Strathmore
UNIVERSITY
THE IMPACT OF DELINQUENT LOANS ON THE PERFORMANCE OF
MICROFINANCE BANKS IN EAST AFRICA
KAVILI JANET KALI
Student Number 078298
A Research paper submitted in partial fulfillment of the requirements for
The Degree of Bachelor of Business Science in Actuarial Science
Strathmore Institute of Mathematical Sciences
Strathmore University
Nairobi, Kenya
November, 2016
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DECLARATION
I declare that this work has not been previousl y submitted and approved for the award of
a degree by this or any other University. To the best of my knowledge and belief, the
Research Proposal contains no material previously published or written by another person
except where due reference is made in the Research Proposal itself.
© No part of this Research Proposal may be reproduced without the permission of the
author and Strathmore University
.k0.\(tJi J0.l~.~t. ~0.\.; [Name a/Candidate]
.~.: [Signature]
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This Resea rch Project has been submitted for examination with my approval as the
Supervisor.
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.. . . ..: [Slgnature]
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Strathmore Institute of Mathematical Sciences
Strathmore University
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Contents
1 Introduction 1
1.1 Background to the study 1
1.2 Problem Staternent. 3
1.3 Research Hypothesis 3
2 Literature Review 4
2.1 Theoretical Literature Review 4
2.1.1 Asymmetry Theory 4
2.1.2 Moral Hazard Theory 5
2.2 Empirical Literature Review 6
2.2.1 Delinquent Loans 6
2.2.2 Impact of Delinquent Loans on Banking Institutions 7
3 Research Methodology 9
3.1 Introduction 9
3.2 Population and Sampling 9
3.3 Data and data sources ; 9
3.4 Data Analysis Method 9
3.4.1 Measuring Non-Performing Loans 10
3.4.2 Financial Performance measures justifications 11
4 Data Analys is, Resul ts and Discussions 12
4.1 Descriptive Statistics 12
4.2 Inferential Statistics 12
4.2.1 Correlation Analysis 12
5 Summary, Conclusions and Recommendations 15
5.1 Introduction 15
. .' . . .
5.2 Summary ~ 15
5.3 Conclusion : 15
5.4 Recommendations 16
5.5 Limitations of the Study 16
5.6 Suggestions for Further Research 16
References 17
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l List of Abbreviations
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MFBs Microfinance Banks
NPLs Non-performing Loans
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CBK Central Bank of Kenya
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FP Financial Performance
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TNT Interest Income
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BS Bank Size
1 ROA Return on Assets
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ROE Return on Equity
1 OLS Ordinary least squares
1 GMM Generalized Method of Moments
J FE Fixed Effects
J RE Random Effects
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1 Introduction
1.1 Background to the study
Financial institutions all over the world are faced with the challenge of loan delinquency which
has necessitated the need for reviewing lending policies to mitigate the delinquency risk as well
as putting in place mechanisms that monitor the behavior of borrowers. Irena (2014) notes that
loan default was one of the major causes of the financial crises of 2008 and consequently, after
the Global Financial Crisis, credit management increased, especially with the purpose of
improving the resiliency of the banking sector by requiring more and higher quality capital and
more balanced liquidity.
Per a study by Alawiye (2013) credit administration and the incidence of bad loans, increase in
the loses ofNigerian Banks result from problem loans and the effects of such loans in the form of
bad debt provisions can be minimized through effective monitoring and evaluation to avoid the
diversion of facilities for unapproved purposes. Similarly, Arko , (2012) , in a study on the causes
and impact ofnonperforming loans on the operations of microfinance institutions in Ghana , states
that some of the loans advanced to beneficiaries underperform and do not earn the projected
returns resulting into the reduced quality of the loan portfolio which constitutes a considerable
percentage of the assets of the MFIs. In regards to these studies, the importance of credit risk
management cannot be taken for granted and this explains why the practice has increased for both
borrowers and lenders.
The Microfinance sector III East Africa has undergone numerous reforms with the aim of
improving delivery of microcredit to the poor as well as small and medium sized Entrepreneurs.
