DIVERGENCE IN COMMERCIAL BANK LENDING DIMENSIONS: EMPIRICAL STUDY ON ETHIOPIA

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Mr. A.S. Kannan and Dr. S. Sudalaimuthu,, “Divergence In Commercial Bank Lending Dimensions:
Empirical Study On Ethiopia” - (ICAM 2016)
DIVERGENCE IN COMMERCIAL BANK LENDING DIMENSIONS:
EMPIRICAL STUDY ON ETHIOPIA
Mr. A.S. Kannan
Associate Professor, Department of Management Studies,
Sri Manakula Vinayagar Engineering College, Puducherry;
Research Scholar in Banking Technology,
Pondicherry University. Pondicherry
Dr. S. Sudalaimuthu
Research Supervisor & Associate Professor,
Department of Banking Technology, School of Management,
Pondicherry University, Puducherry
ABSTRACT
Quite a number of studies in the past in various countries accentuated the significance of
demographic variables in lending decisions of bank-officials. Do the dimensions of
commercial bank lending diverge by gender, age-group, banking experience, sector of the
bank, and designation held by bank-officials in Ethiopia? This is the key issue that is tried to
be answered by empirical testing in this study. For the purpose of this descriptive study of
cross-sectional design, data were collected by means of a pilot-tested questionnaire from
bank-officials across the country between February and July 2015. The study presented a
conceptual framework of various dimensions of commercial bank lending. Tested hypotheses
revealed that there is a significant association: (i) between gender and every dimension of
commercial bank lending; and (ii) between sector of the bank and certain dimensions.
ANOVA results discovered that there is statistically significant differencebetween age-group,
banking experience, designation and various dimensions of commercial bank lending.
Duncan Multiple Range Test recognized significant difference across of groups of bank-
officials with respect to age-group, banking experience, and designation. The dimension
‘overall loan determinants’ is influenced by all the demographic and institutional profile
variables that have been tested in this study.
Key words: Commercial Bank Lending, Creditworthiness, Ethiopia, Overall loan
determinants.
Cite this Article: Mr. A.S. Kannan and Dr. S. Sudalaimuthu. Divergence In Commercial
Bank Lending Dimensions: Empirical Study On Ethiopia. International Journal of
Management, 7(2), 2016, pp. 764-777.
http://www.iaeme.com/ijm/index.asp
INTERNATIONAL JOURNAL OF MANAGEMENT (IJM)
ISSN 0976-6502 (Print)
ISSN 0976-6510 (Online)
Volume 7, Issue 2, February (2016), pp. 764-777
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International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 -
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BRIEF INTRODUCTION
Among various types of banks, Commercial banks are the major ones. They are known for accepting
deposits of money from the public, operating current accounts for business enterprises, granting loans
to businesses and others, and investing in approved categories of investments. Thus, lending is a major
function of a commercial bank. They thrive on lending in the sense – they cover their establishment
costs only on the margin (the difference between the lending rates and the deposit rates). There are
many types of advances commercial banks normally grant, viz., clean loans (without any security as
such), term loans (for a defined period), working capital advances (to cover the operational costs of an
enterprise), secured loans (against mortgage or pledge or hypothecation of certain properties of value),
overdraft facilities (by allowing the reputed customer to overdraw their accounts), etc.
STATEMENT OF THE PROBLEM
There are varying dimensions of commercial bank lending decisions. These dimensions may or may
not be influenced by the demographic and institutional variables, which needs to be investigated.
There have been many studies which proved the influence of gender on lending decisions. Those
studies focused mainly on the gender of the borrowers as such, and occasionally on the loan officer’s.
Similarly the age-group to which the bank official belongs to, and the experience commanded by the
official in banking industry, as well as the position held by the official concerned might have their own
impact on the lending decisions. Again the sector to which the bank in which the official is employed
would have its own persuasions on the lending decisions of the official. These thoughts raise the
following questions in the minds of the researcher:
1. Whether there is a significant association between the gender of the bank official and various
dimensions of commercial bank lending (such as loan size, repayment tenure, interest rate, overall
loan determinants, implications of financing gap, lending related issues, and creditworthiness)?
2. Whether there are significant differences among the different age-groups of bank officials across
commercial bank lending dimensions?
3. Whether there is a significant relation between experience of the official in banking industry and
commercial bank lending dimensions?
4. Whether the sector of the bank in which the official is employed has some influence on the
dimensions of commercial bank lending?
and finally,
5. Whether the designation of the bank official has any bearing on the dimensions of commercial
bank lending?
OBJECTIVE OF THE STUDY
The objective of this study is to ascertain whether there are any influences of (a) Gender, (b) Age
group, (c) Experience in banking industry, (d) Sector of the bank, and (e) Designation of the official
over the various dimensions in commercial bank lending, with reference to Ethiopian Banking
Industry.
