Institutional framework and capital structure of microfinance institutions

JBR-08005; No of Pages 13 Journal of Business Research xxx (2014) xxx–xxx

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Journal of Business Research

Institutional framework and capital structure of microfinance institutions Hubert Tchakoute Tchuigoua ⁎ KEDGE Business School, 680 Cours de la Libération, 33 405 Talence cedex, France

a r t i c l e

i n f o

Article history: Received 6 April 2013 Received in revised form 14 January 2014 Accepted 23 January 2014 Available online xxxx JEL classification: G21 G24 G32

a b s t r a c t This article addresses the question of whether institutional frameworks matter in the capital structure of microfinance institutions. We studied a sample of 292 MFIs between 2004 and 2009. Our findings suggest that creditor rights, a country's legal tradition, and the level of financial sector development are significantly related to MFIs' level of external finance. Furthermore, the positive relationship between banking sector development and borrowings enables us to conclude that the microfinance sector and the formal banking sector are complementary. In addition, a split sample technique is used in order to assess the external validity of the model. Findings from this cross-validation strengthen the results obtained from the whole sample and indicate that our model seems to predict well the effect of institutional variables on the capital structure of MFIs. © 2014 Elsevier Inc. All rights reserved.

Keywords: Strength of legal rights Financial sector development Capital structure Microfinance

1. Introduction According to the 2011 State of the Microcredit Summit Campaign Report (Reed, 2011), MFIs are playing an increasingly important role in the financial system in most developing countries. The results of several recent impact studies are controversial1 regarding the effectiveness of microfinance in alleviating poverty and fighting financial exclusion. Hartarska and Nadolnyak (2008b), Becchetti and Castriota (2011), and Rai and Ravi (2011) provide strong evidence that MFIs improve the welfare of population and alleviate microbusiness financing constraints, whereas Duflo, Banerjee, Glennerster, and Kinnan (2013) find heterogeneous effects of microfinance on financial inclusion. As Earne and Sherk (2013) argue, funding is crucial in improving financial inclusion because it ensures that MFIs have the resources needed to expand through increasing the number of clients served and geographical and product diversification. Efficient MFIs that access cheap external funding may thus offer cheap loans to poor borrowers and to income-generating activities and micro enterprises, thereby promoting and supporting their development (Ghosh & Van Tassel, 2011). According to the existing literature in corporate finance, access to external funding sources on attractive terms is determined not only by firm-level characteristics but also by the institutional environment (Demirgüç-Kunt & Maksimovic, 1999). Several empirical studies extensively reviewed the relationship between institutions and corporate ⁎ Tel.: +33 556 854 55 67, +33 659 74 65 67(mobile). E-mail address: [email protected]. 1 The debate on the effectiveness of microfinance has been amplified by the Andhra Pradesh crisis.

finance decisions in developing and developed economies. Differences in legal and financial systems seem to be responsible for differences in investment policies (Acharya, Amihud, & Litov, 2011; Demirgüç-Kunt & Maksimovic, 1998; Houston, Lin, Lin, & Ma, 2010), dividend policies (Brockman & Unlu, 2009; Byrne & O'Connor, 2012), bank lending policies (Bae & Goyal, 2009; Ge, Kim, & Song, 2012; Haselman, Pistor, & Vig, 2010; Qian & Strahan, 2007), and firms' capital structure (Fan, Titman, & Twite, 2012; Giannetti, 2003; González & González, 2008; La Porta, López-de-Silanes, Shleifer, & Vishny, 1997; Öztekin & Flannery, 2012). The literature on the relationship between institutional environment and capital structure converges on the idea that firms operating in a better institutional environment may benefit from easier access to external funding with attractive conditions (Antoniou, Guney, & Paudyal, 2008; Booth, Aivazian, Demirgüç-Kunt, & Maksimovic, 2001; Demirgüç-Kunt & Maksimovic, 1999; Fan et al., 2012; Li & Ferreira, 2011). The main argument of these studies is that, better institutional environments can overcome information asymmetries in credit markets and consequently affect firms' funding policies. This seems to be the case with MFIs. As argued by Garmaise and Natividad (2010), information asymmetries likely contribute to raising the cost of finance in less developed and emerging markets where MFIs operate. In credit markets with weaker institutions, information frictions will make it difficult and expensive for MFIs to raise funds and may even appear to constrain their growth (Garmaise & Natividad, 2010). Moreover, the regulatory framework, and the openness and level of development of the financial system affect MFI funding policies (Earne & Sherk, 2013). Our article thus assumes that institutional environment and financial sector development affect the funding policies of microfinance institutions.

