Measuring Rural Bank Efficiency in Ghana: An Application of the Data Envelopment Analysis (DEA) Approach

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Journal of Human and Social Science Research Vol. 3, No. 2 (2014), 41-59

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Keywords: Rural and Community Banks Technical Efficiency Data Envelopment Analysis Ghana

Received: 02-22-2014 Accepted: 05-08-2014

Abdulai Iddrisu MPhil, Department of Finance and Banking, University of Ghana Business School, University of Ghana, Legon. Tel:+233 24 3220084 /233 266 814570. Email: [email protected] Professor Anthony Q. Q. Aboagye Head, Department of Finance and Banking, University of Ghana Business School, University of Ghana, Legon. Email: [email protected] Professor Kofi Osei Senior Lecturer, Department of Finance and Banking, University of Ghana Business School, University of Ghana, Legon. Email: [email protected]

Measuring Rural Bank Efficiency in Ghana: An Application of the Data

Envelopment Analysis (DEA) Approach Corresponding Author: Abdulai Iddrisu MPhil, Department of Finance and Banking, University of Ghana Business School, University of Ghana, Legon.

Abstract

The main objective of this study is to investigate whether the establishment of the ARB

Apex Bank which started business in 2002 has helped improve the production efficiency of

the Rural and Community Banks (RCBs) in Ghana. The study is based on published data

gathered from the annual statements of 40 RCBs covering the period of 2000 to 2010. The

study applied the Data Envelopment Analysis (DEA) methodology, using the

input-oriented and the Charnes, Coopers and Rhodes (CCR) Model to measure the

technical efficiency of the Ghanaian RCBs. The intermediation approach was applied to

select input and output variables. Consequently, labour cost, capital cost, deposit expense

and shareholders equity were used as input variables while loans and advances, investments and income from non-interest earning assets were employed as output

variables. Using the Efficiency Management System (EMS) software, the efficiency scores

were calculated for the study period of 2000 to 2010. Thereafter, the scores were compared

for pre and post establishment of Apex Bank efficiency estimates using 2002 as a cut-off

year. The results show that two (2) RCBs were relatively efficient throughout the study

period with an average efficiency score of 1 or 100% while the yearly average efficiency of

all RCBs for the period varied from 0.734 to 0.920. The findings also indicate that

efficiency of the RCBs has significantly improved after the establishment of the ARB Apex

bank in 2002. Eight RCBs were identified as the best practice banks in the industry. The

policy implication is for the Bank of Ghana to continue to encourage the programmes being

implemented by Apex Bank since it could help these banks improve efficiency further over

a period of time. Also, the management of RCBs should reduce expenditure on their input

resources in order to make cost savings given the amount of outputs produced. The results

show that expenditure on inputs can be reduced to the tune of 14.5% at the current output

level.

1. INTRODUCTION The need for efficiency in Rural and Community Banks (henceforth

referred to as RCBs) in providing financial services for the growth and

development of a predominantly agro-based economy like Ghana cannot be

over emphasized. The survival and longterm viability of RCBs is linked to

the levels of efficiency with which they operate (Ellinger, (1994). Since

their inception in the mid-1970s, there are over 135 RCBs across the 10

regions of Ghana and this constitutes about half the total banking outlets in

the country (IFAD, 2008). They were established to provide both micro

finance and commercial banking services structured to fulfill the banking

needs of the rural communities in which they operate. Therefore the

efficiency with which these banks operate goes a long way to affect the

economic development of the rural communities and the national economy

at large.

42 | Measuring Rural Bank Efficiency in Ghana: An Application of the Data Envelopment Analysis (DEA) Approach

Journal of Human and Social Science Research

This is because, an efficient financial sector is a pre-condition for the rapid economic development and

growth of any country (King and Levine, 1993). To support an efficient rural banking sector, the

Government of Ghana in conjunction with African Development Bank (AfDB), the World Bank and the

International Fund for Agricultural Development (IFAD), under the Rural Financial Services Project (RFSP)

have initiated series of policy interventions resulting into the establishment of the ARB Apex Bank in the

year 2000. The focus of this study is the establishment of the ARB Apex bank as a major intervention aimed

at improving the operational efficiency of RCBs in the country.

Apex bank was established with the mission to provide banking and non-banking support services to RCBs

with the aim to improving their operational efficiency and thereby transforming them into efficient and

credible financial institutions which can effectively address the banking needs of the rural communities. The

bank also has the vision to promote accelerated development of the rural economy by the provision of

cost-effective information and communication technology- based banking, through RCBs and micro finance

institutions (MFIs).

Apex bank is therefore mandated to serve as a mini Central Bank for RCBs to assist and supervise existing

and new rural banks to develop into efficient, viable, sustainable and integrated financial systems through

the pooling of their resources. The bank took over the daily, monitoring, supervision and evaluation of the

RCBs with the Bank of Ghana now playing an indirect supervisory function through it. Among the key

functions of the bank are; cheque clearing, fund management, training of directors and managers of RCB

staff as well as monitoring, inspection, examination and supervision of rural and community banks in

accordance with relevant rules, regulations, and policies. It provides training and capacity building support

services to the different categories of staff of RCBs in order to improve the professionalization of rural and

community banks and to help turn them into financially solid institutions capable of offering a wider array

of products, particularly savings products (IFAD, 2008). The question that remain is whether or not its

establishment in year 2000 has impacted positively on the efficiency of the rural banking sector.

Despite the important role Apex bank and RCBs play in the economy, they have not been the subject of

academic studies. Studies by (Aboagye et al, 2009; Ziorklui et al, 2001; Frimpong, 2010; and Korsah et al,

2001) have examined various issues relating to efficiency of Ghanaian banks but they have largely focus on

the universal banking sector. A good number of other studies or reports such as (Nair and Fissha, 2010) have

been conducted on the performance of RCBs, but none of these studies has been subjected to rigorous

empirical investigation like the DEA.

Investigating the level efficiency of RCBs will help identify sources of inefficiencies and to enable the Apex

bank, RCBs, the Bank of Ghana and all stakeholders to take a fresh look at their operations and initiate

policies and programmes to strengthen them.