HYPOTHESES DEVELOPED FOR THE STUDY
In order to attain the set objective of this study, the following null hypotheses have been formulated
and tested in this paper:
H01: There is no significant association between gender of the bank official and various dimensions
of commercial bank lending (such as loan size, repayment tenure, interest rate, overall loan
determinants, financial gap implications, issues in lending, and creditworthiness).
H02: There is no significant difference among age-group of bank-official with respect to various
dimensions of commercial bank lending in Ethiopia.
H03: There is no significant difference among experience of bank-official in respect of various
dimensions of commercial bank lending in Ethiopia.

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H04: There is no significant relation between sector of the bank (in which the official is employed)
and the dimensions of commercial bank lending in Ethiopia.
H05: There is no significant difference between the designation of the bank official and of the
various commercial bank lending dimensions in Ethiopia.
BRIEF REVIEW OF RELATED LITERATURE
According to Carter et al. (2007), previous research provides unequivocal evidence that women-owned
businesses start with both lower levels of overall capitalization and lower ratios of debt finance.
Structural dissimilarities between male-owned and female-owned businesses explain most, but by no
means all, of these contrasting funding profiles. Explanations of residual differences, viewed in terms
of supply-side discrimination or demand-side debt and risk aversion, remain controversial. Using
experimental and qualitative methodologies, their study explores the role of gender in bank lending
decisions, focusing on the criteria and processes used by male and female loan officers. Results reveal
similarities in the criteria used to assess male and female applicants but show modest differences in the
emphasis given to certain criteria by male and female lending officers. The processes used by male and
female lending officers to negotiate loan applications revealed the greatest differences.
Beck et al. (2012) examined the effects of group identity in the credit market. Exploiting the quasi
random assignment of first-time borrowers to loan officers of a large Albanian lender, the researchers
tested for own-gender bias in the loan officer-borrower match. They found that borrowers pay, on
average, 28 basis points higher interest rates when paired with a loan officer of the other sex.
According to them, the results indicate the presence of a taste-based rather than a statistical bias, as
borrowers’ likelihood of going into arrears is independent of loan officer gender. Ending up with an
opposite-sex loan officer also affects demand for credit, with borrowers being 11 percent less likely to
return for a second loan. The evidence further suggests that the bias originates with both female and
male loan officers. The bias is more pronounced when the social distance, as proxied by difference in
age between the loan officer and the borrower, increases and when financial market competition
declines. This is consistent with theories that predict a tastebased bias to be stronger when the
psychological costs of being biased are lower and the discretion in setting interest rates is higher. In
their opinion, together their results showed that own-gender preferences can have substantial welfare
effects.
The paper by Dietrich & Johannsson (2005) tests for the presence of age and gender discrimination
in the loan underwriting process.The researchers modified the tools used during the past exams to test
for racial discrimination and applied them in their study to test for the presence of disparate treatment
on the basis of age and gender. Using HMDA data along with data from 18 fair lending exams recently
conducted by the OCC, between1996 – 2001, they found no evidence of systematic discrimination on
the basis of age or gender.
In the views of Bellucci et al. (2010), loan officers are not only the conduit of bank policies and
operations in credit markets but also the crux between entrepreneurs, small businesses and lending
institutions. They are at the heart of two important problems of information asymmetry pertinent to
banking: the asymmetric information between banks and loan applicants and the moral hazard within
the banking organization itself. Until recently, the economic literature considered loan officers as
rational agents with unlimited information-processing capacity. In their review, the researchers
provided a brief overview of a more recent stream of research which recognizes that lending decisions
could be affected by behavior, character and even feelings or emotions of loan officers. Their focus
falls on gender-based factors which have been shown to have the potential to affect the tasks performed
by loan officers. Different degrees of risk-aversion and overconfidence between man and women result
in male and female loan officers reaching different lending decisions. Social preferences and gender-
pairing also lead to gender-specific outcomes of lending. Finally, negotiation skills, stereotypes and
perceptions, career concerns and discrimination have been shown to vary significantly with gender.
The extant literature for most of these factors is scarce and thus they remain important topics for future
research. Furthermore, most of the recent studies which have addressed the importance of loan officer’s
gender using real data on large samples could only provide indirect insights into their behavior as
characteristics such as degree of overconfidence or career concerns are not directly observable. Studies
which try to directly measure factors such as perceptions and stereotypes are either based on small
samples or do not address all aspects of the outcome of the lending process. Further according to the

767
researchers, often the observed pattern in the data is consistent with more than one explanation and
differentiating between the alternatives remains an hard open question.
Uchida et al. (2008) opine that previous research suggests that loan officers play a critical role in
relationship lending by producing soft information about SMEs. They empirically confirmed this
hypothesis, and also examined whether the role of loan officers differs from small to large banks as
predicted by Stein (2002). While the researchers found that small banks produce more soft information,
the capacity and manner in which loan officers produce soft information does not seem to differ
between large and small banks. In the views of the researchers, although large banks may produce
more soft information, they likely tend to concentrate their resources on transactions lending.
