A Study on Non-Performing Loan: from the Perspective of the Banking Industry in Bangladesh

Thesis Paper
On
“A Study on Non-Performing Loan: from the Perspective of
the Banking Industry in Bangladesh”

i
Submitted To:
Department of Finance
University of Dhaka
Supervised By:
Sultana Shahreen Karim
Lecturer
University of Dhaka
Submitted By:
Ulfat-Ara-Joya
ID: 18-838
University of Dhaka
Date of Submission: 27/08/2017

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Letter of Transmittal
August 27, 2017
Sultana Shahreen Karim
Lecturer
University of Dhaka
Subject: Submission of Thesis Paper on “A study on Non-Performing Loan: from the
perspective of the Banking industry in Bangladesh”
Dear Madam,
As per the requirements of my M.B.A. program offered at Department of Finance, University
of Dhaka, I hereby submit my thesis paper on “A study on Non-Performing Loan: from the
perspective of the Banking industry in Bangladesh”
The paper has been prepared based on data from various sources, mainly from the annual
reports and company websites, secondary sources, Bangladesh Banks’s financial data achieve
and based on my findings. I believe that the knowledge and experience that I gained will be
of great importance for my future life. I have tried my best to make the report a perfect one. I
hope you will appreciate all my efforts and accept my submission.
I would also like to thank you madam for your support and advice in preparing the paper. No
part of this report will be reproduced for use in any other form of publication in the future
without your written permission. I shall be available for any clarification, if required.
Sincerely yours,
Ulfat-Ara-Joya
M.B.A 18th
Batch,
Section: B, ID: 18-838.

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Acknowledgement
All praise to Almighty Allah. At first I am thankful to the almighty who gave me the strength
to complete the thesis paper. In the process of preparing my thesis paper I would like to pay
my gratitude and respect to some important persons for their co-operation.
Above all I am very much thankful to my honorable instructor Sultana Shahreen Karim who
has given me the opportunity to prepare the thesis paper on “A study on Non-Performing
Loan: from the perspective of the Banking industry in Bangladesh”. Besides, she guided
me very often to prepare the thesis paper in an organized manner.
Secondly I want to say that I have done great deal of hard work for preparing the thesis paper.
I have collected information from browsing the internet, reading newspaper article, talking
with experienced pupil, from real life experience, different types of journals and many other
sources. After gaining proper knowledge I have prepared the thesis paper.
Finally, I am grateful to my family who always gives me constant support and
encouragement. I would like to thank my seniors who helped me greatly to complete the
thesis paper. In addition, I want to mention my friends who helped and inspired me to finish
my work.
Sincerely yours,
Ulfat-Ara-Joya,
M.B.A 18th
Batch,
Section: B, ID: 18-838.
University of Dhaka

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Abstract
A loan that is in default or close to being default is considered to be non-performing loan and
higher amount of non-performing loan indicates inefficiency of a bank in providing good
loans. The most important task related to the study is to determine the most influential factors
that contribute to the change in Gross NPL ratios in Bangladeshi banking industry. In order to
conduct the study data from the financial statements of 24 commercial banks including four
state-owned commercial banks has been used. For the purpose of estimation in the study a
balanced panel regression model will be effective because the data that will be used in the
study is both cross sectional and time series. In the case of using balanced panel regression
model the degree of freedom is increased and collinearity among the explanatory variables is
reduced so the economic efficiency and predictive power of the model increase and
ultimately the effectiveness and appropriateness of the model also increase.
According to the Hausman test the value of chi2 is more than 5% that means null hypothesis
cannot be rejected and so random effect model is the most appropriate model for the dataset
according to the results of Hausman test. The random effect model shows that “GDP
Growth”, “Inflation”, “Unemployment” and “Performance Efficiency” are the most
significant predictor variables in explaining the changes in Gross NPLs ratios. The coefficient
of GDP Growth is not consistent with the hypothesis which may create doubt about the
effectiveness of the model.

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Contents
Chapter One: Introduction......................................................................................................................1
Background of the Study:....................................................................................................................2
Objective of the Study: .......................................................................................................................2
Scope of the Study:.............................................................................................................................3
Limitations of the study: .....................................................................................................................3
Chapter Two: Literature Review .............................................................................................................4
Chapter Three: Methodology of the study.............................................................................................7
Chapter Four: Banking Industry Overview............................................................................................11
Trend of Default Loans in Bangladesh: .............................................................................................12
Chapter Five: NPL and Different Aspects of NPL ..................................................................................15
Guidelines of Bangladesh Bank regarding NPL:................................................................................18
Minimum Provision Requirement:....................................................................................................19
Determinants of NPL in Bangladesh: ................................................................................................19
Chapter Six: Data Description, Variables, and Research Design...........................................................22
Econometric Model:..........................................................................................................................23
Data Description: ..............................................................................................................................24
Correlation:.......................................................................................................................................25
Chapter Seven: Analysis & Results........................................................................................................26
Fixed effect model: ...........................................................................................................................28
Random Effect Model: ......................................................................................................................29
Hausman Test: ..................................................................................................................................30
Test for Heteroscedasticity:..............................................................................................................33
Unit Root Testing: .............................................................................................................................33
Multicollinearity:...............................................................................................................................36
Chapter Eight: Conclusion.....................................................................................................................37
References: ...........................................................................................................................................38

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List of Tables:
Table 3.1: List of Hypotheses ………………………………………………………….10
Table 4.1: Banking Industry's L&A and NPLs ratios ………………………………….13
Table 5.1: Minimum Provision Requirement …………………………………………..19
Table 6.1: Variables and Expected Signs ……………………………………………....23
Table 6.2: Descriptive Statistics ………………………………………………………..24
Table 6.3: Descriptive Statistics ………………………………………………………..25
Table 7.1: Regression Analysis ………………………………………………………...27
Table 7.2: Fixed Effect Model …………………………………………………………29
Table 7.3: Random Effect Model ………………………………………………………30
Table 7.4: Hausman Test ……………………………………………………………….31
Table 7.5: Second Step Random Effect ………………………………………………..32
Table 7.6: Summary of Unit Root Tests ………………………………………………..35
Table 7.7: Results of VIF ………………………………………………………………36

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Non-performing loan is an important item related to the balance sheet of a bank and the
performance of a bank is negatively affected because of non-performing loan so every bank
tries to minimize the amount of its non-performing loan as much as possible. In order to
control the non-performing loan of a bank it is necessary to identify or determine the factors
that influence the amount of non-performing loan of a bank. Though it is not easy to
determine all the factors that influence the amount of non-performing loan of our country in
this study it will be tried to determine some of the factors that are closely related to the
increase or decrease of non-performing loan of Bangladeshi banking industry.
Background of the Study:
The study has been conducted in order to develop the understanding of NPLs of Banks from
the perspective of the banking industry in Bangladesh. It was assigned to me as a significant
part of MBA program of Department of Finance which is under Faculty of Business Studies.
The paper has been generated under the academic supervision of Sultana Shahreen Karim,
Lecturer of Department of Finance of University of Dhaka. I have prepared the thesis paper
for the fulfillment of partial requirement of my MBA degree. The topic is “A study on Non-
Performing Loan: from the perspective of the Banking industry in Bangladesh” ‘The
paper is basically assigned to collect practical data and work with that. According to the
instruction of my supervisor I, Ulfat-Ara-Joya has made the framework of the thesis paper.
Objective of the Study:
In order to understand the condition and performance of the banking industry of a country it
is necessary to analyze the non-performing loans of the banks that are operating under the
banking industry of the country. The main objective of the study is to get a clear
understanding of the present condition of the NPLs of Bangladeshi banking industry and to
get an idea about the major determinants of NPLs and to forecast the trend of NPLs in the
near future. In broad sense, the objective of the report is to focus on the determinants of NPLs
and to determine the impact of NPLs on the overall performance of the banking industry. In
order to get idea about the variables that can be helpful in predicting the NPLs and to
understand the treatments of NPLs is another objective of the thesis paper.