This study is set out to measure the relative efficiency of RCBs and to determine whether the establishment

of the ARB Apex bank has helped improve the efficiency of these banks using the DEA methodology.

1.1 An Overview of the Rural Banking Sector in Ghana

Rural and Community Banks are unit banks owned and managed by the residents of the rural communities

in which they operate. They are incorporated under the companies code of Ghana 1963 (Act 179) and

licensed under the Banking law 1989 (PNDC Law 225). The key characteristics of RCBs include: 1. they

operate in restricted geographical area, 2. they belong to the people in the community and management and

control are vested in them and finally because they are unit banks, they are only allowed to open agencies

and not branches (Anin, 2000).

Abdulai Iddrisu, Anthony Q. Q. Aboagye, Kofi Osei| 43

ORIC Publications/2014

The idea of rural banking was conceived 32 years ago by the Bank of Ghana when it opened a dialogue with

the Ministry of Finance about what was called junior league of banking institutions to serve the

special needs of the rural population in Ghana (Aseidu-Mante, 2011).

Since then, the number of RCBs expanded rapidly in the early 1980s in response to the demand for rural

banking services created by the governments introduction of a special cheque system instead of cash

payment to cocoa farmers. The number of rural outlets of commercial banks was woefully inadequate and

could not meet the demand to cash these cheques, let alone provide other banking services. This created

undue hardships on farmers who often had to travel long distances or spend days at the bank to cash their

cheques. More RCBs and agencies were, therefore, hurriedly opened to help provide banking services in

areas where without banking facilities were lacking (Steel and Andah, 2004).

By 1984, RCBs had increased to 106 from 20 in 1980. In 1998, the number had increased to 134 and later

dropped to 121 in 1999 when 23 RCBs were declared bankrupt and subsequently closed down. In 2008,

RCBs numbered 127 and this increased to 135 as at 2010. Currently the total number of rural banks in

Ghana as at March 2012 stands at 140. Two banks are however distressed and are targets for a possibly

closed down.

Available data indicated that as at 2008, RCBs recorded a total deposits of GHc 349.3 million (US $265.1

million) from more than 2.8 million clients, and loans and advances of GHc 224.7 million (US $173.2

million) with about 680,000 clients (Nair and Fissha2010). This is an indication of a tremendous growth in

the rural banking sector since their inception.

Ghana has a population of about 24.6 million which has been growing at about 2.4% per year (2010 census

estimates). Recent statistics (2010 Population census) indicate that about 49% of the population lives in rural

areas and 51% in urban areas.

Rural and Community Banks are therefore important in the socio-economic development of Ghana, as

over 48% of the population live in rural areas. Majority of Ghanaians are engaged in agricultural activities,

which contribute over 39% of the country's GDP and over 60% of employment.

RCBs and their agencies represent about 5% of the total banking assets and account for about half (584

outlets as at 2008) of the total banking outlets in the country. Thus, institutions such as RCBs and Micro

finance service providers play an important role in addressing the problem of access to financial services in

these areas.

IFAD (2008), imply that 60% of the money supply is outside the commercial banking system. RCBs

together with savings and loans companies, semi-formal and informal financial systems play

important role in the mobilization of rural financial resources for their development. Steel and Andah

(2004), added that RCBs could help improve Ghanas private sector development and poverty reduction

strategies. RCBs also support development projects and provide employment opportunities in the rural areas

(Adu-Amoah et al, 2008). Due to all the predefined purpose

of RCBs in the socio-economic development of Ghana especially the rural communities, there should be a

commensurate attention to this sector of the economy in order to ensure the realization of these anticipated

benefits. RCBs have contributed to improvements in banking culture, access to financial services and

household consumption patterns in rural Ghana (Gallardo, 2002). Efficiency in this sector would help

improve the development of the entire financial system as well as the economic development of Ghana.

With the rapid growth of the rural banking sector, the ARB Apex Bank was set up to focus on assisting and

supervising the sector to achieve efficiency since the Bank of Ghana could not cope with the size of the

sector due to logistical and personnel constraints.



2. LITERATURE REVIEW

Coelli et al, (2005), identified four methods of measuring efficiency. They include; the Econometric

Estimation of Average Response, Index Numbers (Total Factor Productivity indices), the Stochastic Frontier

Analysis (SFA) and Data Envelopment Analysis (DEA). The Econometric estimation of average response

method and the Stochastic Frontier Analysis (SFA) are classified into econometric estimation of parametric

functions which requires econometric assumptions on the shape or parameters of the underlying production

function. The accuracy of the estimated technical efficiency is sensitive to the nature of the functional form

specified (Frimpong, 2010). Data Envelopment Analysis (DEA) and the TFP are classified as

non-parametric approaches that do not require assumptions on the form of the production function.

In this study, the non-parametric technique of DEA approach is applied. This approach is applied ahead of

other methods because it is comparatively robust (Seiford and Thrall, 1990). It also allows for the use of

multiple inputs and output variables in efficiency estimation and do not require econometric assumptions on

the shape and form of the production function. Emrouzneijad et al, (2008) also argues that measuring

efficiency of organisations, involving complex multiple input and output structure, is not a simple exercise.

In their view, the DEA approach by design, naturally accounts for these multi input-output issues more

efficiently and effectively.

DEA is an alternative analytic technique to regression analysis. Regression analysis approach is

characterized as a central tendency approach and it evaluates DMUs relative to an average. In contrast, DEA

is an extreme point method and compares each DMU with the only best DMU. The main advantage of DEA

is that, unlike regression analysis, it does not require an assumption of a functional form relating inputs to

outputs. Instead, it constructs the best production function solely on the basis of observed data; hence

statistical tests for significance of the parameters are not necessary (Chansarn, 2008).

The main idea behind DEA approach is that if a producer is able to produce goods and services with given

inputs, then other producers with similar features should be able to do same if they operate efficiently.

There are many studies on efficiency of banks the world over, but only a few have looked at the efficiency

of rural banks. Key studies on rural banks are discussed as follows.

Khankhoje and Sathye, (2008), investigated whether the restructuring of Regional Rural Banks in India

undertaken in 1993-94 has helped improve efficiency of these banks since they are an important arm of the

rural credit system. It was found that efficiency had significantly improved after the restructuring exercise and

that the policy to restructure the rural banks had shown positive results.