In spite of hard efforts, the researchers could not find any standard work on the issue in the
Ethiopian Banking Industry so far. There has been no study conducted on Ethiopian Commercial
banks to find out whether the demographic and institutional variables have a bearing on the different
dimensions of lending. Hence, it is thought fit to undertake this study and so this paper.
CONCEPTUAL FRAMEWORK
Miles and Huberman (1994) defined a conceptual framework as a visual or written product, one that
“explains, either graphically or in narrative form, the main things to be studied—the key factors,
concepts, or variables—and the presumed relationships among them”. The most important thing to
understand about your conceptual framework is that it is primarily a conception or model of what is out
there that you plan to study, and of what is going on with these things and why—a tentative theory of
the phenomena that you are investigating. (Maxwell, 2005).
CONCEPTUAL FRAMEWORK SHOWING
DIMENSIONS OF COMMERCIAL BANK LENDING
The conceptual framework for this study is presented in a diagrammatic form above. The flow
diagram presents the key elements of dimensions of commercial bank lending and the inter-relationship
among them. Accordingly, it features the three basic factors, viz., the loan size, the repayment tenure,
and the interest rate as the contributing factors of ‘overall loan determinants’. The overall loan
determinants are influenced (mostly negatively) by the ‘implications in financing gap’ and by the
‘issues in lending’. The ‘creditworthiness’ factor is the result of the outcome of interactions between
Loan Size
Repayment
Tenure
Interest Rate
Overall Loan
Determinants
Implications of
Financing Gap
Issues in
Lending
Credit Worthiness

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overall loan determinants, and the influencers – implications in financing gap and the issues in lending.
The sense in which each of these variables are taken in this study are explained briefly in the following
page:
Loan Size: This refers to the amount of loan granted to the borrower by the lending bank. There
are about 14 variables which are found to influence this element, and data pertaining to them were
collected in the study.
Repayment tenure: The time duration assigned to complete the loan repayment is what is
referred as repayment tenure in this study. There are 8 variables representing this factor.
Interest rate: The rate of interest charged by the lending bank on the borrower is what is referred to
as interest rate. There are 5 variables representing this.
Overall loan determinants: The combined effect of “loan size, repayment tenure, and interest
rate” is collectively referred to as “overall loan determinants”. Accordingly, there are 27 variables
representing this combine.
Financing gap implications: The Financing Gap refers to the difference between the loan
amount demanded by the borrower, and the loan amount actually granted by the banker. This gap
(especially when the supply is considerably lower than the demand) results in certain implications, and
there are 5 statements of Likert type that are measuring this variable.
Issues in Lending: There are a number of issues in lending that are confronted by the lending
bank. There are about 17 variables that are trying to gauge the issues in lending from the viewpoint of
the lending bank.
Creditworthiness: The creditworthiness of the borrower is the ultimate factor that is
influencing the lending decision. If, in the opinion of the assessing official, the borrower is
creditworthy, a loan may be granted, otherwise may not be. This decisive factor is measured with the
help of 14 variables in this study.
Anticipated Influences: The three factors (loan size, repayment tenure, and interest rate) are
expected to positively influence the ‘overall loan determinants’. The two factors, viz., implications of
financing gap, and issues in lending are expected to negatively influence the ‘creditworthiness’. That
means, the more the implications (of financing gap) and the issues in lending, less likely is the
creditworthiness of the borrower. The inclination of the lender to lend will be negatively affected by
the presence of these two factors (viz., financing gap implications and issues in lending).
METHODOLOGY
This descriptive study uses cross-sectional research design. Data for this study are primary in nature,
and are collected by means of a survey questionnaire administered to the bank officials in public sector
and private sector commercial banks in Ethiopia. 390 questionnaires were distributed among branch
managers, loan officers, credit analysts and relationship managers of the two public sector and sixteen
private sector commercial banks in Ethiopia between February 2015 and July 2015, and out of the
responses collected 342 were found to be fit for analysis, thus representing 89% success rate. For the
purpose of analysis, Statistical Package for Social Sciences (SPSS) version 20 has been used. For
hypotheses testing, the study used (i) independent samples test, and (ii) one-way ANOVA, with
Duncan Multiple Range Test in order to identify the differences that exist within the groups as such.
The findings are presented in the form of appropriate tables in the following section.
RESULTS AND DISCUSSION
This section of the paper discusses the analysis results at length. It starts with the profile of the
officials of Ethiopian Commercial Banks who participated in the Commercial Bank Lending Survey,
2015. Table 1 below presents the profile in a summary manner.

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Table 1 Demographic and Institutional Profile of the Officials of Ethiopian Commercial Banks
Demographic & Institutional Profile of the Bank-officials
Sector of Bank
Total
Public Private
Gender
Male 21.9% 52.9% 74.9%
Female 14.0% 11.1% 25.1%
Age Group in years
Below 30 6.4% 22.2% 28.7%
30-45 28.1% 36.8% 64.9%
Above 45 1.5% 5.0% 6.4%
Experience in Banking in years
Below 3 8.2% 9.6% 17.8%
3-10 14.6% 36.0% 50.6%
Above 10 13.2% 18.4% 31.6%
Designation
Branch Manager 6.7% 21.6% 28.4%
Loan Officer 8.8% 19.9% 28.7%
Analyst 8.2% 17.5% 25.7%
Relationship Manager 12.3% 5.0% 17.3%
Source: Ethiopia Commercial Bank Lending Survey, 2015.
