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.

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ISSN 0976-6502 (Print)

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Volume 7, Issue 2, February (2016), pp. 764-777

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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



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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)


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”.



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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**


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).



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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.



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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’.



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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.



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