Governance and Performance: Evidence from African Microfinance Institutions

Governance and Performance: Evidence from African Microfinance Institutions Thierno Amadou Barry and Ruth Tacneng 1 Université de Limoges, LAPE, 5 rue Félix Eboué, 87031 Limoges Cedex, France May 2011 Abstract: In this paper, we analyze how a microfinance institution s (MFI) organizational structure and external governance affect its performance. We analyze performance in three aspects: sustainability, outreach, and portfolio quality or risk. Using a panel of 281 MFIs in Africa from 1996-2008, we find that non-government organizations (NGOs) perform better in terms of higher profits and at the same time outreach to the poor compared with other institution types. This suggests that NGOs may be the best conduits in attaining the social goal of microfinance, which is to cater to poorer households. However, the results indicate that their higher profits do not translate to being more operationally self-sufficient, implying the possible need to raise additional grants and subsidies to cover losses. Moreover, it also questions the viability of NGOs to provide credit and other services to the poor on a sustainable basis. We also find that larger MFIs attract more client borrowers and savers but at the expense of lending less to the poor. On the other hand, the presence of regulating bodies to oversee and monitor MFIs serves as an effective external governance mechanism, but only in terms of improving efficiency and productivity but not portfolio quality. The results are robust even when the factor analysis method is employed. JEL Classification: G30, I30, L31, 27, O16, O55 Keywords: Governance, Performance, Microfinance, African Economies, Risk, on Government Organizations 1 Corresponding authors: Tel: +33-555-14-92-08, thierno.barry@unilim.fr (T.Barry); ruth.tacneng@gmail.com (R.Tacneng) 1

1. Introduction Microfinance has increasingly become a popular tool in the fight to reduce poverty and promote economic growth especially in developing countries. Over the past twenty years, microfinance institutions (MFIs) have generally been viewed as an important conduit to expand access to finance especially among the poor, in order to improve their welfare (Morduch, 1999; Armendariz de Aghion). As Bellman (2006) has reported, over 100 million customers worldwide borrow small loans from around 10,000 MFIs. In Sub-Saharan Africa (SSA), the informal sector, which includes microenterprises is large and play an important role especially for those that cannot be employed in the formal sector. The proliferation of MFIs has enabled increase in financial access as small enterprises and most of poor population in SSA has very limited access to deposit and credit facilities and other financial services provided by formal financial institutions. In Ghana and Tanzania, for example, only about 5-6 percent of the population has access to the finance from the banking sector (Basu and Yulek, 2004). In addition, financial systems in SSA countries are still underdeveloped both in terms of the size and scope of financial services offered. Even where capital markets exist, they are very shallow and illiquid (Ndikumana, 2003). Despite the series of financial sector reforms that the African countries have undertaken since the 1980s, financial systems still exhibit substantial degrees of inefficiencies in their savings mobilization and allocations of resources into productive activities (Senbet and Otchere, 2006). Financial systems in Africa are generally weak primarily because of two reasons. First is the presence of high interest rate spreads due to lack of competition and weak management in the banking sector. Second, credit allocation tends to be concentrated into short-term and speculative activities, which may be explained by the lack of stable long-term finance and of the high risk aversion exhibited by banks. Given these, MFIs may serve as an important alternative in extending credit and even in providing other banking services when there s limited access to formal financial institutions. Whether or not and how microfinance is able to make a significant and long-term contribution to reducing poverty has been the subject of numerous studies (i.e. Armendariz de Aghion and Morduch, 1999; Cull et al., 2007). As discussed in the literature, financial sustainability may be a prerequisite for microfinance in making a substantial contribution to poverty reduction. Morduch (1999) discusses that meeting the full promise of microfinance, 2

which is to reduce poverty without ongoing subsidies requires translating repayment rates into profits. Indeed, financial sustainability can be determined by the efficiency of MFIs in using their resources and turning them into products and/or services. Outreach, on the other hand, is another aspect of MFI performance mainly indicating the social benefits of microfinance. Schreiner (1999) identifies six important aspects of outreach worth to clients, cost to clients, scope, length, depth and breadth. The latter two, however, are the ones most examined in the literature as data are publicly more available. While depth of outreach indicates how the society values the net gain from microfinance, breadth measures the extent MFIs are able to cater to more clients. Knowledge of the determinants and possible tradeoffs between sustainability and outreach offer a more holistic approach to the investigation on MFI performance. One of the key factors stressed in the literature towards MFI success comes from its ownership and governance. Empirically, these studies have focused on the impact of corporate governance in explaining performance and find that the constitution, experience, monetary compensation and independence of the board of directors as well as the establishment of a supervisory board and control of MFI s management may strengthen the MFI s performance and improve its sustainability and outreach (Otero and Chu, 2002; Labie, 2001; Campion, 1998; Rock et al., 1998). However, more comprehensive, cross-sectional studies by Mersland and Strom (2007) and Hartarska (2004) struggle to find statistical evidence that best practice in corporate governance mechanisms of formal banks operating in mature markets has an impact on the social and economic success of MFIs, more specifically microbanks. Meanwhile some authors that focus their studies on institution types find that bank-mfis are the most efficient under intermediation approach while non-government organization (NGO)-MFIs are the most efficient under the production approach (Haq et al., 2010). Another issue developed in the literature is the relationship between external governance mechanisms and an MFI s financial performance. Hartarska (2004), working on a small sample of rated and unrated Eastern European MFIs from 1998 to 2002, find a positive relationship between public auditing and outreach, whereas banking regulation and rating systems do not have significant impact on the MFI s performance. Meanwhile, Hartaska and Nadolnyak s (2007) study of the impact of regulation on operational self-sufficiency and outreach of 114 MFIs from 62 countries do not find any direct empirical evidence between regulatory involvement of MFIs and economic success (either in terms of operational selfsufficiency and outreach) after controlling for the macroeconomic and institutional framework 3

