© 2014 Research Academy of Social Sciences

http://www.rassweb.com 93

International Journal of Financial Markets

Vol. 1, No. 3, 2014, 93-101

Bankers Perception of Economic Determinants of Non-Performing

Loans in Ghana

Peter Kwame Kuutol1, Benjamin Agyeman

2 , Clement Owusu-Adjei

3

Abstract

The study looks at perception of bankers on economic variables causing Non-Performing Loans (NPLs) in

Ghana since 2005. The research was design to use primary data collection from bankers who evaluate loans

and approving authorities in the banking sector. This study was steered with the aid of structured

questionnaire. In all 401 questionnaires were returned well answered out of 500. Cross sectional Regression

analysis was carried out to analyze the impact of Interest Rate, Energy Crisis, Unemployment, Inflation and

Exchange Rate on the Non-Performing Loans in the Ghanaian banking sector. The study concluded that

Bankers in Ghana identify Interest Rate, Energy Crisis, Unemployment, and Exchange Rate to have

significant positive relationship with Non-Performing Loans. As the exchange rate drives weaken other

macro-economic indicators in the Ghanaian economy, there is the need for government to arrest the

exchange rate to make the economy resilience.

Key words: Non-performing loans, bankers’ perception, energy crisis, Banking Sector, Ghana

1. Introduction

Non-performing Loans (NPLs) are continuously gaining the attention of the globe in recent times as a

result of the global financial crisis in the academic cycles. Over decades now even before the emergence of

the global financial crisis in 2007 as it was official declared, Barr and Siems (1994) have already concluded

that NPLs are increasing and causing banking crisis which are turning into banking failures. NPLs are one of

the core causes of insolvency of financial institutions and in the end injured the entire economy (Hou, 2007).

There is therefore the need to manage NPLs well to foster economic growth in a nation, if not financial

resources can be channeled into non-profit ventures which will not only affect the financial stability

negatively but also growth as well.

Richard (2011) states that no person or society can disagree with the importance of financial institutions

in any economy and that these financial institutions not only affluence the credit flow in the economy but

also enhance the productivity by stimulating investments. Good show of these financial institutions is a

symbol of success and economic growth in any nation and poor show of these institutions equally not only

impedes the economic growth and development of a nation but also affects the entire growth of the world

(Khan & Senhadji, 2001). Brown bridge & Harvey (1998) argued that the world has seen several failures in

the banking sector in some decades now and these banking failures negatively harms the economy in which

these banks operate in many ways. Banks failures causes banking crisis by destroying the banking sector and

also diminishes the credit flow in the nation which eventually affects the competence and productivity of the

business units. Most financial institution failures are attributed to non-performing loans (Brown bridge,

1998).

1 University College, Okwahu Campus, P. O. Box 59, Abetifi-Kwahu, Ghana

2 Presbyterian University College, Okwahu Campus, P. O. Box 59, Abetifi-Kwahu, Ghana

3 Presbyterian University College, Okwahu Campus, P. O. Box 59, Abetifi-Kwahu, Ghana

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According to Adebola et al. (2011) they advanced that if we take into consideration the causes of

recession 2007-2009 which has be placed second to the great economic recession in 1930, not only the

economy of USA was shocked but also many economies across the length and the breath of the world, it was

observed that NPLs is one of the main causes of the recession. It is significant to appreciate the sensations

and nature of NPLs; it has many implications as fewer loan losses is indicator of quite more efficient

financial system, however, high level NPLs is an indicator of unsecure financial system and a disturbing

indication for bank management and regulatory bodies to put proper systems to curb the situation. Clugston

(2009) as cited in Farhan et al. (2012) high risk loans were granted to the unqualified borrowers and these

loans were secured against overestimated resources or against nothing, and when this economic boom “went

bust” those high risk loans turned into non-performing loans and as loans were given to unqualified

borrowers those turned into non-performing loans, as a whole this collection of non-performing loans

irrespective of its causes was one of the main factor of recession which did not only hampered the USA

financial sector but also economy of the entire world.

