1. i
Economic Determinants of Non-Performing Loans among
Nine Banks In the Ghanaian Economy
By
Emmanuel Ebo Arhin
(Msc. Economics)
A thesis submitted to the Institute of Distance Learning (IDL), Kwame Nkrumah
University of Science and Technology (KNUST) in partial fulfillment of the
requirements of the degree of Master of Science in Economics
MAY, 2016

2. ii
DECLARATION
I hereby declare that this submission is my own work towards the M.Sc. in
Economics and to the best of my knowledge, it contains no material previously
published by another person nor material which has been accepted for the award of
any other degree of the University, except where due acknowledgment has been
made in the text.
Emmanuel Ebo Arhin ………………………………… ……………….
(PG2756114) SIGNATURE DATE
CERTIFIED BY:
Dr.Anthony Osei Fosu …………………………… …………………
(SUPERVISOR) SIGNATURE DATE
CERTIFIED BY:
Dr. Yusif Hadrat …………………… …………………
(HEAD OF DEPARTMENT) SIGNATURE DATE

3. iii
ACKNOWLEDGMENT
I would like to express my profound gratitude to the God of Israel for the strength, life,
knowledge and wisdom provided to me to undertake this research work. I also thank all those
who in anyway contributed to the successful completion of this work. My heartfelt gratitude goes
to Mr.Ludwig Aferi (Orphanage International Ministries), Mrs. Juliana Aferi (Bank of Ghana),
Mrs. Rita Sraha( Kwame Asante and Associates), Mr. Frank Adu Junior(CAL Bank Ltd.).I
cannot fail to also acknowledge my supervisor, Senior Economist and lecturer Dr. Anthony Osei
Fosu (KNUST) for his directions, suggestions, pieces of advice and sharing of knowledge to
make this work successful.
Finally, I am eternally grateful to Princess Newman for her lovely support, sacrifices,
encouragement and understanding in the course of undertaking this project work.

4. iv
Abstract
Since 2007 to the first quarter of 2015, the Ghanaian banking system has experienced a high growth rate
of non-performing loans which have affected the overall performances of most banks in Ghana. In that
sense, increasing non-performing loans in some part of the banking sector has threatened the financial
stability of the whole sector and the economy at large.
This research was to identify and confirm the relationship between macroeconomic factors with energy
crises and non-performing loans of nine selected banks (CAL Bank Limited, Uni Bank Limited, HFC
Bank Limited, Prudential Bank Limited, Fidelity Bank Limited, Ecobank Ghana Limited, Agricultural
Bank Limited, Stand Chart Bank Ghana Limited and Barclays Bank Ghana Limited) in the Ghanaian
banking sector of the economy. The macroeconomic variable includes the rate of GDP, 91 Day Treasury
bill rate, rate of Domestic Credit to the Private Sector and Energy Crises as independent variables.
The study was based on the banks using their respective NPLs rate from individual unconsolidated
financial reports from 2007 to 2014 as the dependent variable. The econometric regression analysis(Panel
Data) revealed that lower economic growth (rate of GDP), lower rate of domestic credits to the private
sector and lower energy or electricity supply are associated with higher rate of NPLs whilst higher interest
rate on government securities associated with lower rate of NPLs. All the variables are significant.
The research recommends that Agricultural Development Bank (ADB), Standard Chartered Bank Ghana
(SCB) and Barclays Bank Ghana (BBG) with the other Banks should consider changes in macroeconomic
indicators such as GDP growth rate, 91 Day Treasury bill rate and rate of Domestic Credit to the Private
Sector with Energy Crises. Commercial banks, especially the local banks should invest more funds in
government securities when rate on government securities are in double digits to mitigate any systematic
risks that may arise in the economy whiles considering extending more funds to their respective genuine
borrowers to keep them in businesses in order to repay their debts .On the other hand, the government
with Bank of Ghana (BOG) must control government expenditure to achieve a reasonable and acceptable
budget deficit rate to reduce the current high interest rate to influence more supply of funds to the private
sector. These with other factors will help in reducing the rate of NPLs in the Ghanaian economy.

5. v
TABLE OF CONTENTS
Title Page
DECLARATION………………………………………………………………….II
ACKNOWLEDGEMENTS………………………………………………………III
ABSTRACT………………………………………………………………………..IV
TABLE OF CONTENTS…………………………………………………………..V
LIST OF TABLES……………………………………………………………....VIII
CHAPTER ONE: INTRODUCTION
1.0 Introduction……………………………………………………………………1
1.1 Background to the study…………………………………………….….……..1
1.2 Statement of the Problem………..…………………………………………….5
1.3 Objectives of the study…………………………………………………………6
1.4 Hypothesis of the study………………………………………………….……...7
1.5 Significance of the study……………………………………………..................7
1.6 Scope and Delimitation………………………………………………….………8
1.7 Organization of the study………………………………………….…………...9
CHAPTER TWO: LITERATURE REVIEW
2.0 Introduction………………………………………………………………….....10
2.1 Evolution of the banking industry in Ghana…………………………………10
2.2 Definitions and Some causes of NPLs…………………………………………..13

6. vi
2.3 BOG’s classifications of NPLs in Ghana……………………………………….16
2.4 Macroeconomic Factors…………………………………………………………17
2.5 Empirical Review………………………………………………………………..19
2.6 Justification of the study………………………………………………………...21
CHAPTER THREE: METHODOLOGY
3.0 Introduction………………………………………………………………………23
3.1 Research Design………………………………………………………………….23
3.2 Population………………………………………………………………………...23
3.3 Sample and Sample Techniques…………………………………………………23
3.4 Data Description………………………………………………………………….24
3.5 Method of Analysis……………………………………………………………….24
3.6 Independent Variables…………………………………………………………….25
3.7 Model Specification…………………………………………………....................27
3.8 Expected Signs……………………………………………………………………28
CHAPTER FOUR: RESULTS AND DISCUSSION
4.0 Introduction………………………………………………………………………29
4.1 Descriptive Analysis…………………………………………………...................29
4.2 Regression Analysis………………………………………………………………30

7. vii
4.2.1 Estimated Model………………………………………………………………..25
CHAPTER FIVE: CONCLUSION AND RECOMMENDATION
5.0 Introduction……………………………………………………………………….35
5.1 Summary…………………………………………………………………………..35
5.2 Conclusion…………………………………………………………………………36
5.3 Recommendation………………………………………………………………….37
REFERENCES………………………………………………………………………39
APPENDICES
Trends of individual bank NPL rate………………………………………………..42
Trends of rate of NPL growth in the Ghanaian economy………………………...43
Trends of macroeconomic indicators……………………………………………….44
Data on the individual bank rate of NPL………………………………………….45

8. viii
LIST OF TABLES
Table Page
3.7 Expected Signs of the independent Variables………………………………………28
4.1 Descriptive Statistics………………………………………………………………….30
4.2 Results on Regression Analysis ……………………………………………………....30

9. 1
CHAPTER ONE
Introduction
1.0 Introduction
This chapter of the study includes background to the study, statement of the problem, objectives
of the study, hypothesis of the study, significance of the study, scope and delimitation and
organization of the study.
1.1 Background of the study
The role of the financial institutions in any given economy is very important to sustain and
increase productivity through investment and enhance easy credit flows from the surplus to the
deficit unit in a given economy (Richard, 2011).
This implies that to ensure a strong financial institutions in an economy accompanied with a
healthy financial stability, macroeconomic indicators must be in line as expected or targeted to
achieve these goals to minimize the growth of non-performing loans which has negative impacts
on financial stability in the financial sector.
According to the Classical economists, interest rate is determined by the interaction between
investment demand schedule and that of the savings schedule. It means that interest rate is
determined by the surplus unit and the deficit unit in the loanable fund market. The surplus unit
creates the saving – schedule whiles the deficit unit creates the investment demand schedule of
which their interaction in the market determines the interest rate. In that view, whenever interest
rate increases, it indicates that the investment demand schedule which is the deficit unit in the
market outweighs that of the saving – schedule represented by the surplus unit. Increments in

