TOSSA COLLEGE OF BUSINESS & ECONOMICS

FACTORS AFFECTING LOAN REPAYMENT PERFORMANCES: A CASE

STUDY IN DEVELOPMENT BANK OF ETHIOPIA, DESSIE DISTRICT

A thesis submitted to the school of graduate studies of Tossa Business &

economics for a partial fulfillment of requirements for award of master degree in
business administration.

BY:

April, 2016
DESSIE, ETHIOPIA

1
FACTORS AFFECTING LOAN REPAYMENT PERFORMANCES: A CASE
STUDY IN DEVELOPMENT BANK OF ETHIOPIA, DESSIE DISTRICT

A THESIS SUBMITTED TO TOSSA COLLEGE OF BUSINESESS AND

EONOMICS FOR A PARTIAL FULFILLMENT OF REQUIREMENTS FOR
AWARD OF MASTER DEGREE IN BUSINESS ADMINISTRATION.

BY:

ADVISOR:

TOSSA COLLEGE OF BUSINESS & ECONOMICS MBA PROGRAM

April, 2016
WOLLO, ETHIOPIA
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 APPROVAL SHEET
TOSSA COLLEGE OF BUSINESS & ECONOMICS
DEPARTMENT OF BUSINESS ADMINISTRATION
Members of the Board of Examiners
External Examiner Signature Date
____________________ ______________________ ___________________
Internal Examiner Signature Date
____________________ ______________________ ___________________
Advisor Signature Date
____________________ ______________________ ___________________
Co-advisor Signature Date
____________________ ______________________ ___________________
Chairperson Signature Date
____________________ ______________________ ___________________

April, 2016
DESSIE, ETHIOPIA

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STATEMENT OF CERTIFICATE

This is to certify that the thesis titled “Factors affecting Loan Repayment Performance:
A Case Study on Development Bank of Ethiopia, Dessie District”, submitted to Tossa
College of Business and Economics, Department of Business Administration for the
award of Degree of Master of Business Administration (MBA) and is a record of
genuine research work carried out by --------, under our guidance and supervision.
Therefore, we hereby declare that no part of this thesis has been submitted to any other
university or institution for the award of any degree or diploma.

Main advisors name Signature Date

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Co-advisor name Signature Date
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 DECLARATION

I hereby declare that this research thesis entitled “Factors affecting Loan Repayment
Performance: A Case Study on Development Bank of Ethiopia, Dessie District”, has
been carried out by me under the guidance and supervision of ……... The research
thesis is original and has not been submitted for the award of any degree or diploma to
any university or institutions.

Declared by signature Date

------------------------- -------------------------- -------------------

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 Abstract

This study was conducted in Development Bank of Ethiopia Dessie District

geographical area. Development bank of Ethiopia is state owned and specialized
financial institution with the mandate of providing long, medium and short term loans
to feasible and viable projects of commercial agriculture, agro processing and
manufacturing sectors following government priority area. This study is conducted on
the factors affecting loan repayment; a Case study of Development Bank of Ethiopia,
Dessie District. Accordingly, endeavors are made to contribute to the empirical gab
regarding factors affecting loan repayment performances specifically in Dessie
District. The main objective of this study was to identify and analyze the major factors
of loan repayment performances in DBE, Dessie District; more specifically from four
different perspectives, borrowers related factors, bank related factors, business/project
related factors and factors related to external environments. Both primary and
secondary data were used in the study. The primary data was collected from 150
selected borrowers through questionnaires and pre-tested structured interview with
staffs and bank managers. To define select the population of the study, stratified
random sampling was used where borrowers were stratified based on their loan status.
Both descriptive statistics and econometric analyses particularly logistic regression
(binary logit) were employed to present the results and findings of the research.

The study was basically conducted from four broad perspectives; factors related to
characteristics of borrowers, factors in the side of lender institution, factors related to
business/project and the other external factors were analyzed through descriptive
statistics such as frequencies, percentages, mean, and standard deviation. A total of
twenty one explanatory variables were included in the logistic regression and out of
these eight were found to be statistically significant to influence the dependent

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variables. The results of binary logistic regression revealed that Educational
qualification of borrowers, family size of borrowers, credit experience, having other
business, proper follow up, duration of service time/time horizon, loan size and loan
diversion were found significant and influenced loan repayment performances. Based
on the descriptive and econometric results/analysis, the researcher has recommended
to the bank to undertake proper screening, disbursing loan at the right time, conduct
proper follow-up, provide sufficient amount of loan as per the feasibility study of the
project, solve other difficulties as identified in this study and work on all other factors
affecting loan repayment performances.
Key words: Bank, borrower, Loan Repayment.

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 ACKNOWLEDGEMENTS

On top of all, I would like to thank the Almighty God, for the charity, kindness, help
and forgiveness, and caring; May your name be praised and blessed forever on me. My
sincere and deepest gratitude goes to my advisor………. for friendly approach,
unreserved assistance in giving me relevant comments and guidance throughout the
study.
I would like to offer my special thanks to my sister ……… who always standby me
and supports who paid everything to give me everything. Thank you dad!! I also want
to extend my sincere gratitude for my father ………... You guys were always there for
me. You win my heart, thank you! Family deserves everything. My beloved family,
words cannot describe how wonderful I feel to have you as a family. You have been
doing all what you could do. I am always begging my God to pay back all the favors
you did for me.
Finally yet importantly, I would like to thank all friends, colleagues and all who helped
me either directly or indirectly. Thank you!
Israel Tenaw
israeltenawe20@gmail.com

Table of Content

viii
Contents Page
APPROVAL SHEET.............................................................................................................................iii
STATEMENT OF CERTIFICATE.......................................................................................................iv
DECLARATION....................................................................................................................................v
Abstract..................................................................................................................................................vi
ACKNOWLEDGEMENTS.................................................................................................................viii
Table of Content.....................................................................................................................................ix
ACRONYMS/ABRIVATIONS............................................................................................................xii
CHAPTER ONE.....................................................................................................................................1
1. INTRODUCTION...............................................................................................................................1
1.1. Background of the Study..............................................................................................................1
1.2. Statements of the Problems..........................................................................................................4
1.3. Objectives of the Study.................................................................................................................8
1.3.1. General Objective..................................................................................................................8
1.4. Research Hypothesis.....................................................................................................................9
1.5. Significance of the Study............................................................................................................11
1.6. Scope of the Study......................................................................................................................11
1.7. Limitation of the Study...............................................................................................................12
1.8 Organization of the Study............................................................................................................12
CHAPTER TWO...............................................................................................................................13
2. REVIEW OF RELATED LITERATURE.....................................................................................13
2.1. Theoretical Review.....................................................................................................................13
2.1.1. Banks and Its Importance.....................................................................................................13
2.1.2. Development Banks.............................................................................................................14
2.1.3. The Difference between a Development Bank and Commercial Banks..............................14
2.1.4. Basic Requirements to Access Credit..................................................................................16
2.1.5. Performing Loans.................................................................................................................17
2.1.7. Loan Classifications in Ethiopia..........................................................................................20
2.2. Development Bank of Ethiopia..................................................................................................21
2.2.1. The Main Functional Areas of the Bank..............................................................................22
2.3. Empirical Literature....................................................................................................................24

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 2.4. Empirical Studies........................................................................................................................29
2.5. Conceptual Frame Works...........................................................................................................31
CHAPTER THREE...............................................................................................................................33
3. RESEARCH METHODOLOGIES...................................................................................................33
3.1. Research Design.........................................................................................................................33
3.2. Description of the Study Area....................................................................................................34
3.3. Data Type and Source.................................................................................................................35
3.4. Method of Data Collection.........................................................................................................36
3.5. Population and Sampling Method..............................................................................................36
3.6. Method of Data Analysis............................................................................................................38
3.7. Model Specification....................................................................................................................38
3.8. Variables of the Study................................................................................................................41
3.8.1. The Dependent Variable......................................................................................................42
3.8.2. Definition and Hypothesis on Independent Variables.........................................................43
3.9. Conceptual Framework...............................................................................................................54
CHAPTER FOUR.................................................................................................................................56
4. RESULTS AND DISCUSSIONS.....................................................................................................56
4.1. Background Information of Respondents...................................................................................56
4.2. Descriptive Analysis...................................................................................................................57
4.2.1. Borrowers Related Factors...................................................................................................58
4.2.2. Business Related Factors.....................................................................................................64
4.2.3. Institutional Related Factors................................................................................................70
4.2.4. External Related Factors......................................................................................................79
4.2.5. Other Major Problems..........................................................................................................81
4.3. Econometric Analysis.................................................................................................................82
4.3.1. Model Tests..........................................................................................................................82
4.3.2. Results of Regression Analysis............................................................................................92
4.3.3. Discussions on Regression Results......................................................................................93
CHAPTER FIVE.................................................................................................................................101
5. CONCLUSION AND RECOMMENDATION..............................................................................101
5.1. Conclusion................................................................................................................................101

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 5.2. Recommendation......................................................................................................................104
6. REFERENCE..................................................................................................................................107
7. APENDIX.......................................................................................................................................111

ACRONYMS/ABRIVATIONS

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ADLI……………………………………………Agricultural Development Led to
Industrialization
BSC……………………………………………..Business Scored Cared
CBE……………………………………………..Commercial Bank of Ethiopia
DBE……………………………………………..Development Bank of Ethiopia
ECG …………………………………………… Export Credit Guarantee
GDP …………………………………………… Growth Domestic Production
GTP …………………………………………….Growth and Transformation Plan
KYC……………………………………………..Know Your Customer
LAT……………………………………………...Loan Approval Team
MFI……………………………………………...Micro-finance Institution
MOFED…………………………………………Ministry of Finance and Economy
Development
NBE……………………………………………..National Bank of Ethiopia
NPL……………………………………………...Non Performing Loan
PCFR……………………………………………..Project Completion Follow up
Report
PIFR……………………………………………...Project Implementation Follow up
Report
POFR……………………………………………..Project Operational Follow up
Report
PRLR……………………………………………..Project Rehabilitation and Loan
Recovery
RUFIP …………………………………………Rural Financial Intermediation
Program
SME …………………………………………........Small and medium Enterprises

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SWOT…………………………………………….Strength, Weakness, Opportunity
and Threat
RRR…………………………………………….....Relative Risk Reference
VIF……………………………………………….Variance Inflation Factor
WTO……………………………………………...World Trade Organization

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 CHAPTER ONE

1. INTRODUCTION
1.1. Background of the Study

Over the past two decades, the Ethiopian economy has gone through numerous
changes; it substantially outperformed the average of Sub-Saharan African countries.
The Government of Ethiopia adopted market oriented economic policy, made
agriculture its primary priority in 1991, and implemented Agricultural Development
Led-Industrialization (ADLI) strategy. Following the change of the government by
1991 the country introduced major economic reforms by accepting capitalist ideology
contrary to the previous communist set up in the economy of the country by 1992
(MoFD, 2015).
Since then, the Ethiopian economy has gone through remarkable economic growth in
all agriculture, service and industrial sector according to the World Bank report of
2016 (World Bank, 2016). Although initially led by agriculture, the growth base is
broadening, with increasing contributions to GDP from services and industry year after
years. In the same token the banking sector reveals dramatic progresses and expansions
in the past twenty years. Banks play a very important role in the economic
development of every nation. They have control over a large part of the supply of
money circulation. Banks are the main stimulus of the economic progress of a country.
The financial sectors contribution to growth lies in the central role it plays in
mobilizing savings and allocating these resources efficiently to the most productive
uses and investments in the sector Tihitina, 2009).

The Ethiopian financial institutions have a long time history. The use of money and
coins in Ethiopia has a long history, and the introduction of modern banking is nearly a

1
century old. The original bank of Abyssinia started operation in February 1905 and its
activities included keeping government accounts and financing exports. Despite the
long history, which precedes the advent of modern banking throughout most of Africa,
the Ethiopian financial sector has not progressed as it beginning. In the period of a shift
from a mixed to a state managed economy, the development of the financial sector was
stunted. Although the financial sector of Ethiopia has grown in the 1990’s, compared
to its state during the preceding decades, it is still in its infancy.

In Ethiopia, there are two government owned banks and sixteen private banks,
seventeen insurance companies (1 public and 16 private), thirty five (35) Micro finance
institutions and five Capital Goods Finance Companies in 2015/16. Commercial Bank
of Ethiopia (CBE) is one of the dominating state-owned banks whose assets represent
about 70% of the sector and Development Bank of Ethiopia (DBE) is the only
development bank having a second place market position in the country (National bank
report, 2016).

Development bank of Ethiopia (DBE) is the only bank of its kind in Ethiopia. It’s
different from other commercial banks in its nature and objectives endowed to it.
Development Bank of Ethiopia is a specialized financial institution established to
finance and provide close technical support to viable projects from the priority areas by
mobilizing fund from domestic and foreign sources while ensuring its sustainability.
The
Bank extends investment credit to creditworthy borrowers and projects that have
received a thorough appraisal and found to be financially and economical viable and
socially desirable. In addition to project financing and rendering technical support to
the selected priority area sectors, DBE has given great task in financing the Small and

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Medium Enterprises through Lease Financing program to enable them to acquire
capital goods and machineries (DBE annual report, 2015).

In 2015/16 fiscal year the Bank has set a target of approving, disbursing and collecting
Birr 14.82 Billion, Birr 13.54 Billion and Birr 5.78 Billion respectively. With regard to
the actual performance of the year; the approval, disbursement, and collection of Birr
11.8 Billion (80%), Birr 6.3 Billion (47%) and Birr 4.1 Billion (71%) were registered,
respectively (DBE, annual report 2016) The Non-performing loan size and ratio of the
Bank in the year 2015/16 was Birr 5.6 Billion (17.71%) which is 53% of the planned
target of Birr 3.6 Billion (9.45%) at the corporate level. Compared to the preceding
year same period performance of 12.5%, it has increased by 44%.

The repayment performances of DBE Dessie district within the past three consecutive
years showed that the bank’s NPL is increasing and going against the plan to minimize
the ratio into a single digit and achieve a 100% performing loans by 2020. Even
though, NPL ratio of the bank showed little improvement from its historical
performance at corporate level the NPL ratio of DBE in the year 2015/16 is 17.71%
and fresh entrants to NPLs has showed sharp increment during 2015/16 fiscal year.
The 2013/14 annual report of the bank indicates, DBE Dessie district NPL ratio is
14.35% while the corporate NPL ratio is 8.23%, with the fresh entrant to NPL 2.58%.
Similarly, the 2014/15 annual report of the bank revealed that the NPL ratio of DBE is
12.54%, while NPL ratio in DBE Dessie district is 18.1%. The main reasons
contributing for such low performances particularly in NPL and fresh entrants to NPL
related to different factors.

According to Nawai & Sharif (2013), Olomola (1998) and Micha'el (2006), Abraham
(2002) and kibrom (2004) and many other studies discussed in empirical studies in
3
chapter two of this study, the main causes of such low repayment and high NPL ratio
performance would emanate from institutions related factors, borrower related factors,
and business related factors and external factors. Hence, assessing and seeking for
solution of factors affecting loan repayment performances become imperative in
providing credit service for different governmental and non-governmental business.

1.2. Statements of the Problems

Development Bank of Ethiopia (DBE) is one of the major state owned institution
established to support the economy development of the country through provision of
project finance and technical support to viable projects that are selected as priority
areas by the government. As a policy Bank, it is entrusted to serve as a tool for the
country‘s development through availing medium and long term credit to agriculture,
industry, mining and energy and SMEs (DBE annual plan, 2016).

Development Bank of Ethiopia is well known and specialized in project financing. The
Bank has been offering medium and long term loans to different kinds of viable
projects. Hence, it is known that the role of Development Bank of Ethiopia is very
important in the economy practically by financing government development priority
areas which are believed to be the engine of growth like Manufacturing Industry, Agro
- Processing, Commercial Agriculture, Mining & Energy and SMEs. In addition to
project financing, following the especial emphasis given by the government to Small
and Medium Enterprises as they are believed to be the foundation for the move to
industrialization, recently DBE is entrusted to support SMEs along with medium and
large scale industrial projects.

4
Coming to the plan and performances of the bank, In 2015/16 fiscal year the Bank has
set a target of approving, disbursing and collecting Birr 14.82 Billion, Birr 13.54
Billion and Birr 5.78 Billion respectively. Whereas the actual performance of the year;
the approval, disbursement, and collection of Birr 11.8 Billion (80%), Birr 6.3 Billion
(47%) and Birr 4.1 Billion (71%) were registered, respectively. In the geographical
area where this research was conducted, Dessie District the case seems little different
due to the fact that Financing agricultural projects especially Dessie District was
suspended in the year 2015/16, following the reportedly land overlapping and other
related problems in the area and the very nature of projects in the District(which is
primarily agriculture). Hence, the performance of the District was highly affected not
only in loan collection/repayment performances but in all aspects. At corporate level
the NPL ratio of the bank shows improvement from its historical performance which is
17.71% and fresh entrants to NPLs has showed increment during last 2015/16 fiscal
year.

At Dessie District the NPL ratio increased from 12.5% in the year 2014/15 to 18% in
2015/16 and the new entrant sharply increased from 1.65 to 2.5 in the same fiscal year.
(Annual reports of DBE, 2014/15 and 2015/16 and national Bank of Ethiopia) DBE
has set a vision of having 100% successful projects by the end of the year 2020. The
performance reports of the bank, which includes the figures in the above paragraph
from the year 2015/16 however didn’t shows the same story, but even though the time
table keeps running the NPL ratio was not reducing from time to time as expected.
Furthermore, the GTPI performance reports of the bank and the reports of National
Bank of Ethiopia and the supervising governmental agency, Public Financial
Enterprises Agency indicated that Development bank of Ethiopia was not achieving its
targeted goal especially in approving, disbursing and collecting loans as expected from

5
the plan cascaded from GTPI. The performance in Dessie District resembles the
corporate performances of the bank and even worst in loan repayment performances.

In this study, focus is given to loan repayment performances which include both
performances and nonperformance of loans. The reasons and factors for performances
of loans or increase in NPLs are related to the cumulative effects of different factors.
This is what necessitated and motivated the researcher to focus in this area. The issue
of Loan repayment performance and NPL has been a subject of major concern for
researchers for many years across the world and in recent years in Ethiopia.
In Ethiopia, there were researches conducted on the related topics by different
researchers. For instance, Wondimagn (2012) conducted a research titled ‘determinants
of NPLs on commercial banks of Ethiopia’ and his study indicated that interest rate has
no significant impact on the level of commercial banks loan delinquencies in Ethiopia.
On the other hand, Mitiku (2014) “Determinants of Commercial Banks Lending” with
the objective of assessing the relationship between commercial bank lending and its
determinants variables (bank size, credit risk, GDP, investment, deposit, interest rate,
liquidity ratio and cash required reserve) by taking financial statement of seven years
and found that there was significant relationship between loan size, credit risk, gross
domestic product and liquidity ratio and commercial bank lending. Kibrom T (2010)
studied about determinants of successful loan repayment performances of private
borrowers in the North Region of Development Bank of Ethiopia by using binomial
model and focused on borrowers’ specific and business type specific in order to
analyze successful loan repayments.

Firafis Haile (2015) conducted a study on related subject area under a title
‘determinants of loan repayment performances; a case study of Microfinance
institutions’ mainly focusing on borrowers specific factors using binary logit model.
6
The result identified loan size, credit experience, training, business type and family
size were significantly affected the repayment performances. Similarly, Abraham G
(2002) conducted a study on DBE Dessie branch on loan repayment and its
determinants in small scale enterprises financing in Ethiopia. The study mainly focuses
on bank specific and borrowers’ specific factors.

Arega Seyoum et, al. (2016) studied about factors affecting non-performing loans in
the DBE central District by using descriptive statistics from bank specific factors and
borrowers specific factors in order to determine factors affecting non-performing loans
in the DBE central District. The result of the study shows that poor credit assessment
and credit monitoring are the major causes for the occurrence of NPL in DBE. Credit
size (includes aggressive lending, compromised integrity in approval, rapid credit
growth and bank’s great risk appetite); high interest rate, poorly negotiated credit terms
and lenient/lax credit terms, and elongated process of loan approval were bank specific
causes for the occurrence of nonperforming loans.

In all the above studies what affects loan repayment performances are evaluated from
bank specific and borrowers’ specific factors. In reality what affects loan repayment
performances were not limited to bank specific factors and borrowers specific factors
but beyond these it includes bank specific, borrower specific, business specific and
other factors (macroeconomic factors). There were studies conducted on
Nonperforming loans in Development Bank of Ethiopia in other area of the bank like
in North region or central region and Dessie Branch but it’s not appropriate to
generalize the findings of these studies especially to Dessie District. Because, loans in
the areas were mainly agricultural and the nature & types of problems differs from the
central and north Districts which are predominantly industry, service and agro
7
processing projects. In addition, there are internal and external changes since these
researches were conducted in the bank, including Changes of policies, organizational
restructuring, change in interest rate and the global climate changes are among the
major occurrences.