as well as bank-specific characteristics. Moreover, as the amount of savings has a positive impact on both measures of economic success, the authors suggest that MFIs do benefit indirectly from banking regulation if being regulated is the only way to access savings. Mersland and Strom (2007) using a sample of 226 MFIs in 57 countries from 2000 to 2006 find that competition affects financial performance but not outreach, while regulation is not significant in explaining performance. Mueller and Uhde (2010), on the other hand, examine the impact of a country s institutions-based and outcome-based external governance quality on the economic success of microbanks for a sample of 558 MFIs in 80 countries from 2002 to 2007. They find that while quality of external governance positively affects an MFI s economic success in terms of increased profitability and self-sufficiency, it has negative impact on the depth of outreach. They do not find evidence however of the presence of a trade-off between profitability and a microbank s ability to serve the poor. While recent studies stress the importance of looking into institutional characteristics and macroeconomic factors in order to understand what makes MFIs stay in the business while socially helping the poor obtain credit, the focus of this paper is to examine the determinants of MFI success by looking both at MFI-specific characteristics (internal) and factors that are external to the MFI. We particularly attempt to answer the following questions: Do regulated MFIs perform better? Is there a significant impact of an MFI being audited or not on its performance? Does the organizational structure of an MFI determine its performance? The objective of this paper is to extend the existing literature dedicated to the economic success of MFIs (risk, sustainability, outreach) in several directions. First, we work on the two dimensions of governance internal and external that includes types of institutions and the presence of a regulating body that oversees MFI activities along with whether financial statements reported are audited. While previous studies have looked into worldwide MFIs, we focus on African MFIs where financial systems are not fully developed. Second, we use several measures of MFI performance, proxying for outreach, sustainability and portfolio quality. We also employ factor analysis along with the seemingly unrelated regression model to evaluate the determinants of MFI performance. In doing so, we are able to translate several observable variables to one synthetic indicator for the different dimensions of performance considered in this paper. Third, we consider the impact of economy in explaining outreach. We disaggregate outreach in two components: the growth of loans and the growth of active borrowers. This allows us to understand better how the macroeconomy can effect how MFIs socially perform. 4

Working on a panel of 281 SSA MFIs from 34 countries over the period 1996 to 2008, we highlight two main results from our econometric investigation. First, we find that non government organizations are the most profitable, efficient, and productive in terms of outreach among all types of institutions but are not necessarily operationally self sufficient. Second, we find that while regulated institutions are more efficient and productive than the unregulated ones, the latter socially perform better, accommodating poor clients more effectively. We do not find however evidence linking external governance to lower portfolio risk. We also check the robustness of our results by using interaction terms to examine the effect of the different institution types and external governance mechanisms across size and age on MFI performance. The reminder of the paper is structured as follows. Section 2 describes our data, definitions of variables and their descriptive statistics. Section 3 presents the methodology and the hypotheses tested. The empirical results are discussed in section 4. Section 5 reports robustness checks and further issues. Section 6 concludes the paper. 2 Data, variables and descriptive statistics 2.1 Data collection and sample selection We first consider in our study all the MFIs (299) in Sub-Suharan African countries registered in the Microfinance Information Exchange, Inc. (MIX). Only 294 of them, however, report their financial statements. From this sample, we apply several measures in order to check for the presence of outliers and influential observations 2. The final sample consists of 281 MFIs in 34 SSA countries, over the period 1996-2008. These MFIs represent 4.8 million active borrowers with 1.4 billion USD in loan portfolio in 2008. As Gonzalez (2007) puts it, there are several self-selection issues that must be taken into account when analyzing the data set. First, all the MFIs in the sample have the ability to produce a minimum set of financial indicators. This is related to the availability of information system used to monitor the daily operations of the MFI. The MFIs that do not have the ability 2 We look at the leverage plot and also computed the DFBETA after estimation of the regression to determine the outliers. The DFBETAs measure the distance that a regression coefficient would shift when an observation is included or excluded from the regression, scaled by the estimated standard error of the coefficient (Baum, 2006). Belsley, Kuh, and Welsch (1980) suggest a cutoff of DFBETAI 2 n DFBETA i = v 2 x v i i ( 1 hj) ' hat matrix where ( ) 1, where v i are the residuals obtained from the partial regression, ' j = i i, where x i is the jth row of the regressor matrix. h x X X x 2 v is their sum of squares, hj is the 5

