LGR

0.0000

-13.7412

TA

0.0000

-2.0998

GDP

0.0000

-8.1288

INF

0.0000

3.801

FISCAL

0.0000

-24.254

TAXC

0.0000

9.4175

UNEMP

0.0000

-19.9473

Source: Bankscope, own estimations.


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5. Empirical results
The estimation results are presented in Tables 5, reporting the respective impacts of
determinants on NPLs. Various specifications of Eq. 3.1 are examined. Specification 1
shows estimated parameters of NPLs, which is subjected to bank-specific characteristics
suggested by the literature. The lagged of the bank-specific variables are added to
specification 2. Specification 3 and 4 respectively show the impact of macroeconomic
variables and the lagged of these variables. Specification 5 presents the results of both
specific-bank variables and macroeconomic variables, and the lagged of these variables
are then included to specification 6.
Table 5
GMM estimation results for Southeast Asian area NPLs, 2010 - 2015
Variables
NPLit-1
ROE

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

0.6797***
(0.0051)

0.5826***
(0.0043)

0.6487***
(0.0063)

0.8719***
(0.0203)

0.6544***
(0.0021)

0.6335***
(0.0021)

-0.0198*
(0.0074)

-0.0937***
(0.0143)

-0.0120***
(0.0020)

-0.0561***
(0.0043)

-0.0393**
(0.0137)

ROEit-1
ETA

0.0466***
(0.0084)

-0.0052***
(0.0012)

-0.0616***
(0.0045

LnTAit-1

-0.0059***
(0.0006)

-0.0366***
(0.0057)

-0.2858***
(0.0619)

0.06935
(0.7024)
-0.2836
(0.6857)

-0.0081***
(0.0025)
0.0021
(0.0016)

-0.0360***
(0.0007)

-0.0100***
(0.0017)

GGLit-1
LnTA

-0.0132***
(0.0017)

0.1123***
(0.0117)
-0.0395**
(0.0132)

0.0066***
(0.0018)

LTDit-1
LGR

0.0635***
(0.0039)

-0.0766
(0.0360)

ETAit-1
LTD

0.0357
0.0359

0.0526***
(0.0067)

-0.0394***
(0.0018)
0.0136***
(0.0007)

-0.2284***
(0.0364)

-0.455

(0.2886)
0.1834
(0.2880)


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Variables

Model 1

Model 2

GDP

Model 3

Model 4

Model 5

Model 6

-0.1467***
(0.0409)

-0.0964**
(0.0340)

-0.0810***

(0.0149)

-0.1418***
(0.0248)

0.1162***
(0.0331)

GDPit-1
0.0564*
(0.0216)

INF

0.0503**
(0.0175)

0.0555***
(0.017)
0.0051
(0.0080)

0.1279***
(0.0279)

INFit-1
-0.1935***
(0.0598)

FISCAL

0.0564
(-0.0550)

-0.0380
(0.0167)
-0.0691***
(0.0049)

-0.0608
(0.5840)

FISCALit-1
-0.1013
(0.0497)

TAXC

-0.0140
(0.0856)

0.0814
(0.0636)

UNEMP

0.4181*
(0.1556)

0.1240***

(0.01293)

-0.0495
(-.0336)
0.2263***
(0.0370)

0.3371***
(0.02568)

-0.4541**
(0.1533)

UNEMP it-1

-0.0837**
(0.0270)
0.047
(0.0219)

-0.0917
(0.0770)

TAXCit-1

-0.0257
(0.0113)

0.4559***
(0.0852)

-0.0870
(0.0789)

8.5471***
(1.4580)

9.2280***
(2.2121)

2.0878**
(0.7301)

2.1810***
(0.4632)

3.9292***
(-0.8075)

4.0543***
(1.1536)

Obs.

