Managerial Finance
Technical efficiency: the pathway to credit union cost efficiency in Ghana
Benjamin Amoah, Kwaku Ohene-Asare, Godfred Alufar Bokpin, Anthony Q.Q. Aboagye,
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"Technical efficiency: the pathway to credit union cost efficiency in Ghana", Managerial Finance, Vol.
44 Issue: 11, pp.1292-1310, https://doi.org/10.1108/MF-10-2017-0431
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44,11 Technical efficiency: the pathway
to credit union cost efficiency
in Ghana
1292 Benjamin Amoah
Department of Banking and Finance, Business School, Central University,
Received 26 October 2017
Revised 4 May 2018 Accra, Ghana
30 July 2018 Kwaku Ohene-Asare
Accepted 11 August 2018
Department of Operations and Management Information Systems,
Business School, University of Ghana, Accra, Ghana, and
Godfred Alufar Bokpin and Anthony Q.Q. Aboagye
Department of Finance, Business School, University of Ghana, Accra, Ghana
Abstract
Purpose – The purpose of this paper is to investigate the factors that tend to influence credit union
efficiency, specifically examining cost efficiency (CE) and technical efficiency.
Design/methodology/approach – Using a two-stage method, the authors first estimate CE using Tones’
SBM data envelopment analysis method and technical efficiency in a variable returns to scale setting during the
period 2008–2014. The authors estimate a mixed-effects and two-limit Tobit regression to examine the effect of
credit union specific characteristics, banking industry and macroeconomic conditions, on efficiency.
Findings – Credit unions’ CE averaged 38.9 percent compared to 54.4 percent for technical efficiency. The
authors find that technical efficiency does not translate into CE and vice versa.
Practical implications – The authors suggest that when targeting CE, credit union managers would have
to make technical efficiency a priority. A monopolized and inefficient banking sector does not challenge
efficiency improvement in the credit unions industry.
Originality/value – This study employs data from a frontier market.
Keywords Efficiency, Banks, DEA, Macroeconomics, Credit union
Paper type Research paper
1. Introduction
Credit unions exist to provide their members financial services that are also offered by other
financial institutions, namely, banks, thrifts institutions and finance companies, among
others. From McKillop and Wilson (2011), credit unions are self-help cooperative financial
organizations geared to attaining the economic and social goals of members and wider local
communities. The credit union is governed by its members, members elect (from within that
membership), unpaid volunteer, paid officers and directors, who establish the policies under
which the credit union operates.
For their members to continue to do business with them requires credit unions’
financial services to be provided at lower cost than other financial institutions. In the
absence of this, the member-owners of these credit unions would see no reason to
patronize the credit union. The effect of this, of course, would be that the credit union
would cease to exist as competing financial institutions would take over the markets they
serve. However, other members join the credit unions for non-pecuniary reasons as noted
by Smith (1984). One way of achieving this survival objective is for credit unions’
Managerial Finance management to make efficient use of resources allotted to them by the owners of the union.
Vol. 44 No. 11, 2018 In the credit union setting, business is conducted with owners and for owners.
pp. 1292-1310
© Emerald Publishing Limited This presents a natural margin squeeze which calls for a good control over expenses if the
0307-4358
DOI 10.1108/MF-10-2017-0431 value creation for the owners of the credit union is to stay intact (Taylor, 1977).
Downloaded by University of Ghana At 04:14 18 June 2019 (PT)
The inability to control expenses might mean managers have to report declines in Technical
performance to owners. The manager of the credit union is therefore presented with a efficiency
situation where they have to continuously work on improving efficiency both from within,
technical efficiency, through their production process, and from without, cost efficiency
(CE), a market space where banks pose a major threat.
Currently there exist 34 licensed banks, 4 licensed representative offices,
71 specialized depository institutions, 140 licensed rural and community banks, 1293
417 licensed Forex Bureaux and over 435 licensed credit unions in Ghana. The current
Ghanaian financial industry is particularly worrying as in July 2015, the central bank,
Bank of Ghana (BoG) revoked the license of 70 microfinance companies and a money
lending company. These institutions had their operations suspended because of
inefficiencies. In the year 2017, two banks (UT Bank and Capital Bank) had their
banking license withdrawn and the BoG declared them illiquid and deficient in capital.
In the midst of all these, the credit union sector of the financial institutions industry in
Ghana has been relatively safe. This situation demands an inquiry into how resources
are used in the credit union, should technical or cost should be made a priority by the
management in the pursuit of overall efficiency in the credit union setting? This is the
problem the paper attempts to explore.
This study sets to achieve a number of objectives. First, it aims to estimate and
discuss the CE of credit unions using the non-parametric Tones’ efficiency from data
envelopment analysis (DEA) approach. Second, we estimate and discuss overall technical
efficiency of the credit unions. Third, appraise the nature of relationship that exists
between cost and technical efficiency. Finally, using regression analysis, we explain
discretional and non-discretional factors, particularly banking sector development and
how they relate to credit union efficiency. The paper is structured as follows: Section 2
discusses literature review, Section 3 discusses method, data, specification of input and
outputs variables and Section 4 discusses results and discussion. Section 5 discusses
conclusion and recommendations.
This paper contributes to the debate on cost and technical efficiency in a cooperative
setting. We empirically assess the heterogeneous ability of credit unions in accessing input
at different prices on efficiency. Our study also examines technical efficiency, an empirical
gap yet to receive sufficient attention in the credit union literature. By including the banking
sector development in our study, the impact of the “no boundary effects” in terms of
conducting business with owners and non-owners in the case of banks especially on credit
unions’ efficiency is carefully examined. Furthermore, by focusing on Ghanaian credit union
industry, we provide empirical evidence of the factors that drive efficiency from a frontier
economy context and make some recommendations.
2. Literature review
Worthington (1998) showed that non-core commercial activities do not have a significant
influence on the level of cost inefficiency although asset size, capital adequacy regulation,
branch and agency networks are significantly associated with CE in Australian
credit unions. In Worthington (1999), a large number of credit unions in Australia were
best-practice efficient, and any efficiency found appears to flow from x-inefficiencies.
Worthington (2000) employed a two-stage procedure to evaluate non-bank financial
institution CE and revealed that the major source of overall cost inefficiency appears to be
allocative inefficiency rather than technical inefficiency. McKillop et al. (2002) examined
relative efficiency of UK credit unions using radial and non-radial efficiency measures to
investigate cost performance and revealed that credit unions have considerable scope for
efficiency gains using both measures of efficiency. Esho’s (2001) sample of Australian
credit unions demonstrates that bond type, size, age, average deposit size and interest rate
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MF spreads are significant determinants of relative CE from stochastic frontier and
44,11 distribution-free methodology.
Frame et al.’s (2003) work estimates a translog cost function for credit unions and mutual
thrifts focusing on the unique objectives of mutually owned depository institutions and
shows that credit unions with residential common bonds have higher costs than mutual
thrifts; however, they noted that single common bond occupational and associational credit
1294 unions are more cost efficient. Using stochastic frontier analysis, McKillop et al. (2005)
concluded that UK credit unions from the period 1999–2001 were subject to high levels of
inefficiency and that the environment in which a credit union operates plays a vital role in
relative efficiency, with large credit unions being more cost efficient.
Battaglia et al. (2010) analyzed the impact of environmental factors on cost and profit
efficiencies of cooperative banks including credit unions in Italy for the period 2000–2005.
