ASSESSING THE IMPACT OF NON-PERFORMING LOANS ON PROFITABILITY …cird.online/EJFAI/wp-content/uploads/2018/10/CIRD-EJAFI... · 2018-10-30 · The results of the study show that non-performing

European Journal of Accounting, Finance and Investment Vol.4, No.10; 2018;

ISSN (3466 – 7037);

p –ISSN 4242 – 405X

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European Journal of Accounting, Finance and Investment

An official Publication of Center for International Research Development

Double Blind Peer and Editorial Review International Referred Journal; Globally indexed

Available www.cird.online/EJFAI: E-mail: [email protected]

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ASSESSING THE IMPACT OF NON-PERFORMING LOANS ON

PROFITABILITY OF TANZANIAN BANKS

Josephat Lotto

Department of Accounting and Finance, The Institute of Finance Management, Tanzania, East Africa;

* Correspondence: [email protected]

Abstract:This paper primarily aimed at examining the relationship between NPLs and the bank profitability for Tanzanian banks

during 2009 and 2015. The results of the study show that non-performing loans indicated a negative impact on profitability. This

relationship demonstrates that NPLs are the financial indicators of the banks’ loan quality. The more the NPLs the lower is the quality

of bank loans and, hence, the lower the bank profitability. On the other hand, when the level of NPLs increases banks may use the

little profits they earn to serve the customers who come for withdrawals. Regarding capital adequacy, the results show a positive

significant relationship between capital ratio and bank profitability. This may be supported by the portfolio theory which insists a

direct relationship between risk and returns. Based on this theory, the capital adequacy requirement is meant to stabilize the banks and

ultimately minimizes banks’ portfolio risks while trying to maximize profitability. Bank size had a positive significant relationship

with profitability aligning to a common explanation that larger banks always have larger asset levels which generate more income for

the banks and ultimately increases the profitability. On the other hand, the bank liquidity shows a positive significant relationship with

bank profitability. The result of the liquidity-performance nexus was expected because bank profitability is commonly improved for

banks that hold liquid assets. The paper has some recommendations to make; Firstly; banks’ management are obliged to ensure that

the credit officers do a reasonable due diligence by strictly sticking on the guidelines and strictly know the borrower when giving out

credit facilities. Secondly; the paper recommends the promotion of bank capitalization to improve the bank performance where the

habit of banks to retain more earnings is encouraged to increase the bank’s capital base.

Keywords: Credit risk, Capital adequacy, Profitability, Performance, ROE, ROA

1. Introduction

Throughout the world the banking sector has been the most

complex sector to manage due to rapid growth of financial

market as reflected by Thiel (2011). The complexity of the

banking sector is fostered by the banking innovation in its

operations and products. Such complexity increases the level of

risk profile of banking institutions which may ultimately end up

to market failure. Governments of different countries including

Tanzania have been very careful to prevent their respective

markets from failing by instituting the regulatory reforms such

as setting the minimum capital requirements. Thiel, (2011).

According to Lotto, (2016), in the modern banking regulation

the minimum capital requirements play a very important role.

The banking industry worldwide has witnessed implementation

of the so-called Basle Accord that sets minimum capital

standards for internationally active banks. During the 1970’s and

1980’s there existed several challenges posed by momentous

declines in the banks’ capital ratios resulted into bank

insolvencies and ultimately failures, Lotto, (2016). Following

this, bank regulators have put their attention on the minimum

bank capital requirements to boost the financial system stability.

A crucial step in that direction was the 1988 Basel Capital

Accord which was the agreement for banks among G-10

countries on minimum risk-based capital requirements of 8% to

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total assets. (BIS, Implementation of Basel II, 2004,

www.bis.org)

In Tanzania, to strengthen the banking sector and extend the

financial sector Bank of Tanzania set a relatively higher

minimum regulatory capital ratio compared to the one stipulated

in the Basel (I-III) which is 8% for total capital and tier-1 capital

4.5% and tier-2 capital 6%. Apparently, Banking and Financial

Institutions Act (2014) requires a bank or any financial

institution, at any time, to maintain; Core capital of not less than

twelve and one half per cent of its total risk-weighted assets and

off-balance sheet exposure; and total capital of not less than

fourteen and one half per cent of its total risk weighted assets

and off- balance sheet exposure. However, regardless of the

capital restrictions instituted by Bank of Tanzania the credit

problem related to capital has been a critical problem to several

banks in Tanzania, in the recent time. For instance, the recent

case of Twiga Bancorp reveals the credit problem which resulted

the bank to be put under receivership by BOT. Similarly, in

Uganda, in 2016, the similar circumstance has happened for

Crane bank where the Central Bank of Uganda took over the

control of the bank due to financial crisis that left the bank

without the sufficient capital, The East African, October 28th

(2016).