to report data to MIX might not have the best tools in monitoring their portfolios. Second the MFIs in the sample are willing to share private data with MIX. 3 This willingness to share detailed financial information is driven by the need of exposure to investors and donors that can invest in their institutions. Gonzalez (2007) also notes that most MFIs reporting to MIX run their operations very efficiently and pay attention to the portfolio quality and profitability of their operations. Thus, the MFIs in the sample are expected to be a random sample of the best MFIs in Sub-Saharan Africa, but definitely not a random sample of all MFIs. 2.2 Definitions of variables We consider two dimensions in measuring the performance of microfinance institutions-sustainability and outreach. The inclusion of the latter in assessing performance is brought about by most MFIs social goal of helping the poorest people. In addition, we also consider portfolio quality to determine MFI performance. 2.2.1. Sustainability In this paper, we measure sustainability in three aspects: profitability, efficiency, and productivity. Profitability indicators summarize performance in all areas of the institution. These therefore reflect how efficient the institutions are, or if their portfolio quality is high or low. Whether an MFI is profitable or not is manifested from its return on assets, and operational self-sufficiency. However, looking just into profitability says little about why the MFI is in its current state. In addition, looking into it in isolation can be misleading. As such, in order to understand how the institution achieve its profits, operational efficiency and productivity must be taken into account. Efficiency and productivity indicators show how well an institution is streamlining its operations. In general, productivity measures the amount of output generated by a unit of input while efficiency takes into account the cost of the inputs and/or price of outputs. These indicators have the advantage of not being easily manipulated by management decisions and are then more readily comparable across institutions (Technical Guide 3rd edition). We include two measures of efficiency cost per borrower and operating expense over loans and another two for productivity- number of borrowers per staff member and number of savers per staff member. The inclusion of savers is important in our study as MFIs have increasingly provided services that allow their clients to save as in banks especially in Africa. 3 This is through their published profiles available at www.mixmarket.org 6

2.2.2. Outreach Most MFIs primary mission is to improve the welfare of the poor. Outreach measures then indicate the social benefits of microfinance. In this paper, we consider only two aspects of outreach from the original six aspects proposed by Schreiner (1999)- depth and breadth. We do not take into account the others (worth to clients, cost to clients, scope and length 4 ) as data are not available to compute these aspects. Depth of outreach indicates how the net gain from microfinance of a given client is valued by the society. In welfare theory, depth is the weight of a client in the social welfare function. Since direct measurements of depth through income or wealth are not readily available, indirect proxies for depth are sex (women are preferred), location (rural is preferred), education (less is preferred), access to public services (lack of access is preferred), among others (Schreiner 1999). We consider five measures of depth of outreach in our study which includes average loan balance per borrower (in US$), percentage of women borrowers, average loan balance per borrower/gni per capita (%), average savings balance per saver (in US$) and average savings balance per saver/gni per capita (%). In our estimations however, only three measures are taken into account as the other two give the same information as with our chosen variables. Data on the percentages of clients below poverty line (%), clients in bottom half of the population below poverty line (%), clients in households earning less than US$1/day per household member (%) and clients starting microenterprise for the first time (%) are also available but because only a few of the MFIs report these depth measures, we do not take them into consideration. The breadth of outreach measures the number of clients- both borrowers and savers. Breadth is important because of budget constraints- the wants and the needs of the poor exceed the resources they have. We make use of three breadth indicators: number of savers, number of active borrowers and their sum, which we name number of clients. 2.2.3 Portfolio Quality in MFIs The loan portfolio is an MFI s largest asset and therefore the quality of this type of asset, the risk attached to it may be difficult to measure. For MFIs, the quality of the loan portfolio is very crucial as loans are not typically backed up by collateral. In the microfinance industry, Portfolio at Risk (PaR) is the most widely used measure of portfolio quality. It measures the portion of the loan portfolio contaminated by arrears as a percentage of the total 4 Although Schreiner (1999) mentioned in his study that profits may be a reliable proxy for length, he says that in principle, profits are not sufficient for an MFIs length of outreach or timeframe to supply microfinance. 7

