Determinants of Non-Performing Loans in Vietnamese Banking System

Dịch vụ viết thuê đề tài – KB Zalo/Tele 0917.193.864 – luanvantrust.com
Kham thảo miễn phí – Kết bạn Zalo/Tele mình 0917.193.864
UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM
ERASMUS UNVERSITY ROTTERDAM
INSTITUTE OF SOCIAL STUDIES THE
NETHERLANDS
VIETNAM – THE NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
DETERMINANTS OF NON-PERFORMING
LOANS IN VIETNAMESE BANKING SYSTEM
BY
NGUYEN THI HONG THUONG
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM
INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS
VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS
DETERMINANTS OF NON-PERFORMING
LOANS IN VIETNAMESE BANKING SYSTEM
A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF
ARTS IN DEVELOPMENT ECONOMICS
By
Academic Supervisor:
A/PROF. NGUYEN VAN NGAI

DECLARATION
I declare that the wholly and mainly contents and the work presented in this thesis
(Determinants of Non-performing loans in Vietnamese Banking System) are
conducted by myself. The work is based on my academic knowledge as well as my
review of others’ works and resources, which is always given and mentioned in the
reference lists. This thesis has not been previously submitted for any degree or
presented to any academic board and has not been published to any sources. I am
hereby responsible for this thesis, the work and the results of my own original
research.
i

ACKNOWLEDGEMENT
Here I would like to show my sincere expression of gratitude to thank my supervisor,
Ass. Professor Nguyen Van Ngai for his dedicated guideline, understanding and
supports during the making of this thesis. His precious academic knowledge and ideas
has motivated me for completing this thesis.
Besides, I would like to express my appreciation to the lecturers and staff of the
Vietnam – Netherlands Program at University of Economics Ho Chi Minh city for
their willingness and priceless time to assist and give me opportunity for this thesis
completion.
Next, I would like to thank all of my classmates for their encouragement and their
hard work, which become a good example for me to do the thesis. I wish all of us will
graduate at the same date.
Lastly, I would like to express my gratitude to my families, my beloved group for
their unlimited supports and encouragement. They are the motivation for me to finish
this course research project.
ii

ABBREVIATION
FE: Fixed-effect estimator
GDP: Gross domestic product
NPLs: Non-performing loans
OLS: Ordinary Least Square
RE: Random-effect estimator
SBV: State Bank of Vietnam
S.GMM: the system generalized method of the moments estimator
iii

ABSTRACT
Credit risk is one of the elements impact on the health of banking systems and
performance of economic activities. Non-performing loans is the general factor
presents for this bank’s credit risk. There are previous researches indicate the close
relations between bad debts and factors from macroeconomic environment and bank
specifications. This is the motivations for this paper to examine both macro and micro
variables of 30 Vietnamese banks from 2006 to 2016. This dynamic panel data is
estimated by the System Generalized Method of Moments. The regression results
support the strong evidence for the impact of macro indicators on problem loans. The
testing results are in accordance with several papers which indicated the negative
relation with economic growth and positive correlation with lending interest rate and
government debts of problem loans. However, due to the type of labor force, the
increase of unemployment rate will lead to the increase in bad loans in Vietnam. In
addition, with bank-specific factors, tests of skimping hypothesis, diversification (with
proxy is banks’ size) hypothesis and procyclical credit policy hypothesis have the
statistical significance in Vietnam.
iv

CONTENTS
DECLARATION .......................................................................................................... i
ACKNOWLEDGEMENT .......................................................................................... ii
ABBREVIATION....................................................................................................... iii
ABSTRACT ................................................................................................................ iii
CONTENTS ................................................................................................................. v
APPENDIX................................................................................................................... 1
LIST OF TABLES....................................................................................................... 2
CHAPTER 1: INTRODUCTION......................................................................................................3
1.1 Problem statements:.....................................................................................................................3
1.3 Research objectives:.....................................................................................................................4
1.4 Research questions: ......................................................................................................................4
1.5 Structure of Research:................................................................................................................4
CHAPTER 2: LITERATURE REVIEWS...................................................................................6
2.1 Macro-economic factors:...........................................................................................................6
2.1.1 Theories:..............................................................................................................................................6
2.1.2 Empirical review:............................................................................................................................9
2.2 Bank-specific factors: ..................................................Error! Bookmark not defined.
2.2.1 Hypotheses:.......................................................................... Error! Bookmark not defined.
2.2.2 Empirical review:..........................................................................................................................14
CHAPTER 3: MODEL SPECIFICATION AND DATABASE .....................................16
3.1 Model specification:....................................................................................................................16
3.1.1 Econometric models:...................................................................................................................16
v

3.1.2 Variable explanation: .................................................................................................................21
3.2 Data:....................................................................................................................................................25
CHAPTER 4: RESULTS AND DISCUSSIONS.....................................................................26
4.1 Summary statistics:.....................................................................................................................26
4.2 Empirical results:.........................................................................................................................28
CHAPTER 5: CONCLUSIONS AND RECOMMENDATION.....................................39
5.1 Conclusion:.…………………………………………………………………..39
5.2 Recommendations:………………………………………………………......40
5.3 Limitations: ………………………………………………………………….41
REFERENCES ..........................................................................................................................................42
APPENDIX ..................................................................................................................................................48
vi

APPENDIX
Appendix 1: Correlation of variables
Appendix 2: Addition estimation test with 2 lag of variables
Appendix 3: The estimated results for the regression models with separate hypotheses
using system generalized method of the moments
AP
Page | 1

LIST OF TABLES
Table 1: Summary statistics
Table 2: Results with Pooled OLS, FE, RE and SGMM estimations
Table 3: Estimation results of one lag variables
Table A1: Estimation without lagged variables
Table A2: Estimation with lagged variables
2

CHAPTER 1:
INTRODUCTION 1.1. Problem statement
Both developed and emerging countries recognize the important of financial
institutions. Nkusu (2011) imply that the health of financial system and economy is the
two-way impact. It means that the performance of financiers could be improved by the
economic growth. On the contract, if bank crisis happens, the economy could be
downturned. Non-performing loans are considered as the general measure for riskiness of
the banks, as well as applied to predict the bank crises. Rajaraman and Visishtha (2002)
indicated the investigation the causality of bad debts is important to control this risk.
Adebola, Wan Yusoff and Dahalan (2011) identify that one of the causalities of economic
crisis in 2008, which affects not only on the U.S economy but also many countries
around the world, is the problem loans. Several loans in this period were issued for the
segments in under standard conditions. Therefore, when the economy goes down, most of
them turn out bad debts. The health of banking system become worse and worse after
that, leads to the negative impact on economy (Nkusu, 2011).
Credit risk is one of the factors to evaluate the health of banking system. This factors
is defined as the problem loans of banks. The non-performing loan ratio of Vietnamese
banking system has a significant increase from 2009 and got a peak at 2012, at 3.44%.
Due to the tighten monetary and lending policies of State Bank Vietnam as well as the
development policies of Government, this ratio has a little decrease after that.
The bad debts in banking system get the obstacles for economic growth, as well as
financial system development recent years. First of all, it is difficult for economic
segments to approach the credit capital. In the controlled period (from the end of 2010),
the increase of credit was limited (approximate 15% per year) and the lending interest
was high (in the range from 17% - 22% per year). In addition, in this stage, there are
several M&A as well as restructured banks lead to the more tightened policies to control
the stability of financial system. The stuck capital flows impacted negatively on
economic activities. The firms were in short of capital to manufacture and investment in
3