10. 2
interest rate therefore puts more burden on the deficit unit to pay more amounts of interest on
funds borrowed from the surplus unit and also reduces the capacity of the borrowers to repay
loans borrowed which may cause loans not to be performed. The opposite enables borrowers to
pay off loans which may also reduce non-performing loans.
Empirically, Curat et al(2013), Espinoza and Prasad (2010) and Farhan et al (2012) confirmed
that indeed higher interest rate have a positive relationship with non-performing loans indicating
that higher interest rate increases non-performing loans.
The recent global financial crisis from 2007 to 2008 revealed the vulnerabilities of banks in the
Gulf Cooperative Council countries in different levels (Espinoza and Prasad, 2010) which the
study believes that it may have some effects on the Ghanaian economy. This means that
achievable economic growth in any given nation cannot be sustained without a strong financial
industry (Farhan et al, 2012).
The Ghanaian financial system which includes the financial institutions, regulators of the
financial institutions such as Bank of Ghana (BOG), Securities and Exchange Commission
(SEC), National Insurance Commission (NIC) and the National Pensions Regulatory Authority
(NPRA). According to the Osei Tuti II Centre for Executive Education and Research
(OTCEER), the financial system in the Ghanaian economy is made up of 1,530 institutions from
which the deposit taking institutions or the Bank Financial Institution (BFIs) accounts for 636
institution or 41.57% whilst the Non-Banking Financial Institutions (NBFIs) account for 894
representing 58.43% of the total financial institutions in the Ghanaian economy.
The BFIs are made up of the universal banks, the rural or community banks, savings and loans
companies and microfinance companies. On the other hand, the NBFIs also consist of the agents

11. 3
from the capital market, insurance and the pension market among others. This study only focused
on the non-performing loans granted to the private sector with respect to the commercial or the
universal banks within the BFIs category whose core business is to receive deposits from the
public and grant loans to the public in Ghana. When these loans are granted to customers and the
customers default to repay the principal and the agreed interest of the loans for at least 90 days,
then, arises the loans which will not perform at the assets side on the balance sheets which is
known as the non-performing loans (NPLs).
Since 2012 till the end of the 2014, per the World Bank database, the Ghanaian economy
experienced some level of significant decline in its Gross Domestic Product (GDP), the growth
rate decreased from 15% in the year 2011 to 8.78% in 2012, then to 7.54% in 2013 and still
continued to decline to 4.2% by end of the year 2014 which the study assumes that when there is
a decline in GDP growth rate reduces the capacity for individual borrower to repay their debt. On
the other hand, when there is an increase in GDP growth rate, the capacity to repay debt also
increases.
Inflation (Consumer Price Index) also increased from 8.72% in 2011 to 9.16% in 2012, it
increased to 11.60% by the end of 2013, and it latter moved up to 15.5% in 2014 and as at
August 2015 the rate of inflation stood at 17.3% and by the end of September 2015 it increased
to 17.4% of which it is expected to increase further if the current state of the economy if not
turned around quickly (World Bank, 2015).
According to Ghanaweb (2015), Ghana has been facing power (electricity) crises for some time
now which have caused layoff of workers in the mining sector, disruption in production
processes and delivery, increased cost of production which affects prices of goods and services.

12. 4
The study therefore investigated if the current power crises have a relationship with the rate of
NPL in Ghana because borrowers of banks who are mostly businesses uses power to produce
their products and services to generate income of which part is used to repay their loans.So when
there is power outages, businesses will not be able to produce to generate income to repay loans
which may increase NPLs.
Another important economic indicator is the interest rate which has increased drastically in the
Ghanaian economy. Using 91 day Treasury bill rate as a proxy of interest rate, the interest rate
stood at 9.6% in 2006 which increased to 22.5% in 2012 and by the end of 2014 it stood at
25.75%. High interest rate indicates high cost of borrowing which puts more debt burden on
borrowers which the study assumes that such high interest rate increases the non-performing
loans. This indicates that cost of borrowing has increased which may have some effects on the
rate of non-performing loans in the Ghanaian economy.
Therefore, this research investigated if GDP growth rate, 91 Treasury Bill Rate, Domestic Credit
to the Private Sector and Energy Crises as economic indicators have impacts on non-performing
loans in Ghanaian Banking Sector.
In 1990, sub-Saharan countries experienced some effects of banking crisis due to the emergence
of systemic risks in the environment that led to an extensive accumulation of non-performing
loans (Fofack, 2005). This implies that systematic risks or unfavorable macroeconomic
indicators and poor management of the economy can negatively distort the financial stability of
any economy through non-performance of loans in the financial sector.
Saba et al (2012) defines a non-performing loan (NPL) as a sum of debt upon which the
customer has not made his or her scheduled payments for at least 90 days. Hence the non-
performing loan ratio is the total debt not paid back for at least 90 days to the total loans granted

13. 5
in percentage. The classifications of non-performing loans in Ghana by the Bank of Ghana Law,
Act 2004 are as follows:
Other Loans Especially mentioned (OLEM) Accounts: They are loans which may get some
trouble in the repayment due to business cycle.
Substandard Accounts: Loans whose interest and principal payments were due for payments for
the past three months with a 25% provision.
Doubtful Accounts: Full liquidation of outstanding debts for a period of six months appears
doubtful. Banks make 50% provision for such loans.
Loss Accounts: Outstanding debts of loan principal and interest amount that are regarded as not
collectable after a year. Banks make 100% provision for loans. But the non-performing loans
consist of the loans in the last three categories, and are further differentiated according to the
degree of collection difficulties.
1.2 Statement of the problem
In connection with the Ghanaian economy with its banking sector, the BOG recorded 11.4% of
NPL by the end of the first three months in 2015 from 11.3% recorded in December 2014 which
implies that generally the financial institutions such as may have experienced some growth in
their individual rate of NPLs. From the World Bank database, history shows that the general rate
of NPL in Ghana was 17.5% as at 1998, reached its peak at 19.6% in the year 2001 and reduced
significantly to 6.4% as at 2007. Therefore the problem is that the significant increase in the
general rate of NPL in the Ghanaian banking sector from the 6.4% in 2007 to 11.4% as at the
first quarter of 2015 of which the study assumes that such growth of NPLs reflects that of the

14. 6
individual bank rate of NPLs which may be due to decline in GDP growth rate, increase in
interest rate, significant decline in domestic credit to the Private Sector and the current energy
crises within the stated period may threaten the stability of the financial sector in the Ghanaian
banking sector.
Also, number of studies conducted outside Ghana indicates that poor economic management
accompanied with imbalanced macroeconomic fundamentals exposed banking institutions to
systematic risks which caused non-performing loans in those banking sectors to increase further.
This study investigated and confirmed the relationship between non-performing loans and some
macroeconomic indicators with energy crises among nine banks in the Ghanaian banking sector
using a secondary data on all the variables.
1.3 Objectives of the study
The general objective was to investigate and confirm the relationship between non-performing
loans and some macroeconomic indicators with energy crises among nine banks in the Ghanaian
banking sector using a secondary data on all the variables. Specifically the research aimed to:
Identify the relationship between non-performing loans and some macroeconomic indicators
with energy crises among nine banks in Ghana.
To estimate the effects of macroeconomic variables with energy crises have impact on NPLs
among the selected banks in the economy.

15. 7
1.4 Hypotheses of the study
The following hypotheses were formulated for the first objective.
There is no significant relationship between the GDP growth rate, 91 Day Treasury bill Rate as a
proxy of interest rate, Domestic Credit to the Private Sector growth and energy crises and the
rate of NPLs among the nine banks in the Ghanaian banking sector.
For the second objective, the hypotheses were the following:
i. There is no negative relationship between the GDP growth rate and Domestic Credit to the
Private Sector growth and the rate of NPLs among the nine banks in the Ghanaian banking
sector.
ii. There is no positive relationship between the 91 day Treasury bill as a proxy of interest rate
and energy crises and the rate of NPLs among the nine banks in the Ghanaian banking sector.
1.5 Significance of the study
All studies focused on macroeconomic indicators including energy crises as determinants have
been done outside Ghana. Some of the researches in respect of the Ghanaian economy have been
conducted by Settor Amediku (2006), Samuel Arko(2012), Amuakwa-Mensah and Boakye Adjei
(2015). However, these researchers did not include energy crises among the macroeconomic
determinants in their study related to the economy of Ghana except Kuutol et al (2014), who
included energy crises by using primary data. This research therefore aimed to use secondary
data to investigate the relationship between macroeconomic determinants including energy crises
(dummy variable) and non-performing loan in the Ghanaian economy. It added knowledge in the
sense that the 91 Day Treasury bill Rate as a proxy to interest rate did not have a positive