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Please cite this article as: Tchakoute Tchuigoua, H., Institutional framework and capital structure of microfinance institutions, Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.01.008

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The literature on microfinance institutional framework and capital structure is still growing. Studies that focus on institutions explain the observed differences between MFIs in terms of performance and efficiency by macroeconomic and macro-institutional features (Ahlin, Lin, & Maio, 2011; Patten, Rosengard, & Johnston, 2001; Vanroose & D'Espallier, 2013). Vanroose and D'Espallier (2013) investigate the relationship between financial sector development and MFI outreach and provide evidence that MFIs flourish and act as a substitute when the formal financial sector fails. Studies that examine capital structure in microfinance can be categorized in at least four ways. The first category explores whether capital structure improves MFI efficiency and financial sustainability (Bogan, 2012; Hoque, Chishty, & Halloway, 2011; Hudon & Traca, 2011; Kyereboah-Coleman, 2007). The second category investigates whether ratings reduce the price of financing and help MFIs raise funds (Garmaise & Natividad, 2010; Hartarska & Nadolnyak, 2008a). Hartarska and Nadolnyak (2008a) show that rating agencies differ greatly in their impact on MFIs' ability to raise funds. Their evidence also suggests that subsidizing rating does not help MFIs raise more funds. Garmaise and Natividad (2010) show that rating significantly reduces the price of financing while having a mix impact on the quantity. They find that being rated does not significantly increase the amount of loans that MFIs receive from outside creditors. The third category describes MFI financing practices and links sources of financing to the stage of MFI development (De Sousa-Shields & Frankiewicz, 2004; Fernando, 2004; Ledgerwood & White, 2006). The fourth category examines the determinants of the international funding of microfinance and provides evidence that profitability and better outreach are more likely to increase an MFI's chance of attracting international commercial debt (Mersland & Urgeghe, 2013). These identified studies seem to overlook the impact of the institutional environment on the capital structure of MFIs. The link between institutional framework and capital structure has not yet been subject to investigation in the field of microfinance. This article intends to fill the gap by examining whether institutional frameworks account for MFI funding policies. The article thus attempts to answer the question of whether institutional framework variables have an effect on the capital structure of MFIs. Our study concentrates on three institutional variables: the strength of legal rights, a country's legal tradition, and the size of the banking sector. Our study relates to those that previously examined the relationship between the institutional environment and firm financing in the nonfinancial sector. MFIs are hybrid organizations insofar as they combine banking logic (profitability, clients as customers) and development logic (poverty alleviation, clients as beneficiaries) (Battilana & Dorado, 2010; Kent & Dacin, 2013). As Gaul (2009) shows, MFIs depend less on subsidies over time. Yaron's Subsidy-Dependence Index (SDI) declined between 2003 and 2007, from 56% to 47%. This reduction in subsidies is likely due to the commercialization of microfinance. This commercialization trend thus accentuates the hybridization of logics in microfinance. To our knowledge, our article is thus the first to analyze whether institutional frameworks matter for the capital structure of hybrid organizations such as MFIs. We thus borrow extensively from general law and finance literature, which examines the link between institutional environment and capital structure. We measure capital structure by the leverage (the ratio of total debt to total assets) and donated equity as a percentage of total assets. Given that the capital structure of some MFIs, similar to those of banks, includes deposits, we then split leverage into deposits and borrowing (non-deposits liabilities). We also conducted an empirical study of 292 MFIs between 2004 and 2009 to verify our main assumption that the institutional environment has an influence on the financial structure of MFIs. The hypothesis is not rejected. We provide evidence that creditor rights, a country's legal tradition, and the size of the banking sector are significantly related to MFIs' need for external finance. In that sense, we add to the general literature on microfinance and to the specific literature on the financial

structure of MFIs. The positive relationship between banking sector development and borrowings enable us to conclude that the microfinance sector and the formal banking sector are complementary. We thus extend the work of Vanroose and D'Espallier (2013), who first assumed that the traditional financial sector and the microfinance sector compete, and show that MFIs respond to a need that banks cannot fulfill. To gauge the robustness of the results, we change the measurement of the size of the banking sector. In addition, we randomly split the initial sample in two sub-samples and use cross-validation techniques in order to assess the external validity of our models. Findings from this cross-validation strengthen the results obtained over the whole sample and indicate that the model seems to predict well the effect of institutional variables on the financial structure of MFIs. The remainder of the article is organized as follows. Part two describes the conceptual framework. Part three develops the research methodology. We present and discuss the results in part four. 2. Background 2.1. How important are institutional framework for microfinance funding? For MFIs, deposits, debts (commercial debts, subsidized debts, or bonds offering), and equity (shareholder equity and donated equity) are the three main sources of external funding (Consultative Group to Assist the Poor (CGAP), 20102; Hoque et al., 2011). MFI debt differs not only by its type and its instruments but also according to its maturity. Based on the study of a sample of 289 MFIs, Cull, Demirgüç-Kunt, and Morduch (2009) drew a picture of MFI funding instruments. Commercial funding and deposits seem to be the main funding source of shareholder-based MFIs, whereas non-commercial borrowings and donations are the main funding source of MFIs registered as nongovernmental organizations (NGOs). A unique feature of MFI funding is that a part of MFIs' external financing can be subsidized. Subsidized external debts, also called soft loans or concessionary borrowings, are contracted at favorable conditions, that is, below market rates. They are often provided by government aid agencies (United States Agency for International Development [USAID]), multilateral banks (World Bank), or apex organizations and foundations. According to the Consultative Group to Assist the Poor (CGAP) (2010) more than US$2 billion per year of public money is being disbursed globally to the microfinance sector through apex funds. Funds are disbursed by apexes to microfinance institutions mostly as subsidized loans, but occasionally as grants. Soft equity, also called subsidized equity, is a financial instrument that is channeled mainly through micro investment vehicles (Hudon & Traca, 2011). Returns expected by donors in this case are below the market rate. Finally, MFIs receive subsidies in the form of donations and cash, and donors in this case are not necessarily expecting any positive returns. Subsidized equity is part of equity. Some recent studies indicate that a large number of microfinance programs are still depending on donor subsidies to meet their costs. Hermes and Lensink (2011) report that 8% of MFIs are close to being profitable and 70% of MFIs still receive subsidies from donors, governments, and so on. The 2010 MFI benchmarks (The MIX, 2011) reports that 50% of the 1300 MFIs studied are financially self-sufficient and have an operational selfsufficiency ratio of over 1.03. This indicates that approximately 50% of MFIs still depend on subsidies. D'Espallier, Hudon, and Szafarz (2013) find that only 23% of the world's MFIs survive without subsidies. Given that donors are often geographically far away from MFI operations (Hudon & Traca, 2011) and that MFIs may have information that donors 2 Consultative Group to Assist the Poor (CGAP) is an independent policy and research center dedicated to advancing financial access for the world's poor. It is supported by more than 30 development agencies and private foundations who share a common mission to alleviate poverty. Housed at the World Bank, Consultative Group to Assist the Poor (CGAP) provides market intelligence, promotes standards, develops innovative solutions, and offers advisory services to governments, microfinance providers, donors, and investors. Source: CGAP Microfinance Gateway website.