order to expand production. The consumption was decrease, effect on firm’s revenue and
profit. As a circle, the total output reduce, the economy went down. Until the problem
loans can resolve, the economy has to allocate the scared resources in order to support
banking activities and maintain the stable of banking system. This is the big obstacle
which will pull down the overall economy (Nguyen Xuan Thanh, 2017).
1.2. Research question
Understanding the root cause of the issue is the best way to solve the problem.
Therefore, this research will try to find the answer for question:
- Which determinant can have the significant impacts on the non-performing loans of
Vietnamese banks?
1.3. Research objectives
The objectives of this paper are expected to have the answer for above question, as
below:
- To estimate the impacts of macroeconomic determinants to the NPLs ratio of the
Vietnamese banks.
- To examine the impacts of bank-specific determinants to the NPLs ratio of the
Vietnamese banks.
1.4. Data and econometric model
A cross-sectional dataset is collected to support the objectives of this paper. This data
includes macro-economic factors, such as: economic growth, unemployment rate and
lending interest rate. The data for bank specification will be collected from annually
audited financial statements of 30 banks from 2006 to 2016 and calculated based on the
financial indexes. Generalized Method of Moments (GMM) is suitable for estimating
influence on banks’ problem loans of these variables with the different lagged orders.
1.5. Structure of thesis
This study is organized in five chapters. The first chapter is the problem statement,
research question and objectives. The second chapter will be review the literature,
4

includes theories and previous researches in order to identify the factors impact on NPLs.
Data collection and model specification for the study will be described in the third
section. Next chapter will present and interpret the results of the econometric analysis
with respect to the research’s theoretical and empirical analyses, which are linked to the
hypotheses of the research paper. The results will show the relationship of the economic
factors and the NPLs ratio of banks. Finally, the conclusions could be presented in last
chapter.
5

CHAPTER 2: LITERATURE REVIEW
Credit risk is the risk in credit activity of bank when the borrowers can not
complete obligations of their liabilities. When this risk occurs, the banks are affected
negatively. The total assets, profit and capital will decrease due to the increase of loan
loss provision amount. The consequence is the negative effect on the economic activities
due to not only the increase of banks’ exposure to economic crisis but also the restriction
off the credit activities. Therefore, an analysis for credit risk is necessary to maintain the
stability of financial system and have the early warning of possible crisis. All of them are
worked for the final target: the growth of economy. The factors, which impact on credit
risk, are divided into two groups: systematic and unsystematic credit risk (Castro, 2013).
Macro-economic factors are considers as the factors influencing the systematic credit
risk. On the contrast, the bank specifications are grouped as unsystematic credit risk,
include financial indexes and the quality of credit management.
2.1. Macro-economic factors
2.1.1. Theories:
The theoretical models of business cycle, which indicates the important role of
financiers, offer the good baseline for NPL models. Williamson (1987) highlights the
counter-cyclicality of business failures and credit risk. After that, the researches of
Bernanke, Gertler and Gilchrist in 1980s and 1990s mention about the financial
accelerator framework. The theory of financial accelerator states that the worsening
financial market conditions can amplify the negative shock to economy. More broadly,
the downturn period of finance and macro-economy is propagated by the disadvantage
conditions in the real economy and financial markets. Bernanke, Gertler and Gilchrist
(1996) and Kiyotaki and Moore (1997) use the framework about “principle-agent” view
of credit market in order to rationalize the financial accelerator theoretically. Their
method becomes the important theoretical framework for the macro-financial linkages
when modeling the interaction between NPL and macro-economy.
6

GDP growth: When economic growth is stable or increases (i.e. expansion stage
of economy), the payment of borrowers is easy to complete, and the bank credit usually
meets the demand and increases over time. The doubtful loans are not the most serious
problem of bank’s managers. On the other hand, when the economy has to face with the
obstacles for growth, even downturns, the reduction of cash inflows is the trend of all
segments. At this time, the debt payment of firms as well as individuals becomes
difficult. It leads to the increase in non-performing loans in the banking system. Because
banks’ capitals are stuck in the recession, the capital for the economy, which is the most
important for all activities, is in the shortage. The consequence is the stagnation of all
business, and the economy is still deeper in the crisis. This is the causality of banks’ bad
debts and economic growth. There is a negative relation between NPL and GDP growth.
The interest rate: the higher interest rate is argued to be relevant with the debt
burden due to the higher of financial obligations. The asymmetric information theory can
explain for this argument. According to this theory, when the interest rate increases, the
debtors have to face an adverse selection and the loans can be their bad choice in this
scenario (Bohachova, 2008).
To have enough income to cover the debts, the borrowers have a tendency to
invest in riskier projects instead of safe projects with lower return. Furthermore, banks
will grow their income from credit activity due to new issued loans. In addition, with
outstanding loans, the banks can have more returns with the floating lending interest rate,
which adjusts the increase of debt’s liabilities. But banks have the role as financial
intermediations, which lend to a large number of borrowers as well as borrow from a
large number of depositors. In some countries, despite of the high cost for fund and high
risk behaviors as their culture, interest rate will be liberalized. It means that high-risk
creditors will be charged at higher rate in order to mitigate risks. The consequence is the
increase of overall risk exposure (Fofack, 2005).
At the recession stage of business cycle, the banks have to pay more interest for
depositors than the returns received from borrowers. This leads to the profit reduction,
7

even the losses. Because total assets of banks include long-term fixed interest rate loans,
the return is not quick enough for banks to handle their liabilities. The temporary solution
is the rise of short-term lending interest rate to pay their liabilities (Mishkin, 1996).
Furthermore, the increase of debt payment for borrowers will lead to the risk for banks’
loan portfolios as their ability is not guaranteed. Therefore, this risk will be compensated
by the higher net interest margin (Ahmad and Ariff, 2007).
The unemployment rate: economic cycle stages have the closed correlation with
the unemployment rate. So this factor is defined a determinant impacts on the credit risk.
According this view, the unemployment rate directly affects the income of households. In
addition, this rate increase will lead the decrease of social consumption, which will
impact on the business production of corporates reflected in sale decline. As the results,
the repayment for obligations has the difficulty to complete, thus the credit risk is
exacerbated (Castro, 2013). The model of Lawrence (1995), implies that low-income-
segment could be charged higher rates than others due to the potential risk of
unemployment and payment inability, based on the life-cycle consumption. According to
Rinaldi and Sanchis-Arellano (2006) results, current income and the unemployment rate,
which are key elements of customer’s bankruptcy ability, are relevant with uncertainty
regarding future income and the lending rates.
Non-performing loans and banks’ losses can increase due to the diminished
employment and corporate returns in the recession stage of economic cycle (Berge and
Boye, 2007). Based on the expectation about the future flow of income and expenditure
of the debtors, the banks will decide the provision amount for their loans. If the
borrowers are unemployed, they have to suffer the higher costs for loan and other
services from banks. The capacity of these customers will be deteriorated due to the
unexpected movements. The result is the increase of credit risk.
The Government debts (the sovereign debt hypothesis): Public debts create the
pressure on economic development to ensure the payment ability for principal and
interest. Therefore, when the ratio of public debt exceeds the acceptable threshold, it will
8

negatively affect the growth. This is the causality of vulnerabilities, which are the
baseline of financial crisis if having no punctual adjustment policies... When the
economy is downturn, the banks are careful to finance the loans. The capital from banks
is tightened due to lending reduction. It leads the reduction of production business as well
as the social consumption. The firms’ revenue and households’ income are decrease. So,
the repayment for bank loans is therefore also affected accordingly, leading to bad debt
ratio tends to increase.
2.1.2. Empirical review
Several previous studies do estimation the impact of macro-economic on non-
performing loans. Shu (2002) indicates the change in macroeconomic factor can
influence on the repayment ability of borrowers and banks’ loan portfolio when
examining the banks in Hong Kong. The finding of this study shows that in the expansion
stage of the economic cycle, the banks have more chances to push lending activity, thus
the risk can reduce.
Salas and Saurina (2002) examine the problem loans in commercial and saving
banks in Spain from 1985 to 1997 by using GMM dynamic panel estimations in order to
estimate which determinants of NPL in Spanish banks. The results are showed that
problem loans in neither commercial banks nor saving banks have a negative relation
with the growth of economy overtime.
After that, the research of Jimenez and Saurina (2006) also investigate the loan
loss of Spanish commercial banks from December 1984 to December 2002. By applying
the Generalized Method of Moments (GMM) estimator for dynamic panel models, they
support a significant evidence for the positive relationship between the interest and
problem loans. This conclusion is also supported by the research of Cural, et al. (2013)
for the Southeastern European banks. The explanation for this relation is the top-up
loans’ obligations for borrowers when the interest rate increases.
Burger and Boye (2007) support an evidence for the positive and significant effect
on non-performing loans of unemployment rate in household and corporate segments
9

