Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Efficiency and Productivity Analysis of Microfinance
Institutions in Cambodia: A DEA Approach
Izah Mohd Tahir and Siti Nurzahira Che Tahrim
This paper employs the non-parametric Data Envelopment Approach (DEA) and
Dynamic Malmquist Productivity Index (MPI) to examine the efficiency and
productivity of Cambodian microfinance institutions during the period 2008-2011.
We found that the microfinance institutions in Cambodia have exhibited an overall
efficiency of 92% during 2008-2011 suggesting an input waste of 8%. The overall
efficiency has improved slightly from 91% in 2008 to 92% in 2009 and remained
stable in 2010 and 2011. In addition, the results suggest that the MFIs in Cambodia
have exhibited productivity growth of 1.7% during the period 2008-2009, a regress
of 0.6% during 2009-2010 and a positive change of 0.9% in 2010-2011.
Technological Change had been consistently influencing the productivity change
during the period of study. The growth in the productivity of the MFIs in Cambodia
in 2008-2009 was mainly attributed to the growth in Technological Change of 1.4%.
In 2009-2010, the decline is also attributed to the decrease in Technological
Change of 1.5%. Similarly, the increase in 2010-2011, is also attributed to the
positive change of 1.5% in Technological Change. When decomposing the
Efficiency change into Pure Technical and Scale Efficiency Change, results from
both DEA and MPI advocate that the dominant source of efficiency was scale
related rather than pure technically related. This implies that MFIs have been
operating at an appropriate scale of operations but relatively inefficient in managing
their assets and operating costs.
Field of Research: Efficiency; DEA; Malmquist Productivity Index; Microfinance
Institutions, Cambodia
1. Introduction
1.1 Microfinance
Microfinance is a term commercialized in 1970s by Bangladeshi economist;
Mohammad Younus. It is the provision of tiny loans, mainly agricultural based with
tailor-made contracts. The loans do not require collateral and the contracts have
flexible payment schemes based on the harvest cycle. This flexibility represents one
of the social orientated features of microfinance. Other than microloans or
microcredit, other services offered are microsavings, microinsurance, pension fund
and scholarships. These services had helped the poor to mitigate risks of loan
defaults and to protect from poor harvest season due to adverse climate.
Furthermore, the saving cultures could be created with microsavings and pension
fund. Scholarships are offered to the children to benefit from education, improve the
employment rate and gradually bring the family out of the poverty.
____________________________________________________________
Prof. Dr. Izah Mohd Tahir: Research Institute for Islamic Product and Civilization, Universiti Sultan Zainal
Abidin, Gong Badak Campus, Kuala Terengganu, Malaysia. (izah@unisza.edu.my)
Siti Nurzahira Che Tahrim: Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan, City
Campus, Pengkalan Chepa, Malaysia. (sitinurzahira.chetahrim@gmail.com)
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Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
The main characteristic of microfinance clients is being poor, which restrict them from
being assessed on creditworthiness and collateral by the financial institutions
(Dusuki, 2008), hence excluded financially. This financially excluded society is also
known as the unbanked. As no financial services are available to this group, they
remain trapped in the poverty spiral which can be inherited by their future
generations. Financially excluded society is often related to social consequence
mainly the lack of ability to afford basic needs such as food, shelter, personal
hygiene, health and education. The absence of such basic needs often leads to
social problems such as crime and domestic violence (Sinha et al., 2001).
Despite some failure stories, microfinance has proved to be one of the best solutions
to this situation. Evidently, the outreach of poorest clients through microfinance
increased significantly over the years. As reported in State of the Microcredit Summit
Campaign Report 2011, the outreach improved from 13.5 million in 1997 to 190.1
million in 2009. This is 1,308% growth within the period of 12 years (Reed, 2011). In
addition, around 100 impact studies since 1986 on microfinance also found a wide
range of evidence that microfinance programs can increase incomes and lift families
out of poverty. Microfinance is not only being “banking for the poor,” but now viewed
by many as an instrument that will aid society development (Roy & Goswami, 2013).
The performance of MFIs is therefore very crucial in order to provide continuous
financial and social support to the poor. Despite social goals strived by the MFIs, the
self-sustainability objective is key to exit from the permanent subsidies recipient
group (Yaron, 1994). This objective can be achieved through good performance
practice, critical to ensure uninterrupted operations of MFIs in providing services.
Based on a longitudinal and geographical wide study from 1995-2010 by Roy &
Goswami (2013), a new conceptual model of performance assessment for MFIs is
introduced. Eight dimensions of performance are proposed to be the more holistic
view of MFIs performance. They are efficiency, productivity, sustainability, social,
institutional characteristics, outreach governance and financial.
This study will focus on two of the eight dimensions suggested by Roy & Goswami
(2013) which are efficiency and productivity. Efficiency analysis will provide
information specifically on the use of resources and magnitude of wastes, while
productivity analysis will contribute additional information on source of efficiency and
productivity through thorough analysis. Besides, the results from both analyses will
strengthen and compliment the findings.