portfolio. A loan is considered to be at risk if a payment on it is more than 30 days late. This day-limit is stricter than what is practiced among commercial banks given the lack of collateral to back up the borrowed loans. To measure risk, we use three measures of portfolio quality aside from Portfolio at Risk the loan loss reserve ratio, risk coverage ratio and write-off ratio. The loan loss reserve ratio gives an indication of the expense occurred by the institution to anticipate future loan losses. Risk coverage ratio meanwhile shows how prepared an institution is for a worst-case scenario. Lastly, the write-off ratio represents the loans that the institution has removed from its books because of fear that they will not be recovered. 2.3 Descriptive Statistics We categorize the MFIs by type of institution, whether they are regulated or not and by region (Table 1). Forty-four percent are in West Africa 5, while the other regions constitute only nineteen percent each of all the MFIs in the study. Bulk of these MFIs is regulated (more than sixty percent 6 of total MFIs by region). Evaluating the distribution of institution types among the different geographic regions, we observe that in West Africa, credit unions and cooperatives dominate and constitute almost forty-five percent (45%) of the total MFIs in the region. On the other hand, non government organizations and nonbank financial institutions make up thirty-eight percent (38%) and forty-five percent (45%) of the total MFIs in South Africa, and East Africa, respectively. Aside from other MFIs (including rural banks), which compose barely five percent of total MFIs, only a few banks cater to microfinance services in all the regions throughout Africa. 5 We note that West Africa is the most populated region in Africa. 6 Sixty-seven percent (67%) in Central Africa, seventy percent (70%) in East Africa, seventy-six percent (76%) in West Africa, and seventyfour percent (74%) in South Africa. 8

MFI Type Table 1: umber of MFIs, by type and by region Central Africa East Africa West Africa South Africa Bank 4 2 4 6 (7.69) (3.77) (3.25) (11.32) Cooperative 17 12 55 9 (32.69) (22.64) (44.71) (16.98) NGO 13 14 40 20 (24.52) (26.41) (32.52) (37.73) Nonbank 15 24 21 15 (28.84) (45.28) (17.07) (28.30) Other 3 1 3 3 (5.76) (1.88) (2.43) 5.66 Total 52 53 123 53 (18.50) (18.86) (43.77) (18.50) Regulated 35 37 94 39 (68.62) (90.24) (82.45) (78.00) Unregulated 16 4 20 11 (31.38) (9.76) (17.55) (22.00) Source: Data are computed from the Microfinance Information exchange. The financial volume and outreach indicators for African MFIs by region are reported in Table 2. We account for both their average and median values for a more robust analysis. In terms of average total assets, West Africa has the lowest and therefore, the MFIs in this region are considered to be smaller than the other regions. Table 2. Summary of financial volume and outreach indicators for African MFIs, by region on average in 2006 Indicator Regulated Audited Central Africa East Africa West Africa South Africa TOTAL VOLUME Number of MFIs 36 44 82 29 191 36 44 82 29 191 Total Assets (in thousand US$) 25 100 10 400 9 793.401 12 100 13 800 3 508.317 22 489.57 1 549.920 2 251.490 1 895.968 GLP (in thousand US$) 9 459.333 7 694.687 5 958.054 5 736.409 7 303.029 1 207.755 1 361.864 86.7659 1 468.257 1 164.134 Total Savings (in thousand US$) 12 500 3 087.106 3 851.967 4 244.391 5 689.391 56.235 164.782 170.614 0 105.361 OUTREACH No. of borrowers 21934 40682 14957 24929 24035 4082 8825 5581 6991 6625 No. of Savers 73363 19431 25813 25225 35675 1230 3293 1646 0 1450 Woman borrowers (%) 56.63 57.14 63.28 54.11 59.54 52.6 64.85 66.1 55.7 62 Average loan size 478.94 381.43 840.33 404.97 595.05 285 153.5 213 289 214 Loans below US$300 (%) 62.9 62.14 56.66 41.63 56.29 9 75 65 59 16.5 64 Source: Data are from the Microfinance Information exchange (MIX). All values are estimates in 2006.

With the exception of South Africa, the West African region has lower average gross loans compared to the other regions. This is a little surprising since when we look at the median, West Africa has lower gross loan portfolio (GLP) compared to South Africa, indicating that the latter s distribution in terms of loans may be concentrated at the both extremes, more particularly in the upper percentile. Meanwhile, Central Africa has the largest financial volume among the four regions, primarily marked by large amount of savings. In terms of outreach, East Africa has the biggest number of borrowers, while Central Africa has the biggest number of savers. West Africa meanwhile has the highest percentage of woman borrowers. It is surprising to note, however, that on average, West Africa has the highest average loan size despite having a very low gross loan portfolio. While this may be partially explained by its low number of borrowers, a closer look on their descriptive statistics 7 reveals that one MFI in the West Africa have a very large average loan size. This is further supported by the median amounts wherein it has actually the second lowest loan size. We also look into the financial statistics of the MFIs in terms of MFI type and external governance. In Table 3 (first two columns), we perform t-tests for the equality of NGO vs Cooperative, Cooperative vs Non Bank, and NGO vs Non bank variable means. For this purpose we use three subsamples by excluding respectively Bank, Non Bank, Rural bank, Other; Bank, Rural bank, NGO and Other; Bank, Rural bank, Coop and Other. In comparing NGO vs Cooperative, we document that NGO perform better than Cooperative in terms of both profitability and depth of outreach. However, in terms of being operationally self-sufficient, the latter dominates the former. In addition, we find NGOs more risky than their Cooperative counterpart in terms of the loan loss reserve ratio. Meanwhile, comparing Cooperative vs Non bank, we find the non bank financial institutions to behave like the NGOs. In addition, we find that in terms of portfolio at risk and write-off ratio, cooperatives display higher risk. We also evaluate the differences in the behavior of Non Bank and NGO. We find NGOs to be superior in terms of both sustainability and depth of outreach. However, they are also more risky as evidenced from higher portfolio at risk and write-off ratio. By comparing regulated vs unregulated MFIs, we are able to see that significant differences exist between them in terms of sustainability and outreach but not in terms of portfolio quality. It is thus observed that regulated MFIs are less profitable but are more selfsufficient than the unregulated ones. The latter however are more efficient in converting inputs 7 Not reported but available upon request to the authors 10