Generally the researcher believes that the problems related to defining factors affecting
loan repayment performances were not properly addressed particularly in Development
Bank of Ethiopia Dessie District due to the fact that there were few empirical studies in
this area and some previous studies conducted in other areas of the bank were limited
to bank specific and borrowers’ specific factors. Hence the researcher is motivated to
study and lay his own contribution on the factors affecting loan repayment
performances in a broader sense. Accordingly, endeavors are made to identify the
major factors that contributed to loan repayment performances specifically in DBE
from four broad perspectives, borrowers related factors, bank specific factors, business
characteristics and external factors.
1.3. Objectives of the Study
1.3.1. General Objective

The objective of this study is to identify Factors affecting loan repayment performance,
identify the major factors from four different perspectives particularly, from borrower
side, from bank/lender side, from business and from other external side of loans of
Development Bank of Ethiopia Dessie District.
1.3.1.1. Specific Objective
To achieve the general objective, the following more specific objectives were
identified under this study:
1. Identify the major borrowers specific factors (Education, Experience…..etc.) on loan
repayment performance of Development Bank of Ethiopia, Dessie District.

8
2. Identify the Bank specific factors (Loan size, Follow up, grace period, due
diligence/KYC), Collateral and equity) on loan repayment performance of
Development Bank of Ethiopia, Dessie District.
3. Identify Business related factors (Business sector, business form…etc.) on loan
repayment performance of Development Bank of Ethiopia, Dessie District.
4. Identify other major factors (market and weather conditions) on loan repayment
performance of Development Bank of Ethiopia, Dessie District.
1.4. Research Hypothesis

To achieve the objective of this study the researcher would test the following
hypotheses concerning the factor affecting loan repayment performance of DBE,
Dessie District. Empirical researches conducted in the area found different results; for
instance Kibirom (2010), in his study on determinants of successful loan repayment
Performance of private borrowers in Development bank of Ethiopia north region,
identified factors that determine loan repayment performance which includes;
borrowers perceived need, that is borrowers have to be given an opportunity to borrow
for their perceived needs, competence, that is the borrowers past personal and profit
record, past prosperity etc. Based on this model, educational level of the borrowers,
repayment period, availability of other source of income, sector, purpose of the loan
and type of labor determine successful loan repayment performance of the borrowers
positively and significantly. Whereas, gender and family size have positive sign, but
are not statistically significant. Moreover, variables such as age, loan diversion, other
source of credit show negative sign but not statistically significant. The variable
experience is statistically significant but show negative sign.

Awunyo-Vitor (2012) searched the determinants of loan repayment default among

farmers in Brong Ahafo District of Ghana. The study employed probit model to

9
investigate factors that influence farmer’s loan repayment default. Data used in this
study was gathered through a survey of 374 farmers in five Districts within Brong
Ahafo District of Ghana. The results showed that farm size, and engagement in off
farm income generating activities reduces the likelihood of loan repayment default
significantly. In addition, larger loan size and longer repayment period as well as
access to training are more likely to reduce loan repayment default. Abraham (2002)
conducted a research with the aim of identifying the major factors behind the loan
default problem of small-scale enterprises with particular reference to Development
Bank of Ethiopia (DBE), by employing to bit model. Sample selection was based on
stratified sampling and 102 borrowers were selected.

The result of econometric model revealed that having other source of income,
education, work experience in related economic activity before the loan and engaging
on economic activities other than agriculture are enhancing while loan diversion, being
male borrower and giving extended loan repayment period are undermining factors of
the loan recovery performance of projects. Firafis Haile (2015) conducted a study on
related subject area under a title ‘determinants of loan repayment performances; a case
study of Microfinance institutions’ mainly focusing on borrowers specific factors using
binary logit model. The result identified loan size, credit experience, training, business
type and family size were significantly affected the repayment performances.
Based on these and other empirical research findings the researcher wants to draw the
following research hypothesis;
H1: There is positive relationship between Borrowers’ specific factors (Education and
Experience) and loan repayment performance.
H2: There is positive relationship between Bank specific factors (Loan size, Follow up)
and loan repayment performance.

10
H3: There is negative relationship between Business specific/project related factors
(business sector and loan diversion) &loan repayment performance.
H4: There is positive relationship between macroeconomic factors) like market and
weather conditions and loan repayment performance.

1.5. Significance of the Study

This study and its finding is significant for many more reasons. The subject of the
study remains problems of every financial institution in our country this day. So, the
findings of this research are expected to contribute a lot for different stakeholders. The
following are among the main significance of this study: it benefits the researcher to
obtain new knowledge about problems under the study and gives clear picture about
the issue of loan repayment performance, Present the current clear picture of NPLs in
DBE Dessie District and tries to show the significant factors (internal as well as
external) that determine the repayment performances, Use as starting point for other
studies which may focus on similar topics and issues related to factor affecting loan
repayment performance in general and factors that influence the level of
nonperforming loan in baking industry in particular and also study will enable lenders
of Development bank of Ethiopia how to overcome potential factors that are highly
affects the level of nonperforming loan in the bank at general.

1.6. Scope of the Study

11
This study is conducted on Development bank of Ethiopia Dessie District under a title
factors affecting loan repayment performances. Hence the scope of the study is limited
to the geographical limitation of Dessie District which includes Dessie branch, Dessie
branch, Kombolcha branch, sekota branch, Shewarobit branch, Bonga branch, Mizan
Tefari and Teppi branches. Dessie District is selected for geographical proximity and
accessibility for data collection. Among eight branches under Dessie District, only two
of them are graded as a branch (Dessie branch and kombolcha branches) and it’s these
two branches that are empowered to handle active customers and provide loan for their
customers for practical purposes. Hence, the data used under this study is data from
these two branches.

On the other hands, the subject matter of the study is limited to identifying major
factors affecting loan repayment performances in the Dessie District. This study
mainly focuses on the issues that extracted in the research objective and research
hypothesis. The other important issue is regarding kind and type of data used in the
study. The study used both primary data collected using questionnaire and interviews
and secondary data from different source as defined in methodology but such
secondary data used in this study is limited to the past fiscal year, 2015/16, which is
one year only.

1.7. Limitation of the Study

In conducting this study, the researcher faced some challenges and shortages from
methodological limitations. The main problem is geographical limitation of the study
in to Dessie district. The other limitation related to the theoretical drawbacks emanated
from the nature of the models used in this study which is beyond the control of the
researcher. The other limitation is regarding lists of independent variables; the

12
independent variables are not limited to those listed, discussed and presented in this
work but many more are not covered due to financial and time limitations.

1.8. Organization of the Study

This research report is organized in five chapters. Chapter one provides the general
introduction about the whole report. Chapter two presents the review of related
literatures. Chapter three provide detail description of the methodology employed by
the researcher. Chapter four contains data analysis presentation and interpretation.
Finally, the last chapter concludes the total work of the research and gives relevant
recommendations based on the findings.
CHAPTER TWO
2. REVIEW OF RELATED LITERATURE
2.1. Theoretical Review
2.1.1. Banks and Its Importance

The term bank refers to an institution that deals with money and provides other
financial services. According to Heffernan (1996), banks are defined as intermediaries
between depositors and borrowers in an economy that are distinguished from other
types of financial firms by deposit collection and offering loan products. Banks role in
the economy of any country is very significant. They play intermediation function in
that they collect money from those who have excess and lend it to others who need it
for their investment. Banks mobilize deposits and allocate the mobilized money
efficiently to the most productive uses of investment in the real sector. Availing credit
to borrowers is one means by which banks contribute to the growth of economies. The
banking sector makes a meaningful contribution to the economic growth of every
country. Banks contribution to the growth lies in the role they play in mobilizing
deposits and allocating the resources efficiently to the most productive uses investment

13
in the real sector. So making credit available to borrowers is one means by which
banks contribute to the growth of economies.

Banks pool resources together for projects that are too large for individual shareholders
to undertake (Bagehot, 1873). They are also considered the most important enabler of
financial transactions in any country’s economy and are the principal source of credit
(Rose, 2002). Bank finance is the primary source of debt funding. Commercial banks
extend credit to different types of borrowers for many diverse purposes, either for
personal, business or corporate clients (Saunders & Cornett, 2003). Besides, banks are
also the custodians of nation’s money, which are accepted in the form of deposits and
paid out on the client’s instructions (Sinkey, 2002; Harris, 2003). Banks accept
deposits, make loans, and derive a profit from the difference in the interest rates paid
and charged respectively. Some banks also have the power to create money (Fasil and
Merhatbeb, 2009).

2.1.2. Development Banks

A development bank is a bank established for the purpose of financing development. A

traditional definition of a development bank is one which is a national or District
financial institution designed to provide medium-and long-term capital for productive
investment, often accompanied by technical assistance, in less developed areas
(Encyclopedia Britannica, 2003). Development Banks are financial intermediation that
provides financing to high priority investment projects in a developing economy. This
definition implies that the purpose of development banking is to bring the country to a
higher level of development. Development banks fill a gap left by undeveloped capital
markets and the reluctance of commercial banks to offer long term financing.

14
2.1.3. The Difference between a Development Bank and Commercial Banks

There are several differentiating factors between a development bank and a

commercial bank. Some extreme observations below are made in order to emphasize
“traditional” differences between the two in order to emphasize the point. Actual
practice, of course, differs from commercial bank to commercial bank and from
development bank to development bank. As the country’s capital markets develop,
there shall be less difference between these specialized institutions and the similarities
shall become more apparent. With this as a premise, the traditional differences between
development and commercial banks are in the following areas (compiled by Asian
Development Bank, ADFIAP, 2007).
Impetus for the Creation of the Institution: A development bank is created as an
instrument of economic development while a commercial bank is created by business
opportunities.
Posture Relative to Business Opportunities: A development bank is supposed to be
pro-active as it should take an active role to promote projects and to develop
institutions (entrepreneurs). The projects chosen are those that are consistent with the
economic development priorities. A commercial bank is known to be reactive to
business opportunities. It requires bankability only after the entrepreneur’s decision has
been made; it waits for the idea to culminate into a funding requirement.
Types of Projects Supported: For a development bank, there is an explicit effort to
support economic development projects. The following desired “impact” projects form
the basis for scanning for opportunities: import substitution (at competitive prices);
exports; increased local demand; District development (for example, tourism); and
increased industrial efficiency through better technology. For a commercial bank, the
abovementioned goals are not the starting point for the identification of projects.
Rather, they would most likely be side-benefits.A commercial bank has little concern

15
for these objectives, except for the viability of the bank transaction. In short, a
development bank’s activities are project-based while that of the commercial bank are
transaction-based.
Search Process for Projects Financed: A development bank goes into a planning
cycle, identifying which are the likely areas to go into. For example, if it determines
that an export is an area to be promoted, then it conducts a marketing study and seeks
entrepreneurs to implement related projects. For the commercial bank, the search
process is different. It asks, “Are you an exporter?”, and then looks at that
entrepreneur’s cash balance to determine if there is a marketing opportunity for the
transaction.
Project Promotion Activities: A development bank offers counseling and advisory
services for enterprise development and promotion as part of its development lending
process. A commercial bank offers legal and business advice, appraisal services and
credit investigation, usually for a fee. It undertakes very little project promotion and
institutional development. Its emphasis is on client development and marketing.
Strategic Goals: A development bank has a more difficult strategic objective because
it is involved with the concerns of the country, specifically economic development.
Aside from this, after providing financing, it is also concerned with developing the
enterprise. Developing them explicitly would mean additional costs to the bank.
Enterprise development dramatically limits the number of accounts that a development
can handle because this is time-consuming.

2.1.4. Basic Requirements to Access Credit

In order to at least minimize the inevitable credit risks, according to (Ghatak and
Guinnane, 1999) a thorough credit assessment should be conducted by the lenders
especially concerning the borrowers` character, collateral, capacity, capital and

16
condition (what is normally referred to in the banking circles as the 5C`s) should be
conducted if they are to minimize credit risk.
Such gathering of information is possible primarily from your credit application and a
credit bureau report, to determine whether borrowers are able and willing to repay the
debt. In the final analysis, every credit grantor attempts to answer the question: how
risky is it to lend or extend credit to this applicant? This decision is relatively easy for
most because the applicants will fall at one end of the continuum or the other of the six
“C” s of credit: Capacity: - is a factor in determining creditworthiness. It is assessed
by weighing a borrower is earning ability and the likelihood of continuing income
against the amount of debt the borrower carries at the time the application for credit is
made.
Capital: - Factor in determining creditworthiness consisting of a borrower’s tangible
assets and resources. The presence of sufficient capital in a borrowers profile is an
assurance that a debt could be paid from the borrowers assets if the need arose.
Character: - Character is determined by analyzing how a borrower has handled past
obligations.
Collateral: - is a real or personal property that a borrower pledges for the term of loan.
When the borrower fails to repay, the creditor may take ownership of the property by
following legally mandated procedures.
Conditions: - A factor often considered with the factors of capacity, capital, and
character when creditors are analyzing an applicant’s creditworthiness. This factor
consists of economic conditions that could affect a borrower’s ability to repay, such as
unemployment, seasonal work.

2.1.5. Performing Loans

17
The principal profit- making activities of banks are loans. In allocating funds, the
primary objective of bank management is to earn income while serving the credit needs
of its community. Therefore, Lending 15 represents the heart of the industry. Loans are
the dominant asset and represent 50-75 percent to total amount of assets at most banks,
that generate the largest share of operating income and represent the banks greater risk
exposure (Mac Donald and Koch, 2006). Loans and advances are defined in the
respective laws of different countries. In Ethiopia, under Article 13 (FDRE 592/2008)
and (NBE/43/2008) Article (4.6) loans and advances are defined as: “… Any financial
assets of a bank arising from a direct or indirect advance (i.e. unplanned overdrafts,
participation in a loan syndication, and the purchase of loan from another lender etc.),
or commitment to advance funds by a bank to a person that are conditioned on the
obligation of the person to repay the funds, either on a specified date or on demand,
usually with interest. The term includes a contractual obligation of a bank to advance
by the bank on behalf of a person. The term does not include accrued but uncollected
interest or discounted interest. Loans and advances are the most profitable of all the
assets of a bank. These assets constitute the primary source of income by banks.
Therefore, managing loan in a proper way not only has positive effect on the banks
performance but also on the borrower firms and a country as a whole.

According to (Mac Donald and Koch, 2006) Loans are the dominant asset and
represent fifty percent to seventy five percent to the total amount of banks assets. In
most banks loans generate the largest share of operating income and represent banks
greater risk exposure. The lending function is considered by the banking industry as
one of the most important function for the utilization of funds. Loans and advances are
the most profitable of all assets of banks and constitute the primary source of income
by banks. Banks provide loans and advances in the existences of asymmetric
information, certain level of risks are inevitable. Accordingly, due to controllable and
18
uncontrollable factors, it is unlikely to have 100% of collection of loan and advances in
reality. Loan defaults are inevitable given the uncertainty of the future economic
conditions and the existences of other controllable and uncontrollable factors. The
main issue is how to minimize the rate of this risk? How to increase asset quality of
financial institutions, or minimize the rate of non-performing loans by identifying
factors that causes it? Non- performing loans are closely associated with banking
crises. Many authors argue that the magnitude of non-performing loans is a key
element in the initiation and progression of financial and banking crises.

Unless properly managed and kept at reasonable standard non-performing loans

(NPLs) often associated with bank failures and financial crises in both developing and
developed countries (Gebru Meshesha, 2015). The definition of NLP varies across
countries; there is no global standard to define nonperforming loans at practical level.
The concept has been defined in different literatures and by different scholars using
different parameters. Criterion for identifying non-performing loans varies throughout
the world even between countries. Some countries use quantitative criteria to
distinguish between “good” and “bad” loans like the number of days overdue, schedule
payments while others rely on qualitative standards like the availability of information
about the client’s financial status, and management judgment about future payments as
used by (Teshome, 2010).
According to the International Monetary Fund, a non- performing loan (NPL) is any
loan in which interest and principal payments are overdue for 90 days or more. A
number of other literatures have also tried to define NPLs in their own ways. Even
though, attempts are made to define NPL by different institutions and scholars in
different ways, still all of them indicate NPLs are Loans that are outstanding in both
principal and interest for a long period of time contrary to the terms and conditions
contained in the loan contract. Different endeavors are also made by a number of
19
writers and authors to define what is meant by bad or Nonperforming loans as per their
understanding of the subject matter.

According to (Guy, 2011), Nonperforming loans are also commonly described as loans
in arrears for at least ninety days and nonperforming loans have been widely used as a
measure of asset quality among lending institutions and often associated with failures
and financial crises in both developed and developing world. Non -performing loans
can also be defined as defaulted loans, which banks are unable to profit from it
(Tihitina, 2009). Likewise, Ethiopia has also defined what is meant by nonperforming
loans under National Bank of Ethiopia’s (NBE‟s) Directive no, SSB/43/2008.

2.1.7. Loan Classifications in Ethiopia

The classification of loans into performing and nonperforming loan is not appropriate
in reality. Loans may take different other status than these two extreme classifications.
As per directive number SBB/43/2007 loans are classified into five classes:
1. Pass loans: - these are the loans that have not become any problem, present no
special risk than the normal risk inherent to any loan. Short term loans past due for less
than 30 (thirty) days and medium and long term loans past due for less than 90 (ninety)
days.
2. Special mention loans: - these are the loans that have shown some early signs of
trouble, such as missing one payment, missing a few financial statements, deterioration
of the collateral, etc. Some other events not under the borrowers control may also
trigger some alarm, such as deterioration of the labor or political or security situation in
the area where the business is located. Short term loans past due for 30 (thirty) days or

20
more, but less than 90 (ninety) days and medium and long-term loans past due 90 days
or more, but less than 180 days.
3. Substandard loans: - these are the loans that have become real problems, missing
payments for two consecutive payments. They also present real weaknesses that
jeopardize the orderly liquidation of the loan. The following non-performing loans at a
minimum shall be classified substandard:
 Short term loans past due 90 days or more, but less than 180 (one-hundred-
eighty) days;
 Medium and long term loans past due 180 days or more, but less than 360 days.
4. Doubtful loans: - there are very serious questions about the borrower’s capacity to
repay, leaving the bank with a strong possibility of loss, at least partial loss. The
following non- performing loans at a minimum shall be classified doubtful:
 Short term loans past due 180 (one-hundred-eighty) days or more, but less than
360 days;
 Medium and long term loans past due 360 (three-hundred-sixty) days, but less
than 3 years.
5. Loss Loans: - These are loans that are beyond hope after all means of recovery have
been Exhausted, or loans that have not been performing for over 1 year. The only
course of possible action is to take legal actions to foreclose and write the loans off the
book as a loss. Short term loans past due 360 (three-hundred-sixty) days or more;
medium and long term loans past due 3 (three) years or more; Based on the above
classification the loan of the banks considered as performing and nonperforming. If the
loan fall under pass and special mention category they are classified as performing loan
otherwise it is considered as non-performing loan (DBE, 2014).

2.2. Development Bank of Ethiopia

21
Among the formal source of credit in Ethiopia, Development Bank of Ethiopia is
providing loan and technical support for viable projects on the bases of individual
credit. In line with this, Development Bank of Ethiopia, Amhara, Dessie District has
scored the following performance during the fiscal year that ended June 30; 2015. The
District has approved 2.123 billion birr and has achieved 386 percent of its plan.
During the same budget year, a total of 1.351 billion birr has disbursed to different
sectors of the economy especially agricultural projects. This revealed 223% of its plan
has achieved. Regarding to loan collection, a total of 106.87 million birr was collected
with registering 60% achievement (DBE annual report, 2015). Based on DBE annual
reports of 2011/12, 2012/13, 2013/14 and 2014/15, Dessie District has scored
somehow good trend of non-performing loan ratio where 47% in 2011/12 fiscal year
towards 9.58% in 2021/22, 2022/23 which is in line with the vision of the bank to be
achieved by 2025. However, it needs also some reduction in the coming years by using
different rehabilitation mechanism. On the other hand, loan repayment performance
from whole projects including both healthy and unhealthy projects were face difficulty
as we seen relative to its own plan as well as number of projects that actually approved,
where scored performance not less than 100% of its plan within in those periods.

2.2.1. The Main Functional Areas of the Bank

The Bank is extending investment credits to creditworthy borrowers and projects that
have received a thorough appraisal and found to be financially and economically viable
and socially desirable. Based on the nature of the projects, DBE is providing long and
medium term loans as well as short-term working capital as a package. The term of
loan is, however, to be determined based on the specific needs and requirements of the
projects.