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Scope of the Study:
The scope of the thesis paper is not limited rather the scope is broad as NPLs are closely
related to the development and performance of the banking industry so there is opportunity to
the implementation of the study practically in understanding the impact of NPLs and to take
proper measures in controlling the NPLs of different banks of Bangladeshi banking industry.
Another important scope is to apply the paper in understanding the constraints to the
operational effectiveness of a bank or the overall banking industry. The paper can also be
effective in understanding the different classifications and treatments of NPLs. To understand
the accounting treatment of the interest of NPL and to understand the economic and financial
implications of NPL are other important scopes of the thesis paper.
Limitations of the study:
Though it was tried hard to make the paper an effective one there are some limitations of the
study that may make the scope of the study limited. One of the main constraints that have
made the scope of the study limited is the insufficiency of information and the unavailability
of relevant information. Another notable constraint that was faced in preparing the thesis
paper is the limited time. Moreover because of strict regulations of some of the banks it
became impossible to collect some relevant and important data that were necessary for
making the thesis paper more relevant and effective. In spite of having the mentioned
limitations it can be expected that the study will be effective in understanding the impact of
NPLs in Bangladeshi banking industry and to understand the treatment of NPLs for
improving the overall performance of the banking industry.

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Chapter Two: Literature Review

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NPL is an important topic regarding banking industry so there are lots of studies on this topic
so it is necessary to understand the conclusions of the studies that have already been
performed by different authors before performing the same study as the previous studies may
help to understand the topic more effectively.
Among different types of risks that a bank faces in operating within the banking industry
credit risk is one of the most influential risks that may force a bank to be bankrupt and credit
risk is influenced by non-performing loans so it is necessary to consider the non-performing
loans of a bank in determining the credit risk appropriately (Zelalem, 2013). In order to
ensure the soundness of the banking system and to ensure economic growth it is necessary to
observe the determinants of non-performing loans so that non-performing loans can be
controlled properly. The existing literature regarding NPLs has been discussed below:
Macroeconomic forces are influential to the overall performance of banking industry,
economic growth and other development programs of a country and these forces are
considered as exogenous. Loss rate of a debt portfolio that is diversified is influenced by the
economic condition and in this case the influence is considered to be important (Carey,
1998). Some studies show that during recession the amount of non-performing loans increase
in the economy that means the gross amount of non-performing loans depends on the
economic state of a country. In other studies it has been shown that the bad economic
conditions of a country influences the NPLs of the banking industry of the country and the
main reason behind this kind of finding is that the bad economic condition affects the income
of the clients of a bank and so they make delay of the repayments of the interests and
principle amounts and in some cases fail to repay.
Problem loans and GDP are negatively related and this kind of relationship means that the
amount of problem loans increase during bad economic condition or economic downturn and
the negative relation between the two factors are significant and this conclusion have been
drawn by Jimenez and Saurina (2006), Das and Ghosh (2007), and Warue (2013). A study
related to the determinants of NPLs which was conducted by Salas and Sourina (2002),
showed that the changes in NPLs are affected by GDP expansion, unemployment rate, real
exchange rate, and the soundness of policies within the banking industry significantly. In
addition to the macro-economic factors some other industry-specific, firm-specific or
country-specific factors may also increase or decrease the amount of non-performing loans of
a banking industry. In some studies the relationship between NPLs and bank-specific factors

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has been clearly shown and so it can be said that the amount of non-performing loans of a
specific bank is somewhat dependent on the effectiveness of the bank’s policies, internal
culture and efficiency of its employees.
A study conducted by Ekanayake and Azeez (2015) based on Sri Lankan banking industry
showed that non-performing loans increases when the efficiency of a bank worsen. In the
study of Zelalem (2013), which was conducted on the Ethiopian commercial banks ROA was
used as a proxy for financial performance in other words performance efficiency. In that
study the relationship between NPL and loan to assets ratio of a bank was all examined and
the relationship was shown as negative. Another study related to NPL conducted by Louzis
(2012), showed that the amount of NPLs in Greek banking industry is significantly
influenced by both the management quality of the banks and the macroeconomic forces of the
country. Same conclusion was also drawn by Mehmood (2013) and his study was performed
based on 13 commercial banks of Pakistan and he chose time period from 2003 to 2012 for
conducting his study.
According to the study of Eurak (2013) which was based on the banking system of South
Europe it was concluded that high interest rate, economic downturn and high inflation rate
are the main reasons of higher non-performing loans in an economy and the study was also
conducted based on 69 banks from ten different countries. Other factors that influence the
credit risk of a bank are the size, performance and solvency of the bank as these factors are
influential to the non-performing loans of the bank.

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Chapter Three: Methodology of the
study

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In order to perform the study on the Non-Performing Loans from the perspective of the
banking industry in Bangladesh some of the private commercial banks and some state-owned
commercial banks that operate under the banking industry in Bangladesh will be selected
randomly at the first step of the study.
For conducting the study 20 private commercial banks and 4 sate-owned commercial banks
will be considered and the financial data from the year 2010 to 2016 of the selected banks
will be used for the purpose of the study. Mainly secondary sources will be used for
collecting the financial data of the banks and some advanced tests including correlation and
multiple regression analysis will be performed for determining the variables that contribute to
determine the change in gross NPL ratio of Bangladeshi banking industry.
NPLs of private commercial banks and state-owned commercial banks will be compared by
calculating the descriptive statistics of the NPLs of these two types of banks. Then finally the
most influential variables of the study will be identified and the conclusion regarding any
significant differences between the NPLs of private commercial banks and state-owned
commercial banks will be drawn.
In order to achieve the broad objective of the study the data of the selected banks has been
collected from their financial statements and the mainly quantitative approach has been used
for conducting the study. Among all the commercial banks in Bangladesh 20 private
commercial banks and 4 state-owned commercial banks have been selected and the selected
state-owned commercial banks are the Sonali Bank Ltd, Rupali Bank Ltd, Janata Bank Ltd,
and Agrani Bank Ltd.
At first some required ratios like net interest margin, gross NPL ratio, net NPL ratio etc. will
be calculated then the pairwise correlation matrix will be run for the data set using data
analysis tool in excel. Then the regression analysis for the data set using stata will be
performed in order to determine the relationships between the change in NPLs and the
independent variables. Hausman test in stata will be taken in order to justify either fixed
effect model or random effect model is appropriate for the study. Finally the appropriate
model for the second step regression will be run for the study.
Then Heteroskedasticity test will be performed as well after that unit root test will be done to
examine whether the variables of the study have unit roots or not. In order to perform the
Unit Root Test Augmented Dickey Fuller (ADF) method will be used instead of Dickey

