ISSN: 2456-9992 Non-Performing Loans: Perception Of Somali … · 2019. 9. 25. · Non-performing...

Volume 1 Issue 5, November 2017

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International Journal of Advanced Research and Publications ISSN: 2456-9992

Non-Performing Loans: Perception Of Somali

Bankers

Hassan Haibe Osman, Vikneswaran

Asia Pacific University of Technology and Innovation, School of Business and Economics, South City Plaza, Persiaran Serdang Perdana,

Seksyen 1, 43300 Seri Kembangan, Selangor, Malaysia, PH-00252 063 411 2721

[email protected]

Author School of Computer Science and Engineering, University, Jalan Innovasi, Technology Park Malaysia, Bukit Jalil, Kuala Lampur,

59000 Malaysia, PH-006 657 567 5676

[email protected]

Abstract: Among various indicators of financial stability, banks‟ non-performing loan assumes critical importance since it reflects on the

asset quality, credit risk and efficiency in the allocation of resources to productive sectors. Non-performing loans has become a concerning

issue for banking sector in recent times. Thusly, this study examines the perception of Somali bankers regarding the determinants of NPLs

of Somali banks using primary information collected from 180 bankers working in twelve Somali banks. The researcher applied multiple

regression and correlation matrix analysis aiming to find out the factors that may have an effect on the non-performing loans of Somali

banks. The bankers perceive that the selected variables of this study have an effect on the NPLs of Somali banks. The empirical results of

the regression and correlation analysis established strong positive and significant relationship between NPLs of Somali banks and the three

variables of unemployment rate, interest rate and inflation rate. The findings also reveal that GDP has a weak but positive and significant

relationship with the NPLs, whilst credit monitoring, political interference and banker‟s incompetence established a weak positive and

insignificant relationship with the NPLs of Somali banks. The study suggests that the bank management should increase their expenditure

for training and development programmes, which would enhance the loan quality monitoring, and debt collection management of the

bankers. It is also suggested to the government and regulatory bodies that the country‟s unemployment rate should be kept low, extra care

should be given to inflation rate and interest rate fluctuations, political interference should be limited or reduced to zero and the country‟s

banking sector need to be revitalised by increasing the commercial banking services capacity of the Central Bank.

Keywords: Non-performing loans, Interest rate, Inflation rate, GDP, Credit monitoring, Political Interference, Banker‟s incompetence

1. INTRODUCTION

Commercial bank is described as a financial institution

whose current operations consist of accepting deposits

from the public and issuing loans. The receiving of

deposits and provision of loans distinguishes „banks‟ from

other financial institutions. The existence of both banks

and non-bank financial institutions in a formal and

organized way is collectively known as the financial

system; it consists of an auxiliary network of institutions

that provide financial services within a given country

(Dhungana and Updhyaya, 2012). To name a few of these

institutions and the functions they perform are: (1) national

banks that issue currency and sets the monetary policy, (2)

investments banks which specialise in capital market

issues and trading, and (3) commercial banks that takes

deposits and gives loans to customers. According to

Karim, Chan & Hassan (2010), the banking sector still

remains the primary form of financial intermediation in

many countries and as such is the biggest channel for the

mobilization of domestic savings and local funds, the main

source of external capital to firms and the key player in a

payment system. Thus, it is undeniable that the banking

sector plays a vital role in the growth and improvement of

the economies (Viswanadham and Nahid, 2015). They

provide loans to individuals; businesses organisations and

governments to support them finance investments and

development undertakings as a means of assisting their

growth in particular and or contributing towards the

economic development of a country in general.

Accordingly, channelling of funds from the surplus-fund

units (SFUs) to deficit-fund units (DFUs) in the form of

loans and advances to numerous sectors of the economy is

the primary function of the commercial banks. As noted by

Kwambai and Wandera (2013), these banks play an

important role in emerging economies where most

borrowers have no access to capital markets. Thus, they

are considered as an intermediary between the depositors

and the borrowers. Hence, banks play a major role with in

the financial system and their soundness and stability are a

main concern for the financial stability of the economic

system of a country (Nkurrunah, 2014). However, some of

the loans given out by the lending institutions

unfortunately become non-performing and eventually

result in bad debts with adverse consequences for the

overall financial performance of the institutions. Loans

become non-performing when it cannot be recovered

within several months after the stipulated time expires

governed by the respective contractual terms and

obligations (Bholat et al., 2016). However, if such assets

do not generate any income, the banks‟ ability to repay the

deposit amount (to the depositors) on the due date would

be in question. Therefore, the banks with such asset would

become weak and such weak banks lose the faith and

confidence of the customers. Ultimately, unrecoverable

amounts of loans are written off as Nonperforming loans

(Mallick, 2010). On the other hand, if the collaterals are

inadequate to cover the costs and at the same time, the

borrowers are insolvent, the banks have to provide a sum

of money as the loan loss provisions from its own capital

that may lead to potential unprofitability (Balgova, Nies

and Plekhanov, 2016). In extreme cases, very high level of

NPLs may lead to an individual bank‟s insolvency and

further to systematic bank failures (Bholat et al., 2016).

Non-performing loans became the source of fear among

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banks and financial sector since the global financial crisis

(GFC) while many developed countries found themselves

having high level of NPLs at respective state banks (Ozili,

2017). Saba, Kouser, & Azeem (2012) are of the opinion

that the issues pertaining to non-performing loans are

necessary to be studied as they are responsible for

numerous financial and economic difficulties of the

developed and developing nations, these issues

incorporate less per capita income, diminishing profits and

financial crisis of the banking sector. Financial System:

The context of Somalia In the context of Somalia, the

banking sector is, in reality, in its early stage. Since 1991,

Somalia has been moving practically without banks. The

Central Bank of Somalia (CBoS) has had very limited

commercial banking services, correspondent relationships

with foreign banks and inadequate supervisory capacity to

oversee the financial sector of the country. Moreover,

foreign banks are also basically absent (Paul et al., 2015).

Hence, customary money exchange frameworks (the

money exchange operators) sprung up to fill the crevices

made by the nonappearance of formal banks. The scenario

is similar to a nation that has worked without an

entrenched central regulatory system or central bank and

without a financial regulation by the authorities; financial

institutions have essentially been working in a vacuum

(Powell, Ford and Nowrasteh, 2008). Therefore, as the

country strives to rebuild its shattered economy, the need

of a viable commercial banking sector is indispensable.

Nevertheless, the banking division in Somalia has realised

some gigantic development in the recent five years. New

banks have been set up and the existing banks have

broadened their administrations to accommodate the

increasing demand of financial services. Some portion of

the force in development have been activated by the

expanded engagement between the government, the

international donors and the financial institutions,

including the 2013 resumption of associations with the

International Monetary Fund (International Monetary

Fund, 2017a). Lately, the country has begun to see some

investment and new business inaugurations from both

local and global businessmen, in spite of the savagery still

overflowing in the nation (Economic Index of Freedom,

2017).

1.1 The extent of non-performing loans in Somalia

In the aftermath of the GFC, non-performing loans became

a major concern for some developing as well as

underdeveloped countries (Akinlo and Emmanuel, 2014).

Especially, in the recent years, the rise of high level of

NPLs and capital provisions by banks to cover the NPLs

in the African region has become major thought for

bankers of the region (see Figure 1.). As it was reported,

close to one third of the loans are non-performing in the

banks of Eastern and Central African countries, which

Somalia is part of geographically (International Monetary

Fund, 2017c). Another report presents that African

Development Bank (AfDB), which has its presence in

Somalia, has very high level of NPLs, as one-fifth of their

loans are dedicated to private sectors (Humphrey, 2014).

Contrarily, some Eastern African countries were reported

to have low level of NPLs earlier in this decade, such as,

Uganda (2.2 percent), Kenya (6.2 percent), Tanzania (6.7

percent) and Rwanda (11.3 percent) (Haniifah, 2015).

However, not a single database (including World Bank

database1) was found to have displayed information on the

level of NPLs in Somali banks. Though the reasons

(factors) that causes rise in NPLs were historically

recognized as the macroeconomic factors, with the

occurrence of the GFC, the idea has broadened to bank-

specific factors as well (Balgova, Nies & Plekhanov,

2016). As the level of NPLs and factors affecting the

NPLs in Somali banking sector are not clear yet, it is vital

to identify the various factors, which may have significant

effect on the loan repayment performance in Somali

banks. Hence, a more particular background check on the

following sub-topics (the selected variables in this study)

would help us to understand the actual scenario and its

possible effect on the loan repayment capacity of the

banks‟ customers in Somalia.


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Figure 1: Percentage of NPLs and capital provisions in the African region

1.1.1 Unemployment rate in Somalia

Somalia is the world‟s 83rd most populous country with

10.6 million people (Jaffer & Hotez, 2016). It is to note

that a large number of the country‟s population are

deprived of the basic needs. Over 730 thousand people in

the country are dependent on survival aid and only 30 per

cent of population has access to clean drinking water.

Additionally, there are more than 1.1 million internally

displaced population and 1 million refugees in the country

(Paul et al., 2015). Along with these, increasing

unemployment has added further problems for the people.

According to UN report, the region is experiencing an

unemployment rate of over 66 per cent (Yusuf, 2015).

Furthermore, in the recent years, Somali MTOs are facing

difficulties in accessing banking services in the developed

countries resulted in resisting them transferring foreign

remittances inside the country. Having faced this high risk,

both banks and MTOs are exiting the sectors, leading to

shortage of funds to the Somali customers living in the

country of whom are dependent on the remittance (Paul et

al.,2015).

1.1.2 Interest rate in Somali banks

In Somalia, interest rate is religiously prohibited, as it is a

100 percent Muslim society. Instead, the country uses

bank profit as a proxy for the interest rate in the market

system to influence the determination of money supply and

demand (Mohamud, 2015). Though no databases was

found to have revealed the trend and level of profit rate in

Somalia, the banks in the Eastern African region (Kenya

and Uganda) were reported to have lower banking profits

due to historically declining interest rates (EY, 2014). The

World Bank development indicators show a historical data

(1990-1996) of declining effective interest rate in Somalia

(see Figure 2); no subsequent reliable information is

available. Hence, it is perceived in the study that Somalia

also may have similarly low profit rate in the current

times.

Figure 1: Effective interest rate in Somalia (1990-1996)

1.1.3 Inflation rate in Somalia

Somalia historically has been facing high inflation and in

the recent years with a high level of fluctuation (Guleid,

Maina & Tirimba, 2015). Since 1961 till 2015, the country

had an average inflation rate of 22.22 per cent, with a

highest 216 per cent of inflation rate recorded in 1990 and

lowest -15 per cent recorded in 2010. In 2015, the rate was

recoded at -3.5 per cent (see Figure 3). The high range of

fluctuation and skyrocketing inflation in the country

resulted from lack of financial regulatory mechanisms and

massive printing of fake Somali shillings over the past

several years. Inflation has further resulted to several


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implications including reduction in the citizens‟ standard

of living and supply of basic needs and overall declining

growth of the economy (Center for Research and

Dialogue,2004).