Please cite this article as: Tchakoute Tchuigoua, H., Institutional framework and capital structure of microfinance institutions, Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.01.008

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do not have, there is an information asymmetry problem between donors and MFIs. Hudon and Traca (2011) note that the lack of reliable information coupled with insufficient disclosure in the sector may accentuate this information asymmetry problem. The lack of reliable information3 makes it difficult to better inform donors about the performance, risk, and ability of MFIs to efficiently use subsidies. Better institutions can overcome this information asymmetry between MFIs and donors who decide to give. We can therefore expect the amount of subsidies to be more important in countries with better creditor protection and better law enforcement. Deposits have several functions in deposits-taken MFIs (Ledgerwood & White, 2006). Deposits in MFIs are seen as financial products consisting of short-term demand deposits (savings accounts) or time deposits—remunerated savings accounts (certificates of deposit [CDs], programmed savings). From a financial intermediation point of view, deposits can be seen as a resource used by MFIs to fund their projects and to make loans (Consultative Group to Assist the Poor (CGAP), 2011; Cull et al., 2009). Moreover some deposits-taken MFIs use deposits as a tool to reinforce contracts. They consider savings a prerequisite to be qualified for a loan. Savings in this case is viewed as financial collateral provided by borrowers to secure a loan (Armendáriz de Aghion & Morduch, 2004, 2010). In countries with better creditor protections and better law enforcement, we expect deposits to be less important. MFI deposits would thus be more significant in countries with weaker institutions. Around the world, the vast majority of MFIs are non-deposits-taken (Galema, Lensink, & Spierdijk, 2011). This lack of short-term resource, coupled with the fact that domestic credit markets are underdeveloped in some countries, limit MFI financing opportunities. Moreover, MFIs that rely on subsidies are financially constrained. To overcome this constraint of access to external funding, MFIs seek commercial sources of funding, local or cross-border, in order to meet the promise of microfinance to alleviate poverty. In recent years, the proponents of commercialization4 have encouraged MFIs to decrease their dependence on subsidies and grant funding (Bogan, 2012; Hoque et al., 2011); the argument is that moving toward commercialization may increase the ability of MFIs to expand their scale by leveraging assets (Armendáriz de Aghion & Morduch, 2010). Commercialization5 of microfinance thus gives an opportunity to MFIs to diversify their funding sources and to be less dependent on subsidies. The results of recent surveys suggest that cross-border funding enables MFIs to diversify their external funding sources. In 2010, local lenders provided 60% of debt financing to MFIs and 40% was from cross-border investors (MicroBanking Bulletin, 2010). Consultative Group to Assist the Poor (CGAP) (2011) provides an illustration of the fact that MFIs moved from donor-dependent projects to sustainable financial service providers. Between 2007 and 2010, foreign investment in microfinance (including debt and equity) quadrupled to reach US$24 billion. The Consultative Group to Assist the Poor (CGAP) (2011) has also estimated as of December 2009 that the amount of international funding for microfinance is approximately US$21.3 billion. More than 56% of these funds are allocated directly to MFIs, 38% transit through microfinance investment vehicles, and 6.1% go through apex organizations. In addition, the Consultative Group to Assist the Poor (CGAP) survey highlights that public donor and investor commitments represent almost 70% of total cross-border funding to microfinance institutions, approximately US$14.6 billion. Microfinance investment vehicles (MIVs)—the intermediaries between MFIs and investors—thus facilitate access to capital 3 It should, however, be recognized that in recent years, the MIX and the rating agencies have helped to strengthen information disclosure in the sector and to improve the reliability of the financial information disclosed by MFIs. 4 Another way to achieve commercialization is to transform a nonprofit entity into a formalized, regulated financial institution that, if so licensed, it can receive intermediate deposits from the public (Ledgerwood & White, 2006). 5 Commercialization may not only have positive consequences for the poor (Cull et al., 2009) but also it can lead to mission drift (Hoque et al., 2011; Ledgerwood & White, 2006).

