Should microfinance institutions specialize in financial services? Robert Lensink Faculty of Economics and Business, University of Groningen, Development Economics Group, Wageningen University, The Netherlands. E-mail: B.W.Lensink@rug.nl Roy Mersland Faculty of Economics and Social Sciences, School of Management, University of Agder, Kristiansand, Norway Vu Thi Hong Nhung Faculty of Economics and Business, University of Groningen Email: t.h.n.vu@rug.nl 1 Abstract Using a global data set of microfinance institutions (MFIs) in 61 countries, this study tests whether those that specialize in financial services perform better in terms of financial returns and/or outreach than those that provide both financial and nonfinancial (i.e., business development and social) services. The results suggest that MFIs that provide social services perform better in terms of reaching out to poorer customers but worse in their financial results. With regard to business development service providers, their performance is similar to that of MFIs that specialize in financial services. Keynotes: Microfinance; Business development services; Outreach; Financial sustainability; Random effects regressions. JEL codes: G21; O16; C23. 2 The impact of microfinance, defined as the provision of financial services to poor populations, has become a hotly contested subject. It enjoys widespread appeal as an antipoverty tool; however, many questions regarding its actual influence remain unanswered (Hermes and Lensink, 2007). Key controversies relate to whether suppliers of microfinance should follow a minimalist approach, providing microfinance only, or should provide microfinance alongside other important social services, in a sort of “microfinance-plus” (Bhatt and Tang, 2001; Morduch, 2000). Initially, microfinance institutions (MFIs) focused on providing small loans and microcredit. The industry soon started to recognize though that the poor needed a wide variety of financial products to improve their lives, such that microcredit evolved into microfinance. The term refers to a broad set of financial services, including loans, savings, insurance, and transfer services, as well as remittances aimed at low income clients. Conventional wisdom suggests that poor households benefit from a combination of these services, rather than just the provision of credit (Aghion and Morduch, 2005). Some MFIs thus began to broaden their activities even further, providing financial services as well as business training, health care, and social services. These so-called “plus” activities acknowledge that though financial services are critical to microfinance, they address only one of the many problems of the poor. For example, the poor have comparatively high disease rates, and few know how to use borrowed funds efficiently. In such conditions, microcredit is insufficient. Most studies in this field analyze the trade-off between serving the poor and financial sustainability (Cull et al., 2007; Hermes et al., forthcoming). To the best of our knowledge, the question of whether specializing in financial services or integrating 3 financial services with nonfinancial services is better for financial sustainability has not been tested empirically. We take up the challenge by comparing microfinanceplus providers, which offer nonfinancial services, and specialized MFIs, which focus only on financial services, according to their financial results and outreach to the poor. As an added contribution, we compare financial performance and outreach across two types of plus services: business development services (BDS) and social services. The remainder of this article proceeds as follows: We outline the concept of microfinance-plus in the next section, followed by our conceptual framework of the impact of these plus services. From our empirical literature review, we derive some hypotheses; we then describe our data and methodology for testing these hypotheses. Finally, we present estimates regarding financial results and outreach and conclude with a discussion of our findings. What Is Microfinance-Plus? Microfinance-plus refers to the provision of developmental services to customers, such as training or health services, alongside financial services. An overall understanding of the concept is clear and relatively simple; a more detailed assessment also is possible. For example, a MFI providing savings, insurance, or money transfers alongside loans is not involved in microfinance-plus, because all its services are financial in nature. Moreover, a MFI that provides informational sessions to potential customers or trains existing customers in the use of credit or the importance of repayment is not practicing microfinance-plus, nor is a MFI that partners with another organization that provides customers with plus services. We 4 define a plus service specifically as a nonfinancial service provided by the MFI itself. Various MFIs have taken on a wide variety of plus services. Maes and Foose (2006) report plus services ranging from access to markets and business development services to health provision and literacy training. Generally though, plus services belong to either BDS or social services. Business development services usually aim to boost competitiveness through higher productivity, better product design, improved service delivery, or enhanced market access (Sievers and Vandenberg, 2007). They comprise a broad range of nonfinancial services, including management or vocational skills training; marketing and technical assistance; technology access; productivity and product design; accounting and legal services; and access to various information about standards, regulations, or ideas in an enterprise field. In contrast, social services integrate