when investigating the Nordic banks from 1993 to 2005. In addition, the finding
emphasizes that the strong effect of income on the capacity of debt-servicing and the
volume of problem loans from household segment. Therefore, when unemployment rate
increases, the income, which is used to cover the borrower’s obligations, can reduce. This
leads the potential increase of bad debt form this segment. At the same time, the income
reduction will effect on individuals’ consumption, includes financial services. The
consequence is the lower domestic demand. The next result is the go down of firm’s
earnings and loan repayment ability. Therefore, banks’ bad debts will increase due to the
higher unemployment rate.
Then, Jakubik (2007) analyses macroeconomic factors effect on the credit risk of
Czech banks by applying the Merton’s methodology. The author concludes that the
decrease of real economic growth will lead the higher credit risk of banks as the negative
impact on the loan portfolio of the reduction from the return of companies, wage growth
and the increase of unemployment rate.
After that, the research of Espinoza and Prasad (2010) estimates the effects of
macroeconomic shocks on non-performing loans, by applying a VAR model for the data
of 80 banks in the Gulf Cooperative Council (GCC) in the period 1995-2008. The set of
macro-economic variables includes non-oil growth, interest rates. Their conclusion is the
increase of NPL is affected by the higher interest rate, as well as the lower real non-oil
GDP growth.
Nkusu (2011) uses the single-equatio panel regressions for the sample of 26
developed countries from 1998 to 2009. His data set is the macro and financial indices,
include economic growth, unemployment rate, inflation, interest rate and the price
variation of housing and stock. The author estimated with many methods, such as: OLS
model, panel-corrected standard error (PCSE) models, lagged dependent variables, fixed
effects and one-step GMM. The regression results indicate that the increase of NPL is
affected by the downturn of macro-economy, which is measured clearly by the lower rate
of economic growth as well as employment.
10

Zribi and Boujelbène (2011) have the same conclusion about the inverse relation between
GDP growth and bank credit risk when analyzing the bank credit risk in Tunisia from
1995 to 2008.
Beside, Louzis, et al. (2012) approaches NPL of each loan categories in Greek
banks by using a set of macroeconomic factors, include the real rate of GDP growth, the
unemployment rate and the real interest rate. The result indicates that the injured debts
have relationship not only with this set of variables but also with the bank’s management
qualify. By using government debts factor in order to formulate the sovereign debt
hypothesis, which is based on the findings of Reinhart and Rogoff (2010) and Perotti
(1996), the authors support a strong evidence for this hypothesis.
Reinhart and Rogoff (2010) use OLS with robust errors and fractional logit to
estimate the relation of bank crisis and debts. Their findings indicate that bank crises are
affected by the external financial obligations. In addition, banking crisis usually goes
with the sovereign debts crisis.
In addition, Messai and Jouini (2013) apply data of 85 banks in three countries:
Italia, Greece, and Spain in the duration 2004-2008. The effects of macro determinants on
loan losses are estimated by variables: real growth rate, unemployment rate, real interest
rate. The regression results are consistent with previous studies. The conclusions indicate
that the NPLs have related negatively with real GDP growth and employment rate but
positively with real interest rate.
Recently, Chaibi and Ftiti (2015) conclude that both French and German banks
increase the problem loans when the unemployment rate rising. By using the growth of
GDP and unemployment rate, they find that the credit risk in French banks is more
sensitive to the economic environment than in Germany.
11

2.2. Bank-specific factors
2.2.1. Hypotheses:
Three prominent hypotheses are investigated by Berger and DeYoung (1997)
when the authors take into account the relationship between non-performing loans of the
bank and its cost- inefficiency
“Bad management” hypothesis: The cost efficiency of banks is expected as the
obviously significant factor impacts on non-performing loans of banks. This indicator is
considered as the index to appraise the quality of management. They are assumed that the
bad management could be caused by the poor skills in credit section, such as: scoring,
loan approval, loan monitor, etc. The banks have to spend more and more cost on
operating but the risk management could not be controlled efficiently. Therefore, the
NPL ratio of banks could increase due to the cost inefficiency.
“Skimping” hypothesis: according this hypothesis, there is the trade-off between
operation expense allocation and future problem loans. Skimping on operation costs,
which devote to underwriting and controlling loans, could have cost efficiency in short-
run when lower operation costs still support the quantity of loans. However, the bank
could be faced to the reduction of cost efficiency when non-performing loans become
higher due to its less effort to maintain the quality of loan in long run.
“Moral hazard” hypothesis: one of the solutions to increase bank’s profit is
increasing their loan portfolio. The bank with lower capital usually serves risky segments.
Their performance could be better in short-term but NPL will grow in the future.
Louzis et al. (2011) added three hypotheses for the impact of bank- specific factors on
non-performing loans. They supply more respects to investigate whether other bank
characteristics (different from bank’s cost efficiency) can impact on its bad debts.
“Bad management II” hypothesis: bad performance in the past could predict the
increase of future NPLs. According this view, bank’s performance in the past is another
proxy to the measure of management ability.
12

“Diversification” hypothesis: this theory states that if the portfolio could be
diversified, the firm could reduce the risk and maintain the revenue. Banks are also a type
of corporate. Therefore, their profit could be table or increase if they could have the
diversification in operation.
This hypothesis could be examined by bank’s size or its multiple income sources.
It is said that with the large size, the banks have many opportunities to diversify their
portfolio. They could not be depended on credit sector as a majority operation. Therefore,
they could control the problem loans but maintain the stable profit.
Another proxy for diversification of banks is income sources. According this view, the
non-interest income of bank is higher, the diversification in operation of bank is better.
Credit section is not only the main income if bank. So, bank could control their loan
efficiently. This could lead the lower NPL.
“Too big to fail” hypothesis: In this view, the too big to fail banks could be
expected the protection from Government in case of its failure. Therefore, these banks
could have tendency to increase their leverage to gain higher profit. The majority capital,
which finances banks’ assets, is their liabilities such as: customer deposits, borrowed
money from Government and other financiers, bonds, etc. This leads the result that the
bank could lend more money to maintain the higher profit. The bank size is bigger, the
higher its pressure of debts payment. So, its standard for borrowers could be lower and
riskier in order to have more income. The consequence is the increase of problem loans in
the future.
“Procyclical credit policy” hypothesis: According this view, the policy could be
decided due to bank’s optimal profit expectation as well as other targets such as it
reputation. So, the banks’ profitability could be desirable in the market in short-term as
the effect of managers to manipulate earnings. However, this index could be considered
as a negative net present value of credit extension period. As below declaration, these
policies are built up for short-term target. After this blooming development, the credit
policies could be tightened. This leads the denial with positive net present value in this
13

stage. And the bank’s problem loans are more serious in downturn time. Overall, the test
of this hypothesis could reflect the liberal magnitude of bank’s credit policy, due to
comparing the performance with the increase of NPL in future growth.
2.2.2. Empirical review:
Berger and Young (1997) use the Granger-causality models to examine which of
four hypotheses, include bad luck, bad management, skimping and moral hazard, is in
accordance with the data of U.S commercial banks in the period 1985 -1994. The
estimations give a strong support for bad management hypothesis, when the result
indicated that higher of cost inefficiency can lead the rising of non-performing loans. In
addition, the possibility of skimping hypothesis is investigated in individual banks. The
moral hypothesis is also supported in their research.
Podpiera and Weeill (2008) examine whether bad luck or bad management
impacts on the bank failures in Czech banks from 1994 to 2005. By extending the
Granger-causality models, which were developed by Berger and DeYoung 91997), the
authors also apply GMM dynamic panel estimations in their research. Their regression
results support a strong evidence for bad management hypothesis. According to this view,
the cost-efficiency and bank’s problem loans are the negative relation. The result
concludes that the banks try to improve on cost-efficiency can lead to the decrease of
problem loans as well as precede bank failures.
After that, Karim and Hassan (2010) using the Tobit models also support the bad
management hypothesis when they research the problem loans in Singapore and
Malaysian banks.
Although support the hypothesis about “bad management” but the results of
Louzis, et al. (2012) cannot support “moral hazard” hypothesis in Greek banking system,
due to the small number of bank.
When investigating the factors impact on non-performing loans in Eurozone
banking system by different GMM estimation, Makri, Tsagkanos and Bellas (2013) show
the negative and significant relation between NPL ratios with banks’ performance, which
14