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markets by offering several types of financial instruments. The MicroRate (2011) survey shows that for more than five years, debt instruments have been the main financing tools for investors in microfinance. They represent about 82% of the invested assets of MIVs, followed by equities (18%). Europe and Central Asia (EECA) and Latin America and the Caribbean (LAC), respectively with 38% and 35%, are the two regions that concentrate the majority of microfinance investments. The share of MIV investment held by private institutions is 43%, public institutional investors is 35%, private individuals is 12%, and not-for-profit investors is 5%. According to the Consultative Group to Assist the Poor (CGAP) (2011) debt instruments represent 50% of total commitments. In addition, some MFIs are funded directly through financial markets (e.g., Equity Bank in Kenya and Compartamos Banco in Mexico). Cull et al. (2009) show that the profit rates of MFIs are becoming interesting for profit-maximizing investors, and some recent studies document that microfinance is becoming an investment opportunity (Galema et al., 2011; Krauss & Walter, 2009). Krauss and Walter (2009) provide evidence that MFIs help to diversify the value of international portfolios. Using a mean-variance spanning test, Galema et al. (2011) show that adding microfinance to an international portfolio leads to diversification gains. Investing in microfinance enables international investors to diversify their asset portfolios, especially bond portfolios. It can therefore be assumed that international capital flows will be more important for MFIs operating in countries that guarantee and protect creditor and investor interests. More generally, international investors who see microfinance as a means for portfolio diversification will tend to invest more in countries with better institutions. Mersland and Urgeghe (2013) find that international commercial debts are related mainly to financial performance and the level of professionalization in the MFIs, whereas international subsidized debts are driven mainly by the targeting of women. MFIs in countries with a better institutional framework will thus benefit from greater access to external funding on attractive conditions as better institutions result in loan availability and better loan contract terms. 2.2. Hypotheses development Information asymmetries exist between firms and investors. These information asymmetries lead to market imperfections in developed, developing, and underdeveloped countries and are explained in part by the quality of the legal and institutional environments (DemirgüçKunt & Maksimovic, 1999; La Porta et al., 1997; Qian & Strahan, 2007). Fan et al. (2012) argue that country-level institutional factors are likely to have a first-order effect on firms' capital structure choice. According to the law and finance literature, the legal and institutional framework can help make information available to investors, enforce their legal rights, and consequently mitigate these agency problems (DemirgüçKunt & Maksimovic, 1999). Thus, firms operating in countries with a legal framework that protects the interests of creditors have easier access to external financing sources on favorable conditions (La Porta et al., 1997). 2.2.1. Strength of legal rights and MFI capital structure Previous theoretical and empirical studies provide strong evidence that the strength of creditor and investor rights matter in loan contracting and firm financing choices. Aghion and Bolton (1992) developed a theory of capital structure based on transaction costs and contractual incompleteness. They showed that control rights attached to financial instruments such as debt and equity explain the choice of firm capital structure. Indeed, to the extent that raising more debt funding increases the risk of bankruptcy, lenders should benefit from bargaining power, which is the ability to seize collateral or to control borrower firms. Thus, firms in countries that strengthen the ability of lenders to force borrower repayment by seizing collateral and increase

Please cite this article as: Tchakoute Tchuigoua, H., Institutional framework and capital structure of microfinance institutions, Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.01.008

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lender ability to take control of these firms are financed on favorable terms. Giannetti (2003) studies a sample of unlisted firms and shows that a strong protection of creditor rights is associated with leverage and debt maturity. González and González (2008) also document that creditor protection reduces agency cost of debt. Firms with higher agency cost of debt find it difficult to get loans in countries where creditor rights are not sufficiently protected. In these countries, external finance seems to be costly, so firms preferably resort to internal financing. Qian and Strahan (2007) show that in countries with better investors and creditor protection rights, banks charge lower interest rates, improving credit availability. If the cost of credit is cheaper, firms will be more leveraged. We expect that leverage and the amount of subsidies will be more important in MFIs that operate in countries where creditor rights are better protected. However, strengthening creditor rights can influence manager (the borrower) behavior. A strict enforcement of creditor rights can be associated with low leverage (Rajan & Zingales, 1995). Two main arguments justify this relationship. On the one hand, managers who fear losing their jobs in the event of bankruptcy and the control by their creditors may choose to maintain a low level of debt. On the other hand, if the enforcement of creditor rights entails a bankruptcy, then managers will maintain low leverage. We thus formulate the two following hypotheses.

difference in terms of firm leverage between bank-oriented economies and market-oriented ones. Fan et al. (2012) do not find any significant relationships between leverage and the level of the banking sector development, measured by the ratio of deposits over the GDP. As noted by Vanroose and D'Espallier (2013), MFIs are directly affected by the state of the development of the banking sector. They investigate whether the microfinance sector and the commercial banking sector substitute for each other. They provide evidence that MFIs flourish where the formal financial sector fails. In this article, we assume that MFIs and the commercial banking sector complement each other. In line with the arguments in favor of commercialization, we claim that a large banking sector enables MFIs to access commercial loans and therefore reduce their dependence on subsidies. Given that the vast majority of MFIs are non-listed and use the domestic credit market as a funding source, we can deduce that the development of the banking sector influences their capital structure. Thus, we formulate the following hypotheses:

Hypothesis 1a. Leverage is positively associated with creditor rights.

3. Sample and methodology

Hypothesis 1b. Subsidies are positively associated with creditor rights.