Figure 2: Inflation rate in Somalia (2006-2015

1.1.4 Gross Domestic Product (GDP)

Somalia‟s GDP is one of the lowest in the world with a

large number of the population being dependent on foreign

aids and remittances. However, in the recent years,

economic growth of the country remained steady (see

figure 4). GDP was calculated as $5.9 billion in 2015 and

projected to reach $6.2 billion in 2016. The country

remains one of the poorest in the world with a GDP per

capita of $450 in 2015 and a poverty headcount of 51.6

per cent. Among the major contributors of GDP, trade

including imports and exports is counted for almost 76 per

cent of the GDP followed by consumption and investment

being 8 per cent of the GDP (World Bank, 2016).

According to the World Bank (2016) in 2015, 32 per cent

of the donor commitments were realized as a result of

many factors, including lower oil prices and other

bureaucratic hurdles. Domestic revenue is still insufficient

to allow the government to deliver services to citizens.

The administrative and security sectors account for more

than 85 per cent of total spending while economic and

social services sectors account for about 10 per cent of

total expenditure. Poor collection capacity, narrow tax

base, absence of the necessary legal and regulatory

frameworks, and lack of territorial control hinder full

revenue mobilization in the country.

Figure 3: Nominal GDP of Somalia (1995-2016)

1.1.5 Credit monitoring in Somali banks

No reliable information on the past or present condition of

credit monitoring in Somalia is available. As information

on the level of NPLs in Somalia is also not available, it

was not possible to estimate the trend or pattern of credit

monitoring among Somali banks. However, the

International Bank of Somalia (IBS) was found claiming

to have a risk management committee that is in charge of

credit monitoring and assessment (Union of Arab Banks,

2017). However, considering the fact that the country

being recovering from its 25 years of civil wars and a large

portion of the loans in the past being dedicated to political

connections and the supervisory capacity of the CBoS

being very weak, the credit monitoring capacity of the loan

managers in other Somali banks have always been

minimal. Furthermore, they were not required to enhance

their monitoring ability and skills. Considering this

scenario, IMF came ahead to facilitate a staff-monitored

programme for the Somali banks starting from May 2016

to April 2017. The programme is aimed at re-establishing

the country‟s macroeconomic stability, building capacity

to strengthen macroeconomic management, improving


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governance and monitoring capacity as well as providing

technical assistance (International Monetary Fund, 2016).

1.1.6 Political interference in Somali banking sector

Somalia's banking system has been vigorously affected by

the political tumult for the last few decades that has

concealed the nation's current history. There are currently

small numbers of banks operating actively in the country.

This, however, is not in stark contrast to pre‐1991

Somalia; the nation had a long history of banking system

instability. According to KPMG (2014) in the late 1970s

and early 1980s the banking system was to a great extent

of financing instrument for public agencies. The

Commercial and Investment Funds Bank was announced

bankrupt, and the Development Bank was not able to

provide new loans in 1989. The budgetary framework fell

into profound emergency and, with the complicity of

degenerate and corrupt authorities, the vast majority of

every day operations of the financial system tumbled to the

bootleg and black market (Timothy, 2016). As noted by

Samatar (2008) prior to the 1991 government collapse,

Somalia had three major banks in addition to the Central

Bank of Somalia, namely, The Somali Commercial Bank,

the Commercial and Savings Bank of Somalia, and the

Somali Development Bank, however, much of the national

banking system collapsed as a result of the civil war.

Generally, credit markets have been to a great extent

pre‐dominated by casual trust‐based group loaning;

assurance of credit‐worthiness and relief of counterparty

risks were both tended to be made through familial and

faction relationships. Pre‐independence provincial powers

had a constrained banking attendance prior to the

administrative crumple, and the central bank additionally

offered a restricted menu of commercial banking services

(Powell, Ford and Nowrasteh, 2008).

1.1.7 Banker’s incompetence at Somali banks

The actual scenario on bankers‟ knowledge, skills

(competencies) and capacities are not clear to the

researcher as no reliable information was found on this

matter. However, a study on the personnel working at the

United Nations Support Office for the African Mission in

Somalia revealed that training and development

programmes have a positive impact on employee

engagement as well as their capacity and competency

improvement (Angela, 2014). Therefore, the need for

T&D programmes to improve bankers‟ competence at the

Somali banks is also essential.

2. STATEMENT OF THE PROBLEM

Issues of non-performing loans have gained more attention

in the past few decades due to their major role in financial

crisis in many parts around the world including Latin

America, East Asia and Sub-Saharan Africa. Saba,

Kouser, & Azeem (2012) are of the opinion that Non-

Performing Loans are necessary to be studied as they are

responsible for numerous financial and economic

difficulties of the developed and developing nations, these

issues incorporate less per capita income, diminishing

profits and financial crisis of the banking sector.

Additionally, due to the increment in non-performing

loans the US economy, which is perceived as a

superpower, has gone under the worst crisis of its history

(Nkurrunah, 2014)As such, many banks have failed and

governments were incurring considerable costs to revive

the banking sector. However, given the relative stability of

the banking system made lately, another form of rivalry

between the banks giving credit to family units and private

ventures has been created. By developing share of bank

advances in these areas, banks are exposed to the dangers

of stuns on macroeconomic factors resulting bad debts and

non-performing loans (Ejigu, 2015). Consequently, in the

context of Somalia academic researchers have always

neglected researches on the non-performing loans of

Somali banks historically. Neither the non-performing

loans itself nor the factors affecting NPLs have come to

forefront in the research arena. Rather other countries

were the preferred locations of many researchers.

Moreover, the existing researches of the recent past

investigated the factors affecting NPLs in scattered

manner. In fact, none of the literatures took account of all

the variables (factors) selected in the current study. The

closest selection of variables were made by Bhattarai

(2014), who did not examine unemployment rate and

banker‟s incompetence. Hassan, Ilyas and Rehman (2015)

ignored inflation rate, unemployment rate and GDP and

Mondal (2016) and Farhan et al. (2012) left credit

monitoring, banker‟s incompetence and political

interference for others to study. Hence, the effect of the

seven selected variables on the NPLs of not only Somali

banks, but also banks of other countries in a single

research paper can be regarded as „extinct‟. Further, in

case of six of the seven selected variables except political

interference, the existing literatures revealed differential

findings in relation to the effect of the variables on NPLs

(notably, all the researchers unanimously found only

positive and significant relationship between political

interference and NPLs). As such, in case of the

relationship between GDP and NPLs, whereas Mondal

(2016), Makri, Tsagkanos and Bellas (2013) and Saba,

Kouser and Azeem (2012) found positive significance,

Messai and Jouini (2013), Farhan et al. (2012) and Nkusu

(2011) found negative significance and even Vatansever

and Hepsen (2013) revealed no significance. Similar

phenomena can be noticed in the relationship of inflation

rate and interest rate with NPLs. These varying results do

not form standard findings like the effect of political

interference mentioned above. Hence, for the effect of the

other variables on NPLs, the concept is still ambiguous

and varies across organisations, industry or country.

Lastly, though there are researchers that adopted the OLS

(Ordinary Least Square) and GMM (Generalized Method

of Moments) method, none of them were found to have

adopted these models for investigating the relationships of

banker‟s incompetence, political interference and credit or

loan monitoring. For instance, having adopted the OLS

model, Bonilla (2012) investigated the effect of inflation,

unemployment and GDP on NPLs, whereas Saba, Kouser

and Azeem (2012) only investigated only the effect of

interest rate on NPLs. On the other hand, using GMM

model, Makri, Tsagkanos and Bellas (2013) examined the

effect of GDP and unemployment and San et al. (2015)

explored the impact of interest rate, inflation rate and

unemployment rate on NPLs. Furthermore, researchers in

investigating the factors affecting NPLs except the



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aforementioned two studies rarely used the GMM model.

As such, considering all above knowledge gaps, the

purpose of this research is to examine the effect of the

seven selected factors (unemployment rate, interest rate,

inflation rate, gross domestic product, credit monitoring,

political interference and banker‟s incompetence) on the

non-performing loans of Somali banks by using

quantitative analysis and through distributing

questionnaires among the sample bankers of the banks.

3. LITERATURE REVIEW

3.1 Defining the Concept of Non-performing Loans

As defined by Balgova, Nies and Plekhanov (2016), a loan

is regarded non-performing when it is overdue or in

default for several months. Likewise, According to Asfaw,

Bogale and Teame (2016), a non-performing loan is a loan

that is in default or close to being default. A loan is said to

be in default when it fails to make the repayments of

principal or interest specified in its loan contract and has

no intention of repaying in the future. When it happens,

from debtor‟s (borrowers) side they face inability to apply

for new loans and from lender‟s (banks) side, they face

funding costs. Thus in order to meet the funding costs;

banks usually foreclose the collaterals provided by banks.

However, if the collaterals are inadequate to cover the

costs and at the same time, the borrowers are insolvent, the

banks have to provide loan loss provisions (a sum of

money) from its own capital, leading to potential

unprofitability (Balgova, Nies & Plekhanov, 2016). Bholat

et al. (2016) also defined that an NPL is a loan, repayment

of which has not been made by a borrower according to

contractual terms and obligations. They argued that an

increase in the number or level of NPL is a bad news for

banking and other financial institutions, as their funding

costs rise with the increase in NPLs. In extreme cases,

very high level of NPLs may lead to an individual bank‟s

insolvency and further to systematic bank failures. In fact,

the concept of NPLs were the most relevant topic in

financial sector during the global financial crisis while

many countries found themselves with high NPLs at the

respective state banks (Ozili, 2017). Cucinelli (2015)

perceived that non-performing loans are crucial to credit

risk of any types of banks, as their lending behaviour is

affected by credit risk negatively. As such, if loans default,

it turns to non-performing loans and credit risk increases,

banks then tend to provide fewer loans to investors.

Therefore, maintaining credit risk to very low level is

essential. Though the reasons (factors) that causes rise in

NPLs were historically recognized as the macroeconomic

factors, with the occurrence of the global final crisis the

idea has changed and gone beyond the economic factors to

bank specific factors such as poor managerial

competencies in evaluating potential debtors and making

decisions on wise loan provision (Balgova, Nies &

Plekhanov, 2016). Awan, Nadeem and Malghani (2015)

found that that the major reasons behind non-performing

loans are ineffective monitoring followed by poor credit

condition, lack of business management competency and

unwillingness. They further asserted that those who made

up the most portions of non-performing loans were

farmers and traders. Farmers face loan default, probably

because they are often the victim of natural calamities like

droughts or heavy rain fall, cyclones and landslides for

which they cannot collect the repayment amount at their

expected time. Traders also face loan default due to their

exposure to foreign exchange rate risk and (any) loss in

the businesses. Chimkono, Muturi and Njeru (2016)

reinstated that non-performing loans are major factors of

banks‟ income source and if the NPLs increases, it

adversely affect the performance of banks.