22
According to revised credit policy on 2016, the bank is providing: New Loans: As per
the current working credit policy and procedure of the bank all borrowers who wish to
obtain financing for new priority area projects are required to provide the minimum
equity contribution of 25% of the total project cost in cash. The cash contribution
placed upfront or gradually over a period not to exceed 6 months from the loan
contract signing date. The Bank will finance the remaining balance up to a maximum
of 75% of the total project cost after utilization of the 25% equity contribution by the
borrower.

Expansion Loan: as per current working credit policy of the bank, all borrowers who
wish to obtain financing for the expansion of an existing priority area project and
whose assets of the existing project are not collateralized can access 100% financing of
the expansion cost provided that the value of the existing asset covers 40% of the total
project cost. This means that the debt to equity ratio stands at 60:40. For any cash
contribution made by the promoter to cover the shortfall, the promoter can access
additional loan from the bank according to the debt to equity ratio of 60:40.
Working Capital Loans: in addition to the permanent working capital that is part of
project cost, working capital loan serves as bridge finance and is availed based on the
cash flow of the project itself. The purpose of working capital finance is for extension
of inventory cycle, increase capacity utilization and cover short term cash flow
problem of existing customers.
Co-financing (Syndicate Financing): in order to maintain the exposure limit,
minimize risks and to overcome occasional liquidity problems, the Bank may finance
projects involving very large amount of investment capital under co-financing
arrangements with other national or international financial institutions. Guarantee
Services: the Bank is providing financial guarantee services to its reliable clients.
Export credit guarantee service, on the other hand, is provided to well performing

23
clients of other banks/financial institutions with reliable and or good record of
accomplishment.

Lending Managed Funds: the bank may undertake lending operations for supporting
development projects from managed fund at the request of governmental or non-
governmental agencies.

Loan Transfer: Healthy loans (loans performing as per the contract entered between
the borrower and the Bank) can be transferred to new clients upon the written request
of both the original and the new clients. However, the new client’s credit worth
capability to run the project should be confirmed by conducting the required due
diligence or KYC assessment and the request should be approved by the loan approval
team.

Loan Buy-out: the Bank may buy-out loans extended by other local banks and local
microfinance institutions. However, the loans to be purchased under the buyout loan
facility should be: Viable/ongoing concern (operational) and Priority sector project
loans.
Lease Financing: lease financing is a service in which the Bank provides financial
service for purchases of machineries, equipment and accessories for priority area
projects. Whereby the lessee pays 10% of the purchasing price of these assets to the
Bank in advance and the lessee either returns the assets to the Bank or purchases them
at agreed price at the end of the lease period (revised credit policy, 2016).

Loan Processes According to DBE loan procedure and manual (2016), the Loan
Process of the Bank is designed to serve the customer with a shortest possible time,
minimum cost and high quality. This process starts its function by attracting and

24
persuading customers to apply for investment loans and ends at loan collection. This
loan process encompasses the following four independent loan-processing teams at
corporate and District level to handle loan-processing activities at various stages and
responsibility levels: Figure 2. 1Loan process of DBE Credit Process; Project
Appraisal teams at District; PRLR team at Districts; and Loan Approval team Credit
process The Bank accepts applications from both recruited and walk-in customers if
they fulfill the Bank’s loan requirements branches (credit process) undertake.

2.3. Empirical Literature

In this part of the proposal different related literatures and studies will be critically
analyzed and presented. Accordingly, the first section emphasizes on any literatures
and studies on factors affecting loan repayment performances anywhere in the globe
followed by related literature reviews conducted in Ethiopian context and finally
attempts will be made to reveal the reason why this study is found essential. Different
studies have been conducted regarding the determinant of loan repayment delinquency
and default especially on commercial banks, microfinance and agricultural credit
borrowers by using different technique of analysis. Among others binomial logit or
probit regression model, multinomial logit or probit and to bit are widely use and some
of them are presented show the effect of those factors on loan repayment delinquency
and default.

To begin with, Munene, et al.(2013), in his study of Factors Influencing Loan

Repayment Default in Micro Finance Institutions: The Experience of Imenti North
District, revealed that there was significant relationship between the type of business,
age of the business, number of employees, business profits and loan repayment default.

25
There is strong link between technical training for loan beneficiaries and the
performance of entrepreneurial businesses among the remote communities.

The study was conducted on Microfinance institutions in Kenya to establish the causes
of repayment defaults in Imenti North District, Kenya using a descriptive survey
design by incorporating 400 respondents of individual microfinance loan beneficiaries
and microfinance institution officials using census and cluster sampling procedures for
micro finance institutions officers and loan beneficiaries respectively. The data
collected using both structured and unstructured questionnaires and analyzed using
descriptive and inferential statistics. Another related study by Vigano, Lawra (1993),
under a title “A Credit Scoring Model for Development

Banks: An African Case Study” has identified some important factors using a credit-
scoring model. Taking the case of Development Bank of Burkina Faso, Vigano found
out that being women, married, aged, proximity to the bank, use of better technology
and being flexible to adjust to market changes, proper use of the loan, project
diversification, frequency of loan maturity, collateral, personal guarantee and being a
preexisting depositor are negatively related to loan default risk. Loans in kind, long
weighing period from application to disbursement and being younger firm, past
default, existence of other loan are those positively related to loan default rate.

Using probit model of data analysis, Yacob (2014) analyzed the socio-economic
factors that affect the institutions loan repayment performance Eritrean Saving and
Micro Credit Program of Dekemh are Sub-Zone using the stratified sampling
technique. The data collected from a sample of 134 respondents, which were 67
defaulters and 67 non-defaulters. A structured questionnaire was used to collect the
primary data and descriptive statistics and the probit model were employed to analyze
26
the data. The socio-economic characteristics of the respondents were described using
averages, percentages while the factors influencing loan repayment performance of the
saving, and Micro Credit Program loans were analyzed using the binary probit
regression model. Results of the regression analysis revealed that the level of
education, loan size and loan category have insignificant effect on the probability of
the loan repayment. On the other hand, age, gender, type of business and credit
experience are significant determinants where age and type of business have negative
relationship and gender and credit experience have positive relationship with the loan
repayment probability. Another research conducted in Ghana using probit model to
identify factors affecting loan repayments by the farmers of Brong Ahafo District of
Ghana.

Awunyo-Vitor (2012) searched the determinants of loan repayment default among

farmers in Brong Ahafo District of Ghana. The study employed Probit model to
investigate factors that influence farmers loan repayment default. Data used in this
study was gathered through a survey of 374 farmers in five Districts within Brong
Ahafo District of Ghana. The results showed that farm size, and engagement in off
farm income generating activities reduces the likelihood of loan repayment default
significantly. In addition, larger loan size and longer repayment period as well as
access to training are more likely to reduce loan repayment default. Theresa, et al.
(2014) examined the determinants of loan repayment among cooperative farmers in
Awka North L.G.A of Anambra state, Nigeria. This study examined the determinants
of loan repayment using SPSS version 17. The study provides empirical evidence on
the farmers “socio-economic characteristics as well as determine which of the
characteristics that influence loan repayment, the range of amount of loan applied for,
amount received and amount repaid by the cooperative farmers and organizational
factors affecting the farmers” credit repayment ability.

27
Two coefficients (educational qualification and farm size) are significant at 5%; and
(loan application cost and collateral value) are significant at 1% respectively. Age,
membership duration, and income of the farmers were not significant but it shows a
positive relationship with loan repayment. There was a significant difference between
the amount of loan received and amount repaid by the cooperative farmers. All the
organizational factors affecting the farmers‟ credit repayment ability were significant
at 0.000 significant levels. The study by Oladeebo and Oladeebo (2008) confirmed that
income, gender, farm size, age of farmers, years of farming experience with credit, size
of loan, family size, timeliness of loan disbursement, level of education of farmers,
sales of crops, degree of diversification, income transfer and the quality of information
were positive and significant determinants of agricultural credit repayment.

The other important study was done by Arene (1992). He evaluated the credit delivery
system of Supervised Agricultural Credit Schemes among smallholder maize farmers
in Nigeria employing multiple regression analysis. The result based on 95 sample
maize farmers showed that high repayment farmers had larger loan size, larger farm
size, higher income, higher age, higher number of years of farming experience, shorter
distance between home and source of loan, higher level of formal education, larger
family size, higher level of adoption of innovations, and lower credit needs than low
repayment farmers.

The regression analysis showed that size of loan, farm size, income, age, number of
years of farming experience, level of formal education and adoption of innovations are
significantly and positively related to repayment rate, Distance between home and
source of loan, family size and credit needs were found to be negatively related to
repayment rate. Causes and treatment of NPLs were studied in detail by Bloem and

28
Gorter (2001). They agreed that “bad loans” may considerably rise due to abrupt
changes in interest rates. They discussed various international standards and practices
on recognizing, valuing and subsequent treatment of NPLs to address the issue from
view point of controlling, management and reduction measures.

A study conducted by Espinoza and Prasad (2010) focused on macroeconomic and

bank specific factors influencing NPLs and their effects in the Banking System. After a
comprehensive analysis, they found that higher interest rates increase NPLs but the
relationship was not statistically significant. The other study evaluated the factors
influencing on repayment performance of farmers in Khorasan Razavi province of
Iran. The logit model seeks to explain the probability of loan on time repayment
because of any of the identified independent variables. The signs of the coefficient of
independent variables and significance of the variables were used determining largely
the impact of each variable on probability of dependent variable.

Results showed that farmer’s experience, income, received loan size and collateral
value have positive effect while loan s loan interest rate, total application costs and
number of installment implies a negative effect on repayment performance of
recipients (Kahansal & Mansoori, 2009).
From all studies discussed above, one can observe different financial institutions in
Nigeria, Spain, China, Kenya, Iran, Tanzania and others are assessed in relation with
different factors that causes nonperforming loans and forwarded their finding and
recommendations for these countries. For example, In Kenya banks shift away from
concentration on security based lending and put more emphases on the customer ability
to meet the loan repayment and in China NPLs is transferred to Asset management
(Waweru and Kalani, 2009). So, it is appropriate to study what exactly affects loan
repayment performances in Ethiopia.
29
2.4. Empirical Studies

In Ethiopia In this part, the Ethiopian literatures will be reviewed. Actually, there is no
sufficient literature (published) in our country, but some of the relevant studies are
reviewed in the next section of this paper. Michael (2006) has analyzed the impact of
factors on loan repayment performance in informal sector of financial institutions in
Addis Ababa by grouping the independent variable (i) Borrower related causes; (ii)
Causes related to business operation; (iii) Lender related causes and (iv) Extraneous
causes, A positive coefficient shows that the variable is associated with a higher
probability of being in the delinquent category than that of being in the good credit risk
category. On the other hand, a negative coefficient indicates that the variable is
associated with a lower probability of being in the delinquent category than that of the
good credit risk category. In another relevant study by Abreham (2002) an
investigation of determinants of repayment status of borrowers and criteria of credit
rationing were conducted with reference to private borrowers around Zeway area who
are financed by the DBE. The estimation result employing to bit model revealed that
having other source of income education, work experience in related economic activity
before the loan and engaging on economic activities other than agriculture are
enhancing while loan diversion, being male borrower and giving extended loan
repayment period are undermining factors of loan recovery performance.

Wondimagegnehu Negera (2012) in his study “determinants of NPLs on commercial

banks of Ethiopia” revealed that underdeveloped credit culture, poor credit assessment,
aggressive lending, botched loan monitoring, lenient credit terms and conditions,
compromised integrity, weak institutional capacity, unfair competition among banks,
willful defaults by borrowers and their knowledge limitation, fund diversion for

30
unexpected purposes and overdue financing has significant effect on NPLs.
Conversely, the study indicated that interest rate has no significant impact on the level
of commercial banks loan delinquencies in Ethiopia. In order to analyze such
determinant factors for successful loan repayment performance at bank, researches has
done at north District of DBE.

According to the study of Kibrom (2010), factors that determine loan repayment
performance include; borrowers perceived need that is borrowers have to be given an
opportunity to borrow for their perceived needs, competence that is the borrowers past
personal and profit record, past prosperity etc. Borrowers personal character which
were related with personal qualities of the borrower including age, gender, educational
level, house hold size, management capacity, loan utilization, availability of other
sources of income, bank experience etc. Factors which are related with the bank such
as structure of the bank, change in the lending policy, way of appraising the project,
responsibility and accountability of the staff members and external factors related with
the macroeconomic condition of the country, government policy and natural factors
had analyzed. Million, et al. (2012) examined the determinants of loan repayment
performance among smallholder farmers in Ethiopia, Amhara Region, South wollo
Zone, Dessie district. Structured questionnaire will be used to gather information from
140 smallholder farmers. Quantitative data will be analyzed using descriptive statistics
such as mean, standard deviation, and percentage used.

The study by Arega seyoum(Dr) and Tadele Tesfaye (2016) on factors affecting
nonperforming loan in central District of Development Bank of Ethiopia, using
descriptive statistics including mean, frequency and percentages and processed through
computer loaded SPSS software and by collecting both primary and secondary data.
Generally, the Empirical researches on loan repayment performance in Ethiopia are not

31
comprehensive enough to cover all scenarios. For instance, the research by Abraham
2003 was conducted in Development Bank of Ethiopia; however it is limited to loan
repayments and its determinants in small scale enterprises.

The study by kibrom Tadesse (2010) conducted on successful loan repayments was on
Development Bank of Ethiopia in North District. It mainly focuses on determinants of
successful loan repayment performances in Northern part of the country where the
loans are mainly allocated for industrial investments, and did not cover the other side
of repayment performances (which is NPLS).

2.5. Conceptual Frame Works

According to Shields (2013), a conceptual framework was expressed as the way ideas
are organized to achieve a research projects purpose. It is connected to the research
purpose. A conceptual framework is a basic structure that consists of certain abstract
blocks which represent the observational, the empirical and the analytical/ synthetically
aspects of a process or system being conceived. The interconnection of these blocks
completes the framework for certain expected outcomes. Hence, the researcher
developed the following conceptual framework to easily assess the relationships
between those factors and loan repayment performances. In addition to methods of data
presentation and analysis, the research frame work has described in the following
diagram considering both dependent and independent variables. This conceptual frame
work was developed based on literatures reviewed under previous chapter and the
variables selected under this chapter of the study.
Fig 2.2. Conceptual frame work

Borrower’s
specific
factors
32
Age

Gender
marriage
 Family size

Bank specific
factors
Business related
factors Follow up grace
period
Income Loan
Loan size
Repayment
Other business
performance Diversion
Business form
business sector Equity

Collateral

Time horizon, Kyc

and interest
Other
external
factors

Source: Prepared by the researcher.

CHAPTER THREE

3. RESEARCH METHODOLOGIES

In the preceding chapter the review of related literature on factors affecting loan
repayment performances, the empirical studies and their respective findings are
presented. As loan repayment performances basically comprises of performing and
non-performing loans, different studies related to both scenarios were thoroughly
reviewed under the preceding chapter. This chapter presents the methodology that
provides a detailed direction about the methods that the researcher used in conducting

33
the research. Hence, the research design, description of the study area, data type and
source, methods of data collection, sampling techniques, methods of data analysis and
definition variable, measurement and description of variables are discussed.

3.1. Research Design

Research design is a comprehensive plan. It is a blueprint for empirical research aimed

at answering specific research questions or testing specific hypotheses (Anol
Bhattacherjee, 2012). Research design is the program that guides the researchers in the
process of collecting, analyzing and interpreting the data. Therefore, the nature of
problem and objective of any study usually determine the type of research design
adopted by researcher. A choice of research design reflects the priority of a researcher
about the dimensions of the research process and methods. The objective of this
research is to identify the factors affecting loan repayment performances in
Development Bank of Ethiopia, Dessie District. The collected data mainly focused on
description of borrower’s characteristics, lending institution/bank related factors,
business/project related factors and external factor that affects loan repayments and
their relationship among the dependent and explanatory variables. Therefore, both
qualitative and quantitative research method were used in the study.

3.2. Description of the Study Area

The geographical study area of this research is located in the south western part of the
Ethiopia and comprises some parts of three National regional states. Accordingly the
geographical coverage of the area includes South Wollo Zone, Dessie Town, and
region of Amhara regional state. This area is categorized as ever green and suitable
area for Borrower side agricultural and commercial farming investments according to

34
DBE loan manual. Currently Development Bank of Ethiopia has about 12 Districts all
over the country. Dessie District is one of such Districts covering the above listed
administrative zones from three regional states. Development Bank of Ethiopia Dessie
District has two grade “A” branches (Dessie branch and Sekita branch) and six grade
“A” branches Pursuant to the currently existing organizational structure of the bank
grade A branches can provide credit to their borrowers and administer their active
loans, while the role of grade C branches were limited to collecting repayments from
old loans and handling inactive loans. There is also a team called Project Rehabilitation
and Loan Recovery Team (PRLR) responsible to handle sick/nonperforming loans
under the District. In this study, grade C loans are excluded from the population of the
research for they are totally dead and write-off and hence, the population of the study
is limited to loans under the District office (grade A branch) borrowers and sick loans
handled by PRLR team under the District.

3.3. Data Type and Source

The data employed in this study is both primary and secondary data. Accordingly, the
primary source of data was collected through questionnaire and interviews from the
sample population. A structured and semi structured questionnaire with open ended
and closed ended type was distributed and collected from 150 borrowers identified
using stratified sampling from the loan position of the District as of June 30, 2016. It
excludes borrowers whose repayment installment has not yet matured because it would
be premature to assess the real performance of the projects as well as credit worthiness
of the borrowers unless they are practically tested by their repayment record. The staff

35
borrowers were also excluded from the population of the sample because, such loan is
either personal loan or housing loan which are basically the privilege to the staff
members of the bank and the repayment performance for such credits are very much
secured as long as the employee remains the worker of the bank. Further, such loans
didn’t qualify the requirements of financing for projects/working capital loans, hence
excluded. The financial position of the borrowers at the end of fiscal year i.e. June 30,
2016 (the bank’s financial statement closing date) is considered was used as another of
source data.
Primary data: The primary data was collected from original source (borrowers)
through questionnaire. The primary data collected through semi-structured
questionnaire distributed to the borrowers; and interviews conducted to the bank
officials and staffs.

The questionnaire included both close and open-ended questions. The close-ended
questions covered the personal information, institutional, external factors, loan and
repayment related questions. The open-ended questions dealt with the perception of
clients towards the bank and their feelings.

All questionnaires translated into Amharic. The questionnaire was pre-tested by three
borrowers before conducted for the whole sample. Besides, interviews were made with
selected loan officers and managers, and relevant documents were reviewed.
Secondary data: secondary data were used as a source of data in this work to
determine the repayment performances of the bank in the previous consecutive years
and to determine the sample size population of the study. Especially, annual reports,
loan portfolio of the banks and others publications of the bank were used as a
secondary data.

36
3.4. Method of Data Collection

In order to achieve the objectives stated in the preceding section and considering the
nature of the problem and the research perspective, the researcher used both
quantitative and qualitative data. The Primary data were collected through primary data
collection techniques mainly using structured and semi structured questionnaire and
interviews with the officials and senior officers of the bank. Secondary Data were
directly gathered from records and published documents of the bank. The data
collected include aggregate loans outstanding balances, NPLs as at the annual closing
date, June 30 2016 and others as found appropriate. For the purpose of comparison the
surveyed banks data for the years preceding and following years performances were
also considered.

3.5. Population and Sampling Method

Determining type and method of sampling mainly depends on the types of population
that the study covers. According to (Kothari, 2004), if the population from which a
sample is to be drawn does not constitute a homogeneous group, then stratified
sampling technique is applied to obtain a representative sample. The usual method, for
selection of items for the sample from each stratum, resorted to is that of simple
random sampling. Hence, Sample selection was based on stratified sampling where
borrowers were selected based on the diversified investment activities they are carrying
on and in proportion to the population classification in terms of their loan status. The
loan position of the District shows that as of June 30, 2016, the total number of
borrowers including the staff loans in Dessie District (Dessie Branch, PRLR team of
the District and the remaining six branches) was three hundred seventy five (375).
From the total loans 84 of them were staffs loans (short and long staff loan), and 18

37
were not matured yet, the remaining 273 loans were listed in the loan position of the
District having different loan status and in all cases their first loan repayments is
mature. Thus, the total population of the study is 273. Out of these, 217 loans were
performing loans/non-defaulters and the remaining 56 were defaulters (loan status
including substandard, doubtful and loss).