into outputs. Unregulated MFIs socially perform better compared to their counterparts, catering to more woman borrowers and lending loans of smaller size. Regulated MFIs moreover have more clients and borrowers, in general. To complete our external governance indicators, we also look into the differences in means of the audited and unaudited MFIs. We find unaudited MFIs to be more profitable but at the same time have higher risk compared to their counterparts. Although they have higher depth of outreach, audited MFIs are able to accommodate and cater to larger number of borrowers and total clients. 3. Hypotheses tested, method and models The empirical analysis aims at testing for differences in MFI performance that might be explained by differences in institutional types and external governance mechanisms. We note that we mainly use four measures of sustainability, three measures for depth of outreach, two for breadth and three for portfolio quality. Hypothesis 1 : MFIs with different organizational structures vary in terms of performance. We use the following equation to test hypothesis 1 : Perform ance = α + α DBank + α D Coop + α D onbank + α D O ther + α D Rural + β ASSET + β AG E + β LO A S + β Years + β LagRISK + β log _ G D P + β + β + ε i, t 0 1 i 2 i 3 i 4 i 5 i 1 it 2 it 3 it 4 it 5 it 6 it 7 I FLATIO it 8GRO W TH it it 11 Where DBank, DCoop, D onbank, DOther, DRural are dummy variables that indicate the type i i i i i of the microfinance institution. We remove the dummy for non-government organizations (NGOs) to avoid singularity. In addition, NGOs are the reference institutions upon which we base and compare the resulting coefficient estimates of our vector of dummies for MFI types. We control for the following in our equation: asset size (ASSET), age of MFI (AGE), loan over assets (LOANS), lagged value of risk (LagRISK), number of years reported by the MFI (Years), real GDP per capita (log_gdp), inflation (INFLATION) and real GDP growth (GROWTH). For portfolio quality as measure of performance, we do not include the lagged value of risk. In microfinance, governance refers to the mechanisms through which donors, equity investors and other fund providers ensure that their funds are used according to intended purposes

(Hartarska, 2004). Such mechanisms are important not only because managers and fund providers may have diverging preferences, but also each fund provider has objectives of its own. (i.e. Donors may prefer outreach to sustainability, while private investors prefer sustainability to outreach). Furthermore, these stakeholders may install their representatives on the board and influence the direction of the manager s effort. Competition for donations and customers, as well as the presence of for profit firms affect the behaviour of non-profit organizations and that of MFIs. These institutions may change their perspective as they strive for survival if this would be in exchange for more donor money (Rose- Ackerman, 1986). This means that some NGOs may want to signal donors that aside from catering the poorest people, they are also sustainable. This therefore indicates that they are capable of improving the welfare of the poor for a long timeframe. In addition, from the perspective of a borrower, it is much easier to borrow from an NGO for smaller amount of loans compared to other types of institutions allowing NGOs to price loans with higher interest rates enabling them to generate higher profit margins. In terms of performance through depth of outreach, we expect the signs of the all the institution types to be negative. This is because in comparison to NGOs, other organization types cater to less poor people that may be explained by institutional constraints to borrowing from a borrower s perspective. However, in terms of breadth, we expect the signs to differ. For example, credit unions and cooperatives may have wider outreach than NGOs because they have larger networks and where other channels like offices are existing. Thus, we expect a positive sign for cooperatives. For the other organization type, we expect a negative sign. From the different institution types, most of the NGOs depend on grants (82% 8 ) followed by nonbank financial institutions (71%) and credit unions and cooperatives (61%). Although the actual shares of donors are not divulged, we assume that in these institutions, donors play very important roles as providers of their funds. Therefore, in terms of sustainability, the sign would be dependent on which of the above mentioned scenarios dominate. Hypothesis 2: MFIs with good external governance are more sustainable and have lower risk but do not achieve better outreach. We use the following equation to test hypothesis 2 : 8 In terms of number of observations. 12