16. 8
relationship with NPLs in the Ghanaian banking Sector. Also, it is a unique one since no study of
its kind in Ghana has added energy crises (a dummy variable) as a determinant to capture the
effects of the current power crises on NPLs.
Furthermore, the study will enable banks and fund managers, financial controllers, regulators of
the financial industry and other stakeholder to forecast the impact these variables will have on
the non-performing loans in order to formulate strategies to minimize such risks in future to
maintain financial stability and the value of shareholder in the Ghanaian economy.
The Bank of Ghana as the regulator of the financial industry in the Ghanaian economy can use
this study as a basis to formulate a policy that will direct all banks to apportion a percentage of
their respective loans and advances to the energy sector to gradually end the energy crises and
mitigate the negative effects of energy crises on non-performing loans in the Ghanaian financial
industry.
1.6 Scope and Delimitation
The study focused on the rate of non-performing loans in the individual banks in the banking
sector of Ghana. Thus, the study sought to establish the macroeconomic determinants of non-
performing loans in the Ghanaian economy. The study did not include factors of bank specific
behaviors that influence non-performing loans. The time frame was limited to eight years (2007
to 2014) due to the reason stated above and lack of availability of data for the years before 2007
on NPLs growth rate for the individual banks, additionally, the research covered non-performing
loans to the private sector only and did not cover non-performing loans to the public sector.
Only secondary data from 2007 to 2014 on NPL rate of individual banks, GDP growth rate, 91
day treasury bill rate, Domestic Credits to the Private Sector growth rate as a proxy for Money

17. 9
Supply whilst energy crises was a dummy variable was used. The data were obtained from Bank
of Ghana (BOG) and the World Bank (WB) and periods of energy crises from Graphic Online.
1.7 Organization of the Study
The study is structured into five chapters. Chapter one includes the introduction, background of
the study, the statement of the problem, the research hypotheses, objectives of the study, the
significance of the study, scope and delimitation of the study and the organization of the study.
The second chapter gives a theoretical and empirical review, brief history of the banking
evolution in the Ghanaian economy, definitions and some causes of NPLs, BOG Loan
classification and provisioning, macroeconomic factors and justification of the study. Chapter
three describes the research design, sample techniques data description, method of analysis,
model specification and the a priori signs. Chapter four presents the data descriptive and
regression analysis with their interpretation and discussions whilst chapter five provides
summary, conclusions and recommendations of the study.

18. 10
CHAPTER TWO
LITERATURE REVIEW
2.0 Introduction
Chapter two of the study reviews the existing literatures and empirical studies related to the
research .It comprises the evolution of the banking industry in Ghana, definitions and some
causes of NPLs, definitions of NPLs, some causes of NPLs, BOG’s classifications of NPLs in
Ghana, macroeconomic factors, empirical review, gross domestic product (GDP), interest rate,
growth rate in domestic credits to private sector, energy crises and justification of the study.
2.1 Evolution of the banking industry in Ghana
The banking sector in Ghana started with Bank of British West Africa (BBWA) which was
registered by end of March 1894 and was initially started in England and Lagos. The year 1896
was when a new branch in Accra was added, by then Ghana was called Gold Coast. Buckle et al
(1999)
this means that the BBWA was handling all Government financial activities. Moreover, it was to
introduce the use of cheques in settlement of Government accounts which helped to advertise the
usefulness of the Bank to the public and by 1918, the operations of BBWA in the Gold Coast had
been so successful that another expatriate bank, the Colonial Bank decided to commence banking
in Accra.
Since 1920s till early 1950s, the banking and financial services in Ghana then called the Gold
Coast was provided by these two banks mentioned earlier. They operated wholly in the form of
commercial banks, processing all transactions of commercial entities and helping in the
collections of revenue and making payments of salaries on behalf of the British Government.

19. 11
The BBWA functioned additionally as the bank of issue for the British Government according to
Buckle et al (1999). In 1953, the Bank of the Gold Coast was set up by the Government and
Alfred Engleston was appointed as the first Governor of the Bank of Ghana. Eventually the Bank
was splited into two which were the Bank of Ghana, operating as a bank of issue, to be
developed into a complete central bank; and the Ghana Commercial Bank, to be developed into
the largest commercial bank with a monopoly on the accounts of public corporations (Osakunor,
2009).
In March 6, 1957 Ghana obtained its independence from the Great Britain and the name Gold
Coast became known as Ghana so the Bank of Ghana took over the management of the currency
and in July 1958 it issued its first National Currency (the Cedi) to replace the old West African
currency notes. The Ghana Commercial Bank assumed the role and functions of Government
bankers and began to take over the finances of most Government departments and public
corporations.
Another body which cannot be excluded in the evolution of the banking sector in Ghana was the
West Africa Currency Board (WACB) which was also created in the year 1912 after a submitted
report from the Departmental Committee which was set up by the Secretary of State to
investigate currencies in use in British West African Regions. The Board was obliged for the
distribution and production of currencies in some of the West African countries including the
then Gold Coast (Ghana), Nigeria, Sierra Leone and Gambia. As part of the movement towards
independence, some West African countries started to produce and distribute their own national
currency. This authority was dissolved in 1965, (www.nationalarchives.gov.uk, (2014).

20. 12
However, the existing foreign banks before independence were Barclays Bank and Standard
Chartered Bank. In order to expand the economy, the government of Ghana established three
solid banks namely; National Investment Bank (NIB) in 1963 for long-term project financing for
the industrial sector of the economy, Agricultural Development Bank (ADB) also established in
1965 to finance projects in the agriculture sector and the Bank for Housing and Construction
(BHC) commenced in 1974, to make loanable funds for the real estate industry. These three
banks were known as the development finance institutions (DFIs), Amoafo (2015).
This indicates that as at 1974, the total number of commercial banks in the financial industry was
six, four for the government of Ghana and two foreign banks. Per the Bank of Ghana database, as
at November 30, 2015, the number of commercial bank institutions in the Ghanaian financial
industry was 28. Out of this number 15 are foreign banks and 13 are local banks. Out of the 13
local banks, 3 are owned by the Government of Ghana. However, according to Osei Tuti II
Centre for Executive Education and Research (2015), the financial system in the Ghanaian
economy currently includes both Bank Financial Institutions (BFIs) and Non-Banking Financial
Institutions (NBFIs), where the BFIs consist of the universal banks, the rural or community
banks, savings and loan companies and microfinance companies. On the other hand, the NBFIs
include capital market, insurance and the pension market among others.
According BOG (2015), total assets of BFIs and NBFIs increased by 40.9% by the end of the
year 2014.The increment in total assets size was due to growth associated with total loans and
advances with cash balances by 40% by the end of 2014 as compared to 35.2% in 2013 whilst
bank balances grew by 69.2% by end of 2014 against 20.8% in 2013. The report indicated that

21. 13
out of the total assets of BFIs and NBFIs, BFIs accounted for 85.2% compared with 84.4% in
2013.
The report recorded nominal growth rate of outstanding credit to the Private Sector went down to
33.3% as at June 30, 2015 from 45.8% against the same period in 2014 whilst 36.4% was
recorded in the first quarter of 2015. Outstanding credit to the private sector as at the end of the
June 2015 stood at GH¢26,045.2 million. It concluded that the yearly growth rate of outstanding
credit to the private sector also declined from 29.85% in 2014 to 17% by the end of the first
quarter of 2015 and further decreased to 13.85% at the end of the second quarter of 2015.
The explanation from above implies that the intension to expand the economy by creating more
local banks and allowing foreign banks into the economy has been executed in one way or the
other. It is also clear the financial industry has significantly increased from 6 in 1974 to 28 banks
as at the end of the second quarter of 2015.
However, in order to continue to strengthen the Ghanaian economy by making the financial
sector stronger, it is very important to minimize the rate at which NPLs ratio grows in the
Ghanaian economy for a stronger financial sector in the future.
2.2 Definitions and Some causes of NPLs
2.2.1 Definitions of NPLs
The International Monetary Fund (IMF) defines a non-performing loan as any loan which its
interest and principal payments are more than 90 days overdue, Central Bank of Ireland (2013) ,
defined non-performing loans as loans more than 90 days past due or loans which has a factor of