3.2 Defining the Concept of Unemployment Rate

Kantar and Aktas (2016) provided the simplest definition

of unemployment; they defined it as the situation of being

without job for a certain short period of time. Musai and

Mehrara (2014) stated that unemployment is when some of

the population of a country are unable to find a suitable

job. According to Sileika and Bekeryte (2013),

unemployment is defined as the economic condition

whereby working-age individuals are unemployed for at

least the last four weeks at a certain point of time and still

in need of a job. Hence, unemployment rate is the measure

of the unemployed people for at least four weeks in a

country. Additionally, unemployment rate is also the

measure of economic health, as lower unemployment rate

indicates better economic situation in a country. There are

also particular definitions of unemployment provided by

different organisations and groups. For instance,

International Monetary Fund (IMF) defines unemployment

as an annual percentage measurement of labour force that

cannot find a job. International Labour Organisation (ILO)

defines that unemployment is the situation in which

people, aged 16 and above, who are without job or

continuously searching for job for the last four weeks and

available to join work in the next two weeks (Aqil et al.,

2014). Furthermore, according to the U.S. Bureau of

Labour Statistics (BLS), unemployed people are those

who are completely unemployed, discouraged to work,

marginally attached to the labour force, working part-time

but want to work full-time and actively looking for jobs

(Thies, 2017). Unemployment impacts on society and

economy in different ways. It decreases the knowledge and

skills of the labour force while increases the costs of

producing labour force. According to the economic theory

of crime, unemployment causes increased level of crimes

such as drug abuse, robbery and smuggling (Musai &

Mehrara, 2014).

3.3 Relationship between Unemployment rate and

Non-performing Loans

In terms of relationship between unemployment rate and

NPLs, some studies found significant relationship between

unemployment rate and non-performing loans, while few

others supported the contrary view. Makri, Tsagkanos and

Bellas (2013) examined the non-performing loans of the

banks in Eurozone and concluded that the non-performing

loans have strong correlation with various macroeconomic

factors such as gross domestic product and unemployment

rate. Balgova, Nies and Plekhanov (2016) reported that

their findings in terms of relationship between economic

condition (such as, unemployment rate) and non-

performing loan supported the theoretical view of the

earlier literatures, which indicates a positive relationship

between the two. Bonilla (2012) applied the Ordinary



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Least Square (OLS) model in examining the factors

affecting NPLs in two European countries, namely, Spain

and Italy. The study considered credit growth, wage,

inflation, unemployment and GDP as the independent

variables among these, only unemployment, wage and

GDP were found to have significant effect on NPLs.

Furthermore, by applying the „6-months lag‟ model for

Spanish banking sector and ‟12-months lag‟ model for

Italian banking sector, the study found that unemployment

rate affects more significantly on Spanish banks than

Italian banks. Mileris (2014) studied the non-performing

loans in the commercial banks of the EU countries and

proved the view that banking non-performing loans are

heavily dependent on economic changes such as, rise in

unemployment. As such, they have a positive relationship

between each other. The study implied that the rise of

NPLs in 2010 is considered to be caused by the increasing

level of unemployment in 2009 in the EU countries.

However, the study by Mondal (2016) on the non-

performing loans of 22 commercial banks in Bangladesh

found that macroeconomic factors such as, unemployment

rate is positively but insignificantly correlated with the

non-performing loans in Bangladeshi banks. He concluded

that though he found statistically insignificant relationship

between unemployment rate and non-performing loans, he

also found that non-performing loan would still increase

by 1.18 per cent, if the unemployment rate increases by 1

per cent, as both of them were positively correlated.

Likewise, Kurti (2016) also found insignificant but

positive relationship between unemployment rate and non-

performing loans in Albanian banking sector.

Additionally, contrary to all above, Klein (2013) revealed

insignificant relationships between unemployment rate and

NPLs in the Central, Easter and South Eastern European

banks. The study used panel data model for the period of

1998 to 2011 and examined the potential impact of GDP

growth, unemployment and inflation on NPLs. However,

only the unemployment turned out to be insignificant.

Bhattarai (2014) examined the possible effect of energy

crisis, lack of timely budgetary expenditure, government

and instable political environment, monitoring and

evaluation of loans, banker‟s perception, interest rate,

unemployment rate and exchange rate on the non-

performing loans of Nepalese commercial banks and also

concluded that unemployment rate is not much important

variable to influence NPLs in Nepal.

3.4 Defining the Concept of Interest Rate

Khon Maynard Keynes postulated one of the most

remarkable concepts of interest rate in his theory of

interest rate; more specifically in his work, “The General

Theory of Employment, Interest and Money” (1936).

According to Keynes, interest rate determines the level of

employment, affects money supply and investment

activities in the economy. Keynes provided three distinct

definition of interest rate. Firstly, it is the compensation or

reward to the lenders for not-hoarding the money with

them. Secondly, it is the price that balances up the desire

to hold cash within lenders and the supply of cash to

borrowers. Thirdly, it is a measure of reluctance to give

out money in liquid form to the borrowers (Appelt, 2016).

Khan and Sattar (2014) provided the simplest definition of

interest rate in their paper. As they stated, interest rate is

the amount of money or fee paid by someone to someone

else for using the latter‟s money. It is paid by debtor

(investors) when money is borrowed and received by

creditor (banks) when money is lent. It is the additional

amount of money charged by banks on the principal

amount of a debt security or loan contract to the loan-

receiving investors. It is expressed in the unit of

percentage on annual basis. Thus, an interest rate can be

easily recognized and understood from its standard figure

used worldwide. Based on Keynes‟ theory of interest rate,

any changes in interest rate tremendously affect

investment activities. As such, rising interest rates increase

the investment cost, since bond prices rise when interest

rates rise; and when bond prices are high, investors are

reluctant to borrow from banks with high price and high

interest rate. Thus, the demand for investment is reduced

when interest rate rises. However, when interest rate falls,

the scenario is completely opposite. Bond prices also fall

accordingly and investors become more interested to

borrow with low price and low rate of interest. This

situation stimulates investment (Khurshid, Wuhan &

Suyuan, 2015). The above situation can be explained in

terms of interest rate spreads. Interest rate spreads is the

difference between the interest rate charged to borrowers

and the rate paid to depositors (Were & Wambua, 2014).

When interest rate falls, the interest rate spreads increases

in turn and investors feel reluctant to save and rather spend

more on investment. However, when interest rate rises, the

interest rate spreads decreases and it makes the investment

decision difficult Khan and Sattar (2014). Interest rates

can be either short-term or long-term. Example of a short-

term interest rate that matures within a year is federal

funds and example of a long-term interest rate that matures

in more than one or more years is yields on private bonds

or Treasury securities. Short-term interest rate usually has

more impact on aggregate demand than its counterpart,

since investors‟ perceptions are normally based on short-

term forecast and demand (Kiley, 2014). Interest rates can

also be either fixed or variable types. Fixed interest rates

are unchangeable regardless of the situation and interest

rate movement and therefore, interest rate risk remains

with the lenders. However, variable interest rates are

adjusted concurrently to hedge against the uncertain future

interest expense faced by borrowers (usually medium to

large firms) and therefore, the interest rate risk is

transferred to the borrowers (Athavale & Edmister, 2011).

3.5 Relationship between Interest Rate and Non-

performing Loans

In case of the association of interest rates with non-

performing loans, researchers were of different views.

Whereas some of them found to have a positive

relationship, some others found negative relationship. Few

others also concluded an insignificant relationship

between the two variables. Warue (2013) examined the

impact of several macroeconomic factors (such as, lending

interest rates, real GDP, GDP per capita, government

expenditure and exports and imports) on non-performing

loans of different bank size (large, medium and small

banks) from 1995 to 2009, employing both pooled and

fixed type of panel econometrics approach. The study

found that lending interest rate has a positive and

significant relationship with non-performing loans in



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larger and medium banks; however, no significant

relationship with small banks‟ non-performing loans.

Jameel (2014) conducted a 10 years long study from 2000

to 2010 also using time-series econometric model aiming

to examine the relationships of GDP, weighted average

lending rate, loan‟s maturity time period, capital adequacy

ratio and credit deposit ratio with the non-performing

loans in Pakistani banking sector. The study concluded

that whereas, determinants such as, GDP growth rate and

capital adequacy ratio negatively affect non-performing

loans, weighted average lending (interest) rate positively

affects the non-performing loans in Pakistan. On the

contrary, Chege (2014) studied on the non-performing

loans of 39 commercial banks in Kenya and revealed that

interest rate has a strong but negative linear relationship

with NPL. As such, higher interest rate will result into

higher non-performing loans in Kenyan banks. In relation

to the duration of interest rate‟s impact on non-performing

loan, Sheefani (2015) asserted that macroeconomic factors

such as, interest rate and inflation rate determines NPLs in

the long run in the Namibian banking sector. However,

since the macroeconomic environment in the country is

very critical, he suggested for continuous monitoring on

the performance of non-performing loan and interest rate

fluctuation. However, Islamoglu (2015) opposed the view

and commented that the relationship between commercial

loan interest rate and non-performing loan is rather short-

term in terms of causality relationship. The study was

conducted based on time-series data from 2002 to 2013

using the VARM (Vector Autro-Regression Model) and

VECM (Vector Error Correction Model). The VARM was

used to test multivariate regression among the variables

and the VECM was used to identify more than one

integration relationship between time-series. Furthermore,

the Granger Causality Test was also used to test the long-

term effect of the selected independent factors.However,

Bhattarai (2014) revealed that interest rate was one of the

variables that was found to have insignificant relationship

with NPLs in Nepalese banking sector. The other variables

used in this study were inflation rate, unemployment rate,

exchange rate, political interference and ownership, loan

monitoring and evaluation, energy crisis, budgetary

expenditure, growth rate of GDP and borrower‟s honesty.

The study used macroeconomic theory as the conceptual

theory, which states that expansionary situation and

declining situation create optimistic environment and

pessimistic environment respectively in the business.

Hassan, Ilyas and Rehman (2015) examined the effect of

various bank-specific factors such as, interest rate, credit

monitoring, credit assessment and rapid credit growth and

social factors such as, banker‟s incompetence and political

interference on NPLs and revealed that only interest rate

had a weak significance on NPLs of Pakistani banking

sector in comparison to the other variables.

3.6 Defining the Concept of Inflation Rate

Inflation is defined as the persistent rise in the general

level of prices (Semuel & Nurina, 2015). However, not

every rise in the price level is regarded as inflation. Only if

the rise in the price level is constant, sustained and

enduring, it is regarded as inflation (Bayo, 2011).

Furthermore, there are more conditions to denote a rise in

the price level as inflation such as, the price level rise

should affect almost all the commodities and should not be

temporal. As such, if the rise in the price level only affects

a single commodity or even occurs for a single day, it is

not regarded as inflation (Kanwar, 2014). Inflation is

measured by inflation rate, generally in the unit of annual

percentage change in the general price index (usually

CPI). Inflation rate is defined as the rate at which the

general level of prices for goods and services rise annually

(Kanwar, 2014). Inflation has both positive and negative

impacts on the economy. From the aspect of positive

impacts, central bank can adjust nominal interest rate to

mitigate future recessions and increase investment in non-

monetary capital projects. However, the negative impacts

are more extensive, with the rise of inflation, the real value

of money decreases, investment and savings may even

become discouraged among investors and they may begin

hoarding money perceiving that the prices would increase

in the future (Uwubanmwen & Eghosa, 2015). Inflation is

measured using three approaches, namely, the Gross

National Product (GNP) implicit deflator, the Consumer

Price Index (CPI) and the Wholesome or Producer Price

Index (WPI or PPI) (Semuel & Nurina, 2015). There are

also several causes of inflation, revealed by academic

literatures. Based on the drivers, inflation is denoted by

particular names. Excessive growth in money supply is the

main driver of inflation, as most of the economists agree.