Mohindra and Kaur, (2011) sought to examine the relative efficiency of Indian Regional Rural Banks (RRBs)

during the post reform period spanning from 1991-92 to 2006-07. The non-parametric technique of data

envelopment analysis (DEA) was applied. The results showed that over the period from 1992 to 2007,

regional rural banks experienced technical efficiency to the tune of 78%. Thus the banks can on an average

decrease their inputs by 22% and still can produce the same level of output. The comparative analysis of the

average efficiency scores of 50 regional rural banks between the pre and post reform periods show that the

degree of input waste was 24% in first-generation reforms period, declined to 20% in second-generation

reforms period.



Ishwara, (2011) looked at the performance of the Regional Rural Banks (RRBs) in India from 1980 to 2009.

In order to know the implications of the transformation of RRBs in 2004, the study focused on financial

results before and after amalgamation using descriptive statistics. The study revealed that, RRBs seem to

have a better Non-Performing Assets (NPA) management with net NPA coming down every year after the

amalgamation.

Ibrahim, (2010) did a study to investigate whether the merger/amalgamation of Rural Banks in India,

undertaken in 2005-06 has helped improve their performance. The study was diagnostic and exploratory in

nature and made use of secondary data. The study found and concluded that performance of the banks

significantly improved after the amalgamation exercise by the Government of India.

In Ghana, various studies by (Aboagye et al, 2009; Ziorklui et al, 2001; Frimpong, 2010; Buchs and

Mathisen, 2005; and Korsah et al, 2001) have been conducted on bank efficiency but none of these studies

examined the efficiency of the rural and community banking sector.

To the best of our knowledge, the efficiency of Rural and Community Banks with the non-parametric

technique of DEA is being studied for the first time in Ghana.

3. DATA AND METHODOLOGY

This study was based on available published data covering the period 2000 to 2010, compiled from annual

financial statements of 40 RCBs obtained from ARB Apex Bank and Bank of Ghana. This gives a balanced

panel data of 440 observations. All the banks selected are coded as B1, B2and B40. A list of the sampled

banks is attached as appendix 1.

Following the works of (Khankhoje and Sathye, 2008 and Mohindra and Kaur, 2011) the study divides the

entire study period viz; pre-establishment of Apex Bank period (2000 - 2002) and the post- establishment

period (2003 to 2010). The year 2002 is taken as the cut-off year to compare pre and post establishment

efficiency scores.

Basically, the DEA has two models namely; Charnes, Coopers and Rhodes (CCR henceforth for short)

model and the Bankers, Charnes and Coopers (BCC henceforth for short) model.

The CCR model originated by Charnes et al, (1978), assumes Constant Returns to Scale (CRS) which means

one unit input can get a fixed value of output. The BCC model which is an extension of the CCR model

assumes Variable Returns to Scale (VRS). This study utilized the CCR-Model for our efficiency

measurement. The CCR-Model was selected because the study assumes Constant Return to Scale (CRS)

and the input-orientated approach, both of which seeks to minimize the use of inputs to produce a given

fixed output. As regards services such as rural banking, output is linked to local demand and for this reason,

cost savings appears to be a rational managerial objective (Erbetta and Rapouli, 2003). Cost savings means

the use of minimum inputs rather than concentrating on output which cannot go beyond their restricted

geographical area. The Input-oriented projection model is therefore adopted to minimize the use of inputs.

Input-orientation and CRS are associated with the CCR-Model. This means that all the units under analysis

are assumed to be performing at an optimal scale.

The first step in measuring efficiency is to specify inputs and outputs of the firms under consideration. The

intermediation approach was used to select the input and output variables. With this approach, financial

institutions are viewed as intermediating funds between savers and investors. Banks produce intermediation



services through the collection of deposits and other liabilities and their application in interest-earning assets.

Under this approach, capital and labor are used to intermediate deposits into loans and other earning assets

(Yudistira, 2004). By adopting the intermediation approach, loans, investments and income from

non-interest earning assets are used as output variables whiles deposits expense, capital cost, labour cost and

shareholders equity are used as input variables. The variables are clearly stated as follows;

Inputs

i) Labour cost

ii) Capital cost

iii) Deposit expense

iv) Shareholders equity

Outputs

i) Loans and Advances

ii) Investments

iii) Income from non-interest earning assets

Due to lack of data on the actual number of employees in each bank as well as specific values on staff cost,

labour input cost is represented by operational expenses which contains personnel expenses and other

non-interest operational cost.

Fixed assets are used as a proxy to capital cost. In this study, fixed assets are referred to as capital assets

which are recorded and reported at the book value of premises and other fixed assets. The cost of capital

assets is used as the capital cost.

For the purpose of this study, deposits are used as a proxy to deposit expense due to challenges of data.

Deposits are used because banks produce intermediation services through the collection of deposits and

other liabilities and their application in interest-earning assets. The use of total deposits in place of deposit

expense as an input variable has also been widely supported in the literature. Few examples of resent studies

that apply total deposits as an input variable under the intermediation approach are (Jayamaha and Mula,

2011; Tahir and Yosuf, 2011; Akhtar et al, 2010; Charnsan, 2008; Yudistira, 2004; Nigmonov, 2010; and

Aboagye et al, 2009).

Shareholders equity is the amount of money invested by shareholders of the rural and community banks.

This provides banks with the primary resource with which they are expected to generate interest earning

assets.

Under this study, output is defined as total earning assets which are made up of loans and advances,

investments in Government securities and non-interest earning assets. Loans & advances together with

investments in Government securities generate Interest-income which forms the bulk of the banks revenue.

Therefore interest-income is represented by loans and advances and investments as output variables. The

investments used in this study are investments in interest earning assets such as government securities.

Income from non-interest earning assets includes fee and commission income and covers diversified range

of services offered by the banks.