is measured by the index of return-on-assets and return-on-equity. Their findings
reconfirm that the deterioration of profitability index can increase the bad debts. The
results are consistent with the research of Louiz, et al. (2012), also support the strong
evidence for the bad management II hypothesis.
Salas and Saurina (2002), Hu, et al. (2004) and Rajan and Dhal (2003) have the
same empirical evidence to support the diversification hypothesis when using proxy is
bank size. Their results indicated that the bigger bank the more diversification
opportunities. However, the study of Louzis, et al. (2012) cannot find the empirical
evidence to support this hypothesis, either by proxy of bank size nor by the proxy of non-
interest income ratio. They explain that the bank size could not be present the
diversification fully, or that is the counter-tendencies since the bigger banks have a higher
degree of risk-taking leads to higher NPLs. Furthermore, their result consist with Stiroh
(2004) in rejection hypothesis when apply proxy of income. This consequence could be
from the “potential dark sides of diversification”. It means that NPLs could increase if
bank could not have either the experienced managers or comparative advantages.
Mattana, Petroni and Rossi (2014) support “too-big-to-fail” hypothesis by
examining in European banks via ROA index. However, the results either of Louzis, et
al. (2012) or Boyd and Gerler (1994), Ennis and Malek (2005) cannot have an empirical
evidence to support this hypothesis.
Berger and Udell (2002) cite the speech of Alan Greenspan – old chairman of
Federal Reserve “the worst loans are made at the top of the business cycle”. However, the
findings of Louiz, et al. (2012) cannot support this hypothesis.
15

CHAPTER 3: MODEL SPECIFICATION AND DATABASE
3.1. Model specification
3.1.1. Econometric models
3.1.1.1. Dynamic panel data estimator
In the literature review, the non-performing loans are impacted by its ratio in the
last year. In the previous papers, the authors research the effect of the non-performing
loans one year ago on its situation at the present (i.e. how could the ratio of NPL in t-1
influence on NPL in t). Therefore, this study also uses the variable about NPL ratio which
has lag of first order to estimate the current problem debts ratio.
Based on the literature review, non-performing loans are affected by themselves in
the past, especially by the nearest values. Therefore, the dependent variable with one time
lag is added into the right-hand side of the model. So, dynamic panel data is built up. The
general formula of dynamic panel data approach is
= 1 −1 + 2 + +∪ ; | 1| < 1, i = 1, …, N; t= 1, …, T (1) where the subscripts i and t denote the cross
sectional and time dimension of the panel sample respectively, is the change in the NPLs, 2 is the lag of multiple vectors,
is the matric of vector of independent variables other than −1, are the unobserved effects of bank specific and ∪ is the
error term. The use of Generalized Method of Moments (GMM) created by Arellano and Bond (1991) and amended by
Arellano and Bover (1995) and Blundell and Bond (1998) is applied to estimate Eq (1). The first difference transformation
of Equation (1) is calculated by Equation (1) at year t minuses Equation (1) at year t-1. This formula not only is consistent
with the GMM estimation of Arellano and Bond, but also eliminates the impact of bank-specific factor:
∆ = 1∆ −1 + 2 + ∆ ∪ (2)
where ∆ is the first difference calculation. ∆ −1 presents for the lag of dependent variable. Due to the correlation between the lagged explained variable and error
term, the estimation result could be discrepant. Nevertheless, −2 has the correlation with ∆ −1 but independence with ∆ ∪ for t = 3, …, T, is an instrument variable
of Equation (2)
16

regression in order to prove that ∆ ∪ are not serially correlated. The dependent variable could be lagged two or more but has to meet the moment conditions:
[ − ∆ ∪ ] = 0, with t ≥ 3 and s ≥ 2 (3)
Nonetheless, the correlation of the independent variables and residual also causes the bias results. To
resolve the endogeneity, there is an assumption about independences between error term and all values of
explanatory variables, as the equation: [ − ∆ ∪ ] = 0, with t ≥ 3 and s is not limited (4)
The two-way causality is the limitation for the strictly exogenous presumption. For example, if t has
smaller value than s, the value of [ − ∆ ∪ ] is not equal 0. With a set of fixed independent variables which is
fragile exogeneity, the valid instruments are the value of at present and lagged time, as below function:
[ − ∆ ∪ ] = 0, with t ≥ 3 and s ≥ 2 (5)
Equation (3), (4) and (5) describe the statistically independent limitations. They
are foundation of the one-step GMM regression, following the assumption about the
independence and homoscedasticity of residuals (both cross sectional and over time),
consistence of parameter estimates. Arellano and Bond (1991) estimate the residuals by
the two-step GMM regression. The result will be a consistent variance–covariance matrix
of the moment conditions. This estimator can enforce the bias in standard errors (t-
statistics) due to its dependence on the estimated residuals. Bond (2002); Bond and
Windmeijer (2002), Windmeijer (2005) indicated that is the reason of unreliable
asymptotic statistical conclusion. Arellano and Bond (1991); Blundell and Bond (1998)
re-confirm this inference by the relatively small cross section dimension data samples.
The specification test of Sagan, which distribution is asymptotical as chi-square, will
utilize to examine the variables’ overall validation, based on the assumption about valid
moment conditions. After that, testing the null hypothesis that the difference of error
terms does not having the second order autocorrelation will give the outcome for the
serially uncorrelated errors (∪ )fundamental assumption. If the result is rejection this
assumption, it means that the error terms exist the serial correlation. However, this
estimation is not consistent with GMM methods.
17

3.1.1.2. Econometric model
In the baseline model, the below equation is built up from Equation (1), which is
followed as Louzis, et al. (2012):
∆ = 1∆ −1 + ∑1 =0 2 ∆ − + ∑1 =0 3 ∆ − + ∑1 =0 4 ∆ − +
+∪ ; | 1| < 1, i = 1, …, 30; t= 1, …, 11 (6)
In this equation, ∆ is the difference of the prolem loans ratio, ∆ , ∆ and ∆ are the
change of GDP growth rate, unemployment rate and the lending interest rate respectively.
In order to test thee “sovereign debt hypothesis”, variable of debt will add into
Equation (6) as below:
∆ = 1∆ −1 + ∑2
=1 2 ∆ − + ∑2
=1 3 ∆ − + ∑2
=1 4 ∆ − +
5∆ −5 + +∪ (7)
The next step, the supplement independent variables about bank’s specification in
the model will be tested by adding them into Equation (6). In this regression, the number
of instruments and exogenous variables is limited by the number of cross sectional units.
Due to this limitation, only one bank-specific factor will be added at a time. So we can
control the total of instruments for not exceeding the number of cross sections. This
method is implemented in order to resolve the GMM restriction (Judson and Owen,
1992). Therefore, Equation (6) will be formulized to explain for the additional bank-
specific variable:
∆ = 1∆ −1 + ∑2 =1 2 ∆ − + ∑2 =1 3 ∆ − + ∑2 =1 4 ∆ − + ∑2 =1 5 − + +∪ (8)
In the Equation (8), presents for variables about bank’s specifications as we
explained in the previous section. The dynamic of regressors year by year will be
controlled due to the lag at the forth order of in this regression (Berger and DeYoung,
1997). At the current level, non-performing loan rate is assumed not being impacted by
the bank-specific variables (Louzis, et al., 2012). The accounting characteristics, the
18

