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Determinant of non performance loans the case of vietnamese banking sector

UNIVERSITY OF ECONOMIC

INSTITUDE OF SOCIAL STUDIES

HOCHIMINH CITY

THE HAGUE

VIETNAM

THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A. IN DEVELOPMENT ECONOMICS

DETERMINANTS OF NONPERFORMING LOANS
THE CASE OF VIETNAMESE BANKING SECTOR

A thesis submitted in partial fulfillment of the requirements for degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS


By
TRUONG NGOC THANH

Academic Supervisor
DR. NGUYEN THI THUY LINH

HO CHI MINH CITY, DECEMBER 2016


Determinants of nonperforming loans – The case of Vietnamese banking sector

ABSTRACT

The main purpose of this study is to examine the determinants of non-performing loans (NPLs) in the
case of Vietnamese banking sector by analyzing the unbalanced panel data of 30 Vietnamese banks
over the period of 2008 – 2012. Both of macroeconomic and bank-specific determinants are employed
when modeling the regression of NPLs’ determinants. Macroeconomic factors including Gross
Domestic Product (GDP) growth rate, unemployment rate, real lending interest rate and sovereign
debt are exogenous variables that effect on NPLs. Besides that, the study examine the bank-specific
determinants by analyzing relevant hypothesis such as ‘bad management’, ‘pro-cyclical credit policy’,
‘skimping’, ‘diversification’, ‘too big to fail’, ‘moral hazard’ hypothesis. According these hypotheses,
return on equity, inefficiency rate, proportion of non-interest income and leverage ratio are the
endogenous variables which effect to NPLs. In addition, credit growth rate is added into model to
examine its effect on NPLs. Moreover, the effects of government intervention and foreign investment
on NPLs are also examined in this study by investigating the difference in NPLs of state-owned banks
and fully foreign-owned banks. The fixed effect of unbalance panel data is employed to test these
hypotheses.
Regarding bank-specific factors, the inefficiency rate and credit growth rate statistically affect on
NPLs. However, return on equity, non-interest income rate, leverage ratio do not statistically
significant effect on NPLs. According to regression result, it shows the negative and significant
relationship between the inefficiency rate and NPLs that is consistent with ‘skimping’ hypothesis.
Moreover, the relationship between credit growth and NPLs is significant and negative.
As the regression result, all of macroeconomic determinants including GDP growth rate,
unemployment rate, real lending interest rate and sovereign debt statistically significant affect on
NPLs. The regression shows the positive and significant relationship between the sovereign debt and
NPLs which is consistent with hypothesis. The increase in sovereign debt will reduce payment ability
that increases the future NPLs. However, the regression shows the positive relationship between GDP
growth rate and NPLs and negative relationships between the unemployment rate, lending interest
rate and NPLs that is not consistent with hypothesis.


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Determinants of nonperforming loans – The case of Vietnamese banking sector

Regarding the government intervention, the regression shows that return on equity and leverage ratio
are affected in state-owned bank that lead to higher NPLs. However, the effect of foreign investment
in fully foreign-owned banks on NPLs is not supported in this study.
There are some policy implications based on the regression results. Firstly, the sovereign debt should
be strictly control in order to enhance the payment ability of debtors. Secondly, the underwriting and
monitoring loans process should be controlled to reduce NPLs expansion at bank level. Finally, the
operations of state-owned banks should be controlled to reduce NPLs expansion in state-owned banks.

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Determinants of nonperforming loans – The case of Vietnamese banking sector

TABLE OF CONTENT
CHAPTER 1:

INTRODUCTION................................................................................................. 1

1.1.

Overview of Vietnamese banking sector and non-performing loans .................................... 1

1.2.

Research problem .................................................................................................................. 2

1.3.

Research objectives and research question ............................................................................ 4

CHAPTER 2:

LITERATURE REVIEW .................................................................................... 6

2.1.

Non-performing loans definition ........................................................................................... 6

2.2.

Bank-specific determinants of non-performing loans ........................................................... 7

2.3.

Macroeconomic determinants of non-performing loans ..................................................... 12

2.4.

Government intervention and foreign investment in banking system ................................. 16

CHAPTER 3:

METHODOLOGY AND DATA ....................................................................... 19

3.1.

Methodology........................................................................................................................ 19

3.2.

Data ...................................................................................................................................... 21

3.3.

Estimation approach ............................................................................................................ 23

CHAPTER 4:

ANALYSIS RESULTS ....................................................................................... 25

4.1.

Descriptive statistics ............................................................................................................ 25

4.2.

Economic results.................................................................................................................. 27

4.3.

Result discussion ................................................................................................................. 30

CHAPTER 5:

CONCLUSION ................................................................................................... 35

5.1.

Main findings and policy implication .................................................................................. 35

5.2.

Limitation of the study ........................................................................................................ 36

REFERENCES ................................................................................................................................... 38
APPENDIX ........................................................................................................................................ 41

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Determinants of nonperforming loans – The case of Vietnamese banking sector

LIST OF TABLE

Table 1: Definition of variables used in modeling NPLs determinants ............................................. 17
Table 2: Specific calculation of variables .......................................................................................... 22
Table 3: Methodology test.................................................................................................................. 24
Table 4: Descriptive statistics ............................................................................................................ 25
Table 5: The correlation matrix .......................................................................................................... 26
Table 6: Summarize NPLs ................................................................................................................. 27
Table 7: The regression result ............................................................................................................ 28
Table 8: Regression result of dummy variables ................................................................................. 29
Table 9: Empirical evidence for tested hypothesis............................................................................. 34

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CHAPTER 1: INTRODUCTION

1.1.