3.1 The DEA Model

DEA is a linear programming technique initially developed by Charnes et al, (1978), to evaluate the

efficiency of public sector non-profit organizations. It was later extended by Banker, Charnes, and Cooper

(1984); DEA calculates the relative efficiency scores of various Decision-Making Units (DMUs) in the

particular sample. The DMUs could be banks or branches of banks. The DEA measure compares each DMU

(RCBs) to the best practice bank within the sample. It tells the user which of the DMUs in the sample are

efficient and which are not. The ability of the DEA to identify possible peers or role models as well as

simple efficiency scores gives it an edge over other methods (Khankhoje et al, 2008). As an efficient

frontier technique, DEA identifies the inefficiency in a particular DMU by comparing it to similar DMUs

regarded as efficient, rather than trying to associate a DMUs performance with statistical averages that may

not be applicable to that DMU (Khankhoje et al, 2008).

Consider, N units (each is called a Decision Making Unit, DMU) that convert J inputs into I outputs, where I

can be larger, equal or smaller than J. To measure efficiency of this converting process for a DMU, Charnes

et al.(1978) propose the use of the maximum of a ratio of weighted outputs to weighted inputs for that unit,

subject to the condition that similar ratios for all other DMUs be less than or equal to one. The efficiency

scores are maximized by solving for the input output weights. Note that DEA is a very flexible methodology

because it does not impose the weights of inputs and outputs: they are calculated solving a mathematical

programming problem and it is not necessary to know them a priori. Consequently, efficiency can be defined

as follows:

Suppose that the N DMUs, each has n number of inputs and m number of outputs, relative efficiency score

of a given DMU is obtained by solving the following linear programming model.

Max For i=1, 2., m, j=1, 2., .n (1)

Subject to For r=1 N (2)

0,

0:

Where isy is the amount of the ith output, produced by the sth bank, jsx is the amount of the jth input used

by the sth bank, iu is the output weight, and jv is the input weight.

jrx = the amount of input j utilized by the rth DMU

iry = the amount of output i produced by the rth DMU

1

,

1

m

i is

is n

j js

j

u y

e

v x

m

1

1

1i ir

i

n

j jr

j

u y

v x

iu jv



iu = weight given to output i of the rth DMU

jv = weight given to input j of the rth DMU

Following the work of Charnes et al. (1978) this fractional linear programme can be transformed in to an

ordinary linear programming problem as follows;

Maximize se = m

i is

n

u y (3)

Subject to ,1

n

j js

j

v x

= 1 (4)

1

m

i ir

i

u y

- 1

0n

j jr

j

v x

(5)

iu , jv 0

j =1n; i =1m; r =1N.

The variable definitions in problems (1) and (2) are the same as that of problems (3), (4) and (5).

4.0 DISCUSSION OF THE RESULTS

4.1 Correlation of input-output variables

The degree of correlation between inputs and outputs is an important issue that has great impact on the

robustness of the DEA model (Yang, 2009).

On the one hand, if very high correlations are found between an input variable and any other input variable

(or between an output variable and any of the other output variables), this input or output variable may be

thought of as a proxy of the other variables. Therefore, this input (or output) could be excluded from the

model. On the other hand, if an input variable has very low correlation with all the output variables (or an

output variable has very low correlation with all the input variables), it may indicate that this variable does

not fit the model (Yang, 2009).

This means that there should be a low correlation among the set of input variables and the set of output

variables. However, there should be high correlation between the input and output variables. One input

variable should be highly correlated with at least one output variable or one output variable having a high

correlation with at least one input variable. Correlation analyses were conducted for each pair of variables

and the detail results is presented in Table 1 below.



Table 1: Correlation coefficients between the input output variable

Labour

cost

Capital

cost

Deposits

exp.

Shareholder

equity

Non int.

income

Loans Investments

Labour cost 1

Capital cost 0.4648 1

0.0000

Deposits exp. 0.3759 0.6408 1

0.0000 0.0000

Holder equity 0.2116 0.4888 0.3524 1

0.0000 0.0000 0.0000

Non int. income 0.5048 0.6252 0.3749 0.1897 1

0.0000 0.0000 0.0000 0.0001

Loans & advances 0.5954 0.7232 0.9176 0.6609 0.2375 1

0.0000 0.0000 0.0000 0.0000 0.0000

Investments 0.6555 0.6303 0.8616 0.5769 0.1113 0.6835 1

0.0000 0.0000 0.0000 0.0000 0.0196 0.0158

In the table above, there is no evidence of very high correlation between any one input variable and any other or a very high correlation among the output variables. Similarly there is no evidence of any one input

variable having very low correlation with all the output variables or between an output variable having very

low correlation across all the input variables. This is a reasonable validation of DEA model used in this

study.

4.2 Overall Technical Efficiency for each Bank in the entire Study Period

Table 2 below provides us with the nature of the overall technical efficiency (OTE) of the sampled RCBs.

The estimated time varying efficiency scores of all the 40 RCBs with the yearly and bank-wise averages are

reported for the entire period from 2000-2010.

The results have been obtained through the running of the CCR model (separately for each year). Note that

any bank that has efficiency score of one (1or 100%) is defined as efficient and a score of less than 1 is

regarded as inefficient. The empirical findings in Table 2 reported that two banks, B25 and B32 (Bonzali

Rural Bank and Kintampo Rural Bank respectively) both obtained full technical efficiency (efficiency

equaling unity) throughout the 11 years period from 2000 to 2010 indicating that they had operated on the

efficiency frontier throughout the years. This means that these two banks are able to minimize expenditure

on their input bundles namely, labour cost, capital cost, deposit expense and shareholders equity to produce

the given levels of outputs.

A further look at the table also indicates that B1, B7, B15, B22, B24, B26, B27, B33, B3 and B5 have

obtained full technical efficiency (with a score of 1) in most years in the study period. For instance, with the

exception of 2001, B26 and B7 recorded full technical efficiency in all the years with a score of 1 or 100%.

These two banks (B26 and B7) recorded full technical efficiency in ten out of the 11 years. B22 was

technically efficient in most of the years namely; 2002, 2003, 2004, 2007, 2007, 2008, 2009 and 2010.

That is the bank obtained an efficiency score of 100% in 8 out of 11 years. Similarly, B3 and B33 recorded

technical efficiency in seven out of the 11 years while B24 and B15 were technically efficient in 6 out the 11

years. B12, B4 and B36 obtained full efficiency in five out of the eleven years. This implies that these banks,

particularly B7, B26, and B22 have operated efficiently with the exception of one or two years in the earlier

period. The scores of inefficient rural banks show a discrepancy on the year-by-year basis but seemed to be

upward in favour of improvement in efficiency.