3.1. Data source and sample selection

2.2.2. A country's legal tradition and MFI capital structure According to La Porta et al. (1997), common-law countries offer better protection to creditors. This increases the availability of credit and eases access to external financing. In addition, Davydenko and Franks (2008) compare default rates in three developed countries (France, Germany, and the United Kingdom), showing that differences in creditor rights across countries lead banks to adjust contractual terms of loans in order to mitigate the cost of bankruptcy law. Banks thus tend to adjust their behavior in debtor-friendly countries, that is, countries where bankruptcy law favors borrowers, such as France. In order to anticipate a borrower's default, banks adjust loan terms by requiring more collateral, for example. Using a partial adjustment model, Öztekin and Flannery (2012) find that firms in common-law countries adjust to optimal capital structure faster than firms operating under civil-law systems. However, Fan et al. (2012) show that common-law systems are associated with lower debt ratios. We can expect MFIs in commonlaw countries to be more leveraged than those operating under other legal traditions. Because we are not able to specify the direction of the relationship between legal tradition and subsidies, we formulate a non-directional hypothesis, expressed in the null form.

Our primary data source is the Microfinance Information eXchange (MIX) database, which is used in a growing number of studies in the field of microfinance (Ahlin et al., 2011; Bogan, 2012; Cull et al., 2009; D'Espallier et al., 2013; Hermes & Lensink, 2011; Servin, Lensink, & Van den Berg, 2012; Vanroose & D'Espallier, 2013). The MIX database is a web-based microfinance platform that provides data on individual MFIs. The MIX ensures financial transparency of MFIs and thus helps to address key challenges faced by MFIs, namely, the lack of reliable, comparable, and publicly available information on the viability and the financial and social performance of microfinance institutions. We extracted capital structure data, MFI-level data, and the indicator of whether the MFI is regulated or not from the MIX database. We use the World Bank World Development Indicators (WDI) website (http://data.worldbank.org/datacatalog/world-development-indicators) to gather data related to the macroeconomic environment and the level of financial development. The creditor rights index comes from the Doing Business website (http://www.doingbusiness.org/custom-query). We obtain the corruption index from Kaufmann, Kraay, and Mastruzzi (2010). The index is available on the World Bank Word Governance Indicators (WGI) website (http://info.worldbank.org/governance/wgi/index.asp). The MIX platform discloses information of about 19006 institutions classified into five categories according to the degree of reliability of information. The MIX data are self-reported. For this reason, MIX data seem to be less reliable compared to data collected and verified by a third party, such as a rating agency (Hudon & Traca, 2011; Mersland, Randøy, & Strøm, 2011; Mersland & Strøm, 2009). To address the issue of data reliability, we focused on MFIs with four and five diamond7 disclosure ratings on the MIX. Financial statements of these MFIs are certified by the auditors and for some of them, by the Big Four firms. Financial statements of level four firms are reviewed by audit firms, and level five firms also are rated by rating agencies. Focusing on MFIs with reliable data from the perspective of the MIX enabled us to build an initial sample consisting of 6526 firm–year observations between

Hypothesis 2a. Leverage is more important in common-law countries. Hypothesis 2b. Subsidies are not associated with a country's legal tradition. 2.2.3. Financial sector development and MFI capital structure Studies in non-financial firms document that the development of the banking sector does matter in firms' financing choices (Antoniou et al., 2008; Beck, Demirgüç-Kunt, & Maksimovic, 2008; Booth et al., 2001; Demirgüç-Kunt & Maksimovic, 1999; Giannetti, 2003). The banking literature suggests that banks overcome information asymmetries by producing information on borrowers and using it for capital allocation (Diamond, 1984). Given that the banking sector is an economy that produces and uses information to monitor the behavior of borrowers, it is expected that its development will facilitate access to external financing sources. According to Giannetti (2003), firms tend to be more leveraged in countries where the banking sector is more developed. Rajan and Zingales (1995) show that in developed economies, there is little

Hypothesis 3a. Leverage is positively associated with the size of the banking sector. Hypothesis 3b. Subsidies are negatively associated with the size of the banking sector.

6 Data collection took place in 2011. To date, the database discloses information for about 2000 MFI. 7 MIX uses a diamond scale ranging from one to five to evaluate the transparency of information given by organizations. The highest diamond level indicates that the organization has supplied audited financial statements and adjusted variables.

Please cite this article as: Tchakoute Tchuigoua, H., Institutional framework and capital structure of microfinance institutions, Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.01.008

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Table 1 Description of variables. Identity of the variable