Overview of Vietnamese banking sector and non-performing loans

There are three types of ownership in Vietnamese banking sector including state-owned commercial
banks, joint stock commercial banks, foreign banks (Kalra, 2012). State-owned commercial banks
play an important responsibility in international financial by lending to main sectors in Vietnamese
economy. In particular, loans of trade and industry sectors central is granted by Bank for Industry and
Trade (ViettinBank) while foreign payments is in-charged by Bank for Foreign Trade (VietcomBank).
In additional, loans of agriculture and fishing are supported by Bank for Agricultural Development
(AgriBank). Concerning the bank market share, state-owned commercial bank account for large bank
market share in 2010 (Kalra, 2012). Besides that, the growth of joint stock commercial banks also
contributes in the banking sectors throughout their financial services.
In Vietnam, banking sector is under the control of government throughout the State bank operations.
Besides the financial responsibility, some duties of state-owned bank are expected. In particular, loans
of main sectors in the economy are financed by state-owned commercial banks. In addition, money
supply and demand are controlled by state bank by opening the market operation, reserve system,
bank rate policy. Moreover, all regulation as well as guideline of banking operations must be complied
with state bank’s regulation.
The Vietnamese banking system is significantly impacted by the economic depression over the period
of 2008 – 2012 which leads to NPLs expansion. The main cause of bank problem is the deterioration
of loan portfolio. As the same situation with international banking system, Vietnam experienced with
a period of the housing bubble and rapid growth in the stock market. Allowing easy access to loans
and rapid credit growth, Vietnamese banking sector had to face with the credit exposure when
economy went down. According to report of State Vietnamese Bank, the loan portfolio significant
increased from 2005 to 2007. Specially, the credit growth rate was 52.42% in 2007 that doubly
increases comparing with this in 2006. In addition, high unemployment rate in period of economic
downturn strongly impact to the payment debt ability. Moreover, the weakness of Vietnamese banking
sector is one cause that expand the problem loans. Excessive loans, loose credit policy assessment,
less mortgage loans, lose control in loan monitoring are the problems of Vietnamese banking sectors.

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As the consequence, the NPLs rate was 3.4% in 2012 which doubly increases comparing with this in
2009.
Many reactions were implemented by State bank of Vietnam to solve the bank’s NPLs. The number
of policies was implemented including increasing capital adequacy ratio to 9%, increasing restriction
for lending credit, establishing Vietnam asset management company (VAMC), buying NPLs of weak
banks, restructuring weak banks, issuing new loan classification, etc. In addition, minimum of charter
capital of banking sector was increased. Interest rate ceilings were re-imposed to control operation of
banking sector as well stable the economy. However, the NPLs rate was not significantly improved.
According the World Bank’s report, the NPLs declined to 3.107% by the end of 2013 because of
transferring bad loans to the VAMC. However, the NPLs in 2013 also emphasizes that this rate could
be 9% if all restructured loans were included (Mellor, Minh, & Thuc, 2014). In the other sides,
according to rating agency Moody’s estimation, NPL could be higher and exceed 15% in the case of
implement international standard assessment.
The concern of NPLs was raised in Vietnamese banking sectors in recent years. In addition, the root
cause of NPLs of bank’s sector was examined to find out best measure for NPLs solving. Therefore,
the main purpose of this research is to examine the determinants of NPLs in the case of Vietnamese
banking sector in order to find out the appropriate policy implication for solving banking NPLs.

1.2.

Research problem

Reviewing empirical studies, there are many approaches to examine the determinants of NPLs. On
the one hand, macroeconomic factors could be employed to evaluate their effect on NPLs. Berge and
Boye (2007) conclude that real interest rate and unemployment are highly sensitive with the problem
loans. They find out that one of primary contribution in real interest rate and unemployment rate
improvement is the problem loans’ declining (Berge & Boye, 2007). Besides that, according to study
of Reinhart and Rogoff (2011), they made conclusion that NPLs could be considered as the one root
cause of banking crisis. According International Monetary Fund working paper, basing on the NPLs
in Central, Eastern and South Eastern Europe, the research indicates that strong feedback of
macroeconomic condition including GDP growth, unemployment and inflation on NPLs (Klein,
2013). The econometric result suggests GDP growth is one of the macro explanatory of NPLs. Besides
that, the significant linkage between macroeconomic condition and NPLs is also supported by the
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investigation of determinant of NPLs of 85 banks in three countries including Italy, Greece and Spain
(Messai & Jouini, 2013). However, this approach does not consider the effect of banking specific
variables that illustrate the characteristic of each bank, which generates different effect on the risk
exposure at the bank level.
On the other hand, some empirical studies attempt to find out the linkage between bank-specific
variables and NPLs including bank capitalization, bank profitability, bank regulation, etc. This
approach is more powerful in explanation of difference of banking NPLs. For instance, using the
aggregate banking data from 59 countries, internal factor including the capital adequacy ratio, prudent
provisioning policy, private or foreign ownership, strengthening the legal system have significant
impact on banks’ NPLs (Boudriga, Taktak, & Jellouli, 2009). Moreover, the insolvency of financial
institution is also the result of high NPLs (Farhan, Sattar, Chaudhry, & Khalil, 2012). In addition,
other study attempts to find out impact of ownership status or market power on NPLs. It generally
accepted that NPLs associated with the inefficiency, failures of the banks in the financial crisis period
(Ahmad & Bashir, 2013).
Other approach to examine NPLs’ determinant is analyzing the effect of both macroeconomic and
bank-specific factors on NPLs. In particular, the macroeconomic and microeconomic factors are
combined to examine the NPLs of commercial and saving bank in Spain. It concludes that all
macroeconomic and microeconomic factors have specific effect on NPLs (Salas & Saurina, 2002).
Using the data of Greek banking system, the empirical study combines both macroeconomic and bankspecific factors to assess NPLs’ determinant. This study finds out that bank-specific factors have a
different impact on NPLs of different loan categories including mortgage, business and consumer loan
portfolios (Louzis, Vouldis, & Metaxas, 2011).
Government intervention and foreign investment are also considered as the endogenous variables that
affect to NPLs. Some arguments show that government intervention play important role to manage
economic in which market failure are balanced (Garcıa-Marco & Robles-Fernandez, 2008). Other
arguments supported for private-sector monitoring hypothesis. Regarding foreign investment, it is
general accepted that bank will get advantages from experience of management as well as capital from
foreign investment. However, its effect varies in different studies.
In summary, the financial problem raise more concern in the NPLs in recent years. The determinants
of NPLs are examined in many empirical studies. However, the determinants of NPLs in the case of