Performance = α + α Re gulated + α Audited + β ASSET + β AGE + β LOA S + β Years + β LagRISK + β log _ GDP + β I FLATIO + β GROWTH + ε i, t 0 1 i 2 it 1 it 2 it 3 it 4 it 5 it 6 it 7 it 8 it it Where Re gulated, Audited are dummy variables, which indicate that an MFI is regulated i i t and audited at time t. We use the same control variables as in previous equations. There exists trade-off between financial sustainability and outreach. (Lensink, Cull) We expect the sign of our external governance indicators to be positive with respect to portfolio quality (or equivalently, negative with respect to risk). Moreover, we also expect these indicators to have negative signs with respect to outreach (or equivalently, positive signs with respect to loan and saving size, and negative with respect to percentage of woman borrower). The control variables in our regression models include: ASSET i, t is the log of ASSETS of each MFI. This variable controls for the size of the MFI. A larger MFI is expected to perform better as they have more funds to invest in technologies to screen higher quality borrowers. We thus expect the sign of ASSET to be positive. AGE i, t is the log of the age of the MFI, the number of years since its establishment. It controls for years of experience of each MFI. This variable allows testing the hypothesis that older, more experienced MFIs perform better. An alternative hypothesis however purport that older institutions have had to learn practices by trial and error, whereas more recently established institutions may profit from the knowledge that has been build up in the past years and may come out to be better performers than their counterparts (Hermes et al., 2009). We therefore expect the sign of AGE to be ambiguous depending on what hypothesis dominates. LOA i, t is the ratio of the gross loan portfolio to total assets. This variable captures the performance of an MFI s lending strategy relative to their other earning assets. YEARS i is the number of years wherein the MFI report data for its financial statements. LagRisk i, t is the lagged value of portfolio at risk >30 days. We control for this variable to account for decisions of the MFIs to increase or decrease their risk-taking strategy for an increased performance trade-off. GROWTH i, t is the real gross domestic product growth. This variable controls for changes in performance and/or portfolio risk that can be accounted for by economic growth. 13

Inflation i, t is the average inflation rate, which controls for changes brought about by price changes. GDP i, t is the real per capita gross domestic product. This controls for the effects of economic development on the performance and portfolio quality of MFIs. Adjusting for heteroskedasticity, we estimate all our equations via OLS regressions adjusted to heteroskedasticity using Hubert/White estimator. The hypotheses and the corresponding equations are discussed below. 4 Regression results 4.1 Does MFI performance vary according to institutional types? We report the results of our estimations on the different institution types as determinants of both financial and social performance in Table 4. We treat non-government organizations (NGOs) as our reference institution upon which the other institution types are compared with. The results show that in terms of financial performance, with the exception of banks, all institutions perform less than NGOs. This is further reflected from the regressions on financial revenue, wherein cooperatives and rural banks specifically have lower financial revenue ratios. However, when taking into account self-sufficiency, we note that credit unions and cooperatives operate more self-sufficiently than NGOs. Meanwhile, the results of our estimations on our efficiency measure indicate that NGOs are less efficient than the other institutional types 9. We also look into the variations in the productivity of member staffs. With the exception of OTHER, all the institutional types are more productive in terms of the number of clients 10 the members are able to cater compared to NGOs. However, when we decompose these productivity figures to account for the number of borrowers and savers served by the staff, it appears that NGOs are actually more productive than banks and cooperatives in terms of the number of borrowers that each staff is able to serve reflecting more stringent lending mechanisms in banks and coooperatives. Moreover, the advantage of these institutions and rural banks over NGOs lies on the number of savers that they are able to offer their services. This additional product, on top of the lending products may be one of the factors that borrowers take into consideration when choosing the MFI where he/she will get involved. Thus, setting aside the actual values of loans and/or savings, we find banks and cooperatives more 9 With the exception of banks. 10 The sum of total number of savers and active borrowers. 14

productive in terms of number of savers they are able to accommodate, and less productive in terms of borrowers than NGOs. Meanwhile, we also look into the aspect of social performance, in terms of outreach using two dimensions: depth and breadth. The findings indicate that NGOs perform better compared to all other institution types (excluding nonbank financial institutions) in terms of average loan size (which measures the depth of outreach). This may reflect the borrowers in these types of institutions. This is binding even after the GNI per capita has been adjusted. Furthermore, we find the percentage of woman borrowers catered by the MFIs highest for NGOs. We also look into another aspect of outreach breadth, which is measured by the number of clients of the MFIs. The results indicate that consistent with our findings in terms of the depth of outreach, NGOs perform better by catering to more borrowers. This is in contrary to the number of savers they accommodate, wherein cooperatives and nonbank financial institutions fair better. We also examine both of the client types (savers and borrowers) added together. The findings indicate that banks and nonbank financial institutions have higher breadth of outreach than the NGOs. Credit unions and cooperatives and rural banks, meanwhile have higher scope of outreach compared to NGOs, marked by more products and other services offered. We also investigate if there are marked differences among the different institutions in terms of their portfolio quality. In comparison with NGOs, there are significant variations only with cooperatives and OTHER. We find cooperatives to have higher percentage of their portfolios that are at risk (>30 days), but have lower loan loss reserve ratio. OTHER meanwhile have lower riskiness in terms of these measures. 4.2 Does MFI performance vary according to external governance mechanisms? We also examine the external governance of MFIs to determine the different aspects of performance. Although the return on average assets and operational self sufficiency does not vary according to the external governance mechanism employed by the MFIs, we find regulated MFIs to be more efficient and productive than their counterparts. Although not tantamount to saying that this group is more sustainable than the unregulated ones due to the non-significant relationship between the presence of regulation and the traditional profitability indicator, the Technical Guide (3rd edition) cites the advantage of efficiency and productivity indicators than the former two. The latter measures are not easily manipulated by management decisions and are therefore more readily comparable across institutions. In terms of external 15