credit with health, education, or other programs intended to raise health consciousness, health practices, and the use of formal healthcare. A MFI can offer plus services in parallel or unified form. A parallel service is offered by specialized personnel, whereas a unified service is offered by the same microfinance staff, normally credit officers (Dunford, 2002). The plus service of unified providers tends to be limited to training, whereas parallel providers can offer a wider spectrum of services by relying on staff members with various specializations (Dunford, 2002). The advantage of unified services is that additional costs should be minimal (Vor der Bruegge et al. 1999), such that the services can be funded mainly by the customers themselves through loan interest payments. Parallel services instead tend to be funded by special donations or customer service charges. Some MFIs also cross-subsidize their parallel services. 5 Conceptual Framework of the Impact of Microfinance-Plus A clear-cut, unambiguous theory about the impact of microfinance-plus activities on financial performance and/or outreach is not available. However, using different theories from extant literature, we can derive a framework to assess the impact of microfinance-plus, as we illustrate in Table 1. We classify impact according to its relationship with identified goals, such as expected or unexpected objectives, and the causation types, whether direct or indirect. First, microfinance-plus can exert a direct, anticipated impact (Quadrant I) if it solves multidimensional poverty problems. Many practitioners and researchers argue that the poor need more than financial services to improve their lives (Dunford, 2002; Khandker, 2005), because poverty is not due just to a lack of funds but also relates to vulnerability, powerless, and dependency (Bhatt, 1998). Economic stress events, such as the birth of child, marriage of family members, severe illness, death of a major breadwinner, or natural disasters, strain household income and resources, which in turn influences the likelihood of timely loan repayments (Noponen and Kantor, 2004). To overcome poverty, the poor usually require access to a coordinated combination of microfinance and other development services (Khandker, 2005; Mosley and Hulme, 1998). Thus microfinance-plus providers should have superior effects in alleviating poverty. Second, plus activities, especially training, can improve microfinance customers’ human capital. In specialized MFIs, borrowers are mainly traders who participate in simple production and service provision (Dawson, 1997). Clients may plan to use loans efficiently, but their attempts may be limited by their lack of or narrow knowledge. Many borrowers never go beyond traditional food processing, handicrafts, or petty trade (Dawson, 1997). Without sufficient cognitive 6 skills, micro-entrepreneurs even might earn a negative return on their capital (de Mel et al., 2008). We argue that improved human capital could enable microfinance clients to service bigger loans, which would enhance the financial performance of MFIs though economies of scale. Third, microfinance-plus may help reduce the risk of default. Training should reduce credit risks that arise when borrowers use loans for consumption rather than production activities (Marconi and Mosley, 2006). Plus activities also may improve customer satisfaction and thus increase repayment and retention rates. Fourth, MFIs may obtain a comparative advantage if they provide a range of integrated services (Khandker, 2005; Mosley and Hulme, 1998). With integrated services, MFIs can better differentiate their products and attract new customers. Fifth and finally, plus services may help MFIs achieve sustainability by providing much needed services to borrowers and encouraging their trust. Borrowers may be more confident about depositing their saving and motivated to achieve timely repayment to keep receiving continuously larger loans. However, microfinance-plus also may create direct, unexpected effects (Quadrant II), including higher operational costs. A study of four Freedom from Hunger affiliates reveals that the direct cost of including learning sessions related to family, health, nutrition, business development, and self-confidence added up to between 4.7 and 10 percent of each MFI’s operational costs (Vor der Bruegge et al., 1999). In addition, integrated plus services may increase administrative burdens. The provision of training and technical assistance may distract MFIs from their credit administration and decrease repayment rates (Berger, 1989). Furthermore, many microfinance borrowers do not consider training useful and do not retain or apply the acquired knowledge, whether due to the poor quality of the offered services or irrelevant 7 training provided. They consider time spent in training an opportunity cost for credit (Goldmark, 2006). Consequently, the adding cost of training does not provide immediate and tangible benefits, which may reduce credit demand (Berger, 1989). Microfinance-plus also can create indirect effects in the community (Quadrant III), such as improved productivity, employment, and sustained growth (Dawson, 1997). It would be hard to generate ongoing increases in employment, technical sophistication, and output if MFIs specialize only in financial services (Hulme and Mosley, 1996). Finally, some indirect, unexpected impacts (Quadrant IV) may be due to the difficulty of evaluating performance, because MFIs need a clear, concise measure of good performance. Performance by MFIs that provide microfinance-plus services is more difficult to measure and requires more time to verify (Tendler, 1989). Some MFIs even offer plus services to distract attention away from their inefficient microfinance services (Berenbach and Guzman, 1994). <insert Table 1> Empirical Literature Review and Hypotheses This study aims to answer three research questions: 1. Do specialist MFIs achieve better financial results than microfinance-plus providers? 