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Fuller test as there may have autocorrelation problem in the case of using Dickey Fuller test.
For the purpose of using the Augmented Dickey Fuller test there equation need to be
considered and they are:
(i) Equation 1> Intercept only
(ii) Equation 2> Trend and Intercept
(iii) Equation 3> No trend, No intercept.
The above-mentioned three Dickey Fuller model will be used to check whether the variables
that influences Change in Gross NPL Ratio has unit root or not. All the three models must
comply that the variables have unit root or not. Finally multicollinearity problem of the
dataset will be checked to make the dataset and the analyses more effective and flawless.
Specification of variables: In order to conduct the study some variables have been specified
and among all the variables there is one dependent variable and other variables are
independent variables.
Dependent variable: As the study is concentrated on the NPLs of the Bangladeshi banking
industry it is necessary to analyze factors that influence the change in gross NPLs ratio in the
banking industry that is why change in gross NPL ratio will be treated as the dependent
variable in this study.
Independent Variables: The independent variables are expected to influence the change of
gross NPLs ratio of the Bangladeshi banking industry and the most important independent
variables include GDP growth, unemployment, inflation, loan growth, loan to total assets
ratio, net interest margin, total assets, cost efficiency, and performance efficiency.

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The hypotheses that will be used in the study have been given in the following table:
HP1: There is a significant negative relationship between a bank’s NPLs and GDP growth
rate of the country in which it operates.
HP2: There is a positive relationship between a bank’s current year’s NPLs and the previous
year’s unemployment rate.
HP3: There is a significant positive or negative relationship between a bank’s NPLs and
inflation rate of the country.
HP4: There is a negative relationship between the NPLs of a bank and its size.
HP5: There is a significant positive or negative relationship between its NPLs and loan
growth.
HP6: There is a positive relationship loan to asset ratio of a bank and its NPLs.
HP7: There is a significant negative relationship between a bank’s net interest margin and its
NPLs.
HP8: There is a significant positive relationship between a bank’s NPLs and its cost
efficiency.
HP9: There is a significant negative relationship between a bank’s NPL and its performance
efficiency.
Table 3.1: List of Hypotheses

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Chapter Four: Banking Industry
Overview

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Like other economic sectors banking sector contributes to the overall development of
Bangladesh. In our banking sector there are Nationalized Commercial Banks (NCBs),
Government Owned Development Finance Institutions (DFIs), Private Commercial Banks
(PCBs), and Foreign Commercial Banks (FCBs) and all these institutions contribute to the
economic development of Bangladesh. The overall banking system in Bangladesh is mainly
overseen by the Bangladesh Bank which is the contra bank of the country. After
independence there were only six nationalized commercialized banks in Bangladesh but with
the passage of time the banking industry in Bangladesh has become more developed. The
banking industry in Bangladesh is strictly governed and monitored by the central bank of the
country. The scheduled banks in Bangladesh are needed to be licensed under the Banking
Company Act of 1991and this Act was amended in 2013.
At present the total number of private commercial banks in Bangladesh is 47 and there are
only 9 foreign commercial banks within all these scheduled private commercial banks. In
addition to the conventional private commercial banks there are eight Islamic private banks
that are operating within the banking industry in Bangladesh. Economic growth and
prosperity of a country is directly related to the overall performance of its banking industry so
in order to ensure economic prosperity in Bangladesh it is necessary to observe the
performance of the banking industry. Like out countries the performance of Bangladeshi
banking industry is related to the net growth of non-performing loans. In the recent years the
performance of our banking industry is observed to be satisfactory because of steady and
sound political environment of our country and effective economic policies and strict rules
and regulations. Compared to most of the East and South Asian countries the financial system
in Bangladesh is less-developed and relatively small in size so the overall performance of the
banking system is influenced because of the small and under-developed financial system of
Bangladesh. Moreover our banking industry suffers from some problems like inadequate
discipline of credit, overstaffing, corruption, lack of effective system of loan recovery,
inefficiency, ineffective regulations to overview the performance etc.
Trend of Default Loans in Bangladesh:
The banking sector of Bangladesh has been dogged by default loans since three decades and
at present various reform efforts have been taken to improve the scenario. But as a result of
rigorous reform projects nowadays many of the honest borrowers have been deprived of
funds and so the economic condition of the country has been influenced negatively.

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According to the latest data of Bangladesh Bank the amount of default loans was Tk 53,365
crore as of June, 2016 and this amount was around 10.06% of the total outstanding loan of
the country’s banking sector. In the case of considering the written-off and rescheduled loans
the sum would definitely cross the 100,000 crore-mark. As per the data of the first quarter of
2016 the non-performing loans of the state-owned commercial banks of Bangladesh was
around 24% of the total outstanding loan whereas in India the NPLs of state-owned
commercial banks was only 6.3% of the country’s total outstanding loans (Rahman, 2017).
The calculated net Loans and advances and percentage of NPLs in Bangladeshi banking
industry from the year 2010 to 2016 have been shown below:
Banking industry L&A portfolio and NPL (%)
Year NPL ratio Loan & Advances
2010 0.046 85882
2011 0.041 101347
2012 0.080 115367
2013 0.072 122821
2014 0.065 138330
2015 0.059 153838
2016 0.063 169347
Table 4.1: Banking Industry's L&A and NPLs ratios
Source: Annual reports and self-calculations
Figure: Trend of default loan in Bangladesh
Source: (Rahman, 2017)
0.046
0.041
0.080
0.072
0.065
0.059
0.063
20000
40000
60000
80000
100000
120000
140000
160000
180000
0.000
0.010
0.020
0.030
0.040
0.050
0.060
0.070
0.080
0.090
2010 2011 2012 2013 2014 2015 2016
Banking industry Loan & Asset portfolio and NPL (%)
NPL ratio Loan & Advances

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From the above figure it can be seen that from 2010 to 2011 there were downward pattern of
default loans that means default loans decreased from 2010 to 2011 but in 2012 there were
sharp increase of default loans in the banking sector of Bangladesh then it decreased over the
years 2013, 2014 and upto 2015 and again showed increasing pattern in 2016. Though default
loans decreased from 2014 to 2015 it had again increased in 2016.
Default loans were always present in the banking sector and in 1989 Bangladesh Bank
introduced new rules in order to classify loans according to the international best practices. In
1993 the default loans were around 32% at the state-owned banks in Bangladesh and in 1999
the amount of default loans increased up to 47%. Moreover in some private banks that were
in trouble classified loans were around 50%. During that period it has been observed that the
reform policies failed to decrease the amount of default loans due to other limitations like
increasing corruption, violation of rules and regulations etc.