Cooperative banks in the Northeast of Italy are shown to be the more cost efficient,
benefiting from a favorable environment. Wilcox and Dopico (2011) considered credit union
mergers and efficiency, in situations where the acquirer is much larger than the target credit
union, target members benefit in terms of lower loan rates and higher deposit rates, while
acquirer members see little change as cost benefits are shared equally when the merging
credit unions are equal in size.
Wheelock and Wilson (2013) used an adapted version of the “order-a quantile” frontier
estimate to compare larger and smaller size credit unions, and showed that small size credit
unions confronted a shift in technology that increased the minimum cost required to
produce given amounts of output and that all but the largest credit unions also became less
scale efficient over time. Glass et al. (2014) demonstrated that Japanese cooperatives over the
period 1998–2009 have secured both technical progress and a decrease in technical
inefficiency. Wijesiri et al. (2015) provided evidence for credit unions from Sri Lanka on
technical efficiency that age, type of the institution and return on assets are the crucial
determinants of technical efficiency.
3. Methodology
The method for estimating efficiency is divided into two: the econometric (parametric) and the
mathematical programming (non-parametric) approach. The distinction between the parametric
methods and non-parametric methods is that the former assigns a density function to the
stochastic component of the model, while the latter only defines the deterministic part. DEA is a
non-parametric method introduced by Charnes et al. (1978) from the works of Farrell (1957),
which presents a mathematical linear programming-based technique for measuring relative
efficiency of decision-making unit (DMU) that has multiple inputs and outputs. Originally
developed for measuring the efficiency of not-for-profit, public sector enterprises, DEA has
found widespread applications in the context of profit-oriented organizations.
DEA has several advantages: it is easy to use, it allows for multiple inputs and multiple
outputs in a single framework, it does not require specification of functional form for the
frontier, it does not require a priori specification of weights for inputs and outputs and,
finally, inputs and outputs can be expressed in different measurement units. The weakness
includes the assumption that the distance from the frontier is entirely due to inefficiency
without considering random errors such as errors of measurement or unforeseen events
impacting the DMU. Also, the technique is sensitive to outlier data. Further DEA does not
measure “absolute” efficiency but relative efficiency and finally statistical hypothesis tests
are difficult to conduct in the DEA method.
For the purpose of this study, we chose the non-parametric DEA technique to examine
the CE of credit unions in Ghana using Tone’s (2002) CE approach. Very few studies have,
however, adopted Tone’s (2002) cost efficiency model in banking more so in credit unions, or
openly discussed it (Tone and Sahoo, 2005; Tone and Tsutsui, 2007; Dong et al., 2014).
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The original DEA by Charnes et al. (1978) which assumes constant returns to scale has been Technical
extended to the variable returns (VRS) (Banker et al., 1984), profit maximization (Fried et al., efficiency
1993) and cost minimization (Färe and Grosskopf, 1985). FromMaudos et al. (2002), CE is the
ratio between the minimum cost at which it is possible to attain a given volume of
production and the realized cost. From this definition, a DMU is cost efficient if it uses
minimum level of cost as compared to the actual cost observed at the end of a production
period. It is the product of allocative and technical efficiency (Fethi and Pasiouras, 2010). 1295
Technical efficiency refers to the ability to use the right resources appropriately. A DMU
is technically efficient only when it uses the least amount of inputs to produce maximum
outputs (Koopmans, 1951 cited in Fried et al., 1993). The CE of observed credit unions is
given by the distance of its detected cost point from a constructed cost frontier (Dong et al.,
2014). Thus, the CE of an evaluated credit union is modeled as follows:
w xn¼ iJCE iJ ; (1)
wijxij
where wiJ is a set of input prices for a vector of inputs i, for credit union J, xniJ denotes the cost
minimizing vector of inputs quantities for the credit union J under observation, x is input
and x* is optimal inputs. The traditional CE DEA model by Färe et al. (1985a) assumed that
input prices are the same across all DMUs, and though actual markets may function under
imperfect competition, unit input prices may not be the same across all DMUs. Tone (2002)
identified these drawbacks in the conventional CE DEA model and henceforth proposed a
new scheme to measure CE. The new CE was further extended to the decomposition
of CE by Tone and Tsutsui (2007). Under the new CE model, DMUs with dissimilar
input prices will provide different measures of CE (Dong et al., 2014). The traditional (Färe
et al., 1985a; Farrell, 1957) and new CE DEA (Tone, 2002) models are expressed in the
following equations:
min nl;xiwiOxiO; (2)
subject to:
Xn
ljyriyroX0 ; r ¼ 1; 2; . . .; s;
j¼1
Xn
l x nj ijxiOp0 ; i ¼ 1; 2; . . .; m;
j¼1
Xn
lj ¼ 1 ðVRSÞ;
j¼1
ljX0 ; j ¼ 1; 2; . . .; n;
where xnjO denotes the cost minimizing vector of inputs quantities for the credit union under
observation, given the vector of output weights yro and input prices wjo. Hence, a
hypothetical credit union with the same input price vector as the observed credit union jwill
have a CE score of w njOxjO:
min C nl;x T ¼ exiO; (3)
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MF subject to:
44,11 Xn
ljyrjyroX0; r ¼ 1; 2; . . .; s;
j¼1
Xn
1296 l x exnj ij iOp0; i ¼ 1; 2; . . .; m;
j¼1
Xn
lj ¼ 1 ðVRSÞ;
j¼1
ljX0; j ¼ 1; 2; . . .; n ;  
where e∈Rm a row vector with all elements equal to 1 and x Tij ¼ w1jx1j; . . .; wijxij .
The λ is the weights. The Tone (2002) cost model differs from the traditional model because
under the latter, for observing the credit union unit cost of credit union j fixed at wio,
we search for the optimal input mix xnO for producing yo. However, under the former the
optimal input xnO that produces the output yo can be found independently of the credit
union’s prevailing unit price wO. Thus, based on the optimal solution (Tone, 2002), CET is
defined as follows:
¼ ex
n
CE iOT : (4)ex
For technical efficiency θTE, the input-oriented value-based framework is set up as follows:
yTE ¼ Min y; (5)
l;y
subject to: P
ljxopyxio; j ¼ 1; :::; m;
j¼J
X
ljyrjXyro; r ¼ 1; :::; s;
j¼XJ
lj ¼ 1; ljX0; 8jA J :
j¼J
3.1 Inputs and outputs specification
In defining the inputs and outputs in financial institutions, the approaches used in
literature include the intermediation approach, the production approach, the user cost
approach and the value-added approach. We use the intermediation approach by
considering financial institutions as intermediaries, converting and using factors of
production paid for by owners to transfer financial saving from surplus units to deficit
units in the form of loans.
Three input variables are used by the credit union: Labor, Non-current asset and
Deposits; the three outputs are: Loan, Liquid-financial investment and Non-loan income. The
three input prices are Price of labor, Price of non-current asset and Price of funds. The cost
of labor is proxied by personnel costs. The study would have benefitted from data on
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fulltime and part-time employees to be able to estimate an exact cost of labor; these were Technical
unavailable. Nonetheless, we believe that the current approach can serve as a good proxy efficiency
for cost of labor.
3.2 Sample and data source
We sample firm-specific data from the annual financial report of 66 credit unions operating
under the association of Credit Unions Association (CUA) of Ghana for the period 1297
2008–2014. The panel data set permits us to control for variables like differences in business
practices across the sampled credit unions which account for firm-specific heterogeneity.