In spite the fact that IMF country report in June 2011 which claims that the financial sector in Ghana is

more vulnerable and prone to various risks associated with the operations of the institutions, the sector can

still experience profitability and stability in this time of financial crisis still on-going.Loan portfolio

constitutes the largest operating assets and source of revenue of most financial institutions’. However, some

of the loans given out become non-performing and adversely affect the profitability and overall financial

performance of the lending institutions. Many lending institutions in Ghana are confronted with the challenge

of rising non-performing loan portfolios despite efforts at curtailing the tide. The banking sector is one of the

pioneering lending institutions with a deteriorating trend in the health of their loan portfolio in recent years.

The situation calls for an effective strategy to remedy it before it gets out of hand. Besides, limited research

has been carried out during and just after the global financial crisis in Ghana to examine the economic

determinants of NPLs in Ghana. Also, the few research that has been carried out in this area

adoptedsecondary data in Ghana,hence this study would adopt primary data approach. This research paper

therefore seeks to examine the economic determinants of NPLs in the banking sector in Ghana.

2. Literature Review

Non-Performing Loans (NPLs) and Banking Sector

Nkusu (2011) highlights that NPLs plays a role in creating the financial crisis and that there is positive

relationship between NPLs and financial crisis. In a study conducted by Kaminsky & Reinhart (1999)

explain that a growing trend of NPLs in any society is an indication of financial crisis in such society. They

went further to state that though literature has not been able to establish that NPLs directly responsible for

any financial crisis yet they explained the negative impact of NPLs on every economy.

According to Louzis et al. (2011) argue that the core economic determinants of NPLs can be taken from

the current theoretical literature of life-cycle consumption models. A look at the research work conducted by

Lawrence in 1995,who studied life-cycle consumption model and presented the probability of default,

following his model, low income borrowers have higher defaulting rates and this is as a result of increased

risk of unemployment thus unable to pay their loan obligations. Further, bank charges higher interest rates to

riskier clients, if a high interest rate is charged to those borrowers who have previously poor record to repay

the loans is also a factor causing NPLs. Lawrence’s model was later expanded by Rinaldi et al. (2006) and

according to them the probability of default actually depends on the current income and unemployment rate,

which is actually associated with the insecurity of the future income and lending rates.

According to Keeton & Morris (1987) as cited in Farhan el at.(2012) a research conducted in USA to

ascertain the factors which are causing NPLs in the banking sector by taking the data from 1979-1985. They

came out that bad performance of agriculture and energy sectors along with poor economic settings or

conditions are the main factors causing NPLs. Again, Sinkey&Greenwalt (1991) as cited in Farhan et al.

(2012) conducted research in USA between the period of 1984-1987 to identify the causes of NPLs and

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according to them high level of interest rate, unnecessary lending along with unpredictable funds are the

factors which have positive relationship with the NPLs in the banking sector. Further, according to the

Sinkey&Greenwalt poor economic conditions are also a cause of loan losses in USA banking sector. In

another studies conducted in USA having quarterly data from 1987-1999 to highlight the effect of macro-

economic variables on loan losses; the result indicates that income and unemployment rates are macro-

economic factors causing loan losses in USA (Gambera, 2000). Vogiazas&Nikolaidou (2011) examined

determinants of non-performing loans in the Romanian banking sector in the period of Greek crisis by taking

the data for 108 months (Dec. 2001 to Nov. 2010) opined that construction and investment expenditure,

unemployment and inflation rate and external debt to GDP and M2 (Narrow money and Intermediate money)

influence the credit risk of country’s banking system.

Bofondi&Ropele (2011) also conducted study in Italy and establish that NPLs are positively associated

with the unemployment rates, lending rates and negatively associated with the growth domestic product rate.

