DE MONTFORT UNIVERSITY LEICESTER BUSINESS SCHOOL MICROFINANCE EFFICACY: THE TANZANIAN EXPERIENCE
JESCA MHOJA NKWABI [BBA] P15241712 A dissertation submitted in partial requirement for the award of
MASTER IN INTERNATIONAL BUSINESS AND FINANCE
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Abstract
AN ABSTRACT OF The Efficacy of Microfinance Institutions: The Tanzanian Experience By JESCA NKWABI Submitted to the faculty of business and law in partial fulfilment of the requirement for the degree of Master of International Business and Finance De Montfort University September 2016
Purpose: The main purpose of this dissertation is to investigate the efficacy of microfinance institutions in Tanzania and to provide evidence of these services to date and to discuss the roles played by the regulatory bodies both internationally and locally. This paper mainly aims to find the technical efficiency of MFIs in Tanzania. Design and Methodology: The study used secondary sources to gather data of the eight MFIs from the mix market.org, using non-probability sampling methods from the period of 2012 to 2014.The DEA model was adopted where selected inputs and outputs were analysed by applying the production approach and intermediation approach. The DEAP software was used for analysis and after that descriptive statisticswere applied. Findings: From the study, it was found that under the production approach the efficacy of MFIs was better in 2013 with scores of 94.2%, 100% and 94%, than in 2014 and 2012, this was because there was more wastage of resources and less production of outputs as well as poor regulatory systems in the country. In contrast, in the intermediation, approach organisations performed better in 2014 than the previous years with scores of 64%, 45% and 73.3% indicating that MFIs were helping in channelling more loans to their customers.
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Limitations of the study: This study was only limited to Tanzania and in particular only to one region and initially the aim was to investigate 21 organisations, but there was theunavailability of data only eight MFIs were taken into count. Furthermore, the duration of study is less than a year and since secondary data are used, the information is not up-to-date. Conclusions and recommendations: To solve the inefficacy of MFIs it is suggested that wastages be reduced and tighter regulations to be set. Future research should consider exploring more regions and pinpointing further, as to why MFIs have sorted to greed and if they should still be considered tools for poverty alleviation Keywords: Efficiency, Tanzania, MFIs, Poor, Poverty alleviation, DEA
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Dedication I dedicate this dissertation to my lovely family members who have shown me their support and encouraged me to do my best especially my father who has always supported me academically and who has taught me to believe in myself and also reminded me about the importance of hard work.
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Acknowledgement
The success and outcome of this project required a lot of support and guidance from many people who helped me in successfully completing it. I would like to express my sincere gratitude to my supervisor Bob Illidge who gave his time to support me and to ensure that I complete this on time. Throughout the process, he gave me a lot of guidance and was able to answer all of my doubts. I express my sincere thanks to the dissertation module leader,Dr.HulyaOztel for her guidance and support. I wish to express my sincere gratitude to Mr Shelton Giwa and Mr William Marathi who guided me on the entire research process. Iam also grateful to my module leader Martyn Kendrick for giving me this wonderful opportunity to complete my project. Iam equally grateful to the faculty of business and law for this wonderful opportunity and for the support and skills that I was able to pick up in other modules as well. Finally, yet importantly, I would like to thank my family members for their unending support. I would also like to thank Prof. S. Gowthaman, MBA, M. PHIL for his support. JescaNkwabi
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Contents Abstract ........................................................................................................................................................ 1 Dedication.................................................................................................................................................... 4 Acknowledgement ...................................................................................................................................... 5 List of tables ................................................................................................................................................ 9 List of Abbreviations................................................................................................................................. 10 1.1 Introduction ......................................................................................................................................... 12 1.2 Meaning of microfinance .............................................................................................................. 12 1.3 Origin of microfinance ................................................................................................................... 12 1.4 The classification of poor people................................................................................................. 12 1.5 Role of microfinance in reducing poverty................................................................................... 13 1.6 Need for microfinance efficacy .................................................................................................... 14 1.7 Background information on Tanzania ......................................................................................... 14 1.8 MFIs in Tanzania ........................................................................................................................... 14 1.10 Regulations governing MFIs in Tanzania ................................................................................ 15 1.11 Capital Requirements ................................................................................................................. 16 1.12 Lending ......................................................................................................................................... 16 1.13 Termination of the MFIs ............................................................................................................. 16 1.14 International rules governing MFIs ........................................................................................... 16 1.15 Objectives of the study ............................................................................................................... 17 1.16 Statement of the problem ........................................................................................................... 17 1.17 Research questions .................................................................................................................... 17 1.18 Limitations of the study ................................................................................................................... 18 1.19 Organisation of the dissertation .................................................................................................... 18 Chapter 2-REVIEW OF LITERATURE ................................................................................................. 20 2.1 Meaning of efficiency ........................................................................................................................ 20 2 .2 Distinction between MFIs and Savings Cooperatives............................................................. 20 2.3 The DEA Model .................................................................................................................................. 21 2.4 The model inputs and outputs ..................................................................................................... 22 2.5 Different DEA Scales .................................................................................................................... 23 2.6 Previous studies on efficiency of MFIs ........................................................................................... 23 6|Page
2.7 Research Gap between Theory and Microfinance Practice ....................................................... 28 2.8 Conclusion .......................................................................................................................................... 29 Chapter-3 METHODOLOGY .................................................................................................................. 31 3.1 Research Methodology ..................................................................................................................... 31 3.2 Location ........................................................................................................................................... 31 3.3 Research design ............................................................................................................................ 31 3.4 Data collection.................................................................................................................................... 32 3.5 Sources of data .................................................................................................................................. 33 3.6 Sampling ............................................................................................................................................. 33 3.7 Number of firms ................................................................................................................................. 34 3.8 Time duration ..................................................................................................................................... 34 3.9 Tools for analysis. .............................................................................................................................. 34 3.10 Rationale for choosing various methods...................................................................................... 35 3.11 Statistics............................................................................................................................................ 36 3.12 Ethical issues ................................................................................................................................... 38 Chapter 4-DATA ANALYSIS .................................................................................................................. 40 4.1 Meaning of data analysis .................................................................................................................. 40 4.2 Production Approach..................................................................................................................... 40 4.3 Overall Production results ................................................................................................................ 40 4.4 Intermediation approach ................................................................................................................... 44 Chapter 5-FINDINGS AND DISSCUSIONS ........................................................................................ 50 5.1 Data collection.................................................................................................................................... 50 5.2 Production approach ..................................................................................................................... 50 5.3 Intermediation approach ............................................................................................................... 51 DISCUSSION............................................................................................................................................ 52 5.4 Purpose of the Study................................................................................................................. 52 5.5 Results of the study ................................................................................................................... 52 5.6 How the results relate to other efficiency studies conducted by other researchers ........ 53 5.7 Limitations of the study ............................................................................................................. 53 5.8 Recommendations for future research ................................................................................... 54 5.9 Conclusion .................................................................................................................................. 54 Chapter 6: CONCLUSION ...................................................................................................................... 56 7|Page
6.1 Reasons why MFIS’s are inefficient................................................................................................ 56 6.2 Conclusion .......................................................................................................................................... 57 RECOMMENDATIONS ........................................................................................................................... 58 6.3 Recommendations for maintaining efficacy and future research ............................................... 58 6.4 Review of laws governing MFIs ....................................................................................................... 58 6.5 Coordination between the government and MFIs ........................................................................ 58 6.6 Reaching populations in need to create better opportunities ..................................................... 58 6.7 Proper utilisation of resources ......................................................................................................... 58 6.8 Further recommendations for future research .............................................................................. 59 Personal Reflection .................................................................................................................................. 60 Introduction................................................................................................................................................ 60 Conclusion ................................................................................................................................................. 61 References ................................................................................................................................................ 62 Appendices................................................................................................................................................ 71 Appendix 1(DEA inputs and outputs) .................................................................................................... 71 Appendix 2: Gantt Chart .......................................................................................................................... 72 Appendix 3: (Production Approach Calculations)................................................................................ 73 Appendix 4 :( Intermediation Approach Calculations) ........................................................................ 78 Appendix 5:( Topic Suggestion Form,Topic Agreeement form, Literature ,Discusion Forms,and Ethics Form) .............................................................................................................................................. 83
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List of tables Name of the Table Table 4.1
Title of the table
Page number
Production Approach 40 Summary(2012)
Table 4.2
Production Approach 41 Summary (2013)
Table 4.3
Production Approach 42 Summary (2014)
Table 4.4
Intermediation approach
44
Summary
(2012) Table 4.5
Intermediation Approach
45
Summary
(2013 Table 4.6
Intermediation Approach
46
Summary
(2014) Table 4.7
Mean Summary for 47 Three Years (2012 2014)
Production
Approach
Table 4.8
Mean Summary for 47 Three Years (2012 2014)
Intermediation
Approach
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List of Abbreviations CRDB: Cooperative Rural Development Bank CRS: Constant Return Scale DEA: Data Envelopment Analysis DMU: Decision Making Unit FINCA: Foundation for International Community assistance IISD: International Institute for Sustainable development IMF-International Monetary Fund MFIs-Microfinance institutions NMB: National Microfinance Bank PRIDE: Promotion of Rural Initiative and Development Enterprise SACCOS: Savings and Credit Cooperative Societies UNDP: United Nations Development Program VRS: Variable Return Scale
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CHAPTER 1
INTRODUCTION