Banking industry indicators are sourced from Global Finance Development database, real
Treasury bill rate calculated from data provided by the BoG and GDP growth rate from the
World Development Indicators.
3.3 Econometric estimation
In examining the factors that could influence credit union efficiency, we adopt the two-stage
semi-parametric process. In the first stage, we estimated cost and technical efficiency scores
for all credit unions in our sample as specified in Equations (4) and (5). We follow Mercieca
et al. (2007) by constructing a Herfindahl–Hirschman Index (HHI) measure for each credit
union to account for income diversification from non-loan income activities. We construct a
combined non-loan income diversification index which measures the income generated from
all non-loan income activities of the credit union. This is given as follows:
 2        
¼ LFI þ OFI
2
þ ENT
2
þ COM
2 OTI 2
HHIðCOMBÞ þ ; (6)TNLNI TNLNI TNLNI TNLNI TNLNI
HHI(COMB) is diversification index for combined non-loan income. LFI is liquid-financial
income, OFI is other liquid-financial income and ENT is one-time entrance fees charged new
members. COM is commission income, OTI is other non-interest income and TNLNI is total
non-loan income. An increasing HHI(COMB) implies the credit union is concentrating on one
type of non-loan income focusing less on other non-loan income generating activity. The
credit union Z-score (ZSCOR) captures financial stability, higher financial performance and
capitalization implies stability. The Z-score measure is given as follows:
Z  ¼ ROAþE=TAscore : (7)
sROA
In the second stage, the efficiency estimates obtained from Equations (4) and (5) are
regressed on the selected internal and external variables as specified in Equation (8) using
mixed-effects and a two-limit Tobit regression method.
We estimate the following panel regression model:
X9 X3 X2
yi;t ¼ b1 CU Factorsi;tþb2 Banking Industryi;tþb3 Macrotþei;t ; (8)
n¼1 n¼1 n¼1
where θi,t is the estimated CE and technical efficiency derived from the input-oriented VRS
Tone DEA, CU Factors is a vector of credit union specific characteristics, where Banking
Industry is a set of banking industry condition and Macro is a set of macroeconomics
variables as presented in Table I, εi,t is the error term, and the subscripts i and t represent
individual credit union and time period, respectively. The use of the mixed-effects allows for
inference on populations but not on individual credit union. Again mixed effect models
credit union change across time. Finally, mixed effect model permits repeated observations
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MF
44,11 Variables Description Source
Inputs
Labor Total expenditures on employees (personal expenses) CUAFS
Non-current asset Non-current asset CUAFS
Deposits Total deposits mobilized CUAFS
1298 Outputs
Loan Total loan CUAFS
Liquid-financial Sum of interest income from liquid-financial investment CUAFS
investment
Non-financial Sum of non-interest income CUAFS
income
Input price
Price of labor Total personal expenses scaled by the total funds Authors’ own
calculation
Price of non- Depreciation expenses scaled by non-current asset Authors’ own
current asset calculation
Price of funds Interest expenses on deposits and non-deposits funds plus other Authors’ own
operating expenses divided by the total funds calculation
Dependent
θ Cost efficiency score generated from the input-oriented VRS Tone Authors’ ownCE
SBM DEA approach calculation
θ Technical efficiency score generated from the input-oriented VRS Authors’ ownTE
approach calculation
Explanatory
SIZE Total asset in natural log CUAFS
ZSCOR Insolvency risk; σ is the standard deviation of ROA for credit union Authors’ own
calculation
NWTA Net worth to total asset (%) Authors’ own
calculation
BDLN Loan loss to loans (%) CUAFS
HHI(COMB) Herfindahl–Hirschman diversification measure for combined non-loan Authors’ own
income calculation
NIETA Net interest expense to total asset (%) CUAFS
LOTA Loan total asset (%) CUAFS
AGE Number of years a credit union has been in existence Authors’ own
calculation
LITA Liquid asset to total asset (%) CUAFS
BKCN Asset of five largest banks as a share of total commercial banking asset GFDD
BKZS It captures the probability of default of a country’s commercial banking GFDD
system. It is estimated as (ROA + (equity/asset))/σ (ROA); σ (ROA) is
the standard deviation of ROA
BOCTA Operating expenses of a bank as a share of the value of all assets held GFDD
RLTB Inflation adjusted 1 year Treasury bill rate (%) BoG
Table I. GDP Gross domestic products (%) WDI
Efficiency and Notes: CUAFS, credit unions’ annual financial statements; GFDD, Global Finance Development Database;
regression variables BoG, Bank of Ghana; WDI, World Development Indicator Series
within individual credit unions types (community, workplace and society). We also use a
two-limit Tobit regression because the cost and technical efficiency estimates are censored
lower and upper values and no estimates fall outside this range. The usage of the Tobit
model uses all of the information, that is censored, and leads to consistent estimates.
In using these regression methods, the embedded maximum likelihood treats the correlation
challenge asymptotically albeit at a measured rate.
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The credit union firm-specific factors are size captured by natural log of total asset, where Technical
LOTA is loan-to-asset ratio and LITA is liquidity (see McKillop et al., 2002). We use BDLN to efficiency
capture loan quality similar toWorthington (2000). Instead of using non-interest income to total
revenue as Worthington did, we capture income diversification using HHI(COMB) and this we
believe gives us a good measure of income from non-loan activities. Age is used to capture the
years of existence of a credit union (see Esho, 2001). NWTA is net worth to total asset, a
measure of equity holder’s contribution. Z-score captures solvency risk; NIETA is the net 1299
interest expense to total asset, used to measure management control expenses.
For the external variables, we employ the following in our regression. BKCN5 (concentration
ratio of the five largest banks in terms of asset) is to measure banking sector concentration,
BKZS is Bank Z-score (banking industry stability), and BOCTA is bank overhead cost to total
asset, a measure of banking sector efficiency. We also introduce GDP (growth in gross domestic
products), to control for cyclical output effects, and RLTB (nominal one-year Treasury bill rate
adjusted for inflation), to control for monetary policy stance.
The descriptive statistics in Table II reveal that during 2008–2014 some credit unions in
Ghana were cost efficient scoring 1, with laggards scoring CE of 0.1078 with a mean value of
0.3888. For technical efficiency, best performing credit unions score 1, with an average
efficiency score of 0.5439. On the average, most credit unions in Ghana were more
technically efficient than cost efficient. The size of credit unions ranges from 17.3709 to
8.9551. The Z-score has the highest variability level of 38.6848, implying unstable solvency
levels during the 2008–2014 period. The average net worth to total asset was 94.3705
percent. The highest bad loan recorded was 59.0278 percent. The NEITA ranged between
0.3810 and 39.7088. Loan scaled by total asset averaged 54.7368 percent. The longevity
measure was from 1 year to 45 years of operations. Top 5 bank asset concentration ranged
55.71–87.32 during the period. Bank Z-score was 6.3229 on the average. Bank over cost to
total asset was 55.5071 on the average with a mean real Treasury bill rate of 5.107.
From Table III, the mean loan granted over the seven-year sampled period was
GH₵920,769.50 with a maximum loan of GH₵22,500,000. Liquid investment ranged
between GH₵832.34 and GH₵7,560,580. Non-financial income had a variability of
GH₵38,903.09, and the highest output of non-financial income is GH₵324,865.10. The
input section of Table IV shows that personal cost hovered between GH₵60 and GH
₵469,839.60. Non-current assets have a mean of GH₵77,633.70. Regular savings ranged
Variable Mean SD Min. Max.