According to Golin, (2013), most banks often face severe

problems related to credit risk which is considered as the core

risk category in the banking industry. This is reflected by the

level of non-performing loans which banks hold in their balance

sheets. Therefore, putting more emphasis on the significance of

credits in the banking industry and their consequences in the

economy and the performance of the banking sector, it becomes

more interesting to examine the impact of credit on banks

profitability.

Theoretical and empirical evidence such as Padmanabhan (1998)

and Agu (1998) propose that credit risk management is an

important determinant of bank`s performance. Such evidence

confirms that non-performing loans can trim down the value of a

bank and undermines the credit system. According to the

arguments of Agu, (1998) delayed payment of loan or defaulting

reduces the resource-base of a bank for future lending,

deteriorates staff moral and affects the borrower’s confidence.

Ultimately, the management of overdue loans tends to be very

difficult and costly. This may decrease banks’ profitability levels

and sometimes affect negatively the banks’ other customers as

they are forced to carry the burden of unpaid loans in the form of

high interest margin charged on loans.

Most recently, there have been some cases of banks’

performance drop due to various reasons. According to the

Citizen of January 31st, 2017, as quoted “the banks used to post

double digits in annual profit growth that has slowed down for

the whole industry even though some individual banks may be

doing well but overall growth is subdued”. It is learnt from this

news that the factors which caused all these drifts are non-

performing loans and liquidity problem. According to the

Citizen of January 31st, 2017 the liquidity problem is said to be

caused, to a great extent, by the order of the government to

instruct all government institutions, agencies, parastatals and

local government to transfer all their funds to Bank of Tanzania.

Following this order about TZS 500bilion which were on hands

of commercial banks were transferred to BOT. This caused

liquidity problems to such commercial lenders. Another problem

which caused the reduction of credit to private sector and slow

money circulation is the austerity measures taken by the

government to introduce the VAT on the financial services

charges.

Of interest is the known financial crisis faced by TWIGA

BANCORP, a government owned bank in Tanzania, whose

affairs were temporarily handled by the Bank of Tanzania in

mid-2016 following extreme poor performance of the bank

which threatened its customers’ deposits. The Citizen newspaper

of October 30th, 2016, in its article titled “How ghost borrowers

brought down Twiga Bancorp” reported the problem of the bank

as lending money to customers without a thorough scrutiny of

their behaviours and financial positions. This ultimately led to

increase in the number of defaulters to an unmanageable level.

According to the Controller and Auditor General 2012/13’s

reports the interest of about TZS 584milions were unable to be

matched with the specific borrowers. There seems to have been a

very poor credit management practice at the bank. Twiga

Bankorp is just an extreme case as other banks are also faced by

similar challenges. It is, therefore, interesting to know how risk

management practice influences the banks’ performance in

Tanzania.

In the DBS Annual Report, (2016), it is shown that “The ratio of

Non-Performing Loans (NPLs) to Gross Loans for the year

ended December 2016 increased to 10.27 percent as compared to

7.88 percent recorded in the previous year, which was an

increase of 2.39 percentage point. The increase of NPLs was due


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to various factors including slowdown in lending activities,

hardcore credits arising from the global financial crisis of 2008

and slowdown in global trade. Likewise, the ratio of NPLs net of

provisions to total capital increased to 23.56 percent from 18.59

percent recorded in 2015 indicating that should all NPLs

deteriorate to loss, the banking sector’s capital will be eroded to

a maximum of 23.56 percent”. (DBS, cit., p. 18)”.

The objective of this paper is, therefore, to assess the impact of

non-performing loans on the profitability of the commercial

banks in Tanzania. It is hypothesised that the increase in non-

performing loans has a decreasing effect to banks’ profitability.

This paper is the first one to study the relationship between

NPLs and the bank profitability in Tanzania, and it contributes

significantly to literature in bank credit risk in Tanzania.