1.2 Overview of Microfinance Institutions in Cambodia
The economy of Cambodia since 2008 is stable with GDP annual growth of 6% to
7%, except for a downturn in 2009. The unemployment is low (below 2 per cent) and
stabilized inflation from 2010 onwards at 5.5 per cent in 2011. The imports are
slightly higher than exports in most of the years with both imports and exports shrunk
in 2009, most possibly affected by the economic downturn. The Gross National
Income (GNI) per capita increased steadily from USD660 in 2008 to USD800 in
2011. This is 21 per cent increase over the period of four years.
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Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
According to World Bank, more than 80 per cent of over 14 million population of
Cambodia lives in countryside. Approximately 39 per cent of the population in 2008,
mainly from the rural area, is living below poverty line. World Bank (2006) reported
that the poverty in Cambodia decline from 1994 to 2007. The ratios continue to
reduce annually to 20.5 per cent in 2011. Earlier, the United Nations Development
Programme 2007 reported that Cambodia was at 131st place out of 177 countries
with relatively high Human Poverty Index (38.6) and low Human Development Index
(0.60).
The employment opportunities are mostly from agriculture, followed by services sand
industry sector. In spite that, the production is the highest in the services sector,
followed by agriculture and least contributed by the industry sector. This partly
explain the high percentage of rural population lives in poverty. In addition, Chhay
(2011) found that the agricultural sources contributed minimally to household
incomes and argued that low incomes are generated by agricultural work compared
to non-agricultural work. This is further explained by rapid economic growth
concentrating in urban area focuses on production of garment, tourism and
constructions and limit the wealth spill over to the rural economy. This kind of
economic growth has increased inequality rapidly. Therefore, diversification of the
economy to rural area is timely to enable the rural poor to have a reasonable share of
contributions and benefits from the economic growth (Lanzavecchia, 2011).
Cambodia struggles to advance economically due to long ruthless histories that had
caused public disorder and total collapse of the financial system. Regardless of
desperate need of credit by the people, most of commercial banks that returned after
1991 peace agreement did not have provincial branches, an evidence of no interest
to close the gap of financing the poor (Conroy, 2003). As alternatives, both poor and
comfortable households turned to financial access provided by either friends,
relatives, exploitative local moneylenders (interest rates ranged from 10 to 15 per
cent) or MFIs (Alldén, 2009).
With the peace agreement signed in 1991 and the international recognition of a new
Cambodian government after 1993, international aid to Cambodia had increased. As
part of humanitarian aid packages, small loans were initially given to refugees along
the Thai border. The „INGOs‟, the international NGO movement took advantage of
governmental vacuum, after the withdrawal of Soviet influence in 1989. They handled
projects which are aimed to provide credit to poor micro-entrepreneurs in the rural
areas. Some local NGOs which incorporated financial intermediation functions,
founded from these successful „microcredit‟ projects. Over the years, microfinance
had developed into an essential part of the national financial sector. This was an
outstanding success of close partnership by international donors with private sectors,
the people and the government (Chhay, 2011; Alldén, 2009; Conroy, 2003).
The success of microfinance in Cambodia is partly attributed to the effective
implementation of regulations for microfinance. The development started in 1995
when the Credit Committee for Rural Development was established followed by the
founding of the Rural Development Bank (RDB) in 1998 with specific mission to
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Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
support the alleviation of poverty and to increase the living standards of the rural
population in Cambodia. Law on Banking and Institutions adopted in 1999
recognized banking institutions into three categories; 1) commercial banks; 2)
specialized banks and 3) microfinance institutions. Three categories of MFIs
supervised by National Bank of Cambodia (NBC) are 1) deposit-taking MFIs; 2)
licensed MFIs and 3) registered MFIs (All & Bose, 2014). As a result, a number of
NGOs are transformed into MFIs (Alldén, 2009; Chhay, 2011; Lanzavecchia, 2011).
Accordingly, microfinance in Cambodia is dominated by NGOs. NGOs which provide
microfinance services were required to register as NGO-MFO‟s (NGO – Micro
Finance Organisation). A precedential transformation took place when Association of
Cambodian Local Economic Development Agencies (ACLEDA) transformed from
NGO to a specialized bank in 1998 and upgraded to a commercial bank in 2003
(Crawford, Skully, & Tripe, 2011). The number of NGOs increased to approximately
ninety organisations in 2000, operating in a number of rural lending institutions. 32
NGOs and MFIs registered by the end of 2001. The top five NGOs which held more
than 80 per cent shares of the aggregated loan portfolio are ACLEDA Bank (a bank),
the EMT and Hatthakaksekar (licensed NGOs), and PRASAC and Seilanithih
(registered NGOs) (CMA, 2013; Chhay, 2011; Alldén, 2009 & Donaghue, 2004).
Another milestone was marked in 2004 with the establishment of Cambodia
Microfinance Association (CMA) by seven of the Cambodian MFIs. The CMA
provides assistance to the members by sharing microfinance information, providing
training and also representing members to negotiate with NBC on microfinance
matters (CMA, 2013; Chhay, 2011; Alldén, 2009 & Donaghue, 2004). NBC together
with the Ministry of Economy and Finance (MEF) and CMA develop the microfinance
sector in Cambodia. They implemented the Financial Sector Development Plan
(FSDP) 2000-2010 focusing on penetrating financial services in the rural area (All &
Bose, 2014). This had encouraged competition among NGOs to provide rural credit
and savings services as an alternative to the high interest rates on loans generally
offered in these areas. The number of financial institutions registered with NBC
increased over the years. Resultantly, the active clients grow rapidly also from
400,000 in 2004 to 800,000 in 2008. The importance of NGOs remains as most of
the country‟s 24 provinces currently have microcredit services perceived as poverty
alleviation tools, managed and funded by NGOs (All & Bose, 2014; CMA, 2013;
Chhay, 2011; Alldén, 2009 & Donaghue, 2004).