The study was conducted for the period of 1990-2010 in quarterly dataset. Berge &Boye (2007) found that

NPLs are extremely correlated with the lending rates and unemployment for the Nordic banking system

covering the time span from 1993-2005. Louzis et al. (2011) used panel data to spot the factors causing NPLs

in the Greek banking sector from 2003 to 2009 considering each loan category (corporate loans, consumer

loans and mortgage loans) according to their result economic growth (GDP), unemployment, lending rates,

public debt and management quality are the determinants of NPLs in the banking sector of Greece. These

literatures above show how NPLs relate to the other economic variables in advanced nations.

Adebola et al. (2011) stated that NPLs are not only the problem of conventional banking but also of

Islamic banking, they carried out a research in Malaysia to examine the determinants of NPLs in the Islamic

banking sector of Malaysia for the period of 2007-2009, they concluded that interest rate has a positive

significant relationship with the NPLs and producer price index has a negative and statistically significant

relationship with the NPLs in the Islamic banking sector of Malaysia. Khemraj& Pasha (2009) examine the

determinants of NPLs in Guyana taking into account the data between 1994-2004; they found that growth in

gross domestic product has an inverse relationship with the volume of NPLs. They further showed that

effective exchange rate has a positive relationship with the NPLs in the Guyana banking sector and also

banks who charge higher interest rate are likely to have high volume of NPLs indicating a positive

relationship between lending rate and NPLs. In a study conducted by Shu (2002) in Argentina NPLs have a

negative relationship with GDP growth, inflation rate; increase in property prices however, showed positive

relationship with interest rate. According to Farhan et al. (2012) in their study conducted in Pakistani bankers

perceive that Interest Rate, Energy Crisis, Unemployment, Inflation, and Exchange Rate has a significant

positive relationship with the NPLs of Pakistan banking sector while GDP growth has significant negative

relationship with the non-performing loans of Pakistan banking sector.

Economic Determinants of NPLs

Unemployment

Louzis et al. (2010) have state that theoretically, explanation for relationship between unemployment

and NPLs exist, a growth in unemployment in a country negatively affects the disposable incomes of the

individuals which leads to growth in debt level, it is evident that when a individuals losses his source of

income how can he/she make repayment of loan. Therefore, any growth in unemployment in the nation

negatively affects the demand of the goods/service of businesses which eventually affects the production and

sales of the businesses, this will trigger decline in revenues of businesses and crumbly debt situations.

However, there is a number of empirical evidence of positive relationship between unemployment in a nation

and NPLs (Berge &Boye, 2007; Nkusu, 2011; Bofondi&Ropele, 2011; Farham et al., 2012).

H1. Unemployment has a Significant Positive Relationship with the NPLs.

Inflation

Nkusu, (2011) opines that relationship between inflation and NPLs can be positive or negative since

inflation affects loan payment capacity of borrowers positively or negatively. Greater inflation can improve

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the loan payment capacity of borrower by reducing the real value of outstanding debt as well bigger inflation

can also deteriorate the loan payment capacity of the borrowers by reducing the real income when personal

emoluments are gluey. And that inflation reduces the debt servicing capacity of the loan holders as lenders

adjust the lending interest rates to adjust their real return. Thus, in literature relationship between inflation

and NPLs can be positive or negative depending on the country of operations. However, empirically

evidence of positive relationship between the inflation in a country and NPLs exist (e.g. Fofack 2005).

H2. Inflation has Significant Negative Relationship with the NPLs.

Energy Crisis

Today energy crisis is one of the biggest problems in the Ghanaian economy, as a result of the energy

crisis load shedding/management of electricity (which is popularly known in the Ghanaian context as

“Dumso-dumso”). High cost of energy as well as high cost of replacement resources of energy has destroyed

several businesses in Ghana. The crisis has caused a huge amount of bad loans in Ghanaian banking sector as

businesses are not able to repay loans. Lee (2005) has argued that energy is one of the most vital

components in developing any economy, regardless of the sector in the economy, cheap and constant flow of

energy is essential for the development of any country, because energy is an essential and complementary

part of production. Lee further found that there is a positive relationship between energy crisis and NPLs in

his empirical study.