The average efficiency scores from 2000 to 2010 ranges from 0.734 to 0.920. The mean efficiency in 2000

was 87.5%. This was reduced to 73.4% and 77.1% in 2001 and 2002 respectively. The average further

increased to as much as 92% in 2005 from which it came down to 86.8% and 86.1% in 2008 and 2009

respectively. Finally, the overall mean efficiency went up to 88.2% in 2010. This means the average

efficiency performance over the period under investigation has been fluctuating.

Still on Table 2, the average efficiency in 2000 was 0.875 and the total number of banks which were fully

efficient in the year 2000 with efficiency of 100% was 17 rural banks. Examples of the less efficient banks

in 2000 are; B2, B6, B9, B11, B18. These banks can curtail expenditure on their inputs and still be able to

produce the same output. For instance B18 recorded an efficiency score of 73.6% meaning that the bank has

an input waste to the tune of 26.4%. This implies that management can reduce expenditure on inputs by

26.4% and still be able to produce the same output. This analogy applies to all the years under investigation.

The average efficiency in 2001 decreased to 0.734 from 0.875. The number of fully efficient banks was 8

RCBS. Inefficient banks include; B2, B3 B4, B5 and so on. The average efficiency in 2002 increased to

0.771 with 18 efficient banks. Year 2005 recorded the highest number of banks operating on the efficient

frontier. A total of 20 RCBs were captured on the efficiency frontier with an average efficiency score of 0.92.

However, the average decreased to 0.868 and 0.861 in 2007 and 2008 respectively, while the number of

efficient banks reduced to 15 in 2007 and further to 13 rural banks in 2008. The number of efficient RCBs

went up to 16 RCBs in 2009 and further to 19 rural banks in 2010. On average, though most of the rural

banks operate less efficiently, their performance in terms of efficiency has been improving.

4.3 Inter Bank Comparison of Efficiency Scores

Table 3 below presents the average technical efficiency (TE) scores for each bank in Pre-Apex bank period,

Post-Apex bank period and the entire period. This table enables us to study the pattern or trend of efficiency

between the pre-Apex bank establishment period and the post-Apex bank period.

It can be seen from the empirical findings that two banks viz; B25 and B32 (Bonzali Rural Bank and

Kintampo Rural Bank respectively) have both maintained mean efficiency of 100% in the two distinct

periods. This implies that before the introduction of Apex Bank, these two banks have been engaged in

efficient utilization of their input resources to generate output.

32 RCBs namely; B1, B2, B3, B4, B5, B6, B7, B9, B13, B14, B15, B17, B18, B20, B21, B26, B28, B29,

B30, B31, B33, B34, B35, B36, B37, B38 and B40 have experienced an improvement or increase in

technical efficiency from the pre-Apex years to post-Apex years. For instance, the average technical

efficiency (ATE) of B1 has improved from 0.91 to 0.95 in the pre and post Apex years respectively.

Similarly, the ATE of B6 moved from 0.74 in the pre-Apex bank period to 0.82 in the post-Apex period and

that of B22 increased from 0.84 in the pre period to 0.95 in the post period. In other words, technical

inefficiencies defined by Mohindra and Kaur, (2011) as [OTIE (%) = (1- OTE) * 100] of B1, B6 and B22

have decreased by 4.4%, 10.8% and 13.1% respectively. This implies that 80% of the sampled banks have

recorded an improvements in the technical efficiencies from the pre period to the post Apex period. This

confirms the findings of Mohindra and Kaur, (2011) which found 34 out of 50 (about 70%) sampled banks

experiencing improvements in technical efficiency in the second generation reforms of Indian regional rural

banks.



Four (4) RCBs namely; B19, B25, B32 and B27 remained unchanged in terms of mean efficiency scores

during the two periods. The average efficiency score of the pre period is the same as that of the post period

mean. This is illustrated in column 5 of table 3. This also implies that these banks have been engaged in the

same practices over the years.

With the exception of the 36 banks above, the remaining banks namely; B8, B12, B16 and 39 have

registered reduction in technical efficiency from the pre-Apex Bank period to post-Apex years. This