22. 14
a risk of it not being paid back in full without collateral realization, regardless of the existence of
any past-due amount or the number of days past due.
Also, The European Banking Authorities (2013) defined a non-performing loan as a loan that is
90 days past-due (material exposure) or a loan that is unlikely to be repaid in full without
collateral realization irrespective of any past-due amount or of the number of day’s past-due.
Shingjergji and Shingjergji (2013) defined non-performing assets as an assets which does not
provide incomes anymore whereby the principal and interest are not provided fully as agreed and
payments are due for 90 and above days.
Islam et al (2005) defined non-performing loans as loans that become non-performing when it
cannot be recovered within certain period of time that is governed by some respective laws.
These definitions given above have some characteristics in common and they are payments that
are at least 90 days due, the risks associated with incomes (both principle and interest) not
recoverable as agreed between the lender and borrower without collateral realization.
2.2.2 Some Causes of NPLs
NPLs arises in any given financial industry based on the environment that the economy finds its
self which means that causes of NPLs are not the same in all countries at the same periods.
Whilst some countries’ major causes of NPLs are macroeconomic factors, others are
microeconomic factors such as bank size, weak institutional processes and due diligence and
existence of asymmetry of information that creates adverse selection and moral hazard among
others. Other countries also experience NPLs due to natural factors such as death that are beyond
the control of policy makers and regulators, bank managers and defaulted borrowers.
Whilst Muritiithi (2011) p35 , found out that macroeconomic factors such as inflation and
interest rate are the major causes of NPLs in Kenya, Richard (2011), on the other hand also

23. 15
concluded that major cause of NPLs in Tanzanian economy are not macroeconomic indicators
but rather microeconomic factors where defaulted borrowers diverted funds from agreed and
intended purposes into other activities.
From the view of the two studies above, it should be noted that macroeconomic variables control
all other microeconomic factors or in other sense, macroeconomic factors reflect in all
microeconomic factors in short or long run periods. Therefore the study conducted in Kenya by
Richard (2011) where it concluded that microeconomic variable such as diversion of funds is the
main cause of NPLs in Kenya cannot not be generalized since it may be due to insufficient
supply of money or insufficient credit to the private sector in the Kenyan banking sector which is
a macroeconomic variable.
In Ethiopia, causes of NPLs are mostly microeconomic problems including poor credit
assessment, failed loan monitoring, less developed credit culture, weak institutions, willful
default of customers and lack of in-depth knowledge on loans (Negera , 2012). The study was
in the same direction just as that of Richard (2011) with microeconomic factors as the main
cause of NPLs, but again macroeconomic indicators may have some level of impact on NPLs in
South Africa.
It is surprising that some causes of NPLs in some other jurisdictions are far from macroeconomic
and microeconomic factors stated in the above three stated studies. For example, a research
conducted in Zimbabwe concluded that causes of NPLs in the economy includes natural disaster
such as rain storm, floods, fire outbreak, diseases and others (Mabvure et al, 2012), but natural
disaster do not occur all the times and may necessarily not have some impacts on NPLs in the
country.

24. 16
A study done by Fofack (2005) revealed that both macro and microeconomic variables are the
causes of NPLs in the Sub-Saharan countries. The research concluded that macroeconomic
variables were more significant in causing NPLs in the region. Ghana was included but the study
was on an aggregate level of which this study singled out Ghana as the main focus.Also, it
focused mainly on the private sector banks which was not in line with that of FOfack(2005)
where it merged both state and privates banks.
Amediku (2006) also discovered that causes of NPLs in Ghana were much more of
macroeconomic indicators which were inflation due to output gap and interest rate in the
Ghanaian economy which is in researches done outside country. The study may not reflect the
current trend of the causes of NPLs in the Ghanaian banking sector. Also the study in relation to
the Ghanaian economy except that of Kuutol et al(2014) did not include the energy crises to
capture its effects on NPLs of which this study has included in its model as a dummy variable.
2.3 BOG’s classifications of NPLs in Ghana
In Ghana, licensed financial institutions by BOG are obliged to review the quality of their
respective loan portfolio to minimize systematic and unsystematic risks associated with their
respective assets side of their respective balance sheet at least once every quarter on a regular
basis to adapt to the macro and microeconomic conditions that the institution may face.
Bank of Ghana Law, Act 2004, states that non-performing assets should be classified into four
grades of risk, these are;
 Other Loans Especially Mentioned (OLEM) accounts are loans that may have some risks
of default due to business cycles and days due are mostly below 90 days.
 Substandard accounts are loans due for 90 days with a rating of 25% provision.
 Doubtful accounts are non-performing assets due for 180 days with 50% provision.

25. 17
 Loss accounts are those due for one year and rated as 100% provision.
2.4 Macroeconomic factors
“Non-performing loans” as a theme has attracted a great attention before and especially after the
2008 financial crises due to its significant negative impact it has on the financial stability of any
economy which causes banks to fail (Barr et al 1993).
Studies in this area may be divided in two major categories which are researches that focuses
only the macroeconomic determinants and those which consider the microeconomic or bank
specific indicators and those that combine both.
Diaconasu et al (2014) used both bank specific or microeconomic and macroeconomic variables
to investigate the determinants of NPLs in the Central and Eastern European countries. The
researchers investigated the power of macroeconomic factors as key factors of NPLs among
countries in Eastern and Central part of Europe. They concluded that GDP growth rate and
unemployment were the key macroeconomic determinants of NPLs in the region whilst the
microeconomic determinant was private indebtedness. Their study was the same as Fofack
(2005) where micro and macroeconomic variables were used but this study only focused on
macroeconomic variables which this study believes that macroeconomic factors have command
over bank specific factors. Farhan et al (2012) conducted a study in Pakistan by using collected
primary data on macroeconomic factors as determinants of NPLs from loan providing and
approving authorities in the Pakistani banking industry, it indicated that macroeconomic factors
are the main cause of NPLs in the Pakistani economy. The study revealed that interest rate,
energy crises, unemployment, inflation and GDP growth rate had a significant impact on NPLs
in the Pakistani banking industry. Hence this study followed the same path as done by Amediku

26. 18
(2006) and Kuutol (2014),the study in Pakistan was done using primary data just as Kuutol
(2014) conducted that of Ghana which this study employed a secondary data for the research.
Saba et al (2012) also found that real GDP per capita is the main determinant of NPLs in the
United States of America (USA), the researchers employed both bank specific and
macroeconomic factors in their study. Interest rate and real GDP per capita rate were the
macroeconomic factors whilst total loans represented bank specific. The research recommended
that US banks should consider real GDP per capita when issuing loans to manage non-
performing loans in the US banking industry. Another research in the Albanian banking system
conducted by Shingjergji and Shingjergji (2013), using macroeconomic variables such as GDP
growth rate, inflation rate, interest rate and real exchange rate and banking specific or
microeconomic factors such as total credits. The results were that macroeconomic factors
especially real exchange rate played a major role in determining non-performing loan level in the
Albanian banking system.
Investigation in the Sub-Sahara African countries also done by Fofack (2005), GDP growth rate,
real exchange rate and real interest rate as macroeconomic variables and net interest income and
interbank loans as microeconomic factors were employed for the investigation. It was found that
macroeconomic factors were the main causes of NPLs in the Sub-Saharan countries.
From the above review, it is obvious that each economy has its own unique causes of NPLs in its
respective financial sector. Whilst these empirical results indicates that some countries
experienced macroeconomic factors as the major causes of NPLs other countries have
experienced microeconomic factors as the major causes of their NPLs.

27. 19
Hence this study only focused on the macroeconomic indicators as determinants of NPLs in
Ghana by including the energy crises that the country has faced for the past four years which still
continues to be a challenge to the country. This variable was employed among others to capture
the effects energy crises on NPLs to give the true and current picture of the macroeconomic
factors of NPLs in the current Ghanaian economy.
2.5 Empirical Review
2.5.1 Gross Domestic Product (GDP)
Various researches have shown that there was a significant empirical evidence of negative
relationship between growth in GDP and non-performing loans ratio (Diaconasu Popescu and
Socoliuc, 2014, Curak,Pepur and Poposki, 2013, Farhan, Sattar, Hussain and Khali, 2012, and
Fofack, 2005) .Theory explains that in any given economy, when there is growth in GDP,
income also increases which increase the capacity of loan repayment by a borrower. On the other
hand, when growth in GDP decreases, income also decreases which consequently decrease the
capacity of a borrower to repay debt owed (Jordan and Tucker, 2013).
2.5.2 Interest Rate
Whilst some researches have empirically proven that there is a positive relationship between
NPLs ratio and interest rate implying that higher interest rate reduces the borrower’s capacity to
repay loans and hence increases the growth of NPLs (Kuutol et al, 2014.p35,Badar and Javid
2013, Curak,Pepur and Poposki, 2013 and Farhan et al, 2012 ) , others have also investigated and
shown that there exists a negative relationship between NPLs ratio and interest rate with the
reason that when an economy experiences unacceptable budget deficits, the government raises
funds in the money market whereby it competes with the private sector for loanable funds and