However, despite the notable progress, loan delinquency is still a major problem that undermines
profitability in institutions. Bikki (2003) states that the microfinance institutions in Tanzania
continue to face the major risk of loan delinquency despite stringent measures like compliance of
Microfinance banks to with Bank of Tanzania prudential standards and internationally accepted
sound banking practice by setting aside adequate reserves to protect against anticipated loan
delinquencies.
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Loses in microfinance institutions have resulted from extending huge amounts of credit with little
or no credit assessment with the aim of increasing profitability, loans which later materialize into
non-performing loans and subsequently erode profits of the institutions (Wangai, 2014). This is
especially evident in Kenya where the Central Bank of Kenya (CBK) sector report for 2013 and
2014 listed the following statistics: bank loans in the amount of Kshs 80.6 billion as at December
2013 had not been serviced for more than three months, which was an increase from Kshs 61.6
billion the previous year.
A new directive by the CBK resulted from the record of high delinquency loans that required
lenders to classify as non-performing loan accounts of a borrower who defaults on the repayment
of any one of their multiple loans for more than three months. According to Wangai (2014), High
interest rates in the year 2012, and the introduction of CBK prudential guidelines obliged banks
to set aside additional cash as provision for defaults measures necessary to delve into how NPLs
affect financial performance in microfinance banks in Kenya.
Digging deeper into the issue of non-performing loans and their effect on profitability, A social
research Commissioned by the Austrian Ministry of Foreign Affairs of Micro finance in Uganda
notes that a significant number ofMFls in the country (Uganda) that are making progress in terms
of profitability have taken important steps towards tackling the issue of non-performing loans by
professionalization and transformation into well-organized, well-managed and commercially
viable institutions that provide financial services to an increasing number of clients with proven
poverty reducing impact. The research states that once the bigger microfinance institutions had
embraced professionalism none faced serious delinquency problems.
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1.2 Problem Statement
Financial institutions face the enormous risks of non-performing loans (NPLs) which adversely
affect their financial performance. Loan delinquency among Microfinance institutions in East
Africa has become so crucial , which, as Warui (2012) argues may be a pointer to increasing
ineffectiveness of financial Institutions. Because of the far-reaching implications of NPLs on
Microfinance Banks as well as the overall economy, stringent evaluation and monitoring
strategies have been put in place by the institutions to ensure repayment of loans by borrowers to
mitigate NPLs . In Kenya , The Central Bank of Kenya has set prudential guidelines which oblige
banks to set aside additional cash as provision for defaults and in Tanzania, Microfinance banks
are to comply with Bank of Tanzania prudential standards and internationally accepted sound
banking practice by setting aside adequate reserves to protect against anticipated loan
delinquencies. Extensive research has been done on the impact ofdelinquent loans on Commercial
banks, however, there has been limited research done on the impact of such loans on Microfinance
banks in the East Africa. From existing literature , it has already been deduced that these loans do
affect the profitability of Microfinance banks, therefore with limited empirical evidence of this
impact on microfinance banks in East Africa there exists a gap that this research seeks to fill by
addressing the question of: to what extent delinquent loans affect the financial performance
microfinance banks in East Africa.
1.3 Research Hypothesis
The main objective of this study is to analyze the impact of delinquent loans on the financial
performance of Microfinance Banks in Kenya , Tanzania and Uganda.
In conceptualizing the impact of delinquent loans on FP of Microfinance banks , it is considered
that interest income is significantly influenced by delinquent loans.
The research hypothesis is:
H0: Delinquent loan does not have a significant impact on interest income
Hi: Delinquent loan has a significant impact on interest income
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2 Literature Review
2.1 Theoretical Literature Review
2.1.1 Asymmetry Theory
The theory explains that in an economic transaction the part y that possesses more information on
the specific product to be transacted is in a position to negotiate optimal terms for the transaction
than the other party. (Auronen,2003) In a lending transaction asymmetry information makes it
difficult to distinguish between a good and bad borrower and according to Bester(l985) the party
that knows less about the same specific item to be transacted is therefore in a position of making
either right or wrong decision concerning the transaction. The adverse selection has led to
significant accumulation of Non-Performing loan in banks, therefore, information sharing is said
to reduce adverse selection by enhancing banks ' information on credit applicants.