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Chapter Five: NPL and Different
Aspects of NPL

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A loan that has already become default or close to be defaulted is called Non-performing loan
(NPL) that means when a lender fails to collect the interest payments or the principal amount
then that loan is considered to be non-performing loan. According to Bangladesh Bank’s
definition a loan that is sub-standard (SS), doubtful (DF), or bad-loss (BL) as per the
guideline of loan classification is considered as non-performing loan. The customers to whom
the non-performing loans were sanctioned is said to be the classified customers.
The definition of NPL as per IMF is:
‘A loan is non-performing when payments of interest and principal are past due by 90 days
or more, or at least 90 days of interest payments have been capitalized refinanced or delayed
by agreement, or payments are less than 90 days overdue, but there are other good reasons to
doubt that payments will be made in full’.
When a lender fails to recover loans within three months (90 days) after the expiration of the
maturity date of a loan the loan is generally considered as a non-performing loan but the term
of the contract need to be considered as well before finally considering a loan as a non-
performing loan. Loans can also be classified as non-performing loan if the borrower uses the
loans for the purpose other than the purpose for which the loan was sanctioned.
The possibility of collecting payments on the loans that have been considered as non-
performing is very low and if a borrower starts to make payments against a non-performing
loan the loan is then considered as a performing loan.
Classified loans:
The term classified loan is used for inferring any loan that is deemed to be default and
classified loans are determined as a measure of precaution to ensure that proper steps have
been taken to face a possible risk and to prevent the risks effectively. An eight-tier system for
classifying loans has been defined by Bangladesh Bank and that system include Superior,
Good, Acceptable, Marginal, Special Mention, Sub-standard (SS), Doubtful (DF), Bad/Loss
(BL) as the classes of loans.
Generally, the lending banks classify the loans and the lending banks make the classification
of the loans when the banks believe that the borrower may fail to repay the loans. Loans that
are provided by a bank can be classified into the following categories:

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Sub-standard loans: In the case of loans not being repaid for three months after the maturity
by the borrowers the loans are classified as sub-standard loans. The characteristics of Sub-
standard loans include:
i) In the case of any of the following deficiencies of the obligor being present assets
are classified no higher than the sub-standard category: low account turnover;
very low and declining profitability; belongs to a volatile industry with declining
demand; cash flow less the required principal and interest repayments;
competitive difficulties; liquidity insufficiency; ineffective management; lack of
integrity of the management; existing conflict in corporate governance; absence of
external audit; litigation that is pending.
ii) When the primary sources are insufficient for making repayments of the debt and
the bank requires depend on the secondary sources that include collateral in this
case assets should be classified no higher than Sub-standard.
iii) Assets must also be classified as no higher than Sub-standard category when the
bank acquires the asset without having the proper documentation of the net worth
of the obligor, liquidity, profitability, and cash flow that are required in the
lending process or policy or there exist doubts regarding the documentation
process’s validity, recurrent overdrawn.
Doubtful (DF):
When the loans are not repaid by the borrowers for six months (180 days) after the maturity
date the loans are considered as doubtful loans. Assets must be categorized no higher than
Doubtful when any of the mentioned deficiencies of the obligor is present: location in an
industry having poor aggregate earnings or markets’ losses, problem of intense competition,
very ineffective management, loses related to operations, lack of liquidity, failure of key
products, permanent overdrawn, lack of co-operation within the management, doubtful
integrity of the management, doubtful ownership, lack of faith of the financial statements.
Bad/Loss:
When a loan is not repaid up to nine months (270 days) after the maturity of the loan then it is
considered to be Bad/Loss and assets must be classified as no higher than Bad/Loss in the
case of any of the following deficiencies of the obligor being present: there is operating
losses and the obligor needs to seek new loans for the purpose of financing, disappearing

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existence in an industry, near to face technological obsolescence, located in the bottom
quartile of the industry regarding profitability, losses that are very high, higher production
cost than cash flow, except liquidation no source for repayment, existence of money
laundering, fraud, and different types of criminal activities.
Guidelines of Bangladesh Bank regarding NPL:
Loan classification:
All loans and advances are categorized into four groups and those are:
(i) Continuous Loan
(ii) Demand Loan
(iii) Fixed-term Loan and
(iv) Short-term Agricultural and Micro Credit.
Continuous loan is the loan account where transactions may need to be within a
predetermined limit and there may have a date to be expired for example: Overdraft, Cash
Credit etc.
Demand Loan is the loan which has turned into a forced loan from any contingent liability.
In other words demand loan is the loan which is payable on demand by the bank. For
example: Foreign bill purchased, Forced Loan against Imported Merchandise etc.
Fixed term Loan is generally repayable under a specified term schedule and within a
specified time period
Short term Agricultural and Micro Credit: Credit in the Agricultural and Micro Credit are
generally repayable within less than twelve months and this category includes any micro-
credits less than 25000 tk.
Higher level of non-performing loan may be the cause of financial distress. In some cases
banks prefer to lend to the sectors that have higher risks in order to make more profit as the
highly-risky sectors can be charged with higher interest rate. Though the highly-risky sectors
can provide greater interest income there are high possibilities of failure to collect the
repayments of both the interest and principal timely. Though high-profit can be made by the
high-risk-taker there are many bad effects of having large amount of NPL.

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Minimum Provision Requirement:
Loans Required Provision (as a % of
outstanding loan)
Unclassified 1%
Substandard 20%
Doubtful 50%
Bad/loss 100%
Table 5.1: Minimum Provision Requirement
Source: Bangladesh Bank’s website
Determinants of NPL in Bangladesh:
There are many factors that may influence and determine the change in NPL over time in any
banking industry. The factors can mainly be divided into two categories and they are
macroeconomic factors and bank-specific factors. Some of the most important factors that
influence the change in gross NPL ratio in Bangladesh have been discussed below:
GDP Growth Rate: This is one of the most important macroeconomic factors that may
Influence the determination of NPL ratio in Bangladeshi banking industry. According to
Carey (1998), Low NPL ratio is associated with an economy which is expansionary.
Increasing GDP of a country indicates that people will have more income and they will be
able to repay the loans taken from banks and so the gross amount of NPL will decrease. So
the hypothesis that will be used in this study of NPL is that there is negative relationship
between change in gross NPL ratio and GDP growth.
Inflation: Inflation is one of the most-important macroeconomic variables or factors that may
have positive or negative impact on the change in gross NPL ratio. According to the study of
KS Rajha (2016), the impact of inflation on the change of NPL can be either positive or
negative depending on the economic condition and some other firm-specific factors. The
debtors’ ability to repay their loans decreases with the reduction of the real value of their
income which is caused by higher inflation. On the other hand lower inflation is essential for
economic growth and stability.