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Vietnamese banking sector are not examined. Therefore, this study will examine the NPLs’
determinants in the case of Vietnamese banking sector.

1.3.

Research objectives and research question

1.3.1. Research objectives
As discussion above, the main purpose of this study is to examine the determinants of NPLs. The
unbalanced panel data of 30 Vietnamese banks over the period of 2008-2012 is used in this study.
Both macroeconomic and bank-specific factors are employed in order to model the NPLs’
determinant. In particular, this study will examine the effect of exogenous variables including GDP
growth, unemployment rate, lending interest rate and sovereign debt on NPLs. The endogenous
variables including return on equity, inefficiency rate, non-interest rate, leverage ratio and credit
growth are also examined. In addition, the effect of government intervention and foreign investment
on NPLs is investigated by assessing the difference of NPLs in state-owned bank and fully foreignowned bank. In finally, the policy implication for NPLs solving is suggested after examining the
regression results.

1.3.2. Research questions
According to the research objectives, this study will attempt to answer following research questions.
The first question is which factors will affect on the NPLs. The second question is how they affect on
NPLs. The third question is what the cause of these effect. And the final question is which policy
applicant could be raise from analyzing the effect of these factors.

The rest of study will be arranged as follows. Chapter 2 briefly presents the theories and empirical
studies regarding NPLs’ determinant. In this part, specific influence of each factor on NPLs will be
analyzed basing analyzing the result of previous studies. Chapter 3 will provide methodology analysis
of previous empirical literature. This part will give overview of all methodologies were applied in
previous study and suitable mythology will be selected to analyze NPLs’ determinants in Vietnamese
banking sector. Detailed data and data sources are also presented in this part. Next chapter will present
the analysis results. The descriptive statistic as well as economic results is provided in this part. This
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part also provides regression explanation and comparison with expectation of literature review. The
conclusion as well as policy implication will be presented in final chapter. Chapter 5 also provides
research limitation as well as guideline for future studies.

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Determinants of nonperforming loans – The case of Vietnamese banking sector

CHAPTER 2: LITERATURE REVIEW

2.1.Non-performing loans definition
Non-performing loans are loans either in default or close to being in default. It means that the borrower
cannot pay the loan back in full. In generally, three kinds of debts could be defined as NPLs. Firstly,
debts whose interest and principal are past due by 90 days or more compared with stipulated time
governed in credit contract. Secondly, at least 90 days of interest payments have been capitalized,
refinanced or delayed by agreement. Finally, payments are less than 90 days overdue, but there are
other good reasons to doubt that payments will not be made in full. In particular, the loan is considered
as NPLs if they belong to following exposures (Basel III, 2011). Firstly, all exposures are classified
as the defaulted or impaired loans in which loans experience with the deterioration of their
creditworthiness. Secondly, other exposures have more than 90 days past due. Thirdly, one exposure
could be considered as the NPL if there is evidence that the customer could not fully pay principal or
interest.
NPLs definition used in empirical researches is consistent. Louzizs at el (2011) used to dataset of
Greek banks to analyze the impact of NPLs. According that, NPLs refer to loan which are 90 days
past due. Basing on the study of Louzis at el (2011), Klein (2013) employed the NPLs of Central,
Eastern and South-Eastern Europe to investigate their determinants in which NPLs is defined as the
loan with 90 days past due.
In Vietnamese banking sector, definition of NPLs is nearly the same with international cases. The
NPLs is the loan is classified as Group 3 to Group 5 (Decision 493/2005/QD_NHNN, 2005). As
stipulated in loan classification regulation, following debts are classified into group 3 to group 5.
Firstly, NPLs are debts overdue for a period of more than 90 days. Secondly, debts are restructured
and extended payment term. Thirdly, loan issuing to customer who is not allowed or restricted to get
loan as regulation is considered to classify as NPLs. In addition, loans must be classified to higher
group if there is evidence of disadvantage change in environment or business that negatively effect to
payment ability of customer.
In summary, NPLs in this study is understand as the NPLs rate which is rate of non-performing loans
over the gross loans. NPLs used in this study will based on the regulation of Decision
493/2005/QD_NHNN and other empirical studies in which non-performing loans is loans with 90
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days past due. Next sections will continue to examine the effect macroeconomic and bank-specific
factors on NPLs by reviewing the discussion in theory and previous empirical studies.

2.2.

Bank-specific determinants of non-performing loans

Besides major studies investigating the effect of macroeconomic factors on NPLs, fewer empirical
research attempt to analyze the effect of bank-specific factors on NPLs. While macroeconomic factors
are reflected as exogenous for bank performance, bank-specific is the endogenous factors which
directly effect on credit exposure. Difference in bank regulation, bank capacity as well as profit
enhance will generate different change in NPLs. In general, moral hazard, operating efficiency, loan
diversification, banking leverage, credit policy, etc. is one of the bank-specific factors which are
usually used to analyze NPLs. Following is the discussion regarding effect of specific factors to NPLs
by analyzing relevant hypotheses in previous empirical studies.