While Cambodia deserves recognition for taking a number of important steps to
improve the regulatory environment for microfinance, the management capacity and
internal controls of many microfinance organisations are relatively weak and their
scale is small (Donaghue, 2004). Despite being ranked as one of the world‟s best
microfinance environments, enabled by its economic growth, strong regulatory
regime and limited outreach of commercial banks (MIX Market), Cambodia is not
alienated from challenges and weaknesses.
Some challenges faced by the MFIs in Cambodia include the exclusion of the poorest
of the poor from the primary target of the institutions. The main clients are often the
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Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
moderately poor or vulnerable non-poor (Alldén, 2009). Nevertheless this targeted
segment of clients are still excluded from getting access from the formal financial
institutions (Lanzavecchia & Enache, 2013). MFIs are also having difficulties in
reaching the poorest of the poor in the remote rural areas. Most of the time, this
segment of the poor has neither collateral nor any possibility of joining a borrowing
group. Other than that, the low and competitive interest rates offered by the MFIs in
Cambodia are still high compared to neighbouring countries (Alldén, 2009). The MFIs
are also struggling to manage risks of their operations. Multiple lending taken by
borrowers had rapidly increased the probability of default, which put pressure on the
MFIs to take unfavourable actions such as writing-off the outstanding loan as
nonperforming, restructuring or rescheduling the loan, or bringing the client to court
(Lanzavecchia, 2011).
2. Literature Review
Literatures are selected to review on efficiency studies, mainly using non-parametric
DEA and Production Approach. The literature review also covers productivity studies
using Malmquist Productivity Index. Although there are many efficiency studies on
formal financial institutions such as banks, the literature in microfinance is somehow
very limited. There are remaining huge gaps for the efficiency study in microfinance
which need to be filled. Most of microfinance studies preferred non-parametric Data
Envelopment Analysis (DEA) to parametric Stochastic Frontier Analysis (SFA)
(Kablan, 2012). Annim (2010) suggests that DEA facilitates a comprehensive
analysis of the various facets of efficiency, notably pure technical and scale efficiency
variants.
As opposed to financial institutions, microfinance studies using DEA are dominated
by production approach compared to intermediation approach in selecting the
variables. One of the reasons is the importance of social goals in serving the poor
clients rather than the role in making profits for the institutions and investors. Some
examples of microfinance studies that chose production approach are done by
Bassem (2008), Ahmad (2011), Kipesha (2012) and Singh, Goyal, & Sharma (2013).
There are also studies that compares both efficiency using production and
intermediation such as studies done by Sedzro & Keita (2009), Haq et al., (2010) and
Kipesha (2013). Other approaches used include microfinance scopes and objectives
(Annim, 2010), also financial and social approach (Kablan, 2012).
There are some good DEA studies in microfinance around the globe which presented
good recommendations based on DEA empirical results. For example, Hassan &
Sanchez, (2009) study shows that technical efficiency is higher for formal MFIs than
non-formal MFIs. In addition, South Asian MFIs have higher technical efficiency than
Latin American and MENA MFIs. The source of inefficiency is pure technical rather
than scale, suggesting that MFIs are either wasting resources or are not producing
enough outputs.
Haq et al., (2010) compares analyses for both uncontrollable and controllable
variable as inputs in Africa, Asia, Latin America regions. The findings show non-
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Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
governmental MFIs particularly; under production approach were the most efficient.
However, bank-MFIs also found to be outperformed in the measure of efficiency
under intermediation approach. This result reflects that banks are the financial
intermediaries and have access to local capital market. It may be possible that bankMFIs may outperform the non-governmental microfinance institutions in the long run.
Ahmad (2011) study shows some differences of efficient MFIs for Constant Return to
Scale (CRS) and Variable Return to Scale (VRS) assumptions. There is a decline in
efficiency to most MFIs in Pakistan in 2009 as compared to 2003. Inefficiencies in
Pakistan are mainly technical in nature. Funding gap is identified as a dominant
external factor which may be closed by uplifting the prohibition of microfinance banks
from pledging security or sourcing foreign currency loans.
Other than assessing the efficiency, Singh, Goyal, & Sharma (2013) went on to also
identify determinants of efficiency Indian MFIs using correlations and TOBIT
regressions of selected variables with efficiency. The study found out that size was
proven a major determinant to efficiency while Southern MFIs were also found
statistically performed better than the rest of the other MFIs. As shown by the
regression results, higher number of staff is suggested to be less important than the
efforts to increase customer base.
As for productivity studies, very limited microfinance studies particularly using
Malmquist Productivity Index. An example is work of Nawaz (2010). This study is the
first attempt to measure the financial efficiency and productivity of global MFIs
considering the effect of subsidies. The three-stage analysis applied consist of
calculation of technical and pure efficiency scores, calculation of DEA-based
Malmquist indices to analyse the inter temporal productivity change and analysis of
Tobit Regression to test a series of hypotheses concerning the relationship between
financial efficiency and other indicators related to MFIs. In conclusion, overall
subsidies contribute to financial efficiency of MFIs albeit marginally. Following the
trade-off theory, MFIs which serve the poor tend to be less efficient than those with
relatively more stable income clients. Interestingly, lending to women is efficient only
in the presence of subsidies. The results also proved that MFIs in South Asia and
Middle East & North Africa tend to be less efficient than the others in 2005 and 2006.