Definition

Leverage Borrowings or non-deposit liabilities Deposits Donated equity

Total liabilities/total assets Borrowings (non-deposit liabilities)/total assets

Legal origin Creditor rights index/ strength of legal rights Corruption index

Financial sector development

Economic growth Regulation Profitability Portfolio at risk 30 days

Asset tangibility Size

Deposits/total assets Donated equity/total assets Donated equity is the accumulated historical donations to the MFI Dummy: 1 for common-law system, 0 otherwise The strength of legal rights index measures the degree to which collateral and bankruptcy laws protect borrower and lender rights and thus facilitate lending. The index ranges from 0 to 10, with higher scores indicating that collateral and bankruptcy laws are better designed to expand access to credit. Source: http://www.doingbusiness.org/methodology/getting-credit This index measures the perception of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests. The index runs between −2.5 (weak) and +2.5 (strong). Source: Kaufmann, Kraay and Mastruzzi (2010); World Bank. Domestic credits provided by banking sector (% of GDP) It includes all credits to various sectors on a gross basis, with the exception of credit to the central government, which is net. The banking sector includes monetary authorities and deposit money banks, as well as other banking institutions where data are available (including institutions that do not accept transferable deposits but do incur such liabilities as time and saving deposits). Source: World Bank database. Domestic credits to private sector as a percentage of GDP. It refers to financial resources provided to the private sector, such as through loans, purchases of non-equity securities, trade credits and other accounts receivable that establish a claim for repayment. Source: Word bank database. Annual growth rate of the GDP per capita of a country Binary variable: 1 if the MFI is subject to prudential regulation, 0 otherwise. (Net operating income)/total assets: measures a MFIs' capacity to use its assets to generate returns. Outstanding Balance on arrears over 30 days + Total Gross Outstanding Refinanced (restructured) Portfolio) / Total Gross Portfolio Measurement of portfolio quality. It shows the part of the portfolio affected by outstanding payments, where there is a risk that they will not be repaid. The threshold is b10% given that financial guarantees in microfinance are not always sufficient. Net fixed asset/total assets Natural logarithm of total assets

1995 and 2011. We limited the data to the period from 2004 to 2009 for a total of 4576 firm–year observations. We excluded MFIs that don't have complete capital structure and other MFI-level data. After merging the MIX database and the data from World Bank statistics, we also excluded MFIs with missing country-level data. In addition, we selected MFIs displaying comprehensive data during the observed period. The implementation of these filters enabled us to get a sample of 1752 firm–year observations for 292 MFIs across 66 countries. Although the MIX database is a worldwide database that takes into account several MFI characteristics, it does not seem representative8 of the microfinance industry (Bogan, 2012; Vanroose & D'Espallier, 2013). However, it is less problematic because the MIX data cover a large part of the sector. MFIs reported in the MIX database serve a large proportion of microfinance clients (Bogan, 2012). Focusing on the MIX database may lead to a selection bias9 given that only MFIs that decide to disclose information are available on the MIX website and therefore are included in the study. 3.2. Methodology 3.2.1. Variables In this section, we define all the variables used in our analysis. Traditionally capital structure variables are measured either by the book value leverage or by the market value of leverage. Given that MFIs are mostly non-listed, the market-based capital structure measures are not available, with the exception of some such as Compartamos, which issues bonds and equities in financial markets. Accounting indicators therefore seem more appropriate to evaluate the capital structure of MFIs and MFI-level determinants. 3.2.1.1. The dependent variable. We use two main measures for the capital structure of MFIs. The capital structure variable is available or computed on the basis of information from the MIX database. The first one is leverage, measured as the proportion of total liabilities to total asset. 8 Consultative Group to Assist the Poor (CGAP) estimates that there are more than 10,000 MFIs in the world. The State of the Microcredit Summit Campaign Report identifies 3652 as of December 2010 (Consultative Group to Assist the Poor (CGAP), 2011). The report also states that 3652 MFIs stated reaching 205,314,502 clients as of December 2010. 9 We neglect the effect of the selection bias.

Given that liabilities consist of commercial borrowing10 and deposits for deposits-taken MFIs, we decompose the leverage in order to estimate the effects of institutional framework in each of its components. Following Bogan (2012), we measure the first component (borrowings) by the ratio of non-deposit liabilities divided by the book value of assets and the second component (deposits) by the ratio of deposits divided the book value of assets. The second main measure is donated equity, which is a part of the MFI equity that is subsidized and received through cash donations or grants (Hudon & Traca, 2011). The existing literature measures donated equity either as the proportion of donated equity (grants) to total assets (Bogan, 2012; Serrano-Cinca & Gutiérrez-Nieto, 2013) or by subsidy intensity measured by the ratio of donated equity divided by total equity (Hudon, 2010; Hudon & Traca, 2011). In this article, we follow Bogan (2012) and normalize donated equity as the leverage and its component by the book value of assets. We note that the measure of donated equity seems to underestimate the magnitude of subsidies received by MFIs given that donations for operating and non-operating expenses are excluded from donated equity (Consultative Group to Assist the Poor (CGAP), 2003; Hudon & Traca, 2011). 3.2.1.2. Country-level explanatory variables. We use a set of three institutional variables. The first institutional variable is the creditor rights index. Unlike previous studies (Acharya et al., 2011; Bae & Goyal, 2009; Qian & Strahan, 2007), which use the five-point-scale index built by La Porta et al. (1997) and extended by Djankov, McLiesh, and Shleifer (2007) to measure creditor rights, we follow Ahlin et al. (2011) and use a new version of this index, which ranges in value from 0 to 10 and is updated annually on the Doing Business website (http://www.doingbusiness.org/ custom-query). The choice of the creditor rights score provided by Doing Business is justified by the fact that the La Porta et al. (1997) and Djankov et al. (2007) score is available only from 1978 to 2003 and is, therefore, unavailable for the period covered in our study (2004–2009). Given that the creditor rights index is measured using a numerical ordinal 10 To date, even if it is possible to access the list of MFIs that received international funding, and those that did not, it is however difficult to get information on the amount of debt contracted in international markets. This information is not yet available from the MIX database or rating reports.