2.2.1. ‘Bad management’ and ‘Skimping’ hypothesis
According to ‘bad management’ hypothesis, cost efficiency is considered as the endogenous factor
effecting to NPLs. This hypothesis suggests that NPLs will expand in bank with low cost efficiency
(Berger & DeYoung, 1997). Problem loans strongly links with the management which is reflected in
internal control system. Furthermore, bank with bad management do not have enough skill to manage
bank’s operation as well as credit risk. According that, poor skill in risk definition, risk assessment
will perform in poor skill of defining risk policy, underwriting, collection as well as problem loans
solving.
In contrast with ‘bad management’ hypothesis, the ‘skimping’ hypothesis suggests that the high cost
efficiency is associates with the NPLs’ increase. According this hypothesis, resources is allocated for
management including underwriting and monitoring loans which are traded off with cost efficiency.
In this assumption, it is expected that the bank with high cost efficiency will lead to NPLs expansion
in future by less focusing on loan monitoring including underwriting, debt collection, credit scoring,
etc. (Berger & DeYoung, 1997).
Employing Ganger-causality technique, Berger and DeYoung (1997) proposes that NPLs associate
with cost efficiency. Using U.S commercial banks in 1985 and 1994, the data and regression result

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confirm the strong linkage between cost efficiency and NPLs. According that, the increase in future
NPLs is the consequence of low cost efficiency that is consistent with ‘bad management’ hypothesis.
In addition, ‘bad management’ hypothesis is also supported by the research in Czech banks in 1994
and 2005. Applying GMM model, this research finds that NPLs is forecasted by cost efficiency’s
deterioration (Podpiera & Weill, 2008). However, the study of Spanish banks over the period 19851997 find out cost efficiency in statistically affects on problem loans that do not support for hypothesis
estimation (Salas & Saurina, 2002). Basing on aforementioned research, ‘bad management’
hypothesis is considered to analyze bank-specific determinants of NPLs in mortgage, business and
consumer loans in Greek banking system (Louzis, Vouldis, & Metaxas, 2011). The inefficiency rate
is use as proxy for this hypothesis. The regression gives result that the linkage between cost efficiency
and NPLs is statistically significant. According that, this study suggests that one of leading indicators
of the increase in NPLs is the low cost efficiency. This conclusion is quite consistent with ‘bad
management’ hypothesis and the results of large of studies. In sum, the ‘bad management’ hypothesis
is more supported by different empirical studies. Therefore, this hypothesis will be continued to
examine by measure the effect of inefficiency of bank on NPLs.
Hypothesis 1: Higher cost inefficiency in bank operations is associated with higher NPLs.

2.2.2. ‘Diversification’ hypothesis
‘Diversification’ hypothesis suggests the bank’s diversification generates a potential of low NPLs.
According that, the negative relationship between bank’s diversification and NPLs is expected (Berger
& DeYoung, 1997). It explains that diversification in income sources will reduce dependence of
bank’s income of one or few of bank’s that could generate centralization risk. According to
centralization risk, bank is not flexible for adapt new operations and it is difficult for bank to cover
lose in case of sudden incident happen for main income source. As the result of centralization risk,
loan portfolio could not be controlled which leading to increase in NPLs. Therefore, diversification
will reduce centralization risk which reduce NPLs expansion.
Many studies attempts to analyze effect of diversification on NPLs, however, the proxy for this
variable distinguish in each research. First of all, size of bank is used as the proxy for diversification.
According this approach, more diversification opportunities are driven by bigger size of bank which
allows reducing NPLs (Salas & Saurina, 2002). The research in India is also supported this argument
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Determinants of nonperforming loans – The case of Vietnamese banking sector

which bigger size will reduce NPLs (Ranjan & Dhal, 2003). Secondly, the entropy index regarding
share of different revenue is set up to analyze the diversification hypothesis. However, the result shows
statistically insignificant effect of diversification on NPLs (Hu, Li, & Chiu, 2004). Income growth is
used to consider verifying impact of benefit from diversification on NPLs. Because of high correlation
between income growth and net interest income, diversification’s benefit does not generate any effect
on NPLs reduction (Stiroh, 2004). In the other hand, proportion of non-interest income is counted
when modeling NPLs determinant (Louzis, Vouldis, & Metaxas, 2011). On that ground, this ratio will
reflect the dependence of income in interest rate income. This study suggests that NPLs negatively
related to banks size as well as the proportion of non-interest income over total income. As the
aforementioned discussion, this study will employ the proportion of non-interest income over the total
income to examine the effect of diversification on NPLs.
Hypothesis 2: Higher non-interest income ratio is associated with lower NPLs.