Many recommendations were put forward for practitioners and researchers based on
these findings.
Productivity studies are also done in other industries. Among others include works of
Moffat (2008), Yahya, Muhammad, Razak, & Hadi (2010), Arjomandi, Valadkhani, &
Harvie (2011), Salleh (2012) and Sufian & Habibullah (2013).
Review of literatures showed limited studies in microfinance using DEA and MPI,
covers several countries outside ASEAN region, and none from Cambodia. This
motivates us to study MFIs efficiency and productivity study of Cambodia.
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Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
3. Methodology
3.1 Data Envelopment Analysis
This study will use DEA method with input orientation to calculate the efficiency
scores of MFIs in Indonesia. Analysis will include pure technical and scale
components of efficiency using VRS technology. Production approach is selected for
the study for the suitability with the sector in study. The efficiency score will be
generated using DEAP program.
Assuming the number of DMUs is s and each DMU uses m inputs and produces n
outputs. Let DMUk be one of s decision units, 1 ≤ k ≤ s. There are m inputs which are
marked with
(i = 1, ..., m), and n outputs marked with
(j = 1,...., n). The
efficiency equals to total outputs divided by total inputs. The efficiency of DMUk can
be defined as follows:
∑
The efficiency of DMUk
=
∑
(1.1)
In this study, the input oriented model is chosen. The dual problem model is used to
solve the problems. The CCR dual model is as follows:
[∑
∑
]
(1.2)
∑
∑
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Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
Where
𝜃 is the efficiency of DMU
is the slack variable which represents the input excess value
is the surplus variable which represents the output shortfall value
𝜀 is a non-Archimedean number represents a very small constant
means the proportion of referencing DMUr when measuring the efficiency of
DMUk
If the constraint below is adjoined, the CCR dual model is known as the BCC model.
∑
(1.3)
Equation (1.3) frees CRS and modifies BCC model to be VRS. For the measurement
of efficiency, the CCR model measures overall efficiency (OE) of a DMU, and the
BCC model can measure both the pure technical efficiency (PTE) and scale
efficiency (SE) of DMU. The relationship of OE, PTE and SE is as the equation (1.4)
below.
OE = PTE X SE
(1.4)
3.2 Malmquist Productivity Index
In measuring total productivity change and changes in the production technology in
each MFI unit‟s distance to the production frontier and in the scale size for each unit
evaluated, the analysis employs the notion of an input distance function proposed by
Shephard (1970). This distance function measures how much unit‟s inputs can be
proportionately increased given the observed level of its outputs. The structure of the
production technology with the input distance function is as follows:
(
)
{
|(
⁄ )
},
(2.1)
which measures the input-oriented technical efficiency of MFI j at time t relative to the
technology at time t (Shephard 1970). Since technical efficiency is measured relative
(
)≤ 1, with
(
) = 1
to the contemporaneous technology, we have
implying that unit j is on the production frontier and is technically efficient, while
(
) < 1 indicates that the unit is below the production frontier and is
technically inefficient. Before describing the Malmquist productivity index (MPI)
method, the distance functions with respect to two different time periods need to be
defined. The efficiency of unit j at time t relative to the technology at time t + 1 can be
represented by
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Proceedings of Eurasia Business Research Conference
16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2
(
)
{
⁄ )
|(
}
(2.2)
Similarly, the efficiency of unit j at time t + 1 relative to the technology at time t is
defined by the distance function:
(
)
{
⁄ )
|(
}
(2.3)
Caves, Christensen, and Diewert (1982) defined the MPI as:
(
(
)
)
(
)
(2.4)
or
(
(
)
)
(
)
(2.5)
The indices in Equation (2.5) provide measures of productivity changes. To avoid
choosing an arbitrary benchmark, two continuous MPIs are combined into a single
index by computing the geometric mean and then multiplicatively decomposing this
index into two sub-indices measuring changes in technical efficiency and technology
as follows (Fare et al. 1989; Fare et al. 1992).
(
)
(
)
(2.6)
and
[
(
(
)
)
(
(
)
)
]
(2.7)
The ratio in Equation (2.6) is an index of technical efficiency change between periods
t and t + 1, measuring whether unit j moves closer to or farther away from the best
practices during the time period. The value of
is greater than, equal to or
less than unity, depending on whether the relative efficiency of unit j is improving,
unchanging or declining during the period. The term
in Equation (2.7) is an
index of technology change, which gives the geometric mean of two ratios. A value of
greater than, equal to or less than unity indicates progress, no change or
regress in technology, respectively, between periods t and t + 1. From Equations
(2.4) to (2.7), the relationship between the MPI and its two sub-indices is:
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Proceedings of Eurasia Business Research Conference
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(2.8)
Clearly, productivity change can be decomposed into changes in both efficiency and
technology with Mt,t+1 greater than, equal to or less than unity representing a gain,
stagnation or a loss of productivity, respectively, between periods t and t + 1.