θCE 0.3888 0.2149 0.1078 1
θTE 0.5439 0.2514 0.1102 1
SIZE 13.4276 1.3662 8.9551 17.3709
ZSCOR 50.1611 38.6848 2.1966 263.778
NWTA 94.3705 5.3158 54.9623 100
BDLN 2.9143 5.3307 0 59.0278
HHI(COMB) 0.6062 0.1982 0.2254 0.9954
NIETA 8.431 4.7808 0.381 39.7088
LOTA 54.7368 15.0991 0 96.8126
AGE 16.7403 10.595 1 45
LITA 1.2867 0.4117 −0.4609 2.0343
BKCN5 63.4629 10.5217 55.71 87.32
BKZS 6.3229 0.8641 4.93 7.53
BOCTA 55.5071 6.5347 46.53 63.02
RLTB 5.1071 3.8059 1.9 14.06 Table II.
GDP 8.076 3.0751 3.9859 14.046 Descriptive statistics
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MF
44,11 Variable Mean SD Min. Max.
Outputs
Loan 920,769.50 1,789,777.00 300 22,500,000.00
Liquid investment 444,078.30 898,365.10 832.34 7,560,580.00
Non-financial income 17,826.51 38,903.09 0 324,865.10
1300 Inputs
Personnel cost 35,407.84 63,221.41 60 469,839.60
Non-current asset 77,633.70 147,144.50 10 980,877.40
Regular savings 1,280,355.00 2,307,906.00 2,874.50 25,200,000.00
Inputs price
Table III. Price of labor 2.6 1.97 0.1 16.27
Variables in Price of non-current asset 190.65 3,047.46 0.03 65,331.40
first-stage DEA Price of regular savings 15.45 5.66 3.16 45.49
between GH₵2,874.50 and GH₵25,200,000. Price of labor averaged 2.57 and a maximum
value of 16.27. Price of non-current asset ranged between 0.03 and 65,331.40. The mean price
of regular savings was 15.45.
4. Cost efficiency analysis
From Table IV, we measure overall improvement in the efficiency over the seven years
using the geometric mean because it is less influenced by very small or large values in
skewed data. The CE overall shows that few credit unions recorded more than 50 percent
CE during the sample period. The top 5 most efficient credit unions during the period
2008–2014 were Abosom, UG, DunkwaTrd, KAMCCU and Dunkwa Area Tea with
efficiency scores of 83.8, 81.3, 73.6, 72.9 and 66.6 percent, respectively. These credit unions
also recorded geometric mean CE as follows: Absomo, UG, DunkwaTrd, KAMCCU and
Dunkwa Area Tea 80.9, 78.9, 67, 71.9 and 57.5 percent, respectively.
The worst performers in CE were Alu works, UGARS, Uni of Edu, NAFTI and St Maggi
with score of 23.5, 12.5, 20.2, 18.6 and 13.7 percent, respectively. On the other hand, Alu
recorded 23.4 percent, UGARS 20.3 percent, Uni of Edu 20 percent, NAFTI 18.5 percent and
St Maggi 13.6 percent geometric mean in CE. On the whole, CE has ranged between 33 and
44 percent, with an average score of 38.9 percent and with a geometric mean of 36.4 percent.
From this, we conclude that CE for credit unions is generally very low, implying production
costs for most credit unions are very high.
5. Technical efficiency analysis
From Table V, the most technically efficient credit union over the period was GRA at
97.1 percent followed by Asawinso 95.4 percent, Minechso 93 percent, TOR 92.5 percent and
KAMCCU 90.4 percent. These top performances GRA, Asawinso, Michenso, TOR and
KAMCCU recorded geometric mean of 97, 94.9, 90.8, 92 and 89.8 percent, respectively.
The least technically efficient credit unions are AAK Teachers, Samatex, Kadjebi, UG Med
and Uin of Edu with scores of 28.6, 25.5, 25, 24.4 and 21.1 percent. For the same period,
AAK Teach, Samatex, Kadjebi Tea, UG Med and Uni of Edu recorded 28.3, 25.3, 24.4,
24.4 and 20.9 percent, respectively, of geometric mean.
On the whole, the average technical efficiency ranged between 50.5 and 59.7 percent
with an average of 54.4 percent. There has been more variation in technical efficiency as
seen in the standard deviation ranging from 21.7 to 27.4 percent with an average of
19.4 percent. The overall geometric mean in technical efficiency is 52.1 percent. We can see
from the technical efficiency scores that resources are being used more efficiently in credit
unions, thereby avoiding a lot of waste.
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Technical
efficiency
1301
Table IV.
Tone cost efficiency
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DMU 2008 2009 2010 2011 2012 2013 2014 Average Rank Geometric mean
Abosom 1 0.501 0.573 0.793 1 1 1 0.838 1 0.809
UG 0.489 0.645 0.709 0.941 0.907 1 1 0.813 2 0.789
DunkwaTrd 0.304 0.375 0.628 0.843 1 1 1 0.736 3 0.670
KAMCCU 0.547 0.577 0.739 0.792 0.843 0.711 0.897 0.729 4 0.719
DunAreaTea 0.299 0.281 0.366 0.676 1 1 1 0.66 5 0.575
Ebenezer 0.372 0.431 0.556 0.696 0.659 0.806 0.778 0.614 6 0.593
Asawinso 0.576 0.465 0.594 1 0.565 0.485 0.586 0.61 7 0.592
TOR 0.596 0.783 0.547 0.879 0.365 0.546 0.52 0.605 8 0.584
Minescho 0.4 0.284 0.444 0.906 0.903 0.759 0.258 0.565 9 0.502
NkoranATea 0.233 0.26 0.325 0.424 0.906 1 0.78 0.561 10 0.480
WATER 0.115 0.359 0.47 1 0.42 1 0.316 0.526 11 0.427
Gh Stats 0.476 0.355 0.583 0.156 1 0.561 0.264 0.485 12 0.419
Adoagyiri 0.412 0.286 0.392 0.532 0.527 0.585 0.66 0.485 12 0.469
Navrongo 0.526 0.485 0.611 0.555 0.375 0.433 0.343 0.475 13 0.467
Aapostolic 0.914 0.655 0.451 0.253 0.276 0.39 0.343 0.469 14 0.425
Bawku hos 0.345 0.293 0.423 0.234 0.538 0.674 0.749 0.465 15 0.430
St Paul 1 0.369 0.356 0.33 0.453 0.423 0.293 0.46 16 0.423
Nkoran Vic 0.307 0.341 0.357 0.252 0.17 0.969 0.807 0.458 17 0.385
Standard 0.392 0.306 0.394 0.494 0.674 0.402 0.406 0.438 18 0.427
West ManT 0.276 0.568 0.641 0.291 0.626 0.335 0.33 0.438 18 0.412
West power 0.29 0.339 0.295 0.318 0.314 0.467 1 0.432 19 0.389
GREL 0.311 0.22 0.223 0.26 0.492 0.681 0.823 0.43 20 0.378
Baw Teach 0.176 0.222 0.357 0.488 0.694 0.558 0.514 0.43 20 0.389
Jointchurch 0.691 1 0.397 0.286 0.287 0.149 0.136 0.421 21 0.333
Kekekrachi 0.541 0.36 0.547 0.483 0.234 0.27 0.491 0.418 22 0.399
JACCU 0.228 0.24 0.32 0.325 0.116 0.823 0.828 0.411 23 0.333
Tec Are Tea 0.196 0.226 0.227 0.34 0.555 0.574 0.583 0.386 24 0.349
CRIG Tafo 0.335 0.257 0.261 0.332 0.367 0.49 0.592 0.376 25 0.361
Wenchi 0.299 0.433 0.347 0.403 0.366 0.547 0.231 0.375 26 0.363
North Tema 0.178 0.184 0.326 0.271 0.428 0.522 0.703 0.373 27 0.333
Sege 0.328 0.298 0.368 0.614 0.443 0.258 0.219 0.361 28 0.342
GRA 0.232 0.282 0.234 0.347 0.364 0.562 0.45 0.353 29 0.337
Bole Cath 0.399 0.392 0.41 0.305 0.243 0.113 0.606 0.353 29 0.318
(continued )
MF
44,11
1302
Table IV.
