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Performance of microfinance institutions in Europe—Does social capital matter?
Accepted Manuscript Performance of microfinance institutions in Europe—Does social capital matter? Gabriela Chmelíková, Annette Krauss, Ondřej Dvouletý PII:
S0038-0121(18)30083-1
DOI:
https://doi.org/10.1016/j.seps.2018.11.007
Reference:
SEPS 670
To appear in:
Socio-Economic Planning Sciences
Received Date: 22 March 2018 Revised Date:
19 November 2018
Accepted Date: 22 November 2018
Please cite this article as: Chmelíková G, Krauss A, Dvouletý Ondř, Performance of microfinance institutions in Europe—Does social capital matter?, Socio-Economic Planning Sciences (2018), doi: https://doi.org/10.1016/j.seps.2018.11.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Performance of microfinance institutions in Europe— Does social capital matter? Gabriela Chmelíková1
Annette Krauss2
Ondřej Dvouletý3
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This version: November 15, 2018
Abstract
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This paper investigates performance drivers of microfinance suppliers in Europe. As such suppliers, in contrast to advanced microfinance suppliers in developing economies, typically focus on uncollateralized microcredit services to individuals at the margins of society and of labor markets, we draw on the theory of social capital and empirically investigate the role that social capital may play in the overall performance of European microfinance suppliers. We build a unique, unbalanced panel data set of 302 microfinance service providers in Europe covering the years 2008 to 2015, and measure their performance in terms of credit risk, financial and social performance, and efficiency. Pursuing an
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econometric approach, we test a series of hypotheses using various measures of conditions conducive to building social capital on both the institutional and the country level, such as the client base of a microfinance supplier and the level of cultural fractionalization in a society. Our findings confirm that a higher intensity of social capital is positively associated with all areas of the performance of microfinance suppliers in Europe. Our conclusions could help in the design and launch of microfinance institutions in those European countries in which microfinance markets are developed not at all or only to a very limited extent. Our paper thus contributes to the nascent literature on microfinance in developed economies by applying and extending the theoretical framework and empirical models on social capital and microfinance that were originally elaborated for developing economies.
Keywords Microfinance institutions, social capital, social performance, financial performance, efficiency, regional economics
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JEL Classification G21, G23, 015, R23
1 Gabriela Chmelíková, Department of Regional and Business Economics, Faculty of Regional Development and International Studies, Mendel University in Brno, Zemědělská 1, 61300 Brno, Czech Republic, e-mail: [email protected], telephone: +420 731 624 694, Fax: +420 545 211 128 (corresponding author). 2 Annette Krauss, Department of Banking and Finance, University of Zurich, Plattenstrasse 32, 8032 Zurich, Switzerland, email: [email protected], telephone: +41 44 634 5168 or 3579, Fax: +41 44 634 4970 3 Ondřej Dvouletý, Department of Entrepreneurship, Faculty of Business Administration, University of Economics, Prague, W. Churchill Sq. 1938/4, 130 67 Prague 3, Czech Republic, email: [email protected], telephone: +420 728 431 027, Fax: +420 224 098 740 Declarations of interest: None Abbreviations: MFI – Microfinance Institution, EMN – European Microfinance Network, EAPN – European Anti-Poverty Network, MFC – Microfinance Centre, VIF – Variance Inflation Factor, PCA – Principle Component Analysis
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ACCEPTED MANUSCRIPT 1. Introduction The role of microfinance in European countries differs from its counterpart in the developing world. The main objective of suppliers and promoters of microfinance services in developing countries is to provide access to a broad range of financial services—namely, savings and checking accounts as well as credit—to large numbers of poor people and their business activities who are outside of the target markets of standard commercial banks. Contrarily, the vast majority of the adult populations of EU countries do hold some type of account at a formal
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financial institution: according to the World Development Indicators database (World Bank, 2016), the EU-28 average of adult residents having a bank account is 89 percent, ranging from Romania with 60 percent of the population to 100 percent in Denmark. Thus, the mission of European microfinance suppliers focuses on microenterprise lending to individuals (or their companies) that are excluded from traditional bank credit services for a variety of reasons (Degryse et al., 2018; Dvouletý, 2017a; Terjesen et al., 2016; Balkenhol, 2015).
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Such exclusion from bank credit services is considered to be not a mere financial market phenomenon, but also an aspect of social exclusion (Erikkson et al., 2011). According to the European Anti-Poverty Network (EAPN, 2016), the term “social exclusion” refers to the processes that push people to the edge of society and, in turn, limit their access to resources and opportunities (EAPN, 2016). These processes result in limited access to credit, and also—for instance—to labor markets,
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vocational training, and higher education. Social exclusion or isolation is considered to be a main societal concern in Europe (EAPN, 2016; Di Cataldo and Rodríguez-Pose, 2016), and poverty is believed to be a direct consequence of it. Socially excluded groups include mothers on and after maternity leave, older people seeking new work opportunities, young or low-skilled graduates, ethnic minorities, and migrants. At the same time, included in this heterogeneous group, are individuals who want to start or further develop their own businesses (through microenterprises or self-employment) instead of searching for formal employment (Dvouletý, 2017b; Dvouletý and Lukeš, 2016). Their exclusion from traditional bank credit services constitutes an obstacle to
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launching new business activities.
European institutions that supply microfinance services (hereafter referred to as microfinance institutions, or MFIs) aim at reaching out to these individuals by providing access to credit and, typically, related counselling services (e.g., Urban Agenda, 2017; Kraemer and Conforti, 2009). Despite the stated differences in the role of
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microfinance and in levels of poverty and social exclusion between European and developing economies, microfinance developed in Europe later than in developing economies and drew from the experiences made in developing countries (see, e.g., Erikkson et al., 2011). Yet, there is a lack of comparative research that investigates the performance of MFIs in European (or other high-income) contexts systematically and that includes both institutional and country-specific factors related to social inclusion. In particular, the mechanisms
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that European MFIs use to support their clients in overcoming not only barriers to credit access but also social exclusion are not systematically explored in the literature, and the extant literature on the building and use of social capital in credit markets does not seek to explain the phenomenon of European microfinance. This paper aims to fill this research gap for European MFIs and to lay an empirical foundation for policy recommendations with regard to building institutional frameworks for microfinance in European countries. First, to fill the research gap, we use exclusive institution-specific data from a pan-European survey of individual microfinance providers and focus on the overarching question of the role of social capital in microlending. We hypothesize that microfinance institutions are more successful in terms of their credit risk, double-bottom line (i.e., financial and social) performance, and efficiency in those contexts in which the resources of social capital can be put to better use. Using an econometric approach, we test our hypotheses using various measures of good conditions for social capital on both institutional and country levels while drawing from various cross-country level data, and link them to performance measurement of European MFIs.
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ACCEPTED MANUSCRIPT As a second contribution, our findings have a bearing on the understanding of the role of social capital in credit markets and on the way new financial institutions take roots in their markets. Building on the theoretical and empirical body of literature on the role of social capital as a factor shaping institutions, including Coleman (1988) and North (1990), our findings contribute to the body of literature that applies the concept of social capital to credit markets, in particular microfinance markets (e.g., Haldar and Stiglitz, 2016; Van Bastelaer, 2002).
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In our empirical analysis, which we base on a model of the role of social capital in credit markets that we elaborate below, we do not find a trade-off between building a framework for formal financial institutions and the use of social capital. Our findings support the notion that social capital is positively associated with repayment, profitability, depth of social outreach, and—albeit less—with the efficiency of European MFIs. The remainder of our paper proceeds as follows: The next part reviews extant theoretical and empirical studies
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that focus on the influence of social capital on the performance of microfinance institutions. As research on Europe is in its infancy, this review draws heavily on related investigations conducted in developing countries, and, to a lesser extent, on US mortgage lending markets. Based on this overview, we develop our hypotheses in the third part, and describe the data and descriptive statistics in the fourth part. The empirical study presented in the fifth part applies an econometric approach to identify the extent to which social capital can boost micro
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lending to socially excluded but economically potentially active people through European microfinance suppliers. We conclude with a discussion of main results and conclusions, particular limitations, and possible further research.
2. Performance of microfinance institutions and social capital—literature review In order to contribute to social inclusion, the supply of credit to microenterprises and the self-employed in European countries must be reliable in the long term. Although not uncontested (e.g., Morduch, 2000),
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theoretical (e.g., Cull et al., 2009) and practice related (e.g., Eriksson et al., 2011) literature claims that long-term reliability implies a need for MFIs to strive for independence from donations or subsidies, as well as the ability to control default, limit operating costs, and use economies of scale. Yet, independence from donations or subsidies is not yet achieved for the majority of MFIs in Europe (Balkenhol, 2015), and a better understanding of factors driving their performance is needed to help to mitigate its subsidy-dependent character.
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Because of the above-mentioned strong emphasis that European microfinance puts on social inclusion, our investigation primarily focuses on the role that social mechanisms can play to improve MFI performance. The role of social capital for microfinance markets in developed economies such as European countries has not been
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addressed in the literature. Extant conceptual and empirical studies mostly focus on developing economies. We contribute to closing this gap in the literature. To do so, we build on the growing literature that investigates the relationship between the conditions conducive to the development of strong social capital and credit market outcomes on both firm- and country-specific levels. This includes a stream of literature on microfinance in developing economies, an emerging stream of literature on mortgage lending and social capital in developed economies such as the US, and we also find links to the body of literature on relationship banking. The microfinance literature identifies as one driver of the reliable long-term and sustainable provision of inclusive financial services the smart use of available social capital through innovative credit contract designs that use the ability of social relationships to mitigate credit risk for MFIs. According to Freedman and Jin (2008), social capital may convey soft information about borrower risk and therefore has the potential to compensate for a lack of hard information. This, according to Pham and Talavera (2018) and Van Bastelaer (2002), consequently decreases a lender’s need to request collateral or charge high interest rates, which are considered to be major barriers to reducing social and financial exclusion.
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ACCEPTED MANUSCRIPT This paper applies the concept of social capital as it has been widely explored in both the sociological and economic literature. While many definitions lack clarity and consistency (Haldar and Stiglitz, 2016; Robison et al., 2000), a commonly accepted economic definition is Putnam’s (1995), that “Social capital means features of social organization such as networks, norms, and social trust that facilitate coordination and cooperation for mutual benefit”. Dufhues et al. (2011) develop an operational definition of social capital, which we adopt for our study. According to Postelnicu et al. (2014), economists view it as a source of economic returns, which are
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driven by the social capital embedded in the ties that are actually mobilized to achieve certain outcomes—in the context of this paper, microcredit market outcomes. The theoretical literature on inefficiencies in credit markets stemming from information asymmetries has been applied to different types of lending relationships for which social capital is shown to play a mitigating role. The microfinance literatures discusses that microcredit markets are particularly affected by the typical information asymmetries between lenders and borrowers about the loan repayment probabilities of borrowers because lenders cannot apply standard remedies such as collateralization or costly screening techniques (Hermes et al., 2005;
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Kritikos and Vigenina, 2005). Both the microfinance literature, and the literature on US-mortgage markets and social capital identify several channels through which the availability of social capital can improve credit market outcomes in the presence of information asymmetries between lenders and borrowers but also between groups of
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borrowers. First, social capital in terms of the ability of lenders to use strong interpersonal ties to microcredit borrowers for information on a borrower’s ability and willingness to repay a loan, thus compensating for standard measures used by lenders such as physical collateral or high interest rates (Van Bastelaer, 2002). This mechanism may be more directly at work in joint liability contracts where group members are directly affected by their peers’ repayment behavior and in which the social capital among borrowers matters most in groups’ selfselection and subsequent monitoring of loan repayment (Al-Azzam and Mimouni, 2012). However, it it is also a feature of group lending without joint liability (De Quidt et al., 2016; Haldar and Stiglitz, 2016), and even of
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individual lending processes. In the latter case, Haldar and Stiglitz (2016) derive how the “specificity” of the lending institution as a locally rooted institution reinfs or even creates repayment norms within a community that is bound through social ties and resources, even in the absence of joint liability contracts (Haldar and Stiglitz, 2016). Second, Postelnicu and Hermes (2015) discuss how informal institutions may be helpful in dealing with the information opacity that exists between MFIs and their clients. Informal institutions stimulate the development of and improve social networks, cohesion, and interaction, which eventually results in trust
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building between lenders and borrowers. This, in turn, increases opportunities to use social collateral as a substitute for the lack of hard information and real collateral. Of course, the ability of social collateral to replace physical collateral depends on the quality of networks and relationships used. Third, a borrower’s loss of
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reputation when defaulting is considered the larger, the tighter the social network. This may have a direct economical reason, as stated by Li et al. (2018) for US mortgage markets, in which mortgage default directly affects property values of one’s community neighbor. But it may also be through the ability of lenders to use, if not build social capital by instilling loan repayment as a generalized norm or even value system (Haldar and Stiglitz, 2016) or, more generally, by contagion of ideas within the community (Guiso et al., 2013) or the stronger preference for fairness and altruism towards community members (Haldar and Stiglitz, 2016; Karlan, 2007). Loan default then results in negative reputation and emotions (Seiler et al., 2012; White, 2010). Resulting from these mechanisms is what Li et al. (2018) model as a socially motivated “disutility of default” of an individual borrower. Finally, the ample literature on relationship banking (Crawford et al., 2018; Banerjee et al., 2017 and the literature cited herein, in particular Petersen and Rajan, 1994) establishes that when the lending institution has private information about a borrower’s credit history in the presence of market imperfections and absence of credit information sharing mechanisms, then the length of the relation and the repetitiveness of interaction with the borrower reduces information asymmetries and improves market efficiency. This literature
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ACCEPTED MANUSCRIPT draws from the concept of social capital in its emphasis on the role of the loan officers and their social ties (Berger and Udell, 2002; Uzzi and Gillespie, 1999). These mechanisms described in the theoretical literature on microcredit, mortgage, and small enterprise lending have in common that from a lender’s perspective, this approach to investment risk management using soft information about borrowers, collective wisdom, and peer pressure in the relationship between lenders and borrowers leads to decreased information asymmetry (Yum et al., 2012). In turn, they contribute to reduced
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operating and risk costs, the ability to lower rates of interest or decreased demands of physical collateral, and better social and financial performance of credit suppliers---albeit notwithstanding that other institutional and contextual (macro) factors may play an equally important role in driving the overall performance of an MFI. To investigate the empirical validity of the above-mentioned theoretical predictions on the role of social capital, an operationalization of social capital is required. According to Mersland and Strøm (2014), several proxies have
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been used in empirical studies to gauge the intensity of social ties. They include factors such as the duration of the relationship, geographic proximity, the character of the relationship, the frequency of contact and of sharing between group members. In general, the extant literature confirms the theoretical prediction that strong social ties among stakeholders determine repayment performance and decrease the riskiness of loans. In turn, they also
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contribute to better social and financial performance of credit suppliers.
Wydick (1999) presents empirical tests carried out on borrower group data from Guatemala. Results from a survey of 41 urban and 96 rural borrower groups indicate that peer monitoring significantly effects borrowing group performance. Hermes et al. (2005) provide an empirical analysis of the impact of monitoring and social ties within group lending programs on the moral hazard behavior of their participants, using survey data on 102 groups in Eritrea. They find evidence that peer monitoring reduces moral hazard through the social ties linking group leaders and borrowers, but no significant association between moral hazard within groups and social ties among group members other than the group leader. In the setting of a natural experiment that helps controlling
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for endogeneity, Karlan (2007) uses data from the village bank FINCA in Peru to test whether groups that are connected socially perform better. He finds evidence to support the hypothesis that monitoring and enforcement activities do improve group lending outcomes, and that social connections facilitate the enforcement of joint liability. He also observes that both cultural similarity and geographic concentration lead to improved group
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lending outcomes—to higher repayment rates, saving rates, and returns on savings. Finally, Dufhues et al. (2013) study social capital’s effects on access to formal credit in rural Thailand. Their results also confirm the general relevance of social capital and highlight the informational advantage, and thus the reduction of ex ante transaction costs, derived from personal ties.
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More specifically, social ties work differently depending on the type of pre-existing relationship that connects the different stakeholders. Cassar et al. (2007) conduct their research on primary data from field experiments in South Africa and America, and find evidence of a positive relationship between the personal trust among group members, social homogeneity, and loan repayment. This is shown to be of greater importance than general social trust or acquaintanceship between members. Wydick et al. (2011) use data from a survey of 465 households conducted in Guatemala in 2004 to confirm that social networks have endogenous effects regarding credit availability. These endogenous effects appear to some extent among geographical neighbors, but most strongly within church networks. Their findings have significant implications for the launch of microfinance operations in new areas. In line with these findings, Mersland et al. (2013) investigate the relationship between religion and the development of microfinance, in particular the efficiency, loan repayments, and social outreach of MFIs with a Christian affiliation compared to those of non-religious institutions. They find that Christian MFIs are as effective in enforcing loan repayments as their secular peers, have lower cost of funding, and serve fewer female clients. To investigate whether social capital can be systematically built and put to use for microcredit performance, Feigenberg et al. (2013) provide experimental evidence from a development program in India that
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ACCEPTED MANUSCRIPT encouraged repeat contacts. They show that this strengthens social ties and enhances social capital among a treatment group of community members, which consequently reduces default risk and improves members’ loan repayment rates. Also, Giné and Karlan (2014) carried out two randomized trials to test the influence of joint liability, which supports the creation of social capital, on the repayment performance of microfinance clients. They find that individual and group liability lead to the same repayment performance. 4 Postelnicu et al. (2014) confirm in their review of the empirical literature the unambiguous supreme role that
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geographic proximity plays compared to other aspects of social capital. If this finding were transmittable to European conditions, it would be recommended to support the creation of local and potentially small microfinance institutions in order to enable the utmost utilization of social capital. However, it is questionable whether geographical proximity’s positive effect is strong enough to offset the disadvantages of small entities, in particular the lack of economies of scale. Indeed, the empirical literature investigating this question in crosssectional institutional comparisons reveals ambiguous results. The majority of studies confirm the theoretical prediction of the positive effects of social capital on the performance of MFIs (e.g., Pham and Talavera, 2018;
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Postelnicu et al., 2016; Feigenberg et al., 2013; Al-Azzam and Mimouni, 2012; Cassar et al., 2007; Abbink et al., 2006; Sharma and Zeller, 1997); however, some studies’ findings are rather more ambiguous (Hermes et al., 2005; Kritikos and Vigenina, 2005) and several confirm adverse effects (Ahlin and Towsend, 2007; Godquin,
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2004).