According to Wangai (2014) , in a world with simple debt contracts between risk-neutral
borrowers and lenders , the presence oflimited liability of borrowers imparts a preference for risk
among borrowers , and a corresponding aversion to risk among lenders. This is because the limited
liability of borrowers implies that lenders bear all the downside risk. On the other hand, all returns
above the loan repayment obligation accrue to borrowers. It is further asserted that,just like moral
hazard, adverse selection can lead to significant accumulation ofNPLs. (Bofondi,2003)
The effect of asymmetry information and adverse selection on loan delinquency can further be
illustrated in joint liability lending. Beatriz (2004) states that in an economy where there exists
safe and risky borrowers who know each other's risk type assortative matching will be induced,
where the safe borrowers will group themselves with other safe borrowers and risky borrowers
will have no choice but to form groups with other risky borrowers who opt to default more often.
Ghatak (1999) states that all borrowers prefer to have safe partners because of lower expected
joint-liability payments, in the case ofjoint liability , borrowers therefore end up with partners of
the same type and as a consequence, the lender can screen borrowers by varying the degree of
joint liability because risky borrowers have risky partners and hence, will prefer a contract with
less joint liability than will a safe borrower for the same reduction in interest rates . This creates a
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situation where the safe borrowers might be forced out of the credit market because the presence
of risky borrowers drives the interest rates from credit institutions too high.
While all borrowers face exactl y the same contracts with exactl y the same interest rates , the fact
of assortative matching means that safe borrowers pay lower effective interest rates because their
expected costs will be lower. This in tum encourages them to enter the credit market, take loans ,
and improve efficiency which lowers the average incidence ofdefault and thus lowers costs, group
lending thereby can eliminate adverse selection inefficiencies.
2.1.2 Moral Hazard Theory
Moral hazard is a consequence of concealment of crucial information pertaining to a transaction
such as a lending and borrowing transaction. According to Wangai (2014), Moral hazard refers
the risk in which a party to a transaction provides misleading information about its assets,
liabilities or credit capacity, or has an incentive to take unusual risks in a desperate attempt to earn
a profit before the contract settles. Usually, a party to a transaction may not enter into the contract
in good faith , thus providing misleading information about its assets , liabilities or credit capacity.
Information from such transactions is vital for the efficiency of financial institutions on which the
economic growth rate is dependent on. According to Marcello (2003), moral hazard has led to the
substantial accumulation of NPLs and the financial systems themselves depend on accurate
information about borrowers and the project the funds are used for. The "moral hazard"
hypothesis, which was discussed by Keeton and Morris (1987) in their study of why banks' loan
losses differ. , argued that banks with the relatively low capital respond to moral hazard incentives
by increasing the riskiness of their loan portfolio, which in tum results in higher non-performing
loans on average in the future.
Banks that tend to take more risks , including in the form of excess lending eventually absorbed
higher losses and excess loss rates were prominent among banks that had relatively low equity-
to-assets ratio . Morris (1996) stated that the accumulation of NPLs is generally attributable to a
number of factors , including economic downturn, macroeconomic volatility, and terms of trade
deterioration, high interest rate and excessive reliance on overly high-priced inter-bank
borrowings, insider borrowing and moral hazard.
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2.2 Empirical Literature Review
2.2.1 Delinquent Loans
The term Non-Performing Loans is used interchangeably with Bad loans and impaired loans as
identified in (Fofack, 2005) . A Non-Performing Loan is a loan that is in default or close to being
in default. A loan is non-performing when payments of interests and principal are past due by 90
days or more, or at least 90 days of interest payment have been capitalized, refinanced or delayed
by agreement, or payments are less than 90 days overdue, but there are other good reasons to
doubt that payment will be made in full. (IMF, 2009)
According to Bismark (2015) , there are several definitions to a delinquent loan, a loan is said to
be delinquent if the debtor is either unwilling or unable to pay the amount borrowed when it is
due or when the debtor does not make required payments or comply with the loan covenant or
agreement between the borrower and the lender. The delinquent loans can also be termed as loans
for which both principal amount and interest charges are outstanding contrary to the terms and
conditions of the loan agreement between the borrower and the lender and mostly are at least 90
days overdue. Delinquent loans also are referred to as non-performing loan or impaired loans or
bad loans or even problem loans, are loans that are ninety days or more past due.