governance through the auditing of financial statements, we find that when audited, MFIs are less efficient and productive. In all our outreach measures (breadth and depth), we find regulated MFIs to perform less than the unregulated ones. This therefore implies that unregulated MFIs are the institutions that really cater to the poorest of the poor and at the same time cater to more borrowers. Thus, they socially perform better than the regulated MFIs. Meanwhile we do not find significant results between AUDIT and outreach which implies that external governance through this mechanism does not really cause variations within sub-groups. Meanwhile, we do not find external governance to be effective in lowering portfolio risk. 5 Robustness check and further issues 5.1 Factor analysis We investigate the determinants of performance using factor analysis. This method has the advantage of translating several observable variables to one synthetic indicator for the different dimensions of performance considered in this paper: sustainability, depth and breadth of outreach. Each dimension is composed of a combination of the observed variables we describe in Appendix A1. 11 From prior classifications we have imposed in our main regressions, we expect OSS, FINREV and ROA to capture sustainability. Meanwhile, it is expected that factor analysis will combine the variables SAVERS and BORROWERS to denote breadth of outreach, and LOANSIZE and WOMAN, depth of outreach. Theoretically, it is assumed in factor analysis that each measured variable some unobserved common factors f k and an idiosyncratic effect s j. x = a f + s j jk k j x j is due to Where x j includes all observed (standardized variable). a jk meanwhile denote the factor loadings and f k are the latent common factors. s j is similar to a residual and includes what is known as the variables unique factors. 11 16 We do not include the other variables previously taken into consideration in our main regressions for the sake of simplicity

We note that since our variables are all continuous, the Pearson s correlation matrix is unbiased and is thus the basis of our factor analysis. The factor loadings defined above however are not uniquely determined. As a solution, several constraints to the parameters in the original model must be introduced. In general, the first factor is required to have maximal contribution to the common variance off the observed variables; the second to have maximal contribution to this variance subject to being uncorrelated with the first, and so on (Luzzi & Weber, 2006). Moreover, a more interpretable solution can be achieved using a transformed model called as factor rotation. While various methods are available to implement this, we use the oblique one (promax with power 3), which allows the factors to be correlated, rather than independent. This is relevant to our study as we expect the different dimensions of performance to be linked. There can be trade-offs but synergies among different dimensions and aspects are also possible (Zeller & Meyer, 2002). In choosing the appropriate number of latent factors, we rely on standard statistical tools commonly used in factor analysis. One method excludes factors with eigenvalues smaller than one. This allows retention of only the factors that account for more variance than the average for the variables. Another method is to keep just enough factors so that the cumulated variance explained is no less than 70%. In applying these methods, factor analysis reveals an appropriate 3 latent factors to be used. We then apply oblique rotation that involves the introduction of correlations between factors. The resulting loadings are presented below. Rotated factor loadings (pattern matrix) and unique variances ----------------------------------------------------------- Variable Factor1 Factor2 Factor3 Uniqueness -------------+------------------------------+-------------- LOANSIZE -0.2393-0.8385 0.1925 0.3546 BORROWER 0.9002 0.1275 0.0892 0.2312 WOMAN -0.1386 0.6623 0.0802 0.4677 ROA -0.1179 0.2477 0.4485 0.6465 OSS 0.1902-0.3459 0.7968 0.3440 SAVER 0.8586 0.0252 0.0320 0.2772 FINREV -0.0498 0.2488 0.6234 0.4626 ----------------------------------------------------------- An examination of the factor loadings reveal to us that BORROWER and SAVER load positively and highly to Factor 1, ROA, O_SS and FINREV load positively and highly to Factor 3, and LOANSIZE and WOMAN, load highly negatively and positively, respectively, to Factor 2. This reflects that changes in BORROWER and SAVER changes Factor 1 in the same 17