Not only the institutional specifics of MFIs, but also the presence of country-specific formal and informal institutions that shape social networks, may contribute to solving agency problems in credit markets. However, the literature researching these country-specific factors’ success is scarce, in particular literature that examines the influence of these conditions on the social capital. The pioneering empirical study on the relationship between macroeconomic and country-specific policy issues and the performance of MFIs is the work of Ahlin et al. (2011). The work’s broad, country-level set of variables include those that are proxy measures for aspects of
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social capital, such as the level of corruption, political stability, government effectiveness, time plus cost to register a new enterprise, or income inequality in the country. They report significant effects of macroinstitutional factors on MFIs’ financial performance and their extensive and intensive growth. For example, inequality (measured through the Gini coefficient) is a negative predictor of financial self-sufficiency and lower corruption is associated with faster extensive growth of MFIs, but greater political stability predicts slower extensive and faster intensive growth of institutions. Interestingly, government effectiveness is a strong predictor
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of high MFI operating costs, and, naturally, cost and time required to register a new enterprise are significantly and negatively related to financial performance (measured as financial self-sufficiency). Building on this approach, Sundeen and Johnson (2012) show how social capital, defined in terms of social
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networks, trust, and social norms, influences outreach to clients and the financial performance (measured in operational self-sufficiency) of MFIs. Analyzing data from 2,000 institutions from across 115 countries, they find that social capital significantly influences the performance of MFIs and that there is a trade-off between outreach and sustainability. Manos and Tsytrinbaum (2014) confirm, in their analysis of 852 institutions from across 30 countries, that cultural environment is a significant driver of MFIs’ social and financial performance. Similarly, Burzynska and Berggren (2015) use a panel of 331 MFIs from across 37 countries to explore the relationship between financial performance and general trust and cultural norms. Their cross-country analysis shows that MFI performance not only relies on the macroeconomic and formal institutional environment but is also associated with the nature of informal institutions. In particular, the level of trust and collectivist culture in a society is positively associated with a reduction in operating and default costs, as well as with lower interest rates 4
Motivated by the empirical results of Giné and Karlan (2014) and Feigenberg et al. (2013), de Quidt et al. (2016) develop a model that predicts that joint liability is used in settings with intermediate levels of social capital and that the use of groups, even without joint liability, is more beneficial in settings with low than in those with high social capital.
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ACCEPTED MANUSCRIPT for MFIs. Burzynska and Berggren (2015) claim that social collateral, supported by trust and a collectivist culture, can work as a substitute for physical collateral. Postelnicu and Hermes (2015) support the suggestion of a positive link between social capital and microcredit repayment through an empirical test carried out on data from 934 MFIs based across 100 countries. In general, their results indicate that strong informal institutions are associated with improved performance reflected in both social and financial indicators. They find strong support for their hypothesis that the fractionalization of society
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and a high level of trust existing in a society are each connected with better financial and social performance, and that an individualistic society leads to the higher social performance of MFIs. Although the nature and shape of the conditions conducive to the development of social capital may arguably be different in high- and low- income economies, we exploit the similarities between the formal structure of microfinance in both types of markets. We empirically verify whether the principles of traditional microfinance
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systems and their strong reliance on social capital could be transmittable to European conditions exactly because in this context, microfinance has the explicit function to improve social inclusion. Therefore, our paper contributes and critically tests this body of empirical literature by focusing exclusively on European microfinance suppliers. Moreover, by combining several data sources with a uniquely generated data set on European MFIs, we are able to use a broader set of explanatory variables describing both firm- and country-
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specific factors. We develop and test a model in which we specify the opportunities for an MFI to make use of the social capital existing among lenders and borrowers, as well as different measures—for formal and informal institutions—conducive to social capital formation found in different societies. We admit that the model suffers from limitation due to raw data not cleansed of subsidies. However, this is the case of majority of the empirical studies carried out for the developing world. To overcome this data set drawback, we include efficiency indicators into our model.
3.1 Definition of social capital
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3. Operationalization and research hypotheses on the role of social capital
Based on the above discussion, we empirically investigate our overarching research question as to which extent the conditions conducive to the development of social capital in a society, arguably crudely defined by country borders, are linked to different levels of financial, social, and repayment performance, as well as to the efficiency
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of MFIs in Europe. We investigate efficiency indicators as a separate performance dimension as this separation is more suitable for subsidized institutions, according to Balkenhol (2015). Our hypotheses build on the extant literature concerning the interaction between the presence and strength of social capital resources and the
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efficiency-related outcomes of MFIs. To develop our hypotheses, we first define and operationalize the concept of social capital and then state our hypotheses. To avoid the ambiguities, present in the definitions social capital used in the extant literature, we employ the definition of Dufhues et al. (2011), who perceive and measure social capital as the availability and intensity of two elements—namely, interpersonal network ties and resources. They claim that social structures are not independent of their context, and that not every social structure will result in the building of social capital. It is the nature of the resource that turns social structure into social capital. Economic returns are then driven by social capital that is embedded in these resources, which—in turn—are mobilized to achieve a certain outcome. The nature of these resources—conditions that are conducive to the development of social capital—constitute the main explanatory variables for MFIs’ performance in our models, controlling for a variety of other institutionlevel and country-level factors. We argue that certain features of the institutional and national environment in which MFIs operate constitute such resources because they support the genesis and the development of social network ties that, in turn, lead to the production of social capital as an effective instrument with which to
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ACCEPTED MANUSCRIPT circumvent information asymmetries. The value of the quality of social capital is assessed in terms of presence and extent of its resources. Figure 1 lists these resources of social capital, showing institutional measures in the top-left box and country-level measures in the bottom-left box of the figure. Fig. 1: Operationalization of MFIs’ resources of social capital
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Years of experience within community
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Local staff working in community
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Access to information about clients
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Access to women’s social networks
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Institution-specific resources of social capital allowing the building of social network ties
MFI’s performance
Fractionalization of society
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Level of corruption
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Generosity within a society
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Trust within a society
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Cost and returns: financial performance
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Outreach to excluded population: social performance
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Loan repayment: portfolio quality
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Country-specific resources of social capital allowing the building of social network ties
Social capital available to an MFI
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Source: Authors’ own construction based on Dufhues et al. (2011) and Postelnicu et al. (2014)
We operationalize our hypotheses based on the resources of social capital identified in the literature discussed above. Because of the ambiguous results of the extant literature, we include a rather broad set of variables in our
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operationalization and empirical investigation. We use the previously identified resources as proxies for the different areas of social capital, and deploy them in our models within the set of explanatory variables for the performance of European MFIs. We control for a variety of other institution-level and country-level factors in order to address the above-mentioned issue that social capital may be one but not the only decisive factor for MFI performance.
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3.2 Proxies for social capital
Social capital is a construct that cannot be measured directly. The operationalization of proxies for social capital is inspired by the empirical models of Ahlin et al. (2011), Postelnicu et al. (2014), and Postelnicu and Hermes (2015; 2016). Based on their results from developing countries and because of the striking similarity of MFI
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performance indicators for European MFIs and MFIs from developing economies, we use a similar set of proxies to investigate the empirical evidence of the relationship between social capital and performance of European MFIs while taking into account limitations of data availability for the European context. The size (SIZE) of an MFI, measured in terms of number of staff as opposed to the more frequently used asset or loan portfolio volumes, can be considered a measure of the intensity of the social ties an MFI enjoys with its stakeholders, as MFIs typically employ a large proportion of local staff. Social ties are considered to positively affect their screening, monitoring, and enforcement efforts, which in turn determine repayment performance and the quality of the loan portfolio. The smaller the MFI, the better are the conditions to develop social ties and to use the advantage of social capital. This prediction is supported by findings of Al-Azzam and Mimouni (2012), who find that geographic proximity improves the repayment performance of borrowers. However, it remains questionable whether the effect is strong enough to offset the disadvantages of small entities, such as low economies of scale. For instance, Ejigu (2009) reports a positive impact of size on the profitability and sustainability of MFIs in Ethiopia, as does a study conducted by Adugna (2014). A possible mechanism via
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ACCEPTED MANUSCRIPT which profitability plays positively on the size of an MFI is provided by Kyereboah-Coleman (2007), who shows that firm size has a positive impact on the yields on the gross loan portfolios of MFIs. The age (AGE) of an MFI tells us about the experience acquired by the institution with regard to operations, clients’ behavior, and market experience, as well as to using the existing social ties with and among borrowers. The longer the history of the institution is, the deeper is its knowledge of its market and clients and the higher is the value of relevant social capital. Kyereboah-Coleman (2007) finds evidence of a negative impact on an MFI’s
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performance, in terms of social performance (outreach) and loan portfolio quality, as age increases. Ejigu (2009), meanwhile, measures a positive impact of MFI age on efficiency. The individual approach of an MFI is proxied through its productivity in lending operations with borrowers, measured as the number of loan officers per loan (LOANS_per_STAFF). This proxy is neutral with regard to the role of joint liability while emphasizing lender–borrower relationships. For instance, Johnson and Rogaly (1997)
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show that Bank Rakyat Indonesia (BRI) managed to lower its screening costs by using insider information about the creditworthiness of borrowers when it launched an individual approach to lending. The lower the ratio of loans per employee, the more time a loan officer can devote to a borrower and the stronger are the social ties that can be developed.
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The proportion of clients from rural areas (RUR) is a strong predictor of the creation of social information channels. Such information channels mitigate information asymmetry, and increase the amount of social collateral involved and the threat of social sanctions in case of default. Postelnicu et al. (2014) provide a theoretical framework that considers both the internal and the external ties of clients that borrow through group arrangements. They claim that information channels are especially dense in rural areas, where tightly-knit networks improve the capacity to collect and transmit information. This theory is supported by empirical findings that microfinance lending works better in rural areas than in urban ones (Ahlin and Townsend, 2007; Wydick, 1999).
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The proportion of female clients (WOM) is considered to be a significant resource of social capital. Several authors argue that contracts drawn up with women are easier to monitor and enforce. For example, Rahman (2008) and Goetz and Gupta (1996) find that women are more sensitive to peer pressure and more responsive to the interventions of loan officers. Ameen (2004) states that women have a lower opportunity cost of time than
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men, and are therefore more inclined to have contact with the MFI, with a positive impact on repayment. However, not all findings favor the idea that women are good borrowers. Phillips and Bhatia-Panthaki (2007) claim that women entrepreneurs tend to be overrepresented in traditional sectors with lower profits, which could
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make them less able to repay their loans. Many studies in the developing world use the proportion of women among clients as an indicator of the depth of outreach (e.g. Gutiérrez-Nieto et al., 2009; Sinha, 2006; Zeller and Meyer, 2002). In a European environment, we use this proportion as a proxy for conditions conducive to the development of the social capital of the MFI, not as a social performance indicator per se. Our reasoning is that in high- and middle-income countries in Europe gender is a predictor of the conditions conducive to the development of social capital. Women are believed to be social capital resources in developing world as well (see, e.g., Burt, 1998; Antonucci, 2001), thus the reasoning for deploying of this proxy is coherent with remaining variables. Cultural fractionalization of society (ETHNIC_FRAC, LANGUAGE_FRAC, RELIGION_FRAC) refers, according to Postelnicu and Hermes (2015), to the probability that two randomly chosen people coming from the same country are not from the same ethnic, religious, or linguistic group. Fractionalization is expected to impede the development of social ties. Postelnicu and Hermes (2015) find a negative correlation between societal fractionalization and both the social and the financial performance of MFIs.
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ACCEPTED MANUSCRIPT It seems intuitive to assume a strong negative link between social capital and the national control of the level of corruption (CORRUPTION). According to Paldam and Svendsen (2002) and Uslaner (2001), countries in which people appear to be more honest and are able to build social networks ought to experience less corruption, and the converse should also be the case. Ahlin et al. (2011) find that lower corruption levels are indeed related to faster extensive MFI growth but have no significant association with intensive growth. Postelnicu and Hermes (2015) confirm a significant positive relationship between the control of corruption and the social performance of MFIs. The relationship to financial performance is found to be insignificant.
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Generosity (GENEROSITY) is defined by those acts that benefit another person and cost the giver time, money, or energy (Glanville et al., 2016). Generous behaviors like volunteering are essential to well-functioning societies and use social networks to increase the availability of information about volunteering opportunities and the likelihood of being asked to volunteer (Glanville et al., 2016). To measure generosity, we use the residual of the regression of the national average of Gallup World Poll responses to the question, “Have you donated money to a charity in the past month” on GDP per capita, this being the best available measure despite there being some
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crudeness to it. The higher the average generosity level within a society is, the better are the conditions required to develop social capital. By way of illustration, one of the most pervasive observations in research into the predictors of formal volunteering is that persons with larger social networks volunteer more (Musick and Wilson,
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2003). Brooks (2005) finds strong links between changes in social capital and in the charitable behavior of individuals. Thus, we hypothesize that it is possible to expect better financial performance and efficiency of MFIs when high levels of generosity are present.
Social trust (TRUST) is defined as a shared set of moral values that helps to create expectations of regular and honest behavior (Fukuyama, 1995). The extent to which trust is prevalent in a society is expected to be positively associated with the development of social capital. Postelnicu and Hermes (2015) indicate, as do Knack and Kneefer (1995), that high-trust societies show better financial and social performance. We measure TRUST
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through a World Values survey question on whether “most people can be trusted”. The indicator of trust is the percentage of individuals who respond positively to the question. Knack and Keefer (1995) demonstrate that the Gini coefficient for income inequality (GINI) is strongly associated with lower civic cooperation. Larraín and Vergara (1997) confirm this conclusion based on empirical
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findings that income inequalities give rise to social pressure and conflicts, which in turn decrease opportunities for the creation of social networks or ties. According to Ahlin et al. (2011), income inequality measured by the Gini coefficient is a negative predictor of the self-sufficiency of MFIs.
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3.2 Research hypotheses
As discussed above, the concept of microfinance as a means to reduce exclusion from financial markets and poverty, which has arisen in the developing world, was transferred to the developed world, using and modifying the principles of the original approach. Our research motivation is to assess whether the positive links of social capital to the overall performance of MFIs known from developing world can be found in developed economies as well. Although empirical findings regarding the effect of social capital on the performance of MFIs in the developing world are ambiguous, our starting point is the assumption that good conditions favoring the development of social capital have a positive effect on all performance areas of MFIs. To research this conjecture in a comprehensive way, we develop four hypotheses with respect to four particular areas of interest—loan repayment (portfolio quality), financial performance, social performance, and the efficiency of MFIs. Based on our review of the literature summarized above, we state the following hypotheses: Loan repayment
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ACCEPTED MANUSCRIPT The most widely used measure of loan repayment is portfolio quality, expressed in the microfinance industry as portfolio at risk, which measures the portion of the loan portfolio outstanding affected by delinquency as a percentage of the total outstanding loan portfolio. Our hypothesis is that loan portfolio quality is positively associated with social capital—that is to say, H1: The quality of an MFI’s loan portfolio, expressed as portfolio at risk, is positively associated with the conditions conducive to the development of social capital.
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Note that the portfolio at risk indicator used in the microfinance literature to express the quality of loan repayment and the portfolio is a negative indicator---the higher the portfolio at risk, the lower the portfolio quality. This explains the prefixes used to summarize our hypotheses in Table 1 below. Financial performance
As microfinance performance indicators become increasingly harmonized with those used in standard banking,
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the most commonly used variable for overall financial performance is return of assets (ROA), which is calculated by dividing net operating income after taxes by total assets. Return on assets is an overall measure of profitability on an accounting basis, and thus we state our second hypothesis as follows:
H2: The financial performance of an MFI in terms of return on assets is positively associated with the
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conditions conducive to the development of social capital. Social performance
The extant literature—for example, Mersland and Strøm (2010)—claims that size of loans in relation to GNI per capita is commonly used as a proxy for an MFI’s outreach to poor clients. The higher the value of this indicator, the lower is an MFI’s depth of outreach to poor clients, which, again, explains the signs used in the Table 1
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below. This outreach is considered a key—although not the only—element of MFIs’ social performance (SPTF, 2014). Given the close link between our definition of social capital conditions and the social mission of European MFIs to target populations that are overall excluded from society, we derive the following hypothesis with respect to social performance:
H3: The social performance of an MFI in terms of reaching out to poor clients is positively associated with
Efficiency
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the conditions conducive to the development of social capital.
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Efficiency, when measured using accounting variables, relates an MFI’s outputs to its inputs, and typically focuses on the operating expenses incurred by the operation of the loan portfolio. The operating expense ratio, which is considered to be a key indicator of the overall efficiency of a microlending institution (Microrate, 2003), is calculated by dividing all expenses related to the operations of the institution by the average gross loan portfolio. The lower this ratio, the more efficient are the processes of the institution. Our review of the literature allows us to hypothesize that the conditions conducive to social capital, in particular country-level conditions, are positively associated with the efficiency of MFIs: H4: The efficiency of an MFI is positively associated with the conditions conducive to the development of social capital. Table 1 summarizes these hypotheses and the expected signs of the associations, with independent variables as derived from the literature, and the different variables used to measure the resources of social capital, as dependent variables. Table 1 Summary of hypotheses
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ACCEPTED MANUSCRIPT Variable name
Hypothesis 1 Portfolio quality measured as default risk
Hypothesis 2 Financial performance measured in return on assets
Hypothesis 3 Social performance measured in average loan balance compared to per capita income
Hypothesis 4 Efficiency measured as operating expense ratio
MFI-specific resources of social capital +
-
+
+/-
+
+/-
-
Individual approach (LOANS_PER_STAFF)
-
+
-
+
Proportion of clients from rural areas (RUR)
-
+
-
+
Proportion of female clients (WOM)
+/-
+/-
-
None
Cultural fractionalization of society (ETHNIC_FRAC, LANGUAGE_FRAC, RELIGION_FRAC)
+
-
+
+
Country-specific resources of social capital -
+
Generosity (GENEROSITY)
+/-
+
Income inequality (GINI)
+
-
4. Data and descriptive statistics
-
-
-
-
+
+
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Control of corruption (CORRUPTION)
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-
MFI age in years (AGE)
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MFI size in number of staff (SIZE)
In order to investigate our hypotheses, we build a data set by drawing on several sources. We combine a panEuropean survey of microfinance data with the country-level data obtained from various existing databases. This section describes the collected variables. 4.1 MFI data
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Our microfinance institution data come from a comprehensive, up-to-date overview of microcredit suppliers in the European Union. This pan-European survey of microfinance providers has been carried out by the Fondazione Giordano Dell’Amore on behalf of the European Microfinance Network (EMN) and the Microfinance Centre (MFC) since 2004, making it the longest series of comparable data for European MFIs to the best of our knowledge. According to Botti et al. (2016), the survey has increased its coverage from 32
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microlenders across 10 European countries participating in 2004 to 149 institutions from across 22 countries participating in the most recently available edition. The EMN survey data include the standard MFI institution
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yearly indicators and is strikingly close to the definition of MFI indicators developed for developing economies (see, e.g., Microrate, 2003) , although the scope of variables has broadened over the years. We use data on microfinance institutions from this survey from the years 2008 to 2015, with several filters. We exclude MFIs from Bosnia and Herzegovina as the country suffered from problems of indebtedness caused by specific conditions after the war and empowered by the financial crisis in 2008, which according to EMN resulted in high default rates, a decline in portfolio size, and negative returns. Furthermore, microfinance institutions reporting a share of turnover from micro lending activities lower than 50 percent are excluded. In total, 90 percent of the remaining institutions in the research sample report that the percentage of their turnover made up of micro lending is higher than 75 percent. The number of institutions in our data changed during the observation period, which led to the creation of unbalanced panel data. In all, we have 302 MFIs5 from across 29 countries, each with 2‒8 years of data over the
5
Although the highest annual number of MFIs participating in the EMN survey is 149 institutions, our sample consists of 302 different organizations. The high count of examining institutions is caused mainly by high fluctuation in the survey sample during the years 2008 - 2015.