2.2.3. ‘Moral hazard’ hypothesis and ‘Too big to fail’ hypothesis
In ‘moral hazard’ hypothesis, the moral management is considered as variables which effecting to
NPLs. According that, it is assumed that the increase in NPLs is the consequence of bank’s lowcapitalization (Berger & DeYoung, 1997). The moral hazard incentive will vary in different bank that
is influenced by bank’s manager. This hypothesis assumes that the riskiness of loan portfolio expanded
by bank’s manager because of thin capital. Furthermore, the hypothesis suggests that there is excessive
risk taking in low-capitalization bank. As the result, credit risk’s increase will associate with problem
loans expansion that increases future NPLs.
Salas and Saurina (2002) strongly confirm this hypothesis. The lagged of solvency ratio is used to
analyze the moral hazard hypothesis. As the result, lagged solvency of bank is significant negative
with NPLs that is consistent with moral hazard hypothesis. In addition, moral hazard hypothesis is
tested in the case of Greek banks. However, the increase of solvency ratio - a proxy for moral hazard
hypothesis insignificantly effects on NPLs declining (Louzis, Vouldis, & Metaxas, 2011).
‘Too big to fail’ hypothesis is based on the moral hazard problem of key banks in the economy.
According that, bank is supported by other institutions in case of incident, there is less effort for defend
and recover the risk (Stern & Feldman, 2004). This hypothesis assumes that moral hazard problem
will maintain key banks having many customers or playing a key role in the banking system of one
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country. The liquidity and solvency of key banks will strong link with other bank in the economy.
Because of their nature, the failure of this bank could create domino effects which continuously
effecting to other banks and their creditors. The failure in whole of banking system is beginning if
there is no prevention of institutions. Therefore, government usually plays a role to support and
maintain the operation of key banks. As the consequence, the moral hazard problem happens in which
risk prevention is not strongly prevented. The banks have a probability to accept excessive risk and
issue loan for lower quality customer that increases future problem loans. In sum, according too big
to fail hypothesis, the increase in NPLs could be driven from the moral hazard problem in the large
bank.
This hypothesis is not clearly supported by empirical study. According the study regarding U.S banks,
the research suggests that riskier portfolio in large bank is motivated by U.S government in 1980s
which supports for too big to fail hypothesis (Boyd & Gertler, 1994). In the other hand, the study
analyze to US bank performance over the period 1983-2003 by investigate size classes do not give
evidence for this hypothesis (Ennis & Malek, 2005). This hypothesis is also applied to analyze NPLs’
determinant in Greek banks. This hypothesis is strong supported at all loan categories including
mortgage, business loans (Louzis, Vouldis, & Metaxas, 2011). However, in the case of consumer
loans, this hypothesis is not supported.
In sum, both ‘moral hazard’ and ‘too big to fail’ hypothesis examine the effect of moral management
on NPLs. According these hypotheses, the lower solvency ratio associates with higher leverage ratio
that lead to an increase the future NPLs.
Hypothesis 3: Higher leverage ratio is associated with higher NPLs.

2.2.4. ‘Bad management II’ and ‘Pro-cyclical credit policy’ hypothesis
‘Bad management II’ hypothesis suggests that bank performance is considered as the proxy for
management skill in lending activities. Lower quality of skill management in lending activities
associates with low performance that will deteriorate future loans. Therefore, past performance or
earnings negatively link with NPLs (Louzis, Vouldis, & Metaxas, 2011). In addition, bank with high
profitability, which is not under pressure increasing loan portfolio and profitability, will be more
careful when assessing new loan and risk exposure reduction will belong. Using return on equity as
proxy for bank profitability, Godlewski (2004) reports the negative relationship between return on
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equity and NPLs. This indicates that higher bank profitability will be lower NPLs (Godlewski, 2004).
Furthermore, ‘bad management II’ hypothesis is applied to consider NPL’s determinant in Greek
banking system. The return on equity is used as the proxy for this hypothesis. The regression result
suggests that there is negative relationship between NPLs and earnings in the case of mortgage loans
(Louzis, Vouldis, & Metaxas, 2011).
In contrast, ‘pro-cyclical credit policy’ hypothesis suggests distinguish assumption with ‘bad
management II’ hypothesis. According that, positive relationship between current bank performance
and future NPLs is expected that supports for liberal credit policy. According to the model of Rajan
(2004), the credit policy is affected by earning expectation as well as management’s concerns
regarding short-term reputation. As the consequence, to increase bank’s profitability, the current
earnings and future problem loans are distorted and inflated. Loan loss provision is also used to adjust
current earnings. Therefore, future NPLs positively links with past earnings. This assumption is
consistent with the result when analyzing risk taking behavior and ownership in the Spanish banks.
This empirical study argues that higher bank profitability will associated with higher NPLs (GarcıaMarco & Robles-Fernandez, 2008). In the other side, relationship between bank profitability and
NPLs is not supported when lagged return on asset is used as proxy of bank profitability. The
researchers argue that return on asset is appropriate when applied in firm level instead of country level
(Boudriga, Taktak, & Jellouli, 2009).
As aforementioned empirical studies, most of studies supported for ‘bad management II’ in which
bank with high profitability will be more carefully in granting credit that lead to NPLs reduction.
Hypothesis 4: Profitability negatively related with lower NPLs

2.2.5. Credit growth
In this hypothesis, the credit growth in banking sector is analyzed to find out their effect on NPLs.
Credit growth is the increasing rate of banking credit loan which reflects the speed of credit growth.
According that, it assumed that rapid growth in credit loan will effect on quality of risk control.
Because of large credit loan assessment, the quality of underwriting as well as credit loans is not
ensured, which enhance risk exposure and NPLs in the future.
Many recent empirical studies give clear evidence that is consistent with this hypothesis. Using data
of Argentine banking sector, the study suggests there is strong linkage between credit growth and
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impaired loans. The study make conclusion that impaired loan and credit growth are relevant (Bercoff,
Giovanni, & Grimard, 2002). In addition, rapid past credit or branch expansion is associated with
NPLs. Using the dataset of Spanish problem loans of both commercial and saving banks, the research
concludes that rapid credit growth associated with problem loans (Salas & Saurina, 2002).
Furthermore, Jimenez and Saurina (2006) suggest the positive linkage between credit growth and
impaired loans. The research concludes that lagged there credit growth positively effects to the loan
losses. It explained that low quality of customer and mortgage loans declining increase in the period
of economy downturn. According that, the credit is more risky which affecting loan losses (Jimenez
& Saurina, 2006). This conclusion is also affirmed by other researches. Khemraj and Pasha (2009)
used the dataset of Guyanese banking system to analyze the relationship excessive lending and NPLs.
Using the panel data with fixed effect model, the regression result is consistent with those of Jimenez
and Saurina (2006). According that, excessive lend could generate the likelihood of higher NPLs
(Jimenez & Pasha, 2009).
In the contrast, high credit growth could be considered as high profitability in a certain period. With
high bank profitability, bank is not under pressure to spread out rapidly their market that affects on
credit quality. Ahmad and Bashir (2013) supported the negative relationship between credit growth
and NPLs. They argued that large bank would diversify the loan portfolio and reduce risk by
increasing their market. However, this dimension is rarely supported.
Hypothesis 3: Credit growth is positively associated with higher NPLs.