Delinquent loans have created a global problem in the banking industry and in Indonesia where
NPLs represented about seventy-five percent of total loan portfolios shows that there were
increase in the NPLs in the period leading up to the financial crisis and over sixty banks collapsed
during the crisis. (Cortavarria, 2000) The growth of banks in both Malaysia and Singapore was
constrained by banks which faced the accumulation of NPLs that eroded their capital. (Karim,
2010)
Gulf Cooperation Council (GCC) in the Middle East had a favorable credit conditions and lower
NPLs as well as significant banking credit between 2003 and 2008 but in 2009 the global crisis
exposed the vulnerabilities of the banks in the Gulf Cooperation Council (GCC) and the NPLs
rose sharply and credit stagnated, causing worries that economic recovery could be stagnated by
credit constraints. NPL levels increase as the economic situation deteriorates and interest
payments rise. (Raphael , 2010)
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2.2.2 Impact of Delinquent Loans on Banking Institutions
The financial performance of banking institutions is measured in terms of profitability and NPLs
have a direct adverse impact on that profitability. As stated earlier the loan portfolio forms the
greatest asset of a microfinance institution. Revenues of banks depend on lending and as a result,
the financial performance and success of banks depend on how effectively lending activities are
managed because the revenue (interest income) is usually generated from performing loans.
Among the studied impacts , one effect of delinquent loans on the institutions is paramount;
limitation on FP. The provisions for delinquent loans reduce total loan portfolio of banks and
consequently reduce the interest earnings on such assets . (Bismark, 2015). According to Kwan
(2003), in his study of the Operating Performance of Banks among Asian Economies empirically
reveals, using regression analysis that an increased delinquent loans negatively influence earnings
on loans.
According to Achou (2007) , NPL ratio has an inverse relationship with banks ' profitability. An
increase in NPLs rate is evidence of the failure of credit and according to Khemraj (2009), high
percentages NPLs are often associated with performance problems of banks and financial crises
in both developing and developed countries. The occurrence of banking crises with a massive
accumulation ofNPLs and further observes that the NPLs account for a significant portion oftotal
assets of insolvent banks and financial institutions. (Fofack, 2005)
Many banks in the Central , Eastern, and South-Eastern Europe (CESEE) region experienced a
rapid deterioration in assets ' quality, leading to substantial losses and reduction of capital buffers.
The fast increase in NPLs not only increased banks' vulnerability to further shocks but also limited
their lending operations with broader repercussions for economic activity (Klein,2013). In the
2012 study with the objective of examining the determinants of commercial banks ' performance
in Tanzania Xuezhi (2012) revealed that liquidity and asset quality had positive impact, whilst
NPLs had a negative influence on profitability.
The Government of Tanzania embarked on financial sector reforms in 1991 with the aim of
creating an effective and efficient financial system. The main and most notable reforms in
Tanzania's financial sector included liberalization of interest rates, elimination of administrative
credit allocation, strengthening the Bank ofTanzania's role in regulating and supervising financial
institutions, restructuring of state-owned financial institutions, and allowing the entry oflocal and
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private banks into the market. These elements of the financial sector reform were embodied in the
Banking and Financial Institutions Act of 1991. (Bikki, 2003)
Bikki (2003) also states that the microfinance institutions in Tanzania face the major risk of loan
delinquency and as precaution banks face a major disincentive to comply with Bank of Tanzania
prudential standards and internationally accepted sound banking practice by setting aside adequate
reserves to protect against anticipated loan delinquencies. Reserves for delinquent loans are
usually included as an item to be expensed in a bank's income statement, which would reduce its
net operating profits and taxable income.