Table 2: Efficiency Scores for all RCBs for the entire Period

Bank

Code 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Mean

B1 1.000 1.000 0.731 0.808 1.000 1.000 1.000 1.000 1.000 1.000 0.786 0.940

B2 0.782 0.673 0.744 0.742 0.750 0.858 0.869 0.816 0.859 0.771 1.000 0.810

B3 1.000 0.671 0.690 1.000 1.000 0.920 0.902 1.000 1.000 1.000 1.000 0.920

B4 1.000 0.803 0.619 1.000 0.744 1.000 0.745 0.800 0.862 1.000 1.000 0.870

B5 1.000 0.608 0.899 1.000 1.000 1.000 1.000 1.000 1.000 0.701 0.698 0.900

B6 0.804 0.629 0.773 1.000 0.752 0.812 1.000 0.811 0.811 0.727 0.757 0.820

B7 1.000 0.619 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.970

B8 1.000 0.699 1.000 0.874 1.000 1.000 0.897 0.795 0.800 0.697 0.881 0.890

B9 0.803 0.604 0.833 1.000 1.000 1.000 0.934 0.917 0.911 0.835 0.926 0.890

B10 1.000 0.711 0.836 1.000 0.853 1.000 1.000 0.973 0.763 0.781 0.772 0.880

B11 0.698 0.699 0.652 0.734 0.753 0.788 0.834 0.886 1.000 1.000 1.000 0.820

B12 1.000 1.000 0.640 0.742 1.000 1.000 0.807 0.790 0.632 0.790 1.000 0.860

B13 0.960 0.682 0.765 1.000 0.981 0.979 1.000 0.858 0.763 0.750 0.769 0.880

B14 0.833 0.671 0.655 0.781 0.791 0.791 0.743 1.000 0.851 1.000 1.000 0.810

B15 1.000 0.669 0.813 0.871 1.000 1.000 1.000 1.000 0.849 0.789 1.000 0.910

B16 1.000 0.638 0.622 0.728 0.857 1.000 0.681 0.719 0.728 0.643 0.633 0.750

B17 0.830 0.705 0.678 0.794 0.770 0.865 0.784 0.767 0.804 1.000 1.000 0.820

B18 0.736 1.000 0.658 1.000 0.852 1.000 0.773 0.705 1.000 0.741 0.672 0.840

B19 1.000 0.489 0.758 0.796 0.861 0.722 0.750 0.716 0.752 0.705 0.666 0.750

B20 0.660 1.000 0.625 0.777 0.839 0.897 0.798 0.747 0.825 0.851 1.000 0.820

B21 0.741 0.594 0.753 0.889 0.916 0.755 0.851 0.740 0.618 0.647 0.720 0.760

B22 0.748 0.775 1.000 1.000 1.000 0.956 1.000 1.000 1.000 1.000 1.000 0.950

B23 1.000 0.657 0.649 0.791 0.749 0.788 0.654 0.709 0.863 1.000 1.000 0.800

B24 0.728 0.604 1.000 1.000 1.000 0.785 1.000 0.869 0.906 1.000 1.000 0.910

B25 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

B26 1.000 0.781 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.980

B27 1.000 1.000 0.717 0.818 0.844 1.000 0.885 0.859 1.000 0.895 0.917 0.910

B28 0.746 0.686 0.671 0.860 1.000 0.894 0.823 0.769 0.756 0.802 0.863 0.810

B29 0.831 0.601 0.870 0.849 1.000 0.854 0.830 0.806 0.877 0.773 0.824 0.830

B30 0.693 0.769 0.710 0.820 0.927 1.000 0.791 0.849 0.773 0.829 0.879 0.820

B31 0.805 0.675 0.581 1.000 0.709 1.000 0.545 1.000 0.665 0.603 0.615 0.750

B32 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

B33 0.712 0.699 0.815 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.736 0.910

B34 0.699 0.655 0.634 0.806 0.757 0.849 0.693 0.757 0.801 0.803 0.836 0.750

B35 0.765 0.570 1.000 1.000 1.000 1.000 0.709 0.708 0.704 0.81 0.960 0.840

B36 1.000 0.688 0.652 0.725 0.740 0.813 1.000 0.816 1.000 1.000 1.000 0.870

B37 0.735 0.712 0.696 0.723 0.752 0.719 0.806 0.810 0.750 0.666 0.739 0.740

B38 0.764 0.585 0.741 0.902 0.773 1.000 0.652 0.662 0.709 0.593 0.616 0.730

B39 1.000 1.000 0.691 1.000 1.000 0.751 0.802 0.774 0.813 0.844 1.000 0.880

B40 0.913 0.725 0.659 0.876 0.828 1.000 0.717 0.768 1.000 1.000 1.000 0.860

Mean

0.875

0.734

0.771

0.893

0.895

0.920

0.857

0.868

0.861

0.851

0.882



constitutes only 10% of the sample banks. In other words, technical inefficiency has increased in the

post-establishment period. For instance, B8 recorded an average pre period efficiency of 0.90 and the post

efficiency score of 0.88, a decrease in efficiency by 0.02. This also implies an increase in inefficiency of

10% from the pre period to 12% in the post Apex period. Similarly, B12 recorded an efficiency score of 0.88

and 0.86 in the pre and post period respectively. This gives an inefficiency of 12% in the pre Apex period

and an increase in technical inefficiency of 14% in the post period. Note the formulae of technical

inefficiency by Mohindra and Kaur, (2011).

The story here is that banks B8, B12, B16 and B39 constituting 10 % are getting worse in input waste over

the years. In other words they continue to employ more inputs which do not actually translate into the

required outputs. This notwithstanding, 80% of the banks are experiencing improvement in technical

efficiency from the pre-Apex Bank period to the post-Apex years.

4.4 Pattern of Efficiency Scores for Post and Pre Periods

Tables 4 and 5 below enable us to assess the impact of the establishment of ARB Apex bank on the technical

efficiency of RCBs in Ghana. In this section the entire period is divided into pre-Apex Bank period spanning

from 2000 to 2002 and post- Apex Bank period from 2003 to 2010.

Table 4 presents the overall technical efficiency (OTE) scores with standard deviation of the RCBs of the

pre-period from 2000 to 2002, the post period of 2003 to 2010 and the entire period of 2000 to 2010. The

empirical findings from the Table reported that the average overall technical efficiency (OTE) for the entire

period from 2000 to 2010 turned out to be 0.855 with a standard deviation of 0.053. This means that the

overall technical inefficiency in the rural banking industry within the period of study is 14.5%. This is

obtained using the definitions of technical inefficiency by Mohindra and Kaur, (2011) which is [OTIE (%) =

(1 OTE) * 100]. This implies that the rural banks can curtail their input expenditures on labour cost,

capital cost, deposit expense and shareholders equity by adopting best practices. In other words the input

resources can be reduced by 14.5% and still be able to produce at the same level of output.

The comparative analysis of the average OTE scores between the distinct periods (Pre -Apex Bank period

and Post- Apex Bank period) show that the mean efficiency has increased in the post Apex bank years as

shown in Table 4. Mean efficiency increased from 0.793 with a standard deviation of 0.060 in the pre-Apex

period to 0.878 with a standard deviation of 0.022 in the post-Apex period. These results imply that the

degree of input waste was 20.7% in the pre- Apex years and this declined to 12.2% in the post-Apex bank

period. In other words, technical inefficiency has decreased after the establishment of Apex bank in 2000.

In order to test the hypothesis whether the establishment of ARB Apex bank in 2000 has helped to improve

technical efficiency of RCBs in Ghana, the study applied ANOVA to determine whether the post-Apex

mean is significantly different from the pre-Apex mean efficiency. The ANOVA test results are shown in

Table 5.

The results from Tables 4 and 5 shows enough evidence to suggest that the mean efficiency after the

introduction of Apex Bank significantly differs from the mean efficiency of the RCBs before the

introduction of Apex bank. In other words, there is a significant difference between the pre-Apex period

mean and the post Apex period mean. The standard ANOVA statistics are significant at 5% with probability

value of 0.009.