According to their loan status, 79.49% of the total populations were performing loans
while the rest 20.51% were nonperforming. Borrower whose maturity date is not yet
due and Staff borrowers are excluded from sampling of population because loans for
employees is mere privilege and didn’t qualify the requirements of provision loans for
other businesses and its repayment is almost granted as long as the employee remains
in the bank. On the other hand, in case of loans which are not matured yet, it was
excluded from sampling because it is difficult to study about the factors affecting loan
repayment performances while the loan is not yet matured, it will be prematurity.
Therefore, based on proportional stratified random sampling the samples of 150
borrowers were selected, out of which 110 were performing loans and 40 were
defaulters. In addition, two principal officers from each team (credit team, appraisal
team and approval team), two senior officers from different teams, Dessie and
kombolcha branch managers and District manager was interviewed, their comments
and ideas are used in interpretation and recommendation of the study.

3.6. Method of Data Analysis

The data collected through the above stated techniques were thoroughly coded and
checked for consistency and analyzed and interpreted using both descriptive statistics
and econometric analysis. Accordingly, the researcher analyzed the data using
descriptive statistics (frequencies, percentages, mean, and standard deviation) to obtain

38
information on the factors affecting loan repayment performances and binary logistic
econometric model (logit) was used to identify the factors of loan repayment ability in
Development Bank of Ethiopia Dessie District. Descriptive statistics was employed to
analyze the data and the results were tested with non-parametric tests of significance,
whereas econometric analysis, specifically binary logistic regression was used to
identify statistically significant variables in relation to the dependent variable.

Loan repayment performances refers to the ability/capability of borrowers to duly

repay loans or fail to repay their loans. Hence, the dependent variable is dummy
variable. If Borrowers experienced well repayment performances the dependent
variable takes a value of 1, and if the borrowers fail to repay their loans as per the
terms of agreements/contracts the dependent variable take the value of 0. So, the level
of significance and influence of each independent variables where defined and
identified using both descriptive and econometric analysis against the dependent
variable. Finally the analyzed data was presented in the form of table and percentage in
order to make the data understandable and attractive detailed statement would support
these tools.

3.7. Model Specification

Data collected through the above stated methods were analyzed using different
techniques. According to (Kothari, 2004) data analysis requires a number of closely
interrelated operations such as establishment of categories, the application of these
categories to raw data through coding, tabulation, and then drawing statistical
inferences.
Hence, the researcher analyzed the collected data using descriptive statistics
(frequencies, percentages, mean, and standard deviation) to obtain information on the

39
factors of loan repayment performances especially to summarize and conclude the
implications of qualitative data and binary logistic econometric model (logit) was used
to analyze the determinants of loan repayment ability of the District borrowers.
According to Vasisht (n.d), logit analysis produces statically sound results, which can
be easily interpreted, and the method is simple to analyses. Assume the following basic
model, it can be express the probability that y = 1 as a cumulative logistic distribution
function.
𝑌𝑖 = 𝛽1 + 𝛽2𝑋𝑖 + 𝜀𝑖
1
𝑃𝑖 = 𝜖 (𝑦 = xi ) = 𝛽1 + 𝛽2𝑋𝑖

Where, Zi=B1+B2xi
The cumulative Logistic distributive function can then be written as:
zi
pi=1 e
(−B 1 +B 2)
= zi
1+ e 1+e
Pi= prob (Y = 1| X) is the response probability.
The non-response probability (1- Pi) is also evaluated as:
Note that the response and non- response probabilities both lie in the interval [0, 1];
Zi ranges from - ∞ to + ∞, and hence, are interpretable. There is a problem with non-
linearity in the previous expression, but this can be solved by creating the odds ratio
pi
and its log-transformation.
1− pi
x
1 ¿ 1+ e
pi yi= ¿ = ezi
1− pi
= 𝑝𝑟𝑜𝑏 = ( xi 0
( yi= ) 1+ e
−zi

xi

pi
Li = ( 1− pi ¿ = zi=B1=B2xi (Gujarati, 2004)

Li is called the logit, thus, the log-odds is a linear function of the explanatory variables.
The above transformation has certainly helped the popularity of the logit model. Note

40
that for the linear probability model it is Pi that is assumed to be a linear function of
the explanatory variables. The odds ratio can be interpreted as the probability of
something happening to the probability it will not happen. Accordingly, the estimated
models used in this study presented as follow.
LRP =β1 + β2(Gedr) + β3(Ag) + β4(Mar) + β5(Educ) + β6(Hhs) + β7(Exp) +
β8(Othbus) + β9(Busfrm) + β10(Bussctr) + β11( Income) + β12(Lnamt) + β13(Div) +
β14 (Eq) + β15 (Grprd) + β16 (Folup) + β17 (Coll) + β18 (Int) + β19 (KYC) + β20
(Timhzn) + β21 (Mrkt) + β22 (Wthr)

Where LRP, Gedr, Ag, Mar, Educ, Hhs, Exp, Othbus, Busfrm, Bussctr. Incm, Lnamt,
Div, Eq, Grprd, Folup, Coll, Int, KYC, Timhzn, Mrkt, Wthr denotes Loan Repayment
performance, Gender, Age, Marriage, Education, House hold size, Experience, Other
business, Business form, Business sector, Income, Loan size, Diversion, Equity, Grace
period, Follow up, Collateral, Interest, Kyc, Time horizon, Market and Whether
respectively.
β1 = an intercept, Where β2, β3, β4, β5, β6, β7, β8, β9, β10, β11, β12, β13, β14, β15,
β16 β17 represent estimated coefficient. On the other hand, binomial logit regression
model of regression was used for econometrical or statistical analysis of the study. The
statistical analysis of model with qualitative dependent variables can be viewed as the
problem of predicting probabilities for the various possible values (responses) of the
dependent variable. Probit and Logit are well-known techniques for the case when
there are only two responses, typically the occurrence or non-occurrence of some
event. Both have essentially the same interpretation - the probit is based off an
assumption of normal errors and the logit off of extreme value type errors. The logit
has slightly fatter tails than the probit possibly making it slightly more robust. In binary
studies probit and logit are largely undifferentiated.

41
In this research, the researcher selected logit model because it is slightly easier to
introduce random parameters to an estimate as a simulated maximum likelihood
regression. Even though, both have simple and fairly elegant representations in the
binary case on paper, in cases of choice with more than two alternatives the logit
quickly becomes the preferred choice as the probit model is difficult to estimate when
alternatives are above 3. So, logit was used to present the econometric results and
analysis of the research. Odd ratios were used to explain the degree of influence of
variable. Odd ratio/ logistic regression coefficients provide information on the
probability of being on time payer and default as we change the independent variable
by on unit reference to on time payer category. Furthermore, Likelihood Ratio (LR)
Chi-Square test used to show that at least one of the predictors' regression coefficients
was not equal to zero in the model than the observed statistic under the null hypothesis;
the null hypothesis was that all of the regression coefficients in the model are equal to
zero.
To see the significant of each explanatory variable t- test was applied. Whereas to
detect the presence of violation on basic classical linear assumptions of the model,
different techniques were applied regards autocorrelation, multi collinearity, and
heteroscedasticity and normality test.

3.8. Variables of the Study

In order to achieve the extracted objectives of this research, the researcher selected
different variables based on literatures that could affect the dependent variables either
positively or negatively. Hence, based on availability of data the variables selected in
this research are to signify the loan repayment performance and the variables which are
attributable and likely to influence the dependent variable were listed down with their
respective expected sign. Selection of variables was based on empirical literature

42
review as presented under the preceding section to establish the factors affecting loan
repayment performances. While guided by the literature review, the researcher also
considered other factors likely to influence loan repayment. To establish the factors
affecting loan repayment, the researcher summarizes these variables under four broad
categories, (i) variables that are related to the characteristics of the borrowers, (ii)
factors related to the lending institution (the bank), (iii), factors related to the
business/project itself and (iv), factors emanated from the external environments.

3.8.1. The Dependent Variable

The dependent variable of the study is loan repayment performances. Loan repayment
performance (LRP) is the ability to repay the loan as per the loan agreement and/or
inability to repay the loan by either failing to complete the loan as per the loan
agreement or neglect to service the loan. Several African studies on loan repayment
performance have estimated the factors of loan repayment performance with a binary
loan outcome – defining borrowers as either current on their loan repayments or in
default. There are a number of different factors that would affect this dependent
variable either positively or negatively.
Dependent variable defined Taking in to consideration the loan status classification by
national bank of Ethiopia, according to the loan manual of Development bank the
dependent variable of this study loan repayment performances is dummy variable and
all other independent variable are encoded as dummy as well as categorical
explanatory variables, which is appropriate to use STATA software in the following
form. Hence, the study has encoded both dependent and independent variables in the
following way by taking in to consideration of being dummy and categorical variable.
Loan repayment performances – encoded as dummy as follows;
1. Pass: due date less 90 (ninety) days

43
2. Special Mention: due date 90 days‟ ≤Y<180days Performing loan ……..….
1
3. Substandard: due date 180 days‟ ≤Y<360 days
4. Doubtful: due date 360 days‟ ≤ Y<3 year Non performing loan ……..
0
5. Loss: due date 3 years ≤ Y
3.8.2. Definition and Hypothesis on Independent Variables

Selection of variables was based on empirical literature on the factors affecting loan
repayment performances. While guided by the literature review, the researcher also
considered other factors likely to influence loan repayment. To establish the factors
affecting loan repayment, the researcher summarized variables in to four categories,
factors related to characteristics of the borrowers, factors related to lending institution,
factors related to the nature of the business/project and other external factors. So,
dependent variable (loan repayment performance) is expected to be explained by the
following independent variables:

3.8.2.1. Factors Related To Characteristics of the Borrowers

These factors are very personal and attached to the behavioral attributes or personal
integrity of the borrower. Such type factors are many in numbers; the followings are
some to be evaluated under this research;
Gender: determines whether male or female borrowers perform better than the other.
It is a dummy variable taking, 0 for female and 1 for male. The female borrowers have
a tendency for better loan repayment. This means that lending to women can lead to
their economic empowerment and inculcate them a culture of hard work and financial
discipline, which can lead to high loan repayment rates, thus women borrowers may

44
have high loan repayment performance. Thus being women expected to have a positive
sign on loan repayment.
Age: age of borrower in years. It is a continuous variable but rearranged as 1) young
age (15-30) 2) mature age (31-50) and 3 old age (above 51). It is argued that older
borrowers are wiser and more responsible than younger borrowers. On the other hand
younger borrowers are argued to be more knowledgeable and more independent. That
means on the other way round, the older person may have a lot of experience on
business, which may lead to loan repayment, and the younger one may have limited
experience attributed to his age and this may lead to loan repayment. Hence, age
contribution to loan repayment performance cannot be predetermined.
Marital status: this variable evaluates whether single, married or divorced borrowers
showed any difference in repayment performances. It’s generally believed that
marriage brings stability to once life and equips how to act towards something
responsibly. It is a continuous variable but rearranged as a dummy variable; taking 1 if
the borrowers are single, 2 if married and 3 if the borrowers are divorced/widowed.
The borrowers who engaged in marriage can have financial management experience in
their home. Thus, having such managing experience can be reflected in their loan
utilization. The expected sign is negative to being default loan.
Education: generally education is among the primary tools that has transformed the
world order as it stands today. Education improves once performance quality. Higher
educational levels enable borrowers to comprehend more complex information, keep
business records, conduct basic cash flow analysis and generally speaking, make the
right business decisions. So, it is important to test whether education level difference
between and among borrowers have brought any change in their loan repayment
performances. This is a continuous variable but arranged as categorical variable, taking
1 if the borrowers have no formal education, 2 where the borrowers attended primary
educational, 3 if borrowers attended secondary educational and 4 if the borrowers
45
attended college/university education. This factor is expected to have a positive impact
in loan repayment performance, because higher educational levels enable borrowers to
comprehend more complex information, keep business records, conduct basic cash
flow analysis, and make the right business decision. Hence borrowers with higher
levels of education may have higher repayment performances.
Family size: this variable is all about the number of dependents on the borrower.
Hence, using this variable comparison is made between borrowers having small family
size with those having medium or large family numbers against their loan repayment
performances. It is a continuous variable (measured in number of members of farm
family but arranged as 1 for small family size(1-3) 2 for medium family size(4-5) and 3
for large family size(above 6); it is assumed that the larger the family size the negative
influence on loan repayment performance which is attributed to higher house hold
expenses. There is a possibility of loans diverted to unintended purposes because of
many responsibilities resulting from meeting the needs of many members of the
family. Hence borrowers with large family sizes may have lower repayment
performances.
Credit Experience: it is a continuous variable but rearranged as dummy taking 0)
where borrower have no any credit experience and 1) if borrower have credit
experience. Borrowers who have been in business longer are expected to be more
successful with their enterprise. They have more stable sales and cash flows than those
who have just started. Thus, those who are more experienced will have high repayment
rates. Hence, it is expected that experience will positively affect loan repayment
performance of borrowers.

46
3.8.2.2. Factors Related To Lending Institution

These factors are mainly related to technical capacity and strength and/or weakness of
lending institution in providing credit services to its borrowers particularly in
screening, appraising, approving, supervising and collecting loans from their
borrowers.
Loan size: It is the amount of money permitted for the borrowers. In case, the amount
of money permitted/lent to borrowers have any influence or not was evaluated using
this variable. In order to operate the investment with its full capacity and cover all
necessary costs, sufficient amount of money is required.

Von Pischke (1991) noted that efficient loan sizes fit borrowers’ repayment capacity
and stimulate enterprise. If amount of loan released is enough for the purposes
intended, it will have a positive impact on the borrower’s capacity to repay. If on the
other hand the amount of loan exceeds what the borrower needs and can handle, it will
be more of a burden than help, thereby undermining repayment performance. Also
positive or negative sign may be expected if the loan is too small. If the loan is too
small it may be easy to repay such loans thus enhancing performance (i.e. positive
sign). However, too small loan may not bring commitment on borrowers to use the
loan productively (Von Pischke, 1991). It may also encourage borrowers to divert the
loan to other purposes, increasing credit risk and undermining performance, in which
case a negative sign for the variable is expected (Vigano, 1993). It is continuous
variable but re arranged as 1 if the amount is from 1-10 million, 2 if the amount is from
11- 20 million and 3 if the amount is above 21 million birr.

Follow up: It is a dummy variable that proper follow up taking as 1 and otherwise 0. It
is done at different stage of the project. Project follow up can be done at the stage of

47
project under implementation, during implementation and commencing to commission.
Undertaking of fledged follow up as per the schedule boost the projects /customers to
accomplish their task duly and the project can generate revenue. The chance of being a
default loan is low if proper follow up has done.
Grace period: As of Abreham (2002) if large grace period is given, the project will
have sufficient time for implementation so that borrowers could properly utilize the
loan for the intended purpose and to generate adequate income after it starts operation.
Therefore, it will not face repayment problem when the loan due later. Grace period is
a dummy variable, 0) if sufficient grace period is not given and 1) if sufficient grace
period is given to the project. The expected sign of grace period to
nonperformance/defaulting is negative.
Know Your Customer/Due diligence (KYC): It is a screening stage evaluation of the
borrower and the business whether it is creditworthy or not. It is an entry point
assessment. Conducting proper due diligence is require in every applications to access
credit from Development bank of Ethiopia. In this stage the borrowers all round
aspects are assessed in relation to its personal characteristics from past to present,
fulfillment legal documents to be a creditor, project management , capital adequacy ,
credit relation and experience, availability of inputs and identification of risk. Know
Your Customer/Due diligence (KYC) is a dummy variable that well done due diligence
taking as 1 and otherwise 0. Therefore, adequate due diligence, the expected sign for
being default is negative.
Lending Interest rate: It is a dummy variable taking 1 if increase in interest rate
negatively affected repayment performance and 0 if otherwise. Increase in lending
interest rate, increases the amount of loan to be repaid per installments. Hence, it is
expected to have positive relation with the default.
Timeliness/time horizon: Timeliness of release of loan (measured as a dummy, 1 for
loans released at the right time and 0 for loans not released at the right time).
48
Investments in DBE Dessie District area were predominantly, farming activities which
is mostly seasonal and rain fed, hence if the loan is not released at the right time yield
will be affected and repayment performances may below. Johnson and Rogaly (1997)
noted that timeliness of loan disbursement is important when loans are used for
seasonal activities such as agriculture. They argued that complicated appraisal and
approval procedures, which might delay disbursement, influence a program of seasonal
loans for farmers who use to buy inputs. Further they noted that this could in turn
worsen the prospects of repayment by diverting loan to non-intended purpose. In such
cases a positive sign is expected.
Collateral: Collateral is the guarantee for repayment performances. If the borrower
secures high valued collateral relative to the loan size, the lender may feel that it will
not be a loser in case the borrower defaults. Borrowers exert their maximum effort to
repay the loan if the collateral towards the loan size is high and vice versa. Collateral is
dummy variable taking 0) if sufficient collateral is not attached for the loan and 1 if the
collateral is sufficient to restore the lent money in case of default. So, collateral will
have positive sign towards repayments of money.
Equity: For this research purpose equity is the share of borrowers in the total
investment capital of a project. The amount of equity of owners in the investment
determines the sense of belongingness and the extent of responsibility. If the amount of
equity is higher, the borrower feels a sense of ownership and will strive to recover the
loan and make the whole asset his sole property. Equity is a dummy variable taking 0 if
equity less or equal to 30 percent of total investment and 1 if the equity is greater than
30 percent of total investment. Thus, a positive sign is expected.
Loan diversion: Diverted loans miss the targets of the investments. The projects will
not generate necessary earning and benefits to repay loans if loan diverted. According
to Abraham (2002), loan diversion is problematic only if the business which received
the diverted money fail to pay back. But the loan manual of DBE prohibits any kinds
49
of loan diversion. Loan diversion is a continuous variable but arranged as dummy
variable taking 0) if not diverted and 1) if diverted. So, loan diversion contributes
positively to default.

3.8.2.3. Business Specific/Project Related Factors

Business Sector: - It is clear that different types of projects have different level of
risks. Thus, borrowers with different types of projects may have different repayment
rates. However, it is clear that borrowers who engage in agriculture and agricultural
related product sectors are expected to have default loan, this is because agriculture
and agricultural related projects are seasonal and more exposed to different risks than
service sectors. Business Sector is continuous variable but arrange as 1 for agriculture,
2 for service and 3 for industry. The expected sign for agriculture is positive for
default.
Other business: - It is a dummy variable taking 0 if didn’t have another business and 1
if having another business. It is all about experiencing some other business in addition
to the current project that the borrower involved. If the borrower has other source of
income, he may not spend the income that is generated from the current business other
than loan repayment, or vice versa. It was expected positive or negative sign because if
the promoter has additional business other than the project, he or she will divert loan
and expend more time on other business. On the other hand, borrower who has other
business might use it as the source of short fall of capital or loan repayment.
Business form: The ownership and the level of responsibility of the business matters
in operating any business. Whether the ownership was sole proprietorship or PLC or
other business form, because some form of business leave individual responsibility and
accountability may cause for business failure and hence low repayment performance. It

50
is continuous variable but rearranged as 1 if sole owner 2 if PLC and 3 if SHC and
others.
Income/profit: it is dummy variable taking 0 if sufficient income is not generated and
1 if sufficient income compared to feasibility study is gained from the business.
Income/profit is amount of money generated from the business itself with a given fiscal
year. Hence the sign it takes may not be single, because if sufficient income is
generated the variable shows positive sign to repayment and otherwise.

3.8.2.4. Other Factors (Macroeconomic Factors)

Market conditions (inflations): this is dummy variable taking 0 if market condition

didn’t affected repayment and 1 if market condition affected repayment of the
borrower. In contemporary world the market condition is unpredictable. There
fluctuation from time to time, hence, shows negative sign to repayments of loan.

Whether conditions: this is dummy variable taking 0 if whether condition didn’t

affected repayment and 1 if whether condition affected repayment of the borrower.
Now days the global whether condition is threatening the life of human beings. El Niño
for instance, caused some unpredictable and unbelievable disasters in the last year. So,
shows negative sign towards repayments of loan.