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DMU 2008 2009 2010 2011 2012 2013 2014 Average Rank Geometric mean
Acc Aca 0.783 0.503 0.156 0.287 0.201 0.207 0.293 0.347 30 0.299
Kwaebibi 0.3 0.274 0.242 0.416 0.335 0.234 0.557 0.337 31 0.322
UG MED 0.394 0.288 0.386 0.343 0.346 0.348 0.241 0.335 32 0.331
Trinity 0.538 0.394 0.334 0.293 0.25 0.214 0.319 0.335 32 0.321
Anajichrist 0.222 0.195 0.414 0.395 0.418 0.316 0.351 0.33 33 0.318
ECG 0.479 0.265 0.293 0.267 0.268 0.284 0.428 0.326 34 0.317
Waworkers 0.289 0.239 0.307 0.277 0.334 0.309 0.518 0.325 35 0.316
Chr of Pent 0.256 0.294 0.305 0.305 0.275 0.393 0.389 0.317 36 0.313
Soil Research 0.321 0.181 0.204 0.291 0.365 0.417 0.428 0.315 37 0.301
TrinityGAR 0.327 0.28 0.321 0.172 0.34 0.37 0.374 0.312 38 0.304
TUC NAT 0.217 0.248 0.315 0.289 0.287 0.375 0.413 0.306 39 0.300
Atico 0.509 0.414 0.209 0.26 0.278 0.243 0.171 0.298 40 0.279
Apapam 0.253 0.36 0.403 0.347 0.192 0.265 0.253 0.296 41 0.288
AAK TEACH 0.34 0.316 0.234 0.299 0.203 0.28 0.338 0.287 42 0.283
UDS 0.295 0.301 0.275 0.31 0.317 0.165 0.342 0.286 43 0.280
GUTA 0.353 0.305 0.392 0.328 0.148 0.226 0.246 0.285 44 0.273
St Paul 0.338 0.27 0.314 0.295 0.282 0.219 0.278 0.285 44 0.283
Kadjebi Tea 0.373 0.361 0.367 0.227 0.249 0.188 0.227 0.285 44 0.275
St Joseph 0.258 0.204 0.257 0.227 0.316 0.305 0.416 0.283 45 0.276
Ghana Nat Cul 0.223 0.218 0.23 0.246 0.348 0.373 0.322 0.28 46 0.274
Kpandnewera 0.215 0.289 0.304 0.305 0.202 0.315 0.175 0.258 47 0.252
Adido Tea 0.193 0.272 0.298 0.258 0.359 0.279 0.137 0.257 48 0.247
Bunso CRIG 0.285 0.278 0.267 0.235 0.233 0.234 0.206 0.248 49 0.247
Nkawkaw 0.288 0.199 0.203 0.317 0.216 0.223 0.276 0.246 50 0.242
Jas/golden 0.2 0.226 0.324 0.21 0.371 0.193 0.177 0.243 51 0.234
Samatex 0.22 0.182 0.179 0.197 0.244 0.319 0.32 0.237 52 0.231
Ghatomic 0.255 0.13 0.223 0.369 0.254 0.195 0.231 0.237 52 0.227
N Fadama 0.323 0.265 0.275 0.188 0.212 0.27 0.123 0.237 53 0.227
Alu 0.248 0.23 0.249 0.252 0.232 0.209 0.224 0.235 54 0.234
UGARS 0.134 0.131 0.181 0.23 0.222 0.231 0.377 0.215 55 0.203
UniEdu win 0.226 0.234 0.197 0.183 0.171 0.174 0.228 0.202 56 0.200
NAFTI 0.212 0.204 0.182 0.142 0.19 0.175 0.196 0.186 57 0.185
St Maggi 0.108 0.178 0.136 0.135 0.143 0.138 0.122 0.137 58 0.136
Mean 0.360 0.330 0.355 0.390 0.408 0.436 0.442 0.389 0.364
SD 0.190 0.152 0.140 0.222 0.240 0.258 0.253 0.151 0.139
Technical
efficiency
1303
Table V.
Technical efficiency
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DMU 2008 2009 2010 2011 2012 2013 2014 Average Rank Geometric mean
GRA 1 0.979 1 1 0.926 1 0.892 0.971 1 0.970
Asawinso 1 0.949 1 1 1 1 0.732 0.954 2 0.949
Minescho 1 1 1 1 1 1 0.509 0.93 3 0.908
TOR 1 1 0.843 1 0.793 0.837 1 0.925 4 0.920
KAMCCU 0.737 0.811 0.97 1 1 0.81 1 0.904 5 0.898
TUC NAT 0.602 0.695 1 0.986 1 1 1 0.898 6 0.881
UG 0.547 0.639 0.888 1 1 1 1 0.868 7 0.846
Abosom 1 0.526 0.65 0.765 1 1 1 0.849 8 0.826
WATER 0.958 0.698 0.641 0.675 0.892 1 0.774 0.806 9 0.794
Ebenezer 0.449 0.54 0.681 0.889 0.804 0.887 1 0.75 10 0.724
St Paul 1 1 0.461 1 0.767 0.601 0.355 0.741 11 0.691
Ghana Nat Cul 1 0.82 0.625 0.521 0.618 0.636 0.78 0.714 12 0.699
DunkwaTrd 0.357 0.4 0.569 0.732 0.91 1 1 0.71 13 0.659
St Paul 0.938 0.772 0.788 1 0.512 0.314 0.429 0.679 14 0.630
Gh Stats 1 0.462 0.62 0.373 1 0.936 0.309 0.672 15 0.609
Jointchurch 1 1 0.718 0.708 0.463 0.413 0.391 0.67 16 0.627
Wenchi 1 1 0.978 0.558 0.383 0.36 0.382 0.666 17 0.602
NkoranATea 0.384 0.386 0.438 0.553 0.916 1 0.983 0.666 17 0.612
ECG 1 1 0.363 0.385 0.548 0.535 0.805 0.662 18 0.614
Acc Aca 1 0.662 0.427 0.793 0.67 0.509 0.503 0.652 19 0.628
Adoagyiri 0.388 0.393 0.553 0.634 0.709 0.717 0.809 0.6 20 0.580
Bawku hos 0.548 0.365 0.642 0.487 0.585 0.723 0.826 0.597 21 0.579
North Tema 0.315 0.3 0.541 0.471 0.727 0.82 1 0.596 22 0.545
N Fadama 0.515 0.604 0.356 0.455 0.552 0.656 0.956 0.585 23 0.561
Navrongo 0.664 0.584 0.616 0.52 0.532 0.63 0.485 0.576 24 0.573
Atico 1 1 0.392 0.418 0.488 0.389 0.319 0.572 25 0.517
Jas/golden 0.281 0.313 0.536 0.384 0.684 0.936 0.831 0.567 26 0.515
NAFTI 0.414 0.564 0.595 0.762 0.589 0.53 0.462 0.559 27 0.550
Anajichrist 0.258 0.276 0.58 0.665 0.745 0.649 0.709 0.555 28 0.514
DunAreaTea 0.202 0.249 0.31 0.439 0.783 0.893 0.944 0.546 29 0.462
Trinity 0.727 0.566 0.611 0.591 0.424 0.39 0.499 0.544 30 0.533
West power 0.473 0.505 0.448 0.405 0.375 0.582 1 0.541 31 0.514
JACCU 0.34 0.348 0.343 0.388 0.319 1 1 0.534 32 0.469
(continued )
MF
44,11
1304
Table V.