direction, which may thus be attributed as actual changes of the breadth of outreach. Meanwhile, the negative loading of LOANSIZE on Factor 2 and at the same time positive loading of WOMAN indicate that the effect of these variables on the depth of outreach. Furthermore ROA, OSS and FINREV, all indicators of sustainability affects Factor 3 highly compared to the other variables. To sum it up, Factors 1, 2 and 3 are mainly determined by the breadth of outreach, depth of outreach and sustainability, respectively. The scores of MFIs obtained through factor analysis will now be used as dependent variable of an equation. We will try to explain why some MFIs perform better than other. Denoting performance or score of MFI i at time t on dimension j=1,2,3 by estimate the following regression model: Where S = x β + z γ + ε 1it 1it 1 1it 1 1it S = x β + z γ + ε 2it 2it 2 2it 2 2it S = x β + z γ + ε 3it 3it 3 3it 3 3it s jit, we x jit consist of the MFI i characteristics at time t that explain both outreach (depth and breadth) and sustainability. z jit meanwhile contains the variables that are presumed to affect the dimensions uniquely. Since the scores may be inter-related by possible trade-offs, and thus the error terms may be correlated, we consider estimating our equations by using seemingly unrelated regressions (SUR). Moreover, the Breusch-Pagan statistic shows whether the equations are to be estimated by SUR or can just be estimated by the least squares method. We report the seemingly unrelated regressions by institutional type and external governance mechanism employed of the MFIs in Table 6. The Breusch-Pagan test indicates that in all our equations, seemingly unrelated regression is preferred than the OLS method. The results indicate that non government organizations perform better in terms of the depth of outreach and sustainability 12 compared to the others but fall short in terms of the breadth of outreach. Moreover, it is also interesting to note that expansion of MFIs in terms of asset increases result in higher number of borrowers and savers, however it may be at the expense of lower depth of outreach as evidenced by the negative relationship between Assets and Factor 2. Meanwhile, we do not find any significant relationship between assets and sustainability. This may be due to the presence of institutional type dummies in the equation which may better explain sustainability than assets. 12 With the exception of banks. 18

In terms of external governance, we find regulated MFIs to have lower depth of outreach than the unregulated ones and also have lower sustainability. This last result is expected as unregulated institutions may choose to pursue riskier but more profitable activites, which may be controlled by authories in regulated institutions. Moreover, we find that MFIs with audited financial statements tend to have lesser clients. Our results also show that lagged values of risk influence depth of outreach negatively. This means that MFIs, which have high risk in period t-1 tend to decrease their depth of outreach. 5.2 MFI outreach growth and the macroeconomy We also investigate the effects of the macroeconomic indicators, specifically the real GDP growth and its lagged values on the growth of MFI outreach. We decompose the latter into borrower growth and loan growth. This mechanism allows us to examine more closely what component of outreach is affected by more stable and vibrant economy and/or economic development. The results of our regressions are presented in Table 7. These indicate that economic growth in time t result in decreases in growth rate of depth of outreach in terms of the average loan per borrower. This suggests that during boom times, borrowers tend to increase the amount of loans they borrow from the MFIs more than during low times. On the other hand, this may also indicate that MFIs are more willing to lend higher amounts of loans when the economy is well. Furthermore, it is worth noting that economic growth does not translate into increased or decreased borrower growth although our main results in Table 5 suggest that increased economic growth translates to more borrowers. This may be explained by the presence of alternative sources of funds and loans for borrowers. It is therefore not tantamount to say that improvement in the economy increases or decreases changes in influx of borrowers. The results indicate that it is asset growth that determines markedly increases in borrower growth. It is equally interesting to note that while economic growth at time t is translates to lower outreach growth at time t, economic growth at time t-1 increases the growth of depth of outreach at time t. 5.3 Use of Interaction terms We also perform several regressions by interacting our institutional type and external governance variables with the MFI size and age and loan focus. The results are presented in 19

Tables 8-12. We find that as asset increases, the level of ROA for OTHER (credit unions) and cooperatives also increases suggesting that MFIs may increase profitability by expanding their asset bases. In addition, we also find that although NGOs have higher depth of outreach through extending smaller average loan sizes compared to cooperatives and nonbank financial institutions, it is worth to note that as asset size increase for the latter two, the depth of outreach also increase. In terms of the percentage of women borrowers on the other hand, we find that as banks and cooperatives increase their sizes, the corresponding involvement of women in those institutions decrease. We do not find however significant results when investigating portfolio risk. Interacting age with our institutional type variables, we find in Table 9 that banks and nonbanks have higher operational self-sufficiency as their ages increase. In addition, we find that in terms of depth of outreach, cooperatives and credit union that are older have lower percentages of women borrowers. Meanwhile, Tables 10 and 11, which presents the interaction between external governance and asset and age, show a clear effect of an increase in asset on regulated MFIs on the breadth of outreach in terms of the number of borrowers. Table 12, which presents the interaction between institutions and external governance, show that regulated cooperatives have higher profitability than the nonregulated ones. They also tend to have higher average loan sizes, indicating lower depth of outreach. 5.4 Differences in MFI performance by region 13 We examine the performance of the microfinance institutions using the equations for Hypotheses 1 and 2 over a subsample of the four regions that make up Sub-Saharan Africa. In doing so, we are able to see whether behavioural and institutional differences are present in determining MFI performance. In terms of profitability, the results on the different regions are consistent with our prior findings- that is, nongovernment organizations are more profitable but not necessarily selfsufficient 14. We also find that outreach and portfolio risk don t vary much by region. In examining the effects of external governance on MFI performance, we find that variations across regions occur only in terms of portfolio risk- in Central Africa, regulation increases risk, while the contrary is found on South African MFIs. 13 The regression results are not reported for the sake of brevity, but they are available upon request to the authors. 14 With the exception of banks being more profitable, and cooperatives and nonbank financial insitutions being less operationally selfsufficient in East Africa. 20