In principle, one may calculate the MPI in Equation (2.8) relative to any technology
pattern. We adopt the CRS technology to compute the MPI and its two sub-indices in
the preceding analysis. Next, we further disaggregate
into a component of
pure technical efficiency change (PEFFCH)
calculated relative to the
VRS technology and a component of scale efficiency change (SECH)
capturing change in the deviation between VRS and CRS technologies
according to the suggestion of Fare et al. (1994). That is,
(2.9)
where
(
)
(
)
(2.10)
(
)⁄
(
)⁄
(
(
)
)
(2.11)
where the subscripts „v‟ and „c‟ denote VRS and CRS technologies, respectively.
> 1 indicates an increase in pure technical efficiency,
<
1 indicates a decrease and
= 1 indicates no change in pure technical
efficiency. Similarly,
> 1 implies an increase in scale efficiency,
< 1 implies the opposite and
= 1 indicates that there is no change in scale
efficiency.
The productivity change and its components will be generated using DEAP program.
4. Empirical Findings and Discussions
In this paper, we use data gathered form MIX Market database and we only select
microfinance institutions that exist during the period (2008-2011) and where variables
used for the analysis are available. In total we have thirteen microfinance institutions
for the analysis. Following most studies in microfinance institutions for example
Bassem, 2008 and Ahmad, 2011,we adopt the production approach. Accordingly,
two outputs and two inputs are variables are chosen. The output vectors used are
Gross Loan Portfolio (Y1), Number of Active Borrowers (Y2), while Total Assets (X1)
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Proceedings of Eurasia Business Research Conference
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and Operating Expenses (X2) are the input vectors. Table 1 presents the descriptive
statistics of outputs and inputs employed in the study.
Table 1:
Descriptive Statistics for Outputs and Inputs Used, 2008-2011
Outputs (Y) and
Inputs (X)
2008
Mean
Y1
54.172
Y2
X1
X2
Std Dev.
Min
Max
124.959
0.376
464.478
67.228
85.978
2.037
226.262
74.762
187.200
0.444
692.877
7.663
16.969
0.289
63.329
61.073
144.910
0.679
538.237
2009
Y1
Y2
71.151
92.570
2.308
247.987
X1
93.013
245.218
0.755
904.821
X2
8.727
20.231
0.292
75.281
744.664
2010
Y1
85.080
200.739
1.263
Y2
80.277
100.091
2.219
265.937
X1
121.645
314.262
1.477
1160.569
X2
10.000
21.277
0.304
79.615
119.065
270.948
1.703
1006.604
88.411
105.935
2.636
275.739
162.055
401.466
2.006
1486.654
2011
Y1
Y2
X1
X2
12.067
23.020
0.386
86.523
Note: All vectors are in million USD except Y2 are in thousand persons
4.1 Efficiency Analysis of the Cambodian Microfinance Institutions
In this section, we will discuss the efficiency of MFIs in Cambodia. All computation
was performed using DEAP program. The efficiency of microfinance institutions in
Cambodia was examined for 2008-2011 using a common frontier to see the efficient
MFIs and years.
On average, overall technical efficiency for the period under study is 92%. The
average efficiency over the years ranged in between 91.1% to 92.7%. The trend is
upward from 91.1% in 2008 to 92.7% in 2011, with a small reduction in 2010. Except
in 2008, the main contributor to efficiency on average in all other years is the scale
efficiency. This means although the MFIs sizes varied, they operated at efficient
scales most of the time. This could be strength of Cambodian MFIs. However, the
scale efficiency starts to reverse its trend in 2011, when it drops to average of 97.6%.
In contrast, the pure technical efficiency trends downward on average and starts to
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Proceedings of Eurasia Business Research Conference
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catch up to 95% efficiency in 2011. This implies that the MFIs had on average
reduced efficiency in managing inputs since 2008 and started to improve from 2011.
The MFIs in Table 2 are ranked based on their average Technical Efficiency (TE).
The highest efficiency score achieved by PRASAC (97.5%) while the lowest average
TE scored by ACLEDA (80.9%). Yearly average TE scores are all above 91%, with
the lowest TE among MFIs was 67.9%. This shows that overall efficiency of MFIs in
Cambodia is high. Nine of the MFIs under study score more than 90% average TE,
while the other four MFIs had average TE of between 80-90%. Top MFI, PRASAC
had achieved frontier efficiency in most years due to strength in both pure technical
efficiency (PTE) and SE. Despite achieving frontier TE in some years, AMK and
Chamroeun had a dip in 2011 to 91.3% and 87.1% respectively. The dip was due to
SE for AMK and both PTE and SE had contributed to TE reductions for Chamroeun.
AMRET had great fall in TE in 2009 and continue to have reduction in TE to 93% in
2011. While CBIRD and IPR performed below average in most years, ACLEDA and
Seilanithih had continuously improved their TEs from below 80% in 2008 to 100%
and 98.9% respectively in 2011. The MFIs in Cambodia have mixed performances
during and post economic crisis, which could mean internal strength and undersupply
of microfinance services. Therefore, the MFIs are less likely to be influenced by
external factors, such as economic factor. The authorities in Cambodia are
recommended to take action particularly on mixed performances of MFIs.
The average PTE ranges from 93.9% to 95.9% has a U-shape trend, when it
recorded lower PTEs in 2009 and 2010 than in 2008 and 2011. MFIs which have
higher than 95% TE, scored PTE higher than average PTE, excluding PRASAC‟s
PTE in 2009 and Chamroeun‟s PTE in 2011. While Maxima, HKL and Sathapan
Limited scored the highest PTEs in 2009, PRASAC, CBIRD, AMK and ACLEDA have
the lowest PTEs in the same year. The AMRET was the only MFI which follows the
average PTE trend and KREDIT scored PTEs very close to the average. ACLEDA
continuously improves its PTEs from 77% in 2008 to 100% in 2011, in contrast with
IPR which remains in PTEs downward trend from 100% in 2008 to 71% in 2010.