Table 3: Average TE Efficiency Scores of RCBs on Individual Basis for Different Periods

DMU Code Pre period Mean Post period Mean Overall Mean Change

B1 0.91 0.95 0.94

B2 0.73 0.84 0.81

B3 0.79 0.97 0.92

B4 0.81 0.89 0.87

B5 0.84 0.92 0.90

B6 0.74 0.86 0.82

B7 0.87 1.00 0.97

B8 0.90 0.88 0.89

B9 0.75 0.94 0.89

B10 0.85 0.90 0.88

B11 0.68 0.87 0.82

B12 0.88 0.85 0.86

B13 0.80 0.91 0.88

B14 0.72 0.84 0.81

B15 0.83 0.94 0.91

B16 0.75 0.74 0.75

B17 0.74 0.85 0.82

B18 0.80 0.85 0.84

B19 0.75 0.75 0.75

B20 0.76 0.85 0.82

B21 0.70 0.78 0.76

B22 0.84 0.99 0.95

B23 0.77 0.81 0.80

B24 0.78 0.96 0.91

B25 1.00 1.00 1.00

B26 0.93 1.00 0.98

B27 0.91 0.91 0.91

B28 0.70 0.85 0.81

B29 0.77 0.85 0.83

B30 0.72 0.85 0.82

B31 0.69 0.77 0.75

B32 1.00 1.00 1.00

B33 0.74 0.97 0.91

B34 0.66 0.78 0.75

B35 0.78 0.86 0.84

B36 0.78 0.91 0.87

B37 0.71 0.75 0.74

B38 0.70 0.74 0.73

B39 0.90 0.88 0.88

B40 0.77 0.89 0.86



This result supports the findings of Khankhoje and Sathye, (2008) which found that efficiency of Indian

regional rural banks had significantly improved after restructuring of the rural banking sector in India in

1993/94. The study also confirms the findings of Monhindra and Kaur, (2011) which found that technical

efficiency of rural banks in India improved in the post reform period spanning from 1991-92 to 2006-07.

From the table above, TE denotes technical efficiency. SD denotes standard deviation. 2000 to 2002 is the

Pre-Apex Bank period and 2003 to 2010 is the Post-Apex Bank period. 2000 to 2010 denotes the entire of

study period.

Table 5: ANOVA test results for mean difference

Table 4: Pattern of Overall Technical Efficiency Scores of RCBs in Ghana.

Year ATE

2000

0.875

2001 0.734

2002 0.771

2003 0.893

2004 0.895

2005 0.920

2006 0.857

2007 0.868

2008 0.861

2009 0.851

2010 0.882

2000 -2002 (Pre Period mean)

Average

0.793

SD 0.060

2003 - 2010 (Post Period mean)

Average

0.878

SD 0.022

2000 -2010 ( Entire Period)

Average

0.855

SD 0.053



4.5 Bank-Wise Pattern of Efficiency Analysis for Best Practice Banks

Table 6 presents the interbank average efficiency scores and depicts the best practice banks in each year.

Khankholeje and Sathye, (2008) posits that knowing which efficient banks are most comparable to the

inefficient banks enable analysts to develop an understanding of the nature of inefficiencies and re-allocate

scarce resources to improve productivity. This feature of DEA is a clear and useful decision making tool

in benchmarking. As mentioned earlier, banks with efficiency score of one is considered fully efficient and

those with less than one are less efficient.

Following the work of Mohindra and Kaur, (2011), this study identifies best practice banks by the number of

times banks operate on the efficiency frontier throughout the period from 2000 to 2010. It can be seen from

the table that B25 and B32 appear 11 times (100% appearance) throughout the period. This means that these

two banks are captured 11/11 times on the efficiency frontier, making them the banks which dominated on

the efficiency frontier throughout the period of study.

B7 and B26 are next to B25 and B32 both of which are found to be operating 9/11 times on the efficiency

frontier. B1 and B22 are found to be operating 8/11 times while B24 and B5 are found to be operating 7/11

times on the efficiency frontier. B36 and B3 are found to be operating 6 times on the frontier. These banks

namely; B25, B32, B7, B26, B1, B22, B36, B24, B5 and B3 are therefore considered as the best practice

banks and the poor performing or inefficient banks should follow their practices in their working processes.

Column 3 & 4 of the table also presents the banks with minimum efficiency scores for each year. As far as

banks with minimum efficiency are concerned, B31 recorded the highest minimum efficiency scores in the

entire period from 2000 to 2010. It obtained the minimum efficiency score in the years 2002, 2004, 2006

and 2010 (four times). B37 and B38 had a minimum efficiency scores 2 times in the period. B37 in 2005

and 2008 whiles B38 is found to be the bank with minimum score in 2007 and 2009. B20, B19 and B21

have obtained minimum efficiency score once in 2000, 2001 and 2008 respectively. The banks with the most

minimum efficiency scores are not making the desired progress. So these banks namely; B38, B37, and B31

will have to adopt substantial changes in their practices to keep in line with best practices.

TE denotes technical efficiency; Practice Banks represents the benchmarks estimated for each year from

2000 to 2010. Minimum level of TE is the least efficient banks for each year.

ANOVA

Arithmetic averages from 2000 to 2010

Sum of Squares Df Mean Square F Sig.

Between Groups .017 1 .017 10.777 .009

Within Groups .014 9 .002

Total .031 10



5. CONCLUSION AND RECOMMENDATION

The objective of this study was to apply DEA to investigate the technical efficiency performance of rural

banks in Ghana from 2000 to 2010 and also to assess whether the establishment of the ARB Apex Bank has

help improve the technical efficiency of rural and community banks.

The results show that over the period of 2000 to 2010, the rural and community banks experienced

improvement in technical efficiency in the post-Apex bank years. The overall technical efficiency for all

banks in the entire period was 85.5% indicating that inefficiency in the sector over the period under

investigation was 14.5%. This implies that rural banks can make input expenditure cuts by 14.5% and still

produce the same level of output.