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ACCEPTED MANUSCRIPT period 2008‒15. Variations in the analyzed data set do not decrease its information content; contrarily, using unbalanced panel data analysis enables us to include a wider range of European MFIs in our analysis. Although the survey covers all European countries that launched any form of microfinance activities, we cannot claim it to be a representative sample of all European MFIs, as the average response rate for the observation period was only 47 percent. (It should be noted that the most recent edition experienced a significant increase in its response rate, to 69 percent.) Nevertheless, this data set is unique as it allows us to identify the performance drivers of
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MFIs with respect to the specific conditions in Europe, which—to the best of our knowledge—has not been explored previously. 4.2 Country-level data
Our country-level data come from several sources. Data on societal fractionalization come from the National Bureau of Economic Research (Alesina et al. 2003), while the GINI coefficient and survey data on trust, social
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support, and generosity are taken from the World Happiness Report. Data on formal institutional variables like the level of corruption or of political stability come from a World Bank database, the Worldwide Governance Indicators. The World Bank’s World Development Indicators are the source for macroeconomic variables such as GDP growth, GNI, and the shares of economic sectors in national economies. An indicator of the efficiency of government regulation of business—namely, the Business Freedom indicator, a country-level score between 0
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and 100, with 100 indicating the freest business environment—is published annually by the Heritage Foundation. We do not use regulatory frameworks for microfinance as an independent or control variable because there is no specific regulation in many European countries (in contrast to developing countries). 4.3 Descriptive statistics
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All variables, including a description of the measures used and their descriptive statistics, are summarized in Table 2.
The dataset includes MFI-year observations from 29 countries: Albania (8), Austria (1), Belgium (9), Bulgaria (23), Croatia (2), Finland (2), France (13), Germany (44), Hungary (20), Ireland (3), Italy (31), Kosovo (5), Latvia (2), Liechtenstein (1), Lithuania (2), Macedonia (3), Malta (1), Moldova (2), Montenegro (1), Netherlands (3), Norway (1), Poland (12), Portugal (5), Romania (44), Serbia (4), Spain (22), Sweden (4), Switzerland (1), United Kingdom (33). It includes the following legal forms: Non-bank Financial Institution (119), Cooperative/Credit Union (25), Non-governmental Organization (75), Bank (30), Government body (12), Community Development Financial Institution (10), Religious Institution (4), Other (27). The following product ranges are reported for the MFI-year observations: Business and Personal microloans (31%), Only Business microloans (54%), Only Personal microloans (15%).
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ACCEPTED MANUSCRIPT Table 2: Description of variables and summary statistics Variable
Abbreviation
Description
Source
Mean
SD
Min
Max
N
Outstanding balance on arrears over 30 days/gross loan portfolio
European MFIs survey—European Microfinance Network (EMN)
10.13
14.76
0
96
483
[(Net operating income - taxes)/total assets] * 100
European MFIs survey (EMN)
7.36
18.89
-15
166
394
Portfolio quality
PAR30
ROA
Social performance
LOAN_SIZE TO_GNIpc
Average loan size in current year/GNI per capita
European MFIs survey (EMN)
.48
Efficiency
OER
[Operating expenses/average gross loan portfolio] * 100
European MFIs survey (EMN)
16.97
Independent variables—explanatory MFI-specific (X0ijt) SIZE
Number of employees (E)
Age
AGE
Number of years since creation
Individual approach
LOANS_per _STAFF
Gross loan portfolio in 000 Euros/ number of employees (in constant prices 2010)
Proportion of female clients
WOM
Women as % of total borrowers
Proportion of clients from rural areas
RUR
Rural population as % of total borrowers
European MFIs survey (EMN)
Probability that two randomly chosen individuals in one country are not from the same ethnic group
Fractionalization Data (Alesina et al. 2003)
Probability that two randomly chosen individuals in one country are not from the same language group
Fractionalization Data (Alesina et al. 2003)
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LANGUAG E_ FRAC
European MFIs survey (EMN)
European MFIs survey (EMN)
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Language fractionalization
ETHNIC_F RAC
European MFIs survey (EMN)
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Country-specific (Y1jt) Ethnic fractionalization
European MFIs survey (EMN)
14
.001
14.10
565
21.12
0
156
413
Ordinal variable with categories: Very small (0‒5 E), Small (5‒50 E), Middle (50‒500 E), and Big (500 E and more)
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Size
.88
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Financial performance
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Dependent variables
11.94
11.94
0
133
858
875.6
3,848
11.42
541.75
541
31.56
28.41
0
100
613
44.14
22.23
.5
100
464
.23
.16
.041
.81
848
.18
.15
.020
.58
848
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CORRUPTI ON
Lack of Corruption
GENEROSI TY
Generosity
GINI
Income inequality
Probability that two randomly chosen individuals in one country are not from the same religious group
Fractionalization Data (Alesina et al. 2003)
Control of corruption index (-2,5 to 2,5; Worldwide Governance Indicators)
Worldwide Governance Indicators (World Bank)
Residual of regression of the national average of Gallup World Poll responses to the question "Have you donated money to a charity in the past month?" on GDP per capita.
World Happiness Report (Sustainable Development Solutions Network)
GINI coefficient
INST_TYPE
World Development Indicators (World Bank)
European MFIs survey (EMN)
Country-specific (Y3jt) BUS_FREE
Business freedom score (0‒100; Heritage Foundation)
.72
848
.68
.85
.88
2.41
861
.18
.32
.02
.28
.40
800
.27
.39
806
Non-bank financial institution, cooperative/credit union, non-governmental organization, bank, government body, community development financial institution, religious institution
Economic Freedom Data (Heritage Foundation)
69.16
34.45
1
95.9
865
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Entrepreneurial environment
.12
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Institutional type
.19
.002
Independent variables—control MFI-specific (X2ijt)
.46
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RELIGION_ FRAC
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Religion fractionalization
5. Empirical analysis
We aim to investigate to what extent the conditions conducive to the development of social capital in a society
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are linked to different levels of financial, social, and repayment performance as well as of the efficiency of MFIs in Europe. Our empirical strategy to evaluate our hypotheses is based on an estimation of multivariate
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regression models with fixed effects. An econometric approach is used to quantify the impact of independent variables on the analyzed outcome/dependent variables under the assumption that other variables are kept constant (ceteris paribus). We evaluate our hypotheses based on the value of the estimated parameters and their statistical significance (Verbeek, 2012). For the estimation of econometric models we use the software STATA 14. Before we proceed to the estimation of the econometric models, we specify them in the following section. 5.1 Model specification
Our comparative framework and model used to analyze the influence of social capital on the performance of MFIs follows that of Ahlin et al. (2011) and Postelnicu and Hermes (2015), which were initially developed for studying MFI performance in developing ecconomies. The EMN states that the fight against poverty and social exclusion is the same worldwide, though the European battle is different in terms of opportunities, challenges, history and especially in setting the restrictions and institutions (EMN, 2017). This statement alludes to the adaptations that European MFIs undertook when introducing the microfinance concept into European contexts. For instance, while joint liability lending models are less used than in the early microfinance approaches in developing countries (see, e.g., Erikkson et al., 2011), other features of our descriptive data discussed above
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ACCEPTED MANUSCRIPT show greater similarity, such as the product mix offered with an emphasis on credit and advisory services, the broad variety of legal forms and ownerships structures, and the standardization of performance indicators. Thus, these adaptations, despite significant economic differences between European economies and developing economies, in particular higher levels of absolute and relative poverty, and a higher share of people who live under or near the poverty in developing economies but are not systematically excluded from production and consumption markets, explains the common motivation to use financial services to address the needs of those – fewer – populations excluded from credit markets. Therefore, our empirical approach is to focus on the different aspects of social inclusion in European contexts through measurable proxies for social capital an appropriate set
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of cross-country data. Indeed, we argue that in both types of regions, a reasonable model specification for social capital builds on the same assumption, namely that interpersonal relationships influence the nature of exchange in financial markets, in particular credit markets. We, therefore, build our model using a similar specification
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than used for developing economies in Postelnicu and Hermes (2015; 2016) in order to evaluate whether social capital works indeed similarly. Quality of the loan portfolio (H1)
To test the first hypothesis, we take the standard measure of portfolio quality, PAR30 (PAR30), which represents
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the balance of the loans with arrears over 30 days, and which is appropriate for microloans with frequent, at least monthly, instalments. Our independent variables are represented by the set of social capital resources specified in the previous section, accompanied by a set of control variables from both the institution- and country-specific levels. Baseline MFI control variables include institutional type (INST_TYPE).Country-level controls include the economic growth measure (GDP_Growth) and an indicator describing the quality of the entrepreneurial environment (BUS_FREE). We eliminated the variables for trust, financial leverage, and cost of capital because of multicollinearity. The specification of the regression model is inspired by Ahlin et al. (2011) and Postelnicu and Hermes (2015;
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2016). We use additional categories in order to include variables of primary interest to us, which are believed to be the resources of social capital on both the country- and the institution-specific level, whereas the two other groups of variables include controls on the country- and institution-specific levels. Beyond the framework of the two mentioned studies, we deploy a variable describing the proportion of rural clients and the institutional
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type of the MFI. We control for the rural aspect because according to the theoretical framework of Postelnicu et al. (2014), information channels are of a special importance in rural areas, where people create more effective social networks than in urban regions, and this argument can be adapted to rural areas in developed economies,
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to the best of our knowledge. Moreover, we deploy a variable for the institutional type as a control variable given the importance attributed to this variable and related institutional features in the more recent MFI performance literature (see, e.g., Mersland and Strom, 2014). To test the influence of conditions conducive to the development of social capital on the loan portfolio quality of European MFIs, the following regression model is estimated:
Model (1):
܀ۯ۾ܑ ߙ = ܜܒ+ ߚ ܺ0௧ + ߚଵ ܻ1௧ + ߚଶ ܺ2௧ + ߚଷ ܻ3௧ + ߝ௧ , where ܴܲܣ30௧ is a year-t quality portfolio indicator of MFI i located in country j. ܺ0௧ is a set of MFI-specific variables that are believed to be resources of social capital for MFI i located in country j in a year t. ܻ1௧ is a set of country-specific resources of social capital describing country j in a year t. ܺ2௧ is a set of MFI-specific control variables for MFI i located in country j in a year t. ܻ3௧ is a set of country-specific control variables describing country j in a year t.
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ACCEPTED MANUSCRIPT Financial performance We analyze financial performance using the return on assets ROA (ROA) as our dependent variable to test the second hypothesis. Our independent variables and control variables are the same as for Model 1. To test the second hypothesis, the following regression model is estimated: Model (2): ࡾࡻ࢚ = ߙ + ߚ ܺ0௧ + ߚଵ ܻ1௧ + ߚଶ ܺ2௧ + ߚଷ ܻ3௧ + ߝ௧ ,
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where ܴܱܣ௧ is a year-t financial performance indicator of MFI i located in country j; the remaining symbols have the same content as in Model 1. Social performance
The third hypothesis focuses on the social performance of institutions. We analyze social return using depth of outreach, which is measured as the average loan size in relation to GNI per capita (LOAN_SIZE_TO_GNIpc).
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Again, our independent variables are represented by the set of social capital resources specified in the previous section and are accompanied by the same set of control variables as for the previous models and as summarized in Table 1. To test the third hypothesis, the following regression model is estimated:
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Model (3):
ࡸࡻࡺ_ࡿࡵࢆࡱ_ࢀࡻ_ࡳࡺࡵࢉ࢚ = ߙ + ߚ ܺ0௧ + ߚଵ ܻ1௧ + ߚଶ ܺ2௧ + ߚଷ ܻ3௧ + ߝ௧ , where ܿܫܰܩ_ܱܶ_ܧܼܫܵ_ܰܣܱܮ௧ is a year-t social performance indicator of MFI i located in country j; the remaining symbols have the same meaning as in Model 1. Efficiency
The last hypothesis focuses on the control of the efficiency of institutions. As the dependent variable we choose
Model (4):
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the operating expense ratio, OER (OER). To test the last hypothesis we estimate the following regression model:
ࡻࡱࡾ࢚ = ߙ + ߚ ܺ0௧ + ߚଵ ܻ1௧ + ߚଶ ܺ2௧ + ߚଷ ܻ3௧ + ߝ௧ ,
content as in Model 1. 5.2 Model estimation
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where ܱܴܧ௧ is a year-t efficiency indicator of MFI i located in country j; the remaining symbols have the same
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Our aim is to consider in our models in a comprehensive approach all potential sources of variation at different levels to find out the impact of our main variable of interest – the social capital, and reduce omitted variable bias as much as possible Thus, we estimate our regression models with a year fixed effects approach and control for cross-country heterogeneity and variance using a set of institutional and economic controls. Variables from the pooled data set used in the regressions were tested for stationarity and were found to be stationary. All presented econometric models are estimated with robust standard errors in order to overcome the potential threats of heteroscedasticity and autocorrelation. To inspect collinearity among the independent variables, we check the values of the Variance Inflation Factors test (VIF) and additionally correlations between independent variables, and conclude that all values are below the generally accepted threshold of 10 (Verbeek, 2012) after eliminating (due to multicollinearity) the variables mentioned above. We also use the alternative estimation strategy of clustered standard errors with respect to countries—doing so as a robustness check (Angrist and Pischke, 2008)—and conclude that our results are stable. Therefore, we are allowed to interpret our estimated econometric models (1) to (4). We use the same data set for all tested hypotheses.
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ACCEPTED MANUSCRIPT 5.3 Results and discussion The results are presented in columns (1) to (4) in Table 3. Table 3: Regression results
SIZE_CAT==Small SIZE_CAT==Very small AGE LOANS_per_STAFF RUR
-11.56*** (3.000) 0 (.) -5.286† (3.088) -7.498** (2.720) -12.09*** (3.221) 0 (.) -0.0918* (0.0363) 28.68** (9.865) Yes
-38.42* (17.56) 0 (.) -32.28† (18.15) -40.94* (17.45) 0 (.) -42.04* (17.52) 0.0634 (0.0400) 8.268 (23.81) Yes
OER ETHNIC_FRAC LANGUAGE_FRAC RELIGION_FRAC
GENEROSITY GINI
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INSTIT_TYPE==Bank
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CORRUPTION
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INSTIT_TYPE==Community Development Financial Institution (CDFI)
INSTIT_TYPE==Cooperative/Credit Union INSTIT_TYPE==Government body INSTIT_TYPE==NBFI INSTIT_TYPE==NGO
INSTIT_TYPE==Other INSTIT_TYPE==Religious institution BUS_FREE Constant Year Dummies
(4) OER 0 (.) 0 (.) -3.382 (3.341) -4.873 (3.481) -0.283** (0.0884) -0.000000112 (0.000000115) -0.0776* (0.0388) 0.202* (0.0796)
0.451*** (0.0964) 0.949*** (0.0792) 0.377*** (0.0846) 0.656*** (0.111) 0.516† (0.274) 0 (.) -0.000771 (0.00131) 0.604† (0.327) Yes
26.09** (8.492) 33.55*** (3.415) 20.54*** (5.040) 17.11*** (5.105) 15.96** (5.597) 0 (.) -0.112 (0.0863) -27.85 (23.67) Yes
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WOM
(3) LOAN_SIZE_TO_GNIpc 0 (.) 0 (.) -0.0429 (0.0687) -0.0677 (0.0800) -0.00332** (0.00126) 7.74e-10 (3.14e-09) 0.000436 (0.000788) -0.00653*** (0.00127) -0.00225** (0.000726) -0.432* (0.204) 0.148 (0.281) 0.322† (0.191) -0.129* (0.0525) -0.428* (0.199) -0.828 (1.131) 0.342** (0.109) 0.607*** (0.127)
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SIZE_CAT==Middle
(2) ROA 0 (.) 0 (.) 1.653 (1.438) 8.726** (2.641) 0.00376 (0.0638) -0.000000171 (0.000000109) 0.0665* (0.0313) 0.0498† (0.0309) -0.0779* (0.0393) 17.81† (10.59) -29.94* (12.78) 10.85 (8.984) 7.992* (3.627) -12.17 (13.67) 49.24 (38.04) -33.38† (17.87) -46.25* (18.36)
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SIZE_CAT==Big
(1) PAR30 0 (.) 0 (.) 1.584 (1.597) 0.172 (1.620) 0.0000380 (0.0353) -8.47e-08 (6.61e-08) -0.0676** (0.0240) -0.0629† (0.0331) 0.0267 (0.0444) -14.12** (5.218) 8.813 (6.282) 9.018 (5.800) 4.187** (1.293) -12.64† (7.344) -28.04 (31.06) -6.831† (3.829) 0 (.)
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22.98* (9.465) -46.65** (15.83) 7.991 (7.406) 5.503 (3.961) 12.67 (17.58) 103.8 (75.50) 10.34 (5.683) 1.742 (8.195)
ACCEPTED MANUSCRIPT Observations 245 237 257 266 R2 0.184 0.295 0.362 0.150 Adjusted R2 0.115 0.230 0.305 0.080 AIC 1,784.4 1,969.7 168.9 2,380.9 BIC 1,854.4 2,042.6 243.4 2,452.5 All presented econometric models are estimated with robust standard errors and with year dummies. Standard errors are reported in parentheses † p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001, (omitted) refers to a reference category or to a category with no observations.
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Table 3 shows the results of estimating the impact of the different social capital resources on the loan portfolio quality, financial and social performance, and efficiency, and does the same for the impact of the control variables. In a nutshell, the results show the direction and strength of the relationship between the measurable influences of social capital resources on repayment, financial performance, and social performance and efficiency, controlling for a number of factors.