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DMU 2008 2009 2010 2011 2012 2013 2014 Average Rank Geometric mean
Standard 0.526 0.379 0.451 0.571 0.636 0.617 0.526 0.529 33 0.522
West ManT 0.678 0.701 0.624 0.372 0.429 0.372 0.402 0.511 34 0.493
Apapam 0.597 0.454 0.484 0.503 0.476 0.525 0.524 0.509 35 0.507
Baw Teach 0.301 0.316 0.394 0.608 0.718 0.569 0.647 0.508 36 0.482
CRIG Tafo 0.42 0.589 0.391 0.451 0.401 0.471 0.694 0.488 37 0.478
St Joseph 0.56 0.392 0.412 0.357 0.482 0.518 0.535 0.465 38 0.459
St Maggi 0.502 0.51 0.42 0.401 0.315 0.376 0.728 0.465 38 0.450
Adido Tea 0.379 0.491 0.504 0.591 0.606 0.501 0.178 0.464 39 0.436
Waworkers 0.341 0.351 0.423 0.359 0.473 0.426 0.753 0.447 40 0.431
Tec Are Tea 0.293 0.302 0.326 0.366 0.558 0.589 0.629 0.438 41 0.417
Sege 0.359 0.434 0.493 0.56 0.628 0.326 0.254 0.436 42 0.418
Bole Cath 0.507 0.432 0.421 0.466 0.396 0.35 0.476 0.435 43 0.433
Church of Pent 0.351 0.397 0.433 0.373 0.385 0.546 0.514 0.428 44 0.423
Ghatomic 0.326 0.269 0.353 0.442 0.535 0.535 0.454 0.416 45 0.405
Aapostolic 0.738 0.434 0.304 0.262 0.348 0.429 0.357 0.41 46 0.389
Nkoran Vic 0.391 0.389 0.417 0.123 0.11 0.709 0.71 0.407 47 0.331
GREL 0.247 0.246 0.282 0.268 0.441 0.604 0.743 0.404 48 0.368
TrinityGAR 0.785 0.43 0.258 0.334 0.306 0.302 0.377 0.399 49 0.373
Soil Research 0.471 0.278 0.268 0.33 0.383 0.497 0.533 0.394 50 0.381
UDS 0.327 0.38 0.391 0.435 0.419 0.312 0.364 0.375 51 0.373
Kwaebibi 0.309 0.291 0.285 0.458 0.353 0.301 0.56 0.365 52 0.354
UGARS 0.332 0.316 0.406 0.348 0.313 0.322 0.516 0.365 53 0.359
Nkawkaw 0.316 0.327 0.301 0.349 0.381 0.373 0.477 0.361 54 0.357
Kpandnewera 0.328 0.429 0.568 0.448 0.206 0.262 0.229 0.353 55 0.332
Bunso CRIG 0.426 0.367 0.338 0.345 0.362 0.312 0.303 0.35 56 0.348
Alu 0.411 0.262 0.253 0.306 0.305 0.272 0.232 0.292 57 0.287
Kekekrachi 0.401 0.275 0.315 0.224 0.215 0.256 0.332 0.288 58 0.282
GUTA 0.405 0.291 0.321 0.281 0.249 0.223 0.235 0.286 59 0.281
AAK TEACH 0.243 0.306 0.297 0.246 0.243 0.293 0.374 0.286 59 0.283
Samatex 0.233 0.226 0.252 0.266 0.226 0.256 0.329 0.255 60 0.253
Kadjebi Tea 0.34 0.298 0.304 0.201 0.195 0.161 0.249 0.25 61 0.242
UG MED 0.259 0.234 0.261 0.239 0.237 0.254 0.226 0.244 62 0.244
UniEdu Win 0.22 0.213 0.172 0.189 0.199 0.2 0.287 0.211 63 0.209
Mean 0.552 0.506 0.505 0.525 0.549 0.573 0.597 0.544 0.521
SD 0.274 0.243 0.217 0.245 0.251 0.261 0.263 0.194 0.191
6. Comparing cost efficiency and technical efficiency; which one should be Technical
pursued first? efficiency
For brevity, we analyze the performance of the top 3 credit unions, comparing their cost
and technical efficiency scores, with the bottom 3 credit unions’ technical and CE score
in Tables IV and V, to verify if technical efficiency translates into cost technical efficiency.
Abosom ranked first with a score of 83.8 percent CE but was ranked 8th in technical
efficiency with even higher score of 84.9 percent. Similarly, the second ranked cost efficient 1305
credit union, UG, with a score of 81.3 percent ranked 7th under technical efficiency with a
score of 86.6 percent. The 3rd ranked cost efficient credit union, DunkwaTrd, with a score of
73.6 percent, ranked 13th under technical efficiency with score of 71 percent.
The worst ranked 63rd technical efficient credit union, Uni of Edu, with a score of
21.1 percent, was ranked 56th under CE with a score of 20.2 percent. The next worst ranked
credit union under technical efficiency was UG Med, 62nd with a score of 24.4 percent,
it ranked 32nd under CE with a score of 33.5 percent. Finally, the 61st ranked credit union
based on technical efficiency, Kadjebi Tea, scored 25 percent and ranked 44th on CE with a
score of 28.5 percent. We can conclude from this analysis that technical efficiency does not
necessarily translate into CE. We are of the opinion that pursuing technical efficiency,
an internal production process, should lead the charge over CE, a technique that requires
prices which the credit union has to compete for in the open market.
The CE estimates show direct and significant relations with the size of the credit union.