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6

H. Tchakoute Tchuigoua / Journal of Business Research xxx (2014) xxx–xxx

Table 2 Summary statistics. Panel A: Overall sample description: number of observation: 1752 Variables

Mean

Standard deviation

Minimum

Maximum

Median

Leverage Borrowings Deposits Donated equity Domestic credit provided by the banking sector Domestic credit to private sector Legal origin Strength of legal rights Corruption index Profitability Portfolio at risk Asset Tangibility Size Regulation Country economic growth

0.68 0.39 0.20 0.11 0.40 0.30 0.19 0.47 −0.58 0.05 0.05 0.04 16.53 0.63 0.04

0.21 0.26 0.27 0.17 0.23 0.14 0.39 0.20 0.33 0.04 0.04 0.03 1.70 0.48 0.03

0.19 0.00 0.00 0.00 0.08 0.10 0 0.10 −1.19 0.00 0.00 0.01 11.17 0.00 −0.02

0.92 0.80 0.78 0.60 0.91 0.63 1 0.80 0.06 0.14 0.16 0.11 21.23 1.00 0.09

0.75 0.40 0.00 0.02 0.39 0.29 0 0.40 −0.60 0.04 0.03 0.03 16.43 1.00 0.04

Panel B: Capital structure and country-level data: mean by region Region

Africa East Asia and the pacific Eastern Europe and central Asia Latin America and the Caribbean Middle East and North Africa South Asia Sample mean

Leverage

Borrowings

Deposits

Donated Equity

Domestic credit provided by banking sector

Domestic credit to private sector

Strength of legal rights

Corruption index

Country economic growth

46 35

0.69 0.73

0.25 0.29

0.37 0.36

0.15 0.05

0.25 0.38

0.18 0.26

0.50 0.38

−0.59 −0.84

0.04 0.04

60

0.63

0.48

0.09

0.15

0.33

0.32

0.58

−0.57

0.06

106

0.69

0.42

0.19

0.07

0.40

0.30

0.39

−0.51

0.03

19

0.53

0.39

0.00

0.29

0.84

0.55

0.32

−0.32

0.04

26

0.78 0.68

0.51 0.39

0.19 0.20

0.07 0.11

0.55 0.40

0.37 0.30

0.67 0.47

−0.70 −0.58

0.05 0.04

MFIs

Panel C: Capital structure and country-level data: mean by country Country

Afghanistan Albania Argentina Armenia Azerbaijan Bangladesh Benin Bolivia Bosnia and Herzegovina Brazil Bulgaria Burkina Faso Cambodia Cameroon Chile Colombia Costa Rica Dominican Republic Ecuador Egypt El Salvador

Leverage

Borrowings

Deposits

Donated equity

Domestic credit provided by banking sector

Domestic credit to private sector

Strength of legal rights

Corruption index

Country economic growth

6 18 6 24 36 30 12 54 72

0.39 0.64 0.84 0.53 0.52 0.70 0.88 0.77 0.71

0.33 0.54 0.77 0.39 0.47 0.30 0.32 0.37 0.64

0.00 0.03 0.00 0.08 0.00 0.35 0.49 0.33 0.04

0.55 0.15 0.01 0.19 0.26 0.04 0.08 0.04 0.11

0.08 0.58 0.33 0.12 0.15 0.57 0.13 0.56 0.52

0.10 0.25 0.13 0.14 0.13 0.37 0.18 0.39 0.52

0.18 0.80 0.40 0.57 0.60 0.70 0.30 0.10 0.47

−1.19 −0.65 −0.45 −0.62 −1.04 −1.10 −0.63 −0.57 −0.32

0.07 0.05 0.06 0.07 0.09 0.05 0.01 0.03 0.05

12 12 6 66 6 12 36 24 12

0.59 0.84 0.82 0.65 0.92 0.75 0.66 0.62 0.73

0.31 0.54 0.01 0.49 0.09 0.29 0.50 0.57 0.28

0.00 0.26 0.68 0.10 0.76 0.34 0.11 0.00 0.35

0.00 0.04 0.03 0.07 0.00 0.00 0.05 0.06 0.01

0.84 0.51 0.14 0.12 0.10 0.87 0.53 0.48 0.37

0.43 0.52 0.17 0.16 0.10 0.63 0.34 0.42 0.22

0.30 0.80 0.30 0.33 0.30 0.40 0.50 0.30 0.30

−0.08 −0.12 −0.27 −1.14 −1.14 0.06 −0.17 0.06 −0.63

0.03 0.06 0.02 0.07 0.01 0.03 0.03 0.03 0.05

96 24 36

0.74 0.42 0.61

0.33 0.38 0.42

0.29 0.00 0.11

0.06 0.36 0.13

0.21 0.85 0.46

0.25 0.46 0.43

0.30 0.30 0.50

−0.79 −0.59 −0.31

0.03 0.04 0.02

n

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H. Tchakoute Tchuigoua / Journal of Business Research xxx (2014) xxx–xxx

7

Table 2 (continued) Panel C: Capital structure and country-level data: mean by country Country

n

Leverage

Borrowings

Deposits

Donated equity

Domestic credit provided by banking sector

Domestic credit to private sector

Strength of legal rights

Corruption index

Country economic growth

Ethiopia Georgia Ghana Guatemala Guinea Haiti Honduras India Indonesia Jordan Kazakhstan Kenya Kosovo Kyrgyzstan Lebanon Malawi Mali Mexico Moldova Mongolia Morocco Mozambique Nepal Nicaragua Nigeria Pakistan Paraguay Peru Philippines Poland Romania Russia Rwanda Senegal Serbia South Africa Swaziland Tajikistan Tanzania Togo Tunisia Uganda Ukraine Venezuela Vietnam Sample mean