28. 20
this causes the interest rate or cost of borrowing to increase to some level restrict borrowers
from the private sector from accessing more credits. Therefore this private sector credit crowding
out caused by the government reduces NPLs ratio since government securities are risk free assets
to most commercial banks (Akowuah 2011 and Amidu 2006). For example whilst Muritiithi
(2011) discovered that inverse correlation existed between NPLs and interest rate by studying all
commercial banks in Kenya, Fofack (2005) also empirically revealed that there is a negative
relationship between NPLs and interest rate for all banks in the sub-Saharan Africa though there
was a positive relationship between NPLs and interest rate for state bank in the region. However,
Saba, Kouser and Azeem (2012), proved empirically that there existed a negative relationship
between NPLs ratio and interest rate in the case of the banking sector in United States of
America (USA).
2.5.3 Growth Rate in Domestic Credits to Private Sector
Investigation done by Fofack (2005), indicated that there is a negative relationship between
domestic credit to private sector as a proxy of monetary expansion and non-performing loans in
the Sub-Saharan African regions. Also, a study conducted by Muriithi (2010) revealed that non-
performing loans in the Kenyan banking sector has a negative relationship with the growth of
loans directed to the private sector in Kenya. Another empirical evidence from Sri Lanka shows
that the relationship among non-performing loans and loans approved to the private sector by
nine commercial banks is negative (Ekanayake and Azeez, 2015).
2.5.4 Energy Crises
Using 401 questionnaires to investigate the relationship between non-performing loans and
energy crises in the Ghanaian banking sector, it was empirically revealed that there existed a

29. 21
positive relationship between the two variables according to Kuutol et al (2014) p 35. In the
Nepali economy, it was also significantly empirical evidence that there was a positive correlation
between energy crises and rate of NPLs within the financial sector (Bhattarai, 2014). It is also
not different in that of the Pakistani economy where energy crises increases non-performing
loans ratio in Pakistan (Hussain, Khalil and Nawaz, 2013 and Farhan,Sattar,Hussain and Khalil,
2012).Hence energy crises is assumed to hinder the smooth operations of businesses which also
creates difficulty for businesses to generate sufficient revenue to repay loans which increases
non-performing loans.
2.6 Justification of the study
The current study in this area is still relevant in the sense that whilst most researchers have
conducted studies on this area outside Ghana, a study was done on this same area on Sub-
Saharan African countries by Fofack (2005), focused at the whole region and not specifically the
Ghanaian banking sector. Also in that same study, energy crises were not employed in the model.
This study therefore looked at the Ghanaian banking sector specifically and included energy
crises as a dummy variable in the model stated in the next chapter. Kuutol et al (2014) conducted
a research in this area but this time it was specifically the Ghanaian banking sector and included
energy crises as a variable in its model but the data collected on all independent variables were
primary data with a cross section regression analysis. Also, in that same study, GDP growth rate
and growth in domestic credit to the private sector were excluded from its model. Whilst Fofack
(2005) and Kuutol et al (2014) used real interest rate and lending interest rate respectively in
their study, this study employed 91 Treasury bill as a proxy of interest rate in the model
specified.

30. 22
Arko (2012) also conducted such a study in the Ghanaian economy but the focus was on
Microfinance institutions and not commercial banks in the economy.
Therefore, the study focused on the Ghanaian banking sector specifically the commercial banks
by using secondary data on all the variables (NPLs and independent variables) including energy
crises as a dummy variable, domestic credit to the private sector, growth in GDP and 91 day
Treasury bill as a proxy of interest rate. Panel data analysis was employed for the study which is
different from cross section analysis and moving trend analysis used by Kuutol et al (2014) and
Arko (2012) respectively in the Ghanaian banking system.

31. 23
CHAPTER THREE
METHODOLOGY
3.0 Introduction
This chapter focuses on the method of analyzing the data to answer the research questions. The
sub-headings include research design, population, sample and sample techniques, data
description, method of analysis, expected signs and model specification
3.1 Research Design
The study focused on nine banks in the Ghanaian banking sector as a case study with the help of
both quantitative and qualitative statistical techniques to analyze the economic determinants of
NPLs in the Ghanaian banking sector. This study employed statistical techniques to examine
whether there is a correlation between macroeconomic variables and NPLs in the Ghanaian
banking sector.
3.2 Population
The research focused on only nine universal commercial banks out of the total number of 29
(BOG, 2015), representing 31.03% of the total universal banks in the banking sector. The
selected local banks had a share of 77.78% of the total universal banks whilst the foreign banks
had a share of 22.22%.
3.3 Sample and Sample Techniques
Out of the total number of commercial bank in Ghana, nine commercial banks were sampled
based on the availability of data. The nine banks represented 31.03% of the total commercial
banks in the entire banking sector in the Ghanaian economy.

32. 24
The banks selected included seven local commercial banks namely CAL Bank Limited, Uni
Bank Limited, HFC Bank Limited, Prudential Bank Limited, Fidelity Bank Limited, Ecobank
Ghana Limited and Agricultural Bank Limited representing 77.77% of the total selected banks.
Whilst the foreign banks included Stand Chart Bank Ghana Limited and Barclays Bank Ghana
Limited representing 22.23% of the total selected banks in the study.
3.4 Data Description
To investigate the economic determinants of non-performing loans in the Ghanaian economy
with the nine commercial banks as stated above, the choice of the number of banks and the time
period were determined by the availability and accessibility of non performing ratios of
individual banks. Again as stated earlier, seven local banks and two foreign banks were selected
due to the availability and accessibility of data on NPLs ratios of the individual banks from 2007
to 2014. The dependent variable which is the non-performing ratios of individual bank were
secondary data and sourced from the unconsolidated financial statements of the respective bank
whilst the independent variables which were macroeconomic indicators such as growth in
domestic credit to the private sector, growth in GDP and 91 day treasury bill as a proxy of
interest rate sourced from World Bank (WB) and Bank of Ghana (BOG). Data on energy crises
periods were collected from Graphic News.
3.5 Method of Analysis
The study used Descriptive Statistical Analysis to derive the mean, maximum and minimum for
the independent variables. It also, employed a random effect Panel Data Regression Analysis to
examine the relationship between the independent variables and the NPLs. The regression

33. 25
analysis produced the magnitude and direction at which the independent variables had impact on
NPLs rate.
3.6 Independent Variables
3.6.1 Gross Domestic Product (GDP) Growth Rate
As explained from above, there are enough evidence that the relationship between NPLs and
GDP growth rate is negative (Diaconasu Popescu and Socoliuc, 2014, Curak,Pepur and Poppski,
2013, Farhan, Sattar, Hussain and Khali, 2012, and Fofack, 2005).
However, from the literature review given above, it implies that higher GDP growth rate
increases the capacity of borrowers from the deficit unit to repay their loans which reduces NPLs
and the vice versa is also true (Jordan and Tucker, 2013). Hence the expected relationship
between NPLs and GDP growth rate was negative.
3.6.2 Interest Rate
One of the major macroeconomic determinants of NPLS is interest rate and in other researches,
lending rate and other interest rate have been used. The study employed 91 day Treasury bill rate
as a proxy of interest rate and empirically whilst interest rate has been confirmed to have a
positive relationship with NPLs (Kuutol et al, 2014, Badar and Javid 2013, Curak,Pepur and
Poppski, 2013 and Farhan et al, 2012 ) other researches have also confirmed that interest rate
have a negative relationship with NPLs (Saba et al, 2012) but the study expect a positive
relationship between NPLs and interest rate. Higher interest rate implies higher burden and
difficulty for borrowers to repay their loan, on the other hand, a lower interest rate indicates

34. 26
lower burden and less difficulty to repay loans. Therefore higher interest rate causes higher NPLs
whilst lower interest rate causes lower NPLs (Kuutol et al, 2014).
3.6.3 Domestic Credit to the Private Sector
According to Moinesceu and Codirlasu(2011), growth in credit produces some level of effects on
the quality of loan portfolio and significant decline in the growth rate of credits is associated with
high levels of NPLs.
The more the supply of funds in the loanable fund market indicates low levels of cost of funds.
So when there is a credit expansion, then borrowers can have access to more funds to pay off all
debts owed and therefore reduce the risk of default. On the other hand, low levels of credit
growth to businesses and households implies the difficulty that borrowers will face when
attempting to pay off their debts which results in to loan default. The study therefore expected a
negative relationship between NPLs and Domestic Credit to the Private Sector.
Fofack (2005), Muriithi (2010) and (Ekanayake and Azeez, 2015) have evidently proven that
indeed there is a negative relationship between the two variables.
3.6.4 Energy Crises
Energy crises is either a large drop in the supply of energy available or a large rise in the price of
energy of which it refers to a shortage of crude oil and electricity as well as other natural
resources of non-renewable resource energy(www.peak-oil-crises.com,2009).The study shortage
of electricity supply as energy crises.
With sustainable supply of electricity, the real sector economy is able to produce and transact
businesses or render services to receive income to repay debt owed to reduce non-performing