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1.1 Introduction This chapter introduces microfinance and its origin, which is Bangladesh. It also explains why microfinance services are needed and how MFIs operate in Tanzania and rules that govern them. Lastly, at the end the organisation of the dissertation is presented. 1.2 Meaning of microfinance Microfinance is the provision of financial services to the poor or to those who do not have banking facilities. According to Bakhtiari (2011), microfinance is the way in which low-income individuals can lift themselves from poverty. However, (Ledgerwood, 1999) notes that MFIs not only providefinancial services, such as credit and savings, but they also help in social services, such as group formations, training and provision of banking literacy. Thus, microfinance is an important tool in reducing poverty in both developing and developed countries and acts as both a banking tool and a development tool. 1.3 Origin of microfinance The origin of microfinance can be traced back to Bangladesh. According to the Grameen Bank Bank for Small Business (2016), Muhammad Yunus, also known as the father of microfinance, introduced microloans in 1983 to help the poor and, in, particular women. Today, microfinance has spread to different parts of the world with a mission to assist those in need by making loans available at a lower interest. However, Batilana and Dorado (2010) argue that the expansion of microfinance to other parts of the world affected the initial purpose of microfinance, which was not profit-oriented, towards profit oriented. As a result, this commercialisationhas then led to an increase in interest rates and, furthermore, to a decline in the industry. Conversely, Lutzenkirchen and Weistroffer (2012) argue that the decline in the industry was due to the crisis of 2008, which slowed the sector, resulting in huge losses. Thus, originally, microfinance was purely non-profit, but, with time, the focus has shifted to profit making 1.4 The classification of poor people The increasing popularity of microfinance services has led to various questions as who exactly are refferedto as poor people. Are they the ones that earn below the average income or are the poor referred to as those who have no income? 12 | P a g e
To answer these questions, (Robinson ,2001;Hulme and Mosley,1996) argue that there are two distinctions thatcan be made to categorise the poor, which are the extremely poor, who earn below the average wages, and the economically poor, who encounter some difficulties along the way, such as low wages and food shortages, but do possess some form of work. On the other hand, (Worldbank.org, 2016) defines the extremely poor as those who live on under $1.25 a day, while the moderate poor lives below $2 per day. Thus, the poor can both be those who do not have any source of income to support themselves and those who earn below and fail to get basic needs. 1.5 Role of microfinance in reducing poverty Microfinance has been playing an important role in the development of different economies in the world by assisting the poor. It has been over a decade now since, in 2005, when the United Nations declared that Microfinance was important and called it the year of microcredit. Bakhtiari (2011) argues that MFIs have largely contributed to the reduction of poverty in different parts of the world. Firstly, they have been able to provide financial assistance to the poor, which formal organisations, such as banks, had not been able to provide due to collateral. Secondly, MFIs have been able to bring aboutthe development of economies by ensuring that they enhance the income capacity of people and educating them on the importance of resource management. Similarly,Kinde (2012) argues that MFIs help in the improvement of financial services through generating income for the poor, which then helps them to raise themselves from poverty. On the other hand, Hulme (2000) argues that, even though MFIs can create opportunities for the poor through provisions of loans to generate their income and reduce poverty, not all loans provided are successful in doing so,because some people who have received grants find it hard to repay loans, due such circumstances as floods, droughts or even theft. Furthermore, he argues that microfinance has to act as a strategy that can help in reducing poverty only to an extent and it should not receive any praise as an important tool for poverty reduction because there is a significant amount of greed involved in the industry. Thus, rather than praising MFIs for poverty reduction, their effectiveness should be measured and, where possible, developments should be made to ensure that those getting loans benefit fully from them and can repay them. 13 | P a g e
1.6 Need for microfinance efficacy Microfinance has grown over the years and has been an effective tool for minimisation of poverty. Nevertheless, there have been many criticisms made regarding the changes on how it has been operating. Armistead (2012) published an article in the Telegraph stating how profit makers have attacked the industry and are under threat from greed. Similarly,Sinclair(2012)notes that many microcredit programs focus on just exploiting people even further through their lending schemes,claiming to help the poor, while, at the same time, profiting themselves. Moreover, (Ledgerwood, 1999),argues that there have been more failures than successes in the microfinance industry. Such failures include mismanagement of funds that result in failure to provide better services and inefficiencies, which cause costs to bear, thus being unable to reach more people. 1.7 Background information on Tanzania Tanzania is a large country in East Africa with a total population of 44.9 million people. According to UNDP (2016), the country has a poverty rate of 65.6%, per capita income of 584$, 7% economic growth, 12.1% inflation and 11% unemployment rate. Tanzania largely depends on agriculture, which contributes to nearly 1/4th of the GDP. IMF.org (2016), states that the country’s GDP is increasing, and areas such as transportation communication and finances have grown significantly. On the other hand, although the country has grown over the years, poverty and underdevelopment remain key issues in the country. A report from Levitan (2014) states that, even though the country’s GDP has been impressive, the countryremains the poorest country in the world Similarly, UNDP ranked Tanzania 152nd out of 187 in a report on the Human Development Index (UNDP in Tanzania, 2016),which implies that poverty rates are still growing. 1.8 MFIs in Tanzania Tanzania has over 52 banks, 5500 savings and credit unions and over 170 NGOs, all providing micro credit (Kasanda and Parkes, 2015). The main providers of microfinance services are, however, CRDB, Akiba Commercial Bank,NMB,Saccos,Seda,Pride-Tanzania, Finca and Seda.The Bank of Tanzania governs these institutions. 14 | P a g e
Although the microfinance sector has grown and has many microfinance providers, poverty is still a big problem in the country, which then leads to the question of whether MFIs in Tanzania are contributing to the reduction of poverty. Kipesha (2013) notes that MFIs still fail to perform effectively in Tanzania, as well as in other parts of the world. To confirm this, previous studies from Hassan and Sanchez (2009) Skully and Pathan (2009) and Basu et al. (2004) all have investigated the efficiency of the services provided in other parts of the world.
1.9 Microfinance laws in Tanzania Microfinance was introduced in 1995 in Tanzania but was ineffective then due to lack of regulatory frameworks (MFTransparency.org, 2016) However, in 2001, a new law, known as the Microfinance National Policy, was introduced (Randhawa and Gallardo, 2003). According to Dimoso and Masanyiwa (2008), the national microfinance policy was established with the aim of ensuring that the country had efficient and effective microfinance services. Similarly, Rubambey (2005) argues that the policy focuses on sustainability and aims to achieve it through proper supervision, regulation and control. However,Kasanda and Parkes (2015) argue that, despite the law being enforced, it has failed to control the MFIs in the country and, as a result, it has led to the poorregulatory framework. Furthermore, other laws that govern the sector, which include the BOT Act (2006),BFI (2006), FSC regulations (2005), etc.,do not clearly specify the laws that govern MFIs, which causes many of these organisations to remain unregulated.Thus, paving a way for poor services and personal gains from the funds that are received from donors. They also argue that, since the laws have given the power of pricing to the MFIs, as such, they can decide on the interest rates to charge, the customers have been left vulnerable to any exploitation from the microfinance providers. Thus, this clearly shows that the regulations in the country need be reframed inorder to monitor the MFIs in the country. 1.10 Regulations governing MFIs in Tanzania The Bank of Tanzania (BOT) regulates MFIs in Tanzania, and monitors and intervenes in all issues relating the operations of MFIs. The board regulates about 1880 institutions. The microfinance laws were proposed on 25/05/2005 and signed by the then Governor Daudi T.S. Balali BOT, (2005). 15 | P a g e
1.11 Capital Requirements MFIs in Tanzania that have branches all over the nation are supposed to maintain a minimum capital of about eighty hundred million Tanzanian shillings, while, for those that do not have branches, the minimum allowance is 200 million shillings. Furthermore, the minimum capital should be reviewed at least once a year. Also, before MFIs can carry on their business, the shareholders must be registered as specified in the Banks Act, with a minimum of two boards of directors as well as trained staff. 1.12 Lending Regarding lending, the MFIs must not charge the borrowers any other amount apart from the one that is specified at the beginning of the agreement, plus interest, usually in the domestic currency. Furthermore, the MFIs, as the lenders, are required to ensure that they adhere to the policies and procedures specified. They are also required to ensure that they know the history of their clients in terms of how well they can pay and what funds have been granted in the past. These institutions are also required to monitor their activities through proper auditing and reporting through the company reports. 1.13 Termination of the MFIs In the case where an MFI fails to meet the legal requirements or engages in the act of cheating, then the firm can no longer proceed with micro-credit services and is no longer licensed to do business. 1.14 International rules governing MFIs MFIs globally are regulated under the BFIS (Bank for International Settlement) as well as the International Institute for Sustainable Development. According to a report from (Bank For International Settlements, 2010), a guide was provided by the Basel Committee to ensure MFIs operating in different parts of the world follow similar principles in the activities undertaken. The main principles outlined were to ensure proper licensing, capital adequacy and consumer protection to avoid a collapse of the financial systems. Similarly, Pouchous, (2012) notes that the IISD is responsible for monitoring the MFIs to ensure that they give minimum interest rates,and also to reduce financial crimes, as well as to ensure more outreach.
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1.15 Objectives of the study The main aim of this study is to examine the emergence of microfinance and assess its efficacy in reducing poverty in Tanzania •
To examine the entities that benefit from microfinance.
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To assess the evidence of the efficacy of microfinance to date.
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To examine the roles of the government and regulatory bodies in supervising the process of lending.
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To consider the prospects of microfinance in helping to reduce poverty.
1.16 Statement of the problem There has been an increasing concern in the country about the provision of better services to microfinance clients from MFIs as well as the various rules and regulations. Previous studies that have been undertaken to measure the efficacy of MFIs include (Kipesha 2013;2012;Marwa and Aziakpono 2016),who all found that MFIs are still ineffective, because of poor regulations and an imbalance of the utilisation of resources. In contrast, outside the country, previous studies from (Hermes, Lensink and Meesters,2009, Kablan (2012), Hassan and Sanchez (2008) and Haq, Skully and Pathan (2010) have found that, while some MFIs are performing better, others are underperformingbecauseofinefficient production of outputs, such as the provision of loans, lower number of borrowers, etc. Thus, there is a need to investigate the MFIs in Tanzania and to find their efficiency level and recommend improvements for the poor performers. 1.17 Research questions What is the importance of MFIs? What rules govern MFIs in Tanzania? Do MFIs contribute to poverty reduction? What is the efficacy of Tanzanian MFIs?
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1.18 Limitations of the study •
The study is undertaken only for less than a year.
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The study uses secondary data, which are not up to date.
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Limited data are available on the number of MFIs that are to be considered. Instead of 21, only eight MFIs are considered due to unavailability of data.
1.19 Organisation of the dissertation This dissertation is organised into five chapters •
Chapter 1 provides an introduction of microfinance, gives background information on poverty in Tanzania, and outlines the statement of the problem, objectives and limitations.
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Chapter 2 gives the literature review on the efficacy of microfinance and gives an outline of the DEA model used to measure the efficiency of microfinance.
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Chapter 3 explains the methods to be adopted in the study.
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Chapter 4 discusses data analysis and interpretation.
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Chapter 5 explainsthe findings and discussion.
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Chapter 6 explains the conclusion and gives recommendations.
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Personal critical reflection explains the skills generated and difficulties encountered during the study.
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CHAPTER 2
REVIEW OF LITERATURE
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Chapter 2-REVIEW OF LITERATURE Chapter 2 reviews the literature from different authors on the efficacy of MFIs and outlines their findings and recommendations. At the end of the chapter, a small section is presented which shows the gap between the theory of microfinance and the actual practice and different views of those in favour of microfinance and those that are against it.
2.1 Meaning of efficiency The efficiency of an organisation refers to how well the firm can use its resources to produce more outputs. When a firm canutilise its inputs by reducing waste, then it is said to be effective. The efficiency of microfinance institutions refers to how well these institutions can reach the poor through provision of the loans. According toBassem (2008), the effectiveness of a microfinance institution depends on how well it uses various inputs, such as assets andgrants to aid those in need of these services. However, over the years, the efficiency of these institutions has been in question, particularly because of an increase in someorganisations providing these services and poor regulatory frameworks, especially indeveloping nations. Many studies have adopted the DEA model developed by Charnes, Cooper and Rhodes (1978), used first in reference to non-profit making organisations. The justification of applying this model ratherthan the ratios is that it illustratesdifferences between different financial providersand MFIs. 2 .2Distinction between MFIs and Savings Cooperatives MFIs are those organisations that offer financial services to the poor and charge low-interest rates for the services provided (Ridder, 2010). Over the years, these organisations have proved to be of great help, in particular with poverty reduction. MFIs can be either formal or non-formal, depending on the nature of how lending takes place. Hassan and Sanchez (2009) notethat formal institutions include banks and non-banking institutions and saving cooperatives, while non-formal MFIs include NGOs. Morduch(2000) argues that banks and microfinance banks, even thoughthey provide the same services, are different. Banks provide financial services to businesses while microfinance banks are those that operate just like banks but charge lower interest rates to their customers. Therefore, while banks and micro banks both aim at providing loans, the way they operate is different. Banks are more profit-oriented, while micro banks aim at ensuring that they uplift people by making cheaper loans available to them at low-interest rates.
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In contrast, Saini (2014) states that the non-banking institutions cannot accept deposits like banks and do not require a licence to undertake their business. Furthermore, they are less regulated than banks. However, even though they are different from banks, they perform banking services Subsequently, cooperative organisations aim to maximise the welfare of their customers. Usually, cooperatives act as a producer and consumer of financial services. Marwa and Aziakpono (2016) state that, as producers, cooperatives accept savings from their customers and, in return, as consumers, they give out loans; as such, these organisations are non-profit making entities since they may not be members to exploit. Furthermore, they aim at benefiting members by granting loans and encouraging them to save. Even though the formal and non-formal MFIs differ, they have the same aim,whichis reaching the financially weaker individuals. However, many studies that have been conducted(Kipesha 2013;2012;Haq,Skully and Pathan 2010; Hermes, Lensink and Meesters,2009), focus more on MFIs than cooperatives because of the availability of data. Marwa and Aziakpono (2016) argue that this is because MFIs have data published and analysed on the mixed market, which is not the case for cooperatives, making it harder for researchers to gain information.