As can be observed from the above table, the officials are dominantly male (74.9%); majority
belonging to middle age-group (64.9%); half of them (50.6%) are ‘experienced’ and about 31.6% are
‘seniors’ with more than 10 years’ service in banking industry; holding positions as ‘Branch Manager’
(28.4%), ‘Loan Officer’ (28.7%) at the branch level, or as ‘Analyst’ (25.7%) and ‘Relationship
Manager’ (17.3%) at the zonal/district/head-quarters level. While total participation from public sector
banks is 36%, that of private banks is 64%.
GENDER-RELATED HYPOTHESIS
Table 2 below presents the descriptive statistics and the results of independent samples test for gender-
related hypothesis (H01).
Table 2: Descriptive Statistics & Independent Samples Test results for Gender Hypothesis
Dimensions in Commercial Bank
Lending
Gender
t-value
Sig. (2-
tailed)
Male Female
Mean SD Mean SD
Loan Size 11.34 2.52 10.02 3.67 3.716 0.000**
Repayment Tenure 5.57 1.80 4.73 2.36 3.438 0.002**
Interest Rate 3.46 1.15 2.94 1.51 3.335 0.001**
Overall Loan Determinants 20.38 4.64 17.7 6.74 4.097 0.000**
Financing Gap Implications 18.05 4.93 20.13 3.96 -3.543 0.000**
Issues in Lending 59.81 21.37 67.07 16.05 -2.888 0.004**
Credit Worthiness 50.45 19.3 43.79 21.51 2.689 0.008**
Source: Author's computation based on Commercial Bank Lending Survey, 2015
** significance at 1% level
As can be observed from the above table, all the seven dimensions have highly
significant (p < 0.01) t-values. Table 3 following gives the results of hypothesis
testing based on independent samples test.

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Table 3: Results of Hypothesis (H01) testing (based on Independent Samples test)
H01: There is no significant association
between Gender of bank-official and:
Hypothesis Status
based on t-test
Accepted Hypotheses
Loan size H0 Rejected
(p < 0.001)
HA(significant
association exists)
Repayment Tenure H0 Rejected
(p < 0.001)
HA (significant
association exists)
Interest rate H0 Rejected
(p < 0.001)
HA (significant
association exists)
Overall loan determinants H0 Rejected
(p < 0.001)
HA (significant
association exists)
Financing gap implications H0 Rejected
(p < 0.001)
HA (significant
association exists)
Issues in lending H0 Rejected
(p < 0.001)
HA (significant
association exists)
Creditworthiness H0 Rejected
(p < 0.001)
HA (significant
association exists)
As found in table 3 above, p-value for all the dimensions is less than 0.01. Hence, the null
hypothesis (that there is no significant association between gender and each of the dimensions in
commercial bank lending) is rejected. As such, there is a significant association between gender and (i)
loan size, (ii) repayment tenure, (iii) interest rate, (iv) overall loan determinants, (v) financing gap
implications, (vi) issues in lending, and (vii) creditworthiness. This implies that the perspective of the
bank official varies by gender as to different dimensions of commercial bank lending in Ethiopian
Banking Industry. Further, this result confirms the iterations of Bellucci et al. (2010) which stated
“Different degrees of risk-aversion and overconfidence between man and women result in male and
female loan officers reaching different lending decisions”.
AGE-GROUP RELATED HYPOTHESIS
Table 4 below presents the descriptive statistics and the results of one-way ANOVA (Analysis of
Variance) for age-group hypothesis.
Table 4: Descriptive Statistics & one-way ANOVA results for Age Group Hypothesis
Dimensions in Commercial Bank
Lending
Age Group in years
F Sig.Below 30 30-45 Above 45
YOUNG MIDDLE MATURED
Loan Size 11.28b
(2.65)
11.04b
(2.92)
9.55a
(3.50)
3.262 0.040*
Repayment Tenure 5.67
(1.84)
5.27
(2.01)
4.82
(2.26)
2.261 0.106
Interest Rate 3.45
(1.20)
3.32
(1.25)
2.95
(1.68)
1.416 0.244
Overall Loan Determinants 20.40b
(4.94)
19.63b
(5.32)
17.32a
(6.91)
3.055 0.048*
Financing Gap Implications 18.28
(4.70)
18.59
(4.83)
19.77
(4.70)
0.882 0.415
Issues in Lending 61.17
(20.55)
61.40
(20.46)
66.05
(19.27)
0.553 0.576
Credit Worthiness
53.04b
(18.43)
48.02b
(20.25)
37.41a
(20.52)
6.081 0.003**

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Note:
1. * denotes significance at 5% level.