35. 27
loans and therefore increase in energy crises increases non-performing loans (Hussain, Khalil
and Nawaz, 2013 and Farhan,Sattar,Hussain and Khalil, 2012).
So the research expected energy crises to have a positive relationship with energy crises.
Kuutol et al (2014) and Bhattarai, (2014) empirically revealed that there exist a positive
relationship between NPLs and energy crises.
3.7 Model Specification
In order to investigate the macroeconomic determinants of NPLs in the Ghanaian banking sector,
panel data analysis for nine banks was used.
The empirical study was focused on a panel data analysis with the following model:
lnNPLit = β1+ β2 lnGDPit + β3 lnTBLit+ β4 lnDCPit + β5dvecit + μit
Where: i represents individual bank of the nine banks from 1 to 9, hence the N sections.
t represents the time period from 2007 to 2014 of the analysis.
lnNPLit is the bank’s non-performing loans ratios of bank i in year t;
lnGDPit is the growth rate of gross domestic product of in year t;
lnTBLit is the 91 day treasury bill as a proxy for interest rate in year t;
lnDCPit is the domestic credit to the private sector in year t;
dvecit is the dummy variable for energy crises in year t;
β1 is the constant term for the NPL ratio holding all other variables constant.

36. 28
β2, β3, β4 and β5 are the coefficients for the respective variables holding other constant.
μit is the error term for each i bank in each year t.
3.8 Expected Signs
Table: 1. Expected Signs of the independent Variables
Variables Symbolic form A priori Signs
GDP growth rate lnGDP (-)
91 Treasury Bill Rate lnTBL (+)
Domestic Credit to Private Sector lnDCP (-)
Energy Crises dvec (+)
Source: Author’s calculations
Theoretically, the GDP growth rate was measured by deducting the previous GDP from the
current GDP and the results was divided by the previous GDP and multiplied by hundred
percent. The 91 Treasury bill rate was taken from the BOG’s time series data as determined by
demand and supply of bonds in the government securities market. The Domestic Credit to the
Private Sector growth rate was calculated by deducting the previous Domestic Credit to the
Private Sector from the current Domestic Credit to the Private Sector and the results was divided
by the previous Domestic Credit to the Private Sector and multiplied by hundred percent. Also,
the energy crises was used as a dummy variable which used a binary values (ie 0 and 1)

37. 29
CHAPTER FOUR
RESULTS AND DISCUSSION
4.0 Introduction
Chapter four presents some statistics of the dependent variables which gives the averages,
maximum and minimum rate of the individual macroeconomic variables and the estimates are
provided. The descriptive statistics of the energy crises was not calculated due to its dummy
variable nature. The chapter is made up of descriptive analysis, estimated model and regression
analysis
4.1 Descriptive Analysis
Applying the panel data method, the study achieved the results presented in table 1 and 2 and the
estimated model provided below.
The results identified the relationship between NPLs and macroeconomic variables with energy
crises used in the study. Also, the study confirmed the relationship that exist between NPLs and
the independent variables using the secondary data except the 91 day treasury bill interest rate
used as a proxy of interest rate contrary to the results of Kuutol et al (2014) p 35, in the same
country.

38. 30
Table 1. Descriptive Statistics
Statistics Mean (%) Max (%) Min (%)
GDP 7.81 15.01 3.4
91 T-Bill 18.53 25.79 10.63
DCPS 16.05 22.86 14.38
DVEC n/a
n/a n/a
Source: Author’s calculations
The statistics in the table above shows the average growth rate of GDP for the period was 7.81%,
its highest and lower rate were 15.01% and 3.40% respectively .The Treasury Bill Rate had an
average rate of 18.53% with its peak reaching 25.79% and its lowest rate at 10.63% for the
period under the study. With that of Domestic Credit to the Private Sector, the average growth
rate was 16.05% and recorded maximum and minimum rate of 22.86% and 14.38% respectively.
4.2 Regression Analysis
Table 2. Results on Regression Analysis (Random Effect Panel Data Analysis)
Variables Coefficients
Standard
Error P > I Z I
lnNPL Dependent
lnGDP Independent -1.22383 0.47034 0.009
lnTBL Independent -1.71473 0.697232 0.014
lnDCP Independent -4.20542 1.455481 0.004
Dvec Independent 0.665391 0.279934 0.017
Cons Constant 20.13505 6.662258 0.003
Adjusted R2
= 51.50%
Source: Author’s calculations
4.2.1 Estimated Model
lnNPLit = 20.135 – 1.2238lnGDPit - 1.7147 lnTBLit – 4.205lnDCPit + 0.665dvecit

39. 31
According to the analysis from table 2, the economic variables represented 51.50% variation in
the non-performing loans in the Ghanaian banking sector.
From the estimated model given above, it implies that at 5% level of significance, the coefficient
for the growth rate of GDP is 1.2238 meaning when GDP growth rate increases by 1%, NPL
rate declines by 1.2238% (with p value of 0.009) in the Ghanaian banking sector. The study also
revealed that there was a significant negative relationship between NPLs growth and GDP
growth rate which confirms earlier studies (Diaconasu Popescu and Socoliuc, 2014, Curak,Pepur
and Poposki, 2013, Farhan et al, 2012, and Fofack, 2005) . Positive economic growth in any
given economy is very important since it paves way to increase the wealth or economic power of
individuals, businesses and other institutions in the private sector to honor their respective debt
payments. In the Ghanaian economy, the study implies that when the economy grows positively,
borrower’s capacity to pay debt increases and consequently reduces NPLs growth rate. The study
therefore accepts the alternative hypothesis of i for the first and second objectives.
Treasury bill rate as the proxy of interest rate shows a beta value of 1.7147 (with p value of
0.014) which indicates that as interest rate on government securities increase by 1%, NPLs
growth rate decreases by 1.7147% in the Ghanaian economy. The research discovered that there
was a significant negative relationship between NPLs rate and Treasury bill rate in the banking
sector in Ghana which is also in line with the previous researches (Saba et al, 2012, Muritiithi,
2011, Fofack, 2005). The finding confirms what is currently happening in the Ghanaian
economy especially in the capital market or the loanable fund market. Economic imbalances in
any given economy forces government to miss most of its macroeconomic targets which lead to
continual increase in cost of doing businesses in the economy and consequently affect the budget
of the government creating a huge gap between its revenue and expenditure (budget deficit). In

40. 32
light of that, central banks issue more government securities that increase its Treasury bill rate to
mobilize funds to finance government expenditure at the cost of crowding out the private sector
and increasing the cost of funds in the capital market. The surplus units made up of households
and businesses in the financial sector redirects most of their deposits through the universal banks
to the government securities for higher return and hence reduce the supply of loanable funds in
the capital market that increases the lending rate further. On the side of private commercial banks
as intermediaries, cheap deposit becomes a challenge. When this happens, private commercial
banks also invest most of their deposits in the government securities to make profit and
maximize return for its depositors and shareholders. From the side of deficit units which includes
households and businesses whose expenditure are more than their revenue are also affected in
the sense that they will encounter challenges in accessing loanable funds in the capital market
where demand for loanable funds will far exceed its supply and therefore increase cost of funds
and default risks which will increase NPLs.
From the reasons given above, it means that for commercial banks to minimize NPLs growth
rate, it will be effective and efficient to invest most of its funds into government securities which
is less riskier than packaging them as loans to the private sector with higher risks due to
economic imbalances which directly affect their capacity to pay off loans and thereby increase
the NPLs rate. Hence the study reveals that banks should invest in government securities when
Treasury bill rate increases to reduce growth of NPLs in the Ghanaian economy. The study
rejects the null hypothesis of the first objective and accepts the null hypothesis of the second
objective.
The third independent variable; the growth in domestic credits to the private sector had a
coefficient of 4.205 (with p value of 0.004) which points out that NPLs increases in growth at a