H3. Energy crisis has a Significant Positive Relationship with the NPLs.

Exchange Rate

According to Fofack (2005) appreciation in exchange rates may have different consequences. It can

unfavorably affect the loan repayment ability of export oriented businesses and also positively affect the loan

payment ability of those borrowers who borrow in foreign currency but the relationship between Nominal

effective exchange rate (includes inflation) and NPLs is unknown. Literature has shown mixed reviews as far

as exchange rate and NPLs is concern. Khemraj& Pasha (2009) found a positive relationship between real

effective exchange rate and NPLs.

H4. Exchange Rate has a significant positive relationship with the NPLs.

Interest Rate

According to Nkusu (2011), appreciation of interest rate weakens loan repayment capability of the

borrower hence NPLs are positively associated with interest rates. So long as interest rate policy is concern,

it plays very vital in NPLs growth in an economy. Espinoza & Prasad (2010) examined the macroeconomic

determinants of non-performing loans in the GCC banking system according to them high interest rates

increases loan defaults but they did not find statistically significant relationship. Asari, et al. (2011) also

found significant relationship between loan defaults and interest rates they also found that an increase in loan

defaults also causes asset corrosion of banks and subsequently capital erosion. Collins &Wanjau (2011) also

revealed that interest rate is a primary factor that boosts NPLs. Lending rates or interest rates are one of the

primary economic determinants of NPLs (Nkusu 2011).

H5. Interest rate has a positive relationship with the NPLs.

3. Methodology

Due to the nature of the study, structured questionnaire was developed to aid in collecting primary data.

Questionnaire was designed for the survey to analyze the economic determinants of NPLs in the Ghanaian

banking sector from bankers who take part in decision making of credit allocation and credit risk

management or assessment as well as handling NPLs portfolios. This questionnaire was designed in way to

stimulate the perception of bankers concerning economic factors triggering NPLs in Ghana. The respondents

were positioned to cast their minds back since 2005 to date what they believe are the economic determinants

of NPLs.

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Data was adduced by credit officers, credit managers and branch managers. Credit risk analysts and

credit risk managers were part of risk assessment division of data collection. Initial 50 sample questionnaires

was surveyed, this pilot sample aided to test the validity and reliability of the questionnaire. Cronbach alpha

co-efficient of 0.0864 showed reliability of the questionnaire. All in all a total 500 was administered for the

study but 401 was returned and well answered which represent 80.20%. Seven point Likert scale was used as

a tool to assess the responses and to measure the impact of economic determinants of NPLs. This includes:

[Strongly disagree 1, 2, 3, 4, 5, 6, 7 strongly agree].

Regression Model Specification

The proposed premise is that Non-performing Loans (NPLs) is a function of the independent variables,

that is,

Non-performing loans (NPLs) = ƒ (Interest rates, Energy crisis, Unemployment, Inflation, Exchange

rate).

The dependent variable, Non-performing Loans (NPLs) is a continuous variable. The individual path

coefficients of the structural model are equivalent to standardized Beta (β) coefficients of ordinary least

squares regressions. Following the above, the model follows as

Y = c+βX (1)

Where Y represents the dependent variable which is NPLs. c represents the constant and β represent the

coefficients and X represents the set of explanatory variables of the estimated model.

Therefore the model used for the study is

NPLsi = c + β1INRi + β2ENCi + β3UNEi + β4INFi + β5EXRi + € i (2)

Where: NPLs = Non-performing Loans, INR= interest rate, ENC = energy crisis, UNE

=unemployment, INF= inflation, EXR = exchange rate and €= error term.

4. Discussion of Result

In this study the researchers discusses descriptive analysis and then move on to discuss the result

emanating from the regression analysis.