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Unemployment Rate: Unemployment rate with one-year lag is an important economic factor
that influences the change of gross NPL ratio in Bangladesh. The cash flows of households
are negatively influenced by increasing unemployment and so it also increases debt burden
by reducing the ability to repay the debts timely. So it can easily be said that increasing
unemployment rate have strong positive impact on the change of gross NPL ratio in a
country. According to Louzis (2012), it can be hypothesized that there is a positive
relationship between unemployment rate and NP; growth in a banking industry.
Bank-specific factors:
Size of the Bank: There is relationship between the size of banks and the growth rate of
NPLs within the banking industry. Bank size is usually used as a proxy for diversification and
in my study total assets will be considered as a measure for bank size. So large size will
indicate higher diversification as a result the amount of problem loans and NPLs will reduce.
The banks that are large in size can perform their credit analysis efficiently and so their gross
amount of NPLs tends to reduce over time on the contrary small banks may fail to perform
their credit analysis effectively and eventually fail to reduce the gross amount of their NPLs.
So it can be hypothesized that there is a negative relationship between the size of banks and
their NPL ratios.
Loan Growth: Growth of loans of a bank can be an important indicator of the change in
gross NPL ratio. Excessive growth of loans may indicate that the bank is not following the
credit policy properly in order to provide loans to their clients and that is why the growth rate
may be very high on the other hand another side is that with the increase of loans the
magnitude of the NPL ratio reduces. Because of these two types of contradictory impacts of
the loan growth on the NPLs of the banks the impact is considered to be ambiguous.
Loan to Asset Ratio: When a bank’s most of the assets consist of loans and advances then its
loan to asset ratio is high and this situation indicates that the bank is aggressive in its lending
behavior and the possibility of increasing problem loans and non-performing loans also
increases. So it can be hypothesized that there is positive relationship between Loan to Asset
Ratio and NPLs of the banks.
Net Interest Margin: The success of a bank’s investment and the performance of the bank
for a certain time period can be measured through the calculation of net interest margin.
Positive net interest margin is expected by banks and it indicates that effective utilization of

21
investments has been achieved by the bank and the bank has few problem loans and non-
performing loans. On the contrary negative net interest margin indicates ineffective
utilization of investments and possibility to have excessive bad loans and non-performing
loans which is a bad signal for the overall performance of the bank. So the hypothesis is that
there is negative relation between net interest margin and NPLs of our banking industry.
Cost Efficiency: According to the study of Louzis (2012), there is a positive relationship
between the cost efficiency of banks and their NPLs and in my study on NPLs I have used the
ratio of operating costs to operating income as the measure for cost efficiency of the selected
banks. So the hypothesis is that PLLs and cost efficiency of banks are positively related and
the logic behind the hypothesis is that the amount of non-performing loans of a bank
increases when it is not cost efficient or have higher amount of operating cost compared to its
operating income.
Performance Efficiency: Good records of past performance of a bank decreases its pressure
to improve performance so the bank can offer loans by maintaining proper credit policies and
regulation so the possibility to have bad loans and non-performing loans is reduced.
Unsatisfactory past performance create pressure on a bank so the bank is forced to provide
bad loans in the hope of improving performance as a result the condition deteriorates. So the
hypothesis is that there is negative relationship between performance efficiency of a bank and
its NPLs.

22
Chapter Six: Data Description,
Variables, and Research Design

23
Econometric Model:
Some hypotheses regarding GDP growth rate, inflation, unemployment rate, total assets, net
interest margin, loan to asset ratio, loan growth, cost efficiency, and performance efficiency
have been developed. After examining and reviewing the literature the indicators have been
selected and the relationships between the independent and dependent variables have been
described below:
Variables Expected sign
Gross NPL Ratio: Ratio of Non-performing loan to total loan at
time t for bank x
Dependent Variable
GDP: Annual growth rate of GDP at time t Negative
Annual Inflation: (CPI) growth rate at time t Ambiguous
Unemployment Growth: Annual unemployment rate of previous
year
Positive
Size of the bank: Size of a bank x at time t measured by total assets Negative
Loan growth: loan growth of bank x at time t Ambiguous
Loan to asset ratio: Total loan to total asset ratio of bank x at time t Positive
Net Interest Margin: Net interest margin of bank x at time t Negative
Cost Efficiency: Operating expense to operating income ratio of
bank x at time t
Positive
Performance Efficiency: Net income to Total assets ratio (ROA) of
bank x at time t
Negative
Table 6.1: Variables and Expected Signs
Recent literature will be focused for performing the study and to account for the time
persistence in NPL structure of the selected banks dynamic approach will be used.

24
Data Description:
For the purpose of conducting the study on NPLs of commercial banks I have selected the
data of 24 commercial banks of Bangladeshi Banking industry and there are four state-owned
commercial banks and twenty private commercial banks among all the twenty four selected
commercial banks. Secondary sources like the website of the banks and their published
financial statements have been used for the purpose of collecting the relevant data. I have
collected data from the 24 commercial banks from the year 2010 up to year 2016. Based on
the availability of data the time period, variables and banks have been selected. Information
about the bank-specific factors has been collected from the annual reports of the selected
banks. Most of the data about the macroeconomic variables have been collected from World
Bank’s World Development Indicators. Now the descriptive statistics for both the
macroeconomic and bank specific variables that have been hypothesized to have relationship
with the NPLs of the selected state-owned commercial banks have been given below:
Variables
SCBs
Mean Median
Standard
Deviation Minimum Maximum
GDP Growth 6.45 6.49 0.36 6.01 7.10
Inflation 7.19 6.60 1.74 5.40 10.70
Unemployment(t-1) 4.28 4.25 0.17 4.07 4.50
Total assets 558836 538374 276047 144836 1200600
Net interest margin 0.003 0.001 0.012 -0.014 0.030
Loan Growth 20267 19395 22372 -34697 61994
Loan to Asset ratio 0.48 0.50 0.07 0.32 0.60
Cost Efficiency 0.562 0.509 0.178 0.300 1.140
Performance
Efficiency -0.001 0.004 0.017 -0.049 0.021
Change in Gross NPL
ratio 2% 1% 7% -8% 19%
Table 6.2: Descriptive Statistics
In the case of calculating the descriptive statistics for the state-owned commercial banks the
number of observation was 24 as I have selected four state-owned commercial banks of our
banking industry. From the above table it can be seen that the mean change in Gross NPL
ratio of the state-owned commercial banks is 2% and the maximum change in Gross NPL
ratio is 19%.