692

692

692

692

692

692

No of banks

204

204

204

204

204

204

Pro>chi2

0.000

0.000

0.000

0.000

0.000

0.000

Hansen test

0.101

0.210

0.391

0.118

0.358

0.442

AR(1)

0.004

0.006

0.014

0.021

0.007

0.003

AR(2)

0.338

0.327

0.352

0.366

0.348

0.340

Constant

Source: Bankscope, own estimations
***, **, * * and ** denote significance at the 10 %, 5 % and 1% levels, respectively 5% và 10%. Standard
errors in parentheses

The estimation results in Table 5 confirm that both bank-level and macroeconomic
factors play a role in affecting the NPLs of Southeast Asian banks. Our finding shows that
the highly significant coefficient value of the NPLs persistence. The coefficient’s size of the
lagged NPL ranges between 0.5826 to 0.8719, thus suggesting that a shock to NPLs is
likely to have a prolong effect on the Southeast Asian banking system.


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For the other explanatory variables, most of the estimated coefficients have signs
compatible with the theoretical arguments in the literature. The bank’s performance
indicator, ROE is found to be significant and negatively related to NPLs in all models,
supporting the bad management hypothesis of Berger and DeYoung (1997) . This implies
that bad managed banks leads to more risky activities and rising of NPLs. In contrast,
banks which is characterized by strong profitability is less likely to participate in unsafe
activities and reducing of NPLs. Banks’ risk attitude, as reflected in the equity to asset
ratio and loans to deposits ratio, impact on NPLs of Southeast Asian banks significantly
in most of models. The positive coefficient of ETA is consistent with Hellmann et al.
(2000) and Stolz and Wedow (2005) who explained that bank raised capital to keep up
their buffer when portfolio risk risen. The change in gross loans also affects NPLs at a
level of significance of 1%. The negative impact of credit growth and loan to deposit ratio
on NPLs imply the instantaneous effect of increases in gross loans, which lowers the ratio
of NPLs within that period. There is a strong evidence that support the size effect, verified
by significant coefficients on TA in Models 1 and 5.
Among the macroeconomic variables, the real GDP growth rate has statistically and
negatively impact on NPLs in all models. This implies higher than expected NPL ratios in
downturns are associated with declines in borrowers’ cash flows and NW, which lower
their debt servicing capacity (Le, 2016). The inflation and unemployment rate both at t
and t-1 are found to have a positive relationship with NPLs. In addition, NPLs is
statistically and negatively affected by FISCAL in most models. This implies a positive
feedback between restrictive fiscal policies and NPLs of Southeast Asian banks.
Furthermore, we find that our new variablesTAXINC and TAXINCit-1 significantly affect
the loan quality in model 5 and 6.
The findings also show the Hansen and the serial-correlation tests do not reject the
null hypothesis of correct specification, which means that the research has valid
instruments and no serial correlation.
6. Conclusions
This study estimate the impact of the determinants on NPLs based on sample of the
204 commercial banks for 8 Southeast Asian countries. Applying the dynamic panel data

techniques with System-GMM estimation, the empirical results provide some evidence to
confirm that both bank-level and macroeconomic factors play a role in affecting the NPLs


Nguyen Thi Hong Vinh & Nguyen Minh Sang | 703

of Southeast Asian banks. The results indicate that the high non performing loans during
these years is associated with low profitability, low credit growth, low equity and small
bank size. In addition, the macroeconomic determinants have the significant effect on
bank loan quality. The results also show fiscal variable have negative effect on nonperforming loans and found to be significant. These findings may be helpful for policy
makers to design macro-prudential and fiscal policies.
Important policy implications emerge from these empirical results. The negative
relationship between NPLs and profitability also suggests that the regulator should apply
closer screening and monitoring of the risk of loan default. Furthermore, higher capital
ratios give more incentive to increase NPLs than lower capital ratios. Thus,
implementation of risk-based capital requirement will also help to prevent risk-taking
behaviour by soothing over-heated lending behaviour for high risk banks. It is also crucial
to improve management mechanism and control risks, thereby implementing credit
analysis based on cash flow and monitoring borrowers’ solvency.
The shortcoming is that the paper could not classify the banks to their size or included
different level of banks’ growth on the market, as well as bank’s non-performing loans
classification. Further study will examine the determinants on non-performing loans by
classifying bank size and different level of banks’ growth on the market.

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