Descriptive Result

Table 1: Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

NPLs 401 3.67 7.00 5.4821 .62457

ENC 401 4.25 7.00 5.1876 .59006

INR 401 3.60 7.00 5.2475 .74681

UNE 401 3.33 7.00 5.1712 .90852

EXR 401 3.50 7.00 5.3292 .67262

INF 401 1.67 7.00 5.2704 .89734

Valid N (listwise) 401

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Table 1 above gives the descriptiveanalysis of all the variables. Following the result above, all the

variables have average score more than 5= agree which shows that Ghanaian bankers believe that these

variables (ENC,INR,UNE,EXR,INF) have significant relationship with the NPLs. The descriptive statistics

result agrees with the findings of Farham et al (2012) for all the variables except exchange rate which in their

studies found the average score to be less than 3.

Regression Result

Table 2: Model Summary

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

Estimate

1 .830a .689 .652 .61869

a. Predictors: (Constant),ENC,INR, UNE, EXR, INF

Deducing from table 2 above, gives R value of 0.830, suggesting that, the regression model between the

dependent variable and the established predictors is appropriate. The R square figure of 0.689 indicates that,

reliance on this model will account for 68.9% of the changes in the dependent variable (NPLs). Therefore,

according to the result economic variables represent or can cause 68.9% variation in the NPLs in the

Ghanaian banking sector.

Table 3: ANOVAb

Model Sum of Squares df Mean Square F Sig.

1

Regression 28.048 4 7.012 18.319 .000a

Residual 12.632 33 .383

Total 40.680 37

a. Predictors: (Constant), ENC,INR, UNE, EXR, INF

b. Dependent Variable: NPLs

From Table 3 above, the regression source of variation of 28.048 is higher than the residual variation of

12.632. Thus, the regression model is able to explain larger portion of the disparities in the dependent

variable(NPL) than the residual source of variation, thus, making the model reliable. This is confirmed by the

result showing statistical significant (0.00), which is indicating that model is a good fit.

Table 4: Coefficientsa

Model Unstandardized Coefficients

Standardized

Coefficients T Sig.

B Std. Error Beta

(Constant) 8.515 .964 8.832 .000

ENC .660 .116 .529 5.671 .000

INT .068 .091 .075 4.746 .008

UNE .201 .097 .252 2.085 .040

EXC .456 .109 .397 4.178 .000

INF .035 .070 .056 .502 .617

a. Dependent Variable: NPL

According to table 4 above, unemployment shows beta value of (0.252) which is statistically significant

(0.040) at 5 percent level of significance. The result indicates that bankers in Ghana perceive that

unemployment has a significant positive relationship with the NPLs. Findings also support the earlier studies

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(Nkusu, 2011; Bofondi & Ropele, 2011; Farham et al, 2012). The Ghanaian banking sector has been

affected, due to unemployment which continue to increase at an increasing rate in the country. If businesses

are operating under capacity due to low demand as a result borrowers unemployed, borrowers cannot afford

to pay any loan facility since the person is jobless and that the borrower will be interested to surviving via

shelter than servicing loan.

Unemployment as it is known in research cycles, is a factor which has caused a huge volume of NPLs in

the consumer financing. If one cannot have any source of income then one cannot be expected to pay loan

installments hence loans turning into NPLs. Therefore, if people can get jobs to have source of income then

demand for goods and service would increase triggering increase in sales of businesses and ultimately

position businesses and individuals to honor loan obligations. However, the reverse of this situation is what

has been observed in Ghana. Base on the finding of the study the hypothesis 1 is accepted.

Again, table 4 above, inflation shows beta value of (0.056) which is statistically insignificant (0.617).

So therefore, the result indicates that bankers in Ghana perceive that inflation has no significant relationship

with the NPLs, but the result showed positive relationship. This result is accepted because in Ghana inflation

figure does not support the reality on the ground. In most cases it is believed that politician manipulate the

result to gain political capital. Several inflation figures have been challenged by key stakeholders and

economists in Ghana. On account of the result the hypothesis 2 is rejected.