2. Are microfinance-plus providers able to reach out to poorer customers than their specialist peers? 3. What types of plus services are most effective for MFIs in terms of financial performance and outreach? 8 Relationship of Financial Performance, Types of MFIs, and Plus Services Many policy makers argue that the only way for MFIs to become self-sufficient, obtain sustainability, and reach optimal scale is to concentrate on financial services (Dunford, 2002; Otero, 1994). However, previous studies also confirm that BDS make substantial, positive contributions to profits for not only microcredit users but also BDS providers and MFIs in general. These results may relate to the quality and type of BDS, which can be improved by tending to the specifics, focusing on vocational skills training and market access instead of traditional management training (Sievers and Vandenberg, 2007). One study, conducted in Sarvodaya’s Rural Enterprise Development Service in Sri Lanka, reveals that adding technical assistance to a standard microcredit program can help increase both loan disbursement and repayment rates (Dawson, 1997). Another study focused on 283 participants of a microfinance program in Zambia suggests that training positively affects the growth of enterprise profits (Copestake et al., 2001). Research using data from 1,798 households across 87 villages in Bangladesh during 1991–1992 investigates credit and noncredit effects specifically (McKernan, 2002). The focal noncredit services include social development workshops, a set of pledges, and specialized skills training (i.e., BDS), as well as health advice (drink boiled water), family advice (educate children), and household management strategies (save money in the bank, plant vegetable gardens) (i.e., social services). The author finds large positive effects of noncredit programs on self-employment profits but does not distinguish between different noncredit aspects. 9 A study of the Bangladesh Rural Advancement Committee shows that beyond the benefits of microcredit, female clients who receive skill training obtain significantly higher income than those who do not. Moreover, the BDS package is highly cost effective for clients, because the added gains to their new business exceed the service charge many times over. However, the fees charged for services cover only 47 percent of its total cost (Halder, 2003). A more recent study also assessed the impact of incorporating BDS, in the form of entrepreneurial training, into a microfinance program by using a randomized control trial (Karlan and Valdivia, forthcoming). They measure the marginal impact of adding business training to a Peruvian group lending program for female borrowers; for borrowers, the treatment leads to limited improvements in business knowledge, practices, and revenues. For the MFI though, the program increases client retention rates. Previous studies also have considered the impact of integrating social services with credit. Dunford (2002) provides expansive evidence of the combined effects of financial and education services, especially health and nutrition training, in “Credit with Education” programs in Ecuador, Honduras, Burkina Faso, Thailand, and other locations. Credit with Education helps borrowers increase their income, enhance their household’s ability to reduce risk, and deal with crises and economic challenges, leading to positive changes in clients’ health knowledge and better dietary quality of the food given to participants’ children. Financial and education services also have positive impacts on women’s self-confidence and status in the community. Some Credit with Education providers, such as CRECER in Bolivia or Lower Pra Rural Bank in Ghana, obtain efficiency and sustainability ratios of 106 and 130 percent, respectively. However, these studies also offer clear evidence that education increases 10 the costs of village banking. The thee-year average additional cost of extra education ranges from 5.9 percent in Bolivia to 9.6 percent in Burkina Faso. Credit with Education providers maintain though that the extra satisfaction and outcomes experienced by their clients, their families, and their communities justify this additional cost of education. Evidence from the Self-Employed Women’s Association, a development organization in India, shows that health services training can help prevent illness, which also reduces illness-related borrowing or drift business loans to pay medical expenses. The findings support arguments for the effectiveness of microfinance-plus rather than specialized credit programs to improve self-employed women’s productivity and enhance their social security (Noponen and Kantor, 2004). Another study of the impact of credit and nonfinancial services, defined as health and education, in Honduras and Ecuador shows that health bank participation significantly raises subsequent healthcare compared with participation in credit only. This plus service helps reduce the tendency to switch from breast-feeding to bottlefeeding when income increases. However, the study finds no clear link between health programs and bank performance (Smith, 2002). This existing literature implies then that adding plus services increases operating expenses for providers, but the synergies that result from linking effective services may help them compensate for these extra costs, such that financial performance may even increase. Regarding the comparison of BDS and