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The role of social capital and portfolio quality (H1)
A negative prefix for the estimated coefficients indicates that we find a positive relationship between the particular independent variables and the loan portfolio quality in Model (1). Specifically, among the institutional
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variables, we find significant positive evidence for the proportion of clients from rural areas (RUR) and the proportion of female clients (WOM). Among the country-specific variables, we find a significant influence of generosity, low levels of ethnic fractionalization of society, and a lack of corruption. In total, we find significant association between PAR30 and five of the nine examined resources of social capital. Our presented results therefore support Hypothesis 1 (H1), that more intense conditions conducive to the development of social capital are connected with better portfolio quality, and thus with better repayment behavior in MFI clients. The proportion of rural clients (RUR) is negatively and significantly associated with PAR30. This result supports
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the idea that microfinance lending works better in rural areas than in urban ones. Postelnicu et al. (2014) provide a theoretical framework in order to measure the social collateral pledged by microfinance borrowers and to show how information channels increase the amount of social collateral involved and the threat of social sanctions when payments are delayed. They claim that information channels are especially dense in rural areas, where tight networks increase the capacity to collect and transmit information. This theoretical model is consistent with our
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findings, as well as with the findings of Wydick (1999) and Ahlin and Townsend (2007) for developing economies. Higher social pressure is also a potential factor contributing to the result that the proportion of female clients (WOM) is negatively and statistically significantly associated with lower rates of late repayments. This finding is
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in accordance with the results of Rahman (2008) or Goetz and Gupta (1996), who indicate that women are more sensitive to peer pressure—a sensitivity that, in turn, makes social collateral more valuable. This indicates that the greater the proportion of female clients in microfinance institutions, the easier it is to collect loans and the better are the indicators of repayment. Next, a set of societies’ structural characteristics that are believed to matter for microfinance institutions are examined. The level of generosity (GENEROSITY) in the society is negatively and significantly associated with bad repayment behavior; in other words, the higher the proportion of generous people in the population, the better is the repayment behavior observed among the clients of MFIs located in that country. This finding is consistent with those of Cowell et al. (2017), who assess the development of generosity and moral cognition across five cultures in populations of children, and find that social cognitive development, including generosity, combined with basic demographics seems to be the best predictor of moral behavior. Our results on the cultural fractionalization of society are not uniform. On the one hand, language (LANGUAGE_FRAC) and religious (RELIGION_FRAC) fractionalization are not statistically significant. On the
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ACCEPTED MANUSCRIPT other, ethnic fractionalization appears to be a significant predictor of portfolio quality. This could be explained by the fact that higher ethnic fractionalization may bring about higher levels of trust among individuals belonging to the same ethnic group, and thus support the genesis of social capital within such groups in a more intensive way than would occur in a non-fractionalized society. Last, a set of control variables, including both country- and institution-specific indicators, was tested. Among the institutional variables we find statistically significant evidence for the institutional type (INST_TYPE). Some
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institutional forms predominate in the portfolio quality over the others. We group the MFIs based on ownership structure into for-profit firms (banks (Bank) and non-bank financial institutions (NBFI)) and non-profit organizations (cooperatives (Cooperative/Credit Union) and non-governmental organizations (NGO)). The second group outperforms the for-profit firms statistically significantly in terms of borrowers’ repayment behavior. This is in contrast to the empirical findings of Morgan (2015), who supports the theoretical prediction
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that regulatory oversight leads, due to the fact that more commercially orientated organizations are constrained and monitored by external parties, to such organizations taking fewer risks than their not-for-profit counterparts. Among the country-specific variables we find a significant association of repayment quality with our control variable, Business Freedom Index (BUS_FREE). The higher this country-level score, the better the portfolio quality of MFIs in the country. Surprisingly, we find a negative and statistically significant association of
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repayment with the level of corruption control (CORRUPTION). The more developed mechanisms to control corruption in the country are, the worse is the portfolio quality of that country’s MFIs. This could be explained by the fact that the countries with lowest levels of corruption—the seven European countries with the best anticorruption mechanisms according to their actual rankings in the Worldwide Governance Indicators (2016); namely, Finland, Denmark, Sweden, Norway, Luxembourg, Liechtenstein, and Switzerland—are also those with the lowest levels of social marginalization. In these countries, financial exclusion is concentrated among people suffering from poverty and living on the absolute edge of society. As a reminder, according to Erikkson et al.
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(2011), the role of microfinance in particular in these countries has shifted from the traditional objective of lending for income-generating activities to social help. Social capital and financial performance (H2)
Looking at financial performance as measured through the return on assets in Model (2), we find some similar
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results to, but also differences to, Model (1). Not surprisingly economies of scale matter, as evidenced in the strongly significant relationship between the size variable (SIZE_CAT==very small) and the ROA. Further, we find strong evidence for the second hypothesis, that more intense conditions conducive to the development of
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social capital are connected with better financial performance of MFIs. We can support this hypothesis for the following five of the nine examined resources of social capital that are shown to be statistically significant in Model (2). Among the institutional variables, we find significant positive evidence for the size (SIZE_CAT==Very small), the proportion of clients from rural areas (RUR), and the proportion of female clients (WOM). Among the country-specific variables, we find a significant influence of fractionalization—that is to say, ethnic (ETHNIC_FRAC) and language fractionalization (LANGUAGE_FRAC)—and of lack of corruption (CORRUPTION).
Economies of scale matter, but not in the expected direction. Indeed, the best profitability is statistically significantly achieved by the smallest institutions. These include institutions with less than five employees. Size (SIZE) is thus a significant predictor of the financial performance of MFIs in Europe. While this finding stands in contrast to the general concept of economies of scale, it is in line with empirical findings from the developing world (e.g., Adugna 2014), which show that MFIs’ sizes and expense ratios are inversely related to their financial performance.
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ACCEPTED MANUSCRIPT The effect of the proportion of rural clients (RUR) is positively and significantly associated with the ROA, which is in line with this proportion’s previously discussed significant and negative effects on portfolio quality. This finding is consistent with Mersland and Strom (2014). A possible explanation for this is that social networks are more tightly knit in rural areas, and thus help in building social collateral and collecting loans. A higher proportion of female clients (WOM) is statistically significantly related to better financial performance in European microfinance institutions. This is in contrast to the majority of findings from empirical studies in
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developing countries, which argue that female entrepreneurs tend to be involved in traditional sectors with lower profits and harder competition, which should make them less profitable clients. Meyer (2015) finds for MFIs in developing countries that the effects of more female borrowers on the ROA and the return on equity (ROE) are very small and not significantly different from zero, as the negative effects of smaller loans and higher operating costs seem to offset the positive effects of portfolio quality—the latter effect also found by D’Espallier et al. (2013). While this finding stands in contrast to the findings from developing countries, it is in line with the finding of study of Greve and Salaff (2003) who studied network activities of entrepreneurs in four developed
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countries (Italy, Norway, Sweden, and the United States) and found that high proportion of females in the network increases the likelihood of starting business. This supports our prediction, that gender in developed economies is an indicator of social capital resource more than an indicator of social exclusion.
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Two of the three fractionalization variables (ETHNI_FRAC and LANGUAGE_FRAC) are significant with respect to financial performance. The significantly positive coefficient for ETHNI_FRAC and the significantly negative coefficient for LANGUAGE_FRAC indicate the opposite influence of these two fractionalization measures on profitability. Compared to the case of Model (1), the effect of ethical fractionalization changes direction, as such fractionalization affects profitability due to the need to cater to smaller separate groups through different distribution channels. As expected, a high level of corruption control (CORRUPTION) is statistically significantly associated with
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improved financial performance. This result is also in accordance with the findings of Ahlin et al. (2011) for MFIs in developing countries. Among the institutional controls, we find again statistically significant but less strong evidence for the importance of institutional type (INST_TYPE). Our empirical results show that profitoriented organizations achieved higher profits in comparison to non-profit ones. Still, the difference between forprofit-type organizations and our broadly defined group of non-profit organizations remains small.
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Social performance and social capital (H3)
Our third hypothesis, that MFIs’ social performance is positively associated with conditions conducive to the
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development of social capital, is examined in Model (3). Overall, our hypothesis is supported through a significant positive relationship of five of all nine measures of social capital resources with the average loan size in relation to GNI per capita (LOAN_SIZE_TO_GNIpc). The results confirm that the longer the history of the institution (AGE) is, the greater is its outreach. This relationship is statistically significant in the observed sample of European microfinance institutions. As expected, a higher proportion of female borrowers (WOM) improves the social performance of MFIs in Europe. We find a strong positive influence of proportion of women on the average loan size, which is consistent with findings in the empirical literature (e.g., Bassem, 2012). Among the structural characteristics of a society conducive to the building of social capital, we find strong evidence that the levels of generosity (GENEROSITY), ethnic fractionalization (ETHNI_FRAC), and corruption (CORRUPTION) move in the directions we hypothesized. The more generous the people who live in the particular country are, the easier is for an MFI to achieve a double bottom line through social performance measured in terms of poverty outreach. Our strong evidence for a positive relationship between social performance and ethnic fractionalization stands in contrast to the results of Postelnicu and Hermes (2015) for
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ACCEPTED MANUSCRIPT developing economies; in the European context, our finding could be explained by a higher level of trust and social responsibility among borrowers and lenders situated within the same ethnic group. Interestingly, of all institution-level controls, we find again statistically significant evidence for the relevance of institutional type (INST_TYPE), as profit-oriented organizations are found to be more successful in achieving their social mission than not-for-profits. Social capital and efficiency (H4)
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Our last hypothesis, regarding the influence of social capital on the efficiency of microfinance institutions, is investigated in Model (4). Our results indicate that MFIs grow in efficiency as they get older (AGE), which is in line with our findings regarding financial and social performance. However, our results differ from the previous hypothesis with regard to the proportion of women among clients (WOM), as the OER ratio grows with a growing proportion of female clients. This is consistent with the empirical evidence from developing countries
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(e.g., Hermes et al., 2011); in contrast to this evidence, however, efficiency does not affect the profitability of European MFIs—possibly due to the extensive subsidy system they enjoy. In contrast, serving rural clients (RUR) means higher levels of efficiency, which is consistent with the theoretical framework of Postelnicu et al. (2014). The fractionalization indicators show, again, opposite results to each other. On the one hand, the ethnic
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fractionalization (ETHNIC_FRAC) in the country leads to a worsening of the efficiency of MFIs; on the other, the more languages are spoken in the country (LANGUAGE_FRAC), the better the efficiency of MFIs seems to be, although we do not find support for this result in the literature. Overall, the fourth hypothesis is supported for only four of the nine resources of social capital identified. 5.4 Discussion
The findings from testing our empirical models indicate that the use of social capital is an effective means to improve the performance of microfinance in European contexts. In particular, our results show that the use of
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social capital as practiced by MFIs in developing country contexts and as proxied by a comprehensive set of variables derived from such contexts is also significant for European MFIs. Generally, our empirical results support our hypotheses that specific conditions that are more conducive to the development of social resources, and to their use for the building of social capital, have a positive influence on loan repayments, profitability, and depth of social outreach, as well as on—to a certain extent—the efficiency of European MFIs.
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We find that certain types of societal fractionalization make the business of lending to socially excluded individuals more challenging than, for instance, mere income inequalities in a society do. Among the countryspecific predictors included in our models, the ethnic fractionalization of a society is statistically significant for
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all measures of MFI performance; but, in contrast to previous findings from developing economies, this measure is associated with better performance of MFIs in all areas. We explain this by the fact that higher ethnic fractionalization may cause more intensive trust among members of the same ethnic group, supporting the genesis of social capital in such groups in more intensive way than might occur in non-fractionalized societies. We also find that a higher proportion of clients from rural areas improves most MFI performance indicators. This finding indicates that it would be useful to promote microfinance supply within rural areas, where people seem to use social networks more intensively than they are used in cities. In other words, promoting the creation of rural microfinance institutions turns out to be a recommended tool for regional policies that aim at decreasing disparities between urban and rural areas. Another implication of our analysis, one which stands in contrast to most studies focusing on developing economies, is that in many respects smaller lending entities perform better than their bigger counterparts. In particular, they are more profitable—quite in contrast to the general principle of economies of scale, but attributable, however, to the effects of social capital. Smaller entities have better conditions for the development
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ACCEPTED MANUSCRIPT of the social ties that make the standard screening, monitoring, and enforcement techniques of micro lenders more effective. Better use of the resources of social capital also comes to mind when we seek to understand the result that the proportion of female clients contributes statistically significantly to all tested areas of MFI performance. We explain this result with the notion that women’s social collateral has a higher value, which leads to better repayment performance and thus to the better overall performance of MFIs. Finally, our empirical results indicate that profit-oriented institutions like banks and non-bank financial
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institutions outperform not-for-profits in all the areas of performance that we measured. This is a strong indication that despite the unresolved question of the subsidy-dependency of European MFIs, a for-profit institutional framework seems more appropriate when aiming to promote the development of microfinance markets in Europe.
A limitation of our approach lies in certain characteristics of the underlying data set and the variables constructed
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to operationalize certain measures. Indeed, the EMN data on MFIs in Europe show the same gaps with regard to data on the prices of services (mainly, interest and fees charged on microloans) and on subsidies received as do the larger, publicly available data sets on MFIs in developing economies. This is another reason why we construct and measure our dependent variables using the same empirical logic as used in the abundant literature on the performance of MFIs in developing economies by examining the different resources that can be used to
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build social capital in a multivariate model specification. Moreover, our analysis is limited to the use of aggregate supply-side data, which does not allow to establish causality nor to differentiate in how far wellperforming MFIs indeed contribute to preventing social exclusion and poverty in Europe. The data set also limits our set of proxy measures for social capital to the supply side of microcredit and does not allow to investigate the social capital using borrower data. Still, our analysis provides important and practical indications on how MFIs could both use and further develop social capital and, thus, improve their performance. 6. Conclusion and Outlook
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One of the main challenges for European policy makers with regard to microfinance is to support institutional frameworks that enable society to unlock the potential of microcredit suppliers in Europe regarding business models that can, in the longer term, help the inclusion of socially marginalized people, increase job creation, and thus make a contribution to economic growth. Economic and societal conditions and institutional frameworks are
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not homogeneous across Europe; Erikkson, et al. (2011), for instance, identify a dichotomy between Western European and Central Eastern European microfinance markets and business models. Still, the interest in defining long-term, reliable business models for microfinance is overarching, and divergences with regard to socially and
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financially excluded populations between these two geographies are tending to become smaller (Di Cataldo and Rodríguez-Pose, 2016). In our study, we therefore investigate performance drivers of MFIs based on a cross-country institution-level, supply-side data set collected from European countries, and derive conclusions for the European microfinance industry in general. Our main focus has been on the role that social capital plays in enabling strong results from microfinance—in particular microenterprise lending—in Europe. We contribute to the body of theoretical and empirical research on social capital in credit markets by emphasizing that it is not only institutional variables, and those influencing contractual security—for instance, levels of corruption or trust in the honesty of contractual partners—that matter in microcredit markets. We, in addition, find high relevance of those measuring the degree of trust and cohesion of societies. In this regard, our empirical results lend further support to the theoretical approaches to the role of social capital in retail credit markets such as microfinance, in particular to the conceptual frameworks of Postelnicu et al. (2014), and Li et al. (2018). Both argue that the credibility of the threat of social sanctions depends on the size and importance of both internal and external ties, and that the extent to which external ties are pledged as collateral depends on the network configuration. The network configuration is, according to Postelnicu et al. (2014), influenced by information channels that are especially
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ACCEPTED MANUSCRIPT dense in rural areas. We find significant confirmation that indeed clients from rural areas achieve higher quality of repayment compared to their urban counterparts. This can be explained by the fact that microfinance in European economies emerged despite the existence of a dense and competent banking network in these countries, and is perceived especially as a tool for promoting microenterprise growth, job creation, social cohesion, and poverty prevention at the margins of society. While there is clear evidence that microfinance is an effective financing channel for job creation and social
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inclusion (Chmelíková and Redlichová, 2013), there are European countries in which microfinance markets are developed not at all or only to a very limited extent. Our main recommendations for an institutional framework to promote the further development of the industry are, thus, that an MFI should aim at using social ties conscientiously and strategically to build trust and a brand as an institution rooted in its community. This includes support to the genesis of smaller but profit-oriented microfinance institutions, taking into account that these may cater to subgroups of the population and may be located especially in rural and marginalized areas. These institutions should design their products based on market research that assesses the demand from female
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clients, as our paper—like many that precede it—finds that a large proportion of female clients is associated with better performance of MFIs, or of specific groups in contexts exhibiting high ethnic fractionalization and other elements of cultural fractionalization.
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Our model, suggesting usage of social capital in microfinance sector, may lead to a better understanding of performance drivers and the possibility to achieve sustainable, long-term provision of services through effective use of the resources of social capital. Future research may be devoted to developing a more composite measure of social capital comparable across European countries and employing adequate methods such as PCA to identify such a measure. To advance the micro foundations of the use of social capital in individual lending to excluded populations, future research could also focus on collecting a more comprehensive data set as well as qualitative case studies on the borrower-level use of European microfinance services and the quality of their use of social
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capital resources from a borrowers’ perspective. Especially, we encourage to monitor those proxies that appeared to be significant for the performance of MFIs in case studies drawn from developing countries, but that are not available in the cross-country dataset for European MFIs. These include the contact frequency among MFIs´ stakeholders (Abbink et al., 2006), geographical proximity of the MFIs to their borrowers (Karlan, 2007), common socio-economic characteristics of borrowers (such as gender, age, education, income, or place of living, Kritikos and Vigenia, 2005), joint social activities (Wydick, 1999), and frequency of interactions among MFI’s
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Acknowledgements
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stakeholders (Feigenberg et al., 2013).
We thank our anonymous referees for important comments and suggestions. We further thank Timo Busch, Marc Chesney, Andrea Frederick, and Julia Meyer for comments on a previous version, as well as the audiences and discussants at the FMA European Conference 2018 and at the World Finance Conference 2018. All remaining errors are ours.
Funding
We thank the Swiss National Science Foundation for financial support to conduct this study, and the European Microfinance Network for providing access to proprietary data of the network. We also thank the Mendel University for partial financial support via internal grant (IGA) No. FRRMS - IGA - 2018/006. This work was also supported by the Internal Grant Agency of Faculty of Business Administration, University of Economics in Prague, under no.: IP300040.
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Research data
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Vitae
Gabriela Chmelíková works as an associate professor at the Department of Regional and Business Economy, Faculty of Regional Development and International Studies, Mendel University in Brno, Czech Republic. Her research interest is focused on the evaluation of business performance and risk drivers with the impact on the regional and social policy. She obtained her Ph.D. degree in Business Economics and Management at the Mendel University in Brno. Previously, she obtained Master´s degree in Business Economy at Masaryk University in Brno, Czech Republic. She serves as a reviewer for several journals and scientific conferences.
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Annette Krauss is a Senior Researcher at the Department for Banking and Finance, University of Zurich, Switzerland. She co-founded the Department's Center for Sustainable Finance and Private Wealth and founded its predecessor, the Center for Microfinance. Previously, she was a Senior Lecturer at the Kellogg School of Management, USA. She obtained her doctorate in Economics at Ruhr-University Bochum, Germany, worked as a visiting research fellow at the University of California at Berkeley, USA, and received her diploma in Economics from the University of Trier, Germany. Her research focuses on microfinance and financial inclusion, and development investments in developing countries.
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Ondřej Dvouletý works as a research assistant at the Department of Entrepreneurship, University of Economics in Prague, Czech Republic. His research interest is focused on entrepreneurship policy evaluation and entrepreneurial economics. He obtained his Ph.D. degree at the University of Economics in Prague. Previously, he obtained Master´s degree in Economic Policy at the University of Economics in Prague and Master´s degree in Entrepreneurship at Linnaeus University in Vaxjo. He is a member of the editorial advisory board of the Journal of Entrepreneurship in Emerging Economies and serves as a reviewer for several journals and conferences.
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Highlights Social capital has a positive influence on repayment, profitability, and depth of social outreach
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Higher proportion of clients from rural areas improves most MFI performance indicators
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Smaller lending entities perform better than their bigger counterparts
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Profit-oriented institutions outperform not-for-profits in all the areas of performance
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ACCEPTED MANUSCRIPT Biography
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Gabriela Chmelíková works as an associate professor at the Department of Regional and Business Economy, Faculty of Regional Development and International Studies, Mendel University in Brno, Czech Republic. Her research interest is focused on the evaluation of business performance and risk drivers with the impact on the regional and social policy. She obtained her Ph.D. degree in Business Economics and Management at the Mendel University in Brno. Previously, she obtained Master´s degree in Business Economy at Masaryk University in Brno, Czech Republic. She serves as a reviewer for several journals and scientific conferences.
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Annette Krauss works as a senior Lecturer at the Department for Banking and Finance, University of Zurich, Switzerland. She is a scientific head of Center for Sustainable Finance and Private Wealth at the University of Zurich. Her research interest is focused on microfinance and financial inclusion, social entrepreneurship and development investments in developing countries. She obtained her Ph.D. in Economics at the Ruhr-University Bochum, Germany. She worked as a research fellow at University of California at Berkley, USA. Her Diploma in Economics is from the University of Trier, Germany.