Energy crisis shows a beta value of (0.529) which is statistically significant (0.000) at 1 percent level of

significance. This result points out that the perception of bankers in Ghana about energy crisis has a

significant positive relationship with NPLs and is consistent with the study conducted by Farham et al.

(2012). Energy crisis from 2005 to date in Ghana has crumbled industries, and have cause businesses to shut

down operation in the period and this went on to affect the banking sector since businesses were not able to

repay facilities given them. Cheap and constant flow of energy is necessary for the development of the

economy of any country, because energy is an essential and complementary part of production (Lee, 2005).

Ghana’s energy crisis from 2005 to date caused most business become sick hence huge level of NPLs

hanging on the heads of these businesses. Since the inception of energy crisis in Ghana most of the evil loans

of businesses have been caused by the undecorated energy crisis in the country. Farham et al. (2012) state

that energy crisis does not only affect the production of businesses but affects also the debt servicing capacity

of the borrowers as alternative sources to produce the energy are very costly causing huge cost of production.

In light of the result, hypothesis 3 is accepted.

Exchange rate shows beta value of (.397) which is statistically significant (0.000) at 1 percent level of

significance. The result indicates that bankers in Ghana perceive that appreciation in exchange rate has a

positive significant relationship with the NPLs thus; the finding is consistent with the earlier studies

(Khemraj& Pasha, 2009; Farham et al., 2012). Ghana Cedi is consistently depreciating to the major trading

currencies and this increases direct cost of production hence higher price and because high level of

employment in the Ghanaian economy businesses make low sales hence inability to repay loan facilities.

Therefore, in light of the result the hypothesis 4 is accepted.

According to table 4 above, interest rate shows beta value of (0.075) which is statistically significant

(0.008) at 1 percent level of significance. The result shows that perception of bankers’ in Ghana is that

interest rate has a significant positive relationship with NPLs. The finding is consistent with the earlier

studies (Nkusu, 2011; Adebola et al., 2011). Ghana like any other country where energy crisis and other

external forces have affected the servicing of debt by borrowers so significantly and has cause banks to

increase interest rate due to high risk and has badly affects the loan repayment capacity of borrowers. On

account of the result the hypothesis 5 is therefore accepted.

5. Conclusion

NPLs are dangerous to Sub-Saharan Africa countries economy particular Ghana which is an emerging

economy, so there is the need to identify the economic factors responsible for NPLs. It is suggested that once

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these factors are identified then policies can be made to prevent any future occurrence (Adebola, et al.,

2011). NPLs were the cause of global financial crisis and financial instability in the recent pass. In Ghana,

studies on determinants of NPLs used secondary data; however, this study looked at bankers’ perception in

Ghana, those who are responsible for loaning decisions and handle the NPL portfolios. Primary research was

designed to come to the understanding of economic variables of NPLs in the Ghanaian banking industries

with the aid of questionnaire. And also this study is the first of its kind to the best of the researchers’

knowledge, to have used primary data to establish the economic determinants of NPLs in Ghana.

In this study all the hypothesis were accepted except hypothesis 2. The Regression result analysis

pronounced that Interest Rate, Energy Crisis, Unemployment, and Exchange Rate have a significant positive

relationship with NPLs while Inflation has insignificant positive relationship with the NPLs. In light of the

result, it is believed that the increase in NPLs is as a result of poor energy supply in the energy sectors

alongside with unfortunate economic conditions are the main variables responsible for NPLs in Ghana.

The result also shows that the module used was good fit and that economic determinants of NPLs

accounted for 68.9%. Going forward government of Ghana should put proper policies in place to address the

economic conditions in the country to help reduce the NPLs because a sound and stable financial sector is an

indicator of economic resilience.

The study emphasized on the economic variables of NPLs. By extension future studies could be carry

out to investigate social and political factors of NPLs in Ghana as it was suggested in the study of Farham et

al., (2012) in the case of Pakistan, so as to have the full understanding of the situation and thereby address

the problem of NPLs which has been a blood licker in the banking sector in Ghana.

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