2. ** denotes significance at 1% level.
3. Figures in brackets represent the standard deviation values, other one is the mean value.
4. Different alphabets among age-groups denote significance at 5% level using Duncan
Multiple Range Test (DMRT).
The table presents the mean values and standard deviation values for each of the dimension for
respective age group, viz., Young (below 30 years), Middle-aged (30 to 45 years), and Matured (above
45 years). Since there are multiple groups in this analysis, the exact difference between the groups is
tested with the help of Duncan Multiple Range Test (DMRT), and the results of that test (wherever it is
found to be significant at 5% level) are presented by using alphabets ‘a’, ‘b’, and ‘c’.
Table 5 below summarizes the results of hypothesis testing for H02 using one-way ANOVA.
Table 5: Results of Hypothesis (H02) testing (based on F-test)
H02: There is no significant
difference among Age-group
of bank-official with respect
to:
Hypothesis
Status based on
F-test
(one-way
ANOVA)
Accepted
Hypotheses
Difference between
Groups (Post-hoc /
DMRT)
(below 30 – Young /
30 to 45 – Middle-aged /
Above 45 – Matured)
Loan Size H0 Rejected
(p < 0.05)
HA (significant
difference exists)
Difference exists between
“Matured group” and the
other two age-groups.
No difference exists
between “Young” and
“Middle” age-groups.
(p < 0.05)
HA (significant
difference exists)
Credit Worthiness H0 Rejected
(p < 0.01)
HA (significant
difference exists)
Repayment Tenure
Failed to
rejectH0
(p > 0.05)
H0 (significant
difference does
not exist)
No significant difference
exists among the three
age-groups.
Interest Rate
Failed to
rejectH0
(p > 0.05)
H0 (significant
difference does
not exist)
Financing Gap Implications
Failed to
rejectH0
(p > 0.05)
H0 (significant
difference does
not exist)
Issues in Lending
Failed to
rejectH0
(p > 0.05)
H0 (significant
difference does
not exist)
Since p-value is less than 0.01 in respect of ‘creditworthiness’ dimension, the null hypothesis is
reject at 1% level of significance. Hence, there exists a significant difference between age-group of
bank official and creditworthiness. Based on the results of Duncan Multiple Range Test (DMRT), it
can be concluded that significant difference exists between “matured group” and “young group”, as
well as between “matured group” and “middle-aged group”. However, the results reveal that there is
no different existing between “young group” and “middle-aged group”. This difference can be
attributed to the point that the “matured group”, by virtue of their long experience in general and in
banking, have different perspective when compared to the other two age-groups.
With respect to ‘loan size’ and ‘overall loan determinants’, the p-value is less than 0.05, thus
rejecting the null hypothesis at 5% level of significance. As such, a statistically significant difference
exists between age-group of bank officials and loan size as well as overall loan determinants. Based on
the results of Duncan Multiple Range Test (DMRT), it can be concluded that significant difference
exists between “matured group” and “young group”, as well as between “matured group” and “middle-
aged group”. However, the results reveal that there is no different existing between “young group” and
“middle-aged group”.

772
Since p-value is greater than 0.05, the null hypothesis is failed to reject in respect of dimensions -
repayment tenure, interest rate, financing gap implications, and issues in lending. Accordingly, there is
no statistically significant differences between age-group and these four dimensions of commercial
bank lending. It stands to mean that the views of bank-officials, irrespective of their age, seem to be in
harmony as to repayment tenure, interest rate, financing gap implications, as well as issues in lending.
BANKING EXPERIENCE RELATED HYPOTHESIS
Table 6 below presents the descriptive statistics and the results of one-way ANOVA.
Table 6: Descriptive Statistics & one-way ANOVA results for Experience Hypothesis
Dimensions of
Commercial Bank Lending
Experience in Banking in years
F Sig.Below 3 3 to 10 Above 10
FRESHER EXPERIENCED SENIOR
Loan Size 9.82a
(3.65)
10.92b
(2.92)
11.82c
(2.05)
9.936 .000**
Repayment Tenure 4.70a
(2.37)
5.42b
(1.88)
5.63b
(1.85)
4.489 .012*
Interest Rate 2.92a
(1.49)
3.24a
(1.30)
3.71b
(.96)
9.026 .000**
Overall Loan Determinants 17.44a
(6.96)
19.58b
(5.24)
21.17c
(3.91)
9.983 .000**
Financing Gap Implications 19.77b
(4.21)
18.75ab
(4.53)
17.61a
(5.31)
4.299 .014*
Issues in Lending 68.59c
(15.66)
62.36b
(19.56)
56.55a
(22.75)
7.282 .001**
Credit Worthiness 42.41a
(21.17)
49.86b
(19.75)
50.65b
(19.38)
3.857 .022*
Note:
1. * denotes significance at 5% level.
4. Different alphabets among Banking Experience denote significance at 5% level using
DuncanMultiple Range Test (DMRT).