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Ondřej Dvouletý works as a research assistant at the Department of Entrepreneurship, University of Economics in Prague, Czech Republic. His research interest is focused on entrepreneurship policy evaluation and entrepreneurial economics. He obtained his Ph.D. degree at the University of Economics in Prague. Previously, he obtained Master´s degree in Economic Policy at the University of Economics in Prague and Master´s degree in Entrepreneurship at Linnaeus University in Vaxjo. He is a member of the editorial advisory board of the Journal of Entrepreneurship in Emerging Economies and serves as a reviewer for several journals and conferences.
S0038-0121(18)30083-1
DOI:
https://doi.org/10.1016/j.seps.2018.11.007
Reference:
SEPS 670
To appear in:
Socio-Economic Planning Sciences
Received Date: 22 March 2018 Revised Date:
19 November 2018
Accepted Date: 22 November 2018
Please cite this article as: Chmelíková G, Krauss A, Dvouletý Ondř, Performance of microfinance institutions in Europe—Does social capital matter?, Socio-Economic Planning Sciences (2018), doi: https://doi.org/10.1016/j.seps.2018.11.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Performance of microfinance institutions in Europe— Does social capital matter? Gabriela Chmelíková1
Annette Krauss2
Ondřej Dvouletý3
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This version: November 15, 2018
Abstract
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This paper investigates performance drivers of microfinance suppliers in Europe. As such suppliers, in contrast to advanced microfinance suppliers in developing economies, typically focus on uncollateralized microcredit services to individuals at the margins of society and of labor markets, we draw on the theory of social capital and empirically investigate the role that social capital may play in the overall performance of European microfinance suppliers. We build a unique, unbalanced panel data set of 302 microfinance service providers in Europe covering the years 2008 to 2015, and measure their performance in terms of credit risk, financial and social performance, and efficiency. Pursuing an
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econometric approach, we test a series of hypotheses using various measures of conditions conducive to building social capital on both the institutional and the country level, such as the client base of a microfinance supplier and the level of cultural fractionalization in a society. Our findings confirm that a higher intensity of social capital is positively associated with all areas of the performance of microfinance suppliers in Europe. Our conclusions could help in the design and launch of microfinance institutions in those European countries in which microfinance markets are developed not at all or only to a very limited extent. Our paper thus contributes to the nascent literature on microfinance in developed economies by applying and extending the theoretical framework and empirical models on social capital and microfinance that were originally elaborated for developing economies.
Keywords Microfinance institutions, social capital, social performance, financial performance, efficiency, regional economics
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JEL Classification G21, G23, 015, R23
1 Gabriela Chmelíková, Department of Regional and Business Economics, Faculty of Regional Development and International Studies, Mendel University in Brno, Zemědělská 1, 61300 Brno, Czech Republic, e-mail: [email protected], telephone: +420 731 624 694, Fax: +420 545 211 128 (corresponding author). 2 Annette Krauss, Department of Banking and Finance, University of Zurich, Plattenstrasse 32, 8032 Zurich, Switzerland, email: [email protected], telephone: +41 44 634 5168 or 3579, Fax: +41 44 634 4970 3 Ondřej Dvouletý, Department of Entrepreneurship, Faculty of Business Administration, University of Economics, Prague, W. Churchill Sq. 1938/4, 130 67 Prague 3, Czech Republic, email: [email protected], telephone: +420 728 431 027, Fax: +420 224 098 740 Declarations of interest: None Abbreviations: MFI – Microfinance Institution, EMN – European Microfinance Network, EAPN – European Anti-Poverty Network, MFC – Microfinance Centre, VIF – Variance Inflation Factor, PCA – Principle Component Analysis
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ACCEPTED MANUSCRIPT 1. Introduction The role of microfinance in European countries differs from its counterpart in the developing world. The main objective of suppliers and promoters of microfinance services in developing countries is to provide access to a broad range of financial services—namely, savings and checking accounts as well as credit—to large numbers of poor people and their business activities who are outside of the target markets of standard commercial banks. Contrarily, the vast majority of the adult populations of EU countries do hold some type of account at a formal
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financial institution: according to the World Development Indicators database (World Bank, 2016), the EU-28 average of adult residents having a bank account is 89 percent, ranging from Romania with 60 percent of the population to 100 percent in Denmark. Thus, the mission of European microfinance suppliers focuses on microenterprise lending to individuals (or their companies) that are excluded from traditional bank credit services for a variety of reasons (Degryse et al., 2018; Dvouletý, 2017a; Terjesen et al., 2016; Balkenhol, 2015).
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Such exclusion from bank credit services is considered to be not a mere financial market phenomenon, but also an aspect of social exclusion (Erikkson et al., 2011). According to the European Anti-Poverty Network (EAPN, 2016), the term “social exclusion” refers to the processes that push people to the edge of society and, in turn, limit their access to resources and opportunities (EAPN, 2016). These processes result in limited access to credit, and also—for instance—to labor markets,
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vocational training, and higher education. Social exclusion or isolation is considered to be a main societal concern in Europe (EAPN, 2016; Di Cataldo and Rodríguez-Pose, 2016), and poverty is believed to be a direct consequence of it. Socially excluded groups include mothers on and after maternity leave, older people seeking new work opportunities, young or low-skilled graduates, ethnic minorities, and migrants. At the same time, included in this heterogeneous group, are individuals who want to start or further develop their own businesses (through microenterprises or self-employment) instead of searching for formal employment (Dvouletý, 2017b; Dvouletý and Lukeš, 2016). Their exclusion from traditional bank credit services constitutes an obstacle to
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European institutions that supply microfinance services (hereafter referred to as microfinance institutions, or MFIs) aim at reaching out to these individuals by providing access to credit and, typically, related counselling services (e.g., Urban Agenda, 2017; Kraemer and Conforti, 2009). Despite the stated differences in the role of
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microfinance and in levels of poverty and social exclusion between European and developing economies, microfinance developed in Europe later than in developing economies and drew from the experiences made in developing countries (see, e.g., Erikkson et al., 2011). Yet, there is a lack of comparative research that investigates the performance of MFIs in European (or other high-income) contexts systematically and that includes both institutional and country-specific factors related to social inclusion. In particular, the mechanisms
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that European MFIs use to support their clients in overcoming not only barriers to credit access but also social exclusion are not systematically explored in the literature, and the extant literature on the building and use of social capital in credit markets does not seek to explain the phenomenon of European microfinance. This paper aims to fill this research gap for European MFIs and to lay an empirical foundation for policy recommendations with regard to building institutional frameworks for microfinance in European countries. First, to fill the research gap, we use exclusive institution-specific data from a pan-European survey of individual microfinance providers and focus on the overarching question of the role of social capital in microlending. We hypothesize that microfinance institutions are more successful in terms of their credit risk, double-bottom line (i.e., financial and social) performance, and efficiency in those contexts in which the resources of social capital can be put to better use. Using an econometric approach, we test our hypotheses using various measures of good conditions for social capital on both institutional and country levels while drawing from various cross-country level data, and link them to performance measurement of European MFIs.
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ACCEPTED MANUSCRIPT As a second contribution, our findings have a bearing on the understanding of the role of social capital in credit markets and on the way new financial institutions take roots in their markets. Building on the theoretical and empirical body of literature on the role of social capital as a factor shaping institutions, including Coleman (1988) and North (1990), our findings contribute to the body of literature that applies the concept of social capital to credit markets, in particular microfinance markets (e.g., Haldar and Stiglitz, 2016; Van Bastelaer, 2002).
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In our empirical analysis, which we base on a model of the role of social capital in credit markets that we elaborate below, we do not find a trade-off between building a framework for formal financial institutions and the use of social capital. Our findings support the notion that social capital is positively associated with repayment, profitability, depth of social outreach, and—albeit less—with the efficiency of European MFIs. The remainder of our paper proceeds as follows: The next part reviews extant theoretical and empirical studies
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that focus on the influence of social capital on the performance of microfinance institutions. As research on Europe is in its infancy, this review draws heavily on related investigations conducted in developing countries, and, to a lesser extent, on US mortgage lending markets. Based on this overview, we develop our hypotheses in the third part, and describe the data and descriptive statistics in the fourth part. The empirical study presented in the fifth part applies an econometric approach to identify the extent to which social capital can boost micro
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lending to socially excluded but economically potentially active people through European microfinance suppliers. We conclude with a discussion of main results and conclusions, particular limitations, and possible further research.
2. Performance of microfinance institutions and social capital—literature review In order to contribute to social inclusion, the supply of credit to microenterprises and the self-employed in European countries must be reliable in the long term. Although not uncontested (e.g., Morduch, 2000),
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theoretical (e.g., Cull et al., 2009) and practice related (e.g., Eriksson et al., 2011) literature claims that long-term reliability implies a need for MFIs to strive for independence from donations or subsidies, as well as the ability to control default, limit operating costs, and use economies of scale. Yet, independence from donations or subsidies is not yet achieved for the majority of MFIs in Europe (Balkenhol, 2015), and a better understanding of factors driving their performance is needed to help to mitigate its subsidy-dependent character.
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Because of the above-mentioned strong emphasis that European microfinance puts on social inclusion, our investigation primarily focuses on the role that social mechanisms can play to improve MFI performance. The role of social capital for microfinance markets in developed economies such as European countries has not been
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addressed in the literature. Extant conceptual and empirical studies mostly focus on developing economies. We contribute to closing this gap in the literature. To do so, we build on the growing literature that investigates the relationship between the conditions conducive to the development of strong social capital and credit market outcomes on both firm- and country-specific levels. This includes a stream of literature on microfinance in developing economies, an emerging stream of literature on mortgage lending and social capital in developed economies such as the US, and we also find links to the body of literature on relationship banking. The microfinance literature identifies as one driver of the reliable long-term and sustainable provision of inclusive financial services the smart use of available social capital through innovative credit contract designs that use the ability of social relationships to mitigate credit risk for MFIs. According to Freedman and Jin (2008), social capital may convey soft information about borrower risk and therefore has the potential to compensate for a lack of hard information. This, according to Pham and Talavera (2018) and Van Bastelaer (2002), consequently decreases a lender’s need to request collateral or charge high interest rates, which are considered to be major barriers to reducing social and financial exclusion.
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ACCEPTED MANUSCRIPT This paper applies the concept of social capital as it has been widely explored in both the sociological and economic literature. While many definitions lack clarity and consistency (Haldar and Stiglitz, 2016; Robison et al., 2000), a commonly accepted economic definition is Putnam’s (1995), that “Social capital means features of social organization such as networks, norms, and social trust that facilitate coordination and cooperation for mutual benefit”. Dufhues et al. (2011) develop an operational definition of social capital, which we adopt for our study. According to Postelnicu et al. (2014), economists view it as a source of economic returns, which are
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driven by the social capital embedded in the ties that are actually mobilized to achieve certain outcomes—in the context of this paper, microcredit market outcomes. The theoretical literature on inefficiencies in credit markets stemming from information asymmetries has been applied to different types of lending relationships for which social capital is shown to play a mitigating role. The microfinance literatures discusses that microcredit markets are particularly affected by the typical information asymmetries between lenders and borrowers about the loan repayment probabilities of borrowers because lenders cannot apply standard remedies such as collateralization or costly screening techniques (Hermes et al., 2005;
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Kritikos and Vigenina, 2005). Both the microfinance literature, and the literature on US-mortgage markets and social capital identify several channels through which the availability of social capital can improve credit market outcomes in the presence of information asymmetries between lenders and borrowers but also between groups of
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borrowers. First, social capital in terms of the ability of lenders to use strong interpersonal ties to microcredit borrowers for information on a borrower’s ability and willingness to repay a loan, thus compensating for standard measures used by lenders such as physical collateral or high interest rates (Van Bastelaer, 2002). This mechanism may be more directly at work in joint liability contracts where group members are directly affected by their peers’ repayment behavior and in which the social capital among borrowers matters most in groups’ selfselection and subsequent monitoring of loan repayment (Al-Azzam and Mimouni, 2012). However, it it is also a feature of group lending without joint liability (De Quidt et al., 2016; Haldar and Stiglitz, 2016), and even of
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individual lending processes. In the latter case, Haldar and Stiglitz (2016) derive how the “specificity” of the lending institution as a locally rooted institution reinfs or even creates repayment norms within a community that is bound through social ties and resources, even in the absence of joint liability contracts (Haldar and Stiglitz, 2016). Second, Postelnicu and Hermes (2015) discuss how informal institutions may be helpful in dealing with the information opacity that exists between MFIs and their clients. Informal institutions stimulate the development of and improve social networks, cohesion, and interaction, which eventually results in trust
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building between lenders and borrowers. This, in turn, increases opportunities to use social collateral as a substitute for the lack of hard information and real collateral. Of course, the ability of social collateral to replace physical collateral depends on the quality of networks and relationships used. Third, a borrower’s loss of
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reputation when defaulting is considered the larger, the tighter the social network. This may have a direct economical reason, as stated by Li et al. (2018) for US mortgage markets, in which mortgage default directly affects property values of one’s community neighbor. But it may also be through the ability of lenders to use, if not build social capital by instilling loan repayment as a generalized norm or even value system (Haldar and Stiglitz, 2016) or, more generally, by contagion of ideas within the community (Guiso et al., 2013) or the stronger preference for fairness and altruism towards community members (Haldar and Stiglitz, 2016; Karlan, 2007). Loan default then results in negative reputation and emotions (Seiler et al., 2012; White, 2010). Resulting from these mechanisms is what Li et al. (2018) model as a socially motivated “disutility of default” of an individual borrower. Finally, the ample literature on relationship banking (Crawford et al., 2018; Banerjee et al., 2017 and the literature cited herein, in particular Petersen and Rajan, 1994) establishes that when the lending institution has private information about a borrower’s credit history in the presence of market imperfections and absence of credit information sharing mechanisms, then the length of the relation and the repetitiveness of interaction with the borrower reduces information asymmetries and improves market efficiency. This literature
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operating and risk costs, the ability to lower rates of interest or decreased demands of physical collateral, and better social and financial performance of credit suppliers---albeit notwithstanding that other institutional and contextual (macro) factors may play an equally important role in driving the overall performance of an MFI. To investigate the empirical validity of the above-mentioned theoretical predictions on the role of social capital, an operationalization of social capital is required. According to Mersland and Strøm (2014), several proxies have
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been used in empirical studies to gauge the intensity of social ties. They include factors such as the duration of the relationship, geographic proximity, the character of the relationship, the frequency of contact and of sharing between group members. In general, the extant literature confirms the theoretical prediction that strong social ties among stakeholders determine repayment performance and decrease the riskiness of loans. In turn, they also
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contribute to better social and financial performance of credit suppliers.
Wydick (1999) presents empirical tests carried out on borrower group data from Guatemala. Results from a survey of 41 urban and 96 rural borrower groups indicate that peer monitoring significantly effects borrowing group performance. Hermes et al. (2005) provide an empirical analysis of the impact of monitoring and social ties within group lending programs on the moral hazard behavior of their participants, using survey data on 102 groups in Eritrea. They find evidence that peer monitoring reduces moral hazard through the social ties linking group leaders and borrowers, but no significant association between moral hazard within groups and social ties among group members other than the group leader. In the setting of a natural experiment that helps controlling
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for endogeneity, Karlan (2007) uses data from the village bank FINCA in Peru to test whether groups that are connected socially perform better. He finds evidence to support the hypothesis that monitoring and enforcement activities do improve group lending outcomes, and that social connections facilitate the enforcement of joint liability. He also observes that both cultural similarity and geographic concentration lead to improved group
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lending outcomes—to higher repayment rates, saving rates, and returns on savings. Finally, Dufhues et al. (2013) study social capital’s effects on access to formal credit in rural Thailand. Their results also confirm the general relevance of social capital and highlight the informational advantage, and thus the reduction of ex ante transaction costs, derived from personal ties.
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More specifically, social ties work differently depending on the type of pre-existing relationship that connects the different stakeholders. Cassar et al. (2007) conduct their research on primary data from field experiments in South Africa and America, and find evidence of a positive relationship between the personal trust among group members, social homogeneity, and loan repayment. This is shown to be of greater importance than general social trust or acquaintanceship between members. Wydick et al. (2011) use data from a survey of 465 households conducted in Guatemala in 2004 to confirm that social networks have endogenous effects regarding credit availability. These endogenous effects appear to some extent among geographical neighbors, but most strongly within church networks. Their findings have significant implications for the launch of microfinance operations in new areas. In line with these findings, Mersland et al. (2013) investigate the relationship between religion and the development of microfinance, in particular the efficiency, loan repayments, and social outreach of MFIs with a Christian affiliation compared to those of non-religious institutions. They find that Christian MFIs are as effective in enforcing loan repayments as their secular peers, have lower cost of funding, and serve fewer female clients. To investigate whether social capital can be systematically built and put to use for microcredit performance, Feigenberg et al. (2013) provide experimental evidence from a development program in India that
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ACCEPTED MANUSCRIPT encouraged repeat contacts. They show that this strengthens social ties and enhances social capital among a treatment group of community members, which consequently reduces default risk and improves members’ loan repayment rates. Also, Giné and Karlan (2014) carried out two randomized trials to test the influence of joint liability, which supports the creation of social capital, on the repayment performance of microfinance clients. They find that individual and group liability lead to the same repayment performance. 4 Postelnicu et al. (2014) confirm in their review of the empirical literature the unambiguous supreme role that
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geographic proximity plays compared to other aspects of social capital. If this finding were transmittable to European conditions, it would be recommended to support the creation of local and potentially small microfinance institutions in order to enable the utmost utilization of social capital. However, it is questionable whether geographical proximity’s positive effect is strong enough to offset the disadvantages of small entities, in particular the lack of economies of scale. Indeed, the empirical literature investigating this question in crosssectional institutional comparisons reveals ambiguous results. The majority of studies confirm the theoretical prediction of the positive effects of social capital on the performance of MFIs (e.g., Pham and Talavera, 2018;
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Postelnicu et al., 2016; Feigenberg et al., 2013; Al-Azzam and Mimouni, 2012; Cassar et al., 2007; Abbink et al., 2006; Sharma and Zeller, 1997); however, some studies’ findings are rather more ambiguous (Hermes et al., 2005; Kritikos and Vigenina, 2005) and several confirm adverse effects (Ahlin and Towsend, 2007; Godquin,
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2004).
Not only the institutional specifics of MFIs, but also the presence of country-specific formal and informal institutions that shape social networks, may contribute to solving agency problems in credit markets. However, the literature researching these country-specific factors’ success is scarce, in particular literature that examines the influence of these conditions on the social capital. The pioneering empirical study on the relationship between macroeconomic and country-specific policy issues and the performance of MFIs is the work of Ahlin et al. (2011). The work’s broad, country-level set of variables include those that are proxy measures for aspects of
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social capital, such as the level of corruption, political stability, government effectiveness, time plus cost to register a new enterprise, or income inequality in the country. They report significant effects of macroinstitutional factors on MFIs’ financial performance and their extensive and intensive growth. For example, inequality (measured through the Gini coefficient) is a negative predictor of financial self-sufficiency and lower corruption is associated with faster extensive growth of MFIs, but greater political stability predicts slower extensive and faster intensive growth of institutions. Interestingly, government effectiveness is a strong predictor
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of high MFI operating costs, and, naturally, cost and time required to register a new enterprise are significantly and negatively related to financial performance (measured as financial self-sufficiency). Building on this approach, Sundeen and Johnson (2012) show how social capital, defined in terms of social
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networks, trust, and social norms, influences outreach to clients and the financial performance (measured in operational self-sufficiency) of MFIs. Analyzing data from 2,000 institutions from across 115 countries, they find that social capital significantly influences the performance of MFIs and that there is a trade-off between outreach and sustainability. Manos and Tsytrinbaum (2014) confirm, in their analysis of 852 institutions from across 30 countries, that cultural environment is a significant driver of MFIs’ social and financial performance. Similarly, Burzynska and Berggren (2015) use a panel of 331 MFIs from across 37 countries to explore the relationship between financial performance and general trust and cultural norms. Their cross-country analysis shows that MFI performance not only relies on the macroeconomic and formal institutional environment but is also associated with the nature of informal institutions. In particular, the level of trust and collectivist culture in a society is positively associated with a reduction in operating and default costs, as well as with lower interest rates 4
Motivated by the empirical results of Giné and Karlan (2014) and Feigenberg et al. (2013), de Quidt et al. (2016) develop a model that predicts that joint liability is used in settings with intermediate levels of social capital and that the use of groups, even without joint liability, is more beneficial in settings with low than in those with high social capital.