41. 33
rate of 4.202% when domestic credit to the private sector reduces by 1%. It also shows that in the
Ghanaian banking sector, there is a significant negative relationship between the growth of NPLs
and that of the rate of growth in domestic credit to the private sector. The study was in the same
pattern as the previous studies of Ekanayake and Azeez, (2015), Muriithi, (2010) and Fofack
(2005). The findings reveals that lower credits to the private sector during economic crises or
economic imbalances increases the rate of NPLs that commercial banks record and that the
government should reduce its competition with the private sector in the loanable fund market to
increase credits to the private sector. It also implies that the government and the central bank
should involve good economic managers or technocrats to stabilize the economy so as to avoid
budget deficits which creates credit crowding out in the private sector which also increases NPLs
since most credits are directed to the government through the open market operation
(OMO).Alternatively, the government can put in place measures to let the private sector get more
access to credit to reduce the rate of NPLs. The study therefore fails to accept the null hypothesis
in both first and second objectives.
According to the model stated above, energy crises as a dummy variable had its coefficient of
0.665 with a p value of 0.017 at 5% significance level. It indicates that there is a significant
positive relation between NPLs rate and energy crises and consistent with studies conducted
(Kuutol et al, 2014, Bhattarai, 2014, Hussain et al, 2013 and Farhan,et al, 2012). Energy crises in
Ghana per the results given above assumes that it may has collapsed many private industries in
the period stated which may also have affected the banking sector since some of the private
industries may owe the banks and were not able to honor their loan repayments as agreed, Kuutol
et al (2014). The research fails to accept the null hypothesis.

42. 34
Based on the results produced in the Descriptive Statistics, the Regression Analysis and the
estimated model given above indicates that interest rate on government securities was highest
comparing to the other indicators due to excessive government borrowing influenced by budget
deficit under the period of study. Whilst the interest rate on government securities was on the
high side and attracting most universal banks to redirect their deposits to the government from
the private sector to reduce their NPLs, the growth rate of GDP was on a low side depicting that
the economy grew slower than that of the interest rate on government securities and hence
having a negative impacts on NPLs. On the side of the growth rate of DCPS, the descriptive
statistic shows that they were closer to that of the interest rate on government securities and
hence more money supply to the private sector helped in reducing NPLs for the period.
The hypotheses were tested using the p and z-values per the results given in

43. 35
CHAPTER FIVE
SMMARY, CONCLUSION AND RECOMMENDATION
5.0 Introduction
The study aimed to investigate and confirm the relationship between non-performing loans and
some macroeconomic indicators with energy crises among nine banks in the Ghanaian banking
sector using a secondary data on all the variables. This chapter therefore presents the summary,
conclusion and recommendation of the study.
5.1 Summary
The findings from the study indicate that from 2007 to 2014, the average rate of NPLs among the
nine selected banks is 7.15%. Agricultural Development Bank(ADB),Standard Chartered Bank
Ghana(SCB) and Barclays Bank Ghana(BBG) representing 33.33% of the selected banks had
double digit rate of NPLs whiles the remaining banks which are mostly the local banks had
single digit rate of NPLs within the stated period. Statistically, SCB recorded the highest rate of
NPLs in 2011 among the selected banks. On the other hand, Ecobank Ghana Limited recorded
the lowest rate of NPLs among the selected banks.
However, trend analysis of the macroeconomic variables shows that the rate of GDP and the
growth rate of Domestic Credit to the Private Sector have declined significantly from higher rate
to lower rates whiles the 91 Day Treasury bill rate increased from lower rates to higher rates
within the period of the study. Energy Crises was experienced from the year 2007 to 2008 and
slowed down between 2009 to 2011, but the crises again intensified within 2012 to 2014.

44. 36
Moreover, the regression analysis shows that rate of GDP, growth rate of Domestic Credit to the
Private Sector and 91 Day Treasury bill rate have a negative relationship with NPLs whiles
Energy Crises has a positive relationship with NPLs in the Ghanaian banking sector. Hence the
study shows that growth rate of Domestic Credit to the Private Sector had more impact on NPLs.
5.2 Conclusion
The research identified and confirmed the relationship between rate of NPLs among the nine
banks and the macroeconomic variables with energy crises using secondary data on both
dependent and independent variables. The study shows that there is a significant relationship
between rate of NPLs and the individual variables which includes growth rate of GDP, growth
rate of Domestic Credit to the Private Sector, 91 Day Treasury bill rate and Energy Crises of
which the research fails to accept the null hypotheses for the first objective. Hence, the study
implies that changes in these macroeconomic variables including energy crises have effects on
individual bank rate of NPLs.
Results of the regression shows whiles GDP growth rate and Domestic Credit to Private Sector
had a negative relationship with NPLs; Energy Crises had a positive relationship with rate of
NPLs among the selected banks. So for the second objective, the research fails to accept the null
hypotheses in regards to GDP growth rate, growth rate of Domestic Credit to the Private Sector
and Energy Crises except the null hypothesis of 91 Day Treasury bill rate which the study fails to
reject. Whilst GDP growth rate had an impact on NPLs rate by 1.22%, 91 Day Treasury rate had
an impact on NPLs rate by 1.71%, growth rate of Domestic Credit to the Private Sector an
impact on NPLs rate by 4.21% and Energy Crises also had an impact on NPLs by 0.66%.

45. 37
5.3 Recommendation
Agricultural Development Bank (ADB), Standard Chartered Bank Ghana (SCB) and Barclays
Bank Ghana (BBG) with the other Banks should consider changes in GDP growth rate whenever
taking decisions to grant loans to its customers. More loans should be granted when GDP growth
rate expected to be in the range of 7.80% to 15.01% or banks should reduce their loans and
advances when GDP growth rate is expected to be within 7.00% to 3.40% to minimize the
impacts on individual bank NPL rate.
Commercial banks, especially the local banks such CAL Bank Limited, Uni Bank Limited, HFC
Bank Limited, Prudential Bank Limited, Fidelity Bank Limited, Ecobank Ghana Limited should
invest more funds in government securities especially the 91 Day Treasury bill during periods of
higher rate from 25.79% to 18.53% and reduce its investment growth in government securities
when interest rate on 91 day treasury bill ranges from 17.50% to 10% to mitigate higher level of
risks in the sector to minimize their individual bank NPL rate.
Both the local and foreign banks as a matter of fact should augment the working capital or
extend more credits to genuine borrowers with good track record during periods whereby growth
rate of Domestic Credit to the Private Sector is within the range of 16.05% to 14.38% and
strategically reduce supply of credit to borrowers when the rate is above 22.86%. This in the
form of supplying more funds to debtors during periods of declining growth rate in credit to the
sector will boost the capacity of borrowers to repay debts to reduce the impact of declining
growth rate of Domestic Credit to the Private Sector on NPLs.
Furthermore, the study implies that cheap and constant supply of energy to private industries is a
necessity to pave way for businesses to operate as expected in order to improve output for the

46. 38
development of the economy. And since the study has shown that there is a negative relationship
between growth rate of GDP and NPLs, it is important that the energy crises should be
permanently fixed to boost businesses’ production to expand the economy and consequently
reduce the rate of NPLs in the Ghanaian banking sector. The Bank of Ghana should encourage
all commercial banks and other financial institutions in the financial sector to have a minimum
percentage of their total loans and advances allocated for power developments in the energy
sector to avoid such occurrence in the future to sustain and grow businesses in the private sector
to be able to pay their loans.
5.3.1 Suggestions for Further Research
The study conducted only looked at few macroeconomic indicators to determine the factors of
non-performing loans in Ghana and suggest that further study should include more indicators
such as inflation, exchange rate, deficit rate, policy rate and others as well as bank specific
determinants such as total bank assets, loan size and others.
The study also recommend time series in further research with at least 30 years or a panel data
regression for 70% of the total commercial banks for the period of 15 years.