Explanatory variables encoded

Explanatory variables are encoded as dummy and categorical variables as follows;
1. Gender ………………………….....male= 1 and otherwise = 0
2. Age ……………1= Young age (15-30), 2 = mature age (31-50), 3= old age (51
above)
3. Marital status ……………. Single = 1 married = 2 divorced/widowed = 3

51
4. Family size --------------------- small = 1 medium = 2 large = 3
5. Education level ...1= no formal educ. 2= primary educ. 3= tertiary educ. 4 =
coll/above
6. Other business ……………Have other business = 1 and not have other business = 0
7. Experience ------------------------- if yes = 1 and if not = 0
8. Loan size ……………small (1-10m) = 1 medium (11-20m) = 2 large (21 above) = 3
9. Kyc/due diligence …………….. If properly undertaken =1 if not = 0
10. Business sector ………………...Agriculture = 1 and service = 2 Industry =3
11. Business form ………………. Sole owner = 1 Plc. = 2 and SC and others = 3
12. Equity …………Equity greater than 30 percent = 1 and less/equal to 30 percent = 0
13. Time horizon……………………….timely = 1 delayed = 2 and too late = 3
14. Loan diversion ------------------- if diverted = 1 and if not = 0
15. Collateral …………………....sufficient collateral = 1 and not = 0
16. Grace period ……………….. Sufficient time given = 1 and if not = 0
17. Follow up …………………... proper follow up = 1 and if not = 0
18. Lending interest rate …change in interest rate affected repayment = 1 and if not = 0
19. Income/profit ---------------------- if sufficient income generated = 1 and if not = 0
20. Market condition ……………..Poor market condition = 1 and were not poor = 0
21. Weather condition …………....Bad weather condition = 1 and were not bad = 0

To sum up, discrete dependent variable (loan repayment performance) was expected to
be explained by listed discrete and categorical independent variables with their sign as
shown in the table 3.1.

Table 3. Summary of Expected Sign (+/-) of Explanatory Variables in this Study

No Explanatory Measurements Definition Expected

52
 variable sign
1 Age Categorical(young, The older the age having +
mature, old age) high experience
contributes a lot for loan
repayment
2 Gender Dummy, male & female Lending to women, lead to -/+
high loan repayment rates
3 Marital Categorical(single, Married borrowers can -/+
status married and take great care than non-
divorced/widowed) married for default
4 Education Categorical (no educ, Being literate borrowers +
primary, high school, well informed and
coll.) contributes for default
negatively
5 Credit Dummy,(yes or no) Borrowers who have no or +
Experience less experience, will
contribute for default
6 Household Categorical(small, The smaller family size _
Size medium and large less probability being
family) default
7 KYC Dummy(properly made Performing due diligence +
and otherwise) thoroughly less probability
being default
8 Follow up Dummy(yes or no) Performing fledged follow +
up as per the schedule the
probability of defaulting is
less

53
9 Business Categorical (agriculture, Agricultural projects are _
sector service and industry) seasonal, the rate for
default so high
10 Equity Dummy (less than thirty The larger the equity of -/+
percent or above) owners, the less the
probability of being
default.
11 Time Categorical(timely, Disburse the loan timely, -/+
horizon delayed and too late) less probability being
default
12 Grace Dummy(sufficient time Large grace period is +
period given or otherwise) given for projects, less
probability being default
13 Collateral Dummy (sufficient When bank loan provided -/+
income or otherwise) with sufficient collateral,
the probability of being
default decrease.
14 Income Categorical(small, The more the profitability +
profit medium and large) of projects, the less the
probability of being
default.
15 Loan size Categorical(small, Increasing loan -/+
medium and large) size ,increasing capital,
generates revenue, less
probability being default
16 Loan Dummy (loan diverted Diverted loan miss the _
diversion or not) target, hence probability

54
 of being default is high
17 Other Dummy (whether the Other business may help -/+
business borrower have another to repay or be a source of
business or not) diverting loan; so the
probability may depend on
scenarios.
18 Business Categorical (sole owner, Some business form limit _
form plc and Shc) the liability of owners,
probability of default is
high in such cases.
19 Interest Dummy (increase of Increase in lending _
lending interest affect or interest rate increases the
no) probability of being
default
20 Weather Dummy (product If whether condition was _
affected by whether or not normal in the
not) probability of being
default is high.
21 Market Dummy(the price of +
products affected by
market or not)

Source: Compiled from the definition and literatures, 2016

3.9. Conceptual Framework

According to Shields (2013), a conceptual framework was expressed as the way ideas
are organized to achieve a research project’s purpose. It is connected to the research

55
purpose. A conceptual framework is a basic structure that consists of certain abstract
blocks which represent the observational, the empirical and the analytical/ synthetically
aspects of a process or system being conceived. The interconnection of these blocks
completes the framework for certain expected outcomes. Hence, the researcher
developed the following conceptual framework to easily assess the relationships
between those factors and loan repayment performances. In addition to methods of data
presentation and analysis, the research frame work has described in the following
diagram considering both explained and explanatory variables. Thus, the simple
diagram shows the effect of explanatory variables (borrower side, lender side, business
side and external) of loan repayment performance of borrowers. This conceptual frame
work was developed based on literatures reviewed under previous chapter and the
variables selected under this chapter of this study.

Borrower’s specific
factors

Age, Gender,
marriage, Family size,
Experience, Education

Business
specific factors
Loan
*Income, Repay
*other ment Bank specific
business, factors

*business form, *Follow Up, *Grace

*business Other external
Period, *Loan Size,
sector factors
*Diversion,
*Whether
condition
*Market
condition

56
Source: Prepared by the researcher.

CHAPTER FOUR

4. RESULTS AND DISCUSSIONS

This chapter reports the results of the study conducted to identify the factors affecting
loan repayment performances. The data collected from survey questionnaire were
carefully coded and checked for consistency and prepared for analysis and
interpretations. The analysis was performed using descriptive statistics and with the
help of STATA. Therefore, this chapter presents analysis of the result and discussion to
achieve research objectives based on data obtained from the questionnaire respondents
and interview made with senior staffs and managers.

The first section of this chapter discusses the back ground of respondents followed by
the result of descriptive statistics of explanatory variables. In this part of analysis,
factors of loan repayment performances included under four categories (borrower
related, business related, lender related and external related) and other challenges of
borrowers which affects repayment performance were analyzed by using descriptive
statics like percentiles, means, standard deviation and frequency. Besides, the second
section discusses the econometrics result of binary logistic & the analysis of significant
variables.
4.1. Background Information of Respondents

Questionnaire response rate and interview success rate: The questionnaire was
distributed to a population selected using stratified random sampling. Accordingly,
there are two groups of population, performing loans (borrowers) and nonperforming
loans (borrowers), 110 and 40 respectively. Out of the one hundred fifty questionnaires

57
physically distributed to the target population, one hundred fifty usable responses were
collected. This represented a response rate of 100 percent and implies there is no
unreturned questionnaire.
Out of the ten projected interviews, nine of them were successfully conducted, giving a
success rate of 90 percent. The left interviews was unsuccessful due to the targeted
interviewees were time constrained. Despite this, the target population was fairly
represented considering that managers who are relevant to the study were interviewed.
The results are shown in table 4.1 below.
Table 4. 1. Questionnaire and interview success rate
Target respondents Actual Success rate
respondents
Questionnaire 150 150 100%
Interview 10 9 90%
Source: Own computation from primary data, 2016

4.2. Descriptive Analysis

The descriptive statistics for dependent and independent variables are presented below.
The dependent variable of the study is loan repayment performances and measured by
performances of loans and/or nonperformance of loans/impaired loans. Scholarly
literatures presented under chapter two of this work, classified the factors affecting
loan repayment performances in to four broad categories. Customer/borrower related
factors (include age, gender, experience, family size and education level of the
borrower), lender institution related factors (includes loan size, time horizon,
Collateral, equity, follow up and grace period), business related factors (such as having
other business, business form, business sector, business income,) and finally eternal

58
factors (like market problem, weather condition and others). The detail descriptive and
discussions were presented for every individual factors under all these groups.

As discussed under sampling techniques in previous chapter, the total population of the
study was 273, out of which 217 were performing loans and the remaining 56 were
non-performing. Using stratified random sampling techniques 150 population were
selected for this study purpose and 110 (73.3%) were performing loans (according to
loan status classification it includes pass, special mention and substandard) and the
remaining 40(26.7%) were nonperforming loans (doubtful and loss status). From this
we have loan repayment category of performing and Nonperforming/defaulters. In
presenting the descriptive statics analysis of the variable, in addition to percentage and
frequencies, chi square test of independence allows the researcher to determine
whether variables are independent of each other or whether there is a pattern of
dependence between them. If there is dependence, the researcher can claim that the two
variables have a statistical relationship with each other. So, Pearson Chi-Square used in
this study to indicate the level of association of the independent variables with loan
repayment.

4.2.1. Borrowers Related Factors

To begin with, borrower’s specific factors are the first most important factor related
with personal characteristics of the borrower and it’s important in determining
performing and nonperforming loans based on the personal behavior of the borrower.
Under this research, gender, age, marital status, education level, credit experience and
family size were identified to evaluate their contribution in loan repayment
performances of the borrower. From among these variables, gender and credit
experience were encoded as dummy explanatory variables whereas age, marital status,

59
education level and family size were encoded and treated as categorical explanatory
variables. So, now let us see all discrete and categorical variables from loan repayment
performances perspectives.
Gender of Borrower: There is a belief among many credit analysis/specialists that
female are better payers than male borrowers, taking into consideration of their being
more entrepreneurial that results from assuming more responsibilities in the internal
affairs of a household. (Vigano, 1993) Also Khanker et al. (1995) explains that loan
recovery rates have been higher for women than for men. Table 4.2 below shows the
relationship between genders of borrowers with their repayment performances. In
terms of gender composition, from the total 150 survey population of the study the
super majority of them 137(91.3%) were male borrowers. The detail information is
presented in the table below.
Table 4. 2. Gender in repayment performances.
Explanatory Repayment performance
Variable Performing Default Total x2 value
Male 99 38 137
72.1 27.7 100%
Female 84.6 2 13 X2 = 3.2603
11 15.4 100% P = 0.071
Total 110 40 1500
74.3 26.7 1500
Source; computed based on own survey, 2016

The table revealed that the number of female borrowers is much lesser than male.
Accordingly, Out of the total respondents, 137(91.3%) were male and 13(8.67%) were
female. More specifically Out of the total male sample respondents, 72.3%, and 27.7%
of male respondents were non-defaulted and defaulters, respectively. Whereas 84.6%

60
and 15.4% of female respondents were non-defaulters and defaulters, respectively.
This reveals that from their respective sex composition, females’ respondents were
found having more repayment performance than male respondents. Accordingly,
Female borrowers generally delight in the hard work ethics and the culture of financial
discipline in repaying their loan and being committed to the contractual agreement.
The chi-square result also shows that the association between sex and loan repayment
is significant (X2=3.2603, at P=0.071) table 4.2. This indicates that female borrower
had strong positive relation to loan repayment performances.
Age: is one of the independent variables related with borrowers’ characteristics and
determined loan repayment performance of the borrowers. The survey results revealed
that from total respondents 54(36%) were at their young age/less than the age of 30
years, 62(41.33%) respondents were at their maturity age/ranging 31-50 years and the
reaming 34 (22.67%)respondents were old/above 60 years old. The age distribution of
borrowers shows no significant difference as the number of one group is not that much
greater than the remaining Performance wise, from total young age borrowers 70.4%
were the non-defaulters and 29.6 % were defaulters. From mature age borrowers
67.7% respondents were the non-defaulters and 32.3% were defaulters. From old age
group of population 88.2% respondents were the non-defaulters and 11.8% were
defaulters.

Table 4. 3. Age and marital status of Borrowers and Loan Repayment

Variables Category Frequency Non- Defaulter Total
Defaulter
N % N % N %
Age of Less than 30 years 54 38 70.4 16 29.6 54 36
responden 31-51 years old 62 42 67.7 20 32.3 62 41.3
Above 60 years 34 30 88.2 4 11.8 34 22.6

61
 t old
Total 150 110 40 150 100
Marital Single 61 43 70 18 29.5 61 40.7
status Married 81 61 75 20 24.7 81 54
Divorced/ 8 6 75 2 25 8 5.3
widowed
Total 150 110 80 40 150 100

Experienc Have Credit 85 68 80 17 20 85 100

e experience(1)
No credit 65 42 64.6 23 35.4 65 100
experience(0)
Total 150 110 73.3 40 26.7 150 100
Source; computed based on own survey, 2016. N=number of respondents

The survey revealed that as the age of borrowers increases the probability of defaulting
decreases and the repayment performances increases. This emanates from the logic that
as age increases the knowledge, experience and skills of handling and administering
businesses and thereby credit management enhances, hence, the probability of
defaulting decreases.
Marital status of borrowers: Regarding the marital status of the borrower’s, out of
the 150 sample borrowers, as depicted on table 4.2, 40.67%, 54%, and 5.3%
respondents were single, married, and divorced respectively. The single respondents
were accounts for non-default and default 70.5% and 29.5%. Married respondents were
75.3% and 24.7% non-defaulter and defaulter respectively. Among of Divorced
respondents, 75% non-defaulters and 25 defaulters. This indicated that compared to
single borrowers married and or divorced borrowers were better in paying their credit.

62
The reason may related to the social responsibility level of married and divorced
borrowers. This result is same with the result of Josephat, et al. (2013) and Wongnaa1,
et al, (2013).
Credit Experience: Another borrower related factor is credit experiences of
respondents. The credit experience of respondents shall be expressed in terms of years
or months, hence it is a continuous variable, but rearranged and encoded as dummy
variable taking 1 if credit experience exists and o otherwise. Credit experience helps
borrowers in utilizing the loan for intended purpose and on how to prepare payments as
per the schedules. Coming to survey results, From table 4.3 out of the total respondents
majority of them 85 respondents or 56.7% of total borrowers have credit experience
and 68(80%) of these experienced borrowers were paying their loan as per the schedule
of the contract, while the remaining 17 respondents or 20% were defaulted. On the
other hand, 65 respondents or 43.3% were completely new for inexperienced and did
not have any experience. Out of such borrowers 42 respondents, i.e. 64.6% were
performing loans and 23 respondents i.e. 35.4% were nonperforming loans. So,
experienced borrowers are better in repaying their loan than those who didn’t have any
experience. However, the chi-square result shows that the association between marital
status and experience and the dependent variable loan repayment is insignificant. This
indicates that being either in any marital status doesn’t statistically determine loan
repayment.
Education: The survey on the educational characteristics of the borrowers shows that
22 (14.67% ) of the borrowers didn’t attended any formal education, some 53/35.33%
borrower attended lower level/primary education, the rest 48(32%) and 27(18%) of the
borrowers attended secondary school or tertiary level and joined college or university
respectively as shown in table 4.4. The loan repayment performance of the borrowers
relative to their educational level as shown on table 4.4 showed that among 22
borrowers who do not have formal education 40.9% of them were repaying their loan
63
successfully whereas the remaining 59.1% were defaulters. From borrowers whose
educational level is at primary level, majority of them 71.7% repaid their loan duly and
28.3% of them defaulted. From 48(32%) borrowers who attended tertiary education,
81.2% of them were non-defaulters while 18.8% were defaulters. Finally, from
27(18%) borrowers who attended college education and above, 88.9% were non-
defaulters and the remaining 11.1% were defaulters. This result indicates as education
level increases, the probability of defaulting decreases and vice versa. This result
contradicts the result of Yacob (2014) that the clients with lower education have fewer
financial options and thus they would improve on their loan repayment performance in
order not to lose their only formal source of credit.

Table 4. 4. Educational Qualification and family size of Borrowers and Loan

Repayment

Variables Categories Non-default default Total Chi-

square
N % N % N % X2=66.
Educational No formal 9 40.9% 13 59.1% 22 14.6 56
Qualificatio education P=0.000
n Primary 38 71.7% 15 28.3% 53 35.3%
education(1-
8)
secondary 39 81.2% 9 518.8 48 32%
education(9- %
12)
College and 24 88.9% 3 11.1% 27 18%
above

64
Total 110 73.3% 40 26.7 150 100.0
%
Family size Small 46 76.7% 14 26.3 60 40 X2=6.83
06
Medium 43 81.15% 10 18.9 53 35.3
P=0.033
Large 21 56.8% 16 43.2 37 24.7
Total 110 73.3% 40 26.7 150 100%
Source; computed based on own survey, 2016. N= number of respondents

The chi-square result also shows the presence of strong and significant association
between educational level and dependent variable at 1% significance level
(X2=66.5646, at P=000). This results of chi square revealed that level of education has
strong and significant relationship with the dependent variable.

Family size: In this study family size is used to express the number of dependents on
the borrower.
Accordingly, the influence of family size on repayment performances of borrowers is
assessed as follows; out of the total sample borrowers, 60 of them have small family
size and the repayment performances of small size household is 26.3% defaulted and
76.7% non-defaulted. The other 53 borrowers were having medium size family, out of
which 18.9% of them were defaulted and the remaining 81.1% non-defaulted. Lastly,
37 borrowers were responded having large family from which 43.2% defaulted and
56.8% non-defaulted. The statistical survey from the above table showed as family size
increases the likelihood of being default increase and vice versa.

The regression result of chi-square shows presence of strong and significant

relationship between family size level and dependent variable at 5% significance level

65
(X2=66.5646, at P=000). This results of chi square revealed that increase or decrease
in family size has strong and significant relationship with the dependent variable.

4.2.2. Business Related Factors

Respondents were found to engage in various business sectors. For this study purpose
the most important businesses are categorized in to three sectors namely agricultural
type businesses, service type business sector and industry type business. The other
source of factors that affects loan repayment performances, emanates from the business
itself. That is, among the group of factors affecting loan repayment performances,
business related factors were another important factors in determining what factors
were affecting performing and non-performing loans. Thus, in this research business
form, business sector, other business and business income are selected as business
related factors. Out of which business form and business sector were encoded as
categorical explanatory variables whereas other business and business income were
encoded and treated as dummy explanatory variables of loan repayment performance.
Business sector: this variable evaluates which economic sector from agricultural,
service and industrial sector of the economy affects loan repayment performances of
the borrowers. As discussed under chapter two of this study, literatures show that
agricultural investments are vulnerable to different natural and manmade problems
than other projects.
With respect to the business sector on which loans were invested out of the total 150
sample populations majority of them were agricultural projects. Located in the south
western direction of the country Development Bank of Ethiopia Dessie district and
surrounding areas were conducive for agricultural investments. As such, out of 150
respondents participated in the questionnaire of this research, 108 or 72% of them were
invested on agricultural projects. Compared to the remaining two economic sectors,

66
agricultural investments are dominated the loans of the district and the role of the
sector determines the performances of the district as a whole. The repayment
performances of agricultural loans as shown in table 4.5, below 72 borrowers or 66.7%
of sample respondents took the loan to engage in agricultural type business, whereas,
32 or 21.3% invested on service investments and 10 borrowers or 6.7% took the loan to
invest on industrial sector of the economy.
Now, out agricultural loans 66.7% were performing and of them or 33.3% of them
were nonperforming. On the other hand, from among loans invested on service sector
of the economy, 90.6% loans were performing and 9.4% were nonperforming loans.
The loans invested on industry sector indicate that 90% or 9 performing and the
remaining 10% or 1 loan was non-performing loans. As the table depicted, the
agriculture sector showed that the percentage of default higher than service and
industry sector. In same line research, Besley and Coate (1995) reveal that agricultural
loans were risky and the probability of high loan default.
Table 4. 5. Business sector and business form against repayment
Explanatory Loan repayment performances Chi square
Variable Category Performing Non- Frequency
performing
Business 72 36 108 X2 =
sector 66.7% 33.3% 100% 10.0736
Service 29 3 32 P = 0.006*
96.4 3.4 100%
Industry 9 1 10
90% 10% 100%
Total 110 40 150
73.3% 26.7% 100%
Business Sole owner 62 7 69

67
 Form 89.9% 10.1% 100%
PLC 33 30 63
52.4% 47.6% 100%
SC 15 3 18
83.3% 16.7% 100%
Total 110 40 1501
73.3% 26.7% 100%
Source: own survey of data, 2016. * = Significant at 1%
The results of the survey as depicted in the above table showed that agricultural sector
as compared to others sectors has contributed 80% of nonperforming loans of the bank.
The main reasons of lion share contributions of agricultural investments in the
nonperforming loans emanated from the nature of rain fed agriculture. Rain fed
agricultures are basically dependent on the natural factors like rain, drought and many
others.
Statistically, chi-square also confirms the presence of strong and a significant
association between business sector and dependent variable at 1% level of significance
(X2 = 10.0736, at P=0.006). This shows that business sectors, especially agricultural
loans have strong negative relationship with that of dependent variable.
Business form: Ethiopian commercial law recognizes different types of business form
having their unique features. Share companies, Private limited companies,
cooperatives, and different kinds of partnerships are widely used forms of business in
our country. Among many different features of business forms, one distinguishing
feature is the level of responsibilities the owners share in cases of debt recovery.
Borrowers of DBE, were categorized under three business forms; sole proprietorships,
private limited companies and share companies. Based on the results of the survey
indicated in the above table, 69 borrowers or 46 percent of the sample population were
sole owners/ private borrowers of their business, while 63 (42) borrowers were Private

68
Limited Company (PLC) and the remaining 18 or 12% of borrowers were share
Companies.

In terms of their Loan repayment performances, from total private borrowers/sole

owners as shown in the table, 89.9% of them were performing loans and 10.1% were
nonperforming. In cases of Private Limited company borrowers, 52.4% were
performing loans while the remaining 47.6% were nonperforming loans. Lastly 83.3 %
and 16.75 were performing and non-performing loans respectively in case of Share
Company.
Maximum numbers of performing loans were found in private borrowers/ sole owners’
business form and service and industry business sector. Based on row relative
frequency of business forms and business sectors, maximum numbers of defaulters
were found in private limited company business form and agriculture business sector,
respectively. Maximum numbers of defaulters were from private limited company
business form and service business sector. The researcher believes that the reason why
greater number of defaulters are from private limited company is related to the level of
responsibilities the shareholders bear in cases of failure to repay the debt of the
company. This result is the same with Arega Seyoum(2016). The regression result of
chi square also confirms the presence of strong and a significant association between
business form and dependent variable at 1% level of significance (X2= 25.2614 at P =
0.000). This shows that business form very specifically Private limited Company’s
loans have strong negative relationship with that of dependent variable. Having another
business: this variable is designed to evaluate the exposure, awareness and familiarity
of borrowers to operate the current business and whether such exposure and experience
helps them in repayment performances of their loan.