social services, BDS training benefits likely exceed the service cost. We thus hypothesize that specialist MFIs 11 obtain better financial results than their plus peers. However, BDS providers should be able to cover their service costs better and obtain better financial performance than those that focus on social services. Relationship of Outreach, Types of MFIs, and Plus Services Although MFIs aim to reach poor people, most of them access the “upper poor” much better than the “very poor.” Thus microfinance offers an effective means of reducing poverty but may not influence extreme poverty (Mosley, 2001). Evidence from Bolivia shows that MFIs effectively reach the poor but not the poorest. A major argument in support of microfinance-plus is that it enables MFIs to reach poorer and more vulnerable customers (Halder, 2003; Maes and Foose, 2006). That is, other antipoverty modalities, including primary health, primary education, and agricultural extensions, are necessary to reach the poorest sectors (Mosley, 2001). In addition, microfinance research generally measures customers’ levels of vulnerability by their average loan size and the fraction of female customers (Bhatt and Tang, 2001). Women appear to accept nonfinancial services more readily and also need them more (Aghion and Morduch, 2005). We therefore expect to find that MFIs practicing microfinance-plus, especially those focusing on social services, exhibit smaller average loan sizes and serve relatively more female clients than specialized MFIs. Data and Estimation Methodology We use observations of 290 rated MFIs from 61 countries. Third-party specialized rating agencies perform these assessments. Submitting to a rating can grant a MFI access to external funding from investors. Because the assessment reports are undertaken by third parties, they often cover a wide range of organizational features, along with financial data and social and financial indicators. Each rating relies on four 12 years of data, and they span the period 2001 to 2007, which means our data cover 1998 to 2007. Most data are from 2001–2005. No data set can be perfectly representative of the microfinance field; ours contains relatively fewer mega-sized MFIs and does not cover the virtually endless number of small savings and credit cooperatives. The former are rated by such agencies as Moody’s and Standard & Poor’s; the latter are not rated. However, our use of rating reports should be relevant for studying the effects of microfinance-plus, because MFIs that are rated have a common interest in accessing funding and increasing their sustainability.i The data set includes both specialists and providers of plus services, so it enables meaningful comparisons. Although we controlled for inflation by including inflation rates in our model, the different inflation rates across 61 countries make comparisons difficult. To solve this problem, we converted the monetary variables into U.S. dollar (USD) amounts at the going exchange rate. According to the purchasing power parity theorem of international finance (Solnik and McLeavey, 2003), conversion into USD implies that local inflation has been taken into account. We also assume that the rating agency has made the necessary corrections to the financial reports to enable a reasonable comparison of MFIs. This assumption is in line with the benchmarking objective outlined by the Rating Fund,ii which funds the rating reports that constitute our data set. Estimation Methodology 13 The general specification of the financial performance and outreach equations we estimate is as follows: yijt 0 1MF _ PLijt M jt MFijt ci ijt , where the dependent variable yijt is a measure of financial performance and outreach of the ith MFI located in country j at time t; β0 is a constant term; MF_PLijt is the key explanatory variable that denotes whether the ith MFI provides plus services in country j at time t; Mjt is a vector of control variables describing the macroeconomic environment in country j at time t; MFjt is a vector of control variables describing the features of the ith MFI in county j at time t; ci is the MFI’s individual unobserved effects; and εijt is a random error term. For our empirical analysis, we use panel data. The main explanatory variable of interest, MF_PLijt is time invariant, so a fixed-effects model is not appropriate; we use a random effects model instead. This model assumes that all explanatory variables ( MF _ PLijt , M jt , MFijt ) are strictly exogenous; that E (uit xi1, ..., xit , ci ) 0 ; and that all explanatory variables are uncorrelated with the MFI heterogeneity term ci , that is, E (ci xi1, ..., xit ) 0 . However, random effects models cannot control for selection bias due to selection on unobservable variables. We lack reliable instruments for the choice of MFI type, so we do not attempt to address selection bias formally with an instrumental variable technique. Instead, we compare the estimates with and without controls to determine the extent to which it is likely that the result disappears with the inclusion of unobserved variables (see Altonji et al. 2005; Bellows and Miguel 2009). Hence, we follow the spirit of the method proposed by Altonji et al (2005). This 14 approach provides an indication of how sensitive ordinary least squares regressions are to any selection bias due to unobservables. We next provide some descriptive statistics of the dependent variables and the explanatory variables. In Appendices 1–3, we also offer definitions, sources, a list of countries, and a correlation matrix of the dependent and independent variables. Dependent Variables In general, a MFI has a dual objective: achieve self-sufficiency by covering its costs and reach many poor clients (outreach). Following Cull et al. (2007), we use the financial self-sufficiency ratio (FINSS) as a main indicator of financial performance. This ratio demonstrates the ability of MFIs to be fully sustainable in the long run by covering all their operating costs and maintaining the value of their capital. The FINSS ratio is a better measure of financial performance than standard financial ratios, such as return on assets or equity, because it can adjust the data and involves a more complete list of inputs and outputs. As a robustness check, we also include operational self-sufficiency (OPERSS) and return on assets (ROA) measures; we expect better financial performance to be associated with higher OPERSS, FINSS, and ROA. Finally, we consider the effect on the portfolio at risk (PAR), for a period of 30 days. Better financial performance is related to a decline in PAR. The outreach measure includes average loan size (AVLOAN) and share of female borrowers (PERCWOMEN), which are proxies for the depth of outreach used widely in prior microfinance literature (Ahlin et al., forthcoming; Cull et al., 2007, 2009; Hermes et al., forthcoming; Olivares-Polanco, 2005; Schreiner, 2002). Average loan 15 size is a proxy for the poverty of borrowers, such that smaller loans roughly imply more depth of outreach, because MFIs appear to be providing more loans to the very poor. A higher percentage of female borrowers also indicates more depth of outreach, because lending to women generally is related lending to the poor. We expect that for better outreach, the average loan size coefficient will be significantly negative and the percentage of women measure will be significantly positive. Explanatory Variables In the first step of the analysis, we examine the impact of plus services in general, using as our main explanatory variable MFI_PL. This binary dummy variable equals 1 if the MFI is a plus provider that integrates BDS or social services along with financial services and 0 if it is a specialized MFI. Next, we differentiate the different forms of plus services with three binary dummy variables: (1) MF_FIN equals 1 if the MFI is a specialist and 0 if it is a plus provider of BDS training or social services. (2) MF_BDS equals to 1 if the MFI is a plus provider that integrates BDS and 0 if it is a specialist or plus provider that integrates social services. (3) MF_SOCIAL equals 1 if the MFI is a plus provider that integrates social services and 0 if it is a specialist or plus provider that integrates BDS training. For control variables related to the macroeconomic environment, we include gross domestic product (GDP, in constant 2000 USD), the Human Development Index (HDI), and inflation. Both GDP and HDI proxy for the overall level of development. The HDI includes measures of health, literacy, and education, as well as per capita income. By controlling for GDP and HDI, we capture institutional differences 16 (Claessens et al., 2001; Lensink and Hermes, 2004; Mersland and Strom, 2009). We include inflation to control for macroeconomic conditions, because all programs tolerate the cost of inflation. Microfinance programs also have a better chance of achieving self-sufficiency if they operate in countries where inflation is controlled and moderate (Rhyne and Otero, 1992). Finally, we include total operating expenses (OPEREXP), number of credit clients per loan officer (CREDOPROD), whether the MFI offers voluntary savings to clients (DMVOLS), the debt/equity ratio (DEBTED), and AGE and AGE2 to control for MFI characteristics. We provide these values in Table 2. <insert Table 2> In Table 3, we also provide general descriptive statistics regarding the relations of different types of plus providers and specialists, as well as between financial performance and outreach. The table clearly suggests that plus providers that combine BDS with financial services perform better financially than their peers that integrate social with financial services; they even perform more effectively than specialized MFIs. In particular, the mean values for FINSS, OPERSS, and ROA are higher for plus providers with BDS than for plus providers with social services or specialists. Moreover, plus providers seem to achieve better outreach. The mean value of AVLOAN is lower when MFIs provide additional BDS and social services than when they specialize in financial services. This finding indicates that plus providers, regardless of the type of services, focus more on poor borrowers, whose average loan 17 sizes are much lower than those of wealthier borrowers. Plus providers that include BDS reach the very poorest to the greatest extent by providing smaller loans than their plus peers with social services. In addition, plus providers, whether with BDS or social services, perform better in terms of reaching female borrowers than specialists. Whereas specialists exhibit 70 percent female borrowers, these values are 76 percent for BDS and 91 percent for social services plus providers. <insert Table 3> Empirical Results Financial Performance Models 1 and 3 in Table 4 suggest that more specialized MFIs perform better financially. However, this relationship may suffer from a selection problem, for which the random effects model cannot control. The random effects model may provide biased results and overestimate the true impact since it assumes that the unobservables affecting outcomes are uncorrelated with our variable of interest. Thus, the positive financial performance of MFIs that specialize in financial services may reflect the correlations between their specialization and unobserved variables that affect financial performance. Based on the spirit of the approach by Altonji et al. (2005), Bellows and Miguel (2009) propose a very simple