In Kenya Wangai (2014) carried out a study in Nakuru (Kenya) to find out the Impact of Non-
Performing Loans on Financial Performance of Microfinance Banks in Kenya: A Survey of
Microfinance Banks in Nakuru Town, the results established that credit risk negates the
profitability of MFBs due to increment in NPLs and as such reduces the MFBs' financial
performance. Failing banks have huge amount of non-performing loans prior to failure and that
asset quality is a significant predictor of insolvency. (Allen, 1997)
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l 3 Research Methodology
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3.1 Introduction
This chapter presents the data and the methodology that will be used to conduct the research.
The Study adopts Panel Data Analysis techniques to answer the research questions.
3.2 Population and Sampling
The study uses panel data from Microfinance banks in a target population (the population the
study findings will be generalized to) specified to East Africa Region. The sample includes
microfinance banks from the following East African countries: Kenya, Uganda and Tanzania. The
criteria used to choose sample data include ease of obtaining data and a sufficient number of
Microfinance banks that exist in the mentioned countries. Panel Dataset of Individual Bank Data
will be used in the analysis.
3.3 Data and data sources
The sample data constitutes major microfinance banks from the three countries (Kenya, Uganda
and Tanzania) over the 2003-2009 period that have information available for all of the variables
analyzed. All data is sourced from the world MIX-database l
3.4 Data Analysis Method
The empirical work on determinants of bank's profitability can potentially suffer from three
sources of inconsistency: highly persistent profit, omitted variables, and endogeneity bias .
(Poghosyan, 2009). This study adopts panel data analysis techniques in the empirical analysis to
correct these potential problems. The linear panel data equation is specified as follows:
ROAit,k = ~1+ ~2 X2,it+ fJ3 X3,it + fJ4 X4,it +Eit
Where:
ROAit,k Represents the dependent variable, Return on Assets, ofa micro finance bank k in country
i during the period t.
X2,it Represents Nonperforming loans , measured using nonperforming loan ratio , of a
microfinance bank k in country i during the period t.
1 Data can be accessed from the MIX Market website on the following link with requisite payment
.and subscription: https://www.themix.org/mixmarket/data-and-analysis#datasets
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X3,it Represents Operational efficiency, measured as a fraction of operating expense to total
Loan portfolio, of a microfinance bank k in country i during the period t.
X4,it Represents Capital adequacy, measured in terms of-Equity (capital) to Asset ratio, of a
microfinance bank k in country i during the period t.
E Represents the error term.
There are two estimation methods that can be used in the panel data set: Fixed effects (FE) or
Random Effects (RE). The FE estimations allow for the unobservable bank heterogeneity and
different constants for each bank. However, the use of a fixed-effects model will eliminate the
time-invariant hidden bank features that affect profitability, and will make FE estimations less
efficient than the RE estimation counterpart. Like the FE model, RE estimations take into
consideration the unobservable bank heterogeneity effects but incorporate these effects into the
error term. In the RE also, constants for each bank are taken as random parameters and again
incorporated in the error term. The choice of the appropriate panel data model, fixed effects or
Random effects, is guided by the Hausman (1978) specification test.
3.4.1 Measuring Non-Performing Loans
According to Maudos (2004) , the bank loans over total assets ratio is mainly used as a measure of
bank liquidity or as a proxy for credit risk. Miller (1997) suggests a negative relationship between
credit risk and profitability because a higher loan to asset ratio increases the exposure of banks to
bad loans and hence lowers profit margins. On the other hand, standard asset pricing arguments
imply a positive relationship between risk and earnings.
Empirical studies find that a higher loan ratio is associated with higher interest margins, which
suggest that risk averse shareholders seek larger earnings to compensate higher credit risk
(Naceur, 2008), the following reviews gives rise to the research hypotheses in this study. In the
methodology adapted for calculation of the loan loss provision in the study by Naceur (2010),
credit risk is measured by loans over total assets similarly in this study non-performing loans
(independent variable) will be measured using the non-performing loan ratio given by non-
performing loans over the total loans.