47. 39
REFERENCES
Azeem,M.,Kouser, R.& Saba,I. 2012. Determinants of Non-Performing Loans: Case of US
Bankind Sector. The Romanian Economic Journal
Arko,S.K.,2012. Determining the Causes and Impact of Non-Performing Loans on the operations
of microfinance institutions: A Case of Sinapi Aba Trust.
Akowuah,G.,2011. Interest Rates and the Demand for credit in Ghana.
Amafo,O., 2015. The Roles of Banks in the Ghanaian Economy.
Amediku,S.,2014. Stress Tests of the Ghanaian Sector: A VAR Approach.
Amidu,M. 2006. THE Link between Monetary Policy and Banks’ Lending Behavior: The
Ghanaian Case. Vol 1,Issue 4
Badar,M.,Javid,A.Y.2013.Impact of Macroeconomic Forces on Non-Performing Loans: An
Empirical Study of Commercial Banks in Pakistan. Wseas Transaction on Business and
Economics
Barr, R.,Siems, F.T.,&Seiford,L.M.,1993. Forecasting Banks Failure: A Non-Parametric Frontier
Estimation Approach.
BOG, 2015. Financial Stability Report from http//:www.bog.gov.gh
BOG, 2004. Banking Act from http//:www.bog.gov.gh
Bhattarai,S.,2012. Determinants of non-performing loans:Perception of Nepali
Bankers:Economic journal of development Isuues Vol 17&18
Central Bank of Ireland (2013). Impairment Provisioning and Disclosure Guidelines.
Curak .M, Pepur.S and Poposki.K 2013. Determinants of Non-Performing Loans : Evidence
from business perspective.
Diaconasu , D.E.,Popescu,M.& Socoliuc,O.R. 2014. Macroeconomic Determinants of Non-
Performing Loans in Emerging Markets: Evidence from Central Europe.
Espinoza, R.A. and Prasad, A., 2010. Non-performing loans in the GCC banking system and
their macroeconomic effects. IMF Working Papers, pp.1-24.
EBA,2013. Final draft implementing Technical Standards.
Ekanayake,E.M.N.N., and Azeez, A.A.,2015. Determinants of Non-Performing Loans in
Licensed Commercial Banks; Evidence from Sri Lanka, Asian Economic and Financial Review.

48. 40
Farhan,M., Sattar A., Chaudhry,A. H. & Khalil F. 2012.Economic Determinants of non-
Performing loans: perceptions of Pakistani bankers. European Journal of Business and
Management,4(19),87-100
Fofack, H., 2005. Non-performing loans in Sub-Saharan Africa: causal analysis and
macroeconomic implications. World Bank Policy Research Working Paper, (3769).
Ghanaweb, 2015. Power Crisis http//:www.ghanaweb.com
Hussain., A, Khalil., A&Nawaz M. 2013. Macroeconomic determinants of non-performing
loans:Evidence from Pakistan. Pakistan Journal of humanities and Social Sciences.pp.59-72
Islam,M.S.,Shil,N.C.,&Manna, M. A. 2005. Non-Performing Loans- its causes, consequences
and some learnings, pp. 3
International Monetary Fund. Retrieved from http//:www.imf.org
Jordan, A.,and Tucker, C. 2013. Assessing the Impact of Non-performing Loans on Economic
Growth in the Bahamas.
Koo, R., 2011. The world in balance sheet recession: causes, cure, and politics. Real-world
economics review, 58(12), pp.19-37.
Kuutul,P.K., Agyeman,B.,&Owusu,A.C. 2014. Bankers of Economic Determinants of Non-
Performing loans in Ghana. International Journal of financial Markets. Vol.1, No.3 pp.35-101.
Muritiithi, M.W., 2011. The Causes of Non-Performing Loans in Commercial Banks in Kenya,
D61/72382, pp. 35
Mabvure,J.T.,Edson,G.,Manuere,F.,Clifford,M.,&Michael,K.,2012. Non-Performing Loans in
Commercial Banks: A Case of CBZ Bank LTD in Zimbabwe.
Negera,W., 2012. Determinants of Non-Performing Loans: The case of Ethiopian Banks
Osakunor, O., 2009. Evolution of Banking in Ghana.
Richard, E., 2011. Factors that Cause Non Performing Loans in Commercial Banks in Tanzania
and Strategies to Resolve them. Journal of Management Policy and Practice, Vol. 12(7).
Saba,I., Kouser, R. and Azeem, M., 2012. Determinants of Non-Performing Loans: Case of US
Banking Sector. The Romanian Economic Journal, Year XV, 44.
Shingjergji,.A and Shingjergji,I,2013. An Analysis of the Non-Performing Loans in the Albanian
Banking System. International Journal of Business and Commerce. Vol.2 pp.1-11
Buckle, V. L. & Co, 1999. The History of Banking in Ghana.

49. 41
World Bank, http://www.worldbank.org/en/country/ghana.com, 2015.

50. 42
APPENDICES
TRENDS OF INDIVIDUAL BANK NPL RATE
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8
CAL BANK(%)
ADB(%)
ECB(%)
BBG(%)
SCB(%)
UNB(%)
FDB(%)
PRD(%)
HFC(%)
Year:2007-
xxxxx2014

51. 43
TRENDS OF RATE OF NPL GROWTH IN THE GHANAIAN ECONOMY
0
2
4
6
8
10
12
14
16
18
20
1 2 3 4 5 6 7 8
NPL(%) in the Banking Sector
Year:2007-2014

52. 44
TRENDS OF MACROECONOMIC INDICATORS
0
5
10
15
20
25
30
1 2 3 4 5 6 7 8
GDP Growth (%)
91 Day T-Bills(%)
DCPS(%)
Year:2007-
xxxx2014

53. 45
DATA ON THE INDIVIDUAL BANK RATE OF NPL
Year BANK
NPL
RATE
(%) Year BANK
NPL
RATE
(%) Year BANK
NPL
RATE
(%)
2007
CAL
BANK 7.3 2007
Agricultural
Development
Bank(ADB) 18 2007
Ecobank
Ghana
Ltd. 3.6
2008
CAL
BANK 5.9 2008
Agricultural
Development
Bank(ADB)
12.44 2008
Ecobank
Ghana
Ltd.
3.1
2009
CAL
BANK 10.4 2009
Agricultural
Development
Bank(ADB)
8 2009
Ecobank
Ghana
Ltd.
3.2
2010
CAL
BANK 11.4 2010
Agricultural
Development
Bank(ADB)
11.82 2010
Ecobank
Ghana
Ltd.
3.1
2011
CAL
BANK 9.7 2011
Agricultural
Development
Bank(ADB)
6.69 2011
Ecobank
Ghana
Ltd.
0.66
2012
CAL
BANK 5.1 2012
Agricultural
Development
Bank(ADB)
10.78 2012
Ecobank
Ghana
Ltd.
5.1
2013
CAL
BANK 7.9 2013
Agricultural
Development
Bank(ADB)
12.42 2013
Ecobank
Ghana
Ltd.
5.9
2014
CAL
BANK 6.2 2014
Agricultural
Development
Bank(ADB)
23.29 2014
Ecobank
Ghana
Ltd.
1.78
Year BANK
NPL
RATE
(%) Year BANK
NPL
RATE
(%) Year BANK
NPL
RATE
(%)
2007
Barclyas
Bank
Ghana 0.77 2007
Standard
Chartered
Bank 4 2007 UniBank 7

54. 46
2008
Barclyas
Bank
Ghana
5.52 2008
Standard
Chartered
Bank
4.5 2008
UniBank
4
2009
Barclyas
Bank
Ghana
9.75 2009
Standard
Chartered
Bank
10 2009
UniBank
3.9
2010
Barclyas
Bank
Ghana
33 2010
Standard
Chartered
Bank
12 2010
UniBank
2.6
2011
Barclyas
Bank
Ghana
25 2011
Standard
Chartered
Bank
10 2011
UniBank
3.32
2012
Barclyas
Bank
Ghana
16.52 2012
Standard
Chartered
Bank
10 2012
UniBank
3.09
2013
Barclyas
Bank
Ghana
11.05 2013
Standard
Chartered
Bank
16 2013
UniBank
3.73
2014
Barclyas
Bank
Ghana
12.3 2014
Standard
Chartered
Bank
27 2014
UniBank
4
Year BANK
NPL
RATE
(%) Year BANK
NPL
RATE
(%) Year BANK
NPL
RATE
(%)
2007
Fidelity
Bank 1.63 2007
Prudential
Bank 4.97 2007 HFC 1.53
2008
Fidelity
Bank
0.76 2008
Prudential
Bank
3.96 2008 HFC 1.64
2009
Fidelity
Bank
1.26 2009
Prudential
Bank
4.85 2009 HFC 1.2
2010
Fidelity
Bank
3.23 2010
Prudential
Bank
3.99 2010 HFC 1.1
2011 Fidelity 3.24 2011 Prudential 6.11 2011 HFC 1

55. 47
Bank Bank
2012
Fidelity
Bank
3.8 2012
Prudential
Bank
8.64 2012 HFC 1.64
2013
Fidelity
Bank
4.86 2013
Prudential
Bank
9.22 2013 HFC 1.96
2014
Fidelity
Bank
2.46 2014
Prudential
Bank
7.1 2014 HFC 2.15