2.3 The DEA Model Charnes,Cooper and Rhodes (1978) first developed the DEA model to act as a tool for measuring the efficiencies of MFIs. Over the years, various researchers have used it to measure both efficiencies and inefficiencies in the field of microfinance. In their study Haq, Skully and Pathan (2010) argue that they adopt the DEA model because it is flexible enough to study even a small sample where prices are not required. Furthermore, they argue that it is a suitable tool for policy makers to determine the inefficiencies of the outputs and inputs applied. They also state that it is an appropriate method, which can include multiple inputs and outputs. Similarly, Kipesha (2013) states that, since the model does not use any functional forms in estimating efficiency, it is the best in producing data with no errors. Again, he states that the DEA model is capable of returning variable and multiple returns, which do not require price inputs, which makes this a more favourable approach than the other models. 21 | P a g e
Conversely, some authors have identified several problems with the use of the DEA model. Jenkins and Anderson (2003) warn against the use of multiple inputs and outputs, as these can result in inefficient firms. Moreover, if inputs and outputs are 100% correlated, then this can result in undesired results. Furthermore, Anderson (1996) adds that, while the DEA can tell how well the firm is doing compared to its peers, it fails to show the real level of efficiency. Again, since the DEA is a non-parametric measure, it becomes difficult to generate hypothesis and test it statistically. Consequently, (Parkin and Hollingsworth, 1997) identify issues in his study and urges individuals to be cautious inadding more variables to avoid errors. Therefore, one must be careful in applying this model to overcome the barriers that it has. 2.4 The model inputs and outputs This model requires a set of inputs that produces a range of outputs. Coelli (1998) notes that Farell, who applied two inputs and one output to measure the technical efficiency, first used this model. He argues that, depending on a numerous number of inputs generated, a firm can be said to be efficient if it returns one when a constant return scale is used. In contrast, he also argues that, to be able to determine the right inputs needed without reducing the outputs, it is important to apply the constant return methods to balance the inputs and outputs that are to be used.This is because, when multiple scales are used, it becomes difficult to determine the exact inputs to be applied to maintain efficacy. The model uses both the CCR models and the BCC models, which takes into account different inputs to produce desirable outputs. However, Vincova (2005) notes that the efficiency level that is computed must not be greater than 1.A firm that produces results that are greater than 1 or less than 0 is considered to be inefficient. In this study, Ihave adopted both the CCR models and BCC models to measure technical efficiencies, as well as scale efficiencies as adopted by Kipesha (2013). Appendix1 In the production approach, the inputs used are assets, capital and personnel and the output is loan portfolio, the number of loans and revenue. In contrast, in the intermediation approach, the inputs are some personnel and operating expenses and the outputs are gross loan portfolio and savings as outputs.
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2.5 Different DEA Scales There are mainly two commonly used scales in the DEA model, which are CRS and VRS.Coelli (1998) notes that, when the DMU are functioning at an optimum level, the CRS model is most suitable, while Banker, Charnes and Cooper (1984) argue that imperfect competition and some financial limitations may result in the CRS model to be unsuitable. Thus, they proposed the VRS model, which can handle such constraints when dealing with efficiency studies. Therefore, it is of great importance to consider both the models when dealing with efficiency studies.
2.6 Previous studies on efficiency of MFIs Rosenberg (1994) notes that the efficiency of the firm is very important to measure inputs that are not easily available, such as time, money and rawmaterials. Secondly, competition within the provision of MFIs services has largely increased, making it vital for these organisations to remain efficient. In contrast, (Hermes, Lensink and Meesters,2009), also state that there has been an increase in the number of banks that are providing microfinance, forcing the other institutions to ensure that the inputs that are used add to the outputs that are expected. Which result into the minimisation of waste and increase in the number of active borrowers. Reynolds and Thompson (2002) argues that efficiency is of great importance as it aids in providing information about the operations of MFIs and how effective they are in reducing waste. The efficiency of MFIs can mainly be divided into two components, which are the financial efficiency and social efficiency (Gutiérrez-Nieto, Serrano-Cinca and Mar Molinero, (2009). The financial efficiency is further divided into production approach and intermediation approach. It considers MFIs effective when they are highly productive regarding providing more loans. In the production approach, microfinance institutions are viewed as important organisations for helping the poor by giving them subsidies with the help of capital labour, etc. (Nghiemetal., 2006;Bassem,2008;Gutiérrez-Nieto, Serrano-Cinca and Mar Molinero, 2009; Haq Skully and Pathan 2010). On the other hand, Kipesha (2012) notes that, in the intermediation approach, microfinance institutions act as a mediator between those with surplus funds and those who lack such funds. However, the social efficiency is different from the financial efficiency approach, as noted by (Stauffenberg et al., 2003). According to them, MFIs are efficient when they can manage their resources, which include assets and personnel, in a better way. 23 | P a g e
Over the years, there have been different studies measuring the efficacy level of MFIs. Haq, Skully and Pathan (2010) conducted a global study, which included Africa, Asia and Latin America. The study involved two assumptions, which were MFIs as producers of loans and as mediators. The results were that the non-governmental MFIs were more efficient as producers of loans and indicated that banks outperform the non-bank institutions in the provision of microfinance institutions. Similarly, in their study, Hassan and Sanchez (2009) compare the effectiveness of MFIs in three regions (Latin America, East and North Africa and South East Asia). They use the DEA model, which integrates two components, both the inputs and outputs, instead of the ratio analysis to analyse performance, and argue that the performance of these institutions largely depends on how effectively they manage their inputs to produce greater outputs. Furthermore, they state that the formal MFIs, such as banks, non-bank organisations and cooperatives, perform better than the non-formal institutions, which are NGOs.This inadequacy of these institutions is largely driven by the misuse of their resources, such as grants, which make it difficult to provide services to the poor. To solve the underperformance of these institutions, the authors recommend proper policy frameworks, especially in Latin America and North and Eastern countries where there a higher number of inefficiencieswere found. Again, Lafourcade et al. (2005), in their study on African MFIs efficiency, foundthat formal MFIs outperformed informal MFIs and conclude that Africa was more productive as compared to other regions in the provision of microfinance services. In contrast,studies conducted by Kipesha (2012) foundthat banks outperform NGOs in providing services, since they effectively manage their resources and control waste. The authors, therefore, urge microfinance providers in East Africa, especially NGOs, to adapt to the technological changes to be able to manage resources better to reduce waste and to be able to remain competitive by ensuring that they provide profitable services in the end. Tahir and Tahrim (2015) also argue that the efficacy of microfinance occurs only when these institutions manage inputs to produce better outputs. However, Haq,Skully and Pathan (2010) argue that NGOs are more effective in the provision of microfinance than banks because they reach a larger group of poor people and can sustain these services in the long run. Furthermore, the organisations promote staff productivity, which, in return, helps the organisations to reach a larger target. Therefore, to ensure that other MFIs
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are ultimately sustainable, formal organisations can adopt a similar regulatory approach, which is followed by the NGOs, to ensure effectiveness in providing loans to help poor people. Toindepi (2016) argues there are two approaches given in microfinance models, which are the commercial and the developmental approach. The two approaches are different from one another, implying that different customers are likely to encounter different experiences depending on the type of model an institution adapts. He furthermore states that the role of microfinance has changed from poverty reduction to profit making, thus affecting the way these organisations
provide services. He further states that, for an MFI to be effective, it must
consider standards that are set and must consider socio-economic as well as environmental aspects when providing such services. Recommendations for future research include developing a new model, particularly one that distinguishes between different models that have been used to address the efficacy of microfinance, as researchers have failed to acknowledge that developments have led to the changes in how some of the institutions operate. Hermes, Lensink and Meesters, (2009), analyse whether the effectiveness of MFIs is determined by the level of development that a country has. They argue that MFIs that are in developed economies are likely to perform effectively because of increased competition from other institutions, such as banks, which force theseMFIs to lowertheir interest rates and lend to more people. However, on the other hand, the same banks that stimulate competition may replace MFIs. Therefore, other financial institutions may also influence the performance of microfinance, leading to the ineffectiveness of the performance of these organisations. They conclude by stating that, even though on the other hand, financial institutions may influence the effectiveness of microfinance institutions, developed economies largely contribute to effective MFIs. Tahir and Tahrim (2013) argue that efficacy is a very important indicator of the success of the microfinance. Furthermore, they state that different models, such as the production approach and intermediation approach, can measure the efficiency. The production approach uses inputs such as total assets and operating expenses and outputs such as total loans provided and the number of borrowers. The intermediation approach uses inputs such as deposits, borrowing loan portfolio and financial revenue
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Similarly, Kipesha (2013) argues that production efficiency outperforms intermediation efficiency. Most of the inefficiencies that occur are a result of the poor management of funds and failure to lend money to a larger population. Furthermore, for Tanzanian microfinance institutions to be effective, utilisation of resources must be adapted to ensure that the number of borrowers is increased and that regulatory rules must be strict. Kablan’s study (2012) argues that there is a conflict between the welfarist view and the institutionalist view with regard to the effectiveness of MFIs. The welfarist focuses on how an MFI is able to deliver services to more people, while the institutionalistfocuses on sustainability. In their study, they analyse whether the effectiveness of microfinance in West Africa is a result of the outreach level or the sustainability level. They findthat financial efficiency is growing in comparison to social efficiency. By taking risks, MFIs become financially ineffective, because they focus more on reaching a larger group, which results in more costs. However, they also analyse the role of regulatory bodies in the microfinance sector, which interfere with the role the microfinance institutions and turn them into commercial banks. They recommend that, in order for MFIs to perform better, they have to be innovative and ensure that they are financially capable of providing loans to the poor. Kinde (2012) analysedthat microfinance institutionunderperforming they totally depend on the loans from donors and if they are financially unsound. He emphasises the need for these institutions to ensure that objectives set go hand-in-hand with the objectives of these institutions, by reducing dependency to increase sustainability. In (Gutiérrez-Gutiérrez-Nieto, Serrano-Cinca and Molinero, 2009) study, the efficiency of MFIs has been measured using the DEA model; however, it is stated that this model can only be effective depending on the specifications that are applied. They analyse four different components, including NGO status, choice of inputs and output, and conclude that, by analysing what components different MFIs use, they can determine how MFIs can obtain efficiency. They recommend the DEA approach instead of basic ratios for the efficiency measurement. Similarly, Abdel Kader, Jemaah and Mekhi (2012), in their study in the MENA region, use the DEA model, which includes variables like assets, operating expenses and employees and outputs like financial revenue and benefits to the poor. Their study also applies Samar and Wilson’s (2002) bootstrap-based approach to measure efficiency and concludes that MFIs in the region were not performing better and that NGOs in the region were the best performers.