Fortunately IPR‟s PTE increases to 85.3% in 2011. The results of PTE imply that
every MFI is struggling to remain technically efficient and had experienced fall in PTE
in some of the years. The close partnership between a few authorities in Cambodia is
an advantage for strategy and policy formulation that focus on assisting the MFIs to
increase their capabilities in managing inputs and reducing waste.
The average SE is high in all years, ranging from 95% to 98%. It is the strength for
most of MFIs, with PRASAC, KREDIT and Sathapana Limited consistently operate at
efficient size in all years of the study period. Maxima and IPR were least efficient in
size in 2008 whereas AMK, Chamreoun and AMRET recorded their lowest in 2011.
While ACLEDA improves its SE every year from 75.2% in 2008 to 100% in 2011,
AMK had a great fall from steady efficient scales in 2008, 2009 and 2010 to 91.3% in
2011. The results demonstrate that despite some reduction in SE for some MFIs and
years, in general most of the MFIs are operating at the right scales. It is important for
the MFIs to maintain high efficiency of scale as this is their strength, of which a
decision to shrink or to expand the MFIs needs to be considered wisely. The
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Cambodian authorities are recommended to advise that the scale expansion or
contraction of MFIs operation must be proportionate to inputs-outputs increase or
reduction.
Table 2
Efficiency Scores of MFIs in Cambodia, 2008 – 2011
Year
MFIs
2008
2009
2010
TE
PTE
SE
TE
PTE
SE
TE
PTE
SE
PRASAC
AMK
SAMIC-Limited
Chamroeun
KREDIT
1.000
1.000
0.976
1.000
0.962
1.000
1.000
0.977
1.000
0.962
1.000
1.000
0.999
1.000
1.000
0.933
0.967
0.953
1.000
0.955
0.933
0.968
0.964
1.000
0.955
1.000
0.999
0.989
1.000
1.000
0.967
1.000
0.977
0.986
0.937
0.968
1.000
0.979
1.000
0.937
1.000
1.000
0.998
0.986
1.000
Maxima
HKL
AMRET
Sathapana Limited
Seilanithih
CBIRD
IPR
ACLEDA
0.850
0.887
1.000
0.936
0.764
0.926
0.824
0.712
0.981
0.892
1.000
0.936
0.770
1.000
1.000
0.947
0.867
0.994
1.000
0.999
0.992
0.926
0.824
0.752
0.974
0.999
0.928
0.942
0.842
0.862
0.941
0.679
1.000
1.000
0.928
0.942
0.847
0.894
0.952
0.859
0.974
0.999
1.000
0.999
0.993
0.964
0.989
0.790
0.963
0.959
0.897
0.893
0.956
0.883
0.701
0.844
0.984
0.960
0.917
0.893
0.959
0.956
0.710
0.942
0.979
0.999
0.978
1.000
0.996
0.923
0.987
0.897
0.911
0.936
0.097
-0.929
0.959
0.981
0.066
-2.297
0.950
0.999
0.083
-1.591
0.921
0.942
0.086
-2.107
0.942
0.952
0.050
-0.708
0.977
0.999
0.057
-3.373
0.920
0.956
0.080
-1.858
0.939
0.959
0.075
-2.634
0.980
0.996
0.033
-2.033
Mean
Median
Std Dev
Skewness
Year
MFIs
2011
Average 2008 - 2011
TE
PTE
SE
TE
PTE
SE
PRASAC
1.000
1.000
1.000
0.975
0.975
1.000
AMK
SAMIC-Limited
0.913
0.966
1.000
0.969
0.913
0.996
0.970
0.968
0.992
0.972
0.978
0.996
Chamroeun
KREDIT
0.871
0.935
0.925
0.935
0.942
1.000
0.964
0.947
0.981
0.947
0.982
1.000
Maxima
HKL
0.955
0.895
0.972
0.895
0.983
1.000
0.936
0.935
0.984
0.937
0.951
0.998
AMRET
Sathapana Limited
0.895
0.905
1.000
0.905
0.895
1.000
0.930
0.919
0.961
0.919
0.968
1.000
Seilanithih
CBIRD
0.989
0.876
0.994
0.899
0.995
0.974
0.888
0.887
0.893
0.937
0.994
0.947
IPR
ACLEDA
0.845
1.000
0.853
1.000
0.990
1.000
0.828
0.809
0.879
0.937
0.948
0.860
Mean
0.927
0.950
0.976
0.920
0.947
0.971
Median
0.913
0.969
0.995
0.939
0.961
0.996
Std Dev
Skewness
0.052
0.145
0.051
-0.531
0.036
-1.545
0.078
-1.429
0.060
-1.870
0.056
-2.492
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Proceedings of Eurasia Business Research Conference
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4.2 Productivity Analysis of the Cambodian Microfinance Institutions
In this section, we will discuss the productivity change of MFIs in Cambodia
measured by the MPI and decomposed the changes in the Total Factor Productivity
Change to Efficiency Change and Technological change. We also decomposed the
changes in efficiency into Pure Technical Efficiency and Scale Efficiency Change.