Likewise, the information derived from this study offers

guidance for banks’ managers, investors and bank supervisors to

understand how credit risk management is crucial in improving

the banks’ profitability.

This paper proceeds as follows; while the second part presents

both related theoretical and empirical literature, the third part of

the paper demonstrates the methodology, model specification

and data used in the paper. The fourth part of this paper presents

the empirical results and the last part concludes and provides the

policy implication and recommendations.

2. Related Literature

Bank risk management has attracted attention of the researchers

in the recent time as it is the most crucial determinant of

financial stability and economic development of the first word

economy countries as previously highlighted by Ferguson

(2003). On the other hand, Van Gestel and Baesens (2008) put

forward the correct risk management procedures as risk

identification, measurement and ultimately establishing the

management strategies.

Adeusi et al (2013) recognizes bank risk management to have

impact beyond the effect if causes to the bank performance.

According to the authors bank risk management has also a

significant influence on national economic growth and in the

development of the business environment. Among all the bank

risk categories, credit risk represents a significantly greater

portion of total bank risks as advocated by Gieseche (2004).

According to Gieseche (2004) a bank which properly manages

this kind of risk is in the better side n as far as its financial

performance is concerned.

The studies of this kind are steered by the theory of information

asymmetry which suggests that a non-alignment of information

between two parties such as buyers and sellers brings about

inefficient operation in a particular market as originally

propounded by George Akerlof, Michael Spence and Joseph

Stiglitz in 1970s. The concept of this theory is that; the absence

of full and complete information about the borrowers limits the

ability of the lenders/banks to differentiate between good and

bad borrowers. This may ultimately result into the problem of

moral hazards and adverse selection. According to Auronen

(2003) because lenders have more information about the lending

transaction than the borrower, they are in the position to

negotiate better terms for the transaction. The theory may

explain better the situation where the borrowers default and

create what is termed non-performing loans. According to the

theory, if lenders would have sufficient information about the

borrower which would indicate the inability of borrowers to

honour their obligations, the transaction would not be

completed, and the borrower’s application rejected outright.

However, due to the information asymmetry most lending

business complete successfully although later on borrowers

default. According to Akerlof (1970) there exist different types

of loan seekers, those with genuine business ideas and those

without. Those with genuine business are expected to be able to

honour their obligations of paying back their loans promptly

unlike those without genuine business ideas. Based on adverse

selection problem banks often time prefer to select high quality

borrowers through examination of available information. The

possible challenge during selection is the gathering of all

relevant information because most borrowers don’t disclose

negative information about their businesses.

On the other hand, based on moral hazards problem, the theory

proposes the information sharing as a way of minimizing the

default rate, interest rates and ultimately increases lending

volume. This may be because either reference bureaus foster

competition by limiting informational rents (Padilla and Pagano,

1996) or because they punish borrowers (Padilla and Pagano,

1997)

Several studies have been conducted on the relationship between

credit risk and bank profitability in various countries and the

focus was inclined on the way an effective credit risk

management can be able to resolve the performance problems of

banks related to credit issue, Alshatti, (2015). Most of these

studies support a positive effect of credit risk management on



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the bank performance and others support the reverse relationship

between the two variables. Among the studies which support a

positive relationship include; Ahmed et al (1998), Ahmad and

Ariff (2007), Boahene et al. (2012). On the other hand, studies

which show a reverse relationship include; Achou et al (2008),

Kargi (2011), Epure and Lafuente (2012), Kolapo et al (2012)

and Kithinji (2010)

Applying multi-variant regression analysis Ahmed et al (1998)

reveal a strong negative and statistically significant relationship

between credit risk and bank profitability in Nigeria. On the

other hand, Ahmad and Ariff (2007) report a positive

relationship between credit risk and profitability in Zimbabwe.

The authors realized that credit risk management practice in

developed countries is more superior to in developing

economies. Another study which revealed a positive relationship

between credit risk and bank performance is that of Boahene et

al. (2012) which used the sample examine the relationship of

selected banks in Ghana. According to the authors, the banks in

Ghana remain profitable regardless of the credit risk exposure

they are facing.

Achou et al (2008) conducted a study that examines the

relationship between profitability and credit risk and the result

showed considerable correlation between performance and loan

performance as the measure of risk management. Achou et al

(2008) insists a role of effective credit risk management on bank

performance and that credit risk management is crucial in

protecting banks’ assets and customers’ deposits.