latency time of management’s decisions changes as well as data variation in balance
sheets of banks are the foundation for this assumption.
The coefficients will be calculated in the long term in order to estimate the the
cumulative effect on bad debt ratio at the current level of each regressor, as the below:
5 = ∑2 =1 5 ⁄(1 − 1) (9)
The variance of the coefficients in the long run is calculated as Stuart and Ord (1998):
(∑2 )
2
(∑2 ) ((∑ =1
2
5 ),(1− 1))
4 5 ( 1)
=1 =1
( 5 ) = [ − 2 + ](10)
2 4 2 4 2
(1− 1) ∑
5
(∑
5
)(1−
1
) (1− 1)
=1 =1
Where (∑2
=1 5 ) = ∑2
=1 ( 5 ) + 2 ∑ ≠1 ( 5 , 5 )
The analysis of cumulative impact of these lagged regressors can be accurate and
robust statistical due to the variance estimation in equation (9). In addition, reviewing the
standard errors in long term can detect the multi-collinearity between the lagged
variables, which could be misled in the lags of each regressors in statistical significance
(Berger and Deyoung, 1997). For that reason, these hypotheses could be examined based
on the long-run coefficients. The general hypothesis test is:
H0: 5 =0
H1: 5 > 0; or H1: 5 < 0
Implementing which H1 depends on which hypothesis is tested.
Among these hypotheses of impact of bank’s specifications, the “too-big-to fail”
hypothesis is special, due to the conditions for testing. The interaction terms between the
size and the leverage will be added in the regression model in order to expand
understanding of the relationship between the NLP rate and leverage ratio in different
sizes. The corresponding specification test in econometric will be:
∆ = ∆
−1
+ ∑2
2
∆ + ∑2
3
∆ + ∑2
4
∆ +
1 =1 − =1 − =1 −
5
+ ∑2
6
+ ∑2
7
× +
=1 − =1 −
+∪
(11)
19

Louzis, et al. (2012) compute the marginal effect of leverage on NPLs conditional
on the banks’ size in the long run by deriving leverage ratio (LR) in Equation (11), as
below:
6
− + 7
− = ∑ =1 6 ⁄(1 − 1) + [∑ =1 7 ⁄(1 − 1)] × (12)
The corresponding variance is computed as Brambor et al. (2006) and Shehzad et
al. (2010) as Equation (10). They also imply that simple parameters t-statistics should not
be the baseline any statistic conclusion of the multiplicative terms. Following Louiz et al.
(2012), the long run marginal effect of this bank-specific factor is the statistic significant
for this hypothesis assessment:
H0: 6 − + 7 − = 0
H1: 6 − + 7 − > 0
The confidence intervals of Louiz, et al. (2011), which based on the researches of
Shehzad, et al. (2010) and Aiken and West (1999), are followed to test the null
hypothesis.
At the first of the study, the macro-economic variables are considered as strict
exogeneity. This is opposite with bank-specific variables. These explanatories are
expected as the predicted factors, due to the expectation that the Board of Managers
always takes into consideration the incurred NPL from their decisions. But there still
have the unpredictable future random shocks to problem loans. With this operation in
reality, Louiz, et al. (2011) supposes micro-economic variables will be a week of
exogeneity. Following Bobba and Coveiloo (2007), this assumption can solve the
endogeneity between error terms and future shocks in non-performing loans, although
there can have the autocorrelation between the errors in the current and past level.
Therefore, equation (4) and (5) are the conditions for the instruments of macro and micro
regressors respectively.
20

3.1.2. Variable explanation
3.1.2.1. Dependent variable
The dependent variable of this paper is change of non-performing loan (∆NPLit) of banks,
which present for the credit risk. NPLs are the loans which are overdue interest and/ or principle
more than 90 days. In Vietnam, State Bank of Vietnam (SBV) divides debt into 5 groups:
 Group 1: outstanding balance has overdue less than 10 days

 Group 2: outstanding balance has overdue from 10 days to 90 days



 Group 5: outstanding balance has overdue more than 180 days
Followed SBV regulations, bad debts are loans belong to group 3, 4 and 5. The NPL ratio is calculated
by total NPLs divide total outstanding balance of the bank at the end of year. This formula is ∆NPLit = − −1
where i and t denote the bank and time series in panel data.
The 1 lag of this variable will be applied in the model as an exogenous variable to
investigate whether the current non-performing loans can be impacted by its history
positively.
3.1.2.2. Macro-economic variables

GDP growth (∆ ):

Due to the finding of Williamson (1987), the business failures and credit risk are
counter-cycle, the impact of economic growth is expected having the negative sign with
the NPL ratio. The common variable of the economic cycle is GDP. The lower GDP
growth leads the lower earnings of companies, income of household, higher level of
unemployment rate, thus the quality of loan portfolio will be deteriorated.
There are several researches indicate the negative relation between GDP growth
and non-performing loans. Jakubik (2007) explains the decrease of GDP rate will lead the
higher unemployment rate and lower corporates’s earning, thus the credit risk can
21

increase potentially. Castrol (2013) supports evidence about the increase of problem
loans in the recession stage of economic cycle as the consequence of the credit expansion
in the blooming period.
This variable (∆ ) presents the change of economic growth, will be applied in the model. The formula for this factor is: − − ,
where j denotes as the lag of this factor and in the range from 1 to 2. This variable is applied to investigate whether the economic growth could
have the negative impact on the credit risk of banks.

Lending interest rate: (∆ )
The influence of interest rate on credit risk is proved by several researches, such
as: Jakubik (2007), Nkusu (2011), Louzis, et al. (2012), Castro (2013) and Chaibi and
Ftiti (2015). The general explanation is the increase of interest rate leads the debt burden
both individuals and corporate. Therefore the credit risk is predicted to be worsen due to
the weakness of repayment capacity.
According the view of theories about asymmetric information, Minskin (1996) concludes
the higher interest rate will lead the bad selection of borrowers when they tend to invest the
riskier projects for more earnings in order to cover the liabilities. . This paper applies the change
of the lending interest rate (∆ ) to present for the interest rate factor. The formula is: − −
where j denotes as the lag of this factor and in the range from 1 to 2. This variable will help to
examine if the interest rate and credit risk could have the positive relation.

Unemployment rate (∆ )
The economic conditions can be considered by the unemployment rate. This rate
impresses the loan repayment ability of borrowers, thus to predict the potential of
problem loans.
The previous researches identify the negative effect of unfavorable increase in the
unemployment rate on the income of households. To remain the capable of debt-
servicing, they can reduce consumption or savings. However, a decrease in the market
demand will lead the decrease of the total production level and revenues of firms, the
22

production cost (such as wages) would be stable or increase. Therefore, firms’ capability
to repay their loans is impacted negatively.
This paper applies the change of the variable (∆ ) to estimate their effect. The formula for the factor is: − − where j
denotes as the lag of this factor and in the range from 1 to 2. Therefore, the unemployment rate may increase the risk of credit
defaults and it is hypothesized that unemployment rate has a positive impact on bank credit risk.

Government debts (∆ )

One of independent variables is the ratio of government debt in GDP from 2006 to
2016. This factor could be used in order to estimate the impact of the Public debts on
problem loans. The testing result was expected to having a positive relation as sovereign
debt hypothesis.
3.1.2.3. Bank –specific variable:

Return-on-equity (ROEit):

This financial index is the ratio of profit and total equity of bank i in year t. The
doubtful loans and ROE of banks are expected to having the negative relation as the bad
management (version II) and positive sign in procyclical credit policy hypothesis
mentioned.