The table presents the mean values and standard deviation values for each of the dimension for
respective experience in banking industry, viz., Fresher (below 3 years’ experience), Experienced (3 to
10 years’ experience), and Senior (above 10 years’ experience). Since there are multiple groups in this
analysis, the exact difference between the groups is tested with the help of Duncan Multiple Range Test
(DMRT), and the results of that test (wherever it is found to be significant at 5% level) are presented by
using alphabets ‘a’, ‘b’, and ‘c’.
Table 7 below summarizes the results of hypothesis testing for H03 using one-way ANOVA.

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H03: There is no
significant difference
among Experience of
bank-official with
respect to:
Hypothesis
Status based
on F-test
(one-way
ANOVA)
Accepted
Hypotheses
Difference between Groups (Post-
hoc / DMRT)
(below 3 – Fresher /
3 to 10 – Experienced /
Above 10 – Senior)
(p < 0.01)
HA (significant
difference
exists)
Difference exists between ‘Fresher’
and ‘Experienced’; ‘Experienced’
and ‘Senior’; ‘Fresher’ and ‘Senior’
Repayment Tenure
H0 Rejected
(p < 0.05)
HA (significant
difference
exists)
and ‘Experienced’; ‘Fresher’ and
‘Senior’. No difference between
‘Experienced’ and ‘Senior’
Interest Rate
H0 Rejected
(p < 0.01)
HA (significant
difference
exists)
and ‘Senior’; ‘Experienced’ and
’Fresher’ and ‘Experienced’
(p < 0.05)
HA (significant
difference
exists)
Financing Gap
Implications
H0 Rejected
(p < 0.05)
HA (significant
difference
exists)
and ‘Senior’; No difference exists
between ‘Fresher’ and
‘Experienced’; between
Issues in Lending
H0 Rejected
(p < 0.01)
HA (significant
difference
exists)
Credit Worthiness H0 Rejected
(p < 0.05)
HA (significant
difference
exists)
and ‘Experienced’; ‘Fresher’ and
Since p-value is less than 0.01, null hypothesis is rejected at 1% level of significance with regard
to loan size, interest rate, and issues in lending. Hence, there is a significant difference between
experience of the official in banking industry and loan size, interest rate, and issues in lending. During
post-hoc analysis based on the results of Duncan Multiple Range Test (DMRT), it is observed that with
regard to loan size, and issues in lending, there exists differences between (i) ‘fresher’ and
‘experienced’, (ii) ‘experienced’ and ‘senior’, and (iii) ‘fresher’ and ‘senior’. This stands to mean that
as experience progresses, the views of the bank-official with respect to the size of the loan and the
issues involved in lending amounts to various borrowers keep changing and modifying. The views of
the ‘experienced’ (with less than 10 years’ experience in banking industry), and of ‘senior’ (with more
than 10 years’ experience) do not match with each other, and so also the views of the ‘fresher’ matches
with neither of the other two categories of officials.
Sector related Hypothesis
Table 8 below presents the descriptive statistics and the results of independent samples test for sector
hypothesis (H04).

774
Table 8: Descriptive Statistics and Independent Samples test results for Sector Hypothesis
Dimensions in
Commercial Bank Lending
Public Sector Bank Private Sector Bank
t-value
Sig.
(2-tailed)Mean SD Mean SD
Loan Size 10.69 3.36 11.19 2.61 -1.533 .126
Repayment Tenure 4.92 2.23 5.61 1.79 -3.116 .002**
Interest Rate 3.15 1.46 3.43 1.14 -1.932 .054
Overall Loan Determinants 18.76 6.31 20.23 4.68 -2.440 .015*
Financing Gap Implications 18.91 4.89 18.38 4.72 .978 .329
Issues in Lending 63.49 20.06 60.59 20.54 1.261 .208
Credit Worthiness 44.85 21.15 50.98 19.12 -2.737 .007**
* Significant at 5% level // ** significant at 1% level
As can be observed from table 8 above and table 9 following, since p-value is less than 0.01, null
hypothesis is rejected with respect to repayment tenure, and creditworthiness dimensions. With respect
to overall loan determinants, p-value stands lower than 0.05 thus rejecting the null hypothesis.
Table 9: Results of Hypothesis (H04) testing (based on Independent Samples test)
H04: There is no significant relation
between Sector of the bank and:
Hypothesis Status
based on t-test
Accepted Hypotheses
Repayment Tenure H0 Rejected(p < 0.01) HA(significant relation)
Overall loan determinants H0 Rejected(p < 0.05) HA (significant relation)
Creditworthiness H0 Rejected(p < 0.01) HA (significant relation)
Loan Size
Failed to reject H0
(p > 0.05)
H0 (significant relation
does not exist)
Interest Rate
Failed to reject H0
(p > 0.05)
does not exist)
Financing Gap Implications
Failed to reject H0
(p > 0.05)
does not exist)
Issues in Lending
Failed to reject H0
(p > 0.05)
does not exist)
As found in table 9in the previous page, p-value for the dimensions repayment tenure and
creditworthiness is less than 0.01. Hence, the null hypothesis (that there is no significant relation
between sector of the bank and repayment tenure and creditworthiness dimensions in commercial bank
lending) is rejected at 1% level of significance. As such, there is a significant relation between sector
of the bank and (i) repayment tenure, and (ii) creditworthiness. The p-value is less than 0.05 for the
dimension ‘overall loan determinants’, thus rejecting the null hypothesis at 5% level of significance.