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In contrast, Jayamaha (2012) evaluate the efficiency of small MFIs in Sri Lanka for theperiod from 2005 to 2010 and use the DEA model to measure efficiency. Three types of efficiencies are measured, technical, and pure and scale. Also, the variables thatused include deposits,and the number of deposits and outputs include loans and the number of loans provided. The study concludes that the small MFIs were ineffective during the period that the study was undertaken, and these institutions needed further reforms. Gutiérrez-Nieto, Serrano-Cinca and Mar Molinero, (2009) compare three studies that assess MFI efficiency. They measure the effectiveness of an MFI by comparing inputs and outputsand conclude that the efficacy of these institutions largely depends on the location and the resources that are applied. The study recommends that better use of resources should be prioritised by MFIs to ensure that they can reach as many borrowers as possible. On the other hand, in their study, (Cinca and Molinero, 2004) analyse MFIs by taking into account the percentage of femaleborrowers as outputs and measuring the effectiveness of MFIs using the DEA model. The results show that the best MFIs are those that have more female leaders. Makame and Murinde (2006) analyse 35 MFIs in five East African countries from 2000 to 2005.Their results show that there is a relationship between the sustainability of MFIs and theiroutreach. In their study, Murdoch and Haley (2002) assess the available literature of microfinance institutions across the world and use various characteristics to compare how different MFIs operate. The results show that these institutions help to increase the client’s level of income, but lack evidence on how these institutions contribute to the improvement of other sectors such as health and nutrition. They recommend that, in order for MFIs to function well, there needs to be a review of different programs, using ranking tools to analyse the performance so as to be able to understand whetherthey contribute more to the poor or not, and also policies that are used by MFIs must be reviewed in order forfunds to be allocated properly. Other studies include Shiraz and Khan (2009) who study the positive impact of microfinance institutions regarding poverty reduction. Similarly, Mahmud (2006) evaluates the impact of development programs and focuses mainly on the outreach of these programs as well as social impacts. Her study compares various MFIs by applying different techniques and concludes that those who have used these services have managed to overcome poverty. 27 | P a g e
Kondo et al. (2008) evaluate microfinance programs in the Philippines and observe a small effect on theincrease of income and food. However, theypoint out that MFIs are shifting from their main aim and are becoming more business-oriented. Moreover, these institutions’ focus is on making profits rather thanhelping the poor in overcoming poverty. In their study, Marwa and Aziakpono (2016) analyse the technical efficiency in Tanzania and find that most of the firms in Tanzania face difficulties in how to utilise their resources to produce more outputs. While large and smaller firms have fewer economies of scale, the middle firms struggle with how to make decisions on the management of resources that they use. Recommendations given include reframing of available policies to ensure that there is a tighter control on how these organisations operate, and to ensure that firms manage resources better to ensure that they enhance their services. Furthermore, they urge MFIs to make use of innovations to be able to control costs.
2.7 Research Gap between Theory and Microfinance Practice Although there have been contributions from various authors in the field, there still exists a gap between theory and practice. Fischer and Ghatak (2010) point out that, to bridge the gap, researchers in the field must be able to link theories with experiments for better determination of a microfinance institution performance. They argue that there must be an interaction between empirical researchers, theoretical researchers and practitioners to be able to link theories and practice. Untested theories cannot be applied in real life, no matter how effective they might appear. Therefore, by implementing both theory and practice, practitioners can take possible actions towards poverty reduction. According to Hina, Lightfoot and Harvie (n.d) another gap that exists is the difference in opinion between those in support of making profits by providing microfinance services and those that are against it. Those that are in favour suggest that, for a microfinance institution to be effective, it must make higher returns. Those that are against, oppose this because it drifts away from its origin, which is to reduce poverty. Thus, these two arguments formulate a gap between the receiver and providers of loans resulting from inappropriate policies being set. Annim (2010) argues that researchers in the field have failed to apply different strategies to differentiate the types of services that MFIs provide because ofthe methodological gaps that have been identified. They suggest that an examination of different strategies and cost implicationsbe consideredto identify the success of microfinance services. 28 | P a g e
Thus even though previous efficacy studies (Bassem,2008;Gutiérrez-Nieto, Serrano-Cinca and Mar Molinero, 2009; Haq,Skully and Pathan 2009;Kipesha 2013;2013;Hassan and Sanchez, 2009;Hermes, Lensink and Meesters 2009) report efficacy levels of MFIs. Annim(2010) argues that efficacy levels alone are not sufficient enough to determine the overall performance of an MFI.Furthermore, there is a difference in how effective MFIs are between developed and developing countries. Therefore, the best practices are yet to be investigated and be put in practice to bridge the gap between the theory and the actual activities.
2.8 Conclusion To conclude, the efficiency of a microfinance institution largely depends on how effectivelyitcan manage both the inputs and outputs that are available. Such inputs include raw materials, labour, etc., and outputs are the number of borrowers generated. However, the efficacy of microfinance can be measured by two approaches, the financial efficiency, and the intermediation efficiency. Financial efficiency views an MFI to be effective when production increases. In contrast, in the intermediation efficiency,an MFI acts as a bridge between lenders and borrowers. Most MFI efficacy studies have adopted the DEA model, which measures the performance of various MFIs to determine the level of success reached. However,Roodman and Murdoch (2009) that, although MFIs help the poor, their contribution to poverty reduction is insignificant argue it. Fischer and Ghatak (2010), Hina, Lightfoot and Harvie (n.d) and Annim (2010) identify various gaps in the field, such as differences in practice and theory, differences in opinions between the supporters of MFIs and the opposers and the methodological gaps that exist. They, therefore, suggest that theories be linked with practice to aid both researchers and practitioners and to adopt different strategies and costs when dealing with some MFIs.
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CHAPTER 3
METHODOLOGY
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Chapter-3 METHODOLOGY This chapter describes in detail the data used and the various methods that have been employed. It also explains the location of the study, which is Tanzania since this is where the MFI are located, study duration and statistical tools that have been adopted. Furthermore, the rationale of adopting certain techniques is explained in detail.
3.1 Research Methodology According to (Kothari, 2004) Research, methodology refers to the process that a researcher uses to collect the information and data needed in the research. The research methodology must be carefully presented to avoid confusion to the researcher as well as the readers of the research report. Furthermore, (Kumar 2014) states that agood research report is normally one with a good research methodology. Thus, it is the responsibility of the researcher to ensure that the methods that are adopted are relevant to and useful in the study. The methodology is the procedure that a researcher follows to carry on an investigation (Bryman and Bell, 2011). The methodology adopted must suit the needs of the study. Additionally, Kothari (2004) argues that even though the researcher is free to adopt any methods, these should be clearly justified to avoid ambiguity and to enable other readers to be clear about why particular methods were chosen. 3.2 Location The study is based in Tanzania and in particular in Dar es Salaam where most of the MFIs in this study are based. 3.3 Research design A research design is a blueprint that is used to help the researcher to decide on how to answer the research questions (Kumar, 2014). It helps the researcher to tell the readers clearly what methods will be applicable during the research and the number of respondents that will be required. A research design usually has two functions, to provide a guideline to other readers on what methods are applied and to justify the choice of selecting one approach over the others. It is of great importance to have a good research design to ensure clarity in the research that is to be done.
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Saunders, Lewis and Thornhill (2015) note that research designs can be either quantitative or qualitative, according to the nature of the study. The quantitative research design usually uses data to develop a theory. It is usually deductive in nature. However, it can also be inductive in given circumstances. Quantitative research involves numbers, which are tested later by applying statistical tools. Furthermore, with quantitative research, a multimethod approach can be applied, which involves more than one quantitative and qualitative method (Bryman, 2001). Qualitative research design applies interpretative philosophy (Denzin and Lincoln, 2011 cited in Saunders, Lewis and Thornhill, 2015). It is usually inductive in nature because it develops a theory. A qualitative research design uses both mono methods and multiple methods in analysing data. Kumar (2014) argues that qualitative studies lack clarity in differentiating between designs and methods of data collection, as there is usually an overlap between the two. Furthermore, it is the responsibility of the researcher to make interpretations reach a conclusion. To achieve a balance between these two methods, Creswell (2014), suggests that a researcher should use a mixed method approach because it enables interpretation of both qualitative and quantitative methods and thus minimising the limitations that these two methods have. Furthermore, he states that this strategy is useful because it enables a researcher to explain quantitative data by showing evidence with qualitative analysis. However,Bryman(2001) argues that even though a mixed method strategy is superior, this approach can be time-consuming, and it requires the researcher to be familiar with both the qualitative and quantitative methods. In this study, however, a quantitative research design has been adopted since only secondary data have been used, which are numerical in nature, and data is analysed in DEAP,Excel and results are further tested using descriptive statistics.
3.4 Data collection Data collection refers to the collection of information needed by the researcher. Data gathered can be either primary or secondary depending on the nature of the study that is to be undertaken. Malhotra, Birks and Wills (2013) state that data can be either primary or secondary. Primary data are fresh and collected at first hand, while secondary data are already available. 32 | P a g e
Primary data can be in the form of interviews, questionnaires, observations, surveys, etc., whereas, secondary data can be in the form of journals, magazine articles, etc. They argue that primary data can be costly as compared to secondary data, because of the high costs that can be encountered by the researcher while collecting information. In contrast, they also argue that secondary data can be biased and may not give accurate results since they are not always current. Thus, a researcher has to be cautious while collecting data, using both sources to ensure that the most reliable and up-to-date data are collected.
3.5 Sources of data In this study, only secondary data has been used. Secondary data has been collected from company reports and websites that compare some MFIs in Tanzania for3years (2012 to 2014). Data from 2015 has not been included due to the unavailability of published reports of the chosen MFIs.
3.6 Sampling A sample is a representation of a small unit of a population (Field, 2005). It takes into consideration a small portion of an entire population. Sampling is done to save money and time, as studying an entire population would be time consuming and expensive. Chaturvedi, (2016) notes that there are two types of sampling methods, probability and nonprobability. On the one hand, in probability techniques, all the elements of a population have the same opportunity of being included in the sample, while non-probability techniques are biased in nature. Elements that are included are in accordance with the sample that a researcher selects. Probability sampling methods can be divided into simple random, stratified random and systematic random. According to Sekaran and Bougie (2013), probability-sampling methods all involve a random selection of elements to be included in the study. This simple sampling method gives an equal chance for all the elements in the population to be included. The systematic method involves drawing samples from a population randomly and from the starting point to a fixed interval. The stratified method involves dividing elements into various groups and, after that, selecting them randomly. They argue that the probability methods are the most appropriate sampling techniques because they are less biased general in nature and are systematic. 33 | P a g e
Saunders,Lewis and Thornhill (2007) argue that probability methods can be costly, and information might not be available as required. Moreover, in the case of simple sampling, a researcher cannot do the research if the survey involves elements that are in different locations and require direct contact with respondents, because of the cost and time involved. Consequently, with the systematic sampling method, careful selection of sampling lists is required to avoid repetition. Non-probability methods mainly rely on the researcher. They give the researcher a wider range of choice to select a particular targeted group.Tthese methods include convenience method, which obtains samples based on convenient elements, judgment sampling, which focuses on the judgment of the researcher, and quota sampling, which is mainly associated with two stages of limited judgment sampling (Malhotra, Birksand Wills, 2012, p.504). However, Cameron and Price (2009) argue that non-probability methods are biased in nature, as they do not have an equal representation of the elements. Moreover, they might not yield better results due to the unclear supervision of data. Thus,careful selection of sampling methods is vital for better data collection and analysis.
3.7 Number of firms The number of MFIs studied is eight which includeAcessBank,Akiba,Eclof,Mwanga,Finca,PrideTz,Nmb and Crdb.
3.8 Time duration This study has been undertakenfor less than a year. All the tasks and the time they took to complete are shown in the Gantt chart. Appendix 2
3.9 Tools for analysis. Descriptive analysis was used in this study. The DEA model was used in measuring the efficiency of the selected MFIs by comparing the outputs and inputs to measure the technical efficiency of various MFIs in the DEAP software. Both production and intermediation efficiency have been analysed by applying the CCR model and VRS model.