Using Nigerian Sample from 2004 to 2008, Kargi (2011)

conducted a study that examined the association between the

credit risk variables and bank profitability. The findings of the

study revealed a negative influence credit risk management has

on bank profitability. In a similar vein, Epure and Lafuente

(2012) in their Costarican study revealed a negative relationship

between non-performing loans as a measure of credit risk and

profitability but the relationship changed to positive when the

credit risk was measured using capital adequacy.

A similar study conducted in Kenya by Kithinji in 2010 reveals a

negative impact on the banks performance. In a different facet,

Kolapo et al (2012) in their study on the relationship between

credit risk management and bank performance show that an

increase in credit risk has a negative effect on bank performance.

Most recently, Li et al (2014), Iwedi and Onuegbu (2014) and

Gizaw et al (2015) used the data set of Europe, Nigeria and

Ethiopia respectively to examine the relationship existing

between credit risk and bank performance. Although they used

different measures of credit risk and they used data collected

from different banking environment all found the similar

significant positive relationship between credit risk and bank

performance. However, Uwalomwa et al (2015) who also used

Nigerian data like Iwedi and Onuegbu (2014) examined the

similar relationship and came up with a significant negative

relationship between credit risk and bank performance.

3. Methodology

3.1 Data

The data employed in this paper is assembled from the

respective banks published annual financial reports for the

period between 2009 and 2015. The paper covers all 34

commercial banks operating in Tanzanian banking sector.

3.2 Model Specification and Variable Definition

This paper primarily aims at examining how credit risk

management influences the profitability of commercial banks in

Tanzania. Since the bank capital and risk are usually correlated

(they are endogenous) and are explanatory variables to each

other in their respective equations, we apply heteroskedasticity-

robust estimators of the variances using a robust regression

method. In this study we specify and test the following

regression model

1. ROA Equation

ROAit = a0 + a1*BSZit + CARit + a3*NPLit + b4* LIQit+ e1it

2. ROE Equation

ROEit = b0 +b1* BSZit + b2* CARit + a3*NPLit b4* LIQit + e1it

Where;

CAR=Capital Adequacy; the share of equity on total assets of the

bank. Capital Adequacy shows the strength of bank capital

against the vagaries of economic and financial environment

BSZ (Size of the bank): logarithm of total assets of the bank. Size

can show the economies of scale.

ROA (Profitability): Returns on Assets; this is the ratio of net

profit before tax to total asset. ROA depicts how the bank uses

its assets to generate profits

ROE (Profitability) = Returns on Equityshows the effectiveness

of management in the utilization of the funds contributed by

shareholders

NPL (Non-Performing Loans) - This is an indicator of credit risk

management. It particularly indicates how banks manage their

credit risk because it defines the proportion of loan losses

amount in relation to Total Loan amount



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LQ -This is measured as the ratio of Liquid Assets to Total

assets

The variables used in this study are summarized in table 1

below

Table 1: Definitions and sources of variables

Variable Definition Adapted From

ROA-Returns on Assets Net profit before tax to total asset Naceur, 2003; Ongore and Kusa, (2013)), Lottob , (2016)

Bank size (BSZ) The natural logarithm of total assets Boyd et al, (2009). Lotto , (2016)

ROE-Returns on Equity Net profit after tax to owners’ equity Khrawish, (2011); Lotto , (2016)

Capital Adequacy Equity-to-total assets Gul, (2011), Lotto , (2016)

Non-Performing Loan NPLs/Total Assets Lotto , (2016)

Liquidity Liquid assets/Total Assets Lotto and Mwemezi , (2016)

4. Empirical Results

4.1 Descriptive Statistics and Trend Analysis

Descriptive statistics in table 2 shows that over a period between

2009 and 2015 banks’ non-performing loans ratio had a

minimum of 0% and reached a maximum of about 33% with

average (mean) of 4.3%. The trend analysis presented in graph 1

shows that in 2009 the average level of NPL in the banking

sector in Tanzania was around 4% but the level picked up to

8.5% in 2011. This level abruptly dropped to around 3.8% in

2012 and then settled at the same level in 2013 before it slightly

increased to 4% in 2014. This roughly indicates that loan

defaulters highly increased between 2009 and 2011 but most

recently, between 2012 and 2014 the level of loan defaulting has

decreased. The fundamental possible cause of this dramatic

decrease in loan loss may be due to the issuance of banks’ risk

management guideline by BOT in 2010. We may argue that

BOT’s scrutiny on banks’ risk management practice, following

the issuance of the guideline, has increased the cautiousness of

banks during loans offering and hence more strictness in loan

assessment.