25
The descriptive statistics for the private commercial banks have been given below:
Variables
PCBs
Mean Median Standard Deviation Minimum Maximum
GDP Growth 6.45 6.49 0.36 6.01 7.10
Inflation 7.19 6.60 1.70 5.51 10.70
Unemployment(t-1) 4.28 4.25 0.16 4.07 4.50
Total assets 193095 163449 115178 67641 778604
Net interest margin 0.022 0.021 0.010 -0.005 0.045
Loan Growth 15124 14006 10236 -19264 53176
Loan to Asset ratio 0.65 0.66 0.06 0.49 0.75
Cost Efficiency 0.48 0.48 0.11 0.07 0.85
Performance Efficiency 0.010 0.010 0.004 0.002 0.022
Change in Gross NPL
ratio 0.3% 0.3% 2.1% -6.7% 11.8%
Table 6.3: Descriptive Statistics
Number of observations for calculating the descriptive statistics of private commercial banks
was 120 and the mean change in Gross NPL ratio is .3% which is much lower than the mean
change in the Gross NPL ratio. The maximum change in Gross NPL ratio is also lower for the
private commercial banks compared to the state-owned commercial banks in Bangladesh and
this clearly indicates that state-owned commercial banks are more desperate to provide loans
and to increase their interest income and for this reason they offer bad loans which is creating
threat for our state-owned commercial banks.
Correlation:
The pairwise correlations for the selected variables that are supposed to have relationships
with the NPLs of the selected commercial banks of Bangladeshi banking industry have been
calculated and the results have been attached in appendix.

26
Chapter Seven: Analysis & Results

27
Estimation of the relationships between NPLs ratios and the selected
independent variables:
In this study a balanced panel regression model has been run to estimate the intensity of
relationships among the independent variables and the dependent variable. As the study
involves both the time series and cross-sectional data a balanced regression model is
considered to be more effective in explaining the variation in the dependent variable because
of the variations in the independent variables. Some of the results of the regression analysis
that are the most important for estimating the relationships between the ratio of NPLs and the
independent variables have been given in the following table:
R-Square: Within = 0.4625
Between = 0.3596
Overall = 0.4463
Predictors Coefficients P-Values Significant/Non-
Significant
GDP .0134169 .052 Insignificant
INF -.0052107 .006 Significant
Unemployment .0746603 .000 Significant
Net Interest Margin .0252464 .911 Insignificant
Loan Growth -2.57 .157 Insignificant
Loan to Asset Ratio .5333 .099 Insignificant
Cost Efficiency -.008497 .647 Insignificant
Performance Efficiency -2.1774 .000 Significant
Table 7.1: Regression Analysis
From the above table it can be seen that the P-values for “Inflation”, “Unemployment”, and
“Performance Efficiency” are very insignificant that means their explanatory power to
explain the “change in Gross NPL ratio” is very high. The overall R-square from the

28
regression analysis is .4463 that means 44.63% variation in the dependent variable can be
explained by the variations in the selected independent variables.
Fixed effect model:
The particular effect of time-variant features is removed by the fixed effect model in
examining the net effect of the independent variables and the distinctiveness of the features in
order to reduce the possibility of having correlation among the variables are also reduced
through the use of the fixed effect model. Pooled regression model cannot be used here as all
the selected commercial banks are not similar from the perspective of their characteristics,
culture, and operational activities.
In the case of using the fixed effect model it has been assumed that all the 24 commercial
banks have different intercept. At first statistics option in stata then Longitudinal/Panel data,
setup & utilities and then the dataset was declared to be panel data and then the panel ID
Variable as the bank code was selected and in the dataset there are 24 commercial banks.
After that time variable and year was selected and finally ok option was clicked.
In order to run the fixed effect model the statistics option then Longitudinal/Panel data,
Linear model and Linear regression (FE, RE, PA, BE) sub-option were selected sequentially.
After that the change in NPL Ratio as the dependent variable and other variables as the
independent variables were selected and then the fixed effect model from the given options
was selected and ok was clicked to get the results. The results from the model have been
shown below:
R-Square: Within = .4842
Between = .1598
Overall = .2191
Predictors Coefficients P-Values Satisfactory/
Non-
Satisfactory
GDP .00757 .363 Non-satisfactory
INF -.00385 .086 Non-satisfactory

29
Unemployment .10022 .000 Satisfactory
Total Assets 1.62 .050 Non-satisfactory
Net Interest Margin .2231 .694 Non-satisfactory
Loan Growth -4.01 .108 Non-satisfactory
Loan to Asset Ratio .1241 .232 Non-satisfactory
Cost Efficiency -.0287 .331 Non-satisfactory
Performance Efficiency -2.6281 .000 Satisfactory
Table 7.2: Fixed Effect Model
In this model the probability value is very insignificant and it is less that 5% that means all
the coefficients of this model are not equal to zero so the model is satisfactory. Now if the
explanatory power of the independent variables to explain the dependent variable is measured
then it can be seen that here “unemployment” and “performance efficiency” are the most
significant variables to explain the change in NPL Ratio as the P-values of the two variables
are very insignificant that is less than 5%. After analyzing the results from the fixed effect
model it was necessary to store the results in the memory.
Random Effect Model:
The most important portion of the results that have been got by running the random effect
model has been given below:
R-Square Within = .4612
Between = .3851
Overall = .4470
Predictors Coefficients P-Values Significant/Non-
Significant
GDP .0137 .049 Significant

30
Total Assets -7.81 .697 Non-Significant
Loan Growth -.0090 .970 Non-Significant
Loan to Asset Ratio .0427 .313 Non-Significant
Cost Efficiency -.0094 .615 Non-Significant
Table 7.3: Random Effect Model
From the above results it can be seen that the P-values for “GDP Growth”, “Inflation”,
“Unemployment”, and “performance efficiency” are less that 5% so their explanatory power
to explain the variations in the dependent variable that is “Change in Gross NPL ratio” is high
or significant.
After using both the fixed effect and random effect model it is now necessary to check which
model is fit for the study.
Hausman Test:
In order to identify the effect of different macroeconomic and bank-specific variables of the
NPLs both fixed effect model and random effect model will be used. In order to justify the
appropriateness of fixed effect model and random effect model to the dataset Hausman test
will be used. The two hypotheses of Hausman test include:
0: Random effect model is appropriate
1: Fixed effect model is appropriate
In order to justify the appropriateness of Fixed effect model or Random effect model the
value of P will be considered and if the p value is statistically significant then fixed effect
model should be used that means if the P-value is less than 5% then the null hypothesis will
be rejected and alternative hypothesis will be accepted. When P-value is more than 5% then
null hypothesis is accepted and so random effect model is considered to be the appropriate
model. The results of Hausman that based on which any one of the previously run model will
be selected have been given below:

31
Independent
variables
Fixed Random Difference sqrt
GDP .007575 .0137256 -.0061507 .0044808
INF -.0038545 -.0051337 .0012791 .0011655
Unemployment .1002231 .0730345 .0271886 .018417
Total Assets 1.62 -7.81 1.70 7.93
Net Interest
Margin
.2231124 -.0090086 .2321211 .5112397
Loan Growth -4.01 -2.26 -1.76 1.48
Loans to Asset
Ratio
.1241736 .0427475 .0814261 .0941684
Cost Efficiency -.0287 -.0094513 -.0193288 .0227211
Performance
Efficiency
-2.628155 -2.189007 -.4391475 .2358775
chi2 (7) = 7.43
prob>chi2 = .3851
Table 7.4: Hausman Test
From the above results I have got the value of chi2 and the probability value and for this
model null hypothesis was that “random effect model is appropriate” and our alternative
hypothesis was that “fixed effect model is appropriate”. Here the probability –value is .3851
or 38.51% which is significantly greater than 5% that means null hypothesis cannot be
rejected rather null hypothesis is accepted. The meaning of accepting the null hypothesis is
that random effect model is the appropriate model in order to explain the outcome so now it
is necessary to estimate the random effect model again. The results of the random effect
model have been given in the following table:
R-Square Within = .4612