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3.10 Rationale for choosing various methods In this study, only quantitative methods have been adopted to compare the efficiency levels of the MFIs.According to Nicholas (2010),quantitative methods are measurable and can be tested statistically. SimilarySaunders,Lewis and Thornhill (2007) argue that quantitative methods are easy to use and can be easily analysed using statistics software. Furthermore, they note that the most popular quantitativemethods are surveys,observations and secondary data. In contrast Bryman(1998),argues that even though quantitative methods are simpler than qualitative,these methods are subject to manipulation. The researcher can easily manipulate the data to prove the hypothesis. Thus it is, therefore, important to be cautious while dealing with quantitative data to avoid manipulation. On the other hand,however,Boeije(2010) argues that qualitative research methods are mainly used to gather insights on the human behaviour. The methods that are used in qualitative studies do not include numbers. Also (Butler-Kisber, 2010) argues that qualitative research methods mainly include focus groups,interviewsquestionnaires, etc.Furthermore, these methods as compared to quantitative methods are flexible as the participants and researcher can easily interact. However even though flexibility is offered in qualitative research these methods can be timeconsuming,and complications can occur while trying to quantify the data. Thus in this study, only quantitative methods have been used and analysed to identify the level of efficacy of Tanzanian MFIs.Furthermore, primary sources like interviews,focus groups and questionnaires have not been taken into account due to limited access to MFI data by firms in Tanzania and due to the nature of the study, which involves just measuring efficacy. Secondary data were collected from eight MFIs in Tanzania from the mixmarket org. According to Malhotra, Birks and Wills (2013), secondary data is the data that is already available and can either be in the form of reports,journals,periodicalsetc.Furthermore, this data is easily available and helps to save time.
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Similarly (Walliman, 2011) argues that this data is easily accessible and is cheaper as compared to primary data. On the other hand, even though this method saves time it has been argued by Blaikie (2003) that secondary data is not updated as it is usually already available. Furthermore, the data that was collected might be to answer other research questions, which are different from the current study that is undertaken. In addition (McNeill and Chapman, 2005) argue that since secondary data is old, the researcher is usually required to determine the quality of the data before using it to ensure that it is feasible with the study that has been undertaken. Thus in this study only secondary data was used because it was easily available and to measure the efficiencies of MFIs published data from company reports had to be used. Convenience sampling is applied for the eight MFIs. According to Conveniencesampling.net (2016), this method is appropriate because it enables the researcher to collect data fast, which, in turn, saves time. Furthermore, with convenience sampling, data are easily available as the researcher can obtain them from any environment that suits him or her without having to travel a long distance. In contrast, Bergh and Ketchen (2009) note that this kind of sampling method can be ineffective if proper care is not considered because its highly biased in nature and might not produce the desired results. Therefore, the researcher has to be cautious when dealing with convenience sampling to avoid bias.
3.11 Statistics According to Seligman (cited in Chikkodi and Satya Prasad, 2009), statistics is both an art and science and deals with the collecting, classifying and comparing of data to generate interpretations to reach a conclusion. Furthermore, it helps to test various assumptions and to either accept or reject them. Statistical methods are divided into two categories, descriptive and inferential Descriptive statistics have been adopted in this study. Geisler (2004) notes that descriptive analysis is used to describe data without any inclusion of inferences and is easy to use since it 36 | P a g e
simplifies complex data. Descriptive statistics include numbers, tables and graphs, which help in summarisingdata. Additionally, Chartejee et al(2014) argue that descriptive statistics are most often used to compute measures of central tendency,dispersion,skewness and kurtosis and help in making the data look more presentable. However, even though descriptive methods are easier to use and are more presentable, Chikkodi and Satya Prasad (2009) argue that, if not carefully used, the results from descriptive statistics can be incomplete and misunderstood. Furthermore, it can lead to misinterpretations resulting from limited information and is subject to bias,which may cause errors. Thus, it is very crucial for the researcher to understand the extensive bias that descriptive statistics can produceto ensure that the right outcome is generated. Inferential statistics,however, go beyond just the data description and provide generalisations, which
enable
reaching
conclusions.
In
other
words,
(Rugg
and
Petre,
2007)
arguesthatwithinferential statistics the 'so what?' Thequestion can be answered, as the researcher has the power to test and prove the data and conclude thereof. Furthermore, he states that the tests that are to be conducted, whether parametric or nonparametric, all depend ononthe nature of data. In contrast,Huck (2004) argues that, even though it is easy to draw conclusions with inferential statistics methods, these are limited to probability samples because they involve much estimation, such as making judgements, forecasting, etc. Furthermore,since there is a lot of guesswork involved, errors aremost likely to occur. In addition to statistics that have been applied, the DEA model was used to measure the efficiencies. Thismodel has been chosen in this study because it the most efficient way of analysing data and has been used by previous MFIs studies (Kipesha 2013; 2012; Haq,Skully and Pathan 2010; Hassan & Sanchez, 2009;Hermes, Lensink and Meesters2009). Furthermore, Anderson (1995) argues that the DEA model can handle multiple data relating to inputs and outputs, and no assumptions are required in using the model. Again, it helps in comparing the data among various peers. Similarly, Haq, Skully and Pathan (2010) and Kipesha (2013) note that DEA is flexible to use and does not require any price inputs, and it enables a multistage analysis of both the inputs and outputs.
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However, on the other hand, even though this model has been applied to numerous studies, Anderson (1995) and Jenkins and Anderson (2003) argue that multiple inputs and outputs can generate undesirable results and, since it is a non-parametric model it is difficult to be measured statistically and also, since the DEA creates a separate linear program for each firm, larger problems can result in extreme difficulties. Thus, with the application of the model, an individual has to be cautious to avoid errors and to gain good results. This study will use the multistage DEA model, using both the CRS and VRS, to measure efficiencies. Firm efficiency will be measured using the multistage model, which has the capacity of identifying inputs and outputs that are the same and matching them with the inefficient outputs (Coelli, 1999).
3.12 Ethical issues According to Denscombe (2010), researchers must ensure that they carry on their research in an ethical manner to avoid biasness. Furthermore, it is the responsibility of the researcher to ensure that all participants give their consent in participating in the research in case of primary data. On the other hand,Kumar(2011),argues that in the case of secondary data it is very important for the researcher to ensure the validity of the information before using it in a particular study. Thus since this research used secondary data,the validity of the information from the mixed market was assessed by checking the authenticity of published data,measuring the credibility to ensure that there were no errors and checking the relevance of the information which was to be used in the research.
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CHAPTER 4
DATA ANALYSIS
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Chapter 4-DATA ANALYSIS Chapter 4 represents the analysis of the efficacy of the MFIs for3 years in both the production approach and intermediation approach. Also, the results are presented in tables and tested using descriptivestatistics. For easier understanding, results are given in percentages instead of decimals.
4.1 Meaning of data analysis Data analysis is an important step that enables a researcher to process the data carefully using appropriate methods. Bala (2005) argues that this is a very crucial stage for the researcher to be able to know what type of data they are dealing with and how to interpret them carefully. In this study, secondary data have been used and analysed both in production approach and intermediation approach to measuring the technical efficiency usingDEAP software. Additionally, results have been expressed using descriptive statistics. 4.2 Production Approach According to (Haq, Skully and Pathan 2010; Bassem,2008), production approach regards MFIs as producers of loans and deposits using appropriate inputs such as assets, capital and personnel. Furthermore,Gutiérrez-Nieto, Serrano-Cinca and Mar Molinero, (2009) argue that the MFIs that can grant more loans to their customers are more efficient than those who are incapable of doing so. In addition, a score of one which is 100% must be achieved by an MFI to be considered effective than the others. In the study, data of eight MFIs has been considered under the production approach from the year 2012 to 2014.The, inputs that have been considered are assets capital and personnel, and the outputs are loan portfolio, the number of loans and revenue. 4.3 Overall Production results. The overall results show that in the production approach in 2012 in the CRS and SCALEthe MFIs that were efficient were Akiba, Eclof, Mwanga and Crdb all with a score 1. However, in the VRS all the firms were efficient except Access Bank.This indicates that the efficient firms were able to generate more loans than the others were as well as to reduce wastages. In 2013, under the CRS and SCALE, the efficient firm’s wereAkiba,Eclof,Mwanga,Pride –Tz and Crdb On the other hand in the VRS all the firms are efficient.
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In 2014 under all the scales only Akiba and Eclof were efficient. This indicates that all the other firms did not produce sufficient loans to their customers, and there were a lot of wastages due to misuse of inputs that in return result in underproduction of outputs. The results of production approach have been presented below from Table 4.1 to 4.3 and inferences have been given below the table.
Table 4.1 Production Approach Summary (2012)
SERIAL
DMU
NUMBER
TECHNICAL
TECHNICAL
SCALE
EFFICIENCY(CRS) EFFICIENCY(VRS) EFFICIENCY(TECRS/TE-VRS
1
ACCESS
63%
67.5%
93%
BANK 2
AKIBA
100%
100%
100%
3
ECLOF
100%
100%
100%
4
FINCA
59%
100%
59%
5
MWANGA
100%
100%
1.00
6
NMB
71.6%
100%
71.6%
7
PRIDE-
84.8%
100%
84.8%
100%
100%
100%
84.8%
95.9%
88.6%
TANZANIA 8
Mean
CRDB
Source:Data from mixmarket.org computed by the author and analysed in Excel and DEAP Software. Table 4.1 shows that, in 2012, the efficient MFIs firms were Akiba, Eclof and CRDB, which implies that these firms outperformed their peers by proper utilisation of the resources that were available and reduction of waste under the CRS model. However, in the VRS model, the Access Bank was the inefficient firm with a lower performance of 67.5% On the other hand, in scale efficiency, the best performers were Akiba, Eclof, Mwanga and CRDB, all returning values of 100%. 41 | P a g e
The total mean of the firms under the TE-CRS, VRS and Scale efficiency, was 84.8%, 95.9%and 88.6%, respectively. Appendix 3(EFFECIENCY SUMMARY 2012)
Table 4.2 Production Approach Summary (2013) SERIAL
DMU
NUMBER
TECHNICAL
TECHNICAL
SCALE
EFFICIENCY(CRS) EFFICIENCY(VRS) EFFICIENCY(TECRS/TE-VRS
1
ACCESS
92.6%
100%
92.6%
BANK 2
AKIBA
100%
100%
100%
3
ECLOF
100%
100%
100%
4
FINCA
71.2%
100%
71.2%
5
MWANGA
100%
100%
100%
6
NMB
90%
100%
90%
7
PRIDE-
100%
100%
100%
100%
100%
100%
94.2%
100%
94.2%
TANZANIA 8
Mean
CRDB
Source:Data from mixmarket.org computed by the author and analysed in Excel and DEAP Software.
Table 4.2 depicts the efficiency of the firms using the TE-CRS, VRSand scale efficiency. Under the TE-CRS Akiba, Eclof, Mwanga, Pride-Tanzania and CRDB operated well in 2013.However, under the TE-VRS, all the firms performed very efficiently. In contrast,scale efficiency, Akiba,Eclof,Pride-Tanzania and CRDB were the best performers, indicating that these firms were using their resources in an optimum way as compared to their peers. The mean reported from the scale efficiency was 94.2%, 100% and 94.2%, respectively. Appendix 3(EFFECIENCY SUMMARY 2013)
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Table 4.3 Production Approach Summary (2014) SERIAL
DMU
NUMBER
TECHNICAL
TECHNICAL
SCALE
EFFICIENCY(CRS) EFFICIENCY(VRS) EFFICIENCY(TECRS/TE-VRS
1
ACCESS
48.1%
70.6%
68.2%
BANK 2
AKIBA
100%
100%
100%
3
ECLOF
100%
100%
100%
4
FINCA
100%
59.7%
96%
5
MWANGA
100%
100%
100%
6
NMB
40.9%
49.7%
82%
7
PRIDE-
25%
27.4%
91%
11.5%
32.9%
34.8%
60.3%
67.5%
84.1%
TANZANIA 8
Mean
CRDB
Source:Data from mixmarket.org computed by the author and analysed in Excel and DEAP Software. Table 4.3 indicates that Akiba, Eclof and Mwanga outperformed their peers with an efficient level of 100% in the TE-CRS.Similarly, in the TE-VRS and scale efficiency; the resourceful firms were Akiba, Eclof and Mwanga. On the other hand, the worst performer was CRDB in the TECRS and Pride-Tanzania in the TE-VRS.In the scale efficiency model, the worst performer was CRDB.
