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) 1 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. 2 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 3 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 4 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- 5 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. 6 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) ∑ ∑ 7 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 8 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: 9 Proceedings of Eurasia Business Research Conference 16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2 (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) 10 Proceedings of Eurasia Business Research Conference 16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2 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 11 Proceedings of Eurasia Business Research Conference 16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2 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 12 Proceedings of Eurasia Business Research Conference 16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2 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 13 Proceedings of Eurasia Business Research Conference 16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2 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 14 Proceedings of Eurasia Business Research Conference 16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2 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 15 Proceedings of Eurasia Business Research Conference 16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2 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. 16 Proceedings of Eurasia Business Research Conference 16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2 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. 17 Proceedings of Eurasia Business Research Conference 16 - 18 June 2014, Nippon Hotel, Istanbul, Turkey, ISBN: 978-1-922069-54-2 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. 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