This is presented in Table 3. In this study, we employ Dynamic Malmquist
Productivity Index which takes into accounts changes of productivity for every two
subsequent years. All indices are relative to the previous year and hence the output
begins with the year 2009. Therefore the Malmquist Productivity Index and its
components between 2008 and 2009 take the initial score of 1.000 in 2008. Hence,
any score greater than 1.000 indicates an improvement (progress) and lower than
1.000 indicates worsening (regress). The similar process is used for 2009-2010 and
2010-2011 productivity change analyses.
Table 3, 4 and 5 depicts the Malmquist results for the microfinance institutions in
Cambodia for the period 2008-2011, which ranked by total factor productivity change.
The results suggest that the MFIs progress in productivity by 1.7% in 2009, regress
by 0.6% in 2010 and make productivity improvement of 0.9% in 2011. The
advancement in the productivity of the MFIs in Cambodia in 2009 was mainly
attributed to the increase in both efficiency change of 0.3% and technological change
of 1.4%. Further decomposition of productivity reveals that the scale efficiency
increase had caused the improvement in efficiency change. In 2010, the productivity
decline is attributed to the decrease in technological change of 1.5%. While the scale
efficiency continuously improved in 2010, the pure technical efficiency reduced
further by 0.9%. However, in 2011, both technical and pure technical efficiency
improvements have increased the productivity by 0.9% despite a regress of scale
efficiency by 0.1%.
This reflects that technological advancement has the biggest influence in the
productivity changes during the study period compared to efficiency change. Further
decomposition of productivity source suggests that the reduction of pure technical
efficiency in 2009 and 2010 reduced the effect of scale efficiency advancements in
those years. Both improvements in technology and skills in managing inputs
contributed to the productivity progress in the final year of study (2011). This implies
that MFIs have been performing well in operating at the right scale but require more
efforts to progress in managing their operating costs, while keeping up with the
technology.
The findings in table 3 shows that MFIs with positive productivity change had
performed well in technology and scale efficiency in 2009. Some MFIs have
outstanding improvements and compensated the regress of more than half of the
total MFIs under study in that year. For example, Maxima and HKL made the highest
improvements of 14.5% and 13.2% productivity change respectively. While Maxima
high productivity progress was due to scale efficiency increase, the HKL made
technological advancement while maintaining the other aspects of operations (scale
and technical). Seilanithih and IPR made more than 10% improvement in total
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productivity with advancement of technology and scale as source of productivity for
IPR and pure technical improvement for Seilanithih. Both Chameroeun and
Sathapana Limited made technological improvements to achieve productivity
progress. As for MFIs which decreased in productivity, they mainly had reduction in
scale efficiency and technology changes.
Table 3
The Dynamic Malmquist Productivity Index of MFIs in Cambodia, 2008-2009
MFIs
TFP
Chg
TECH
Chg
EFF
Chg
PTE
Chg
SE
Chg
2008-2009
Maxima
1.145
0.974
1.176
1.000
1.176
HKL
1.132
1.132
1.000
1.000
1.000
Seilanithih
1.104
0.973
1.134
1.138
0.996
IPR
1.101
1.058
1.041
0.974
1.069
Chamroeun
1.054
1.054
1.000
1.000
1.000
Sathapana Ltd
1.012
1.037
0.976
0.992
0.983
KREDIT
0.999
1.009
0.990
1.002
0.988
SAMIC-Ltd
0.990
0.999
0.990
0.976
1.015
ACLEDA
0.975
1.148
0.849
1.000
0.849
AMK
0.952
0.952
1.000
1.000
1.000
PRASAC
0.945
0.982
0.962
1.000
0.962
CBIRD
0.931
0.975
0.955
0.890
1.073
AMRET
0.919
0.919
1.000
1.000
1.000
Average
1.017
1.014
1.003
0.997
1.006
In 2010, seven of MFIs regressed in productivity with Seilanithih‟s productivity
deteriorates for more than 30% as shown in table 4. The rest of the MFIs made slight
progress, except for ACLEDA and Seilanithih. This had results in total productivity
regress by 0.6% in the year. IPR had large reduction in pure technical efficiency
which is the main source of its productivity deterioration. On contrary, ACLEDA has
made a breakthrough performance when it successfully improved in scale efficiency
by 34.6%. This explains ACLEDA‟s high productivity progress of 28.5%. On the other
hand, the source of high productivity by Seilanithih was pure technical change, while
AMK‟s 5.6% productivity progress was mainly due to technological change
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Table 4
The Dynamic Malmquist Productivity Index of MFIs in Cambodia, 2009-2010
2009-2010
ACLEDA
1.285
0.955
1.346
1.000
1.346
Seilanithih
1.134
0.997
1.138
1.133
1.005
AMK
1.056
1.056
1.000
1.000
1.000
PRASAC
1.039
1.000
1.039
1.000
1.039
SAMIC-Ltd
1.026
0.996
1.030
1.025
1.005
CBIRD
1.024
0.996
1.028
1.124
0.915
Maxima
0.989
0.996
0.993
1.000
0.993
KREDIT
0.984
0.998
0.986
0.981
1.005
AMRET
0.950
0.976
0.973
1.000
0.973
Sathapana Ltd
0.949
1.001
0.949
0.933
1.017
HKL
0.932
0.939
0.992
0.994
0.998
Chamroeun
0.903
0.903
1.000
1.000
1.000
IPR
0.743
1.000
0.743
0.744
0.999
Average
0.994
0.985
1.009
0.991
1.019
Based on results in table 5, ACLEDA continues to make the highest productivity in
2011 with a progress of 21.4%. As opposed to improvement in 2010, the source of
productivity in 2011 is the technological advancement. ACLEDA outstanding
performances demonstrated the implementation of systematic strategy in achieving
high efficiency and productivity. IPR reversed its position from the previous year by
making a remarkable productivity progress of 20.4%, mainly due to pure technical
efficiency improvements as well as some technological progress. Most of other MFIs
made technological progress and totalled to yearly technological advancement of
1.5%. Apart from IPR, the rest of the MFIs maintain their pure technical efficiency as
the previous year. Operational scales for most of the MFIs are maintained with no
change in efficiency and some MFIs reduced scale efficiency slightly from the year
before.