69
Accordingly, the survey result of the study as depicted on the table 4.6, out of total
sample borrowers only 66 borrowers or 44% have other business and 88 borrowers or
56% of them didn’t have any other business prior to the current project/business
established with help of Development Bank of Ethiopia. The repayment performance
statistics as tabulated in the table below shows that out of those borrowers having other
business 50 borrower’s or 75.8% have performing loans and the left 16 borrowers or
24.2% were defaulted. Likewise out of those borrowers having no other business 60
borrowers or 71.4% were paying their loan as per the schedule of their contract with
the lending bank, while the remaining 24 borrowers or 28.6% were defaulted.
Obviously, this result shows that having other business contributes to repay the loan as
per the schedule of the contract whereas having no other business contributes to fail to
repay the loan. Similarly, the chi-square result reveals the strong and significant
association between having other business and loan repayment at significant level 5%
(X2=25.8248, at P = 000). This implies that having business experiences enhances the
probability to repay loan more than no business experience.

Table 4. 6. Other business and income vs. loan repayment performances

Explanatory Frequency Loan repayment performances
variable Performing Nonperforming Total
Loan Loan
Have Other 62 50 16 66 X2=
business(1) 44 75.8% 24.2% 100 25.8248
Total 84 60 24 84 P =
56 71 28.6 100 0.000*
Income 150 110 40 150 X2 =
generated 0.4219
61 43 48 89

70
Sufficient(1) P = 0.516
Not 40.7% 70.5% 24.7 100
sufficient(0) 89 67 22 89
Total 59% 110 40 150
73.3 26.7 100
Source: own computation of data, 2016. * Significant at 1%

Income/ profit generated from the project is a continuous variable, in this study it was
rearranged as dummy variable. It is generally believed that if a business generated
sufficient income/profit, the probability of repayment performance is high and vice
versa. So, based on the results of the survey from the above table 4.6, 67 borrowers or
44.7% answered their business generated sufficient profit. The left 83 borrowers or
55.3% answered their business did not rewarded them with sufficient income.
Accordingly, among the respondents that generated sufficient income 67 borrowers,
70.5 % (43) were performing while 29.5% (18) were defaulters. From the projects that
fail to generate sufficient income 89 borrowers, 75.3% (67) were performing/ non-
defaulted and the remaining 24.7% (22) were defaulters. The table showed that
projects that have not generated sufficient income/profit expected to repay the loan
from the other source of finance otherwise the probability of loan to default is high.
Despite most respondents who answered this questionnaire, there were some
respondents who left it blank space but they responded. The respondents, who got
profit from their loan, were high loan repayments rates. The result is the same as
Stephen (2012); and Wongnaa and Awunyo (2013). However, the chi-square result
shows that the association between income and loan repayment is insignificant
(X2= .4219, at P=.516) table 4.6. This indicates that being generating income by itself
doesn’t determine loan repayment performances.

71
4.2.3. Institutional Related Factors

Development Bank of Ethiopia is a public financial institution that has been financing
for viable projects in line with country’s developmental policy and programs. The bank
has its own policy and procedural manual for the flow of work that can be used during
providing credit services on those feasible projects. There are different factors
affecting loan repayment performances while using Policy and procedures of the bank.
Among institutional related factors, loan size, loan diversion, sufficient equity, grace
period, follow up, collateral, interest rate, KYC and time horizon problems were taken
as factors of loan repayment performance. From these variables, loan size and time
horizon were encoded as categorical variables and the remaining were encoded and
treated as a dummy variables having their own features. Now, let’s discuss the results
and present the survey of the data.
Loan size: this is the amount of money the bank permitted for the project and whether
the size of such loan determines the repayment performances of loan is assessed as
follows. Loan size is a continuous variable to be expressed in terms of currency or
figure but for the purpose of this study it is categorized and coded as (1= 1million-10
million, 2=11 million to 20 million, 3= 21million and above). From the total
respondents 40 borrowers or 26.67% borrowed from 1-10 million birr, 58 borrowers or
38.67% were borrowed from 11-20 million birr and the remaining 52 borrowers or
34.67% were borrowed above 21 million birr. When it comes to repayment
performances, out of those who borrowed 1-10 million birr, 27 borrowers, i.e. 67.5%
were paying their loan as per the terms of their contract with the bank, and the
remaining 13 borrowers, i.e. 32.5% were defaulted. The next respondents were those
who borrowed from 11-20 million birr, out of 58 borrowers, 42 of them i.e. 72.4%
were performing and the remaining 16 borrowers, i.e. 27.6% were defaulted. Lastly,
from 52 borrowers who borrowed 21 million and/or above, 41(78.8%) were

72
performing loans and the left 11 borrowers or 21.2% were nonperforming loans.
Generally, the results of this statistical analysis show that when loan size increases, the
probability of default decreased. It can be the fact that an increase in the loan size,
borrowers can do their project in a wide range with the inclusion of quality and
quantity of products.

Therefore, their project can generate huge revenue and can repay the due amount of
loan on time. The chi-square result reveals the strong and significant association
between having loan size and loan repayment at significant level 5% (X2=9.1793, at P
= 0.010). This implies that getting sufficient loan amount contributes to repayment
performances. This is the same as Ali AL Sharafat, et al. (2013) that the volume of
loans borrowed the most important factor and had a positive effect on the repayment
performance of the investigated agency. This is also the same as (Ifeanyi and Blessing,
2012).

Table 4. 7. Loan size and time horizon against repayment

Explanator Category Loan repayment performances

y variables Frequency Performing Non per Mean
forming
Loan size 1- 40 27 13 2.08 X2 =
10million 26.67% 67.5% 32.5% 9.1793
11- 58 42 16 P =
20million 38.67% 72.4% 27.6% 0.010*
≥ 21 52 41 11
million 34.67% 78.8 21.2%

73
Total 150 110 40 1.9533 X2
100 73.3 26.7 =70.3838
Time Timely 39 37 2 Pr =
horizon 26% 94.9% 5.1% 0.000**

Delayed 79 68 11
52.7% 86.1% 13.9%
Elongated 13.9% 5 27
21.3% 15.6% 84.4%

Source: Output from Survey Data, 2016. ** Significant at 1% and significant at %5

Time horizon: this variable assess whether the lending institution deliver its services
(includes services like credit team processes, appraisal and approval) within a shortest
possible time or otherwise. Time horizon is a continuous variable, but rearranged as
categorical variable and encoded as 1 for timely response 2 for delayed services and 3
for elongated services. According to Johnson and Rogaly (1997), timeliness of loan
disbursement is important when loans are used for seasonal activities such as
agriculture. It’s argued that complicated appraisal and approval procedures, which
might delay disbursement, influence a program of seasonal loans for farmers who use
to buy inputs. Further they noted that this could in turn worsen the prospects of
repayment by diverting loan to non-intended purpose.

The survey of data as stipulated in the table above shows that 26% of total population
was served within a reasonable time expected from customers, while 52.7% get
delayed service and the remaining 21.3 got service after such length dalliance. Out of
timely served borrowers, 94.9% were performing their loan, while the 5.1% failed. On
the other hand out of borrowers who got service (especially disbursement and

74
approval) after some dalliance, 86.1% were repaying their loan as per the requirements
of the bank and 13.9% were defaulted. Finally among those borrowers who got service
after lengthy dalliance, 15.6% were performing and 84.4% were nonperforming. The
result from the survey is also backed by statistical chi square. Hence, chi-square result
reveals the strong and significant association between time horizon and loan repayment
at significant level 1% (X2=70.3838, at P = 0.000). The statistics results in this survey
indicate the fact that getting service within the shortest possible time contributes to
well performance and vice versa.

Collateral: this is a continuous variable, but arranged as dummy variable taking 0 if

the collateral is sufficient in cases of failure and 1 if the collateral is not sufficient.
Lending institutions needs grantee for the money they provide for their customers. The
value of such collateral is believed to be more or equal to the amount of money
permitted for the borrower. The case is little different in development bank of Ethiopia,
because the loan policy of the bank do not require for collateral that is more or equal to
the amount of money lent to customers. The only collateral of the bank is the
business/project itself, which in some cases particularly in agricultural projects were
open fields or empty store.
The table below revealed that, 54.7% respondents believes that the bank has no
sufficient mortgage in case the loan defaulted, if the banks go for recovery and the
remaining 45.3% believes the bank has sufficient collateral. In terms of performances,
out of those who believe the bank has no sufficient collateral, 69.5% of them were
performing their duty of repaying their loan and 30.5% were non-performing. From
those who believe the bank has sufficient collateral, 77.9% were no defaulted while
22.1 were defaulted.
Equity: is a continuous variable, but encoded as dummy taking 1 where the equity is
greater than 30% percent of total investment of the project and 0 when the equity
75
amount is less or equal to 30% percent of total investment cost. Accordingly, from the
table below, 76% of equity contribution is less or equal to thirty percent of the total
investment cost and 24% were more than thirty percent of the investment. Further the
table revealed that, out of 114 borrowers or 76 %whose equity contribution is less or
equal to thirty percent, 66.7% were performing and 33.3 were nonperforming loan.
And from those 36 borrowers or 24% whose equity contribution is greater than thirty
percent, 94.4% were performing loans and the left 5.6 were non-performing loans. This
indicates that the probability of defaulting decreases as equity contribution of borrower
increases.

Table 4. 8. Collateral, equity and diversion against repayments.

Explanatory Loan repayment performances
variables Frequency Performing Nonperforming Tota
l
Sufficient 68 53 15 68 X2= 1.3636
Collateral(1 45.3% 77.9% 22.1% 100 P = 0.243
)
Insufficient 82 57 25 82
Collateral(0
)

76
 Equity≤30 54.7 69.5 30.5 100
percent(0)
Total 150 110 40 150 X2= 1.3636
100 73.3% 26.7% 100 P = 0.243

Equity>30 36 34 2 36 X2= 0.8485

percent(1) P = 0.357
24% 94.4% 94.4% 100

Equity≤30 114 76 38 114

percent(0) 76% 66.7% 33.3% 100
Total 150 110 40 150
1001 73.3% 26.7% 100
Diverted 8 3 5 8 X2= 0.8485
loan(1) P = 0.357
Not diverted 142 107 35 142 X2= 4.7944
(0) 94.7 75.4% 24.6% 100 P = 0.029*

Total 150 110 40 150

100 73.3% 26.7% 100

Source: Output from Survey Data, 2016. * Significant at 5%

Regarding loan diversion, out of the total sample respondents, 94.7% borrowers were
not diverted their loan to other business, but 5.3% respondents diverted their loan from
intended business to some other purposes. Out of those who diverted respondents
62.5% was defaulted and 37.5% was performing. And out of those who did not
diverted 75.4% were performing and the remaining 24.6% was defaulted. The
statistical result of this survey shows that, loan diversion contributes to the probability

77
of defaulting and vice versa. Statistically, chi-square result also confirms that there is
strong and significance association between loan diversion and loan repayment at 5%
(X2= 4.7944, at P =0.029) could find from Table 4.8.

Table 4. 9. Interest rate, grace period, follow up and KYC with repayment
Explanatory Loan repayment performances
variables
Frequency Performing Nonperforming Total
Given grace 59 44 15 29 X2 =
period(1) 39.3% 74.6% 25.4% 100% 0.0771
Pr = 0.781
Not given grace 91 66 25 91
period (0)
60.7% 72.5% 27.5% 100%

Total 1500 110 40 150%

100 73.3% 26.7% 100%
Interest rate 86 61 25 86 X2 =
affected (1) 57 70.3% 29% 100% 0.6001
Pr = 0.439
Interest rate not 64 49 15 64
affected(0)
42.7% 76.6% 23.7% 23.4%

Total 150 110 40 150

100 73.3 26.7 100%
Proper follow 41 32 9 41 X2 =
up (1) 9.4665
27.3% 78% 22% 100% Pr = 0.002**

No follow 109 8 31 109

up(0) 72.7% 71.6% 28.4% 100%

Total 150 110 40 150

78
 Proper KYC 70 57 13 70 X2= 4.4836
(1) Pr = 0.034*
46.7% 81.4% 18.6% 100%
No proper 80 53 27 80
KYC (0) 53.3% 66.2% 33.8% 100%

Total 150 110 40 150

100 73.3% 26.7% 100%

Source: Output from Survey Data, 2016. *Significant at 5% ** significant at 1%

As read from the table, 60.7% respondents said they were not given sufficient grace
period so as to begin operation with full capacity and establish itself strongly. Only
39.3% respondents answered their business is given grace period. The
business/projects that had no grace period contribute for default and non-default loan
was 27.5% and 72.5% respectively. In the other hand, projects that was given grace
period contributes for default and non-default loan was 25.4% and 74.6% respectively.
The table below demonstrates that when the projects have no grace period, projects
faced repayment problem when the loan due later due to insufficient time for
implementation. But the chi square didn’t show the existence of strong and significant
relationship between grace period and the dependent variable. In the same token,
increase in lending interest rate also has affected repayment performances.
Accordingly, 57.3% respondents answered their repayment capability was affected by
increase in the lending interest rate and 42.8% respondents said such increase in
interest rate did not affected their repayment capacity. Out of those affected by
increase in lending interest rate change, 70.9% were performing their repayment duty
as per the schedule arranged in their loan contract, while 29.1% failed to carry on their
duty of repayment as per the schedule. From those who did not affected by change in
lending interest rate, 76.6% were performing their duty while 23.4% were
nonperforming. The statistical analysis of chi square didn’t show the presence of any

79
strong and significant association between increase in interest rate and dependent
variable. With regard to follow up, 72.7% of total sample population responded that
there is no proper follow-up to their business and some 27.3% responded the bank was
conducted proper follow up with their business/project.

As shown in the above table, out of 72.7% respondents whose projects did not have
appropriate follow up, 71.6% were performing loans and 28.4% were defaulted.
Similarly, out of those projects that appropriate follow up was conducted, 78% were
performing and the left 22% were defaulters. Therefore there is a significant statistical
difference between defaulters and non-defaulters in these averages, at 1% significance
level (Table 4.9). This indicates that the continuous follow up of borrowers reminds
them to pay attention toward their business and enables to increase their perception of
responsibility toward loan repayment Know your customer (KYC) also known as due
diligence is a screening stage sorting of credit worthy customers. Reading of the table
above, 53.3% respondents said such screening is not properly conducted while the left
46.7% answered their business/project have appropriate KYC study. Out of businesses
that KYC was not conducted properly, 66.2% were performing loans while the
remaining 33.8% were defaulters. Likewise, out of those KYC properly conducted,
81.4% were performing and 18.6% were defaulters. Statistically, chi-square result also
confirms that there is strong and significance association between Kyc and loan
repayment at 5% (X2 = 4.4836 P = 0.034) could find from Table 4.9. This reveals that
the due diligence/KYC plays very important role in repayment of loan.

4.2.4. External Related Factors

External factors are factors of repayment performance which are beyond borrower,
lending institution and the business itself. Most of the time these factors are

80
unpredictable and uncontrollable by these bodies, thus it makes difficult in case of
decision making in different institutions. In this study weather condition and market
condition were selected as external factors of loan repayment performance on
development Bank of Ethiopia, Dessie District customers. Since the majority of
projects financed in this District were agricultural, the possibility of being affected by
such external factors is natural. Therefore, the table below has provided number of
respondents who were challenged or were not challenged due to bad weather condition
during running their business. Accordingly, 88 respondents or 58.7% were affected by
bad weather condition and 41.3% were not affected by such conditions.
Regarding the repayment performances, out of 88(58.7%) respondents that were
affected by bad weather conditions, 57(64.8%) were performing loans and 31(35.2)
were defaulters. Out of those 62(41.3%) respondents who were not affected by bad
weather conditions, 53(85.5%) were performing loans and the remaining 9(14.5%)
were defaulters. The results of chi square didn’t show the presence of significant
relationship between bad weather condition and dependent variable.

Table 4. 10. Whether condition and market condition against repayment.

Explanatory Frequency Loan repayment performances

variable Performing Nonperforming Total
Loan Loan
Bad 88 57 31 88 X2 =
weather(1) 0.1555
58.7% 64.8% 35.2% 100%

81
 No whether 62 53 9 62 P = 0.693
problem(0) 41.3% 85.5% 14.5% 100%
Total 150 110 40 150
100 73.3% 26.7% 100%
Market 75 52 23 75 X2 =
problem(1) 50% 69.3 30.7% 100% 1.2308
No market 75 58 17 75 P = 0.267
problem (0) 50% 77.3% 22.7% 100%
Total 150 110 40 150
100 73.3% 26.7% 100

Source: Output from Survey Data, 2016

As regards to market conditions, 75 (50%) of sample respondents answered that there

were a problem of poor market condition especially selling at lower price than
expected one and the main reason of unfavorable market condition were due to
international price fluctuation and less demander for the product and high amount of
supplies and the remaining 75(50%) were not affected by market fluctuation.
Therefore, out of 75 respondents affected by market conditions, 52(69.3%) were
performing loans and 23(30.7%) were nonperforming. And out of 75 respondents that
were not affected by market situations, 58(77.3%) were performing loans and the
remaining 17(22.7%) were defaulted. The chi square also didn’t recognize the
existence of significant relationship between market conditions and the dependent
variable.

4.2.5. Other Major Problems

82
In addition to the lists of variables discussed above, there are different challenges that
hinders the repayment performances of loans by borrowers. So, respondents have
stipulated different reasons, factors and challenges they faced and what they think was
affecting their repayment performances. Here are some of them; majority of the
responders sited that unavailability of skilled and unskilled labor, price fluctuation of
the product, poor quality of the product and land overlap were other challenges of
sampled customers. Availability of skilled and unskilled labor: the establishment of
projects especially agricultural farming projects needs the availability of skilled man
power both for direct agricultural activities and administrative works. However, even if
casual workers particularly during the time of harvesting and sowing are very
important in farming, majority of the farm found in desert area were challenged by this
factor. Pricing and quality of product: the price of cotton, sesame and coffee product is
largely degree depended on demanded quality, number of suppliers, production, market
condition etc. (the price maker is the market itself). Thus, most of agricultural
producers; especially cotton, coffee and sesame producers were challenged by price
fluctuation of the market. Due to the presence of new entrants, unexpected weather
condition and uncontrollable diseases and pest which is common for all agricultural
products, had affected quality of their produce and hence production.
Land overlap: especially, projects found in Gambella region were been challenging
by this problem because of using unreliable way of land providing to those potential
investors from concerned institutions. Thus, based on citation from majority of
borrowers even if it was not significant factor in econometrical analysis, land overlap
problem was one of the causes for lag of loan process hence weak project performance
leads to delinquency.