Additionally, excessive aggregate demand causes

inflation, which is known as demand-pull inflation. When

inflation arises from upward pressure of production costs,

it is regarded as cost-push inflation. However, while

inflation arises from inefficient production, distribution or

marketing system in the economy, it is denoted as

structural inflation (Kanwar, 2014; Bayo, 2011).

3.7 Relationship between Inflation Rate and Non-

performing Loans

Similar to the relationship of interest rate with NPLs,

researchers also found both positive and negative

relationship with non-performing loans. Farhan et al.

(2012) conducted a perception study of the bankers in 10

Pakistani banks on the effect of macroeconomic factor on

non-performing loans. They examined the possible

association of inflation, unemployment, GDP growth,

exchange rate, interest rate an energy crisis with NPLs.

The study revealed that along with all other variables

(except GDP growth), inflation had a positive and

significant relationship with non-performing loans in

Pakistan. They stated that economic determinants of NPLs

could be derived using life-cycle consumption model. The

model argues that low-income borrowers have more

probability to be default on loans as they have more

chances to be unemployed and are unable to repay loans

with high interest rates. Mehmood, Younas and Ahmed

(2013) studied on the macroeconomic effect on non-

performing loans in Pakistani commercial banks and

concluded that inflation rate is positively significant in

relationship with non-performing loans. As such, if 1 unit

decreases inflation rate, the NPL will as well decreased by

2.59 units (2.6 times). However, Abebrese, Pickson and

Opare (2016) found a negative but significant relationship

between inflation and loan performance. As such, if

inflation increases, it reduces loan performance. In another

word, the level of non-performing loans increases with the



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rise of inflation (positive relationship). However, Anjom

and Karim (2016) reported a negative but significant effect

of inflation on non-performing loans in Bangladeshi

banking sector. As such, an increase in inflation rate

would decrease the level of NPLs. Though both these

studies found contrary findings, Khemraj and Pasha

(2009) uniformed these views. According to them, high

inflation in the current period would reduce the level of

non-performing loans; however, the high inflation from the

previous period would cause increment in the level of non-

performing loans. As such, he found both negative and

positive relationship between inflation and non-performing

loans in Guyanese banking sector, but in two different

time perspectives. Some researchers also found

insignificant relationship between inflation rate and NPLs.

Gezu (2014) found that inflation rate (INFR) has negative

and insignificant relationship with non-performing loans in

Ethiopian commercial banking sector. It indicates that

though rising inflation may reduce the amount of NPLs,

the relationship is not very clear or established. Bhattarai

(2014) revealed that inflation rate has insignificant

relationship with NPLs along with the other variables,

which are interest rate, unemployment rate and exchange

rate in the Nepalese banking sector. Likewise, the study by

Ali and Iva (2013) who examined the bank-specific factors

on NPLs in the Albanian banking sector revealed that

inflation rate had an insignificant effect on NPLs. They

further found that real exchange rates and loan growth rate

have a positive relationship with NPLs, whereas GDP

growth and interest rate have negative association with

NPLs.

3.8 Defining the Concept of Gross Domestic Product

Gross Domestic Product (GDP) is the basic measure of the

economic status, performance and development of a

country (Semuel & Nurina, 2015). GDP is defined as the

total market values of all officially recognised final goods

and services in a country in a given period of time (year)

(Rahman, 2013). As Desmond et al. (2015) stated, GDP

includes the products and services produced and delivered

respectively in a country both by nationals and non-

nationals residing in the country. However, in

accumulating the market values, GDP does not simply add

up quantities of the goods and services directly. Moreover,

the values of goods and services produced by nationals at

foreign country do not fall in the scope of GDP. The

values of non-market goods, illegal goods and leisure

activities also are not included in the scope of GDP. In

addition, no intermediate goods are considered in

calculating GDP, rather only final goods. GDP benefits us

in several ways. Firstly, it is used to measure the

healthiness of a country. Secondly, it is used to determine

the standard of living of individuals in the country.

Thirdly, it is used to determine the growth prospects of the

economy of a country. Fourthly, it is used to compare the

size of economies from all over the world. Furthermore, it

is also used to compare the growth rate of world

economies (Desmond et al., 2015). GDP is generally

categorised into two types, namely, Real GDP and

Nominal GDP. Real GDP does not take account of the

rising prices and hence, it ignores any possibility of

inflation. On the other hand, Nominal GDP assumes that

prices of goods and services may increase. Hence, it

increases while an economy faces inflation or rising

prices. The relationship between the two types is Real

GDP = (Nominal GDP / price index) X 100. GDP is also

expressed in terms of GDP per capita, which is the total

output of the country per person (Mallett & Keen, 2012).

There are three methods to measure GDP, namely

Expenditure method, Value-Added or Production method

and Income method. The foremost method takes into

account of consumption (C), investment (I), government

expenditure (G) and gross exports (X) and imports (M).

As such, the equation to find GDP according to the

expenditure method stands as GDP = C + I + G + (X – M).

Under the income method, GDP is calculated as GDP =

Compensation of employees + Rent + Interest +

Proprietor‟s Income + Corporate Profits + Indirect

business taxes + Depreciation + Net foreign factor income.

Net foreign factor income is the difference between

income earned by the rest of the world and income earned

from the rest of the world. Moreover, under the value-

added method (also known as output method or net

product method), GDP is calculated as GDP = value of

sales of goods – value of purchase of intermediate goods

to produce the goods sold (Konchitchki & Patatoukas,

2014).

3.9 Relationship between Gross Domestic Product and


Similar to the characteristics of the association of both

inflation rate and interest rate with NPLs, researchers also

revealed both positive and negative relationship of gross

domestic product (with GDP) with NPLs. It is hereby

important to note that GDP in this study can be

interchangeably referred to the GDP growth rate in on

other academic literatures. In case of positive and

significant relationship between GDP and NPLs indicate

that the level of non-performing loans rise with the rise of

GDP growth rate and vice versa. Mondal (2016) examined

the effect of four macroeconomic variables (GDP,

unemployment, inflation and interest rate spread) on the

NPLs of 22 commercial banks in Bangladesh applying the

time-series data from 2005 to 2014. The study revealed

that whereas both GDP and unemployment is positively

significant to NPLs, interest rate and inflation rate behaves

otherwise. Additionally, Makri, Tsagkanos and Bellas

(2013) examined the factors affecting non-performing

loans of the banks in 14 out of 17 Eurozone countries and

concluded that the non-performing loans have strong

positive correlation with various macroeconomic factors

(annual percentage GDP growth rate, unemployment and

public debt) and bank-specific factors (capital adequacy

ratio, return on equity and rate of previous year‟s NPLs).

Their study employed the Generalised Method of the

Moments (GMM difference) estimation method, as it

provides unbiased and consistent results. Saba, Kouser and

Azeem (2012) investigated the effect of Real GDP per

capita, interest rate and total loans on the NPLs of US

banking sector using a panel data from 1985 to 1997 using

OLS regression model. The study revealed that Real GDP

per capita has the strongest and significant relationship (68

percent) with NPLs followed by interest rate (40.7

percent) and total loans (28.1 percent). However, majority

of the researchers revealed that GDP affects NPLs

significantly but negatively. It indicates that declining



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growth rate of GDP would increase the amount of NPLs.

As such, Messai and Jouini (2013) investigated on the

association of both macroeconomic variables (GDP

growth rate, interest rate and inflation rate) and bank-

specific factors (loan loss reserves to total loans ratio,

return on assets and profitability of bank assets) with the

NPLs of 85 banks in three European countries, namely,

Italy, Greece and Spain from 2004 to 2008. The study

found that while GDP growth rate and profitability of bank

assets shows significant negative relationship with NPLs,

the remaining variables show contrary or positive

relationships. Moreover, Farhan et al. (2012) also found

significant negative relationship of GDP growth with

NPLs of Pakistani banks, whereas the other variables

(interest rate, inflation, exchange rate and energy crisis)

were found to have positive relationships. Additionally,

Tsumake (2016) studied on the Botswana banking sector

using 10 years of data and revealed that whereas GDP

growth and inflation have a negative and significant

relationship with the NPLs, the other two variables, real

interest rate and unemployment rate affect significantly

and positively on the NPLs of Botswana banks. Nkusu

(2011) conducted a study to investigate the linkage

between non-performing loans and macroeconomic

performance of 26 advanced economies from 1998 to

2009 and concluded that poor macroeconomic

performances such as slower GDP and higher

unemployment rate could be associated with increasing

non-performing loans in advanced economies. The study

adopted a panel vector autoregressive (PVAR) approach

(as it helps to identify how the variable respond to the

shock affecting each variable) and relied upon the impulse

response functions (IRFs) to identify the significance of

the response of each variable over a four-year forecast

period. There are also very few studies, which found

insignificant relationships between GDP and NPLs.

Vatansever and Hepsen (2013) investigated the Turkish

banking sector and revealed that the variables such as,

Turkey‟s GDP growth, the Euro Zone‟s GDP growth, debt

ratio, loan to asset ratio, consumer price index, real sector

confidence index, USD/Turkish Lira rate and the volatility

of the Standard & Poor‟s 500 stock market index have

insignificant effect on NPLs.

3.10 Defining the Concept of Credit Monitoring

Credit Monitoring generally refers to information

gathering for not only new loans but also evaluating the

information gathering process for existing loans

(Instefjord & Hiroyuki, 2015). Regular monitoring of

loans involves daily, weekly or fortnightly reviewing the

borrowers‟ position in terms of transactions, fund

utilisation and fund diversion (Alam, Haq & Kader, 2015).

The main rationale of credit monitoring is to detect signs

of difficulty in repaying loans by the borrowers and

minimize potential losses (Ugoani, 2016). Loan

monitoring is an integral part of banks or bank-specific

(internal) factor which aims to ensure credit facilities

remain within the performing loan circle or simply, credits

are collected in not more than 90 days of the loan maturity

or specific credit collection period according to the

particular credit terms. Hence, credit monitoring takes

place to ensure full compliance of loan agreement from the

borrowers as to whether the loan is being used for eligible

purposes, the quality of the loan is being maintained or the

repayment sources of the borrowers are protected so that

no unexpected default occurs (Idris & Nayan, 2016).

There are several tools used by banks in monitoring their

loans such as, relationship management, transaction

account monitoring, loan stress testing, internal credit

scoring and ratings. Among all of these, credit bureau

ratings have been suggested as one of the timely method as

it has more capacity of forecasting the likelihood of loan

default (Nakamura & Roszbach, 2013).