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ACCEPTED MANUSCRIPT for MFIs. Burzynska and Berggren (2015) claim that social collateral, supported by trust and a collectivist culture, can work as a substitute for physical collateral. Postelnicu and Hermes (2015) support the suggestion of a positive link between social capital and microcredit repayment through an empirical test carried out on data from 934 MFIs based across 100 countries. In general, their results indicate that strong informal institutions are associated with improved performance reflected in both social and financial indicators. They find strong support for their hypothesis that the fractionalization of society
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and a high level of trust existing in a society are each connected with better financial and social performance, and that an individualistic society leads to the higher social performance of MFIs. Although the nature and shape of the conditions conducive to the development of social capital may arguably be different in high- and low- income economies, we exploit the similarities between the formal structure of microfinance in both types of markets. We empirically verify whether the principles of traditional microfinance
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systems and their strong reliance on social capital could be transmittable to European conditions exactly because in this context, microfinance has the explicit function to improve social inclusion. Therefore, our paper contributes and critically tests this body of empirical literature by focusing exclusively on European microfinance suppliers. Moreover, by combining several data sources with a uniquely generated data set on European MFIs, we are able to use a broader set of explanatory variables describing both firm- and country-
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specific factors. We develop and test a model in which we specify the opportunities for an MFI to make use of the social capital existing among lenders and borrowers, as well as different measures—for formal and informal institutions—conducive to social capital formation found in different societies. We admit that the model suffers from limitation due to raw data not cleansed of subsidies. However, this is the case of majority of the empirical studies carried out for the developing world. To overcome this data set drawback, we include efficiency indicators into our model.
3.1 Definition of social capital
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3. Operationalization and research hypotheses on the role of social capital
Based on the above discussion, we empirically investigate our overarching research question as to which extent the conditions conducive to the development of social capital in a society, arguably crudely defined by country borders, are linked to different levels of financial, social, and repayment performance, as well as to the efficiency
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of MFIs in Europe. We investigate efficiency indicators as a separate performance dimension as this separation is more suitable for subsidized institutions, according to Balkenhol (2015). Our hypotheses build on the extant literature concerning the interaction between the presence and strength of social capital resources and the
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efficiency-related outcomes of MFIs. To develop our hypotheses, we first define and operationalize the concept of social capital and then state our hypotheses. To avoid the ambiguities, present in the definitions social capital used in the extant literature, we employ the definition of Dufhues et al. (2011), who perceive and measure social capital as the availability and intensity of two elements—namely, interpersonal network ties and resources. They claim that social structures are not independent of their context, and that not every social structure will result in the building of social capital. It is the nature of the resource that turns social structure into social capital. Economic returns are then driven by social capital that is embedded in these resources, which—in turn—are mobilized to achieve a certain outcome. The nature of these resources—conditions that are conducive to the development of social capital—constitute the main explanatory variables for MFIs’ performance in our models, controlling for a variety of other institutionlevel and country-level factors. We argue that certain features of the institutional and national environment in which MFIs operate constitute such resources because they support the genesis and the development of social network ties that, in turn, lead to the production of social capital as an effective instrument with which to
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ACCEPTED MANUSCRIPT circumvent information asymmetries. The value of the quality of social capital is assessed in terms of presence and extent of its resources. Figure 1 lists these resources of social capital, showing institutional measures in the top-left box and country-level measures in the bottom-left box of the figure. Fig. 1: Operationalization of MFIs’ resources of social capital
•
Years of experience within community
•
Local staff working in community
•
Access to information about clients
•
Access to women’s social networks
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Institution-specific resources of social capital allowing the building of social network ties
MFI’s performance
Fractionalization of society
•
Level of corruption
•
Generosity within a society
•
Trust within a society
•
Cost and returns: financial performance
•
Outreach to excluded population: social performance
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•
Loan repayment: portfolio quality
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Country-specific resources of social capital allowing the building of social network ties
Social capital available to an MFI
•
Source: Authors’ own construction based on Dufhues et al. (2011) and Postelnicu et al. (2014)
We operationalize our hypotheses based on the resources of social capital identified in the literature discussed above. Because of the ambiguous results of the extant literature, we include a rather broad set of variables in our
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operationalization and empirical investigation. We use the previously identified resources as proxies for the different areas of social capital, and deploy them in our models within the set of explanatory variables for the performance of European MFIs. We control for a variety of other institution-level and country-level factors in order to address the above-mentioned issue that social capital may be one but not the only decisive factor for MFI performance.
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3.2 Proxies for social capital
Social capital is a construct that cannot be measured directly. The operationalization of proxies for social capital is inspired by the empirical models of Ahlin et al. (2011), Postelnicu et al. (2014), and Postelnicu and Hermes (2015; 2016). Based on their results from developing countries and because of the striking similarity of MFI
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performance indicators for European MFIs and MFIs from developing economies, we use a similar set of proxies to investigate the empirical evidence of the relationship between social capital and performance of European MFIs while taking into account limitations of data availability for the European context. The size (SIZE) of an MFI, measured in terms of number of staff as opposed to the more frequently used asset or loan portfolio volumes, can be considered a measure of the intensity of the social ties an MFI enjoys with its stakeholders, as MFIs typically employ a large proportion of local staff. Social ties are considered to positively affect their screening, monitoring, and enforcement efforts, which in turn determine repayment performance and the quality of the loan portfolio. The smaller the MFI, the better are the conditions to develop social ties and to use the advantage of social capital. This prediction is supported by findings of Al-Azzam and Mimouni (2012), who find that geographic proximity improves the repayment performance of borrowers. However, it remains questionable whether the effect is strong enough to offset the disadvantages of small entities, such as low economies of scale. For instance, Ejigu (2009) reports a positive impact of size on the profitability and sustainability of MFIs in Ethiopia, as does a study conducted by Adugna (2014). A possible mechanism via
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ACCEPTED MANUSCRIPT which profitability plays positively on the size of an MFI is provided by Kyereboah-Coleman (2007), who shows that firm size has a positive impact on the yields on the gross loan portfolios of MFIs. The age (AGE) of an MFI tells us about the experience acquired by the institution with regard to operations, clients’ behavior, and market experience, as well as to using the existing social ties with and among borrowers. The longer the history of the institution is, the deeper is its knowledge of its market and clients and the higher is the value of relevant social capital. Kyereboah-Coleman (2007) finds evidence of a negative impact on an MFI’s
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performance, in terms of social performance (outreach) and loan portfolio quality, as age increases. Ejigu (2009), meanwhile, measures a positive impact of MFI age on efficiency. The individual approach of an MFI is proxied through its productivity in lending operations with borrowers, measured as the number of loan officers per loan (LOANS_per_STAFF). This proxy is neutral with regard to the role of joint liability while emphasizing lender–borrower relationships. For instance, Johnson and Rogaly (1997)
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show that Bank Rakyat Indonesia (BRI) managed to lower its screening costs by using insider information about the creditworthiness of borrowers when it launched an individual approach to lending. The lower the ratio of loans per employee, the more time a loan officer can devote to a borrower and the stronger are the social ties that can be developed.
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The proportion of clients from rural areas (RUR) is a strong predictor of the creation of social information channels. Such information channels mitigate information asymmetry, and increase the amount of social collateral involved and the threat of social sanctions in case of default. Postelnicu et al. (2014) provide a theoretical framework that considers both the internal and the external ties of clients that borrow through group arrangements. They claim that information channels are especially dense in rural areas, where tightly-knit networks improve the capacity to collect and transmit information. This theory is supported by empirical findings that microfinance lending works better in rural areas than in urban ones (Ahlin and Townsend, 2007; Wydick, 1999).
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The proportion of female clients (WOM) is considered to be a significant resource of social capital. Several authors argue that contracts drawn up with women are easier to monitor and enforce. For example, Rahman (2008) and Goetz and Gupta (1996) find that women are more sensitive to peer pressure and more responsive to the interventions of loan officers. Ameen (2004) states that women have a lower opportunity cost of time than
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men, and are therefore more inclined to have contact with the MFI, with a positive impact on repayment. However, not all findings favor the idea that women are good borrowers. Phillips and Bhatia-Panthaki (2007) claim that women entrepreneurs tend to be overrepresented in traditional sectors with lower profits, which could
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make them less able to repay their loans. Many studies in the developing world use the proportion of women among clients as an indicator of the depth of outreach (e.g. Gutiérrez-Nieto et al., 2009; Sinha, 2006; Zeller and Meyer, 2002). In a European environment, we use this proportion as a proxy for conditions conducive to the development of the social capital of the MFI, not as a social performance indicator per se. Our reasoning is that in high- and middle-income countries in Europe gender is a predictor of the conditions conducive to the development of social capital. Women are believed to be social capital resources in developing world as well (see, e.g., Burt, 1998; Antonucci, 2001), thus the reasoning for deploying of this proxy is coherent with remaining variables. Cultural fractionalization of society (ETHNIC_FRAC, LANGUAGE_FRAC, RELIGION_FRAC) refers, according to Postelnicu and Hermes (2015), to the probability that two randomly chosen people coming from the same country are not from the same ethnic, religious, or linguistic group. Fractionalization is expected to impede the development of social ties. Postelnicu and Hermes (2015) find a negative correlation between societal fractionalization and both the social and the financial performance of MFIs.
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ACCEPTED MANUSCRIPT It seems intuitive to assume a strong negative link between social capital and the national control of the level of corruption (CORRUPTION). According to Paldam and Svendsen (2002) and Uslaner (2001), countries in which people appear to be more honest and are able to build social networks ought to experience less corruption, and the converse should also be the case. Ahlin et al. (2011) find that lower corruption levels are indeed related to faster extensive MFI growth but have no significant association with intensive growth. Postelnicu and Hermes (2015) confirm a significant positive relationship between the control of corruption and the social performance of MFIs. The relationship to financial performance is found to be insignificant.
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Generosity (GENEROSITY) is defined by those acts that benefit another person and cost the giver time, money, or energy (Glanville et al., 2016). Generous behaviors like volunteering are essential to well-functioning societies and use social networks to increase the availability of information about volunteering opportunities and the likelihood of being asked to volunteer (Glanville et al., 2016). To measure generosity, we use the residual of the regression of the national average of Gallup World Poll responses to the question, “Have you donated money to a charity in the past month” on GDP per capita, this being the best available measure despite there being some
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crudeness to it. The higher the average generosity level within a society is, the better are the conditions required to develop social capital. By way of illustration, one of the most pervasive observations in research into the predictors of formal volunteering is that persons with larger social networks volunteer more (Musick and Wilson,
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2003). Brooks (2005) finds strong links between changes in social capital and in the charitable behavior of individuals. Thus, we hypothesize that it is possible to expect better financial performance and efficiency of MFIs when high levels of generosity are present.
Social trust (TRUST) is defined as a shared set of moral values that helps to create expectations of regular and honest behavior (Fukuyama, 1995). The extent to which trust is prevalent in a society is expected to be positively associated with the development of social capital. Postelnicu and Hermes (2015) indicate, as do Knack and Kneefer (1995), that high-trust societies show better financial and social performance. We measure TRUST
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through a World Values survey question on whether “most people can be trusted”. The indicator of trust is the percentage of individuals who respond positively to the question. Knack and Keefer (1995) demonstrate that the Gini coefficient for income inequality (GINI) is strongly associated with lower civic cooperation. Larraín and Vergara (1997) confirm this conclusion based on empirical
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findings that income inequalities give rise to social pressure and conflicts, which in turn decrease opportunities for the creation of social networks or ties. According to Ahlin et al. (2011), income inequality measured by the Gini coefficient is a negative predictor of the self-sufficiency of MFIs.
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3.2 Research hypotheses
As discussed above, the concept of microfinance as a means to reduce exclusion from financial markets and poverty, which has arisen in the developing world, was transferred to the developed world, using and modifying the principles of the original approach. Our research motivation is to assess whether the positive links of social capital to the overall performance of MFIs known from developing world can be found in developed economies as well. Although empirical findings regarding the effect of social capital on the performance of MFIs in the developing world are ambiguous, our starting point is the assumption that good conditions favoring the development of social capital have a positive effect on all performance areas of MFIs. To research this conjecture in a comprehensive way, we develop four hypotheses with respect to four particular areas of interest—loan repayment (portfolio quality), financial performance, social performance, and the efficiency of MFIs. Based on our review of the literature summarized above, we state the following hypotheses: Loan repayment
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ACCEPTED MANUSCRIPT The most widely used measure of loan repayment is portfolio quality, expressed in the microfinance industry as portfolio at risk, which measures the portion of the loan portfolio outstanding affected by delinquency as a percentage of the total outstanding loan portfolio. Our hypothesis is that loan portfolio quality is positively associated with social capital—that is to say, H1: The quality of an MFI’s loan portfolio, expressed as portfolio at risk, is positively associated with the conditions conducive to the development of social capital.
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Note that the portfolio at risk indicator used in the microfinance literature to express the quality of loan repayment and the portfolio is a negative indicator---the higher the portfolio at risk, the lower the portfolio quality. This explains the prefixes used to summarize our hypotheses in Table 1 below. Financial performance
As microfinance performance indicators become increasingly harmonized with those used in standard banking,
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the most commonly used variable for overall financial performance is return of assets (ROA), which is calculated by dividing net operating income after taxes by total assets. Return on assets is an overall measure of profitability on an accounting basis, and thus we state our second hypothesis as follows:
H2: The financial performance of an MFI in terms of return on assets is positively associated with the
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conditions conducive to the development of social capital. Social performance
The extant literature—for example, Mersland and Strøm (2010)—claims that size of loans in relation to GNI per capita is commonly used as a proxy for an MFI’s outreach to poor clients. The higher the value of this indicator, the lower is an MFI’s depth of outreach to poor clients, which, again, explains the signs used in the Table 1
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below. This outreach is considered a key—although not the only—element of MFIs’ social performance (SPTF, 2014). Given the close link between our definition of social capital conditions and the social mission of European MFIs to target populations that are overall excluded from society, we derive the following hypothesis with respect to social performance:
H3: The social performance of an MFI in terms of reaching out to poor clients is positively associated with
Efficiency
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the conditions conducive to the development of social capital.
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Efficiency, when measured using accounting variables, relates an MFI’s outputs to its inputs, and typically focuses on the operating expenses incurred by the operation of the loan portfolio. The operating expense ratio, which is considered to be a key indicator of the overall efficiency of a microlending institution (Microrate, 2003), is calculated by dividing all expenses related to the operations of the institution by the average gross loan portfolio. The lower this ratio, the more efficient are the processes of the institution. Our review of the literature allows us to hypothesize that the conditions conducive to social capital, in particular country-level conditions, are positively associated with the efficiency of MFIs: H4: The efficiency of an MFI is positively associated with the conditions conducive to the development of social capital. Table 1 summarizes these hypotheses and the expected signs of the associations, with independent variables as derived from the literature, and the different variables used to measure the resources of social capital, as dependent variables. Table 1 Summary of hypotheses
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ACCEPTED MANUSCRIPT Variable name
Hypothesis 1 Portfolio quality measured as default risk
Hypothesis 2 Financial performance measured in return on assets
Hypothesis 3 Social performance measured in average loan balance compared to per capita income
Hypothesis 4 Efficiency measured as operating expense ratio
MFI-specific resources of social capital +
-
+
+/-
+
+/-
-
Individual approach (LOANS_PER_STAFF)
-
+
-
+
Proportion of clients from rural areas (RUR)
-
+
-
+
Proportion of female clients (WOM)
+/-
+/-
-
None
Cultural fractionalization of society (ETHNIC_FRAC, LANGUAGE_FRAC, RELIGION_FRAC)
+
-
+
+
Country-specific resources of social capital -
+
Generosity (GENEROSITY)
+/-
+
Income inequality (GINI)
+
-
4. Data and descriptive statistics
-
-
-
-
+
+
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Control of corruption (CORRUPTION)
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-
MFI age in years (AGE)
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MFI size in number of staff (SIZE)
In order to investigate our hypotheses, we build a data set by drawing on several sources. We combine a panEuropean survey of microfinance data with the country-level data obtained from various existing databases. This section describes the collected variables. 4.1 MFI data
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Our microfinance institution data come from a comprehensive, up-to-date overview of microcredit suppliers in the European Union. This pan-European survey of microfinance providers has been carried out by the Fondazione Giordano Dell’Amore on behalf of the European Microfinance Network (EMN) and the Microfinance Centre (MFC) since 2004, making it the longest series of comparable data for European MFIs to the best of our knowledge. According to Botti et al. (2016), the survey has increased its coverage from 32
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microlenders across 10 European countries participating in 2004 to 149 institutions from across 22 countries participating in the most recently available edition. The EMN survey data include the standard MFI institution
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yearly indicators and is strikingly close to the definition of MFI indicators developed for developing economies (see, e.g., Microrate, 2003) , although the scope of variables has broadened over the years. We use data on microfinance institutions from this survey from the years 2008 to 2015, with several filters. We exclude MFIs from Bosnia and Herzegovina as the country suffered from problems of indebtedness caused by specific conditions after the war and empowered by the financial crisis in 2008, which according to EMN resulted in high default rates, a decline in portfolio size, and negative returns. Furthermore, microfinance institutions reporting a share of turnover from micro lending activities lower than 50 percent are excluded. In total, 90 percent of the remaining institutions in the research sample report that the percentage of their turnover made up of micro lending is higher than 75 percent. The number of institutions in our data changed during the observation period, which led to the creation of unbalanced panel data. In all, we have 302 MFIs5 from across 29 countries, each with 2‒8 years of data over the
5
Although the highest annual number of MFIs participating in the EMN survey is 149 institutions, our sample consists of 302 different organizations. The high count of examining institutions is caused mainly by high fluctuation in the survey sample during the years 2008 - 2015.