Hence, there is statistically significant relation between sector of the bank and the overall loan
determinants’ dimension of commercial bank lending in Ethiopia.
Hypotheses testing by independent samples further found that p-value for the dimensions loan size,
interest rate, financing gap implications and issues in lending is more than 0.05. Thus, the null
hypothesis is failed to be rejected in all these cases. Accordingly, the null hypothesis that there is no
significant relation between sector of bank and loan size, interest rate, financing gap implications and
issues in lending stands firm and valid in this study.

775
DESIGNATION RELATED HYPOTHESIS
Table 10 below presents descriptive statistics and ANOVA for the designation hypothesis.
Table 10: Descriptive Statistics & one-way ANOVA results for Designation Hypothesis
Dimensions
of
Comm. Bank
Lending
Designation
F Sig.
Branch
Manager
Loan
Officer
Analyst
Relationship
Manager
Loan Size 12.00b
(1.88)
10.38a
(3.75)
10.39a
(3.09)
11.37b
(1.68)
7.356 .000**
Repayment
Tenure
5.75
(1.76)
5.20
(2.42)
5.20
(1.92)
5.20
(1.53)
1.779 .151
Interest Rate 3.76c
(.93)
2.99a
(1.59)
3.13ab
(1.21)
3.49bc
(.99)
7.635 .000**
Overall Loan
Determinants
21.52b
(3.46)
18.57a
(7.18)
18.72a
(5.56)
20.07ab
(2.75)
6.536 .000**
Financial Gap 17.82
(5.27)
19.01
(5.03)
18.82
(4.49)
18.71
(3.83)
1.160 .325
Issues in
Lending
57.40
(22.24)
62.21
(22.05)
63.22
(19.40)
65.27
(14.12)
2.247 .083
Credit
Worthiness
52.67
(19.06)
49.27
(21.11)
45.07
(20.76)
47.10
(17.96)
2.407 .067
Note:
3. Different alphabets among Designations denote significance at 5% level using
DuncanMultiple Range Test (DMRT).
The table 10 presents mean and standard deviation values for each of the dimension for respective
designation of the bank-official, viz., Branch Manager, Loan officer, Analyst, and Relationship
Manager. Since there are multiple groups in this analysis, the exact difference between the groups is
tested with the help of Duncan Multiple Range Test (DMRT), and the results of that test (wherever it is
found to be significant at 5% level) are presented by using alphabets ‘a’, ‘b’, and ‘c’.
H05: There is no
significant difference
among Designation of
bank-official with
respect to:
Hypothesis
Status based
on F-test
(one-way
ANOVA)
Accepted
Hypotheses
Difference between Groups (Post-hoc /
DMRT)
(B.Manager / Loan Officer /
Analyst / R.Manager)
(p < 0.01)
HA (significant
difference
exists)
Difference exists between ‘B.Manager’ and
‘Loan officer’; ‘B.Manager’ and ‘Analyst’;
‘Loan officer’ and ‘Relationship
Manager’; ‘Analyst’ and ‘R. Manager.; No
difference exists between ‘B.Manager’ and
‘R.Manager’; ‘Loan officer’ and ‘Analyst’.
Repayment Tenure
Failed to
reject H0
(p > 0.05)
H0 (significant
difference
does not exist)
Interest Rate
H0 Rejected
(p < 0.01)
HA (significant
difference
exists)
‘Loan officer’; ‘B.Manager’ and ‘Analyst’;
‘Loan officer’ and ‘R.Manager’. No
difference exists between ‘Loan officer’
and ‘Analyst’; ‘Analyst’ and ‘R.Manager’;
‘B.Manager’ and ‘R.Manager’.

776
Overall loan
determinants
H0 Rejected
(p < 0.01)
HA (significant
difference
exists)
‘Loan officer’; ‘B.Manager’ and ‘Analyst’.
No difference exists between ‘B.Manager’
and ‘R.Manager’; ‘Loan officer’ and
‘Analyst’; ‘Loan officer’ and ‘R.Manager’;
‘Analyst’ and ‘R.Manager’.