Capital Adequacy is an essential mechanism to protect banks’

solvency and profitability because the banks’ business is among

the riskier business in the financial market. The reason for this is

since there is a potential information asymmetry between the

banks and the borrowers which may result into loan defaulting.

This consequently leads to bank losses and, therefore, banks are

obliged to have adequate capital, not only to remain solvent, but

to avoid the failure of the financial system. The level of capital

adequacy in Tanzania is determined by the Bank of Tanzania.

Apparently, Banking and Financial Institutions Act (2014)

requires a bank or any financial institution, at any time, to



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maintain Core capital of not less than 12.5% of its total risk-

weighted assets and off-balance sheet exposure; and total capital

of not less than 14.5% of its total risk weighted assets and off-

balance sheet exposure.

A descriptive statistics table 6 shows that on average every bank

in the Tanzanian banking sector holds a capital requirement of

about 16.9%, a level well above the stipulated capital adequacy

requirement in the BFIA (2014). The maximum level of capital

adequacy reported in table 6 is about 94% while the minimum

goes as little as -0.003%. Although the Tanzanian banks, on

average, have the capital ratio above the requirement, most of

them are financed by roughly 17% equity showing that they rely

more on the long-term liabilities to finance their assets. The

average capital adequacy ratio of Tanzanian commercial banks

has taken a falling trend since 2009 to 2014 from around 18% in

2009 to around 7.5% in 2014 as stipulated in graph 2

Table 2: Descriptive Statistics

variable Obs. mean std.dev min max

CAR 200 .169304 .1334687 -.0003 .9446

ROA 200 -.010617 .0713755 -.382 .2302

ROE 200 -.0300525 .4990143 -5.0069 1.4051

NPL 197 .0427756 .0564863 0 .3324

BSZ 208 11.26123 1.7753124 7.435 14.814

LQ 196 .2176413 .2382629 0 .85

4.2 Regression Diagnostics

Before presenting the regression analysis, we test our model for

multicollinearity and heteroscedasticity. Multicollinearity is a

situation where the explanatory variables are nearly linear

dependent (Jurczyk, 2011, p. 262). In table 3 and table 4, we can

observe that the highest correlation among all the variables is -

0.3625 which is the correlation between CAR and ROA.

However, researchers always prefer an absolute value larger than

0.8 to be enough to cause multicollinearity (Studenmund, 2011,

p. 258). Considering that -0.3625 is quite far from 0.8, we

conclude that there is no problem of multicollinearity among our



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variables. The following tables show the results for multicollinearity test:

Table 3: Correlation Matrix for the regression 1 (ROE)

ROE NPL BSZ CAR LQ

ROE 1.000

NPL 0.0472 1.000

BSZ 0.1627 0.1975 1.000

CAR -0.0569 -.0.1028 -.0.2035 1.000

LQ -.0.0669 0.3104 0.2626 0.0497 1.000

Table 4: Correlation Matrix for the regression 2 (ROA)

ROA NPL BSZ CAR LQ

ROA 1.000

NPL 0.0479 1.000

BZS 0.1353 0.1975 1.000

CAR -0.3625 -.0.1028 -.0.2035 1.000

LQ 0.0229 0.3104 0.2626 0.0497 1.000

After the test for multicollinearity, we also performed a White

test for heteroscedasticity. Heteroscedasticity concerns if the

variances of the residuals are homogenous or not. It is another

requirement for conducting OLS regression. The results of

White test for regression 1 and 2 are presented in table 5 and 6.

The results demonstrate a Chi value that is greater than the

critical value, meaning that we could reject the hypothesis for

homoscedasticity.

According to Huber, (1980) the homoscedasticity assumption is

needed to show the efficiency of OLS. The heteroskedasticity

test shows that the variances of the OLS estimators are biased.