method that can be used to test how sensitive OLS regressions are to any selection bias based on unobservables. They argue that by adding further control variables, we can examine if our main result disappears when we include these unobserved variables that both explain financial performance and correlate with specialization. That is, if adding controls 18 attenuates the coefficient for the MFI specialization dummy, our results likely suffer from an omitted variable bias. Specifically, we compare the coefficient of the variable of interest in a model without controls with the coefficient in a model with a full set of controls and thereby calculate the extent to which adding controls attenuates its magnitude. With the assumption that we can proxy for the possible bias due to selection on unobservable variables with the bias due to selection on observable variables, we estimate how large the former would need to be to explain away the entire effect of MFI specialization on financial performance (see also Altonji et al. 2005; Bellows and Miguel 2009). Models 2 and 4 contain the results when we add controls: The significant impact of MFI specialization disappears. However, a significant effect on PAR remains. Adding controls does not reduce the impact of MFI specialization on PAR, and the effect even becomes bigger. Adding nine observed variables to the unconstrained model increases the coefficient from 0.015 to 0.026. It follows that it seems highly likely that adding unobserved variables could not explain away the effect of MFI specialization on PAR. < Insert Table 4> We also distinguish the three different types of MFI services: (1) only specialized financial services, (2) financial services and BDS, and (3) financial services and social services. We include the mf_social and mf_bds dummies, as well as a constant. This specification implies that the constant measures the impact of MFIs that specialize; the impact of MFIs that also provide social services equals the sum of the constant and mf_social; and the impact of MFIs that also provide BDS equals the sum of the constant and mf_bds. Significant values of mf_bds or mf_social imply that the impact of plus providers differs from that of MFIs that specialize in financial services. 19 The results in Table 5 indicate that BDS have a positive effect on MFIs’ financial performance. The positive coefficient for MF_BDS is not significant though, so their financial performance does not differ from that of MFIs that specialize in financial services. The negative, significant coefficient of MF_SOCIAL confirms that MFIs that provide social services, such as health services, literacy, and nutrition training, perform worse financially than do specialized MFIs. Adding the controls does not have a notable impact on the coefficient; following the reasoning suggested by Altonji et al. (2005), the unobserved variables appear unlikely to explain away the effect. Social services also may impose additional costs on clients without ensuring clear financial benefits for MFIs. That is, health or nutrition training might not provide clients immediate and tangible benefits that help them increase their income. Our results confirm that providing social services significantly decreases the selfsustainability and profits of MFIs. Providing social services may have a sizeable social impact, but in terms of self-sustainability and profits, MFIs that use Credit with Education can hinder the drive toward sustainability (Dunford, 2002). < Insert Table 5> Outreach We have hypothesized that higher values for AVLOAN are associated with lending to wealthier people and hence lower outreach. In contrast, an increase in PERCWOMAN implies better outreach. Similar to our estimates of financial performance, we use a random effects panel model. We have only one observation per MFI for the 20 percentage of female borrowers and thus cannot use panel estimators to explain PERCWOMAN. Instead, we use simple ordinary least squares and provide the results in Tables 6 and 7. < Insert Table 6> According to the results in Table 6, MFIs with plus activities tend to focus more on the poor and female borrowers. In all regressions for LNAVLOAN, MF_FIN is significantly negative, but it is positive for the regressions with PERCWOMAN. These results remain stable, with the same signs and significant effects, when we add the control variables. Because the coefficient does not substantially change with the inclusion of the controls, it is unlikely that the results would disappear were we to include unobserved variables to control for possible selection bias. Regarding the effects of different types of plus activities, Table 7 suggests that both types of plus providers focus more on the poor; the coefficients of MF_BDS and MF_SOCIAL are negative in the models explaining AVLOAN and positive in the regressions with PERCWOMAN. However, this effect appears significant only in the case that MFIs provide social services to borrowers. < Insert Table 7> Conclusions We investigate whether MFIs that specialize in financial services perform differently financially or in terms of outreach than do MFIs that also provide BDS and/or social services. Using a large data set, we find that MFIs that provide social services in addition to financial services perform worse financially but better in terms of reaching 21 out to the poor. Yet we do not find any significant difference in performance between MFIs that specialize and those that also provide BDS—in contrast with conventional wisdom that implies nonfinancial services drag down sustainability efforts. Providing BDS services does not hurt financial self-sustainability. Moreover, even though social service provision imposts costs, it also offers