Financing Gap
Implications
Failed to
reject H0
(p > 0.05)
H0 (significant
difference
does not exist)
Issues in Lending
Failed to
reject H0
(p > 0.05)
H0 (significant
difference
does not exist)
Credit Worthiness
Failed to
reject H0
(p > 0.05)
H0 (significant
difference
does not exist)
Since p-value is less than 0.01, null hypothesis is rejected at 1% level of significance with regard
to loan size, interest rate, and overall loan determinants. Hence, there is a significant difference
between designation of the official in banking industry and loan size, interest rate, and overall loan
determinants. During post-hoc analysis based on the results of Duncan Multiple Range Test (DMRT),
it is observed that with regard to loan size, there exists differences between (i) Branch Manager & Loan
officer; (ii) Branch Manager & Analyst; (iii) Loan officer & Relationship Manager; and (iv) Analyst &
Relationship Manager. However, the views of (a) Branch Manager & Relationship Manager; and of
(b) Loan officer & Analyst converge. By profession, the functions of branch manager and relationship
manager are analogous; and that of loan officer and analyst are comparable. That is why
branchmanager and relationship manager have views different from that of loan officer and analyst.
With regard to interest rates, divergent views are held between (i) branch manager & loan officer; (ii)
branch manager & analyst; and (iii) loan officer and relationship manager. No differences exist in the
views of (a) loan officer & analyst; (b) analyst & relationship manager; and (c) branch manager &
relationship manager. In respect of overall loan determinants, differences exist between (i) branch
manager & loan officer; and (ii) branch manager & analyst. No difference exists between (a) branch
manager & relationship manager; (b) loan officer & analyst; (c) loan officer & relationship manager;
and (d) analyst & relationship manager.
Since p-value is greater than 0.05, null hypothesis is failed to reject at 5% level of significance
with regard to repayment tenure, financing gap implications, issues in lending and creditworthiness.
Hence, it can be concluded that there exists no statistically significant difference in the views of branch
manager, loan officer, analyst and relationship manager as to repayment tenure, financing gap
implications, issues in lending, and creditworthiness. In Ethiopian banking industry, these officials
seem to hold the same view with regard to these four dimensions of commercial bank lending. Though
there are some variations in their professional functions, these officials converge on the said
dimensions grossly.
FINDINGS AND CONCLUSION
The study attempted to empirically test the influences of certain demographic and institutional profile
variables on various dimensions of commercial bank lending in Ethiopia. It formulated hypotheses and
tested them with the help of independent samples t-test and Analysis of Variance F-test. Major
findings of the study established that there is a significant association: (i) between gender and every
dimension of commercial bank lending; and (ii) between sector of the bank and certain dimensions.
ANOVA results revealed there is statistically significant difference: (a) between age-group, banking
experience, designation and various dimensions of commercial bank lending. Duncan Multiple Range
Test revealed significant difference across of groups of bank-officials with respect to age-group,
banking experience, and designation. Thus, the study successfully confirmed in Ethiopian context
some of the earlier findings by researchers in various countries that demographic (such as gender and
age-group) and institutional profile (such as banking experience, sector of the bank, and designation
held) variables have statistically significant associations with certain dimensions of commercial bank
lending. More specifically, the dimension ‘overall loan determinants’ is found to be significant (at 5%
level) with each of the profile variable empirically tested in this study.

777
REFERENCES
[1] Andrea Bellucci, Alexander Borisov and Alberto Zazzaro. (2010). Do Male and Female
Loan Officers Differ in Small Business Lending? A Review of the Literature. Available
at: http://docs.dises.univpm.it/web/quaderni/pdfmofir/Mofir047.pdf, accessed in
Jan.2016.
[2] Andy Field. (2009). Discovering Statistics using SPSS. ISBN 978-1-84787-906-6, Sage
Publications: London
[3] Colin D. Gray and Paul R. Kinnear. (2012). IBM SPSS Statistics Made Simple. ISBN
978-1-84872-069-5, Psychology Press: New York.
[4] Hirofumi Uchida, Gregory F. Udell and Nobuyoshi Yamori. (2008). Loan Officers and
Relationship Lending to SMEs. Available at: http://www.frbsf.org/economic-
research/files/wp08-17bk.pdf, accessed in Jan.2016.
[5] Jason Dietrich and Hannes Johannsson. (2005). Searching for Age and Gender
Discrimination in Mortgage Lending. Available at:
http://www.occ.treas.gov/publications/publications-by-type/occ-working-papers/2008-
2000/wp2005-2.pdf, accessed in Jan.2016.
[6] Maxwell, J. A. (2005). Qualitative research design: An interactive approach (2nd Ed.).
Thousand Oaks, CA: SAGE Publications.
[7] Sara Carter, Eleanor Shaw, Wing Lam and Fiona Wilson. (2007). Gender,
Entrepreneurship, and Bank Lending: The Criteria and Processes Used by Loan Officers
in Assessing Applications. Entrepreneurship Theory and Practice, Vol.31, Issue 3,
pp.427-444, May 2007, DOI: 10.1111/j.1540-6520.2007.00181.x
[8] Thorsten Beck, Patrick Behr and Andreas Madestam. (2012). Sex and Credit: Is there a
Gender Bias in Lending? Available at:
http://siteresources.worldbank.org/INTFR/Resources/ThorstenBeck_Jan_12_2012.pdf,
accessed in Jan. 2016.

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