2.3.

Macroeconomic determinants of non-performing loans

Reviewing the empirical studies regarding NPLs’ determinant, the major of study assess NPLs at the
aggregate level by investigating macroeconomic environment. GDP growth rate, unemployment and
lending interest rate are general investigated when modeling macroeconomic determinants of NPLs.

2.3.1. Economic growth
Many previous studies confirm the linkage between NPLs and business cycle. GDP growth gets the
negative effect to the NPLs rate (Salas & Saurina, 2002). GDP growth rate and other macroeconomic
factors such as family indebtedness, rapid past credit are taken into account to explain the credit risk

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Determinants of nonperforming loans – The case of Vietnamese banking sector

in Spanish bank over the period 1985-1997. Two categories of banks are taken into account to analyze
the determinant of problem loans including commercial and saving banks. The result shows that NPLs
is negatively affected by GDP growth rate. This is explained that the ability to serve the debt including
problem debt is improved by macroeconomic development (Salas & Saurina, 2002). As the same
expectation of previous studies, the NPLs of Italian banks are largely affected by the business cycle
over the period 1985-2002 (Quagliariello, 2007). This study suggests that bad debt as well as loan loss
is tentative to be low in period of rapid growth. As the result, the research confirms that the revolution
have significant impact on new bad debts (Quagliariello, 2007).
Furthermore, the effect of business cycle on credit default is affirmed by analyzing the relationship
between production cycle and credit default in Turkish financial system (Ciftera, Yilmazerb, &
Cifter1, 2009). At the different time scale over the period 2001-2007, this study finds that production
cycle generated impact on NPLs and the effect vary in different levels. Reviewing the data of 26
advanced countries over the period 1998-2009, the result show the strong linkage between NPLs and
macroeconomic exposure (Nkusu, 2011). This study finds that the macroeconomic performance is
vulnerable by sharp increase in NPLs. Nkusu (2011) also points out the key indicator of
macroeconomic performance is GDP growth which effected to NPLs.
Based on previous study regarding the determinants of NPLs, the study of NPLs in Greece affirmed
the macroeconomic impact on NPLs. The result shows that GDP growth rate mainly explains the
NPLs of all loan categories in Greek bank (Louzis, Vouldis, & Metaxas, 2011). Using dynamic panel
data of Greek banks database, this study attempts to combine both macroeconomic and bank-specific
factors to analyze NPLs of mortgage, business as well as consumer loan portfolio. The research
suggests that all GDP growth rate statistically impacts on NPLs. However, the quantity effect to
different category of loans is not consistent. The mortgage and business loans are less sensitive to the
change of macroeconomic factors compared with consumer loans. As regression result, the increase
of GDP growth rate is associated with NPLs declining. It could be explained that the slowdown of
economic generate negative effect on NPLs.
In addition, basing on the NPLs in Central, Eastern and South Eastern Europe, the research indicates
that strong feedback of macroeconomic condition including GDP growth, unemployment and inflation
on NPLs (Klein, 2013). The econometric result suggests GDP growth is one of the macro explanatory
of NPLs. Besides that, the significant linkage between macroeconomic condition and NPLs of bank

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Determinants of nonperforming loans – The case of Vietnamese banking sector

is also supported by the investigation of determinant of NPLs of 85 banks in three countries including
Italy, Greece and Spain (Messai & Jouini, 2013).
Hypothesis 6: Economic growth negatively related with lower NPLs

2.3.2. Unemployment
Unemployment is other primary contribution in the increase of NPLs. Many previous studies confirm
the linkage between NPLs and unemployment rate. Current income as well as unemployment rate
effect could be considered as the probability of credit default (Rinaldi & Sanchis-Arellano, 2006).
Using dataset of seven euro areas over the period 1989-2004, this study finds that unpredictability of
future income is the consequence of current income as well as unemployment, which effect on the
likelihood of credit default. In addition, Berge and Boye (2007) conclude that unemployment is highly
sensitive with the problem loans. The NPLs in household as well as enterprise sector in the Norges
bank is respectively investigated. They find out that one of primary contribution on unemployment
rate improvement is the problem loans’ declining (Berge & Boye, 2007).
Furthermore, reviewing the data of 26 advanced countries over the period 1998-2009, the result show
the strong linkage between NPLs and macroeconomic factors (Nkusu, 2011). This study points out
the key indicator of macroeconomic performance including unemployment effect to NPLs. The
linkage of unemployment rate and NPLs is confirmed when analyzing panel data of Greek banks. The
result shows that unemployment rate mainly explains the NPLs of all loan categories in Greek bank
(Louzis, Vouldis, & Metaxas, 2011). According to regression result, the NPLs are also positively
affected by unemployment rate. It could be explained that NPLs is reduced when unemployment rate
declined and customer had enough capacity to pay the overdue debts. Basing on the NPLs in Central,
Eastern and South Eastern Europe, Klein (2013) indicates strong feedback of macroeconomic
condition including unemployment and inflation on NPLs. The econometric result suggests that
unemployment rate is one of the macro explanatory of NPLs.
Hypothesis 7: The higher unemployment rate is associated with higher NPLs.