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ACCEPTED MANUSCRIPT period 2008‒15. Variations in the analyzed data set do not decrease its information content; contrarily, using unbalanced panel data analysis enables us to include a wider range of European MFIs in our analysis. Although the survey covers all European countries that launched any form of microfinance activities, we cannot claim it to be a representative sample of all European MFIs, as the average response rate for the observation period was only 47 percent. (It should be noted that the most recent edition experienced a significant increase in its response rate, to 69 percent.) Nevertheless, this data set is unique as it allows us to identify the performance drivers of
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MFIs with respect to the specific conditions in Europe, which—to the best of our knowledge—has not been explored previously. 4.2 Country-level data
Our country-level data come from several sources. Data on societal fractionalization come from the National Bureau of Economic Research (Alesina et al. 2003), while the GINI coefficient and survey data on trust, social
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support, and generosity are taken from the World Happiness Report. Data on formal institutional variables like the level of corruption or of political stability come from a World Bank database, the Worldwide Governance Indicators. The World Bank’s World Development Indicators are the source for macroeconomic variables such as GDP growth, GNI, and the shares of economic sectors in national economies. An indicator of the efficiency of government regulation of business—namely, the Business Freedom indicator, a country-level score between 0
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and 100, with 100 indicating the freest business environment—is published annually by the Heritage Foundation. We do not use regulatory frameworks for microfinance as an independent or control variable because there is no specific regulation in many European countries (in contrast to developing countries). 4.3 Descriptive statistics
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All variables, including a description of the measures used and their descriptive statistics, are summarized in Table 2.
The dataset includes MFI-year observations from 29 countries: Albania (8), Austria (1), Belgium (9), Bulgaria (23), Croatia (2), Finland (2), France (13), Germany (44), Hungary (20), Ireland (3), Italy (31), Kosovo (5), Latvia (2), Liechtenstein (1), Lithuania (2), Macedonia (3), Malta (1), Moldova (2), Montenegro (1), Netherlands (3), Norway (1), Poland (12), Portugal (5), Romania (44), Serbia (4), Spain (22), Sweden (4), Switzerland (1), United Kingdom (33). It includes the following legal forms: Non-bank Financial Institution (119), Cooperative/Credit Union (25), Non-governmental Organization (75), Bank (30), Government body (12), Community Development Financial Institution (10), Religious Institution (4), Other (27). The following product ranges are reported for the MFI-year observations: Business and Personal microloans (31%), Only Business microloans (54%), Only Personal microloans (15%).
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ACCEPTED MANUSCRIPT Table 2: Description of variables and summary statistics Variable
Abbreviation
Description
Source
Mean
SD
Min
Max
N
Outstanding balance on arrears over 30 days/gross loan portfolio
European MFIs survey—European Microfinance Network (EMN)
10.13
14.76
0
96
483
[(Net operating income - taxes)/total assets] * 100
European MFIs survey (EMN)
7.36
18.89
-15
166
394
Portfolio quality
PAR30
ROA
Social performance
LOAN_SIZE TO_GNIpc
Average loan size in current year/GNI per capita
European MFIs survey (EMN)
.48
Efficiency
OER
[Operating expenses/average gross loan portfolio] * 100
European MFIs survey (EMN)
16.97
Independent variables—explanatory MFI-specific (X0ijt) SIZE
Number of employees (E)
Age
AGE
Number of years since creation
Individual approach
LOANS_per _STAFF
Gross loan portfolio in 000 Euros/ number of employees (in constant prices 2010)
Proportion of female clients
WOM
Women as % of total borrowers
Proportion of clients from rural areas
RUR
Rural population as % of total borrowers
European MFIs survey (EMN)
Probability that two randomly chosen individuals in one country are not from the same ethnic group
Fractionalization Data (Alesina et al. 2003)
Probability that two randomly chosen individuals in one country are not from the same language group
Fractionalization Data (Alesina et al. 2003)
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LANGUAG E_ FRAC
European MFIs survey (EMN)
European MFIs survey (EMN)
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Language fractionalization
ETHNIC_F RAC
European MFIs survey (EMN)
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Country-specific (Y1jt) Ethnic fractionalization
European MFIs survey (EMN)
14
.001
14.10
565
21.12
0
156
413
Ordinal variable with categories: Very small (0‒5 E), Small (5‒50 E), Middle (50‒500 E), and Big (500 E and more)
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Size
.88
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Financial performance
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Dependent variables
11.94
11.94
0
133
858
875.6
3,848
11.42
541.75
541
31.56
28.41
0
100
613
44.14
22.23
.5
100
464
.23
.16
.041
.81
848
.18
.15
.020
.58
848
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CORRUPTI ON
Lack of Corruption
GENEROSI TY
Generosity
GINI
Income inequality
Probability that two randomly chosen individuals in one country are not from the same religious group
Fractionalization Data (Alesina et al. 2003)
Control of corruption index (-2,5 to 2,5; Worldwide Governance Indicators)
Worldwide Governance Indicators (World Bank)
Residual of regression of the national average of Gallup World Poll responses to the question "Have you donated money to a charity in the past month?" on GDP per capita.
World Happiness Report (Sustainable Development Solutions Network)
GINI coefficient
INST_TYPE
World Development Indicators (World Bank)
European MFIs survey (EMN)
Country-specific (Y3jt) BUS_FREE
Business freedom score (0‒100; Heritage Foundation)
.72
848
.68
.85
.88
2.41
861
.18
.32
.02
.28
.40
800
.27
.39
806
Non-bank financial institution, cooperative/credit union, non-governmental organization, bank, government body, community development financial institution, religious institution
Economic Freedom Data (Heritage Foundation)
69.16
34.45
1
95.9
865
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Entrepreneurial environment
.12
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Institutional type
.19
.002
Independent variables—control MFI-specific (X2ijt)
.46
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RELIGION_ FRAC
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Religion fractionalization
5. Empirical analysis
We aim to investigate to what extent the conditions conducive to the development of social capital in a society
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are linked to different levels of financial, social, and repayment performance as well as of the efficiency of MFIs in Europe. Our empirical strategy to evaluate our hypotheses is based on an estimation of multivariate
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regression models with fixed effects. An econometric approach is used to quantify the impact of independent variables on the analyzed outcome/dependent variables under the assumption that other variables are kept constant (ceteris paribus). We evaluate our hypotheses based on the value of the estimated parameters and their statistical significance (Verbeek, 2012). For the estimation of econometric models we use the software STATA 14. Before we proceed to the estimation of the econometric models, we specify them in the following section. 5.1 Model specification
Our comparative framework and model used to analyze the influence of social capital on the performance of MFIs follows that of Ahlin et al. (2011) and Postelnicu and Hermes (2015), which were initially developed for studying MFI performance in developing ecconomies. The EMN states that the fight against poverty and social exclusion is the same worldwide, though the European battle is different in terms of opportunities, challenges, history and especially in setting the restrictions and institutions (EMN, 2017). This statement alludes to the adaptations that European MFIs undertook when introducing the microfinance concept into European contexts. For instance, while joint liability lending models are less used than in the early microfinance approaches in developing countries (see, e.g., Erikkson et al., 2011), other features of our descriptive data discussed above
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ACCEPTED MANUSCRIPT show greater similarity, such as the product mix offered with an emphasis on credit and advisory services, the broad variety of legal forms and ownerships structures, and the standardization of performance indicators. Thus, these adaptations, despite significant economic differences between European economies and developing economies, in particular higher levels of absolute and relative poverty, and a higher share of people who live under or near the poverty in developing economies but are not systematically excluded from production and consumption markets, explains the common motivation to use financial services to address the needs of those – fewer – populations excluded from credit markets. Therefore, our empirical approach is to focus on the different aspects of social inclusion in European contexts through measurable proxies for social capital an appropriate set
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of cross-country data. Indeed, we argue that in both types of regions, a reasonable model specification for social capital builds on the same assumption, namely that interpersonal relationships influence the nature of exchange in financial markets, in particular credit markets. We, therefore, build our model using a similar specification
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than used for developing economies in Postelnicu and Hermes (2015; 2016) in order to evaluate whether social capital works indeed similarly. Quality of the loan portfolio (H1)
To test the first hypothesis, we take the standard measure of portfolio quality, PAR30 (PAR30), which represents
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the balance of the loans with arrears over 30 days, and which is appropriate for microloans with frequent, at least monthly, instalments. Our independent variables are represented by the set of social capital resources specified in the previous section, accompanied by a set of control variables from both the institution- and country-specific levels. Baseline MFI control variables include institutional type (INST_TYPE).Country-level controls include the economic growth measure (GDP_Growth) and an indicator describing the quality of the entrepreneurial environment (BUS_FREE). We eliminated the variables for trust, financial leverage, and cost of capital because of multicollinearity. The specification of the regression model is inspired by Ahlin et al. (2011) and Postelnicu and Hermes (2015;
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2016). We use additional categories in order to include variables of primary interest to us, which are believed to be the resources of social capital on both the country- and the institution-specific level, whereas the two other groups of variables include controls on the country- and institution-specific levels. Beyond the framework of the two mentioned studies, we deploy a variable describing the proportion of rural clients and the institutional
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type of the MFI. We control for the rural aspect because according to the theoretical framework of Postelnicu et al. (2014), information channels are of a special importance in rural areas, where people create more effective social networks than in urban regions, and this argument can be adapted to rural areas in developed economies,
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to the best of our knowledge. Moreover, we deploy a variable for the institutional type as a control variable given the importance attributed to this variable and related institutional features in the more recent MFI performance literature (see, e.g., Mersland and Strom, 2014). To test the influence of conditions conducive to the development of social capital on the loan portfolio quality of European MFIs, the following regression model is estimated:
Model (1):
܀ۯ۾ܑ ߙ = ܜܒ+ ߚ ܺ0௧ + ߚଵ ܻ1௧ + ߚଶ ܺ2௧ + ߚଷ ܻ3௧ + ߝ௧ , where ܴܲܣ30௧ is a year-t quality portfolio indicator of MFI i located in country j. ܺ0௧ is a set of MFI-specific variables that are believed to be resources of social capital for MFI i located in country j in a year t. ܻ1௧ is a set of country-specific resources of social capital describing country j in a year t. ܺ2௧ is a set of MFI-specific control variables for MFI i located in country j in a year t. ܻ3௧ is a set of country-specific control variables describing country j in a year t.
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ACCEPTED MANUSCRIPT Financial performance We analyze financial performance using the return on assets ROA (ROA) as our dependent variable to test the second hypothesis. Our independent variables and control variables are the same as for Model 1. To test the second hypothesis, the following regression model is estimated: Model (2): ࡾࡻ࢚ = ߙ + ߚ ܺ0௧ + ߚଵ ܻ1௧ + ߚଶ ܺ2௧ + ߚଷ ܻ3௧ + ߝ௧ ,
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where ܴܱܣ௧ is a year-t financial performance indicator of MFI i located in country j; the remaining symbols have the same content as in Model 1. Social performance
The third hypothesis focuses on the social performance of institutions. We analyze social return using depth of outreach, which is measured as the average loan size in relation to GNI per capita (LOAN_SIZE_TO_GNIpc).
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Again, our independent variables are represented by the set of social capital resources specified in the previous section and are accompanied by the same set of control variables as for the previous models and as summarized in Table 1. To test the third hypothesis, the following regression model is estimated:
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Model (3):
ࡸࡻࡺ_ࡿࡵࢆࡱ_ࢀࡻ_ࡳࡺࡵࢉ࢚ = ߙ + ߚ ܺ0௧ + ߚଵ ܻ1௧ + ߚଶ ܺ2௧ + ߚଷ ܻ3௧ + ߝ௧ , where ܿܫܰܩ_ܱܶ_ܧܼܫܵ_ܰܣܱܮ௧ is a year-t social performance indicator of MFI i located in country j; the remaining symbols have the same meaning as in Model 1. Efficiency
The last hypothesis focuses on the control of the efficiency of institutions. As the dependent variable we choose
Model (4):
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the operating expense ratio, OER (OER). To test the last hypothesis we estimate the following regression model:
ࡻࡱࡾ࢚ = ߙ + ߚ ܺ0௧ + ߚଵ ܻ1௧ + ߚଶ ܺ2௧ + ߚଷ ܻ3௧ + ߝ௧ ,
content as in Model 1. 5.2 Model estimation
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where ܱܴܧ௧ is a year-t efficiency indicator of MFI i located in country j; the remaining symbols have the same
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Our aim is to consider in our models in a comprehensive approach all potential sources of variation at different levels to find out the impact of our main variable of interest – the social capital, and reduce omitted variable bias as much as possible Thus, we estimate our regression models with a year fixed effects approach and control for cross-country heterogeneity and variance using a set of institutional and economic controls. Variables from the pooled data set used in the regressions were tested for stationarity and were found to be stationary. All presented econometric models are estimated with robust standard errors in order to overcome the potential threats of heteroscedasticity and autocorrelation. To inspect collinearity among the independent variables, we check the values of the Variance Inflation Factors test (VIF) and additionally correlations between independent variables, and conclude that all values are below the generally accepted threshold of 10 (Verbeek, 2012) after eliminating (due to multicollinearity) the variables mentioned above. We also use the alternative estimation strategy of clustered standard errors with respect to countries—doing so as a robustness check (Angrist and Pischke, 2008)—and conclude that our results are stable. Therefore, we are allowed to interpret our estimated econometric models (1) to (4). We use the same data set for all tested hypotheses.
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ACCEPTED MANUSCRIPT 5.3 Results and discussion The results are presented in columns (1) to (4) in Table 3. Table 3: Regression results
SIZE_CAT==Small SIZE_CAT==Very small AGE LOANS_per_STAFF RUR
-11.56*** (3.000) 0 (.) -5.286† (3.088) -7.498** (2.720) -12.09*** (3.221) 0 (.) -0.0918* (0.0363) 28.68** (9.865) Yes
-38.42* (17.56) 0 (.) -32.28† (18.15) -40.94* (17.45) 0 (.) -42.04* (17.52) 0.0634 (0.0400) 8.268 (23.81) Yes
OER ETHNIC_FRAC LANGUAGE_FRAC RELIGION_FRAC
GENEROSITY GINI
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INSTIT_TYPE==Bank
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CORRUPTION
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INSTIT_TYPE==Community Development Financial Institution (CDFI)
INSTIT_TYPE==Cooperative/Credit Union INSTIT_TYPE==Government body INSTIT_TYPE==NBFI INSTIT_TYPE==NGO
INSTIT_TYPE==Other INSTIT_TYPE==Religious institution BUS_FREE Constant Year Dummies
(4) OER 0 (.) 0 (.) -3.382 (3.341) -4.873 (3.481) -0.283** (0.0884) -0.000000112 (0.000000115) -0.0776* (0.0388) 0.202* (0.0796)
0.451*** (0.0964) 0.949*** (0.0792) 0.377*** (0.0846) 0.656*** (0.111) 0.516† (0.274) 0 (.) -0.000771 (0.00131) 0.604† (0.327) Yes
26.09** (8.492) 33.55*** (3.415) 20.54*** (5.040) 17.11*** (5.105) 15.96** (5.597) 0 (.) -0.112 (0.0863) -27.85 (23.67) Yes
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WOM
(3) LOAN_SIZE_TO_GNIpc 0 (.) 0 (.) -0.0429 (0.0687) -0.0677 (0.0800) -0.00332** (0.00126) 7.74e-10 (3.14e-09) 0.000436 (0.000788) -0.00653*** (0.00127) -0.00225** (0.000726) -0.432* (0.204) 0.148 (0.281) 0.322† (0.191) -0.129* (0.0525) -0.428* (0.199) -0.828 (1.131) 0.342** (0.109) 0.607*** (0.127)
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SIZE_CAT==Middle
(2) ROA 0 (.) 0 (.) 1.653 (1.438) 8.726** (2.641) 0.00376 (0.0638) -0.000000171 (0.000000109) 0.0665* (0.0313) 0.0498† (0.0309) -0.0779* (0.0393) 17.81† (10.59) -29.94* (12.78) 10.85 (8.984) 7.992* (3.627) -12.17 (13.67) 49.24 (38.04) -33.38† (17.87) -46.25* (18.36)
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SIZE_CAT==Big
(1) PAR30 0 (.) 0 (.) 1.584 (1.597) 0.172 (1.620) 0.0000380 (0.0353) -8.47e-08 (6.61e-08) -0.0676** (0.0240) -0.0629† (0.0331) 0.0267 (0.0444) -14.12** (5.218) 8.813 (6.282) 9.018 (5.800) 4.187** (1.293) -12.64† (7.344) -28.04 (31.06) -6.831† (3.829) 0 (.)
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22.98* (9.465) -46.65** (15.83) 7.991 (7.406) 5.503 (3.961) 12.67 (17.58) 103.8 (75.50) 10.34 (5.683) 1.742 (8.195)
ACCEPTED MANUSCRIPT Observations 245 237 257 266 R2 0.184 0.295 0.362 0.150 Adjusted R2 0.115 0.230 0.305 0.080 AIC 1,784.4 1,969.7 168.9 2,380.9 BIC 1,854.4 2,042.6 243.4 2,452.5 All presented econometric models are estimated with robust standard errors and with year dummies. Standard errors are reported in parentheses † p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001, (omitted) refers to a reference category or to a category with no observations.
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Table 3 shows the results of estimating the impact of the different social capital resources on the loan portfolio quality, financial and social performance, and efficiency, and does the same for the impact of the control variables. In a nutshell, the results show the direction and strength of the relationship between the measurable influences of social capital resources on repayment, financial performance, and social performance and efficiency, controlling for a number of factors.
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The role of social capital and portfolio quality (H1)
A negative prefix for the estimated coefficients indicates that we find a positive relationship between the particular independent variables and the loan portfolio quality in Model (1). Specifically, among the institutional
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variables, we find significant positive evidence for the proportion of clients from rural areas (RUR) and the proportion of female clients (WOM). Among the country-specific variables, we find a significant influence of generosity, low levels of ethnic fractionalization of society, and a lack of corruption. In total, we find significant association between PAR30 and five of the nine examined resources of social capital. Our presented results therefore support Hypothesis 1 (H1), that more intense conditions conducive to the development of social capital are connected with better portfolio quality, and thus with better repayment behavior in MFI clients. The proportion of rural clients (RUR) is negatively and significantly associated with PAR30. This result supports
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the idea that microfinance lending works better in rural areas than in urban ones. Postelnicu et al. (2014) provide a theoretical framework in order to measure the social collateral pledged by microfinance borrowers and to show how information channels increase the amount of social collateral involved and the threat of social sanctions when payments are delayed. They claim that information channels are especially dense in rural areas, where tight networks increase the capacity to collect and transmit information. This theoretical model is consistent with our
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findings, as well as with the findings of Wydick (1999) and Ahlin and Townsend (2007) for developing economies. Higher social pressure is also a potential factor contributing to the result that the proportion of female clients (WOM) is negatively and statistically significantly associated with lower rates of late repayments. This finding is
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in accordance with the results of Rahman (2008) or Goetz and Gupta (1996), who indicate that women are more sensitive to peer pressure—a sensitivity that, in turn, makes social collateral more valuable. This indicates that the greater the proportion of female clients in microfinance institutions, the easier it is to collect loans and the better are the indicators of repayment. Next, a set of societies’ structural characteristics that are believed to matter for microfinance institutions are examined. The level of generosity (GENEROSITY) in the society is negatively and significantly associated with bad repayment behavior; in other words, the higher the proportion of generous people in the population, the better is the repayment behavior observed among the clients of MFIs located in that country. This finding is consistent with those of Cowell et al. (2017), who assess the development of generosity and moral cognition across five cultures in populations of children, and find that social cognitive development, including generosity, combined with basic demographics seems to be the best predictor of moral behavior. Our results on the cultural fractionalization of society are not uniform. On the one hand, language (LANGUAGE_FRAC) and religious (RELIGION_FRAC) fractionalization are not statistically significant. On the
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ACCEPTED MANUSCRIPT other, ethnic fractionalization appears to be a significant predictor of portfolio quality. This could be explained by the fact that higher ethnic fractionalization may bring about higher levels of trust among individuals belonging to the same ethnic group, and thus support the genesis of social capital within such groups in a more intensive way than would occur in a non-fractionalized society. Last, a set of control variables, including both country- and institution-specific indicators, was tested. Among the institutional variables we find statistically significant evidence for the institutional type (INST_TYPE). Some
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institutional forms predominate in the portfolio quality over the others. We group the MFIs based on ownership structure into for-profit firms (banks (Bank) and non-bank financial institutions (NBFI)) and non-profit organizations (cooperatives (Cooperative/Credit Union) and non-governmental organizations (NGO)). The second group outperforms the for-profit firms statistically significantly in terms of borrowers’ repayment behavior. This is in contrast to the empirical findings of Morgan (2015), who supports the theoretical prediction
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that regulatory oversight leads, due to the fact that more commercially orientated organizations are constrained and monitored by external parties, to such organizations taking fewer risks than their not-for-profit counterparts. Among the country-specific variables we find a significant association of repayment quality with our control variable, Business Freedom Index (BUS_FREE). The higher this country-level score, the better the portfolio quality of MFIs in the country. Surprisingly, we find a negative and statistically significant association of
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repayment with the level of corruption control (CORRUPTION). The more developed mechanisms to control corruption in the country are, the worse is the portfolio quality of that country’s MFIs. This could be explained by the fact that the countries with lowest levels of corruption—the seven European countries with the best anticorruption mechanisms according to their actual rankings in the Worldwide Governance Indicators (2016); namely, Finland, Denmark, Sweden, Norway, Luxembourg, Liechtenstein, and Switzerland—are also those with the lowest levels of social marginalization. In these countries, financial exclusion is concentrated among people suffering from poverty and living on the absolute edge of society. As a reminder, according to Erikkson et al.