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Table 5
The Dynamic Malmquist Productivity Index of MFIs in Cambodia, 2010-2011
MFIs
TFP
Chg
TECH
Chg
EFF
Chg
PTE
Chg
SE
Chg
2010-2011
ACLEDA
1.214
1.214
1.000
1.000
1.000
IPR
1.204
1.031
1.168
1.222
0.956
PRASAC
1.088
1.088
1.000
1.000
1.000
Sathapana Ltd
1.060
1.085
0.978
0.978
1.000
Seilanithih
1.029
1.014
1.014
1.008
1.006
KREDIT
0.997
1.012
0.986
0.979
1.008
CBIRD
0.990
1.019
0.971
1.000
0.971
Maxima
0.990
1.026
0.964
1.000
0.964
SAMIC-Ltd
0.989
1.004
0.985
1.000
0.985
AMRET
0.976
1.002
0.974
1.000
0.974
HKL
0.930
1.030
0.903
0.902
1.001
Chamroeun
0.871
0.871
1.000
1.000
1.000
AMK
0.842
0.842
1.000
1.000
1.000
Average
1.009
1.015
0.994
1.005
0.989
5. Conclusions
The objective of this study is to investigate the efficiency and productivity change of
microfinance institutions in Cambodia during the period of 2008-2011. Using a nonparametric approach, Data Envelopment Analysis enables us to distinguish between
technical, pure technical and scale efficiencies. The total factor productivity change
and the sources of change are analysed using Malmquist Productivity Index, which
helps us to recognise the differences of technological, pure technical and scale
efficiency changes.
The results suggest that the mean overall or technical efficiency has upward trend
from 91.1% in 2008 to 92.7% in 2011. MFIs in Cambodia have high technical
efficiency with yearly average of above 91%. The lowest technical efficiency (TE) of
67.9% is scored by ACLEDA in 2009. The average TEs are stable throughout the
years and very less likely to be influenced by economic crisis. Scale efficiency is
identified as the strength for most of MFIs and had improved remarkably in 2010. The
4-year average TE of 9 MFIs are above 90% and the lowest average TE is 80%.
PRASAC, KREDIT and Sathapana Limited had achieved efficient scale throughout the
study period. Also, PRASAC was on the frontier in most years and being the top MFIs
in this study. ACLEDA and Seilanithih scored TE of 100% and 98.9% respectively in
2011, a remarkable improvement from the lowest TE in 2008.
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As for productivity, the progress and regress change minimally every year. Productivity
progress in 2009 is due to technological and scale efficiency improvements, with some
MFIs improved more than 10%. The productivity regressed by 0.6% in 2010 because
most MFIs experienced technological deterioration and technical efficiency reduction.
In 2011, technological and technical efficiency improvements contributed to
productivity progress. However, the SE starts to reduce in that year. ACLEDA made
breakthrough improvements every year: 30% and 20% technological advancement in
2009 and 2011, and 15% SE progress in 2010 to successfully achieve frontier
efficiency in 2011. Maxima made the highest SE progress in 2010, Seilanithih made
higher than 10% PTE progress in 2010 and IPR achieved the biggest PTE
improvements in 2011.
During the period of study we found that the results from efficiency analyses are
evidently consistent with productivity change analyses, particularly on overall
movements of pure technical and scale efficiencies as well as by MFIs. In addition,
results from efficiency analysis show that scale efficiency has mainly contributed to
efficiency in all years. This implies that scales of operation are highly efficient and are
improving over the years of study. Nevertheless, conclusion made from the
productivity analysis suggests that besides scale efficiency, technological progress
was also found to contribute to productivity in 2008 and 2011. The Malmquist
Productivity Index confirms the technological efficiency effect which implies that
technology used in managing inputs is the main influence in improving the growth of
productivity change of Cambodian MFIs.
The findings of this study provide empirical evidence on the performance of
microfinance institutions in Cambodia in terms of efficiency levels and productivity
changes. This empirical evidence would have implications and contributions to policy
making related to microfinance institutions.
This study is also hoped to carry significant contribution to theory and application in
the field as MFIs in Cambodia have relatively high efficiency in particularly ASEAN
region. The results in this study has potential to catalyst more initiatives in the study of
MFIs efficiency in Cambodia and other ASEAN countries, which in turn would promote
further lessons exchanging and improvement in the level of efficiencies of the
microfinance institutions in countries in this region.
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