2.3.3. Lending interest
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Besides other macroeconomic factors, lending interest rate is other macroeconomic factors affecting
to NPLs. Many previous studies confirm the linkage between NPLs and lending interest rate. This
study concludes that riskier financial position is set up for household having debt increase (Rinaldi &
Sanchis-Arellano, 2006). In addition, Berge and Boye (2007) conclude that real interest rate is highly
sensitive with the problem loans. The NPLs in household as well as enterprise sector in the Norges
bank is respectively investigated. They find out that one of primary contribution in real interest rate is
the problem loans’ declining (Berge & Boye, 2007).
Based on previous study regarding the determinants of NPLs, the study of NPLs in Greece affirmed
the effect of lending interest rate on NPLs of all loan categories in Greek bank (Louzis, Vouldis, &
Metaxas, 2011). According that, the NPLs are positively affected by real lending rate. It could be
explained that higher lending interest rate is usually charged for riskier loans that have more ability to
debt default.
Hypothesis 8: Higher lending interest rate is associated with higher NPLs

2.3.4. Sovereign debt
Sovereign debt plays an important role in investigating NPLs’ determinant, especially after recent
financial crisis. There are two effects of sovereign debt on banking system. Firstly, because of the
public finance failure, market evaluation ‘ceiling’ is set up. As the consequence, the bank’s liquidity
is affected in which lending is decreasing and debtors could not be able to finance their debt. This lead
to credit default in the banking system. In addition, fiscal measures are applied in the case of high
sovereign debt. As the consequence, the social expenditure and wage for government are cut.
Affecting by these measures, the debtors are shocked and could not be able to serve their debts which
increase future NPLs. Therefore, it is expected that sovereign debt will increase future NPLs.
Many studies give evidence for the linkage between sovereign debt and financial crisis. According to
the regression result, this empirical study concludes that financial crisis leads to sovereign debt
(Reinhart & Rogoff, 2011). Based on previous study, Louzis et al (2011) confirm the effect of
government debt on NPLs by investigating all loan categories in Greek banks. This study uses ratio
of central government debt over the nominal GDP as the proxy for sovereign debt. According to
regression, the results show that sovereign debt statistically effect to NPLs of all loan categories
including mortgage, business and consumer loan portfolio.
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Determinants of nonperforming loans – The case of Vietnamese banking sector

Hypothesis 9: Sovereign debt positively related with higher NPLs.

2.4.

Government intervention and foreign investment in banking system

2.4.1. Government intervention
Government intervention plays important role in bank’s performance by controlling regulation as well
as credit policy. The effect of government intervention in bank varies in different studies. In the one
hand, government intervention could be offset the market failure, particularly in Pigouvian where the
monopoly as well as the information asymmetries exist. As the consequence, government intervention
play as important role to manage economic in which market failures are balanced and social welfare
is strengthened. In addition, other empirical researches suggest that state-owned bank with better
regulation will reduce more NPLs compared with private-sector monitoring. By examining the
relationship between risk and ownership structure, Garcia-Marco and Robles-Fernandez (2008)
document that commercial banks are more risk exposure compared with state-owned banks.
In the other hand, some argument suggests that the private-sector monitoring in which government
intervention is excluded will be more effective. In this case, the private-sector is allowed to control
bank regulation which no more effected by government intervention. With profitability enhance,
private-sector is more incentive to control their profitability, cost efficiency which reduce NPLs.
Moreover, others suggest that state-owned banks are incentive to finance riskier project in order to
finance for public good and country’s economic development. As the consequence, that lead to higher
NPLs. Using dataset regarding bank regulation and supervisory of 107 countries, Barth et al. (2004)
argues that empower the private-sector to monitoring of banks are associated with better banking
soundness including outcomes, higher development as well as small NPLs. This argument is strongly
confirmed by regression result. According that, the private-sector is more effective when controlling
NPLs compared with government intervention (Barth, Jr., & Levine, 2004). The role of private-sector
monitoring is also confirmed in the report of Boudriga et al (2009). This paper indicates that due to
weaker credit recovery capacities compared with private monitoring, state-owned banks tend to
increase NPLs (Boudriga, Taktak, & Jellouli, 2009). As the same result, the investigating ownership
reform in China confirms the role of private monitoring in generating better performance and NPLs
reduction (Lin & Zhang, 2009). According aforementioned researches, this study will investigate the
effect of government intervention in NPLs, particular in state-owned banks.
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Determinants of nonperforming loans – The case of Vietnamese banking sector

Hypothesis 10: State-ownership is positively associated with higher NPLs.

2.4.2. Foreign investment
Foreign investment generates negative effect in banks’ performing, particularly in NPLs. The bank
efficiency seems to be higher in bank with higher foreign investment ratio compared with domestic
ownership. Bank with foreign investment is usually get advantages from experience of management
as well as capital from foreign investment. With their advantages, the strictly internal control system
is usually set in foreign investment bank that reduces credit exposure. In addition, because of capacity
independence, foreign investment bank is rarely effected by profitability enhance in which control
points are loosen to archive sales target. In particular, developing countries get experience of human
capital improvement including skill and technology from foreign investment company (Lensink &
Hermes, 2004). All of that will increase banks’ soundness that positively encourages reducing bad
loans.
This hypothesis is supported by many empirical studies. Barth et al (2004) investigate the effect of
foreign bank entry limitation on bank performance by counting the number of bank entries to economy
in investigating period. According to regression results, the study finds that the bank fragility
positively links with the limitation in foreign investment entry. Besides that, poor bank development
is associated foreign investment entry limitation. In conclusion, the effect of foreign investment varies
in different examinations. Therefore, foreign investment is applied in this study to analyze their effect
on bank performance, particularly in NPLs. Besides that, the study also attempts to analyze the
distinction of NPLs’ determinant in bank with direct foreign investment and the others.
Hypothesis 11: Full foreign ownership negatively related with lower NPLs.