3.11 Relationship between Credit Monitoring and


Most researchers indicated poor credit monitoring to be

the cause of increased non-performing loans. In another

word, they found strong negative relationship between

credit monitoring and NPLs. As such, the weaker the

credit monitoring becomes, the higher the non-performing

loans rise up. Asfaw, Bogale and Teame (2016)

investigated on the major factors affecting the NPLs of

Development Bank of Ethiopia (DBE) using the samples

from 77 NPLs. The independent variable they selected

was bank-specific factors (credit assessment, credit

monitoring, high interest rate, credit size and lax credit

terms) and customer-specific factors (loan diversion,

wilful defaulting and poor credit culture of customers). As

the result of their study revealed, along with all the above

factors, credit monitoring was identified as a major cause

of NPLs. Abdeta (2015) also studied on the Development

Bank of Ethiopia (DBE) and asserted a similar view that

credit monitoring and follow ups directly affect NPLs of

the bank. The study revealed that poor monitoring and

follows up increases NPLs by 31.8 percent compared to

strict monitoring and follow up. As such, the probability of

NPLs is high when the banks undertake poor monitoring

and follow up. However, the study also pointed out that

even if a loan is poorly assessed, it may avoid loan default

by adequate monitoring. Similarly, Geletta (2012) also

revealed that failed loan monitoring, poor credit

assessment, aggressive lending, underdeveloped credit

culture, borrower‟s lack of knowledge, willful default and

fund diversion, lenient credit terms and excessive

financing by banks are the main causes of loan default of

both the state-owned and private banks in Ethiopia.

Hassan, Ilyas and Abdul Rehman (2015) examined the

effect of bank-specific factors (credit monitoring, credit

growth, interest rate and credit assessment) and social

factors (political interference and banker‟s acceptance) on

the NPLs of Pakistani banks. The findings of their study

showed that except interest rate, all other factors have

significant effect on NPLs. As such, lack of credit

monitoring is also a major factor to cause high NPLs in

Pakistan. There are also some other literatures that

explained the above findings in the light of hypotheses of

„bad luck‟ and „skimping‟ postulated by Berger and

DeYoung in 1997. For instance, as Rajha (2016) Abdeta

(2015) and Klein (2013) reinstated, the „bad luck‟

hypothesis argues that bad management that is

characterised by poor credit scoring, loan underwriting,

monitoring and controlling skills often pose the probability

to have increased NPLs. Accordingly, the „skimping‟

hypothesis argues that high cost efficiency affects the

NPLs. Banks often try to achieve short-term profits by



215

reducing the costs on monitoring. Though it may help the

banks in short-term, they pose themselves in risks in the

long-term. Thus, the NPLs grow high in the long run when

the banks dedicate less effort in ensuring loan quality.

However, there are researchers who found contrary

findings. Joseph et al. (2012) examined the factors

affecting NPLs of CBZ Bank Limited in Zimbabwe and

revealed that internal factors such as poor credit

monitoring, insider loans, inadequate risk management,

weak credit analysis and poor credit policy have very

limited influence on the NPLs, rather external factors such

as government policy, natural disaster and borrower‟s

integrity significantly affects the NPLs in Zimbabwe.

Defining the Concept of Political Interference Shen and

Lin (2012) defined political interference as the situation in

which executives of government banks are substituted

within 12 months of the political elections in a country.

They are of the view that financial performance declines

when a bank is politically involved. As such, a politically

influenced bank displays the worst performance compared

the non-political and private banks. They also argued that

developing countries are largely affected by political

interference than developed countries. Moreover,

withdrawing any political interference would lead a bank

to revive its profitability and performance. According to

Kumar (2014), political interference causes significant

costs to economy. A political interference may be imposed

in the form of regulating banks‟ lending decisions to

certain sectors. Hence, if government banks are regulated

to stop lending to certain sectors like manufacturing firms,

those firms may face significant costs and the banks at the

same time are to make adequate decision to mitigate the

situation and increase the amount of loans to other

permitted sectors. Ozaki (2014) asserted that government-

influenced state banks‟ operations in Nepal are politically

interfered in many ways. For example, lending decisions,

planning, budgeting as well as appointment, transfer and

promotion of a chief executive officer and other

executives may be under strict political intervention from

time to time. The study revealed that even in the case of a

politically-influenced bank which was being managed by

external management after restructuring takes place, the

Nepalese government still continued to control its lending

decisions by offering loan waivers and lending to

politically priority sectors. Agarwal et al. (2016) defined a

politically-influenced bank as only if it has 45 percent or

more loans provided to politically connected people or

businesses. They also found that politically connected

loans increase the loan volume of commercial banks by

0.7 percent and decrease the interest rate spreads by 5 to 6

percent, lowering down the banks‟ profitability. They

perceived that political interference may take two forms:

one is by controlling lending decisions and the other one is

by appointing a politically involved person as the

chairman of the bank‟s board management. For example, a

politically engaged bank would offer favourable loan

terms to its politically connected clients with longer

maturities, larger loan quantities and lower collateral

requirements. Furthermore, if a politically engaged person

becomes the chairman of the bank‟s management board,

the recruitment of unskilled, unexperienced and politically

favoured employees increases in the bank and decisions on

almost all the aspects of the bank are interfered by the

chairman.

3.12 Relationship between Political Interference and


As reviewed below, all the recent studies unanimously

revealed the existence of a significant effect of political

interference on non-performing loans of banks around the

world. As such, Hassan, Ilyas and Abdul Rehman (2015)

revealed significant relationship between political

interference and NPLs from the Pakistani banking

perspective. They added that when terms of the loan

advancements are compromised and when loans are being

disbursed under political pressures to politicians it leads to

even higher non-perming loans. Similarly, having

investigated on the Nigerian state-owned banks, Adeyemi

(2011) discovered that political interference has an impact

on non-performing loans. As the study revealed, most

public-owned financial institutions in Nigeria were

politically influenced to grant loans and overdrafts to

politicians, which eventually became bad debts and

remained unpaid. Agarwal et al. (2016) examined the

political interference on the Mexican commercial banks

and revealed that though the loan terms are better for

political loans rather than non-political loans, they often

result to higher default rates. In fact, the default

probability rises by 12 percent upon provision of a loan to

a politically involved client. Likewise, Bhattarai (2014) in

her study on the Nepalese commercial banks indicated that

instable political environment increases the level of NPLs.

Collins and Wanjau (2011) also revealed that political

interference was the major contributor to the bad loans in

the Kenyan banking sector that took place in the form of

insider lending (provision of loan given to insiders of the

banks who are politically engaged). For example, most of

the large local bank failures such as, Continental Bank and

Pan African Bank befallen due to extensive insider lending

to politicians.

3.13 Defining the Concept of Banker’s Incompetence

Incompetence, in general, refers to the underlying

knowledge, skills and attributes that enable people to

deliver effective and improved job performance (Brits &

Veldsman, 2014). Besides these, competence may refer to

expertise and emotional intelligence all of which are key

elements in improving a banker‟s competence (Tejada,

2015). In fact, competence is regarded as one of the

crucial component of professionalism in banking industry.

According to Bashah et al. (2012), competencies can be of

two types: essential competencies and differentiating

competencies. Examples of the prior one is knowledge,

skills and abilities that are easy to develop. However, the

latter one is difficult to develop, such as, self-images,

motives and social roles. They further asserted that the

differentiating competency enables a banker to provide

superior performance being differentiated from the

average performance. Julius (2015) reinstated seven

characteristics of managerial competencies in banks, such

as, technical (job-related) skills, interpersonal skills,

conceptual skills (to understand the goals of the banks),

diagnostic skills (to be able to assess each individual

borrower), communication skills, decision-making skills

(to be able to accurately decide on who to provide loans)



216

and time management skills (to facilitate the debt

collection process effectively). As banker‟s competence

has been widely discussed with its importance since the

global financial crisis, the researcher was also of the view

that banker‟s competence helps to prevent loan defaults,

financial losses and bank failures to many extent.

3.14 Relationship between Banker’s Incompetence and


While referring to banker‟s incompetence, some

researchers denoted it as poor management, bad

management or unskilled management. Though there are

few researchers who found both significant and

insignificant relationships between the banker‟s

competencies and the level of non-performing loans, this

relationship theory has been disregarded by majority of the

researchers historically or even in the recent times.

Hassan, Ilyas and Rehman (2015) in their study on the

Pakistani banks found significant relationship between

banker‟s incompetence and NPLs. They found that about

83 percent of the bankers commented that adequate and up

to date training plays key role in making effecting loan

decisions. In fact, this is the only study that used the term

„banker‟s competence‟ in the recent times aiming to find

its effect on NPLs. According to them, bankers with high

qualification are in a place to judge the credibility of a

borrower more effectively, contributing to lower loan

default cases. They placed banker‟s incompetence under

the category, social factor rather than the bank-specific

factors as they argued that competent bankers can

understand and communicate their customers to convert

their NPLs into performing loans. Likewise, Islam and

Nishiyama (2016) examined the banks in four South Asian

countries, namely, Bangladesh, India, Pakistan and Nepal

and revealed that besides, inflation, GDP growth, bank

size and cost inefficiency, bad management also explains

the level of NPLs in the banking sector. Similar findings

were revealed by Iuga and Lazea (2012) who asserted that

incompetence of the banking personnel is the main cause

of increasing NPLs. They further pointed out few

characteristics of the incompetency, such as, inappropriate

interview, inadequate financial analysis and monitoring of

the clients as well as inaccurate documentation and

acceptance of poorly guaranteed loans. However, Quadt

and Nguyen (2016) revealed that bad management does

not have any significant relationship with NPLs, rather

only external factors explains the level of NPLs. They

examined the relationship between efficiency and NPLs at

40 Nordic (Denmark, Finland, Norway, Sweden and

Iceland) banks by applying the Granger-causality

technique. As the study concluded, the level of NPLs is

increased with the increase in external or macroeconomic

factors (inflation, interest rate and GDP) rather than any

internal factor of banks.

3.15 Research Gaps

Gap identification is vital to a research project, which

involves the existence of a practical problem to be

investigated, clear contradiction with and among the

existing literatures (Dissanayake, 2013). As such, for a

research gap or knowledge gap to be justified, the reasons

might be that the chosen research site has always been out

of others‟ research focus, not many researchers

investigated the selected variables previously, some of the

researchers have made their researches inconclusive by

recommending future research on the topic or particular

variables or when there are evident contradiction among

the previous literatures in terms of the causality-effect

relationship. In the following paragraphs, the above

rationales are discussed in separate points. Firstly, though

quite a number of researchers (Asfaw, Bogale and Teame

(2016), Tsumake (2016), Abdeta (2015), Chege (2014),

Gezu (2014) and Joseph et al. (2012)) have come ahead to

investigate the factors affecting NPLs in some countries in

the African continent, not a single existing research of the

recent or far past focused on the non-performing loans in

the Somali banking context. Hence, this research may

focus on the Somali banks‟ NPLs and possible associated

the factors to fill the gap precisely. Secondly, none of the

reviewed literatures investigated effect of all the variables

(selected in this study) in a single research paper. Though

Mondal (2016), Ilyas and Abdul Rehman (2015),

Bhattarai (2014) and Farhan et al. (2012) took account of

four to five of these variables in their studies, other

researchers were not even closer to this consideration

(selection). Therefore, considering the seven independent

variables (possible factors) in this study, it will be adding

new information to the research database. Thirdly, some of

the researchers made their studies inconclusive by

recommending few variables to be studied in the future

researches. It is found that not many researchers examined

the effect of credit monitoring, political interference and

banker‟s incompetence on NPLs, rather they suggested

these to be studied in future. For instance, Sheefani (2015)

suggested that the effect of credit monitoring needs to be

studied in the future. Fourthly, in case of political

interference, no contrary literature stating insignificant

relationship was found. Hence, if the current study finds

insignificant effect of political interference on NPLs, it

will be a unique information in the field of non-performing

loans and banking. Last but not least, all the variables

except political interference were found to have significant

or insignificant relationship with non-performing loans.