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(2011), the role of microfinance in particular in these countries has shifted from the traditional objective of lending for income-generating activities to social help. Social capital and financial performance (H2)
Looking at financial performance as measured through the return on assets in Model (2), we find some similar
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results to, but also differences to, Model (1). Not surprisingly economies of scale matter, as evidenced in the strongly significant relationship between the size variable (SIZE_CAT==very small) and the ROA. Further, we find strong evidence for the second hypothesis, that more intense conditions conducive to the development of
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social capital are connected with better financial performance of MFIs. We can support this hypothesis for the following five of the nine examined resources of social capital that are shown to be statistically significant in Model (2). Among the institutional variables, we find significant positive evidence for the size (SIZE_CAT==Very small), the proportion of clients from rural areas (RUR), and the proportion of female clients (WOM). Among the country-specific variables, we find a significant influence of fractionalization—that is to say, ethnic (ETHNIC_FRAC) and language fractionalization (LANGUAGE_FRAC)—and of lack of corruption (CORRUPTION).
Economies of scale matter, but not in the expected direction. Indeed, the best profitability is statistically significantly achieved by the smallest institutions. These include institutions with less than five employees. Size (SIZE) is thus a significant predictor of the financial performance of MFIs in Europe. While this finding stands in contrast to the general concept of economies of scale, it is in line with empirical findings from the developing world (e.g., Adugna 2014), which show that MFIs’ sizes and expense ratios are inversely related to their financial performance.
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ACCEPTED MANUSCRIPT The effect of the proportion of rural clients (RUR) is positively and significantly associated with the ROA, which is in line with this proportion’s previously discussed significant and negative effects on portfolio quality. This finding is consistent with Mersland and Strom (2014). A possible explanation for this is that social networks are more tightly knit in rural areas, and thus help in building social collateral and collecting loans. A higher proportion of female clients (WOM) is statistically significantly related to better financial performance in European microfinance institutions. This is in contrast to the majority of findings from empirical studies in
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developing countries, which argue that female entrepreneurs tend to be involved in traditional sectors with lower profits and harder competition, which should make them less profitable clients. Meyer (2015) finds for MFIs in developing countries that the effects of more female borrowers on the ROA and the return on equity (ROE) are very small and not significantly different from zero, as the negative effects of smaller loans and higher operating costs seem to offset the positive effects of portfolio quality—the latter effect also found by D’Espallier et al. (2013). While this finding stands in contrast to the findings from developing countries, it is in line with the finding of study of Greve and Salaff (2003) who studied network activities of entrepreneurs in four developed
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countries (Italy, Norway, Sweden, and the United States) and found that high proportion of females in the network increases the likelihood of starting business. This supports our prediction, that gender in developed economies is an indicator of social capital resource more than an indicator of social exclusion.
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Two of the three fractionalization variables (ETHNI_FRAC and LANGUAGE_FRAC) are significant with respect to financial performance. The significantly positive coefficient for ETHNI_FRAC and the significantly negative coefficient for LANGUAGE_FRAC indicate the opposite influence of these two fractionalization measures on profitability. Compared to the case of Model (1), the effect of ethical fractionalization changes direction, as such fractionalization affects profitability due to the need to cater to smaller separate groups through different distribution channels. As expected, a high level of corruption control (CORRUPTION) is statistically significantly associated with
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improved financial performance. This result is also in accordance with the findings of Ahlin et al. (2011) for MFIs in developing countries. Among the institutional controls, we find again statistically significant but less strong evidence for the importance of institutional type (INST_TYPE). Our empirical results show that profitoriented organizations achieved higher profits in comparison to non-profit ones. Still, the difference between forprofit-type organizations and our broadly defined group of non-profit organizations remains small.
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Social performance and social capital (H3)
Our third hypothesis, that MFIs’ social performance is positively associated with conditions conducive to the
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development of social capital, is examined in Model (3). Overall, our hypothesis is supported through a significant positive relationship of five of all nine measures of social capital resources with the average loan size in relation to GNI per capita (LOAN_SIZE_TO_GNIpc). The results confirm that the longer the history of the institution (AGE) is, the greater is its outreach. This relationship is statistically significant in the observed sample of European microfinance institutions. As expected, a higher proportion of female borrowers (WOM) improves the social performance of MFIs in Europe. We find a strong positive influence of proportion of women on the average loan size, which is consistent with findings in the empirical literature (e.g., Bassem, 2012). Among the structural characteristics of a society conducive to the building of social capital, we find strong evidence that the levels of generosity (GENEROSITY), ethnic fractionalization (ETHNI_FRAC), and corruption (CORRUPTION) move in the directions we hypothesized. The more generous the people who live in the particular country are, the easier is for an MFI to achieve a double bottom line through social performance measured in terms of poverty outreach. Our strong evidence for a positive relationship between social performance and ethnic fractionalization stands in contrast to the results of Postelnicu and Hermes (2015) for
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ACCEPTED MANUSCRIPT developing economies; in the European context, our finding could be explained by a higher level of trust and social responsibility among borrowers and lenders situated within the same ethnic group. Interestingly, of all institution-level controls, we find again statistically significant evidence for the relevance of institutional type (INST_TYPE), as profit-oriented organizations are found to be more successful in achieving their social mission than not-for-profits. Social capital and efficiency (H4)
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Our last hypothesis, regarding the influence of social capital on the efficiency of microfinance institutions, is investigated in Model (4). Our results indicate that MFIs grow in efficiency as they get older (AGE), which is in line with our findings regarding financial and social performance. However, our results differ from the previous hypothesis with regard to the proportion of women among clients (WOM), as the OER ratio grows with a growing proportion of female clients. This is consistent with the empirical evidence from developing countries
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(e.g., Hermes et al., 2011); in contrast to this evidence, however, efficiency does not affect the profitability of European MFIs—possibly due to the extensive subsidy system they enjoy. In contrast, serving rural clients (RUR) means higher levels of efficiency, which is consistent with the theoretical framework of Postelnicu et al. (2014). The fractionalization indicators show, again, opposite results to each other. On the one hand, the ethnic
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fractionalization (ETHNIC_FRAC) in the country leads to a worsening of the efficiency of MFIs; on the other, the more languages are spoken in the country (LANGUAGE_FRAC), the better the efficiency of MFIs seems to be, although we do not find support for this result in the literature. Overall, the fourth hypothesis is supported for only four of the nine resources of social capital identified. 5.4 Discussion
The findings from testing our empirical models indicate that the use of social capital is an effective means to improve the performance of microfinance in European contexts. In particular, our results show that the use of
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social capital as practiced by MFIs in developing country contexts and as proxied by a comprehensive set of variables derived from such contexts is also significant for European MFIs. Generally, our empirical results support our hypotheses that specific conditions that are more conducive to the development of social resources, and to their use for the building of social capital, have a positive influence on loan repayments, profitability, and depth of social outreach, as well as on—to a certain extent—the efficiency of European MFIs.
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We find that certain types of societal fractionalization make the business of lending to socially excluded individuals more challenging than, for instance, mere income inequalities in a society do. Among the countryspecific predictors included in our models, the ethnic fractionalization of a society is statistically significant for
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all measures of MFI performance; but, in contrast to previous findings from developing economies, this measure is associated with better performance of MFIs in all areas. We explain this by the fact that higher ethnic fractionalization may cause more intensive trust among members of the same ethnic group, supporting the genesis of social capital in such groups in more intensive way than might occur in non-fractionalized societies. We also find that a higher proportion of clients from rural areas improves most MFI performance indicators. This finding indicates that it would be useful to promote microfinance supply within rural areas, where people seem to use social networks more intensively than they are used in cities. In other words, promoting the creation of rural microfinance institutions turns out to be a recommended tool for regional policies that aim at decreasing disparities between urban and rural areas. Another implication of our analysis, one which stands in contrast to most studies focusing on developing economies, is that in many respects smaller lending entities perform better than their bigger counterparts. In particular, they are more profitable—quite in contrast to the general principle of economies of scale, but attributable, however, to the effects of social capital. Smaller entities have better conditions for the development
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ACCEPTED MANUSCRIPT of the social ties that make the standard screening, monitoring, and enforcement techniques of micro lenders more effective. Better use of the resources of social capital also comes to mind when we seek to understand the result that the proportion of female clients contributes statistically significantly to all tested areas of MFI performance. We explain this result with the notion that women’s social collateral has a higher value, which leads to better repayment performance and thus to the better overall performance of MFIs. Finally, our empirical results indicate that profit-oriented institutions like banks and non-bank financial
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institutions outperform not-for-profits in all the areas of performance that we measured. This is a strong indication that despite the unresolved question of the subsidy-dependency of European MFIs, a for-profit institutional framework seems more appropriate when aiming to promote the development of microfinance markets in Europe.
A limitation of our approach lies in certain characteristics of the underlying data set and the variables constructed
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to operationalize certain measures. Indeed, the EMN data on MFIs in Europe show the same gaps with regard to data on the prices of services (mainly, interest and fees charged on microloans) and on subsidies received as do the larger, publicly available data sets on MFIs in developing economies. This is another reason why we construct and measure our dependent variables using the same empirical logic as used in the abundant literature on the performance of MFIs in developing economies by examining the different resources that can be used to
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build social capital in a multivariate model specification. Moreover, our analysis is limited to the use of aggregate supply-side data, which does not allow to establish causality nor to differentiate in how far wellperforming MFIs indeed contribute to preventing social exclusion and poverty in Europe. The data set also limits our set of proxy measures for social capital to the supply side of microcredit and does not allow to investigate the social capital using borrower data. Still, our analysis provides important and practical indications on how MFIs could both use and further develop social capital and, thus, improve their performance. 6. Conclusion and Outlook
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One of the main challenges for European policy makers with regard to microfinance is to support institutional frameworks that enable society to unlock the potential of microcredit suppliers in Europe regarding business models that can, in the longer term, help the inclusion of socially marginalized people, increase job creation, and thus make a contribution to economic growth. Economic and societal conditions and institutional frameworks are
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not homogeneous across Europe; Erikkson, et al. (2011), for instance, identify a dichotomy between Western European and Central Eastern European microfinance markets and business models. Still, the interest in defining long-term, reliable business models for microfinance is overarching, and divergences with regard to socially and
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financially excluded populations between these two geographies are tending to become smaller (Di Cataldo and Rodríguez-Pose, 2016). In our study, we therefore investigate performance drivers of MFIs based on a cross-country institution-level, supply-side data set collected from European countries, and derive conclusions for the European microfinance industry in general. Our main focus has been on the role that social capital plays in enabling strong results from microfinance—in particular microenterprise lending—in Europe. We contribute to the body of theoretical and empirical research on social capital in credit markets by emphasizing that it is not only institutional variables, and those influencing contractual security—for instance, levels of corruption or trust in the honesty of contractual partners—that matter in microcredit markets. We, in addition, find high relevance of those measuring the degree of trust and cohesion of societies. In this regard, our empirical results lend further support to the theoretical approaches to the role of social capital in retail credit markets such as microfinance, in particular to the conceptual frameworks of Postelnicu et al. (2014), and Li et al. (2018). Both argue that the credibility of the threat of social sanctions depends on the size and importance of both internal and external ties, and that the extent to which external ties are pledged as collateral depends on the network configuration. The network configuration is, according to Postelnicu et al. (2014), influenced by information channels that are especially
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ACCEPTED MANUSCRIPT dense in rural areas. We find significant confirmation that indeed clients from rural areas achieve higher quality of repayment compared to their urban counterparts. This can be explained by the fact that microfinance in European economies emerged despite the existence of a dense and competent banking network in these countries, and is perceived especially as a tool for promoting microenterprise growth, job creation, social cohesion, and poverty prevention at the margins of society. While there is clear evidence that microfinance is an effective financing channel for job creation and social
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inclusion (Chmelíková and Redlichová, 2013), there are European countries in which microfinance markets are developed not at all or only to a very limited extent. Our main recommendations for an institutional framework to promote the further development of the industry are, thus, that an MFI should aim at using social ties conscientiously and strategically to build trust and a brand as an institution rooted in its community. This includes support to the genesis of smaller but profit-oriented microfinance institutions, taking into account that these may cater to subgroups of the population and may be located especially in rural and marginalized areas. These institutions should design their products based on market research that assesses the demand from female
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clients, as our paper—like many that precede it—finds that a large proportion of female clients is associated with better performance of MFIs, or of specific groups in contexts exhibiting high ethnic fractionalization and other elements of cultural fractionalization.
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Our model, suggesting usage of social capital in microfinance sector, may lead to a better understanding of performance drivers and the possibility to achieve sustainable, long-term provision of services through effective use of the resources of social capital. Future research may be devoted to developing a more composite measure of social capital comparable across European countries and employing adequate methods such as PCA to identify such a measure. To advance the micro foundations of the use of social capital in individual lending to excluded populations, future research could also focus on collecting a more comprehensive data set as well as qualitative case studies on the borrower-level use of European microfinance services and the quality of their use of social
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capital resources from a borrowers’ perspective. Especially, we encourage to monitor those proxies that appeared to be significant for the performance of MFIs in case studies drawn from developing countries, but that are not available in the cross-country dataset for European MFIs. These include the contact frequency among MFIs´ stakeholders (Abbink et al., 2006), geographical proximity of the MFIs to their borrowers (Karlan, 2007), common socio-economic characteristics of borrowers (such as gender, age, education, income, or place of living, Kritikos and Vigenia, 2005), joint social activities (Wydick, 1999), and frequency of interactions among MFI’s
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Acknowledgements
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stakeholders (Feigenberg et al., 2013).
We thank our anonymous referees for important comments and suggestions. We further thank Timo Busch, Marc Chesney, Andrea Frederick, and Julia Meyer for comments on a previous version, as well as the audiences and discussants at the FMA European Conference 2018 and at the World Finance Conference 2018. All remaining errors are ours.
Funding
We thank the Swiss National Science Foundation for financial support to conduct this study, and the European Microfinance Network for providing access to proprietary data of the network. We also thank the Mendel University for partial financial support via internal grant (IGA) No. FRRMS - IGA - 2018/006. This work was also supported by the Internal Grant Agency of Faculty of Business Administration, University of Economics in Prague, under no.: IP300040.
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Vitae
Gabriela Chmelíková works as an associate professor at the Department of Regional and Business Economy, Faculty of Regional Development and International Studies, Mendel University in Brno, Czech Republic. Her research interest is focused on the evaluation of business performance and risk drivers with the impact on the regional and social policy. She obtained her Ph.D. degree in Business Economics and Management at the Mendel University in Brno. Previously, she obtained Master´s degree in Business Economy at Masaryk University in Brno, Czech Republic. She serves as a reviewer for several journals and scientific conferences.
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Annette Krauss is a Senior Researcher at the Department for Banking and Finance, University of Zurich, Switzerland. She co-founded the Department's Center for Sustainable Finance and Private Wealth and founded its predecessor, the Center for Microfinance. Previously, she was a Senior Lecturer at the Kellogg School of Management, USA. She obtained her doctorate in Economics at Ruhr-University Bochum, Germany, worked as a visiting research fellow at the University of California at Berkeley, USA, and received her diploma in Economics from the University of Trier, Germany. Her research focuses on microfinance and financial inclusion, and development investments in developing countries.
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Ondřej Dvouletý works as a research assistant at the Department of Entrepreneurship, University of Economics in Prague, Czech Republic. His research interest is focused on entrepreneurship policy evaluation and entrepreneurial economics. He obtained his Ph.D. degree at the University of Economics in Prague. Previously, he obtained Master´s degree in Economic Policy at the University of Economics in Prague and Master´s degree in Entrepreneurship at Linnaeus University in Vaxjo. He is a member of the editorial advisory board of the Journal of Entrepreneurship in Emerging Economies and serves as a reviewer for several journals and conferences.
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Highlights Social capital has a positive influence on repayment, profitability, and depth of social outreach
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Higher proportion of clients from rural areas improves most MFI performance indicators
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Smaller lending entities perform better than their bigger counterparts
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Profit-oriented institutions outperform not-for-profits in all the areas of performance
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ACCEPTED MANUSCRIPT Biography
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Gabriela Chmelíková works as an associate professor at the Department of Regional and Business Economy, Faculty of Regional Development and International Studies, Mendel University in Brno, Czech Republic. Her research interest is focused on the evaluation of business performance and risk drivers with the impact on the regional and social policy. She obtained her Ph.D. degree in Business Economics and Management at the Mendel University in Brno. Previously, she obtained Master´s degree in Business Economy at Masaryk University in Brno, Czech Republic. She serves as a reviewer for several journals and scientific conferences.
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Annette Krauss works as a senior Lecturer at the Department for Banking and Finance, University of Zurich, Switzerland. She is a scientific head of Center for Sustainable Finance and Private Wealth at the University of Zurich. Her research interest is focused on microfinance and financial inclusion, social entrepreneurship and development investments in developing countries. She obtained her Ph.D. in Economics at the Ruhr-University Bochum, Germany. She worked as a research fellow at University of California at Berkley, USA. Her Diploma in Economics is from the University of Trier, Germany.
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Ondřej Dvouletý works as a research assistant at the Department of Entrepreneurship, University of Economics in Prague, Czech Republic. His research interest is focused on entrepreneurship policy evaluation and entrepreneurial economics. He obtained his Ph.D. degree at the University of Economics in Prague. Previously, he obtained Master´s degree in Economic Policy at the University of Economics in Prague and Master´s degree in Entrepreneurship at Linnaeus University in Vaxjo. He is a member of the editorial advisory board of the Journal of Entrepreneurship in Emerging Economies and serves as a reviewer for several journals and conferences.