Thus, the usual OLS t statistics and confidence intervals are no

longer valid for inference problem. Using OLS estimator without

adjustment will render estimations biased. To solve this

problem, we can improve the OLS estimators by finding

heteroskedasticity-robust estimators of the variances using a

robust regression method. Robust regression is an alternative to

least squares regression when data are contaminated with

outliers or influential observations as in our case. Robust

regression is a good strategy since it is a compromise between

excluding some data points entirely from the analysis due to

errors and including all the data points and treating all them

equally in OLS regression. The idea of robust regression is to

weigh the observations differently based on how well behaved

these observations are. Roughly speaking, it is a form of

weighted and reweighted least squares regression.

Table 5: Test for heteroscedasticity for return on

equity ROE

White’s test for Ho: homoscedasticity



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against Ha: unrestricted heteroskedasticity

chi2 (20) = 110.84

prob > chi2 = 0.0000

Cameron & Trivedi’s decomposition of IM-test

Source chi2 Df. p

Heteroskedasticity 110.84 20 0.0000

Skewness 17.75 5 0.0033

Kurtosis 1.09 1 0.2962

Total 129.68 26 0.0000

Table 6: Test for heteroscedasticity for return on Assets ROA

White’s test for Ho: homoscedasticity

against Ha: unrestricted heteroskedasticity

chi2 (20) = 82.19

prob > chi2 = 0.0000

Cameron & Trivedi’s decomposition of IM-test

Source chi2 Df. p

Heteroskedasticity 82.19 20 0.0000

Skewness 5.67 5 0.3394

Kurtosis 10.42 1 0.00122

Total 98.28 26 0.0000

4.3 Regression Results

The regression analysis comprised of two profitability models,

one profitability model measured by ROE and the other one

measured by ROA as indicated in table 7 and 8 respectively.

The results presented in table 7 and 8 shows that Non-

performing loan as a measure of credit risk has a statistically

significant relationship with both ROE and ROA at 1%

significant level. Based on the correlation coefficient in table 7

and 8 we may conclude that for every one percent increase

Non-performing loan, assuming all other factors remain fixed,

ROE and ROA for commercial banks in Tanzania decrease by

1.04% and 0.11% respectively.

This relationship demonstrates that NPLs are the financial

indicators of the banks’ loan quality. Commercial banks are

often vulnerable to default risk or delayed payment of the

loans from the borrowers. Such default is recognised by banks

as loan losses and more of these losses negatively affect the

ability of banks to honour its lending function. The

consequence of this is the failure to maintain or increase the

efficiency of banks’ investment. Likewise, lower NPLs are

associated with decline in deposits rate which ultimately

impact on banks’ operation and profitability. The relationship

observed between NPLs and profitability, both measured as

ROE and ROA, is consistent with some previous studies such

as Kargi, (2011), Uwalomwa et al (2015) and Iwedi and

Onuegbu (2014)

in Nigeria; Epure and Lafuente (2012) in Costa-Rica; Ara et al

(2009) in Sweden and Felix and Claudine (2008) in Ghana.

Regarding capital adequacy, the results in table 7 and 8 shows

a strongly statistically negative relationship between both

profitability measures and capital adequacy at 1% significance

level. The correlation coefficient in table 7 and 8 show that for

every one percent increase capital adequacy assuming all other

factors remain constant, ROE and ROA for commercial banks

in Tanzania decrease by 0.45% and 0.07% respectively. The

explanation of this finding may be supported by the portfolio

theory. Normally, capital adequacy rules limit the banks to

adjust their capital structure flexibly. Realistically, a bank

explores the business opportunities which maximize the bank

profits.

According to the portfolio theory, corporate risks and returns

are directly related and the capital adequacy requirement is

meant to stabilize the banks and ultimately minimizes banks’

portfolio risks while trying to maximize profitability or

returns. At the very outset, therefore, the regulatory capital

adequacy ratio inversely relates to the banks’ profitability. All

these explanations reveal that the enforcement of the capital

adequacy norms would result into improvement of banks’

assets quality. It follows, therefore, that the higher the banks’

assets quality the higher is the expected bank profitability.

This finding is consistent with Berger and Patti (2006),

Fiordelisi et al. (2011) and Calomiris and Kahn (1991).