4.3. Econometric Analysis

83
In contrast to descriptive analysis, an econometric analysis or statistical analysis is the
method of data analysis where mainly focus on coefficients, R-square, chi-square,
standard error, tests, log likelihood ratio etc., which can be done using different
software’s such as STATA, SPSS and others. In this study STATA version 12 was
adopted for the analysis of binary logistic regression coefficients and different tests.
So, before running the binary logistic regression, the explanatory variables were
checked using the following tests.
4.3.1. Model Tests

According to (Gujarati, 1995), for the econometric estimation to bring about best,
unbiased/reliable and consistent result, it has to fulfill the basic linear classical
assumptions. The basic assumptions include: linearity in parameters of the regression
model, for given explanatory variables the mean value and the variance of the
disturbance term (Ui) is zero and constant (homoscedastic) respectively, there is no
correlation in the disturbances, no correlation between the regressors and the
disturbance term, no exact linear relationship (multicollinearity) in the regressors and
the stochastic (disturbance) term Ui is normally distributed. Naturally, therefore, if
these assumptions do not hold well on what so ever account, the estimators derived
may not be efficient. Based on the type of data (cross sectional type) used in this study,
the most important tests such as heteroscedasticity, multi collinearity are conducted
and the appropriate remedies were taken.
4.3.1.1. Test for Multi collinearity Assumption

If an independent variable has exact linear combination with the other independent
variables, then we say the model suffers from perfect collinearity. This assumption is
concerned with the relationships which exist between explanatory variables. In the
construction of an econometric model, it may happen that two or more variables giving

84
rise to the same piece of information are included, that is, we may have redundant
information or unnecessarily included related variables and such situation is called a
multi collinearity (MC) problem. One of the assumptions of the CLRM is that there is
no exact linear relationship exists between any of the explanatory variables. When this
assumption is violated, we speak of perfect MC. If all explanatory variables are
uncorrelated with each other, we speak of absence of MC. Multi collinearity usually
exists in most applications. Therefore, the question is not whether it is present or not;
it is a question of degree! In addition, MC is not a statistical problem; it is a data
(sample) problem. Therefore, we do not test for MC; but measure its degree in any
particular sample (using some rules of thumb). There is no consistent argument on the
level of correlation that causes multi collinearity.
There are two measures that are often suggested to test the presence of multi-
collinearity. These are: Variance Inflation Factor (VIF) for association among the
continuous explanatory variables and contingency coefficients for dummy variables.
The technique of variance inflation factor (VIF) was employed to detect the problem of
multi collinearity among the continuous variables. According to Gujarati (2003), VIF
1
can be defined as: 𝑉𝐼𝐹 (𝑋𝑖) = 2
1−Rj

Where, VIF is variance inflation factor, Ri 2 is multiple correlation coefficient and is

explanatory variables Xi is explanatory variables. The result of VIF test is annex as
Annex 3 at the end of this paper.
Table 4. 11. VIF of the Explanatory Variables used in the study
Variable VIF 1/VIF
Education 1.71 0.583593
Kyc 1.44 0.693435
Loan size 1.44 0.695333
Time horizon 1.34 0.711450

85
 Grace period 1.29 0.747682
Other bus in ~s 1.26 0.772964
Businesses ~r 1.26 0.791117
Income 1.24 0.791368
Marriage 1.23 0.804159
Collateral 1.22 0.813179
Gender 1.22 0.817818
Interest 1.20 0.818259
Business form 1.19 0.822914
Age 1.20 0.830964
Households ~e 1.19 0.839626
Whether 1.16 0.859426
Follow up 1.13 0.883061
Equity 1.13 0.884816
Market 1.13 0.887345
Experience 1.12 0.891935
Diversion 1.09 0.913764
Mean VIF 1.26

Larger value of VIF shows co-linearity across variables, thus if VIF exceeds 10
indicates that there was multi collinearity within continuous variables. The result
shows that no categorical and dummy explanatory variables which have variance
inflation factor near to 10 i.e. the maximum value among those dummy explanatory
variables was 1.50 in case of loan size while 1.26 was an average VIF of all variables.
In addition to VIF, contingency coefficients were computed to check the existence of
multi collinearity problem among the discrete explanatory variables. A contingency
coefficient is a measure of the degree of relationship, association of dependence among
variables included in the study. The contingency coefficient is calculated as follows
(Garson, 2008 cited in Fikirte, 2011): ……………………

86
Where: C = contingency coefficients, X2 = the value of Chi-square, N = total sample
size.
The decision rule for contingency coefficient is the larger the value of this coefficient,
the greater the degree of association. The maximum value of the coefficient is never
greater than 1. The results of contingency coefficients reveal that there was no serious
problem of association among the discrete variables.

87
Table 4. 12 Correlation matrix of coefficients of regress model.
e(V) Gender Age Marriage Educat~n Househ~e Experi~ Otherb~s Busine~m Busine~r Incom Loanam~t
e e
Gender 1.0000
Age 0.0123 1.000
0
Marrage 0.0119 0.113 1.0000
2
Education -0.1016 0.080 0.1678 1.0000
5
House holds -0.1766 -0.209 -0.1919 -0.0241 1.00
00
Experiance 0.0499 0.079 -0.0379 0.0028 0.0430 1.0000
6
Otherbusin~s -0.0107 -0.184 -0.0094 -0.1512 0.0063 0.0688 1.0000

Businessfor -0.1868 0.071 -0.1180 0.0908 0.0261 0.1947 0.0404 1.0000

m 5
Businessse~r 0.0417 -0.078 -0.0571 -0.2016 0.0607 -0.0116 -0.0415 -0.0690 1.0000
Income 0.0888 -0.170 -0.0591 -0.2683 0.0464 0.0007 0.0406 -0.1400 0.1151 1.0000
Loan amount -0.0001 0.045 -0.2583 -0.0890 -0.0358 -0.0142 -0.0166 0.1724 -0.3131 - 1.0000
9 0.1162
Diversion -0.0680 -0.136 -0.0075 0.1029 -0.0013 -0.0530 -0.0022 0.0074 0.0265 - 0.0207
0.0277
Equity 0.0886 - -0.0693 -0.1436 0.1023 -0.0128 -0.0264 -0.1039 0.0921 0.1862 -0.0282
86
 0.074
2
Grace period 0.0291 0.123 -0.1207 -0.0873 0.0859 0.1042 -0.2372 0.0207 0.0983 - -0.1448
1 0.0490
Follow up -0.0379 0.003 0.0315 -0.0572 -0.0324 -0.0139 0.0240 0.1050 -0.0874 - -0.0017
0 0.1106
Collate al -0.0424 - -0.0517 -0.0296 0.0273 -0.0512 -0.0909 0.0981 0.0643 0.0451 0.0303
0.076
2

Interest -0.0943 - 0.1511 -0.1199 0.0053 -0.0717 0.0976 -0.0510 0.0308 - -0.2071
0.032 0.0127
7
KYC -0.0943 - 0.1511 -0.2337 -0.0726 -0.1079 -0.1155 0.0374 0.0033 0.0883 0.0891
0.052
3
Time 0.0648 - -0.0289 0.3210 -0.0571 0.0919 0.0993 -0.0321 -0.0122 0.0936 -0.1239
horizone 0.035
5
Market 0.1758 - 0.0771 -0.0591 0.0704 0.0601 0.0460 -0.0052 0.0902 0.0317 0.0671
0.026
5
Whether 0.1610 0.082 0.0861 0.0529 -0.1941 0.0710 0.0632 -0.0234 -0.0334 0.0452 0.0125
4
Cones -0.2784 - -0.2912 -0.4279 -0.0671 -0.2707 -0.0251 -0.3133 -0.0877 0.0123 -0.1038
0.289
8

87
Divers~n Equity Gracep~d Followup Collat~l Interest Kyc Timeho~ Market Wheth _cons
n r

88
Table 4. 13 Results of Binary Logistic regression, loan repayment performances.
Repaymen Co ef. Std. Err. Z P>|z| [95% Interval]
t Conf.
Gender -2.234012 64.67539 -0.03 0.972 -128.9954 124.5274
Age 1.498252 .938601 1.60 0.110 -.3413725 3.337876
Marriage .1965257 1.236763 0.16 0.874 -2.227485 2.620537
Education 4.994262 2.29616 2.18 0.030* .4938714 9.494653
Family -2.783878 1.394197 -2.00 0.046* -5.516454 -.0513033
size
Experience 4.997728 2.54503 1.96 0.050* .0095619 9.985895
Other 5.08024 2.396175 2.12 0.034* .3838236 9.776657
business
Business -.664479 .9807302 -0.68 0.498 -2.586675 1.257717
form
Business -1.532271 1.395945 -1.10 0.272 -4.268272 1.203731
sector
Income 1.793644 1.533704 1.17 0.242 -1.21236 4.799648
Loan size 2.806922 1.503764 1.87 0.062* -.1404005 5.754245
Diversion -8.941219 4.74869 -1.88 0.060* -18.24848 .3660423
Equity 1.589673 1.459438 1.09 0.276 -1.270772 4.450119
Grace 4.900312 31.71605 0.15 0.877 -57.262 67.06263
Period
Follow up 3.399658 1.992316 1.71 0.088* -.5052096 7.304525
Collateral 2.710324 1.934826 1.40 0.161 -1.081866 6.502513
Interest -2.105947 1.666111 -1.26 0.206 -5.371464 1.15957
KYC .8590335 1.453748 0.59 0.555 -1.990261 3.708328

90
 Time -6.440273 2.561279 -2.51 0.012* -11.46029 -1.420258
horizon
Market 2.231671 1.952738 1.14 0.253 -1.595625 6.058967
Weather -.0583181 1.189738 -0.05 -2.390161 0.961 2.273525
_cons -1.443807 64.80903 -0.02 0.982 -128.4672 125.5796
Source: STATA version 12 Logistic regression result 2016,
Note: Coef. = coefficient, Std. Err = standard error, Pseudo R2 = 81%, Log likelihood
= -18.25, Logistic Regression Chi-square =141.26
* Significance at 5% ** significant at 10%
4.3.1.2. Measures of Goodness of Fit
The conventional measure of goodness of fit, R 2, is not particularly meaningful in
binary regress and models. A measure similar to R 2, called pseudo R2, is available, and
also ranges between 0 and 1(Gujarati, 2004).According to Kibrom (2010), the use of
conventional R2 for goodness of fit when the dependent variable takes either 1 or 0 is
not appropriate. “A summary measure used similar to the conventional R2 that have
been suggested for models with qualitative dependent variable is pseudo R2. It should
be noted, however, that in binary regress and models, goodness of fit is of secondary
importance. What matters are the expected signs of the regression coefficients and their
statistical and/or practical significance? As noted previously more meaningful
interpretation is in terms of odds, which are obtained by taking the antilog of the
various slope coefficients” (Gujarati, 2004, p .605-606). Thus for this study, the model
pseudo R2 is 81% or 0.81 (as it is depicted in the logistic regression). This result
indicates that, the logit model explained about 81% of the variation and it lies in the [0,
1] interval.

91
4.3.1.3. Test for Normality assumption

Normality assumption (ut ~ N (0, σ2) state that a normal distribution is not skewed and
is defined to have a coefficient of kurtosis 3. Bera-Jarque formalizes this by testing the
residuals for normality and testing whether the coefficient of skewedness and kurtosis
are zero and three respectively. Skewness measures the extent to which a distribution is
not symmetric about its mean value and kurtosis measures how fat the tails of the
distribution are. To make sure that this assumption is valid or not, the residuals
generated out of the regression model is plotted against the fitted values of the
dependent variables. If this curve is like bell shaped distribution it can be concluded
that the disturbance term is normally distributed with mean zero and constant variance
one (i.e. N~ (0, 1)).
To get the residuals normally distributed first we have to make sure that each variables
employed are found to be normally distributed. In this case, most of the variables are
found to be normally distributed, the variables that are not normally distributed were
transformed to logarithmic form, and the disturbance term becomes normally
distributed. Therefore, normality test was checked out by using Kernel density estimate
test. According to Kernel, using command “kdensity r, normal” whiles after the
command of ‘predict r’ the graph shows normal distribution on estimated residual as
compared with normal distribution reference line.

4.3.1.4. Hetero Schedasticity

In general, hetero schedasticity is one of the problems of cross sectional data where it
has assumed that homoscedasticity or constant variance in basic classical linear
regression assumptions. Due to the indication for presence of such defects in the data
were collected according to White's test, Breusch-Pagan test and residual plot test, the

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study was applied robust technique of estimation in the STATA set up which can
easily detect the problem. The result is annexed at the annexation part of this paper.
4.3.2. Results of Regression Analysis

As shown in chapter three, the model used to find out and explain the association
between the dependent variable and the independent variables is:
LRP =β1 + β2(Gdr) + β3(Ag) + β4(Mar) + β5(Educ) + β6(Hhs) + β7(Exp) +
β8(Othbus) + β9(Busfrm) + β10(Bussctr) + β11( Income) + β12(Lnamt) + β13(Div) +
β14 (Eq) + β15 (Grprd) + β16 (Folup) + β17 (Coll) + β18 (Int) + β19 (KYC) + β20
(Timhzn) + β21 (Mrkt) + β22 (Wthr)
Where LRP, Gedr, Ag, Mar, Educ, Hhs, Exp, Othbus, Busfrm, Bussctr. Incm, Lnamt,
Div, Eq, Grprd, Folup, Coll, Int, KYC, Timhzn, Mrkt, Wthr denotes Loan Repayment
performance, Gender, Age, Marriage, Education, House hold size, Experience, Other
business, Business form, Business sector, Income, Loan size, Diversion, Equity, Grace
period, Follow up, Collateral, Interest, KYC, Time horizon, Market and Whether
respectively.

Under the following regression outputs the beta coefficient may be negative or
positive; beta indicates that each variable’s level of influence on the dependent
variable. P-value indicates at what percentage or precession level of each variable is
significant. R2 values indicate the explanatory power of the model.

The adjusted R-squared value for the model is around 81%, suggesting that almost
81% variance in repayment performances were explained by all mentioned explanatory
variables. And also adjusted R2 value show that the overall goodness of the model.
Accordingly, the value of R2 showing that model used in this study has good statistical

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health. F-statistics of the model has a p-value of 0, suggesting that all explanatory
variables jointly can influence the repayment performances.

As shown in the above regression table the output of variables like Education, family
size, Credit experience, Other business and time horizon were statistically significant
factors affecting the repayment performances in Development Bank of Ethiopia at 5%
significant level while loan size and loan diversion are statistically significant factors
affecting the repayment performances in Development Bank of Ethiopia at 10%
significant level. The coefficients of three significant variables, time horizon, family
size and loan diversion were negative and the left five, education, experience, other
business, loan size and follow up were positive. The negative coefficient indicates that
the dependent variable was associated with the independent variables negatively and
the positive coefficient shows the positive influence of the variable on the dependent
variable.

On the other hand, thirteen (13) variables were found insignificant on dependent
variable namely gender, age, marital status, business form, business sector,
income/profit, equity, grace period, collateral, interest rate, KYC/ due diligence,
market condition and weather conditions were statistically insignificant influence on
loan repayment performances. From these insignificant variables gender, family size,
business form, business sector, interest rate and weather conditions are having a
negative sign and the remaining insignificant variables bear positive sign. Overall, the
binary logistic model predicted factors contributing to 81% of Development Bank of
Ethiopia Dessie District loan repayment performances as revealed in the above table.

4.3.3. Discussions on Regression Results

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The preceding sections present the overall results of the study. Thus, this section
presented detail analyses of the results for each explanatory variables and their
importance in loan repayment performances in accordance with the above regression
result. In addition, the discussions analyses the statistical findings of the study in
relation to the previous empirical evidences. According to the binary logistic result, the
significant variables were significant at different level of significance and discussed as
below.
Time horizon: The result of logit model in table 4.13 indicated that time horizon have
a negative influence on loan repayment performances and statistically became
significant predictor of borrowers’ loan repayment performance at 5% significance
level. As indicated table 4.13 and 4.14, timely disbursement of loan increases the
borrowers’ loan repayment probability by 5.6%. Thus, the result is in accordance with
the research hypothesis (time horizon has negatively influence repayment
performances). This implies that getting service timely or after long time waiting,
keeping the other thing constant has a resultant change of 5.6% increase or decrease
the repayment in the opposite direction. I.e. The odd ratio of the econometric result
indicates that disbursing the loan timely can reduce the probability of being default by
65% other things remain constant (table 4.13). There are a number of studies found
negative relationships between time of disbursement and loan repayment
performances.
Table 4. 14 Odds ratio of binary logistic regression, loan repayment performances.
Repayment Odds St. err z P>|z| [95% Interval]
ratio Conf.
Gender .107097 6.926597 -0.03 0.972 9.51e-57 1.21e+54
9
Age 4.47386 4.199169 1.60 .7107941 0.110 28.15924
Marriage 1.21716 1.505347 0.16 0.972 .1077992 13.7431

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 7
Education 147.564 338.8306 2.18 0.110 1.638648 13288.48
Family size .061798 .0861591 -2.00 0.874 .0040201 .9499905
4
Experience 148.076 376.8588 1.96 0.030 1.009608 21717.96
4
Other business 160.812 385.3354 2.12 0.046 1.467887 17617.66
7
Business form .514541 .5046264 -0.68 0.050 .0752699 3.517381
5
Business .216044 .3015863 -1.10 0.034 .014006 3.332528
sector 6
Income 6.01131 9.219578 1.17 0.498 .2974944 121.4676
7
Loan size 16.5588 24.90063 1.87 0.272 .8690101 315.5271
7
Diversion .000130 .0006215 -1.88 0.242 1.19e-08 1.442016
9
Equity 4.90214 7.154379 1.09 0.062 .2806149 85.63712
8
Grace Period 134.331 4260.473 0.15 0.060 1.35e-25 1.33e+29
7
Follow up 29.9538 59.67753 1.71 0.276 .6033791 1487.014
5
Collateral 15.0341 29.08845 1.40 0.877 .3389625 666.8155
4
Interest .121730 .2028162 -1.26 0.088 .0046473 3.188561

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 3
KYC 2.36087 3.432122 0.59 0.161 .1366598 40.78555
8
Time horizon .001596 .0040877 -2.51 0.012 .0000105 .2416515
Market 9.31542 18.19058 1.14 0.253 .2027818 427.9332
1
Weather .943349 1.122339 -0.05 0.961 .0916149 9.713582
8
_cons .236027 15.29671 -0.02 0.982 1.61e-56 3.46e+54
5

The result was consistent with the descriptive analysis result in preceding section of
this same study and consistent with the study and findings of other research like studies
by Shaik and Tolosa( 2014) confirmed that timely disbursement of loan increases the
borrowers loan repayment probability. Accordingly, the null hypothesis H2 is fail to
rejected. That is the null hypothesis that expected positive relationship between
delayed service and defaulting is accepted. Because the result of the regression proves
that as the time of delivering service elongated the likelihood of defaulting increases
and the probability of performing decreases. Educational qualification; Table 4.13 and
4.14, shows that educational level is significant at 5% and positively related to
borrowers ability to repay their loans. An increasing the level of education has the
effect of decreasing the likelihood of defaulting by 14% ceteris paribus. This figure
reveals that the borrowers whose educational is at tertiary level have the probability of
decreasing default rate by 14 percent than the borrowers who is at elementary
education level/ illiterates table 4.13. This implies that borrowers that were more
educated may have access to business information, use their personal knowledge, skill
and experience to properly manage their loan and repay timely. This result was

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consistent with preceding descriptive analysis of this study and these results are
resembled with the output of Michael (2006) and Olomola (2009) described that
default rate decreased with education level of the borrower increased.

The null hypothesis H1 is fail to reject. The null hypothesis stated that there is positive
relationship between education and loan repayment performances, which found true
under the regression result. So, the null hypothesis is accepted.

Family size: As shown under the logit regression table above, family sign showed
significance to the dependent variable. It was hypothesized that a borrower having
larger family number is likely to default than a borrower having small family number,
and vice versa. The coefficient of family size is negatively related to the dependent
variable, loan repayment performances and is strongly significant at 5% level.
Increasing borrower’s family size by one person decreases the likelihood of being able
to repay one’s loan. This means that the smaller the size of the borrower family, the
higher the probability that borrowers will be able to repay their loans and vice versa.
This result and conclusion is similar with the results under descriptive analysis is part
of the study and the reason of such may resulted from the fact that large household
sizes increased the household head’s domestic responsibilities and thereby constituted
leakage to the household’s income stream. As household income depleted, liability of
the household increased and there would be greater tendency to divert loans meant for
borrower production resulting in default in loan repayment. The odds ratio indicates as
family number increases the likelihood of defaulting increases by 60 percent compared
to small family holders. This result is similar with (Abbafita, 2005 and Berhanu,
(2005).

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The null hypothesis H1 is accepted. The null hypothesis stated that the increase in
number of dependents/family increases the probability of defaulting and decreases the
likely hood of performing the loan. The logistic regression result indicated this same
result; hence accepted.
Other business: The variable other business has a positive sign as expected and is
statistically significant at 5%. The result shows that as the borrower have other source
of income his/her capacity to repay his loan increase. Under the descriptive analysis
part of the study, having other business positively contributed to repayment
performances and this result is almost the same with the results of this econometric
logistic regression result. This implies that incases income from the project under
consideration fail to meet their debt obligation income from such other source could
help to settle their repayments. The studies made by (Kibrom, 2002) and (Abraham
2002) supports this result. The logistic regression and chi square of fail to reject
hypothesis H3. The null hypothesis which stated having other business in addition to
the current one is better for repayment and there is positive relationship with
repayment performances. The regression result showed this assumption true and the
hypothesis is accepted.