For instance, whereas Balgova, Nies and Plekhanov

(2016), Mileris (2014), Makri, Tsagkanos and Bellas

(2013) and Bonilla (2012) found significant relationship

between unemployment rate and NPLs, Mondal (2016),

Kurti (2016), Bhattarai (2014) and Klein (2013) found

contrary or insignificant relationship. Likewise, Asfaw,

Bogale and Teame (2016), Abdeta (2015), Geletta (2012)

revealed significant relationship, whereas Joseph et al.

(2012) concluded insignificant relationship between credit

monitoring and NPLs. Hence, the identified gap is whether

significance or insignificance is the real phenomenon in

terms of the causality of each variable with NPLs in the

context of Somali banks. This study is directed to be the

first-hand and reliable source of the above information

4. DATA ANALYSIS AND FINDINGS

4.1 Pearson Correlation

According to Pallant (2013) Pearson correlation

coefficient is a measure of the strength of a linear

association between two variables and is denoted by (r).

Basically, a Pearson correlation attempts to draw a line of



217

best fit through the data of two variables. It takes the range

values from +1 to -1, the sign out the front indicates

whether there is a positive correlation or negative

correlation. Therefore, a value of 0 indicates that there is

no association between the two variables while a value of

1 indicates a positive association; that is, as the value of

one variable increases, so does the value of the other

variable, and a value of -1 indicates a negative association;

that is, as the value of one variable increases, the value of

the other variable decreases. Therefore, though different

authors suggest different interpretations, for this research

the strength of the relationship is based on Cohen (1988)‟s

guidelines in determining the strength of the correlation

between the variables as shown in table one below.

Table1: Cohen's 1988 Guidelines

1 Small r = .10 to .29

2 Medium r = .30 to .49

3 Large r = .50 to 1.0

The following tables illustrate the correlation coefficient

between the dependent variable (Non-performing loans)

and the independent variables of this study (unemployment

rate, interest rate, inflation rate, GDP, credit monitoring,

political interference, banker‟s incompetence). The

Pearson correlation test indicates a significant and a

positive correlation between the dependent variable and all

the seven independent variables of this research, the

strongest relationship exists between the first independent

variable (unemployment rate) and the dependent variable

(non-performing loans) where the r= (.543**). This is

followed by interest rate, r= (524**) and inflation rate

r=(.511**), then political interference (.291**), credit

monitoring r=(.264**), GDP r=(.252**), banker‟s

incompetence (.194**). Correlation between Non-

performing loans and Unemployment Rate As table 2

exhibits, the relationship between unemployment rate and

non-performing loans was investigated using Pearson

correlation test (r), with the value of alpha set at 0.05, this

shows strong positive correlation between the two

variables (r=0.543, p-value = 0.000 0.05). This supports

the alternative hypothesis that there is a relationship

between unemployment rate and non-performing loans of

Somali banks.

Table 1 Correlations between DV and IV1

Correlations

DV IV1


Pearson Correlation 1 .543**

Sig. (2-tailed) .000

N 180 180

Unemployment Rate

Pearson Correlation .543** 1


N 180 180

**. Correlation is significant at the 0.01 level (2-tailed).

Source: The researcher computed from SPSS software version 22.0

Correlation between Non-performing loans and

Interest Rate

Pearson correlation test (r) was used to investigate the

relationship between interest rate and non-performing

loans of Somali banks. With Alpha value set at 0.005, the

correlation analysis established a strong positive

relationship between the variables (r= 0.524, p-value =

0.000 0.05) as can be seen in table 3. We reject the null

hypothesis and support the alternative hypothesis that

there is a relationship between interest rate and the non-

performing loans of Somali banks.

Table 3: Correlations between DV and IV2

Correlations

DV IV2



Sig. (2-tailed) s .000

N 180 180

Interest Rate



N 180 180





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4.1.1 Correlation between Non-performing loans and

Inflation Rate

Based on the Pearson correlation analysis test in table 4,

inflation rate and non-performing loans of Somali banks

are found to have a strong positive relationship at 0.000

significance level as the p-value of these two variables is

less than 5% and their correlation coefficient r=0. 511.

This supports the alternative hypothesis that there is a

relationship between inflation rate and non-performing

loans of Somali banks.


Correlations

DV IV3




N 180 180

Inflation Rate



N 180 180




GDP

By using Pearson correlation test (r) to examine the

relationship between GDP and non-performing loans of

Somali banks. With Alpha value set at 0.005, the

correlation analysis established a positive relationship

between the variables (r= 0.252, p-value = 0.001 0.05) as

shown in table 5. This supports the alternative hypothesis

and we reject the null hypothesis that there is a

relationship between GDP and the non-performing loans

of Somali banks.


Correlations

DV IV4




N 180 180

GDP



N 180 180




Credit Monitoring rate

As table 6 exhibits, the relationship between credit

monitoring and non-performing loans was investigated

using Pearson correlation test (r), with the value of alpha

set at 0.05, the evidence shows that there is strong positive

correlation between the two variables (r=0.264, p-value =

0.000 0.05). This supports the alternative hypothesis that

there is a relationship between credit monitoring and non-



Correlations

DV IV5




N 180 180

Credit Monitoring



N 180 180





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4.1.4 Correlation between Non-performing loans

and Political Interference

Based on the Pearson correlation analysis test in table 7,

political interference and non-performing loans of Somali

banks are found to have a positive relationship at 0.000

significance level as the p-value of these two variables is

less than 5% and their correlation coefficient r=0. 291.

This supports the alternative hypothesis that there is a

relationship between political interference and non-



Correlations

DV IV6




N 180 180

Political Interference



N 180 180



4.1.5 Correlation between Non-performing loans and Banker’s Incompetence


Correlations

DV IV7




N 180 180

Political Interference



N 180 180



As table 8 exhibits, the relationship between banker‟s

incompetence and non-performing loans was investigated

using Pearson correlation test (r), with the value of alpha

set at 0.05, the findings and results shows a very weak

positive correlation between the two variables (r=0.194, p-

value = 0.009 0.05). This supports the alternative

hypothesis that there is a relationship between banker‟s

incompetence and non-performing loans of Somali banks.

4.2 Regression Model

4.2.1 Model Summary

The regression results comprise three tables the Model

Summary, the Annova and the Coefficients table. The first

table (Model Summary) provides information about the

regression line‟s ability to account for the total variation in

the dependent variable. Table 9 demonstrates model

summary of the dependent variable of this research that is

non-performing loans. In accordance to table, the R

Square value is 0.420 and the Adjusted Square value is

0.396. R Square is known as the coefficient of

determination with its value ranging between (0) and (+1)

and measures the proportion of variation in a dependent

variable that can be explained statistically by the

independent variables. Moreover, it also shows the degree

of goodness of fit for the multiple regression equation

estimated by the researcher.

Table 9: Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .648a .420 .396 2.33854

a. Predictors: (Constant), IV7, IV3, IV4, IV6, IV1, IV5, IV2

This indicates if the independent variable of this study can

be explained by the dependent variables, the coefficient of

determination R Square would have been equal to (1).

However, in this research, 42% of the variation of the

dependent variable could be explained by the independent

variables, in other words, 42% of the values fit the model.

Additionally, the R Square and the Adjusted R Square

respectively values at 0.420 and 0.396 and could indicate

there is a moderate degree of goodness of fit in the

regression model of the researcher.



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4.2.2 Anova and Coefficients of the Study

The analysis of variance tests (ANOVA) tests whether the

model is significantly better at predicting the outcome.

Precisely, the F-ratio represents the ratio of the

improvement in prediction that results from fitting the

model (labelled „Regression‟ in table 31), relative to the

inaccuracy that still exists in the model (labelled

„Residual‟ in table 10) (Pallant, 2013). If the improvement

due to fitting the regression model is much greater than the

inaccuracy within the model then the value of F will be

greater than 1 and SPSS calculates the exact probability of

obtaining the value of F by chance (Piaw, 2013). For the

data of this research the F=17.8, which is very unlikely to

have happen by chance (p < .001). This can be interpreted

that the final model significantly improves the ability to

predict the outcome variable

Table 10: ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 680.171 7 97.167 17.768 .000b

Residual 940.629 172 5.469

Total 1620.800 179

a. Dependent Variable: Non-performing Loans

b. Predictors: (Constant), IV7, IV3, IV4, IV6, IV1, IV5, IV2

In another words, table 31 shows that F = 17.768 with

significance (Sig.) of 0.000, which means that the

probability of these results occurring by chance is less than

5%. The F-Test determines the probability of the

relationship between dependent variable and the

independent variables occurring by chance (Greener,

2008). Additionally, table 32 below shows the coefficients

of this research. The T-test is generally used to determine

the probability of the impact of the independent variables

on the dependent variable occurring by chance (Greener,

2008). The results of the T-test for the individual

regression coefficients for the IVs of this research

(unemployment rate, interest rate, inflation rate, GDP,

credit monitoring, political interference and banker‟s

incompetence) are respectively 4.392; 2.378; 2.809;

0.776; 1.236; 0.103 and 0.050. The probability of

occurrence of the first four variables is less than 5%; On

the other hand, the probability of occurrence of the last

three variables is greater than 5%.

Table 11: Coefficientsa

Model Unstandardized Coefficients

Standardized

Coefficients

t

Sig.

B Std. Error Beta

1

(Constant) 2.496 2.284 1.093 .276

Unemployment Rate .356 .081 .321 4.392 .000

Interest Rate .181 .076 .191 2.378 .002

Inflation Rate .245 .087 .219 2.809 .000

GDP .051 .066 .050 .776 .001

Credit Monitoring .092 .075 .090 1.236 .218

Political Interference .009 .082 .008 .103 .918

Banker‟s Incompetence .004 .086 .003 .050 .961

a. Dependent Variable: Non-performing Loans

However, the beta values tell us about the relationship

between the DV and the IVs. The positive sign of the beta

indicates that there is a positive relationship between the

predictor and the outcome. Thusly, for this data all the

predictors have positive values and the beta for the seven

independent variables are 0.356 for unemployment rate,

0.181 for interest rate, 0.245 for inflation rate, 0.051 for

GDP, 0.092 for credit monitoring, 0.009 for political

interference and 0.004 for banker‟s individual.

Furthermore, each of these beta values has an associated

standard error indicating to what extent these values would

vary across different samples, and these standard errors

are used to determine whether or not the b value differs

significantly from zero. Therefore, if the t-test associated

with b values is significant (sig. is less than 0.05) then the

predictor is making a significant contribution to the model.

Accordingly, as table 11 implies the “sig.” value of the

first four IVs is less than 0.05 indicating that these

predictors are making contribution to the model, while the

“sig.” value of the last three IVs shows to be greater than

0.05 meaning that these predictors do not contribution to

the model.