The mean efficiency scores of the pre-Apex years and post-Apex years were compared using ANOVA to

test whether there is a significant difference in the two distinct periods. The ANOVA test result showed a

significant difference between the pre-Apex mean and the post -Apex mean. In other words, the degree of

input waste (inefficiency) was 20.7% in the pre-Apex period and this significantly declined to 12.2% in the

post-Apex period, implying that technical inefficiency in the study period has decreased after Apex bank

intervention.

Eight RCBs namely, B25, B32, B7, B26, B1, B22, B36, B24, B5 and B3 were identified as the best practice

banks.

Table 6: Average DEA Efficiency Scores and Best Practice Banks of RCBs: 2000 to 2010

Year TE

Min. of level of TE

Name of Bank

TE

Best Practice Banks

2000 0.875 Upper Amenfi RB 0.660 B1, B3, B4, B5, B7, B8, B10, B12, B15,

B16, B23,B25, B6, B27, B32, B36, B39

2001 0.734 Ankobra West RB 0.489 B1, B12, B18, B20, B25, B27, B32, B39

2002 0.771 Drobo Comm. Bank 0.581 B7, B8, B22, B24, B25, B26, B32, B35

2003 0.893 Lower Pra RB 0.723

B3, B4, B5, B6, B7, B9, B10, B13, B18,

B22, B24, B25, B26, B31, B32, B33, B35,

B39

2004 0.895 Drobo Comm. Bank 0.709 B1, B3, B5, B7, B8, B9, B12, B15, B22,

B24, B25, B26, B28, B29, B32, B35, B39

2005 0.920 Lower Pra 0.719

B1, B4, B5, B7,B8, B9, B10, B12, B15,

B16, B18, B25, B26, B27, B30, B31, B32,

B33, B38, B40

2006 0.857 Drobo Comm. Bank 0.545 B25,B26,B32,B33,B36,B24

B1,B5,B6,B7,B10,B13,B15,B22,

2007 0.868 Ada Rural Bank 0.652 B24,B25,B26,B31,B32,B33,B36

B1,B5,B6,B7,B10,B13,B15,B22,

2008 0.861 La Comm. Bank 0.618 B27,B32,B33,B36,B40

B1,B3,B5,B11,B18,B22,B25,B26,

2009 0.851 Ada Rural Bank 0.590 B32,B23,B24,B25,B26,B33,B36,B40,

B1,B3,B4,B7,B11,B14,B17,B22,

2010 0.882 Drobo Comm. Bank 0.615

B32,B36,B39,B40

B17,B20,B22,B23,B24,B25,B26

B2,B3,B4,B7,B11,B12,B14,B15,

TE denotes technical efficiency; Practice Banks represents the benchmarks estimated for each year from 2000 to 2010.

Minimum level of TE is the least efficient banks for each year.



The overall conclusion of the study is that there is significant improvement in the operational efficiency of

rural and community banks after the establishment of ARB Apex Bank. This result is consistent with the

view in the literature that reforms in the rural banking sector impact positively on efficiency (see Mohindra

and Kaur, 2011 and Khankhoje and Sathye; 2008).

Since efficiency of the RCBs has improved during the post-Apex Bank period, especially the post-Apex

years, it appears the policy of the Government of Ghana to restructure the rural banking sector by bringing

in the Apex bank to absorb some of the functions of the Central Bank has shown positive results and this

study recommends its continuity. Accordingly this study calls for the Bank of Ghana to empower Apex bank

by granting the institution with more powers to enable them monitor and supervise the RCBs to operate

efficiently. For instance, the bank should be given the authority to sanction the rural banks when they fail to

file their returns in full. Currently, RCBs that fail to send copies to Apex bank cannot be sanctioned by Apex

bank.

It is also recommended that the management of rural and community banks should review their input

resources to improve efficiency. This can be done by curtailing expenditure on the input resources of labour

cost, shareholders equity, capital cost and deposit expense. The results indicate that RCBs can make input

expenditure cuts to the tune of 14.5% and still be able to produce the same level of output.

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Appendix 1: List of Sampled Banks

Serial No. Bank Code Bank Name Region

1 B1 Sonzele Rural Bank Upper West

2 B2 South Akim Rural Bank Eastern

3 B3 Odwen Anoma Rural Bank Eastern

4 B4 Mamuadu Rural Bank Eastern

5 B5 Kwahu Rural Bank Eastern

6 B6 Dumpong Rural Bank Eastern

7 B7 Gomoa Rural Bank Central

8 B8 Otuasekan Rural Bank Ashanti

9 B9 Bosomtwe Rural Bank Ashanti

10 B10 Kumawuman Rural Bank Ashanti

11 B11 Amansei West Rural Bank Ashanti

12 B12 Ahafo Ano Rural Bank Ashanti

13 B13 Adansi Rural Bank Ashanti

14 B14 Assinman Rural Bank Central

15 B15 Eastern Gomoa Assin Central

16 B16 Akim Bosome Rural Bank Eastern

17 B17 Atiwa Rural Bank Eastern

18 B18 Asawinso Rural Bank Western

19 B19 Ankobra West Rural Bank Western

20 B20 Upper Amenfi Rural Bank Western

21 B21 La Community Bank Greater Accra

22 B22 Averno Rural Bank Volta

23 B23 Mepe Rural Bank Volta

24 B24 Weto Rural Bank Volta

25 B25 Bonzali Rural Bank Northern

26 B26 Kwamaman Rural Bank Ashanti

27 B27 Odotobiri Rural Bank Ashanti

28 B28 Okomfo Anokye Ashanti

29 B29 Sekyeredomase Ashanti

30 B30 Amantin &Kasei Comm BA

31 B31 Drobo Comm Bank BA

32 B32 Kintampo Rural Bank BA

33 B33 Kwahu Praso Rural Bank Eastern

34 B34 Upper Manya Rural Bank Eastern

35 B35 Jomoro Rural Bank Western

36 B36 Kaaseman Rural Bank Western

37 B37 Lower Pra Rural Bank Western

38 B38 Ada Rural Bank Greater Accra

39 B39 Unity Rural Bank Volta

40 B40 Bessfa Rural Bank Upper East

Documents

Measuring Rural Bank Efficiency in Ghana: An Application of the Data Envelopment Analysis (DEA) Approach