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3.4.2 Financial Performance measures justifications
A study by Flamini (2009) on the Determinants ofCommercial Bank Profitability in Sub-Saharan
Africa uses Return on Assets as a measure of profitability defined as the banks ' after tax profit
over total assets. The study uses ROA as an alternative profit measure to ROE and justifies this
by stating that ROE disregards financial leverage and the risks associated with it. Similarly, a
study by (Saira , 2011) also uses ROA as a measure of profitability calculated by dividing net
income to total assets , this study therefore adopts the same methodology and uses ROA, as a
measure of profitability.
There are several studies that have been conducted to explain whether the capital structure and
capital adequacy determine the performance of microfinance institutions. In his 2007 study,
Kyereboah states that highly leveraged micro finance institutions have higher ability to deal with
moral hazards and adverse selection than their counterparts with lower leverage ratios. Wambugu
and Ngugi (2012) in the study on factors influencing financial sustainability of MFIs in Kenya
found a positive correlation between capital structure and financial sus tainability of MFIs.
According to Upchurch (2005) , capital adequacy is an important factor in the sustainability if any
given organization. With the support of the aforementioned research, this paper employs capital
adequacy as a control variable that affects performance of microfinance banks.
In this research operational efficiency is also used as a control variable, as it is believed to also
affect the performance of microfinance banks. Generally management competency can be
expressed through their ability to operate management expenses. Efficiency in expense
management should ensure a more effective use ofMFIs loan able resources, which may enhance
MFIs profitability. (Catherine, 2015). According to Basu, Blavy & Yulek (2006) ,Microfinance
institutions require ensuring high customer satisfaction, customers make their choice depending
on the perception quality, service and value they are receiving.
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4 Data Analysis, Results and Discussions
This chapter presents data analysis, results and discussion made from the study on the effects of
nonperforming loans on performance of microfinance banks in East Africa.
4.1 Descriptive Statistics
Regression analysis was done using the following variables: Return on Assets as the dependent
variable, non-performing loans as the independent variable and operational efficiency and capital
adequacy as control variables. The population consisted of Microfinance banks in East Africa
during the period 2003-2009, data is from the MIX database, data obtained was transferred to
Eviews 8 as variables for regression analysis and results were obtained. The sample consisted of
35.71% of the total population, the researcher is of the opinion that the data well represents the
population.
4.2 Inferential Statistics
4.2.1 Correlation Analysis
Before carrying out regression analysis the researcher sought to establish the correlation between
the variables. The results are shown below and as expected the correlation(r) between return on
assets and non-performing loans is negative.
Table 1: Correlation Matrix of Variables
Capital Return on Assets Operational Non -performing
Adequacy Efficiency loans
Capital 1.0000
Adequacy
Return 0.4576 1.0000
on Assets
Operational -0.3665 0.2278 1.0000
Efficiency
Non-performing 0.1256 .:0.0114 0.2851 1.0000
loans
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2 The matrix gives the correlation(r) between the pairs of all the data set. Negative correlation
indicates that variables move in the opposite direction and positive correlation signifies that both
variables move in the same direction. When r is +1, it signifies a perfect positive relationship.
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Regression Analysis
Regression analysis was used to determine the effects ofnon performing loans on the performance
of micro finance banks. To conduct the regression analysis, the following model was used:
ROAit,k = ~1+ ~2 Xit,k+fh Xit,k+ /34 Xit,k +Eit
Where:
ROAit,k Represents the dependent variable, Return on Assets, ofa microfinance bank k in country
i during the period t.
X 2,it Represents Nonperforming loans, measured using nonperforming loan ratio, of a
micro finance bank k in country i during the period t.
X3,it Represents Operational efficiency, measured as a fraction of operating expense to total
Loan portfolio, of a microfinance bank k in country i during the period t.
X4,it Represents Capital adequacy, measured in terms of Equity (capital) to Asset ratio, of a
micro finance bank k in country i during the period t.
s Represents the error term.
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Sample: 2003 2009
Dependent Variable: Return On Assets
Table 2: Regression Analysis
0.0708
Prob.