32
Between = .3851
Overall = .4470
Predictors Coefficients P-Value Significant/Non-
Significant
GDP .0137256 .049 Significant
Total Assets -7.81e-15 .697 Non-Significant
Net Interest Margin -.0094513 .970 Non-Significant
Loan Growth -2.26e-13 .256 Non-Significant
Loan to Asset Ratio .0427475 .313 Non-Significant
Cost Efficiency -.0094513 .615 Non-Significant
Table 7.5: Second Step Random Effect
So it can be clearly said that “GDP Growth”, “Inflation” “Unemployment” and “Performance
efficiency” are the significant explanatory independent variables to explain the dependent
variable “Change in NPL Ratio” as the P-values of these two variables are less than 5%. The
coefficient of GDP Growth is positive which is not consistent with the hypothesis the
coefficient of inflation is negative according to the random effect model’s result that means
non-performing loans increases in the case of having low inflation in any country. On the
other hand the coefficient of unemployment rate of previous year is positive that means NPLs
increase with the increase in unemployment rate and this is consistent with the hypothesis.
Lastly the coefficient of Performance efficiency is negative that means non-performing loan
decreases when performance efficiency increases and this result is consistent with the
economic theory so it can be said that the model is fine and effective.

33
Test for Heteroscedasticity:
Though the results of random effect model do not show any reason to have significant doubt
about the appropriateness of the model still will the test for Heteroscedasticity would be
effective. The two hypotheses of the test are:
0: The error term is homoscedastic
1: The error term is heteroscedastic
If the P-value is less than 5% then null hypothesis can be rejected otherwise null hypothesis is
accepted. From the above results of the Breusch-Pagan test for heteroscedasticity test it has
been seen that the P-value is very insignificant which is around zero and less than 5% so the
null hypothesis can be rejected and the error term is heteroscedastic. In order to control for
the heteroscedasticity “robust” option can be added to the random effect model.
By “robust” at the end of the random effect model it can be seen that “GDP Growth”,
“Unemployment”, “Loan to assets ratio”, and “Performance Efficiency” are the most
important explanatory variables to explain the variations in “Change in NPL ratio” as their P-
values are less than 5%. The coefficients of all the significant explanatory independent
variables except GDP Growth are consistent with the hypotheses of the study. The results of
both the tests have been attached in appendix.
Unit Root Testing:
In order to do unit root testing the following hypotheses have been considered:
Null hypothesis: HO : Variable is not stationary or got unit root
Alternative H1: Variable is stationary
From the “Levin-Lin-Chu” unit-root test for the variable GDP, it can be seen that the
value of adjusted t* is negative and the P-value is around zero in other words less than 5%
that means zero probability the null hypothesis can easily be rejected and the alternative
hypothesis can be accepted. So the variable which is GDP does not contain unit root in other
words GDP is stationary in this case.
The result of “Levin-Lin-Chu” unit-root test for the variable inflation shows that the P-
Value is 1 that is very high and so the null hypothesis Ho cannot be rejected and the panels
contain Unit Roots that means INF is non-stationary.

34
“Levin-Lin-Chu” unit-root test for Unemployment shows that the P-Value is very small
for Unemployment that means the null hypothesis can be rejected and the alternative
hypothesis will be accepted that means the variable Unemployment is stationary.
The results of the unit-root test using “Levin-Lin-Chu” model for the variable Total Assets
shows that the P-Value is 1.0000 that means the null hypothesis Ho cannot be rejected that
means the variable is non-stationary.
The P-Value for the “Levin-Lin-Chu” unit-root test for Net Interest Margin variable is
very small so the null hypothesis can be rejected and the alternative hypothesis will be
accepted and so Net Interest Margin does not contain unit roots and is stationary.
From the “Levin-Lin-Chu” unit-root test for Loan Growth negative value has been got for
the adjusted t* value and the P-Value is very small and so the null hypothesis can be rejected
and the alternative hypothesis will be accepted. The variable Loan Growth is stationary.
The Adjusted t* value is -18.0481 and the P-Value is very insignificant in the case of “Levin-
Lin-Chu” unit-root test for Loan to Assets Ratio so it can be said that the null hypothesis
Ho can be rejected and the alternative hypothesis can be accepted in other words the variable
is stationary.
“Levin-Lin-Chu” unit-root test for Cost Efficiency Provides the Adjusted t* value of -
28.5910 and very small P-Value so the variable Cost Efficiency is stationary and the null
hypothesis Ho can be rejected.
The results that have been got from the “Levin-Lin-Chu” unit-root test for Performance
Efficiency shows that the Adjusted t* value is -74.6756 and the P-Value is very small so the
null hypothesis will be rejected and the alternative hypothesis will be accepted and the
variable Performance Efficiency is stationary.
*All the results from the Unit Root tests have been attached in appendix.

35
Finally the results of the unit-root test have been shown in a summarized form in the
following table:
Variables statistics
P-
Value
HO= Panels
contain unit
roots
Ha=
Panels
are
stationary
Stationary/Non-
Stationary
GDP -8.1418 0.0000 Rejected Accepted Stationary
INF 25.1754 1.0000 Not Rejected Rejected Non-Statioanry
Unemployment -29.799 0.0000 Rejectd Accepted Stationary
Total Assets 19.5331 1.0000 Not Rejected Rejected Non-Stationary
Net Interest
Margin -120.00
0.
0000 Rejected Accepted Stationary
Loan Growth -17.1276 0.0000 Rejected Accepted Stationary
Loan to Asset
Ratio -18.0481 0.0000 Rejected Accepted Stationary
Cost Efficiency -28.591 0.0000 Rejected Accepted Stationary
Performance
Efficiency -74.6756 0.0000 Rejected Accepted Stationary
Table 7.6: Summary of Unit Root Tests
In order to summarize the results of the Unit Root test using the Levin-Lin-Chu unit root test
of the variables the null hypothesis and the alternative hypothesis need to be remembered.
The null hypothesis was that the variables are not stationary or got unit roots one the other
hand the alternative hypothesis was that the variables are stationary. From the summary table
it can be clearly seen that all the variables except “Inflation” and “Total assets” are stationary
that means they do not have unit roots. Only “INF” and “Total assets” have unit roots that
mean they are not stationary and in case the unit roots tests of these two variables the null
hypothesis “Ho” has been accepted.