Basing on aforementioned theories and empirical studies, these researches will consider both
macroeconomic and bank-specific factors in modeling NPLs’ determinants in different type of banks.
All macroeconomic and bank-specific factors used to modeling NPLs’ determinants are summarized
as following table.
Table 1: Definition of variables used in modeling NPLs determinants

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Determinants of nonperforming loans – The case of Vietnamese banking sector

Variables

Definition

Expected
sign
+

Hypothesis tested

Inefficiency

Operating expense/ operating income

Non-interest income

Noninterest income/ Total income

-

Diversification (-)

Leverage ratio

Total liability/ total assets

+

Too big to fail (+)

Return on equity

Profit/ Total equity

-

Bad management II (-)

Credit growth

(Loant - Loan t-1)/ Loant-1

+

Credit growth (+)

Economic growth

GDP growth rate

-

Economic growth (-)

Unemployment

Unemployment rate

+

Unemployment (+)

Lending rate

Real lending interest rate

+

Lending rate (+)

Government debt

Central Government debts/ Nominal
GDP

+

Sovereign debt (+)

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Determinants of nonperforming loans – The case of Vietnamese banking sector

CHAPTER 3: METHODOLOGY AND DATA

3.1.

Methodology

Panel data is usually employed in recent empirical studies regarding to modeling the regression of
NPLs’ determinants. Because of panel data advantages, combining both time series and cross-section
data, panel data increase number of observations and degree of freedom. This method will reduce colinearity among variables and reduce risk in omitting variables. Therefore, simple pooled regression
may not be well designed to examine NPLs and its determinant (Boudriga, Taktak, & Jellouli, 2009).
Lin and Zhang (2009) employed year fixed effect to control bank size, change in market and regulatory
conditions over the year. In order to analyze the determinants and impact of NPLs in macroeconomic
performance in Europe, Nir Klein (2003) also used panel data of countries in Europe from 1998 y
2001. In additional, Ahlem Selma Messai and Fathi Jouini (2013) also used panel data of 85 banks
from Italy, Greece and Spain period from 2004 to 2008 to assess macro and micro determinants of
NPLs. Therefore, unbalanced panel data of 30 Vietnamese banks over the period 2008 – 2012 is
applied to model the regression of NPLs’ determinants.
According to recent researches regarding NPLs’ determinant, dynamic panel regression is employed
to analyze effect of factors in NPLs. Salas and Saurina (2002) applied dynamic approach to investigate
problem loans in commercial and saving banks in Spanish. In addition, Klein (2013) applied the
dynamic panel regression to analyze macroeconomic determinant of NPLs. Furthermore, dynamic
panel regression is widely applied when investigate macroeconomic and bank-specific determinant of
NPLs in Greek banks (Louzis, Vouldis, & Metaxas, 2011). The specification of a dynamic panel data
is presented as following:
yit = αyit-1 + βXit-1 + εit (Eq. 1)
where cross section and time are denoted by the subscript i and t respectively. yit denotes for NPLs
change of observations. Firstly, first lag of dependent variable (yit-1) is counted to explain dependent
variable. Second, Xit-1 is other independent variables. According to empirical studies, it is important
to count lagged variables into regression model. It is explained that NPLs is the consequence of change
of independent variables. However, this impact is not immediately affected and time is necessary to
count in the model. Therefore, all lagged variables are applied in order to count accurate effect of
independent variables on NPLs. Finally, εit presents for error term in equation.
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Determinants of nonperforming loans – The case of Vietnamese banking sector

Applying dynamic panel regression for testing effect of macroeconomic factors, economic specific of
model is presented of follows:
NPLs it = NPLs it-1 + β1 GDP it-1 + β2 UNit-1 + β3 RLR it-1 + εit (Eq. 2)
In Equation 2, i is denoted for cross section which present for each banks in panel dataset. t will
presents for time series from 2008 to 2012. NPLs it-1 is the first lag NPLs. GDP it-1, UNit-1 and RLR it1

are the first lag of GDP growth rate, unemployment rate and real lending interest rate respectively.

For testing the ‘sovereign’ hypothesis, lag of government debt is counted into regression to analyze
their effects on NPLs. Finally, bank-specific factor is counted into regression to analyze their effects.
Lagged variables are also used when modeling the regression. The model could be present as follows:
NPLs it = NPLs it-1 + β1 GDP it-1 + β2 UNit-1 + β3 RLR it-1 + β4 Debtit-1 + β5 ROEit-1 + β6 INEFit-1 +
β7 NIIit-1 + β8 LRit-1 + β9 CREit-1 + εit (Eq. 3)
in which Xit-1 is the lagged variable of bank-specific factors as discussion in aforementioned empirical
studies which is summarized in Table 1:


ROEit-1: return on equity of bank i year t-1



INEFit-1: cost inefficiency of bank i year t-1



NIIit-1: proportion of non-interest income on total income of bank i year t-1



LRit-1: total liability over total asset of bank i year t-1



CREit-1: credit growth of loan portfolio of bank i year t-1

Finally, dummy variables are added into Equation 3 for testing effect of state ownership and foreign
investment. There are two dummy variables presented as follows:


STATE variable: state-owned bank is 1 and others is 0



FOREIGN variable: bank with 100% foreign capital is 1 and others is 0

To analyze effect of these variables on bank-specific factors, dummy variables are generated by
multiple dummy variables including STATE and FOREIGN with other bank-specific factors.
In addition, some fundamental tests are employed to examine variables including correlation among
variables, multi-co linearity and stationary.
Multicolinearity is the problem of model in which there is one or more relationship among the
regressors. As the consequence of multicolinearity, the model exist the large confidence interval in
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