0.0062
0.0073
0.0517
2.9588
2.8919
t-Statistic
-2.0322
-1.87800.0280
Std. Error
0.0851
0.2572
0.0684
0.2462
0.2023
Coefficient
-0.0526
-0.5226
Variable
Periods included: 7
Operational Efficiency
Capital Adequacy
Intercept
Non-Performing Loans
3 Table 2 shows results from Regression Analysis when Return on assets is regressed on Non-
performing loans and the two control variables, operational efficiency and capital adequacy.
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Interpretation of results
The coefficient ofNon-performing loans is -0.52 with a p-value 5%, which means that an increase
in non-performing loans decreases the return on assets, as expected NPL has a negative impact on
revenue growth . The coefficient of capital adequac y is 0.25 with a p-value of 0.73%, significant
at a level of 5% significance. An increase in the capital adequac y therefore increases the return on
assets. The coefficient of operating efficiency 0.20 with a p-value of 0.62%, significant at a level
of 5% significance, therefore an increase in operating efficiency increases return on assets.
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5 Summary, Conclusions and Recommendations.
5.1 Introduction
The research set out to establish the impact of non-performing loans on the performance of
microfinance banks in East Africa. The following chapter presents discussions of the key findings ,
conclusion and recommendations from the findings .
5.2 Summary
The study is on the impact of delinquent loans on the performance of microfinance banks. The
key concepts include non-performing loans, which was measured using the non-performing loans
ratio and performance, measured in telms of Return on assets , in context of microfinance banks
in East Africa. The control variables considered are capital adequacy and operational efficiency.
The study used a descriptive design where the population comprised of major microfinance banks
in East Africa and the analysis covered the period 2003-2009.
A regression model was used to analyze the data. The findings concluded that delinquent loans
negativel y affects the performance of microfinance banks and that nonperforming loans ratio
measured by nonperforming loans over total loans, is a good measure of nonperforming loans as
the findings indicate that it is appropriate and statistically significant in explaining variance with
return on assets . The study also indicates that capital adequacy and operational efficiency affects
performance of microfinance institutions in East Africa.
5.3 Conclusion
The study as supported by Berger et al (1997) , confirms that that nonperforming loans negatively
affects performance of microfinance institutions. The results from the regression analysis confirm
that nonperforming loans negatively affects the performance of microfinance banks in East Africa
and that nonperforming loans ratio measured by nonperforming loans over total loans is
appropriate and significant in explaining effect of nonperforming loans on performance of
micro finance banks. The findings also indicated that multi linear regression model is appropriate
for testing the effects of nonperforming loans on performance using nonperforming loans ratio as
independent variable and change in revenue as a dependent variable respectively. It was concluded
that profitability of MFBs is negated due to increment in NPLs and as such reduces the MFBs '
financial performance.
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5.4 Recommendations.
Through the use of effective internal control systems, diversification, good credit appraisal
procedures along with efforts to improve asset quality in the balance sheets mic rofinance banks
can mitigate moral hazard and adverse selection risks when advancing loans to minimize non-
performing loans. It is also recommended that, potential borrowers should be critically analyzed
to assess their credit worthiness before they are awarded loans or alternatively offer the loans
depending on borrower's ability to service these loans. Moreover, MFBs should fully abide with
the stipulated credit regulations as well as work closely with other loaning institutions to allow
sharing of credit information in order to identify serial defaulters and desist from awarding such
borrowers loans.
5.5 Limitations of the Study.
The study gives past trends and does not give indicators on what to improve on. Descriptive
research is essentially static whereas the data maybe dynamic and this cannot be captured in the
study. Most data used in the study was annual data and it would have been more preferable to
obtain quarterly or semi-annual data to better prove the accuracy of the data. This would mean
that the data points were not as sufficient as it might have been desired. For some of the MFBs
data from particular years was missing and some data from existing years was not complete for
all variables, these affected the accuracy of the results from the analysis.
5.6 Suggestions for Further Research
Future research should include other factors as controlling variables and include other parameters
that can be used to measure performance and non-performing loans of microfinance institution. It
would also be important to conduct a comparative study with other MFIs apart from micro finance
banks , as well as expand scope to other countries in order to establish any similarities and
differences. In the case where data is available it would be more prudent to extend the time period
to establish whether the same conclusions will be reached.
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