Solvency ratio (SRit):

This variable is applied in the model to examining the moral hazard hypothesis.
This variable, which reflects the strength of banks capital, is calculated by the owned
capital in total assets of banks. It is generally considered that higher level of capital
allows bank to absorb shocks that may appear in the credit market. Berger and DeYoung
(1997) find evidence supporting the ‘moral hazard’ hypothesis, implying impact of bank
capital to NPLs, which is a negative association. The explanation for the hypothesis lies
on the role of banks’ managers, who decide to accept the high riskiness rate in their loan
portfolio even though their banks’ capitals are thin. On the other hand, Curak et al. (2013)
propose that even the bank are higher capitalized, it could encourage banks to take more
23

risk in lending activities resulting in loan losses because of moral hazard behavior of
bank managers. Thus, the negative sign in this test is expectation results.

Inefficiency ratio (IEit):


The operation efficiency of banks is calculated by formula as:=
. The result is expected that if the bank could not operate efficiently
within year, its NPL could increase at the end of the year, as the hypothesis about bad
management and skimping stated. Therefore, the positive sign is expected in bad
management hypothesis testing, otherwise for skimping hypothesis.

Bank’s size (Sizeit):


The diversification hypothesis is tested by variable about size of the bank (Size) as
the formula: = ∑30 =1
. This hypothesis indicated that the small banks
have less possibility to diversify their
portfolio than big competitions. Therefore, these banks could have more NPL ration than other group. Expectation about
the testing result is a negative sign of coefficient.

Non-interest income ratio (NIit):

This variable, is a proxy for the bank diversification, is also described by the
portion of non-interest income in total income (NI). It means that the majority in total
income is from interest income, the bank has to push the lending activity to maintain the
growth rate of earnings. The potential problem loans will increase in the credit expansion
stage. So the estimation result is expected a negative relation between non-performing
loans and percentage of non-interest income.

Bank’s leverage ratio (LRit)

This variable is calculated by the proportion of total assets which are financed by
total liabilities. The expectation is the positive sign, which means that this ratio is high
will increase the non-performing loans of the banks.
24

3.2. Data
This paper will use two kinds of data in the period 2006 – 2016. One is
macroeconomic data and the other is the bank-specific data of Vietnamese banks.
3.2.1. Macroeconomic data
With the data-set about World Development Indicators (WDI) of WB, data for the
variables: GDP growth, Unemployment rate and lending interest rate from 2006 to 2016
can be collected
However, the figures of Central Government Debt are blank in WDI. For that
reason, data from IMF could supply variable about General government gross debt.
Therefore, the macro-economic indicators for this study could be complete.
3.2.2. Bank-specific data
This paper uses a panel data which was collected from the audited annual financial
statements and annual reports of 30 Vietnamese banks in the period from 2006 to 2016.
Balance Sheet supports information of Total Assets, Total equity, Owner capital, Total
Liabilities and The total outstanding balance. With Profit and Loss report, the figures of
income and expenses could be collected, such as: Total income, Operation Income, Non-
interest income, Operation expense, Profit. The Notes of Financial statements and the
annual reports supply the NPL ratio each year.
25

CHAPTER 4: RESULTS AND DISCUSSIONS
4.1. Summary statistics
The period 2005-2010 is considered as the significant growth of Vietnamese
banking system, which is described by the increase of total assets and total outstanding
balance (Nguyen Xuan Thanh, 2016). The author indicated that the total outstanding
loans of join-stock commercial banks and state-owned banks grew up at 10.2 times and
2.9 times respectively in 2010. However, this growth also showed off the weakness of the
system, such as the unbalance of loan portfolio, weaken liquidity, bad debts. However,
after this blooming period, the banking system has been suffered the serious
consequence. The number of bad loans increased significantly. Some large bad loans,
which have not any solution yet, can be identified as An Khang Ltd., Co. (credit limit is
over 600 million dong), Lifepro VN (2.754 million dong in Agribank, etc). The NPL ratio
gets a peak in 2012. Up to now, several policies have been issued in order to restructure
the banking system as well as solve the non-performing loans. However, as many
researches mention, understanding the root cause is usually the best method to resolve the
problem.
The statistics summary of the variables for this study is presented in the Table 1.
The observations of each independent variable are 316. However, the change of non-
performing loans ratios is the smallest at 298 observations. . When collecting the data of
30 banks in 11 years (2006 – 2016), some data about bad loans are hidden with public.
That is the reason of missed observations of the dependent variable. In addition, some
banks were established in the stage after 2006. The change of problem loans ratio varies
from -0.0773% to 0.1118%. The banks maintain the change at an average around
0.0002%, as the mean value shows.
Besides, the change of economic growth lies in the ranges from -1.4677% to 1.
0253%, and its medium value is -0.1186%. 68% value of ΔGDP lies in the interval [-
83.31%; 59.71%] with the median -18%. Besides, the maximum value of unemployment
rate changes is 0.32 while -0.62 is its minimum. The median of this change is 0.03, with
26

the macro-economic factor about lending interest rate, the changes of this variable have
the smallest value at -5.71 and the biggest at 4.6. The average value is -0.3886. Thee
changes’ government debts vary from -2.3 to 5.75 with the median at 2.49.
The return on equity of these banks ranges from -82% to 32%, and its medium is
8.86%. The inefficiency in bank’s operating has the minimum and maximum value at -
5.15 and 8.56 respectively. Its median is -0.52. In the above table, minimum value of
banks’ size and solvency ratio is 0.00. However, in fact, the value of these variables is too
small, 0.00085 and 0.0022 respectively. Their maximum values are 0.27 and 0.44 in that
order. The largest proportion of non-interest income in total income is 11.65, while the
smallest is -0.55. The other bank-specific factor is banks’ leverage ratio. This index lies
between 0.54 to 0.97, with its average value is 0.8883
The correlation of each pair of variables s described in the Table 2. The diagrams
about the correlation of NPL with each explanatory variable are presented in Appendix II.
Except the relations between the change of government debts with the unemployment
rate and lending interest rate; between leverage ratio and size of bank, there is no
correlation has index more than 0.8 or less than -0.8. Therefore, the serious multi-
collinerity among these variables cannot be occurred in the regression.
Table 1: Summary statistics
Variable Obs Mean Var SD Skeness Kurtosis Min Max
ΔNPL 298 0.0002 0.0003 0.0171 1.2540 13.9459 -0.0773 0.1118
ΔGDP 316 -0.1186 0.5102 0.7143 -0.2196 2.2553 -1.4677 1.0253
ΔUN 316 -0.0170 0.0619 0.2488 -1.0292 3.7999 -0.6200 0.3200
ΔLIR 316 -0.3882 9.6320 3.1036 0.1128 2.0385 -5.7146 4.6036
ΔGD 316 2.1839 4.9193 2.2180 -0.7424 2.9461 -2.2970 5.7480
ROE 316 0.0886 0.0064 0.0797 -4.3016 54.9448 -0.8200 0.3012
IE 316 -0.5599 0.5740 0.7577 5.0336 78.0259 -5.1513 8.5564
27

SR 316 0.0977 0.0047 0.0684 2.4469 10.4348 0.0022 0.4438
Size 316 0.0348 0.0021 0.0463 2.2220 7.8159 0.0008 0.2670
NI 316 0.2668 0.5395 0.7345 12.7985 189.9411 -0.5516 11.6503
LR 316 0.8879 0.0049 0.0697 -2.4161 10.1881 0.5374 0.9736
4.2. Empirical results
This study estimates the impact on non-performing loans ratios by four methods:
Pooled Original Least Square, Fixed Effect, Random effect and Generalized Method of
Moments. The issues about heterogeneity and unobserved individual effects such as
bank-specifics are controlled better by Fixed Effect method than by Pooled OLS. On the
contrast, the endogeneity of variables, which have lag orders, and effects in the error
terms can be occurred in the Fixed Effect or Random Effect models. To restrict the
residual issues, the models of Different Generalized Method of Moments – D.GMM
(Arellano and Bond, 1991) and System Generalized Method of Moments – S.GMM
(Arellano and Bover, 1995; Blundell and Bond, 1998) are proposed to apply. These
methods use the separated data and lagged variables as the instrument variables (IV). In
addition, with the limited and small data sample, the S.GMM with the first lag order of
dependent variable is suitable for estimating the impact of positively unobserved
explanatories (Roodman, 2009). The macroeconomic factors are considered as the strictly
exogenous and instrumented by themselves as instrument variables in the S.GMM model.
The lagged value of NPLs and bank specifications in level are endogenous and
instrumented with GMM-style instruments. Moreover, the lag of the endogenous
variables with a restricted the maximum lag period will be hold constant in the GMM-
style lag specification. Table 2 presents the estimation results of methods: POLS(1), FE
(2), RE (3) and SGMM (4).
28