36
Multicollinearity:
Whether the independent variables are correlated to each other or not can be tested through
multicollinearity test and this test is generally performed to avoid those independent variables
that are somewhat redundant that means related to other independent variables that have
already been included in the study. For this study Variation Inflation Factor will be calculated
in order to determine whether the model suffers from multicollinearity problem or not. The
results of the VIF test have been given in the following table:
Variable VIF 1/VIF
INF 2.19 0.457660
Unemployment 2.17 0.461349
LoantoAsse~0 1.78 0.563040
Netinteres~n 1.76 0.567596
Performanc~y 1.45 0.689288
GDP 1.33 .750374
CostEffici~y 1.23 0.815898
LoanGrowth 1.21 0.828362
Mean VIF 1.64
Table 7.7: Results of VIF
The results of the VIF test shows that none of the variables of my model has more than 7 and
the mean VIF is also less than 7 so it can be said that the model does not suffer from
multicollinearity problem.

37
Chapter Eight: Conclusion
NPLs are closely related to the operational efficiency of banks so in order to ensure smooth
operations of a banking industry it is necessary to identify the factors that influence the gross
amount of NPLs in a certain time period. This study has mainly been conducted as a
summary form by analyzing previous studies related to the NPLs of different country’s
banking industry. Mainly quantitative research approach has been followed for completing
and concluding the study on non-performing loans of Bangladeshi banking industry.
From the study it can be concluded that the state-owned commercial banks in Bangladesh
have higher NPLs ratios compared to those of the private commercial banks and so it can be
clearly stated that private commercial banks are more efficient in controlling their NPLs and
they have less bad loans because of their strict compliance of credit policies.
The most influential factors that are contribute to the changes in the Gross NPLs of banks in
Bangladesh include GDP Growth, Unemployment, Loans to assets ratio, and performance
efficiency as their calculated P-values after controlling the heteroscedasticity is less than 5%.
Moreover performance efficiency is the most influential one as it has been got from all types
of regression models so it can be said that banks should try to increase their performance
efficiency in other words should try to increase their ROA in order to reduce non-performing
loan and to achieve growth.

38
References:
 B karica (2013) [online] Available at: http://hrcak.srce.hr/file/201674 [Accessed 14
Aug. 2017].
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http://www.iiste.org/Journals/index.php/EJBM/article/download/3588/3637
[Accessed 14 Aug. 2017].
 Jimenez, G., Salas, V. and Saurina, J., (2006). Determinants of collateral.Journal of
financial economics, 81(2), pp.255-281.
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Finance Bi-annual Conference on Banking and Finance, St. Augustine, Trinidad.
 KS, Rajha. (2016), [online] Available
at:http://jfbmnet.com/journals/jfbm/Vol_4_No_1_June_2016/9.pdf [Accessed 14
Aug. 2017].
 Louzis, D., Vouldis, A. and Metaxas, V. (2012). Macroeconomic and bank-specific
determinants of non-performing loans in Greece: A comparative study of mortgage,
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 Makri, V., Tsagkanos, A. and Bellas, A. (2014). Determinants of non-performing
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 Mehmood, B., Younas, Z.I. and Ahmed, N., (2013). Macroeconomic and bank
specific Covariates of non-performing loans (NPLs) in Pakistani commercial banks:
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 Messai, A. and Jouini, F. (2017). Micro and Macro Determinants of Non-performing
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Aug. 2017].

39
 Rahman, R. (2017). Default loans soar. [online] The Daily Star. Available at:
http://www.thedailystar.net/frontpage/default-loans-soar-1275622 [Accessed 7 Aug.
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 Zelalem. (2013). Determinants of Non-Performing Loans in Licensed Commercial
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pp.868-882.

40
Appendix:
A portion of the bank-specific data that I have inputted in excel has been shown below:

41
Macro-economic information that I used for completing my study on NPLs in Bangladeshi
Banking industry has been given below:
Year GDP
growth,
%
GDP (current LCU) Unemployment,
total (% of total
labor force)
Inflation ∆GDPt UNEt-1
2010 5.57 7975387000000.00 4.53 8.13
2011 6.46 9158287999999.99 4.50 10.70 1182901 4.53
2012 6.52 10552040389300.00 4.47 6.22 1393752 4.50
2013 6.01 11989231718700.00 4.26 7.53 1437191 4.47
2014 6.06 13436744000000.00 4.24 6.99 1447512 4.26
2015 6.55 15158022000000.00 4.12 6.19 1721278 4.24
2016 7.10 4.07 5.51 -15158022 4.12
A portion of the ratios that I have calculated for the purpose of my study have been given
below:

42
Table: Pairwise Correlations
GDP INF
Unemploym
ent
Total
assets
Net
interest
margin
Loan
Growth
Loan to
Asset
ratio
Cost
Efficiency
Performa
nce
Efficiency
Change
in NPL
Ratio
GDP 1
INF -0.34959 1
Unemployme
nt -0.32036 0.66710 1
Total assets 0.12288 -0.19738 -0.2467 1
Net interest
margin 0.01742 0.18212 0.23233 -0.6604 1
Loan Growth 0.35027 -0.12998 -0.1249 0.27275 -0.01934 1
Loan to Asset
ratio 0.09036 0.10055 0.06311 -0.73406 0.61039 0.058702 1
Cost
Efficiency 0.14474 -0.22936 -0.2917 0.22371 -0.254
-5.10E-
05 -0.30577 1
Performance
Efficiency -0.14079 0.31450 0.04764 -0.37963 0.32572
-
0.221735 0.32833 -0.179707 1
Change in
Gross NPL
ratio 0.17222 -0.21271 0.14697 0.03736 -0.03957 0.073016 -0.02536 -0.001017 -0.57314 1
7.1 Regression:

43
7.2 Fixed effect model:
7.3 Random Effect Model

44
7.4 Hausman Test:
7.5 Random effect model:

45
7.6 Heteroskedasticity test:

49
The NPLS ratios of both state-owned commercial banks and private commercial banks in
Bangladesh have been shown from the year 2010 to 2016 below:
NPL ratio
Year PCBs NPL Ratio SCBs NPL Ratio
2010 3.05% 13.41%
2011 3.03% 10.07%
2012 4.72% 25.29%
2013 5.14% 18.99%
2014 4.81% 16.59%
2015 5.13% 18.20%
2016 4.85% 19.76%
Table: NPL Ratios of both private commercial banks and state-owned commercial banks.
Source: Self calculations
Figure: Category-wise NPL ratio
Source: Self-generated.
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
2010 2011 2012 2013 2014 2015 2016
Bank category-wise NPL ratio
PCBs NPL Ratio SCBs NPL Ratio

50
Now the bank category-wise loan and advances portfolio with graphical presentation have
been shown below:
L&A Portfolio
Year PCBs' L & A SCBs' L & A
2010 65925 185545
2011 78042 219226
2012 90408 246441
2013 99386 234288
2014 112701 254379
2015 129812 270881
2016 150669 307149
Table: Loans and advances for Private and state-owned commercial banks
Source: Annual Reports
Figure: Bank category-wise loans and advances
Source: Self-generated
50000
100000
150000
200000
250000
300000
350000
2010 2011 2012 2013 2014 2015 2016
Bank category-wise L&A
PCBs' L & A SCBs' L & A

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