a clear gain in terms of better outreach to the poor. Our study cannot offer a decisive conclusion about the preferred type of MFI, nor do we argue conclusively whether MFIs should specialize or not. Rather, our findings suggest that different approaches have different advantages. If a MFI is mainly interested in reaching the poor, it should provide social services, in addition to financial services. However, this choice will impose the cost of lower financial performance. Furthermore, our study represents a first attempt to understand the effects of different types of microfinance services on financial performance and outreach. The importance of including nonfinancial services demands continued research efforts. Of particular interest would be an investigation of how “smart” subsidies might help sponsor the additional costs of providing plus services, as well as how coordinated nonfinancial services provided by non-MFIs, in cooperation with the MFIs, might influence MFI performance. 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Davis: 25 University of California Press. 26 Appendix 1: Variable description Variables Description Sources FINSS Adjusted operating revenue / Adjusted ( Authors’ calculation1 financial expense + loan loss provision expense + operating expense OPERSS Operating revenue / ( Financial expense + loan Authors’ calculation loss provision expense + operating expense ROA Net operating income / average total assets PAR30 Portfolio at risk (30 days) LNAVLOAN Log(Total value of loans / number of credit Authors’ calculation Authors’ calculation clients) PERCWOMAN Percentage of female borrowers Authors’ calculation MF_BDS if MFI provides BDS 1, otherwise 0 Authors’ calculation MF_SOCIAL if MFI provides social services 1, otherwise 0 Authors’ calculation MF_FIN If MFI provides only financial services, Authors’ calculation otherwise 0 MF_PL if MFI is plus provider 1, specialist 0 Authors’ calculation OPEREXP Total operating expenses at the end of given Authors’ calculation year CREDOPROD Total number of credit clients / number of loan Authors’ calculation officer DMVOLS if MFI offers voluntary savings to clients 1, Authors’ calculation otherwise 0 DEBTEQ debt/equity ratio Authors’ calculation AGE The years since an MFI started microfinance Authors’ calculation operations AGE2 1 Square of age Authors’ calculation Authors’ calculation based on data from www.ratingfund.org 27 INFLATION Inflation rate World Development Indicators (WDI) HDI_VALUE Human Development Index United Nations Development Program (UNDP) GDP_CONST_2000 GDP (constant 2000 US dollar ) World Development Indicators (WDI) 28 Appendix 2: List of countries Albania Argentina Chad Chile Honduras India Morocco Mozambique Armenia Azerbaijan Colombia Croatia Dominican Republic Indonesia Jordan Nepal Nicaragua Kazakhstan Nigeria Benin Bolivia Bosnia and Herzegovina Brazil Bulgaria Ecuador Egypt Kenya Kyrgyzstan Pakistan Paraguay El Salvador Ethiopia Georgia Burkina Faso Cambodia Cameroon Guatemala Guinea Haiti Madagascar Mali Mexico Moldova, Rep. Of Mongolia Montenegro Peru Philippines Romania Russian Federation Senegal South Africa Bangladesh Sri Lanka Tajikistan Tanzania, U. Rep. Of Timor-Leste Togo Trinidad and Tobago Tunisia Uganda Vietnam 29 Appendix 3: Correlation matrix explanatory variables, financial performance, and outreach regressions (1) (1) FINSS (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) 1 (2) OPERSS 0.83 1 (3) ROA 0.65 0.69 (4) PAR30 -0.29 -0.11 -0.21 (5) LNAVLOAN 0.19 0.25 0.2 0.15 1 1 1 (6) PERCWOMAN -0.12 -0.22 -0.08 0.08 -0.65 1 (7) MF_PL -0.03 -0.11 -0.01 0.05 -0.18 0.27 1 (8) MF_BDS 0.08 0.04 0.04 0.06 -0.09 0.03 0.58 1 (9) MF_SOCIAL -0.11 -0.17 -0.05 0.01 -0.15 0.29 0.76 -0.1 (10) OPEREXP 0.16 0.11 0.06 0.03 0.1 -0.06 -0.06 -0.03 -0.05 1 1 (11) CREDOPROD 0.07 0.11 0.07 -0.06 -0.28 -0.02 -0.06 -0.05 -0.04 0.22 (12) DMVOLS -0.08 -0.03 -0.04 0.15 -0.05 0.23 -0.07 -0.12 0.01 0.2 0.23 (13) DEBTEQ 0.01 0.01 0.03 -0.01 -0.02 0.11 -0.02 -0.01 -0.01 0.02 0.03 0.08 1 (14) AGE -0.01 -0.02 0.07 0.16 0.08 0.07 0.04 -0.02 0.07 0.2 0.22 0.28 0.01 1 (15) AGE2 -0.01 -0.02 0.03 0.1 0.09 0.01 0.01 -0.03 0.04 0.14 0.18 0.24 0 0.86 (16) INFLATION -0.09 -0.03 0.04 -0.04 -0.03 -0.01 -0.04 -0.06 0.01 -0.03 -0.05 -0.05 1 1 1 0.01 -0.01 -0.01 (17) HDI_VALUE 0.07 0.03 0.15 -0.01 0.35 -0.1 -0.01 0.07 -0.06 0 -0.26 -0.32 -0.04 0.01 0 1 0.11 1 GDP_CONS (18) T_2000 -0.14 -0.12 -0.02 -0.06 -0.2 0.33 0.15 0 0.18 -0.04 -0.07 -0.03 0.06 0.01 0.06 0.15 0.26 1 30 Table 1: The impacts of microfinance-plus - Direct - Indirect Expected Solve multidimensional problems of poverty reach the poorest Improve MFI customers’ human capital MFIs’ economies of scale Reduce default risk Comparative advantage for MFIs Achieve sustainability Improve productivity, employment generation in community - - Unexpected Higher operational cost Administrative burden Poor quality or irrelevant plus services Difficulty to measure good performance Need long time to verify the impact of plus services Serve as a veil to hide inefficient performance 31 Table 2: Descriptive statistics Mean S.D. FINSS 0.93 0.312 OPERSS 1.12 0.383 ROA 0.015 0.125 PAR30 0.07 0.103 LNAVLOAN 6.037 1.139 PERCWOMAN 0.729 0.251 MF_PL 0.181 0.385 MF_BDS 0.068 0.252 MF_SOCIAL 0.113 0.317 OPEREXP 9.23E+05 1.83E+06 CREDOPROD 273.775 180.784 DMVOLS 0.312 0.464 DEBTEQ 3.268 53.795 AGE 9.914 8.404 AGE2 168.887 454.217 INFLATION 0.066 0.11 HDI 0.648 0.127 GDP 1.37E+11 2.31E+11 Note: Extreme outliers (observations with age<0) are ignored Min 0.06 0.08 -0.9 0 0 0.09 0 0 0 1144 14 0 -880.41 1 1 -0.08 0.3 2.84E+08 Max 1.94 2.95 0.79 0.98 10.11 1 1 1 1 3.80E+07 2073 1 1358.67 84 7056 1.7 0.898 8.13E+11 32 33 Table 3: Descriptive statistics for specialists and plus