Table 2: Results with Pooled OLS, FE, RE and SGMM estimations
Variables POLS FE RE SGMM
ΔGDP -0.00524*** -0.00510*** -0.00524*** -0.00499***
(-4.18) (-3.88) (-4.05) (-11.94)
ΔUN -0.00932 -0.00927* -0.00932* -0.00439
(-1.62) (-1.91) (-1.87) (-1.16)
ΔLIR 0.000746 0.000713 0.000746 0.000864***
(1.18) (1.25) (1.29) (5.45)
ΔGD 0.000589 0.000554 0.000589 0.000151
(0.62) (0.59) (0.61) (0.38)
IE -0.000455 -0.000517 -0.000455 -0.00128***
(-0.54) (-0.66) (-0.60) (-2.61)
SR 0.0617 0.148** 0.0617 0.0454**
(0.73) (2.34) (1.07) (2.35)
Size -0.0281 -0.087 -0.0281 -0.000347
(-1.56) (-1.05) (-1.25) (-0.03)
LR 0.0544 0.141** 0.0544 -0.00855***
(0.68) (2.06) (1.00) (-3.09)
ROE 0.0154 0.0234 0.0154 0.0300***
(0.93) (1.14) (0.96) (4.15)
NI 0.00181 0.0023 0.00181 0.00231***
-1.29 -1.36 -1.32 -4.02
29

Variables POLS FE RE SGMM
Constant -0.0568 -0.141** -0.0568
(-0.71) (-2.09) (-1.04)
ΔNPL 0.154*
(1.83)
N 298 298 298 267
Hansen 20.8
0.107
AR1 -2.405
0.0162
AR2 1.489
0.136
(*), (**) and (***) are the significant level at 10%, 5% and 1% respectively.
Values in the parentheses are T-statistics.
The regression results of Fixed Effect and S.GMM show the effect of macro and
micro variables on bank’s problem loans. The result of Hansen test in the S.GMM model
identifies the non-correlation between the instruments and the error terms. Besides, the p-
values of Arellano–Bond tests for first and second-order autocorrelation in the first
differenced errors are 0.0162 and 0.136 respectively. It means that the hypothesis about
the auto-correlations between errors cannot be rejected in the first order (AR1) but not in
the second order (AR2).
Except the leverage ratio, the signs of both macro and micro variables in Fixed
Effect and S. GMM models are the same. However, the lag of dependent variable is
removed from FE estimator, due to the endogenous issue. With S.GMM estimation
results, the change of NPL ratio with the first lagged order affects positively and
30

statistically significant on its current rate at level 1%. It means that the increase of
problem loans can predict the go up of this ratio in the next year. Besides, the GMM
model support estimation results about the impact on NPLs ratio of more variables than
FE. So, the detailed results will be described in the next part.
The impact of the economic growth on bank’s problem loans is constant through
four estimation methods. The coefficient of this variable in the S.GMM model is negative
and statistically significant at 1%. This result indicates that when economic growth
increases, the bad debt ratios of banking system can decrease. The impact of this factor is
in accordance with the previous researches, such as: Louiz, et al. (2012), Messan and
Jouini(2013), Salas and Saurina (2002), etc.
The estimation result about impact of unemployment rate is inconsistence with the
previous studies. In our paper, this test supports a positive and statistically significant
relation of this factor and problem loans. Firstly, non-performing loans are the over-90-
days loans which the borrowers have not paid yet. It means that they have the financial
distresses impacts on debts payment. If borrowers are individuals, unemployment rate
effects on customers’ ability for payment obligations (Louiz, et al., 2012). If borrowers
are firms, they can save operation costs by cutting their labor. The consequence is the
increase of unemployment rate. The income of households is impacted negatively. Their
demands will be decrease, so that the production will be declined as well (Messai and
Jouini, 2013). As the cycle, the NPL ratio continues to rise when the unemployment rate
cannot be improved.
However, in Vietnam, the situation has some differences. The result is associated
with the research of Nguyen Huu Quang and Nguyen Xuan Nhi (2017) when they
examine the bad loans of commercial banks in Viet Nam. One possible explanation for
the negative effect of this variable is the distribution of labor force. In the report of
Ministry of Information and Communications in 2011, over 71% labor force is in rural.
However, the employment rate in this area is lower than in urban, leads the overall rate of
Vietnam is small. And most labor in the countryside work in agricultural segment,
31

household business and factory. In fact, workers are fired from factory’s managers can
come back their agricultural jobs. The internal transfer in this side can make no
significant impact on unemployment rate. However, their income has been reduced in
reality. That is the possible reason for the negative relation between this macro-economic
variable versus non-performing loans in banking system.
The impact of the lending interest rate is consistent with the previous studies. The
result supports the significant evidences that the higher lending interest rate can make the
higher the NPL ratio, due to the increase of financial pressure. However, the estimation
result about the influence of the change in government debt on NPL is insignificant,
although the sign is the same as the expectation.
S.GMM model supports the strong evidences for skimping hypothesis by using the
inefficiency index. The consequence of the improvement in banks’ cost-effectiveness by
cost reduction from loan quality control can be predicted the increase of problem loans.
In fact, Mr. Huong, chairman of Lien Viet Post Bank, used to say that due to the legal
difficulties, many debtors rejected to pay their obligations. Therefore, if we exchange the
cost decrease for underwriting and monitoring loans into having the higher cost
efficiency, the bad debts will increase rapidly. This is the reality in credit flow in
Vietnam.
The coefficient on the solvency ratio is a positive determinant of credit risk and
significant in both FE and S.GMM estimator at a level of 1%. This evidence cannot
support for moral hazard hypothesis, which is supported by Salas and Saurin (2002),
Berger and Deyoung (1997). However, this result is consistent with finding of and
Louzis, et al. (2012). The possible reason to explain this issue is that the small sized
market is the obstacle for bank’s managers to take more risk in short term due to the
reputation targets. Furthermore, the SBV can take account for the risk level of bank’s
loan portfolio and intervene if this is necessary. This leads the reduction of incentives for
the moral hazard.
32

The next factor is bank’s size. Although the sign of this variable is the same as the
expectation but thee estimation result is insignificant. On the contrast, the sign of
leverage ratio is negative in the S.GMM but positive in FE model; and the estimation
result has the statistically significant meaning. This paper applies the estimation result of
S. GMM model as this model can solve the endogenous issues better than others.
However, this finding cannot support for “too-big-to-fail” hypothesis, but this is the
evidence for finding of Salas and Saurina (2002). They explain that in case the quality of
credit policies and credit management is better enough for the bank expands their credit
activity, their portfolio can reduce the problem loans, due to the segment diversification.
The impact of profit index on bank’s problem loans change is positive and
significant at 1%. This finding supports a strong evidence for “procyclical credit policy”
hypothesis. The conclusion indicates that in reality, Vietnamese banks have a tendency to
create a best performance in short-term for the reputation target. The example for this
achievement is the awards they showed off each year such as Best Banking Brand in
Vietnam, The Asian Banker Vietnam Country Awards top banks, etc. Besides, an event
lasting from the end of 2010 can be the proof for the appearance of the procyclical credit
hypothesis in Vietnam banking system. This is the restrictions for the credit increase of
SBV. State Bank of Vietnam divided banks into three groups with different credit rooms.
It means that the bad performance last year will limit the lending ability this year.
The sign of non-interest income is contrary with the expectation about the inverse
relation between this factor and NPLs. This sign shows the positively and statistically
significant relation between two variables. The result indicates that the increase of non-
interest income can make the non-performing loans of banks are worse. This finding is
not the content of “diversification” hypothesis. One probably explanation for this
difference in Vietnamese banks is the efficiency of the non-credit activities. The
diversification hypothesis mentions that if the bank diversifies their activities, the
pressure on credit activity can reduce and they can collect better customers. However, in
Vietnam, the quality of non-credit activities is not substantial as these activities
33