Table VI suggests that increasing the size of the credit union would improve CE in all the
four models. The inference here is that bigger-sized credit union is more cost efficient than
small-sized credit unions. This agrees with Esho (2001) that size is a significant determinant
of credit union efficiency. Increasing provision for bad and doubtful debt infers that credit
unions are, on the average, most likely not to be efficient, an issue that should be of concern
to managers of credit unions. Income diversification activities, HHI(COMB), in non-loan
activities significantly associate with CE, and increase the inefficiency of credit unions,
Variables 1 2 3 4
SIZE 0.0529*** (7.6200) 0.0551*** (7.1294) 0.0548*** (7.1006) 0.0809*** (4.2721)
ZSCOR −0.0002 (−1.1061) −0.0003 (−1.1625) −0.0003 (−1.1631) 0.0030 (0.9703)
NWTA 0.0027* (1.7551) 0.0029* (1.8363) 0.0028* (1.7945) 0.0011 (0.4540)
BDLN −0.0047** (−2.5594) −0.0045** (−2.4659) −0.0046** (−2.4925) −0.0060*** (−3.1238)
HHI(COMB) −0.0935** (−2.1178) −0.0942** (−2.1099) −0.0905** (−2.0122) −0.0770 (−1.4652)
NIETA −0.0106*** (−4.8663) −0.0107*** (−4.8369) −0.0107*** (−4.8446) −0.0120*** (−4.2960)
LOTA −0.0014** (−2.2201) −0.0015** (−2.3240) −0.0015** (−2.2791) −0.0020*** (−2.8257)
AGE 0.0030*** (3.3771) 0.0030*** (3.4117) 0.0030*** (3.3791) 0.0027 (0.4775)
LITA 0.1455*** (6.1449) 0.1465*** (6.1967) 0.1461*** (6.1849) 0.1194*** (4.1326)
BKCN5 −0.0013 (−1.1963) −0.0008 (−0.7370) −0.0015 (−1.3179)
BKZS −0.0613** (−1.9902) −0.0675** (−2.0882) −0.0891*** (−2.6404)
BOCTA −0.0081* (−1.9369) −0.0095** (−2.1442) −0.0104*** (−3.0385)
RLTB −0.0007 (−0.2982) −0.0005 (−0.2613)
GDP 0.0037 (0.9976) 0.0034 (1.1742)
Constant −0.5660*** (−3.3880) 0.3144 (0.6486) 0.3862 (0.7855) 0.5543 (1.2887)
Credit union No No No Included
dummy
Wald χ2 279.74 286.24 288.08 873.95
Prob.Wχ2 0.0000 0.0000 0.0000 0.0000
Observations 462 462 462 462 Table VI.
Credit unions 66 66 66 66 Regression results of
Notes: t-statistics in parentheses. *po0.1; **po0.05; ***po0.01 Tone cost efficiency
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MF a conclusion similar to Worthington (2000). Thus, credit unions aiming at reducing cost
44,11 must be wary of non-loan income activities.
Increasing management expense, NIETA implies that CE level in credit union would
decline, a relation that is very significant in Table V. The loan business of the credit union
captured by LOTA reveals a negative coefficient, implying that high levels loans granted can
cause inefficiencies; in Model 1, a 1 percent increase in loans would result in a 0.0014 decrease
1306 in CE. In Models 2 and 3, a 1 percent increase in loans would imply a 0.0015 reduction in CE.
An increase in Age of the credit union suggests an improvement in CE as Esho (2001) and
Wijesiri et al. (2015) also observed. This may be attributable to experience and learning curve
effect as the credit union would not want to repeat decisions that might have led to inefficiency
in the past. Liquidity implies improved CE for credit unions in all three models.
In Model 2, all the relationships afore-mentioned still hold with the same level of
significance. As the banking sector’s cost to total asset increases, credit unions stand to
become cost efficient. This can be as a result of the direct competition that exists between
credit unions and banks. Additionally, the temptation to win the same customer would mean
each competitor would end up spending much more, hence this nature of relationship while
top 5 bank size concentration is insignificant. All banking sector development indicators
have negative coefficients to credit union cost efficiencies. From this, it is obvious that the
banking sector matters in credit union efficiency as also found in Battaglia et al. (2010).
This can be attributable to the competition that exists between these two competing
financial institutions in the financial market.
On the economy wide level, there exists a negative, insignificant relation between CE of
credit unions and real return one-year Treasury bill rate. A growing economy can lead to
improvement in CE for credit unions as indicated by the positive coefficient between GDPs.
In Column 4 of Table VI, the insignificant coefficient of NWTA, HHI(COMB) and Age is when
credit union fixed effect is accounted for as an explanatory variable in the model.
Technical efficiency measures management’s ability to make use of resources and
avoiding wastage as much as possible. In Table VII, size has positive and significant
relations with technical efficiency comparable to Wijesiri et al.’s (2015) conclusions. Credit
unions with higher new worth to asset are more technically efficient. The relationship
between HHI(COMB) that is income diversification and technical efficiency is positive,
i.e. an increase in credit unions’ investment in non-loan income leads to an increase in
technical efficiency. It must be explained that these non-loan investments need a lot
of management commitment; less technically efficient credit unions may stick to loan
business and make less adventure into non-loan activities. The more credit unions spend on
non-interest expense, NIETA, the less technically efficient that credit union.
Loan to asset of credit unions shows an inverse relation with technical efficiency.
Inherent is the loan business is bad loans, increasing loans comes with commensurate bad
loans, loan increase implies a decrease in technical efficiency in all case. Increasing bad
loans leads to higher levels of inefficiencies on the part of management of the credit union.
The Age of the credit union has the same nature of relationship as in the case of CE.
The more stable the banking sector, as measure by the BKZS, the less technically efficient
the credit unions become, probably because labor may then move to banks, denying the
credit union of quality management. An increase in the banking sector’s efficiency implies a
decline in technical efficiency of credit unions. Top 5 bank concentration, real Treasury bill
and GDP growth do not significantly influence technical efficiency in the credit union.
When credit union fixed effect is accounted for in the model as an explanatory variable in
Column 4 of Table VII, the coefficient of SIZE, NWTA, HHI(COMB), NIETA and AGE
becomes insignificant while BDLN becomes significant.
We again use a two-limit Tobit regression to evaluate Equation (7). The range CE scores
are scaled from 0.1078 to 1.000 and for technical efficiency, scaled from 0.1102 to 1.000 for
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Technical
Variables 1 2 3 4 efficiency
SIZE 0.0158* (1.7672) 0.0169* (1.6962) 0.0165* (1.6600) 0.0133 (0.5880)
ZSCOR −0.0002 (−0.6075) −0.0002 (−0.6178) −0.0002 (−0.6216) −0.0012 (−0.3158)
NWTA −0.0051** (−2.5582) −0.0050** (−2.5162) −0.0051** (−2.5641) −0.0028 (−0.9904)
BDLN −0.0030 (−1.2502) −0.0025 (−1.0715) −0.0027 (−1.1269) −0.0038* (−1.6590)
HHI(COMB) 0.2392*** (4.1954) 0.2315*** (4.0158) 0.2394*** (4.1246) 0.0479 (0.7633)
NIETA −0.0044 (−1.5649) −0.0048* (−1.6825) −0.0048* (−1.6804) −0.0054 (−1.6105) 1307
LOTA −0.0020** (−2.3379) −0.0020** (−2.3819) −0.0020** (−2.3176) −0.0009 (−1.0154)
AGE 0.0023** (1.9805) 0.0023** (2.0275) 0.0023** (1.9777) 0.0031 (0.4691)
LITA 0.1857*** (6.0729) 0.1885*** (6.1791) 0.1877*** (6.1582) 0.1382*** (4.0066)
BKCN5 −0.0006 (−0.4392) 0.0001 (0.0622) 0.0003 (0.2197)
BKZC −0.0708* (−1.7822) −0.0842** (−2.0194) −0.0712* (−1.7669)
BOCTA −0.0094* (−1.7512) −0.0118** (−2.0647) (−2.7150)
RLTB 0.0003 (0.1168) −0.0000 (−0.0112)
GDP 0.0058 (1.2320) 0.0049 (1.3911)
Constant 0.5541** (2.5690) 1.5472** (2.4738) 1.6787*** (2.6466) 1.8294*** (3.5635)
Credit union dummy No No No Included
Wald χ2 147.32 153.13 155.15 821.93
Prob.Wχ2 0.0000 0.0000 0.0000 0.0000
Observations 462 462 462 462 Table VII.