4.3 Comparisons of Findings with the Literature

4.3.1 Unemployment Rate

The first objective of the study was to examine the effect

of unemployment rate on the non-performing loans of

Somali banks. Thusly, the empirical correlation results of

this study suggest that there is a strong positive (r=0.543)

relationship between unemployment rate and NPLs of

Somali banks. Moreover, the regression coefficient results

indicate significant relationship (Sig.=0.000) between the

two variables. Together the findings of correlation and

regression this study establishes a strong positive and

significant relationship between the two variables. These

findings are in line with the previous literatures of Makri,

Tsagkanos and Bellas (2013) who examined the non-



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performing loans of banks in Eurozone and found a strong

positive correlation and others such as Balgova, Nies and

Plekhanov (2016), Bonilla (2012), Mileris (2014) who all

found a positive correlation between unemployment rate

and non-performing loans. However, the results of this

study also contradict the literatures of Mondal (2016),

Kurti (2016) and Klein (2013) who all found insignificant

relationship between unemployment rate and NPLs. As

noted by to Musai and Mehrara (2014) unemployment

impacts on society and economy in different ways. It

decreases the knowledge and skills of the labour force

while increases the costs of producing labour force.

According to the economic theory of crime,

unemployment causes increased level of crimes such as

drug abuse, robbery and smuggling. However, in Somalia

the unemployment rate is very high, 66% as of 2015

according to the World Bank and this high unemployment

could be one of the main reasons of the high non-

performing loans in Somali banks.

4.3.2 Interest Rate

The second objective of the study was to examine the

effect of interest rates on the NPLs of Somali banks. Both

the regression and correlation results indicate that there is

a strong positive and significant (r=0.524, Sig.0.002)

relationship between the interest rate and NPLs. The

finding of this variable is consistent with the findings

revealed by Jameel (2014) and Warue (2013) who both

found positive and significant relationship of interest rate

on NPLs. On the other hand, the finding of this variable is

inconsistent with the studies of chege (2014) who studied

commercial banks of Kenya and found positive but

negative linear relationship, Bhattari who revealed that

interest rate have insignificant relationship with NPLs in

Nepalese banks and Hassan, Ilyas and Rehman (2015)

who examined the effect of various bank-specific factors

including interest rate and revealed that only interest rate

had a weak significance on NPLs of Pakistani banking

sector in comparison to the other variables. However, as

argued by Nkusu (2011) an upsurge in interest deteriorates

the credit disbursement ability of the debtors, therefore

banks should constantly review the interest rates on loans

since loan failures are higher for banks which increase

their real interest rates.

4.3.3 Inflation Rate

The third objective of this study was to examine the effect

of inflation rate on the NPLs of Somali banks. Similar to

the unemployment and interest rates the empirical

correlation results of the third IV (Inflation rate) reveals

strong positive relationship (r=0.511) between NPLs of

Somali banks and the fluctuation of inflation in the

country, while the correlation results also show significant

relationship between the variables (Sig.=0.000) which is

consistent with the findings of Mehmood, Younas and

Ahmed (2013) and Farhan et al. (2012) who found

positive and significant relationship of NPLs and inflation

rate of Pakistani banks; and Abebrese, Pickson and Opare

(2016), Anjom and Karim (2016), who discovered

negative but significant relationship of NPLs and inflation

rate of Bangladeshi banks. However, the findings of this

variable are inconsistent with the literature of Gezu

(2014), Bhattari (2014) and Ali and Iva (2013) who

established a negative and insignificant relationship

between NPLs and inflation rate in Ethiopian commercial

banks, Nepalese banking sector and Albanian banking

sector respectively. Therefore, based on the literatures

reviewed the relationship between inflation rate and non-

performing loans seems ambiguous. According to Nkusu

(2011) higher inflation rate can affect the level of NPLs

positively or negatively. Theoretically high inflation

should reduce the real value of debt and hence make debt

servicing easier. However, high inflation may pass through

to nominal interest rate and weaken some borrower‟s

ability to service debt by reducing real income when

wages are adhesive (Louzis, Vouldis and Metaxas 2010).

4.3.4 Gross Domestic Product (GDP)

The fourth objective of this study was to examine the

effect of GDP on the NPLs of Somali banks. The

empirical correlation results of this variable established a

very weak positive relationship (r=0.252, Sig.=0.001)

between GDP and NPLs of Somali banks. Additionally,

the regression coefficients‟ results indicate significant

relationship (Sig.=0.001) between the variables. Though,

the results of the correlation and the regression were

similar by direction, the correlation results indicate

weakness of the relationship. This infers that increase in

GDP growth rate results in a decrease of the level of NPLs

of loan giving banks and vise versa. These findings are

being consistent with the findings of Mondal (2016),

Tsumake (2016), Makri, Tsagkanos and Bellas (2013),

Messai and Jouini (2013), Saba, Kouser and Azeem

(2012) and Farhan et al. (2012) who all found positive

relationship between GDP and NPLs and Nkusu (2011)

and Vatansever and Hepsen (2013) who also revealed

insignificant relationship between the NPLs and GDP

growth rate. Nevertheless, Beck, Jukubik and Piloiu

(2013), insinuate that in theory an improvement in the real

economy should see an instantaneous reduction in the

NPLs of a country. This is because the growing economy

increases the borrowers‟ income and ability to repay debts

and generally increases the overall financial soundness and

stability. Thusly, when all subjects in the economy are

supposedly getting higher incomes, they will be more

capable of settling their debts and this will lead to lesser

bad debts, defaults and non-repayments.

4.3.5 Credit Monitoring

The fifth objective of this study was to examine the effect

of credit monitoring on the NPLs of Somali banks. The

empirical results of the correlation indicate a very weak

positive correlation (r=0.264) between credit monitoring

and the NPLs of Somali banks. Moreover, the regression

coefficients results show insignificant relationship between

the variables (Sig.=0.218). Thusly, both the regression and

correlation results establish weak positive insignificant

relationship. The findings of this study are in line with the

findings of Asfaw, Bogale and Teame (2016), Hassan,

Ilyas and Abdul Rehman (2015), Abdeta (2015) and

Geletta (2012), who all found a positive relationship

between NPLs and credit monitoring. There are other

literatures that explained the above findings in the light of

hypotheses of „bad luck‟ and „skimping‟ postulated by

Berger and DeYoung (1997). For instance, as Rajha

(2016) Abdeta (2015) and Klein (2013) reinstated, the



222

„bad luck‟ hypothesis argues that bad management that is

characterised by poor credit scoring, loan underwriting,

monitoring and controlling skills often pose the probability

to have increased NPLs. Accordingly, the „skimping‟

hypothesis argues that high cost efficiency affects the

NPLs. Banks often try to achieve short-term profits by

reducing the costs on monitoring. Though it may help the

banks in short-term, they pose themselves in risks in the

long-term. Thus, the NPLs grow high in the long run when

the banks dedicate less effort in ensuring loan quality and

this might be the reason of this finding causing the weak

positive insignificant relationship between the NPLs of

Somali banks and credit monitoring.

4.3.6 Political Interference

The sixth objective of this study was to examine the effect

of political interference on the NPLs of Somali banks. The

empirical results of the correlation established a very weak

positive correlation (r=0.291) between NPLs and political

interference of the Somali banks. Moreover, the regression


the variables (Sig.=0.918). Thusly, both the regression and

correlation results establish weak positive insignificant

relationship and the findings of this variable are consistent

with the results of Hassan, Agarwal et al. (2016), Ilyas and

Abdul Rehman (2015), Bhattarai (2014), Wanjau (2011)

and Adeyemi (2011), who all unanimously revealed the

existence of a significant effect of political interference on

the NPLs of banks around the world, though the results of

this study establish insignificant. The reason behind this

insignificant positive effect of political interference on the

NPLs of Somali banks could be the initiatives that the

Somali central government is undertaking for the last

couple of years to mitigate and reduce the political

corruption involving state banks.

4.3.7 Banker’s Incompetence

The seventh and last objective of this study was to

examine the effect of banker‟s incompetence on the NPLs

of Somali banks. The correlation findings of this variable

established the weakest positive correlation (r=0.194) of

this study, which is between NPLs of Somali banks and

banker‟s incompetence. Moreover, the regression


the variables (Sig.=0.961). Thusly, together the regression

and correlation results establish weak positive and

insignificant relationship between NPLs and banker‟s

incompetence. The findings of this variable are consistent

with the results of Islam and Nishiyama (2016), Hassan,

Ilyas and Rehman (2015) and Iuga and Lazea (2012).

Adequate and up to date training of the bank officers

involving the loan giving process plays a key role in

making effective loan decisions. In fact, Hassan, Ilyas and

Rehman (2015) done the only study that used the term

„banker‟s competence‟ in the recent times aiming to find

its effect on NPLs of Pakistani banks. According to them,

bankers with high qualification are in a place to judge the

credibility of a borrower more effectively, contributing to

lower loan default cases. They placed banker‟s

incompetence under the category, social factor rather than

the bank-specific factors as they argued that competent

bankers could understand and communicate their

customers to convert their NPLs into performing loans.

5. SUMMARY AND DISCUSSION OF THE FINDINGS

As Somali banking sector has long been facing high non-

performing loans, the situation played as a motive behind

conducting this study. Furthermore, since no earlier

research was found that examined the NPLs of Somali

banks, this knowledge gap also motivated the study. This

study considered non-performing loans as the dependent

variable and unemployment rate, interest rate, inflation

rate, gross domestic product, credit monitoring, political

interference and banker‟s incompetence as the

independent variables. Interestingly, this is the first ever

study that took into account all the above variables in a

single study. The research objectives of the study were to

investigate the effect of all the aforementioned variables

on the NPLs of Somali banks. Accordingly, the research

considered both public and private Somali banks as the

research site and the respondents were selected using a

non-probability sampling method. It also implemented a

quantitative analysis by distributing and collecting survey

questionnaire to the respondent bankers. The collected

primary data were analysed using SPSS tool. The results

from the demographics of the respondents show that

majority of the respondents were male (72.2 %), young,

below 30 years old (56.7%), completed degree level

(55%) and have experience of more than 3 years (54.4%).

The null hypotheses of the study recognise that each of the

independent variables has an impact on the NPLs of

Somali banks. However, the findings of the study show

that all the null hypotheses are rejected. Firstly, the study

found that unemployment rate has an effect on the NPLs

of Somali banks. This finding is consistent with the

findings of Balgova, Nies and Plekhanov (2016), Mileris

(2014), Makri, Tsagkanos and Bellas (2013) and Bonilla

(2012). Secondly, this study revealed that interest rate has

an impact on the NPLs of Somali banks. The finding is

consistent with the findings revealed by Jameel (2014) and

Warue (2013). Thirdly, the study also found that inflation

rate has an effect on the NPLs of Somali banks which is

consistent with the findings of Abebrese, Pickson and

Opare (2016), Anjom and Karim (2016), Mehmood,

Younas and Ahmed (2013), Farhan et al. (2012) and

Khemraj and Pasha (2009). Fourthly, gross domestic

product was found to have an impact on the NPLs of

Somali banks, being consistent with the results of Mondal

(2016), Tsumake (2016), Makri, Tsagkanos and Bellas

(2013), Messai and Jouini (2013), Saba, Kouser and

Azeem (2012) and Farhan et al. (2012). Fifthly, credit

monitoring was also found to have an impact on the NPLs

of Somali banks, which is consistent with the findings of

Asfaw, Bogale and Teame (2016), Hassan, Ilyas and

Abdul Rehman (2015), Abdeta (2015) and Geletta (2012).