providers MF_BDS=1 FINSS OPERSS ROA PAR30 LNAVLOAN PERCWOMAN MF_SOCIAL=1 MF_FIN=1 1.026154 .822963 .9342094 (.2293195) (.4185673) (.3012288) 1.168077 .9222951 1.139637 (.2580093) (.4320123) (.3811682) .0353333 -.0034444 .0159338 (.080327) (.1414624) (.1261973) .0940984 .0715663 .0675 (.1224252) (.1204208) (.0991225) 5.665549 5.512657 6.129878 (1.272797) (1.303492) (1.082931) .7585714 .9128571 .6949398 (.2683548) (.1694075) (.2492495) Note: Standard errors are given in parentheses. Extreme outliers (observations with age<0) are ignored 34 Table 4: Financial results and microfinance plus (2) (3) Operss Finss1 -0.07877 -0.04806 (0.273) (0.373) Operexp 0.00000** (0.037) Credoprod 0.00019* (0.094) Dmvols -0.04391 (0.572) Debteq 0.00031 (0.265) Age 0.00476 (0.624) age2 -0.00013 (0.627) Inflation -0.00485 (0.963) hdi_value 0.10761 (0.565) gdp_const_2000 -0.00000 (0.296) Constant 1.12865*** 0.96709*** 0.92119*** (0.000) (0.000) (0.000) Observations 609 459 561 Number of id 199 146 194 within r2 e(r2_w) 0.0220 e(r2_w) between r2 0.0269 0.0478 0.00529 overall r2 0.0112 0.0380 0.000962 VARIABLES MF_FIN (1) Operss1 -0.12947** (0.028) (4) (5) (6) (7) (8) Finss Roa1 Roa par1 par -0.03820 -0.00795 -0.00109 0.01553 0.02596* (0.537) (0.624) (0.955) (0.276) (0.075) 0.00000*** 0.00000* -0.00000 (0.003) (0.096) (0.153) 0.00025*** 0.00006** -0.00001 (0.003) (0.048) (0.553) -0.14885** -0.02364 0.03248** (0.025) (0.210) (0.023) 0.00033* 0.00001 0.00005 (0.072) (0.957) (0.532) 0.02349*** 0.00995*** 0.00305 (0.002) (0.000) (0.117) -0.00047** -0.00023*** -0.00007 (0.026) (0.002) (0.205) -0.11935* -0.02977 -0.00033 (0.092) (0.338) (0.990) 0.15593 0.11764** 0.00733 (0.256) (0.017) (0.852) -0.00000 -0.00000 -0.00000 (0.159) (0.980) (0.861) 0.61513*** 0.01441** -0.14011*** 0.06691*** 0.04310 (0.000) (0.032) (0.000) (0.000) (0.159) 448 905 742 884 726 148 276 225 278 224 0.147 e(r2_w) 0.0713 e(r2_w) 0.00535 0.0410 0.00154 0.0323 0.00449 0.0813 0.0583 0.000132 0.0350 0.00238 0.0527 Notes: Random effects estimates. p-values in parentheses. *** p < 0.01. ** p < .05. * p < .1. 35 Table 5: Financial results and different types of microfinance plus (1) (2) (3) (4) (5) (6) (7) (8) Operss Operss Finss Finss Roa Roa Par Par 0.02026 0.05692 0.08562 0.09215 0.01816 0.01243 0.02814 0.02845 (0.809) (0.546) (0.284) (0.269) (0.463) (0.659) (0.194) (0.175) mf_social -0.24379*** -0.22210** -0.13763** -0.16441** -0.02423 -0.01150 0.00757 0.02405 (0.001) (0.022) (0.039) (0.046) (0.225) (0.645) (0.667) (0.200) operexp 0.00000** 0.00000*** 0.00000* -0.00000 (0.050) (0.004) (0.100) (0.153) credoprod 0.00019* 0.00025*** 0.00006* -0.00001 (0.099) (0.003) (0.050) (0.554) dmvols -0.03398 -0.14060** -0.02268 0.03265** (0.658) (0.032) (0.231) (0.023) debteq 0.00031 0.00033* 0.00000 0.00005 (0.265) (0.072) (0.958) (0.532) age 0.00397 0.02262*** 0.00988*** 0.00302 (0.680) (0.003) (0.000) (0.122) age2 -0.00007 -0.00042** -0.00023*** -0.00007 (0.773) (0.047) (0.003) (0.215) inflation 0.00159 -0.11620 -0.02930 -0.00015 (0.988) (0.101) (0.346) (0.995) hdi_value 0.08448 0.13850 0.11555** 0.00696 (0.650) (0.311) (0.020) (0.860) gdp_const_2000 -0.00000 -0.00000 0.00000 -0.00000 (0.355) (0.197) (0.984) (0.869) Constant 1.12874*** 0.98230*** 0.92123*** 0.62703*** 0.01441** -0.13883*** 0.06691*** 0.04342 (0.000) (0.000) (0.000) (0.000) (0.031) (0.000) (0.000) (0.157) Observations 609 459 561 448 905 742 884 726 Number of id 199 146 194 148 276 225 278 224 within r2 e(r2_w) 0.0223 e(r2_w) 0.149 e(r2_w) 0.0718 e(r2_w) 0.00524 between r2 0.0570 0.0801 0.0309 0.0721 0.00903 0.0340 0.00743 0.0811 overall r2 0.0302 0.0582 0.0178 0.0811 0.00387 0.0362 0.00421 0.0530 VARIABLES mf_bds Notes: Random effects estimates. p-values in parentheses *** p < 0.01. ** p < 0.05. * p < 0.1. 36 Table 6: Outreach and microfinance plus (9) (10) (11) (12) Avloan1 Avloan Percwoman1 Percwomen -0.64553*** -0.40032** 0.16649*** 0.11858* (0.000) (0.015) (0.006) (0.094) Operexp 0.00000*** 0.00000 (0.002) (0.940) Credoprod -0.00079*** -0.00007 (0.000) (0.758) Dmvols -0.18316 0.01130 (0.220) (0.899) Debteq 0.00005 -0.00028 (0.886) (0.931) Age 0.05420*** 0.02221 (0.000) (0.189) age2 -0.00081* -0.00077 (0.076) (0.224) Inflation -0.31677*** -0.78166 (0.006) (0.289) hdi_value 0.55144** -0.50530* (0.010) (0.094) gdp_const_2000 -0.00000* 0.00000** (0.061) (0.013) Constant 6.05792*** 5.64021*** 0.69494*** 0.91041*** (0.000) (0.000) (0.000) (0.000) Observations 919 752 104 77 Number of id 283 227 within r2 0 0.139 e(r2_w) e(r2_w) between r2 0.0472 0.110 e(r2_b) e(r2_b) overall r2 0.0331 0.111 e(r2_o) e(r2_o) VARIABLES MF_FIN Notes: Random effects estimates. p-values in parentheses *** p < 0.01. ** p < 0.05. * p < 0.1. 37 Table 7: Outreach and different types of microfinance plus VARIABLES mf_bds mf_social (9) LNAvloan1 -0.51481* (0.052) -0.72634*** (0.001) operexp credoprod dmvols debteq age age2 inflation hdi_value gdp_const_2000 Constant Observations Number of id within r2 between r2 overall r2 6.05791*** (0.000) 919 283 0 0.0487 0.0338 (10) (11) (12) LNAvloan Percwoman1 Percwomen -0.27837 0.06363 0.05557 (0.246) (0.505) (0.574) -0.49074** 0.21792*** 0.16448* (0.019) (0.002) (0.061) 0.00000*** 0.00000 (0.002) (0.988) -0.00079*** -0.00008 (0.000) (0.729) -0.17323 0.01482 (0.249) (0.868) 0.00005 -0.00094 (0.888) (0.776) 0.05390*** 0.02314 (0.000) (0.172) -0.00079* -0.00081 (0.085) (0.202) -0.31627*** -0.83731 (0.006) (0.259) 0.54588** -0.46610 (0.011) (0.126) -0.00000* 0.00000** (0.067) (0.023) 5.64070*** 0.69494*** 0.89050*** (0.000) (0.000) (0.000) 752 104 77 227 0.139 e(r2_w) e(r2_w) 0.112 e(r2_b) e(r2_b) 0.113 e(r2_o) e(r2_o) Notes: Random effects estimates. p-values in parentheses *** p < 0.01. ** p < 0.05. * p < 0.1. i ii See www.ratingfund.org. See www.ratingfund.org. 38