Credit unions 66 66 66 66 Regression results of
Notes: t-statistics in parentheses. *po0.1; **po0.05; ***po0.01 technical efficiency
both lower limit and upper limit, respectively. In these results, we see that if credit unions
increase their size by 1 percent, the expected size effect would increase 0.056 and 0.002 on
cost and technical efficiency, respectively, while holding all other variables in the model
constant. For every 1 percent increase in NWTA, there exists 0.0029 increase in CE and
0.0061 decline in NWTA in the technical efficiency. Increase loan loss would lead to a
decrease in the expected bad loan value by 0.0047 in CE.
The expected value of non-loan income diversification activities declines by 0.0977 in the CE
model, and 0.2645 increase in the technical efficiency for every 1 percent increase in HHI(COMB);
these relations are significant. There exists a decrease of 0.0108 for every increase in NIETA in
relation to CE. The loan business exhibits a reduction as expected both in the CE and technical
efficiency estimation. A 1 percent increase in loan to asset leads to a 0.0016 and 0.0022 decline
in expected values of loan to asset. An increase in Age by one year implies an increase in value
as given in the coefficient under both cost and technical efficiency. Liquidity captured by LITA
shows an increase in expected value of 0.1494 under CE and 0.2052 under technical efficiency,
on the average, from a 1 percent increase in LITA. The stability of the banking sector shows a
decline of 0.0701 and 0.1036 decline in expected values for a unit increase in the case of the two
dependent variables, respectively, in Table VIII. The efficiency level of the banking sector
implies negative relations with CE and technical efficiency, a 1 percent increase in banking
sector overhead cost leads to an increase of 0.0099 and 0.0014 in expected value. The real
Treasury bill rate and GDP growth are not statistically significant. With the addition credit
union fixed effect in the model in Table VIII, there was no change in the significance of
coefficient under the CE model; however, in the fixed effect technical efficiency (FSTE), SIZE,
NWTA, HHI(COMB), NIETA, AGE become insignificant while BDLN is significant.
7. Conclusion
The current study estimates cost and technical efficiency for credit unions for Ghanaian credit
unions during the period 2008–2014. Credit unions operated at CE average of 38.9 percent,
while technical efficiency averaged 54.4 percent for the period. The results show that many
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MF
44,11 Variables CE TE FSTE
SIZE 0.0561*** (6.9523) 0.0229** (1.9985) 0.0179 (0.6973)
ZSCOR −0.0003 (−1.2511) −0.0003 (−0.8129) −0.0013 (−0.2962)
NWTA 0.0029* (1.8077) −0.0061*** (−2.6123) −0.0037 (−1.0859)
BDLN −0.0047** (−2.4768) −0.0030 (−1.1134) −0.0047* (−1.7917)
HHI(COMB) −0.0977** (−2.0797) 0.2645*** (3.9982) 0.0385 (0.5317)1308 NIETA −0.0108*** (−4.6967) −0.0053 (−1.6162) −0.0054 (−1.4016)
LOTA −0.0016** (−2.2806) −0.0022** (−2.2509) −0.0008 (−0.8233)
AGE 0.0033*** (3.4929) 0.0024* (1.8426) 0.0036 (0.4882)
LITA 0.1494*** (6.0609) 0.2052*** (5.9469) 0.1523*** (3.8649)
BKCN5 −0.0009 (−0.7525) 0.0000 (0.0018) 0.0001 (0.0666)
BKZS −0.0701** (−2.0786) −0.1036** (−2.1817) −0.0875* (−1.9266)
BOCTA −0.0099** (−2.1540) −0.0140** (−2.1516) −0.0133*** (−2.8620)
RLTB −0.0007 (−0.3000) 0.0003 (0.0991) −0.0002 (−0.0896)
GDP 0.0036 (0.9537) 0.0071 (1.3150) 0.0060 (1.5199)
Constant 0.4056 (0.7910) 1.9229*** (2.6530) 2.1736*** (3.6636)
Credit union dummy No No Included
Observations 462 462 462
Credit unions 66 66 66
2
Table VIII. LR χ 219.23 128.58 465.04
Tobit Prob.Wχ
2 0.0000 0.0000 0.0000
regression results Notes: t-statistics in parentheses. *po0.1; **po0.05; ***po0.01
credit unions are on the average technically efficient. We also realized that CE does not
necessary translate into technical efficiency as there exist varying scores and positions for the
same credit union among the sample credit unions. From the mixed effect and the two-limit
Tobit regression estimates, the efficiency of the credit union is driven by factors like size, net
worth to asset, bad loans, non-loan income, non-interest expense, loan to asset, liquidity and
Age. Credit union efficiency is also significantly associated with competition and efficiency of
the banking sector, with the wider economy bearing no statistical significant relations.
From the evidence in this study, managers must aim at deploying every resource fully to
avoid waste in their efforts to create value for the owners. Managers of credit unions must
improve their production aspects of operations to cut down production cost. Pursuing technical
efficiency should lead CE in the credit union setting. A monopolized banking sector and an
inefficient banking sector do not challenge efficiency improvement in the credit unions industry.
It is therefore appropriate for credit union managers to monitor the banking industry and craft
strategies that would help improve their efficiency. It is recommended that policy makers for the
financial institutions industry also look beyond the banking sector and consider the effect of
their actions on smaller deposit taking financial institution like the credit union, since from this
study we see that there exist rippling effects of banking sector activities on credit unions.
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About the authors
Benjamin Amoah holds a PhD degree in Finance and is Lecturer at the Department of Banking and
Finance at the Central Business School, Central University, Accra, Ghana. His research interest includes
investment fraud, financial institutions management, cooperative studies with special emphasis on credit
unions, efficiency, risk in financial institutions, financial literacy, pension and retirement planning.
Benjamin Amoah is the corresponding author and can be contacted at: benjamintoamoah@yahoo.com
Dr Kwaku Ohene-Asare holds a PhD degree in Operations Research and Management Science and is
Economics Lecturer at the University of Ghana Business School. He holds a PhD degree from Warwick
Business School, the University of Warwick. He previously taught in the Economics Department and
Warwick Business School at the Warwick University and is now Associate Fellow with them. Areas of
teaching interest include data envelopment analysis, operations research andmanagement science techniques.
Professor Godfred Alufar Bokpin is currently Head at the Department of Finance. His research
focuses on macro-finance linkages, market microstructure, asset pricing, fiscal policy or monetary
interaction, economic and firm-level governance, disclosure and transparency. Professor Bokpin
investigates issues with funding mostly from African Economic Research Consortium (AERC), African
Development Bank, Canadian Government and University of Ghana as well as Corporate Ghana.
Anthony Q.Q. Aboagye is currently Associate Professor and former Head of the Department of
Finance, University of Ghana Business School. He received PhD in Finance from McGill University. He
also holds an MBA degree from the University of Toronto, an MA degree from York University, Canada
and a BSc degree from the University of Ghana. Professor Aboagye is an active consultant. A partial list
of his clients includes: Ministry of Finance and Economic Planning and the Ministry of Trade and
Industries, Ghana, USAID, Ghana Health Service and the Ghana Stock Exchange.
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