Sixthly, the study revealed that political interference also

has an impression on the NPLs of Somali banks. The

result is consistent with the results of Hassan, Agarwal et

al. (2016), Ilyas and Abdul Rehman (2015), Bhattarai

(2014), Wanjau (2011) and Adeyemi (2011). Last but not

least, banker‟s incompetence was also found to have an

impact on the NPLs of Somali banks, which is consistent

with the findings of Islam and Nishiyama (2016), Hassan,

Ilyas and Rehman (2015) and Iuga and Lazea (2012).



223

5.1 Contributions of the Study

5.1.1 Theoretical contributions

Firstly, not a single existing research focused on the non-

performing loans in the Somali context, as far as our

knowledge concerns. Hence, this study fulfils the lacking

of literatures on the NPLs of Somali banking sector.

Secondly, none of the earlier literatures took account of all

the variables (factors) selected in the current study.

However, this study has covered the gap by examining all

the seven variables in a single research. Thirdly, some of

the reviewed literatures recommended for future

researches, especially in case of the impact of several

variables on non-performing loans that those studies did

not investigate. For example, Sheefani (2015) suggest that

the impact of credit monitoring need to be studied by

future literatures. Thus, this study fulfils the suggestions

made by the researchers at large extent. Fourthly, the three

variables, namely, credit monitoring, political interference

and banker‟s incompetence were not studied in a single

research by many researchers. Only Hassan, Ilyas and

Abdul Rehman (2015) was found to have studied the

impact of these three variables on NPLs. Hence, this study

also added important information in the case of impact of

the three factors on non-performing loans. Fifthly, the

study is found to have empirical evidence that the findings

of this study were inconsistent with the conclusion made

by several researchers such as, Bhattarai (2014) and Klein

(2013) in case of the impact of unemployment rate on

NPLs; Hassan, Ilyas and Rehman (2015) and Bhattarai

(2014) in case of the impact of interest rate on NPLs;

Gezu (2014), Bhattarai (2014) and Ali and Iva (2013) in

case of the impact of inflation rate on NPLs; Vatansever

and Hepsen (2013) in case of the impact of GDP on NPLs;

Joseph et al. (2012) in case of the impact of credit

monitoring on NPLs and Quadt and Nguyen (2016) in

case of the impact of banker‟s incompetence on NPLs.

5.1.2 Practical contributions

The measure of non-performing credit is one of the

markers of a financial system‟s strength or weakness, the

less the non-performing loans the better the financial

soundness of the economy. In the event that the non-

performing credit is more, there will be poor financial

related wellbeing and emergency may bring about the

economy. Therefore, studying and identifying factors that

contribute to the borrowers‟ inability to repay the

advances is necessary. As the bankers‟ perception towards

the major factors triggering non-performing loans were

considered, the study recognised the selected independent

variables and their impact on the NPLs. It is expected that

the study will benefit the researchers of the field and

similar interests. As the research has added new

information in the context of Somali banking sector and in

case of the impact of the selected seven variables on

NPLs, the future researchers will be able to obtain

necessary information from the study‟s findings

investigating NPLs in Somali banks. The study will also

benefit bankers (loan managers) of the Somali banks. The

loan managers will be able to identify the major causes or

factors of NPLs in their banks more effectively as all the

seven factors in this study were found to have impact on

the NPLs. Additionally, they will be able to analyse

customers‟ financial background, identify the required

collaterals, measure the loan quality and take necessary

steps to facilitate a better debt collection process while

facing the possibility of loan defaults. The findings of the

study would also encourage the Somali banking authority

to organise internal and spend for external trainings and

development programmes that would increase the level of

bankers‟ competency, especially in case of loan quality

monitoring and assessment. It may consequently reduce

the level of non-performing loans. The study further

benefits the government and national regulatory

organisations, as they will be able to understand and

identify the root causes of high NPLs in the country‟s

banking sector. Accordingly, it would encourage them to

keep unemployment rate, interest rate and inflation rate

and political interference low for a healthy financial

system.

5.2 Limitations of the Study

Limitations are normally those factors that limit a study

from obtaining expected outcomes. This study has also

several limitations addressed below. Firstly, time

constraint is a major limitation to this study as the primary

data were collection over a period of only one month.

Hence, the study does not reflect the banker‟s perception

regarding the impact of the selected variables on NPLs in

the long run. Secondly, financial constraint is also another

limitation to this study that restricted the researcher from

obtaining data from a large number of banks. As such, not

all the Somali banks were approached mentioned in the

chapter 1. Thirdly, lack of cooperation from the

respondents was also another limitation of this study.

While approached, a number of bankers were reluctant to

respond to the questionnaire and consequently, the

response rate was primarily not high. However, the

researcher managed to obtain expected primarily data

from the pre-calculated number of samples eventually.

Last but not least, though this study aimed to examine the

possible effect of the variables on the NPLs, it did not

investigate the direction of the impact such as, positive or

negative relationship. Hence, this is also a limitation of the

research.

5.3 Recommendations for Future Research

This study did not investigate the direction (positivity or

negativity) of the impact of the selected variables on the

non-performing loans of Somali banks. Hence, it is

suggested that the future researchers may investigate the

direction of the selected variables in a single research.

Furthermore, this research did not take account of the

bank-specific factors such as, credit assessment, credit

size, credit terms, credit growth, capital adequacy ratio,

return on equity, return on assets, and loan loss reserves to

total loans ratio, and profitability of bank assets and

macroeconomic factors such as, public debt, exchange

rate, government expenditure and exports and imports.

Hence, the future researchers may investigate the possible

impact of the aforementioned variables on the NPLs of

Somali banks. Recommendation to the Management of the

Somali Banks In order to ensure a better relationship

management with the customers, an effective loan quality

assessment and an efficient debt collection process, there

is no other means than improving the competencies (the



224

knowledge and skills according to recent trends) of the

bankers, especially loan managers. Furthermore, Banks

need to deliberately and regularly organise training and

development (T&D) programmes focusing on credit

enhancement process, credit approval conditions, loan

quality monitoring and debt collection process for

respective employees of loan and risk management

departments. Furthermore, monitoring programmes such

as, the SMP organised by IMF (mentioned in chapter 1)

can be collaborated with international agencies that will

make the bankers and loan managers understand the bigger

picture of credit monitoring and its effect on reducing loan

defaults in various countries.

5.3.1 Recommendation to the Government and

Financial Regulatory Authorities

One of the reasons of high NPLs in the Somali banks is the

high unemployment rate in the Somali region. Hence, it is

clear that due to high unemployment, many debtors are

unable to repay loans within the loan maturity. In order to

avoid the situation, the government should ensure a job or

income source for all the citizens. This will increase the

demand for more expenditure among the public and in turn

will increase the growth of GDP, resulting to lower the

NPLs. Therefore, managing unemployment is critical for

political dependability. The unemployed youth populace

(67 per cent) contributes essentially to unpredictable

movement and cooperation in fanatic exercises, including

Al-Shabaab the activist jihadist group, which is seen as

another type of employment in the country. With high

youth unemployment and low overall labour force

participation, the Somali experts set up the National

Development Plan that spotlights on the accompanying

key points: how to accomplish higher financial

development, make employments, and assimilate the

Somali outcasts and refugees coming back from Kenya;

remittances streams; and organizing social wellbeing nets

and squeezing philanthropic conditions (International

Monetary Fund, 20017a). As several earlier study found

both positive and negative of interest rate on non-

performing loans, it is ambiguous that whether government

needs to increase interest rate reduce it. The local

government should be extra careful in navigating the

monetary policy of the region so that they can avoid any

inverse action to increasing NPLs and may fluctuate the

level of interest rate depending on the financial situation of

the banks. The government of Somalia also needs to be

careful in endorsing policy on maintaining the inflation

rate in the country. For the past few years, the country has

been facing both ups and downs in the inflation rate. In

2015, the country faced deflation after an inflation in the

previous year (see Figure 3 in chapter 1). However, it is

suggested that the government should maintain upwards

within 2 to 3 percent inflation rate in the upcoming years

so that the industry remains competitive, the wages (salary

scales) of productive workers are adjusted and, in some

cases, may boost economic growth. GDP was also found

to have ambiguous relationship with NPLs in the reviewed

studies. As currently, the GDP growth in the country is

steady, the government should ensure that it does not

decline and remains steady or go upwards in order to meet

the goals of higher average incomes, better standards of

living, lower unemployment, lower government

borrowings and improved public services and investment.

One major concern in Somalia is the issue of corruption,

which needs laws to be implemented to ensure fair, and

transparent dealings across the spectrum of financial

markets and companies to have proper disclosures.

Recently Anti-Corruption Act is brought to Somali

parliament waiting to be passed. Furthermore, the

government needs to ensure that the banks especially the

state banks have limited political involvement and fewer

amounts of loans provided to politically engaged debtors

(Sufi, 2017). However, if the government can ensure very

limited or no political loans, that will result into lower

level of NPLs indeed. Somalia is one of the world‟s most

remittance-dependent country being the recipient of

US$1.3 billion every year that accounts for 25-45 per cent

of the country‟s economy and about 80 per cent of start-up

capital of small businesses in the region (International

Monetary Fund, 2017b). However, the problem facing the

regional banking section is that till now, there is

inadequate commercial banking services and supervisory

capacity of the Central Bank (Paul et al., 2015).

Furthermore, the Central Bank has very limited

corresponding relationship with the foreign banks and

money transfer operators. Hence, it is suggested that the

government should build more sustainable financial

industry by increasing the commercial banking services

capacity of the Central Bank and maintaining good

relationships with foreign governments in order to ensure

quick arrival of the remittances and lower NPLs.

6. CONCLUSION

Non-performing loans are not only a threat to the

economic and financial stability of a country but also

globally as we have seen financial crisis by the theses

loans in East Asian Countries, America and Sub-Saharan

Africa. Moreover, a colossal volume of non-performing

loans fills in as preface to financial fragility. Thusly, there

is a need of identifying the factors responsible for these

loan defaults, as many authors and researchers believe that

once these factors are identifying then policies and

regulations can be put in place to minimise and or prevent

the causes. The existing literature in the area of non-

performing loans concentrated on analysing secondary

data in finding out the major factors contributing to the

loan defaults, however, this study focused on the views

and perceptions of the Somali bankers who deal with and

handle the daily activities of the loan portfolios. It is the

researchers‟ believe that the bankers who are actually

dealing with these issues on a daily basis could better

describe the factors or elements causing or contributing

the loan defaults. Therefore, this study incorporates the

first ever investigation of the effect of unemployment rate,

interest rate, inflation rate, GDP, credit monitoring,

political interference and banker‟s incompetency on NPLs

in the context of Somali banking sector. The findings of

the study revealed that all the factors were found to have

an effect on the NPLs of Somali banks. It is expected that

the study has added valuable information in the field of

NPLs and its factors. The research is expected to be

equally beneficial for prospective researchers,

management of the banks and the government. Finally, it

is suggested that the bank management should increase



225

their expenditure for training and development

programmes, which would enhance the loan quality

monitoring, and debt collection management of the

bankers. It is also suggested to the government and

regulatory bodies that the country‟s unemployment rate

should be kept low, extra care should be given to inflation

rate and interest rate fluctuation, political interference

should be limited or reduced to zero and the country‟s

banking sector need to be revitalised by increasing the

commercial banking services capacity of the Central Bank.

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