Credit experience: The coefficient of this variable was expected to influence the
repayment capacity positively and the result of logit regression shows the same as
expected. P-value of the credit factor is statistically significant at 5% (0.050) and has a
positive influence on the dependent variable, which is in line with the research
hypothesis (there is a positive relationship between credit experience and repayment
performances). The coefficient value of the variable (i.e.4.007) indicated a percentage
rise/decline in years of experience resulted performing/ nonperforming of the loans.

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The coefficient value tells us there is a strong positive relationship between credit
experience and repayment performances. The implication of this result is that those
who had long credit experience have good knowledge of managing and handling the
financial aspects of their business and at better position than those who never had such
exposure. This result is the same with results presented under descriptive part of this
research. This result agreed with (Firafis Haile, 2003).The logistic regression and chi
square of fail to reject hypothesis H1. The null hypothesis which stated having credit
experience better for repayment and there is positive relationship with repayment
performances. The regression result showed this assumption true and the hypothesis is
accepted.

Loan Diversion: The coefficient sign of loan diversion shows that there is a negative
relationship between loan diversion and loan repayment performances. This variable
adversely and significantly influence loan repayment performances at 10% significance
level, borrowers who diverted the loan other than the intended purpose are found to be
defaulters. An application of entire loan for intended and productive business lessens
the probability of defaulting by 0.060 (table 4.13). It is obvious that diverted loans miss
their intended target and out of the sight of lending institutions. Hence, unless the
responsible borrowers willingly pay their loan from such other business it would be
difficult to repay the loan according to the terms of the contract. This result is the same
with (Jemal, 2003), and results under descriptive analysis part of this research in
preceding section. The null hypothesis H2 is failed to reject. The null hypothesis stated
that there is negative relationship between loan diversion and loan repayment
performances, which found true under the regression result. So, the null hypothesis is
accepted.
Loan size: this variable also was found to influence borrowers’ loan repayment
performance positively and significantly at 10% significance level. Keeping the other
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factors constant, having sufficient loan size and operating business with adequate
amount of capital decreases the probability of being defaulter by 0.088 (table 4.13).
Large amount of money creates huge capacity to performance with full capacity and
effective manner. Accordingly, loan size showed positive relationship with loan
repayment performances indicating that the increase in the loan size likely increases
the loan repayment capacity of the borrowers. This same result was found by Olomola
(2009), Nawai and Shariff (2013), Abafita (2003). Shaik Abdul Majeeb PASHA
(2014).The result here is almost presented in descriptive part of this study too. The null
hypothesis H2 is accepted. The null hypothesis expected the positive relationship
between loan repayment performances and loan size, which found to be true under
logistic regression result. Hence, the null hypothesis is accepted.

Follow up: This variable was to have positive and significant association with the
dependent variable. It is significant predictor loan repayment performance at 10%
significance level. If other variables held constant, continuous follow up and visit of
respondents reduces their probability of being defaulter by 0.088. The importance of
follow up is unquestionable in every credit monitoring and loan collection. The odds
ratio of the variable indicated that a project that follow up is properly made is 29.95%
more likely to repay its loan than that never proper follow up is made. This result is the
same with finding of Wongnaa (2013).

The null hypothesis H2 expected the follow-up to have positive relationship with
repayment. The result of the logistic regression showed that there is strong positive
relationship between loan follow-up and repayment of loans. Hence, the null
hypothesis is accepted.

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 CHAPTER FIVE

5. CONCLUSION AND RECOMMENDATION

The preceding chapter presented results and discussion of the study, while this chapter
deals with conclusion and recommendation of the study based on the findings.
Accordingly this chapter is organized into two subsections. The first section of this
chapter discusses the conclusions part briefly and the second section presents
recommendation for the findings.

5.1. Conclusion

The objective of this research is to identify and determine factors affecting loan
repayment performances at DBE Dessie District. To achieve this broad objective, the
study used both qualitative and quantitative data and the primary data was collected
from 150 borrowers, nine senior expertise and three managers at different level of the
bank using semi structured open ended and close ended questionnaire. For data
analysis purpose both descriptive statistics and binary logistic model were employed.
Therefore, this study was intended to identify and discuss factors which affect
borrowers’ loan repayment performance and finally concludes that low repayment
performance was one of the main problems of the District as compared to its plan and
other performances such as loan appraisal, loan approval and disbursements. The
descriptive statistics findings shows that there were significant association between
dependent variable with respect to time horizon, level of education, family size,
experience, other business, follow-up, loan size and loan diversion variables were
significantly influenced the repayment performances of the loans. On the other hand,
twenty one explanatory variables were entered in to binary logistic model and out of

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which eight variables were found significant to determine loan repayment performance
of borrowers.

The results of this study revealed that the time horizon negatively and significantly
affected the loan repayment performance of borrowers. Time lag between loan
application and disbursement should be reduced to increase repayment rate. The
complicated loan processing procedures, which might lead to delay in disbursement,
further, it will increase default rate. When the bank deliver its services timely, the
probabilities of paying loan and in the reverse if the bank fail to provide services after
a long time of waiting and after time of utilizing opportunity is lapsed, the probabilities
of defaulting is very high. Time of loan disbursement was also another significant
variable with default loan negatively. Thus, unless the bank faces strange problems, the
risk of being default most probably decreases when disbursements performed on time.

Therefore disbursing the loan on time, we can expect high loan repayment
performance.
The education qualification level determines loan repayment positively and
significantly. The borrowers who attained higher education level able to pay better than
the borrowers who were in lower level schooling and/or illiterates. Therefore,
institution should motivate educated people and also easy to provide training. The
selection of educated borrowers decreases the probability of being default. This is the
fact that the literates can easily grasp knowledge, information, capable to manage their
business, adopt new technologies and workable strategy for their business than the
illiterates.

Family size also influenced the repayment performances of loans significantly and
shows negative sign. This indicates that increasing in the number of family size,
103
increases the probabilities of default and vice versa. Borrowers who have small number
of or no dependents in the household perform better in loan repayment. The borrowers
who support large number of dependents face difficulties of repayment. The logic
behind is that the borrower having larger family size as compared to those having
smaller family size have tremendous challenges to administer the demands of his/her
family and run the business simultaneously. The larger family size have different needs
and high consumption, while the small size borrowers can focus on administering their
business without much challenges and difficulties

Loan size; is the other variable showing positive relationship with loan repayment
performances and statistically significant. Repayment capacity of borrowers depends
on the capacity of investments and the profit they generate from the business itself. A
project, In order to operate with full capacity and without any financial constraints
needs to have full financial support, including investment cost and working capitals.
So, when huge capacity is created, the probability of defaulting is low and vice versa.
Loan diversion was also found as essential and significant factors of loan repayment
rate negatively. Loan diversion is negatively affecting the loan repayment capacity. It
is clear that diverted loans miss their target and cannot repay the loan according to the
duty. This means, diverting loan into non-income generating activities increases
default rate. Therefore, it is recommended that the institution should give attention to
continuous follow-up on proper loan utilization

Credit experience is also another significant variable. A borrower having credit

experience is at better position to repay its loan than those new comers. Because, while
experienced borrowers use their skill, knowledge and familiarity in carrying out their
duty, new borrowers faces new environment to begin from the scratch. Having another
business is another significant variable influencing the dependent variable positively.
104
The help of having addition business is to use the experience such other business in
running the current one and financial support in case of default. But this should be
handled very carefully because the existence of another business may also be the cause
of failure if diversion of money to such other business occurred. The positive sign and
the significance is from the support and help it gives to the current business while the
other side should be considered with caution.

The other significant variable was follow up. This variable influence borrower’s loan
repayment performance positively and significantly. Giving Projects proper follow up,
the probability of default decrease since problems will be tackled immediately and
utilize their loan effectively, generate revenue, and then make loan repayment. The
follow-up and supervision made by the loan officers and concerned bankers should be
increased and it leads to increase repayment performances.

Generally, the finding of the study failed to reject two research hypotheses that indicate
the relationship between loan repayment performances and borrower related factors,
specifically education level, family size of borrowers and experience and bank related
factors like having other business, follow-up, loan size, time horizon and loan
diversion whereas the remaining were insignificant.

5.2. Recommendation

It is apparent that DBE has to work to avert the loan repayment problems. The source
of loan repayment performances as indicated under this research is from four different
areas and the bank is required to work on the solution to bring about better
performances. Financial performances and wellness is one criteria of measuring
financial institution healthiness, which in cases of DBE is possible through loan

105
approval collection and disbursement. Now, Based on the analysis and findings of this
study, the researcher therefore recommends that: The study revealed that among
personal or borrower characters, educational level, credit experience and family size
were the main and significant factors of loan collection performance which was
unattractive in the past consecutive years. From bank specific factors, time horizon,
follow up, loan diversion and loan size were found significant variables and from
business specific factors having another business is found significant variable.
Therefore, the bank is recommended to select and screened out those customers who
are more educated and have credit experience in running related business. Proper due
diligence should be conducted in screening customers with better educational level and
credit experience. The major activities of screening is knowing the personal traits and
history of the borrower and the feasibility and viability of the business. Hence,
customer with better educational qualification and experience should be selected. The
researcher also recommends timely disbursement of loan. Since projects are sensitive
to season (production, market, and implementation) for these hold proper amount and
disburse when the need arises.

Disbursement dalliance was the problem, where the main challenge of the bank in the
last four consecutive years. The main justification behind such dalliance was less
number of contact officers and engineers as compared with financed projects which
could unable to make necessary follow up and progress report. Follow-up being one
significant variable by itself, when properly implemented solve other related problems
too. The bank has to increase the number of officers and engineers who has
responsibility of taking full-fledged follow up and revision as well as progress report,
respectively. Thus, follow up also as being one of significant factor, increasing the
number of contact officers and give more attention on follow up can increase good
performance of projects hence loan collection performance of the bank.

106
The other important recommendation is regarding loan diversion. The bank is highly
recommended to follow the money released for project development and avert the
diversion of loan. The main cause of loan diversion is lack or loose of following the
money and progress supervision. The other important recommendation is regarding
loan size. In order to implement the project with full potential and capacity necessary
capital should be allocated. Such loan size shouldn’t be more than what is needed or
less than what is required. So, the bank is recommended to conduct critical feasibility
Finally, the researcher recommends other researchers to do by including the other
Districts & head office, and the determinants of other variables like loan repayment
performance, outreach, using innovative features of the bank and the other variable.

Generally, internal factors can be easily controlled while external factors can be a
threat to the viability of banks. Banks have to be vigilant in their lending decisions so
as to avoid loan losses and the accumulation of non-performing loans. Banks need to
concentrate on sectors that are performing well and avoid lending to those sectors
which have already recorded a significant amount of non-performing loans. One thing
to note is that, this result can be generalized to the whole banking sector in Ethiopia as
almost all the banks have been affected by non-performing loans. Therefore, the
recommendations generated are a prescription for all banks engaged on similar
investment activities in Ethiopia.

107
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Zelalem, G., Hassen, B., & Jemal, H. (2013). Determinants of Loan Repayment Performance of
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7. APENDIX
Appendix 1. Questionnaire paper
TOSSA COLLEGE OF BUSINESS AND ECONOMICS
DEPARTMENT OF BUSINESS DMINISTRARTION
MBA RESEARCH QUESTIONARY
This Research Questionnaire is for academic purpose only!
Dear respondent, this questionnaire is prepared to collect data on loan repayment
performances, for the purpose of MBA research to be conducted under a title Factors
affecting loan repayment performances in Development Bank of Ethiopia Dessie
District. Get relaxed and feel free to respond the questions and focus on providing the
required information to help the researcher do his/her job rightly. Hence, I kindly
request you to fill the questionnaire very carefully and provide genuine information so
as to help me find the actual reason for the identified problems. Advance thanks for
your patience and time
Factors of loan repayment performance
A. Borrower’s related information
B. Name of borrower (optional) ___________________________________________
C. Gender 1) Male 0) Female
D. Age: 1. 15-30 2. 31-50 3. Above 51
E. Marital status 1. Single 2. Married 3. Divorced/widowed

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F. What is your family size? ---------------------------
1. Small (1-3) 2. Medium (4-5) 3. Large (above 6)
G. Educational Background: 1. No formal education 2. Primary school completed 3.
High School completed 4. College/University graduate
H. Do you have any credit experience in running similar project? 1) Yes 0) No.
If yes, did it help you for current business? Explain how ___________________
I. Business related questions;
A. What is the current status of your business? 1. Performing good/successfully
operating 2. Not good/ defaulted (substandard, doubtful and loss)
B. What is your business form? 1. Sole proprietor 2. PLC 3. SHC and Others
C. What is your business sector? 1. Agriculture 2. Service 3. Industry
D. Have you gained sufficient income compared to your plan? 0) No 1. No If no, why
(list reasons) ……………………………………………………………………………
E. Do you have other business? 1) Yes 0) No
II. Institution related questions;
A. What is a Loan size permitted for the project? ----------------------- Birr
B. Do you believe such amount is sufficient for your project as compared to feasibility
study? 0) No 1) Yes
If no, explain how it affected your repayment
performances----------------------------------------------------------------------------------------
-------------------------------------------------
C. Have you used any amount of money from the loan to operate some other business
or used for your personal consumption? 0) No 1) yes
If yes, explain the amount, ----------------------------------------
D. Amount of equity contribution? ------------------ Birr
Is such amount 1) Exceed 30% of total investment 2 . 30% only
E. Do sufficient grace period granted to begin repayments? 0) No 1) Yes
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If No, explain how it affected your repayment capacity?
---------------------------------------------------------------------------------------------------------
-------------------------------------
F. Do you think the bank has made a proper follow up to the project? 0) No 1) Yes
G. Do you think the bank has secured its loan with enough/sufficient collateral in cases
of default? 0) No 1) Yes
H. What is the collateral of the bank for the loan? Is there any property other than the
project itself? 0) No 1) Yes
If yes, explain the amount-------------------------------------------------------------------------
I. What is the ratio of debt to collateral value? -----------------------------------------------
J. How do you evaluate the change (increase) in interest rate, do you think it affected
your repayment performances? 0) No 1) Yes
If yes, is that positively or negatively? Explain
---------------------------------------------------------------------------------------------------------
------------------------------------------------
K. Do you believe the KYC (know your customer) assessment was performed duly
according to policy and procedures of the bank? 0. Yes 1. No
L. How do you evaluate the service period (time to conduct KYC, appraisal and
approval)? 1. Timely
2. Elongated 3. Too late
If such time has affected your repayment capacity, explain your reason.
---------------------------------------------------------------------------------------------------------
------------------
III. External factors
A. Do you think International/national market fluctuation affected your repayment
performances?
0) Yes 1) No
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If yes, what is the reason, how? ------------------------------------------------------------------
--------------------------------------------------------------------------------------.
B. Were the project attacked by pest and weed problem?
0) Yes 1) No
If yes, what are the causes?
---------------------------------------------------------------------------------------------------------
-------------------------------------------------------------
C. Were the project faced bad weather condition problem like flood, too less or too
much rainfall? 0) No 1) No
If yes, what are the causes, explain
---------------------------------------------------------------------------------------------------------
--------------------------------------------------------------
Other factors
Elaborate other major challenges and factors that challenged the repayment of bank
credit and over all performances of your business.
---------------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------------
--------------------------------------------
Thank you for your cooperation!
Appendix.2 Interview questions

TOSSA COLLEGE OF BUSINESS AND ECONOMICS

DEPARTMENT OF BUISNESS ADMINISTATION
MBA RESEARCH INTERVIEW
This interview questions is for academic purpose only! (For Bank staffs & officials)

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This interview questions are prepared to collect relevant data for my MBA research
under a title ‘Factors affecting loan repayment performances, the case of DBE Dessie
District’ and your answers thereby will be utilized for the same purpose. Thank you in
advance for your willingness and cooperation and please help me in providing a
genuine information. The confidentiality of the information you provide will be kept.
1. How do you analyses the current performances of the bank and what do you think
contributed for such performances?
2. Do you believe the screening process and due diligence are duly conducted to select
credit worthy borrowers? What are the limitations seen during the due diligence
assessment?
3. Do you think the bank conducted the appraisal and approval activities duly as per
the policy of the bank?
What drawbacks do you observe regarding appraisal and approval process?
4. What do you think about the lengthy and time taking process in all screening,
appraisal, approval and disbursement process of the bank? What do you think the bank
should do to solve such problems?
5. Do you believe that the bank has done fledged follow up for its customers /loan?
What are the results after project follow up?
6. How do you explain the impact of due diligence, loan appraisal, and project follow
up with loan repayment?
7. What are the crucial confronting factors for loan repayment in the District?
8. What alternative measures were taken on the side of the bank to improve the
repayment Situation?
9. Were the measures taken brought an improvement in repayment status of the
project?
Thank you!

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Appendix 3. Correlation matrix of coefficients of regress model.

e(V) Gender Age Marriag Educat~ Househ~ Experi~ Otherb~ Busine~m Busine~ Incom Loanam~
e n e e s r e t
Gender 1.0000
Age 0.0123 1.0000
Marrage 0.0119 0.1132 1.0000
Education -0.1016 0.0805 0.1678 1.0000
House holds -0.1766 -0.209 -0.1919 -0.0241 1.00
00
Experiance 0.0499 0.0796 -0.0379 0.0028 0.0430 1.0000
Otherbusin~ -0.0107 -0.183 -0.0094 -0.1512 0.0063 0.0688 1.0000
s

Businessform -0.1868 0.0715 -0.1180 0.0908 0.0261 0.1947 0.0404 1.0000

Businessse~r 0.0417 0.0780 -0.0571 -0.2016 0.0607 -0.0116 -0.0415 -0.0690 1.0000
Income 0.0888 -0.170 -0.0591 -0.2683 0.0464 0.0007 0.0406 -0.1400 0.1151 1.0000
Loan amount -0.0001 0.0459 -0.2583 -0.0890 -0.0358 -0.0142 -0.0166 0.1724 -0.3131 -0.116 1.0000
Diversion -0.0680 -0.139 -0.0075 0.1029 -0.0013 -0.0530 -0.0022 0.0074 0.0265 -0.027 0.0207
Equity 0.0886 -0.072 -0.0693 -0.1436 0.1023 -0.0128 -0.0264 -0.1039 0.0921 0.1862 -0.0282
Grace period 0.0291 0.121 -0.1207 -0.0873 0.0859 0.1042 -0.2372 0.0207 0.0983 -0.049 -0.1448
Follow up -0.0379 0.0030 0.0315 -0.0572 -0.0324 -0.0139 0.0240 0.1050 -0.0874 -0.110 -0.0017
Collateral -0.0424 -0.072 -0.0517 -0.0296 0.0273 -0.0512 -0.0909 0.0981 0.0643 0.0451 0.0303

Interest -0.0943 -0.037 0.1511 -0.1199 0.0053 -0.0717 0.0976 -0.0510 0.0308 -0.012 -0.2071
KYC -0.0943 -0.053 0.1511 -0.2337 -0.0726 -0.1079 -0.1155 0.0374 0.0033 0.0883 0.0891
Time 0.0648 0.0355 -0.0289 0.3210 -0.0571 0.0919 0.0993 -0.0321 -0.0122 0.0936 -0.1239
horizone
Market 0.1758 -0.0265 0.0771 -0.0591 0.0704 0.0601 0.0460 -0.0052 0.0902 0.0317 0.0671
116
Whether 0.1610 0.0824 0.0861 0.0529 -0.1941 0.0710 0.0632 -0.0234 -0.0334 0.0452 0.0125
Cones -0.2784 -0.2898 -0.2912 -0.4279 -0.0671 -0.2707 -0.0251 -0.3133 -0.0877 0.0123 -0.1038
Divers~n Equity Gracep~ Followup Collat~l Interest Kyc Timeho~n Market Whethr _cons
d

117
Appendix 4. VIF test result
Variable VIF 1/VIF
Education 1.71 0.583593
KYC 1.44 0.693435
Loan amount 1.44 0.695333
Time horizon 1.41 0.711450
Grace Period 1.31 0.747682
Other business 1.29 0.772964
Businessse~r 1.29 0.791117
Income 1.26 0.791368
Marriage 1.26 0.804159
Collateral 1.24 0.813179
Gender 1.23 0.817818
Interest 1.22 0.818259
Business form 1.22 0.822914
Age 1.22 0.830964
House hold 1.19 0.859426
Follow up 1.16 0.884816
Equity 1.23 0.887345
Market 1.13 0.891935
Experience 1.12 0.913764
Diversion 1.09 0.839626
Mean VIF 1.26

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