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Concerning the control variables bank size and liquidity, table

7 and 8 show that, the bank size has an expected positive

relationship with performance which is strongly statistically

significant at 1% significant level. This converges to a

common explanation that larger banks always have larger

asset levels which generate more income for the banks and

ultimately increases the performance. On the other hand, the

bank liquidity has a positive significant relationship with bank

performance. The relationship is reported to be significant at

1% significant level. The results of the liquidity-performance

nexus were expected and not a surprise because bank

profitability is commonly improved for banks that hold liquid

assets.

Table 7: Regression results on ROE Model

Robust regression Number of obs = 192

F (4, 187 = 34.95

Prob > F = 0

ROE Coef. Std. Err. t P> (t) (95% CI)

LQ .0726477 .034178 2.13 0.035** 0.0052237 0.140072

CAR -4521626 .0626407 -7.22 0.000*** -5757359 -3285894

NPL -1.04319 .1426411 -7.31 0.000*** -1,324,578 -0.76179

BSZ 0.025491 .0045428 5.61 0.000*** 0.0165294 0.034453

Cons -92023 .0533724 -1.72 0.086 -1973124 132664

** means statistically significant at 5% significant level, *** means statistically significant at 1% significant level

Table 8: Regression results on ROA Model

Robust regression Number of obs = 192

F(4, 187 =

29.92

Prob > F =

0

ROA Coef. Std. Err. t P> (t) (95% CI)

LQ 0.008345 0.005195 1.61 0.11 -0.001904 0.018599

CAR -0.07328 0.009521 -7.7 0.000*** -0.092062 -544973

NPL -0.11557 0.021681 -5.33 0.000*** -0.158341 -728016



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BSZ 0.003414 0.000681 4.94 0.000*** 0.0020518 0.004776

Cons -0.01339 0.008112 -1.65 0.101 -0.02939 0.002616

** means statistically significant at 5% significant level, *** means statistically significant at 1% significant level

5. A Concluding Remark and Recommendations

This paper primarily aimed at examining the relationship

between NPLs and the bank profitability for Tanzanian banks

during the period between 2009 and 2015. The results of the

study show that non-performing loans indicated a negative

impact on profitability. It is a clear fact that banks generate loans

from customers’ deposits and therefore the inability of banks to

recover the loans issued will affect the profit which the banks

earn because the profits will be used to serve the customers who

comes for withdrawals. The relationship reported between non-

performing loans and performance demonstrates that NPLs are

the financial indicators of the banks’ loan quality. Commercial

banks are often vulnerable to default risk or delayed payment of

the loans from the borrowers. Such default is recognised by

banks as loan losses and more of these losses negatively affect

the ability of banks to honour its lending function.

Regarding capital adequacy, the results show a positive

significant relationship between capital ratio and bank

profitability. The explanation of this finding may be supported

by the portfolio theory. Normally, capital adequacy rules limit

the banks to adjust their capital structure flexibly. Realistically, a

bank explores the business opportunities which maximize the

bank profits. According to the portfolio theory, corporate risks

and returns are directly related and the capital adequacy

requirement is meant to stabilize the banks and ultimately

minimizes banks’ portfolio risks while trying to maximize

profitability or returns. At the very outset, therefore, the

regulatory capital adequacy ratio inversely relates to the banks’

profitability. All these explanations reveal that the enforcement

of the capital adequacy norms would result into improvement of

banks’ assets quality which ultimately improves bank

profitability.

Bank size had a positive significant relationship with

profitability which aligns to a common explanation that larger

banks always have larger asset levels which generate more

income for the banks and ultimately increases the profitability.

On the other hand, the bank liquidity shows a positive significant

relationship with bank profitability. The result of the liquidity-

performance nexus was expected and not a surprise because

bank profitability is commonly improved for banks that hold

liquid assets.

Finally, the paper provides several recommendations to banks to

reduce the effect of credit risk. Firstly; banks’ management are

obliged to ensure that the credit officers do a reasonable due

diligence by strictly sticking to the guidelines and strictly know

the borrower when giving out credit facilities. Secondly; the

paper recommends the promotion of bank capitalization to

improve the bank performance. In this recommendation the

paper encourages the habit of banks to retain more earnings

instead of distributing such large sums as bonuses to increase the

bank’s capital base.

In future this paper suggests the coming studies to examine the

impact of credit risk management on individual bank

profitability as every bank has different levels of credit risk

capital ratio.

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