contribute a very little percentage, even negative, in total income. This leads to the
increase of credit activities to maintain the stability and growth of revenue and profit. The
consequence is the increase of problem loans.
The results in Table 2 express the impacts on the change of problem loans rate by
year. However, some effects can be recognized after some years. Table 3 will show the
impact of these variables with the first lagged order, except government debt ratio.
Table 3: Estimation results of one lag variables
OLS FE RE SGMM
ΔGDPit-1 0.00440** 0.00462*** 0.00440*** 0.00621***
(2.51) (2.78) (2.74) (13.30)
ΔUN it-1 -0.00964* -0.00912* -0.00964** -0.00307*
(-1.75) (-1.81) (-1.98) (-1.70)
ΔLIR it-1 -0.000221 -0.000455 -0.000221 -0.00108***
(-0.37) (-0.96) (-0.50) (-8.54)
ΔGD it-1 -0.000638 -0.000983 -0.000638 -0.00222***
(-0.58) (-1.04) (-0.69) (-11.29)
IEit-1 -0.000925 -0.000768 -0.000925 -0.000715
(-0.94) (-0.61) (-0.80) (-1.27)
SRit-1 -0.205*** -0.332*** -0.205*** 0.00967**
(-3.37) (-2.98) (-3.06) (2.25)
Sizeit-1 -0.0237 0.0308 -0.0237 -0.0299***
(-1.23) (0.41) (-1.07) (-2.98)
LRit-1 -0.211*** -0.339*** -0.211*** -0.00218*
34

(-3.48) (-3.28) (-3.37) (-1.72)
ROE it-1 0.0324** 0.0481** 0.0324** 0.0557***
(2.08) (2.75) (2.43) (7.44)
NI it-1 0.00522*** 0.00654*** 0.00522*** 0.00536***
(4.15) (4.60) (5.20) (8.80)
ΔNPLit-1 -0.222***
(-19.47)
Constant 0.206*** 0.328*** 0.206***
(3.41) (3.17) (3.26)
N 282 282 282 267
Hansen 25.27
0.448
AR1 -2.359
0.0183
AR2 -0.64
0.522
(*), (**) and (***) are the significant level at 10%, 5% and 1% respectively.
Values in the parentheses are T-statistics.
Most of the estimations results are consistent in four models. This means that the
endogenous issue in the variables is not serious and S.GMM method has solved this
problem better.
The first surprising result is the negative and significant of the coefficient at 10%
of the non-performing loan ratio two years ago. This is not consistent with the estimation
result by year. One reason to explain is relevant with the restructuring project of the Bank
35

State of Vietnam (SBV). The project implies that when the problem loans of bank reach
the threshold, this bank will be controlled by SBV especially. In addition, SBV requires
the maximum rate of NPLs is 3% and the credit activity will be limited after that. So
when the bad debts of bank increase, managers have to find the solutions to control the
ratio, as SBV’s requirements. Therefore, if the non-performing loans increase two or
more years ago can be expected the improvement of the bad debts in current.
The next surprise is the coefficient of the economic growth with first lag order is
positive and significant with the interval confidence at 99%. Beck, Jakubik and Piloiu
(2013) explain that many loans with low quality, are issued in the blooming period, can
deteriorate the bank’s assets with the lag responses in the growth of economy positively.
Other reason is argued by Bohachova (2009). The author identifies the prudence of bank
in developed countries in capital increase in the boom period of economy. When the
economy turns down, banks’ capital ratios reduce. This result reflects the falling of asset
quality and equity values. This leads to the positive relationship between economic
growth and non-performing loans of banks. Furthermore, Poudel (2013) explains that
credit activities are controlled carefully in the recession phrase in order to prevent the risk
exposure. Banks will limit the credit volumes, categorize the segments with higher and
more tightened credit conditions, and prefer the secured loans than unsecured loans.
These solutions are expected to control the quality of credit portfolio to keep banks from
risk exposure in the downturn of economy.
The impact of unemployment rate is proved with the 1 lag of this variable, when
the result supports the significant coefficient at 10%. This result confirms the effect of
this factor on non-performing loans of Vietnamese banks, which is difficult to be
observed year by year.
For the long time, the increase of lending interest rate can be the solution to
decrease the non-performing loans of banks. The estimation result indicates the negative
relation between the NPL ratio and this variable with 1 lag. Coefficient is significant at
the level of 99%. One possible explanation is based on the debt-burden. For the long
36

term, if lending interest rate is still high, the borrowers tend to pay off their loans to
increase available income for their demand and savings. If borrowers are corporates, they
will find other funds, such as: bond market, stock market, partners, etc. in order to
alternate the banks’ loans as well as achieve the profit target. Therefore, this factor will
support to reduce the non-performing loans in the long term.
With 1 lag, the impact of government debt is observed. The coefficient is
significant at 1%. However, the negative sign is not the expectation of the sovereign
hypothesis testing. One possible explanation for this negative impact of this factor is from
the threshold of government debt. It means that if the government can control the ratio of
debt under threshold, the bad debt issue can be improved as the economy continues to
developing.
The coefficient of inefficiency ratio is insignificant when estimating this variable
with one lag, although the sign is still negative. The result is consistent with the finding
of Salas and Saurina (2002). The quality of cost management impacts on the change in
problem loan within one year. The consistence in sign confirms the existent of skimping
hypothesis in Vietnamese banks, although the results can have the significant meaning or
not.
The result from estimation of the first lagged solvency ratio remains unchanged
and cannot support evidence for the moral hazard hypothesis in Vietnamese. However,
with the longer time, the impact of size is observed. The coefficient of this variable is
negative and statistically significant at 1%. This result supports the diversification
hypothesis. The banks have bigger size than others can have more chances to diversify
their activities. Abundant activities can reduce the pressure on the growth of credit, thus
banks have many opportunities to collect the better segments for financing.
The results of estimation for the influence of ROE and leverage ratio of banks on
the non-performing loans remain unchanged. The procyclical credit policy hypothesis is
supported strongly due to the banks’ branding strategies in order to take as more shares as
possible in the market. The too-big-to-fail hypothesis may be not exact for Vietnam at
37

this time. In reality, the biggest Vietnamese banks, include the stated owned banks, have
to spend their capacity to help SBV control and maintain the stability of financial system.
In addition, the large banks in Vietnam have the diversified activities efficiently. Their
non-credit activities, such as foreign exchange, lending in the interbank market, are have
more chances to be success than others.
Other impact which is unchanged in the 1 lag estimation is the non-interest income
ratio. It means that in really, the diversification in bank’s activities is not efficient as
expectation. The negative effects of fluctuations from foreign exchange and golden
market lead banks’ income decrease, even the losses, of course some big banks could be
the exceptions. To maintain the stability of banks’ performance, credit section is pushed
to grow, even if serving the worse segments trade off with high interest rate. The
consequence is the increase of doubtful loans in the future.
38

CHAPTER 5: CONCLUSIONS, RECOMMENDATION AND LIMITATIONS

Determinants of Non-Performing Loans in Vietnamese Banking System

Recommended

Recommended

More Related Content

Similar to Determinants of Non-Performing Loans in Vietnamese Banking System

Similar to Determinants of Non-Performing Loans in Vietnamese Banking System (20)

More from Dịch vụ viết thuê đề tài trọn gói Liên hệ ZALO/TELE: 0973.287.149

More from Dịch vụ viết thuê đề tài trọn gói Liên hệ ZALO/TELE: 0973.287.149 (20)

Recently uploaded

Recently uploaded (20)

Determinants of Non-Performing Loans in Vietnamese Banking System