Appendix 3(EFFECIENCY SUMMARY 2014)
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4.4 Intermediation approach According to Kipesha (2013), the intermediation approach regards MFIs as intermediaries between servers and borrowers. In addition, he argues that even though the intermediation approach is the most appropriate method for measuring technical efficiency MFIs do not often use it. Similarly, (Ahmad, 2011 and Bassem, 2008) argue that most MFIs do not utilise funds suchas deposits rather they use debts whichhave led production approach to be used more than intermediation approach. This study has adopted the intermediation approach, and it has used inputs such as loan portfolio,thenumber of loans and outputs are gross loan portfolio and savings. The overall results of intermediation approach in 2012 show that efficient firms were only Access Bank and Eclof in the CRS and SCALE however in VRS the efficient firms were Access Bank, Eclof, Finca, Mwanga and Nmb. In 2013, only Eclof and Akiba were efficient in all the threescales(CRS,VRS and SCALE). On the other hand, in 2014 only Akiba and Mwanga were efficient in all the three scales Thus, the intermediation results indicate that the efficient firms were able to channel their loans well to the customers and to generate more outputs. However, the inefficient firms failed to do so due to misuse of outputs or lack of proper funding.
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Table 4.4 Intermediation Approach Summary (2012) SERIAL
DMU
NUMBER
TECHNICAL
TECHNICAL
SCALE
EFFICIENCY(CRS) EFFICIENCY(VRS) EFFICIENCY(TECRS/TE-VRS
1
ACCESS
100%
100%
100%
BANK 2
AKIBA
17.4%
32.1%
54%
3
ECLOF
100%
100%
100%
4
FINCA
97.3%
100%
97.3%
5
MWANGA
37.1%
100%
37.1%
6
NMB
21.8%
100%
21.8%
7
PRIDE-
42.2%
63%
66.9%
25.8%
74.1%
34.9%
55%
83%
64%
TANZANIA 8
CRDB
Mean
Source: Source: Data from mixmarket.org computed by the author and analysed in Excel and DEAP Software.
Table 4.4 shows that Access Bank and Elf performed well in 2012 in the TE-CRS.In contrast, under the TE-VRS, Access Bank, Eclof, Finca, Mwanga and NMB did well. However, Finca was the only firm that maintained efficiency in the scale efficiency Appendix 4(EFFICIENCY SUMMARY 2012)
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Table 4.5 Intermediation Approach Summary (2013) SERIAL
DMU
NUMBER
TECHNICAL
TECHNICAL
SCALE
EFFICIENCY(CRS) EFFICIENCY(VRS) EFFICIENCY(TECRS/TE-VRS
1
ACCESS
45.7%
59%
77%
BANK 2
AKIBA
100%
100%
100%
3
ECLOF
100%
100%
100%
4
FINCA
93%
33.9%
27.4%
5
MWANGA
28.9%
100%
28.9%
6
NMB
25%
50%
50%
7
PRIDE-
97%
50.9%
19%
29%
55%
53%
37.4%
68.6%
45%
TANZANIA 8
Mean
CRDB
Source:Source:Data from mixmarket.org computed by the author and analysed in Excel and DEAP Software.
Table 4.5 shows that only two firms did well under the T-E CRS, VRS and scale efficiency. Appendix 4(EFFECIENCY SUMMARY 2013)
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Table 4.6 Intermediation Approach (2014) SERIAL
DMU
NUMBER
TECHNICAL
TECHNICAL
SCALE
EFFICIENCY(CRS) EFFICIENCY(VRS) EFFICIENCY(TECRS/TE-VRS
1
ACCESS
65.5%
100%
65%
BANK 2
AKIBA
58.2%
61.7%
94%
3
ECLOF
100%
100%
100%
4
FINCA
33%
34.8%
94.7%
5
MWANGA
100%
100%
100%
6
NMB
31%
41.3%
32.1%
7
PRIDE-
29.5%
49.6%
59.5%
26.7%
66.4%
40.2%
53%
69.2%
73%
TANZANIA 8
CRDB
Mean
Source:Source:Data from mixmarket.org computed by the author and analysed in Excel and DEAP Software.
Table 4.6 shows that Akiba and Mwanga are the only firms that outperformed the other firms in all the three measures. Appendix 4(EFFECIENCY SUMMARY 2014)
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Table 4.7 Mean Summary for Three Years (2012 -2014) Production Approach SERIAL NUMBER SCALE
2012
2013
2014
1
TE-CRS
84.8%
94.2%
60.3%
2
TE-VRS
95.9%
100%
67.5%
3
SCALE
86.6%
94.2%
84.1%
EFFECIENCY Source:Data from mixmarket.org computed by the author and analysed in Excel and DEAP Software. Table 4.7 shows the average means of the firms from 2012 t0 2014 indicating that in the production approach the best scores of the efficiency of MFIs was in 2014
Table 4.8 Mean Summary for Three Years (2012-2014) Intermediation Approach SERIAL
SCALE
2012
2013
2014
1
TE-CRS
52.2%
37.4%
53%
2
TE-VRS
83.7%
68.6%
69%
3
SCALE
64%
45.4%
73%
NUMBER
EFFICIENCY Source:Data from mixmarket.org computed by the author and analysed in Excel and DEAP Software.
Table 4.8 shows the mean scores of the firms from 2012 to 2014 and indicates that efficiency scores were bad in 2013 than in 2012 and 2013.
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CHAPTER 5
FINDINGS AND
DISCUSSIONS
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Chapter 5-FINDINGS AND DISSCUSIONS Chapter 5 is divided into two sections, which are the findings and the discussion. The findings give details on the performance of MFIs from 2012 to 2014.Under the production approach MFIs performed better in 2013 than the other years with an overall score of mean average of 94.2%,100% and 94.2% in the three scales(CRS,VRS and SCALE). However under the intermediation approach the MFIs did well in 2014 with average scores of 64%,45.4%and 73.3%. The discussion summarises the findings of this research, explains how this study differs, and relates to the other efficacy studies that have been conducted earlier.
5.1 Data collection Secondary data were collected from the mix market website, and data was analysed using the DEAP software. Furthermore, descriptive statistics were usedinExcel to evaluate the efficiency. 5.2 Production approach It was found from the analysis that, in the production approach, the results of 2012 showed that the technical efficiency of Akiba and Eclof, Mwanga and CRDB was constant throughout the TECRS, TE-VRS and Scale efficiency, returning 100%, which indicates that these firms were utilising their resources while minimising waste to reduce costs. Furthermore, it implies that the inputs, such as assets, personnel and loans, were used in an effective manner to generate more outputs. On the other hand, the mean was 84.8% in the TE-CRS, showing that the firms performing below the average areFinca, Access Bank and NMB. Consequently, in the TE-VRS, the mean is 95.9%,and the only firm that did not do well was the Access Bank. Furthermore,Finca and NMB underperformed under the scale efficiency. During the following year, 2013, the results that were derived from the analysis showed that the firms below the average of 94.2% in the TE-CRS and scale efficiency were Access Bank, NMB and Finca. However, in the VRS model, all firms were efficient, implying that they made use of their resources to produce more outputs. In 2014, it was foundthat the firms that outperformed their peers in all the models were Akiba, Eclof and Mwanga, performing higher than the average means. All the other DMUs performed
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poorly, indicating that they were not generating more loans for their customers, and some of the inputs were not utilised effectively. 5.3 Intermediation approach The analysis of the intermediation approach showed that Access Bank and Eclof were the best microfinance providers, as of 2012, about the TE-CRS, as well as the scale efficiency. However, in the TE-VRS, fivefirms were efficient as compared to the others. On the other hand, the average mean computed in the TE-CRS was 55.2%, and again Eclof and Akiba reported to be more efficient than the other firms. Similarly, Eclof, Finca, Mwanga, Access Bank and NMB were above the average mean of 83.7% in the TE-VRS.The scale efficiency showed that Access, Eclof, Finca and Pride-Tanzania were better as compared to the other MFI service providers. The results of 2013 under the intermediation approach indicated that only Akiba and Eclof were the most efficient performers, implying that the other firms were not able to provide good services to their customers. In contrast, the firms that performed above the means computed were CRDB, NMB and Mwanga in TE-CRS, Finca, Access Bank, NMB, Pride-Tanzania and CRDB in the TE-VRS, while, in the scaleefficiency, again only Akiba and Eclof performed well. The firm, which performed poorly in all the scales, was CRDB, proving to be more inefficient than the other firms. In 2014, the analysis found that Mwanga and Eclof performed better than the other organisations in all the three scales. However, the worst performers were CRDB and Mwanga, who were below the means in all the scales. All in all, the MFI’s efficacy under the production approach indicated that, in 2013,MFIs performance was better than that of 2012 and 2014.The means for 2013 were 94.2%, 100%and 94.2%. In contrast, in the intermediation approach, organisations did well in 2014 with a score of 64%,45.4%and 73.3%.
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DISCUSSION This chapter will summarise the main findings of the study and will provide answers to the research questions. 5.4 Purpose of the Study The main purpose of this dissertation was to find whether the microfinance service providers in Tanzania are effective and whetherthey have contributed to the reduction of poverty. Previous studies by Kipesha 2013;2012; Marwa, and Aziakpono,2016) allfound that MFIs in Tanzania are still performing poorly due to poor government regulations and the inability of these firms to manage their resources. Elsewhere, studies by Hassan and Sanchez (2008), (Hermes, Lensink and Meesters, 2009, Haq, Skully and Pathan (2010) and Kablan (2012) found that MFIs were underperforming because of ineffective production of outputs, which was led by misuse of resources available. Therefore, there was a need to analyse the efficiency level of MFIs in Tanzania, which was done using the DEA model by taking the company information of eightMFIs in Tanzania, which were available on the mix market, using both the production and intermediation approach. 5.5 Results of the study The findings of the study show that, under the production approach, four firms (Akiba, Eclof, Mwanga and CRDB) were most effective, as the results were constant throughout the threescales(TE-CRS,TE-VRS and scale efficiency), indicating that these firms were utilising resources well and, at the same time, minimising waste. However, on the other hand, in 2013, all the eight firms were efficient in the TE-VRS scale, which returned 1, implying that the firms were able to provide better services and also to use their inputs to produce better outputs. On the other hand, three firms were reported to perform below the average of 94.2%, which were Access Bank, NMB and Finca, indicating that they were inefficient. Again, in 2014, the results showed that, in all the scales, Akiba, Eclof and Mwanga performed better, implying that these firms generated more loans for customers and made use of the resources available. However, all the other firms performed very poorly due the mismanagement of their resources and not being able to meet the targeted outputs,
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In the intermediation approach, however, the best performers were Access Bank and Eclof in 2012, in both the TE-CRS and Scale efficiency. On the other hand, Eclof, Finca, Mwanga, Access Bank and Akiba were efficientcompared to the other firms The results of 2013 only showed two MFIs as best microfinance providers, implying that these firms were the only ones that were able to provide better services to their customers. In 2014, it was found that Mwanga and Ecloff performed better than other organisations in all the three scales. However, firms that did not perform were CRDB and Pride-Tanzania, who were below the means in all the scales. Overall, under the production approach, the best results were obtained in 2013, where the means were 94.2%, 100% and 94.2%. In contrast, in the intermediation approach, firms performed well, returning 64%, 45.4% and 73.3% in all the scales. However, it is to be noted that, for firms to be efficient, they should return a score of 1;the results, therefore, indicate that, even though other firms outperformed others by scoring higher than average means, the efficiency was still low, because not all were able to return mean score of 100% 5.6 How the results relate to other efficiency studies conducted by other researchers The findings from this study are not new, from the observations, we note that they report similar results to previous studies done by(Hassan and Sanchez 2009; Haq, Skully and Pathan 2009; and Marwa and Aziakpono 2016), which showed that firms were underperforming because of their incapability to produce more outputs (generating more loans, getting more customers) due to misuse of their inputs. In contrast, the results under the production approach show that banks were more efficient compared to other MFIs. On the other hand, under the intermediation approach, someMFIs are better than banks. This is different tothe results reported by Haq, Skully and Pathan (2009), where banks were more efficient under the intermediation approach and less effective under the production approach. This implies that some MFIs have been able to channel more loans to their customers than the banks. 5.7 Limitations of the study The study undertaken was limited to a period of less than a year and, since only secondary data were used, the information that was collected was not up-todate. Moreover,not enough data
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were available because, initially, 21 MFIs were to be taken into account, but, due to the insufficient data of some of the organisations, only eightMFIs and their data were considered. Additionally, the organisations that were considered were only in one region in Tanzania and not in other cities due to unpublished data of the MFIs operating in other regions. 5.8 Recommendations for future research Future research measuring the efficiency of Tanzanian MFIs should consider more firms and ensure that up-to-date data are taken into account and more regions shouldbe considered. Furthermore, it should show the progression of MFIs after new regulations have been passedandshow why MFIs fail to manage their resources, which makes them inefficient. Also, MFI studies should be able to provide a direction in which will help MFIs to perform better. 5.9 Conclusion To conclude, the main aim of the dissertation was to findwhether Tanzanian MFIs were efficient; whetherthey contributed to poverty reduction and to shed light on the various rules and regulations that are available in the country. From the findings, it was found that, under the production approach, banks were better, while, under the intermediation approach, non-banking institutions were best performers. Overall, the results of 2013 werethe best of all the years in the production approach and, in theintermediationapproach,the results of 2014 were better as compared to the other twoyears. Although there were best performers, not all met the 100% criteria, which shows that firms have to ensure they manage their inputs in theright way to produce more outputs. Furthermore, new laws in place must ensure that they are tight enough to have more regulated microfinance services to avoid these organisations benefiting themselves instead of working for the poor.
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CHAPTER 6
CONCLUSIONS AND RECOMMENDATIONS
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Chapter 6:CONCLUSION This chapter will review the entire research and discuss the literature, the various methods used for data analysis, the results found,conclusions and recommendations will be given. Microfinance efficacy has been an area of concern for a long period. Researchers from all over the world have noted that the industry has changed over time and that MFIs are not performing as expected.
6.1 Reasons why MFIS’s are inefficient Microfinance institutions face certain risks, which make them face more failures than successes. (Ledgerwood, 1999),argues that some MFIs fail to reach a larger population because they target people who do not have access to business opportunities. Also, some MFIs fail because of the mismanagement of cash or a failure to meet the efficiency levels that are being set. In contrast, Kipesha (2013) argues that poor regulation of these institutions causes them not to function as expected. Thus, without proper regulations, theseinstitutionswillcontinue to misuse the available resources, which, in turn, will greatly affect the borrowers, as this would lead to their exploitation. Furthermore, in Tanzania, previous studies by Kipesha 2013;2012; Marwa, and Aziakpono (2016) all found inefficiencies of some MFIs due to wastage of resources as well asthelack of clarity in the regulations in the country. In the study, secondary data were collected from the mix market website of the eight MFIs and a quantitative approach was adopted. To analyse the data, the DEA model was applied because previous studies had done so and it was suggested to be the most effective tool for measuring the efficiency of microfinance firms. Both the production and intermediation approach were considered as factors for viewing the best performers, as well as the worst ones. Data were also analysed using the DEAP software, and also descriptive analysis, and measured the overall performance of the eight MFIs for the period of threeyears from 2012 to 2014. From the analysis, it was found that, under the production approach, MFIs in Tanzania performed better in 2013 than in 2012 and 2014.The mean scores for 2013 were 94.2%, 100% 56 | P a g e
and 94.2%. In contrast, under the intermediation approach, it was found out that MFIs did well in 2014compared to the previous years, with scores of 64%, 45%and 73%. The findings imply that firms need to control their inputs to produce better outputs by managing their resources in an appropriate way. This also implies that proper governance must be put in place to be able to control the activities ifMFIs are to perform better.
6.2 Conclusion In conclusion, the efficiency of an MFI is very crucial for its success and, as these organisations largely depend on donors to fund them, it is advisable for the firms to ensure that loans given can contribute to the growth of the MFIs as well as ensure that more loans benefitborrowers in need of exploring various opportunities to overcome poverty. Future research should be able to consider the measurement oftheefficacy of a large number of MFIsin Tanzania and consider exploring different areas in the country, as the study was only based in Dar esSalaam. Furthermore, it would be advisable to take a longer period to study the causes of inefficiencies, as this study was limited to a period of less than a year. Tighter regulations also have to be considered by the government, so that borrowers can be safeguarded from MFIs who wish to exploit them by charging higher interest
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RECOMMENDATIONS The following are some of therecommendations on how firms can maintain efficacy and areas where future researchers can look into.
6.3 Recommendations for maintaining efficacy and future research The study has reported some inefficiencies as found earlier researchers in the field (Kipesha 2013; 2012; Haq Skully and Pathan, 2010), that poverty cannot be easily eradicated if there is a poor performance from the MFIs. Therefore, both the government and the MFIs must work closely to ensure that amendments take place to shape the sector, so that it contributes to reducing poverty by providing various opportunities to individuals. To do this, the following recommendations are laid forth.
6.4 Review of laws governing MFIs Firstly, the laws in the country have to be carefully reviewed and applied to all the MFIs, regardless of their size. This will help to ensure a standard to all the providers of microfinance. Furthermore, a proper understanding of the nature of the MFI and its customers must be in place before such laws are enforced (ledgerwood, 1999).
6.5 Coordination between the government and MFIs Secondly,to ensure that MFIs perform better the government as well as financial regulators should coordinate with one another to enable MFIs to deliver better services. Lack of coordination between the government and other financial regulators can make it difficult for MFIs to perform better (World Bank, 2012).
6.6Reaching populations in need to create better opportunities Thirdly, firms must be able to target populations who have more opportunities of generating incomes for themselves, as targeting people who are closed to opportunities would not bring any difference, whether they receive the loans or not.
6.7Proper utilisation of resources Fourthly, proper utilisation of inputs would help in ensuring that desirable outputs are produced, thus saving costs and being able to utilise the donors’ money to provide better services. 58 | P a g e
Furthermore the clients and MFIs are able to fully understand how to use loans and to be clear of the purpose behind the loan eg (education, household needs etc) then this would enable to make sure that the loan is utilised in an effective manner (World Bank, 2012).
6.8 Further recommendations for future research Lastly, as this study only was based in one country (Tanzania) future researchers should look at more regions and compare the reasons for better or poor performances between MFIs in different locations. Furthermore, this study was conducted for less than a year, therefore, future researchers can study in detail about MFIs for a longer period and follow up whether the rules that are being set to control efficacy are useful. Additionally, future research should consider reviewing the entire industry and state if it is still necessary for those in need to go to MFIs and also to make a follow up on the regulations if they are effective in ensuring fair practice as well as controlling efficacy levels of MFIs
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Personal Reflection
Introduction I started my dissertation in March. During the start of my research, choosing a topic was the most difficult part as I was not sure of what to research about and what topic to choose from to suit both my needs as well as the university’s requirements. However, after talking to friends and the module leader, I was able to think about three topics, which seemed to be of interest. I, therefore, listed them down and submitted them as three areas I would like to research. After having a chat with my supervisor Bob, I was able to decide and finalise that I would be researching on the efficacy of MFIs in Tanzania. At the start of my project, I was only aware of the basic meaning of microfinance institutions and how they operate. Another challenge was to research more on how these institutions operate and mainly their origin and how they operate in different parts of the world. My supervisor was very supportive again and recommended that I read more on MFIS’s and their founder Muhammad Yunus was then able to gain more insights, and this became interesting. However, as writing a dissertation is a process at the next stage of the review of the literature, I found it challenging and wrote it about three times. In the end, I was able to link up what various authors wrote about efficacy. From the module People management and was able to pick up a few skills of writing critically, and linking ideasand differentiating them. This helped me with the review of the literature, which I completed successfully. The data collection stage was another stage where I faced difficulties; I spent nearly two weeks just learning about the Deap software l, which was very important in my study. It was hard to follow through, but I had to keep trying and to focus even more to learn more. Perseverance as an important virtue is what I was able to develop during the start of my masters. This greatly paid off, as I was able to learn more about the DEA more as well as the DEAP software. Furthermore, I was able to analyse my data and provide interpretations as well. Another important skill that I had to enhance was the time management skill, which was the overall important one to be able to finish assigned task on time.
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Writing up other chapters even though challenging I, was able to write and rewrite them repeatedly without feeling disheartened because I believe in putting all my effort to get better results?
Conclusion Overall, I would say that the most important skills that I gained through this research were time management, perseverance, acting in an ethical manner and being able to make stronger judgments and to understand their implications as well. Through my experiences, I have been able to learn more, and to know how to go about a difficult task given to me. As Kolb (1884), cited by (Skills for learning.leedsbeckett.ac.uk, 2015) states that experiences teach us and shape us into becoming better. Throughout the writing process, I have been able to gain new skills, reflect on them, and apply them and to actually understand what contributions these have made.
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70 | P a g e
Appendices
Appendix 1(DEA inputs and outputs) Inputs and outputs selected
INTERMEDIATION APPROACH
PRODUCTION APPROACH
DATA ENVELOPMENT ANALYSIS
INPUTS • • •
INPUTS ASSETS CAPITAL PERSONNEL
• •
PERSONNEL OPERATING EXPENSES
OUTPUTS • • •
OUTPUTS LOAN PORTFOLIO NUMBER OF LOANS REVENUE
• •
GROSS LOAN PORTFOLIO SAVINGS
Note: Inputs and outputs have been adopted from Kipesha (2013 71 | P a g e
Appendix 2:Gantt Chart
72 | P a g e
Appendix 3: (Production Approach Calculations) RAW DATA FROM MIXMARKET.ORG (2016) 2012
Assets
Capital
Personnel Loan
number of
portfolio
loans
Revenue
ACCESS BANK
527575
507578
126950
803100
500000
425
AKIBA
739901
732020
170475
122600
824718
468
ECLOF
2198586
6700
895292
229654
1701561
53
FINCA
966900
626474
293986
245857
2939886
689
MWANGA
508524
492589
949070
799152
106007
33
NMB
137598
134593
333287
279587
329688
278
PRIDE
420000
360000
527009
876636
103236
556
CRDB
183934
180668
805430
307500
400000
1898
TANZANIA
Results from DEAP Version 2.1 Instruction file = Eg2-ins.txt Data file
= eg2-dta.txt
Input orientated DEA Slacks calculated using multi-stage method
EFFICIENCY SUMMARY: 2012 Firm
CRSTE
VRSTE
SCALE
ACCESS BANK
0.630
0.675
0.933
AKIBA
1.000
1.000
1.000
ECLOF TANZANIA
1.000
1.000
1.000
FINCA
0.591
1.000
0.591
MWANGA
1.000
1.000
1.000
NMB
0.716
1.000
0.716
PRIDE
0.848
1.000
0.848
CRDB
1.000
1.000
1.000
73 | P a g e
MEAN
0.848
0.959
0.886
Results from DEAP 2.1 Note: crste = technical efficiency from CRS DEA 0.630+1.000+1.000+0.591+1.000+0.716+0.848+1.000/8=0.848 Vrste = technical efficiency from VRS DEA 0.675+1.000+1.000+1.000+1.000+1.000+1.000+1.000/8=0.959 Scale = scale efficiency = crste/vrste 0.630/0.675=0.933 1.000/1.000=1.000 1.000/1.000=1.000 0.591/1.000=0.591 1.000/1.000=1.000 0.716/1.000=0.716 0.848/1.000=0.848 1.000/1.000=1.000
Raw data from Mixmarket.org 2013
Assets
Capital
Personnel Loan
number of
portfolio
loans
Revenue
ACCESS BANK
898185
864573
227543
145658
500000
575
AKIBA
7621147 748690
163392
125000
824718
487
ECLOF
2706230 852886
116116
408138
137887
53
FINCA
840600
828793
355799
309053
355799
689
MWANGA
707650
688250
132706
104620
113026
48
NMB
165243
160635
398405
328018
421804
286
PRIDE
688411
704768
255275
113937
269913
628
CRDB
202839
199310
843780
355866
400000
2158
Results from DEAP Version 2.1
Instruction file = Eg2-ins.txt Data file
= eg2-dta.txt
Input orientated DEA 74 | P a g e
Slacks calculated using multi-stage method
EFFICIENCY SUMMARY:2013 Firm
Crste
Vrste
Scale
ACCESS BANK
0.926
1.000
0.926
AKIBA
1.000
1.000
1.000
ECLOF
1.000
1.000
1.000
FINCA
0.712
1.000
0.712
MWANGA
1.000
1.000
1.000
NMB
0.900
1.000
0.900
PRIDE
1.000
1.000
1.000
CRDB
1.000
1.000
1.000
Mean
0.942
1.000
0.942
Source: Results from DEAP 2.1 Note: crste = technical efficiency from CRS DEA 0.926+1.000+1.000+0.712+1.000+0900+1.000+1.000/8=0.942 vrste = technical efficiency from VRS DEA 1.000+1.000+1.000+1.000+1.000+1.000+1.000+1.000/8=1.000 scale = scale efficiency = crste/vrste 0.926/1.000=0.926 1.000/1.000=1.000 1.000/1.000=1.000 0.712/1.000=1.000 1.000/1.000=1.000 0.900/1.000=0.900 1.000/1.000=1.000 1.000/1.000=1.000
75 | P a g e
Raw data from Market mix.org (2016) 2014
Assets
Capital
Personnel Loan portfolio
number of
Revenue
loans ACCESS
124258
118594
272773
191164
500000
514
AKIBA
794635
772192
343973
135000
860715
561
ECLOF
253447
7247504 1237354
407754
130430
53
FINCA
636600
949333
413750
297414
413750
689
MWANGA
869260
851575
172233
115770
131663
51
NMB
204676
198616
471530
388195
523312
300
PRIDE
420000
360000
527009
770057
700190
679
CRDB
257614
254529
95645
421009
400000
1356
BANK
Results from DEAP Version 2.1
Instruction file = Eg2-ins.txt Data file
= eg2-dta.txt
Input orientated DEA Slacks calculated using multi-stage method
EFFICIENCY SUMMARY: 2014
Firm
Crste
Vrste
Scale
ACCESS BANK
0.481
0.706
0.682
AKIBA
1.000
1.000
1.000
ECLOF
1.000
1.000
1.000
FINCA
0.573
0.597
0.961
MWANGA
1.000
1.000
1.000
NMB
0.409
0.497
0.822
PRIDE
0.250
0.274
0.914
CRDB
0.115
0.329
0.348
76 | P a g e
Mean
0.603
0.675
0.841
Results from DEAP 2.1 Note: crste = technical efficiency from CRS DEA 0.481+1.000+1.000+0.573+1.000+0.409+0.250+0.115/8=0.603 vrste = technical efficiency from VRS DEA 0.706+1.000+1.000+0.597+1.000+0.497+0.274+0.329/8=0.675 Scale = scale efficiency = crste/vrste 0.481/0.706=0.682 1.000/1.000=1.000 1.000/1.000=1.000 0.573/0.597=0.961 1.000/1.000=1.000 0.409/0.497=0.822 0.250/0.274=0.914 0.115/0.329=0.348
77 | P a g e
Appendix 4 :( Intermediation Approach Calculations) Raw data from Mix market.org (2016) 2012
Loan
Number of loans
Gross loan portfolio
Savings
527575
219942205
425
126506
AKIBA
739901
34090163
468
1892620
ECLOF
2198586
582554
53
885958
FINCA
966900
44180546
689
261767
MWANGA
508524
70062
33
875331
NMB
137598
22899979
278
198934
PRIDE
420000
25376700
556
290400
CRDB
183934
2591033
1898
170640
portfolio ACCESS BANK
Results from DEAP Version 2.1
Instruction file = Eg2-ins.txt Data file
= eg2-dta.txt
Input orientated DEA Slacks calculated using multi-stage method
EFFICIENCY SUMMARY:2012 Firm
Crste
Vrste
Scale
ACCESS BANK
1.000
1.000
1.000
AKIBA
0.174
0.321
0.542
ECLOF
1.000
1.000
1.000
FINCA
0.973
1.000
0.973
MWANGA
0.371
1.000
0.371
NMB
0.218
1.000
0.218
PRIDE
0.422
0.630
0.669
CRDB
0.258
0.741
0.349
78 | P a g e
Mean
0.552
0.837
0.640
Results from DEAP 2.1 Note: crste = technical efficiency from CRS DEA 1.000+0.714+1.000+0.973+0.371+0.218+0.422+0.258/8=0.552 vrste = technical efficiency from VRS DEA 1.000+0.321+1.000+1.000+1.000+1.000+0.630+0.741/8=0.837 scale = scale efficiency = crste/vrste 1.000/1.000=1.000 0.174/0.321=0.542 1.000/1.000=1.000 0.973/1.000=0.973 0.371/1.000=0.371 0.218/1.000=0.218 0.422/0.630=0.669 0.258/0.741=0.349
2013
Loan portfolio
Number of loans
Gross loan portfolio
Savings
ACCESS BANK
898185
306229884
575
190651
AKIBA
7621147
753474
487
213764
ECLOF
2706230
395550000
53
112593
FINCA
840600
78354279
689
331876
MWANGA
707650
59360
48
125956
NMB
165243
2582625
286
225298
PRIDE
688411
24081833
628
221322
CRDB
202839
3024429
2158
203400
Instruction file = Eg2-ins.txt Data file
= eg2-dta.txt
Input orientated DEA Slacks calculated using multi-stage method
79 | P a g e
EFFICIENCY SUMMARY:2013 Firm
Crste
Vrste
Scale
ACCESS BANK
0.457
0.591
0.774
AKIBA
1.000
1.000
1.000
ECLOF
1.000
1.000
1.000
FINCA
0.093
0.339
0.274
MWANGA
0.289
1.000
0.289
NMB
0.025
0.500
0.050
PRIDE
0.097
0.509
0.191
CRDB
0.029
0.554
0.053
Mean
0.374
0.686
0.454
Results from DEAP 2.1 Note: crste = technical efficiency from CRS DEA 0.457+1.000+1.000+0.093+0.289+0.025+0.097+0.029/8=0.374 vrste = technical efficiency from VRS DEA 0.591+1.000+1.000+0.339+1.00060.5000+0.509+0.554/8=0.686 scale = scale efficiency = crste/vrste 0.457/0.591=0.744 1.000/1.000=1.000 1.000/1.000=1.000 0.093/0.339=0.274 0.289/1.000=0.289 0.025/0.500=0.050 0.097/0.509=0.191 0.029/0.554=0.053
80 | P a g e
Raw data from Mix market.org (2016) 2014
Loan
Number of
Gross loan
portfolio
loans
portfolio
124258
731350247
873
262944
AKIBA
794635
896093
561
239110
ECLOF
253447
480000000
53
113083
FINCA
636600
130213635
689
394706
MWANGA
869260
70062
51
152179
NMB
204676
3006640
300
273741
PRIDE
420000
0
679
249084
CRDB
257614
3390921
1356
170640
ACCESS
Savings
BANK
Results from DEAP Version 2.1 Instruction file = Eg2-ins.txt Data file
= eg2-dta.txt
Input orientated DEA Slacks calculated using multi-stage method
EFFICIENCY SUMMARY:2014 Firm
Crste
Vrste
Scale
ACCESS BANK
0.655
1.000
0.655
AKIBA
0.582
0.617
0.944
ECLOF
1.000
1.000
1.000
FINCA
0.330
0.348
0.947
MWANGA
1.000
1.000
1.000
NMB
0.132
0.413
0.321
PRIDE
0.295
0.496
0.595
CRDB
0.267
0.664
0.402
Mean
0.533
0.692
0.733
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Note: Crste = technical efficiency from CRS DEA 0.655+0.582+1.000+0.330+1.00060.132+0.295+0.267/8=0.533 Vrste = technical efficiency from VRS DEA 1.000+0.617+1.000+0.348+1.000+0.413+0.496+0.664/8=0.692 Scale = scale efficiency = crste/vrste 0.655/1.000=0.655 0.582/0.617=0.944 1.000/1.00=1.000 0.330/0.348=0.947 1.000/1.000=1.000 0.132/0.413=0.321 0.295/0.496=0.595 0.267/0.664=0.402
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Appendix 5:( Topic Suggestion Form,Topic Agreeement form, Discusion Forms,and Ethics Form)
P15241712
Leicester Business School Topic suggestion / Supervisor request form We encourage all postgraduate students to select research topics that reflect the areas within which they wish to deepen their business knowledge. This has to be aligned with their programme of study. Please use this form to indicate the area you wish to study, bearing in mind supervisor research and teaching areas and the requirements of your programme of study. We cannot guarantee that students will be supervised by their preferred supervisor. Applicant Last Name:
Nkwabi
First Name:
DMU Email Address:
[email protected] mu.ac.uk
Programme of study:
Ms. International Business and Finance
Student no.
Jesca p15241712
The research Indicative Titles for possible dissertation topic areas: 1. An analysis of financial performance between two major banks in Tanzania: CRDB and Nmb 2. A study on how Microfinance has played in bringing development in Tanzania 3. A study on the impact of privatization of banks. Evidence from Tanzania
Indicative aims of the preferred research project: -
To study the differences between Crdb and Nmb benchmarking their financial performances for the last ten years To analyze how Microfinance has contributed in shaping the economy of Tanzania and what challenges have the organizations in charge of Microfinance encountered over the years To examine whether the privatization of banks has had significant growth of the economy in
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Tanzania or whether it has contributed in undermining the economy
Supervisor preferences NB. It is not guaranteed that we will be able to accommodate your preference, but we will endeavour to do so when appropriate and if possible.
List of possible supervisors in order of preference 1. Mrs Katarzyna Jaskowiec 2. Samuel Komakech 3. John Margerison
Signature of Researcher j.mhoja. Date: 27/2/2016
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