60 42 24 42 6 12 30 72 6 24 18 18 30 12 12 6 12 24 6 6 48 18 18 84 12 30 24 126 132 12 6 18 6 18 24 6 6 12 24 12 6 24 12 6 6

0.57 0.58 0.58 0.52 0.53 0.82 0.60 0.86 0.80 0.37 0.58 0.78 0.65 0.65 0.48 0.85 0.78 0.73 0.70 0.88 0.69 0.58 0.87 0.69 0.78 0.69 0.78 0.69 0.77 0.46 0.76 0.65 0.35 0.64 0.52 0.66 0.57 0.75 0.80 0.89 0.49 0.78 0.63 0.87 0.53 0.68

0.30 0.45 0.23 0.43 0.12 0.40 0.45 0.63 0.47 0.30 0.53 0.19 0.42 0.53 0.06 0.51 0.17 0.35 0.65 0.39 0.51 0.21 0.45 0.61 0.25 0.51 0.18 0.39 0.19 0.32 0.69 0.33 0.06 0.09 0.28 0.59 0.42 0.47 0.30 0.15 0.42 0.28 0.37 0.41 0.21 0.39

0.22 0.07 0.27 0.04 0.42 0.00 0.09 0.14 0.05 0.00 0.00 0.56 0.13 0.00 0.00 0.02 0.59 0.31 0.00 0.47 0.00 0.25 0.38 0.02 0.51 0.11 0.54 0.23 0.51 0.00 0.00 0.26 0.20 0.49 0.15 0.00 0.08 0.20 0.35 0.66 0.00 0.41 0.21 0.39 0.27 0.20

0.28 0.25 0.24 0.15 0.31 0.00 0.09 0.02 0.02 0.32 0.18 0.00 0.17 0.10 0.38 0.38 0.06 0.01 0.16 0.01 0.22 0.15 0.01 0.09 0.12 0.15 0.00 0.08 0.03 0.00 0.00 0.12 0.29 0.05 0.17 0.14 0.00 0.06 0.18 0.03 0.27 0.05 0.16 0.00 0.31 0.11

0.41 0.27 0.27 0.36 0.11 0.28 0.46 0.63 0.42 0.91 0.38 0.39 0.10 0.12 0.91 0.20 0.14 0.36 0.37 0.27 0.82 0.12 0.52 0.75 0.19 0.48 0.22 0.17 0.49 0.47 0.33 0.25 0.08 0.24 0.33 0.91 0.12 0.22 0.13 0.21 0.65 0.10 0.57 0.18 0.80 0.40

0.20 0.23 0.14 0.27 0.10 0.14 0.47 0.43 0.26 0.63 0.45 0.28 0.25 0.12 0.63 0.11 0.18 0.20 0.30 0.34 0.54 0.15 0.39 0.33 0.23 0.28 0.22 0.21 0.30 0.39 0.32 0.35 0.11 0.23 0.34 0.63 0.22 0.23 0.13 0.18 0.59 0.11 0.48 0.18 0.62 0.30

0.40 0.53 0.65 0.45 0.30 0.22 0.60 0.72 0.30 0.40 0.40 0.80 0.67 0.68 0.30 0.70 0.30 0.50 0.80 0.60 0.30 0.20 0.70 0.30 0.80 0.60 0.30 0.57 0.40 0.80 0.80 0.30 0.30 0.30 0.77 0.80 0.60 0.30 0.80 0.30 0.30 0.70 0.75 0.10 0.70 0.47

−0.68 −0.29 −0.08 −0.62 −1.05 −1.18 −0.78 −0.40 −0.75 0.06 −0.95 −0.94 −0.60 −1.15 −0.76 −0.57 −0.46 −0.27 −0.67 −0.60 −0.29 −0.52 −0.70 −0.65 −1.03 −0.92 −1.10 −0.28 −0.71 0.06 −0.20 −0.92 −0.21 −0.36 −0.34 0.06 −0.33 −1.05 −0.44 −0.95 −0.08 −0.81 −0.80 −1.02 −0.66 −0.58

0.08 0.06 0.04 0.01 0.01 0.01 0.03 0.07 0.04 0.05 0.06 0.02 0.04 0.04 0.05 0.03 0.01 0.01 0.05 0.06 0.04 0.05 0.02 0.03 0.04 0.03 0.02 0.05 0.03 0.05 0.06 0.06 0.05 0.01 0.05 0.03 0.02 0.07 0.04 0.00 0.04 0.05 0.05 0.06 0.06 0.04

scale, and following some recent studies in banking (Poon & Firth, 2005) and in microfinance (Beisland & Mersland, 2012), we carried out a transformation of these variables into a ratio scale. Creditor rights thus take a value between 0 and 1; the higher the ratio value, the better the score. The second institutional variable is the development level of the financial sector. Given that the domestic credit market seems to be a funding source for MFIs, the development level of the country's financial sector may facilitate access to external financing sources. We use two measures of the financial sector development that are taken from the World Development indicator provided by the World Bank and available on the World Bank website (http://data.worldbank.org/datacatalog/world-development-indicators). The first measure is the domestic credit provided by banking sector as a percentage of the GDP used in a recent study in microfinance (Vanroose & D'Espallier, 2013). The second measure used in previous studies (Beck, Demirgüç-Kunt, & Maksimovic, 2006; Qian & Strahan, 2007) is the domestic credit to private sector as a percentage of the GDP. The third variable is the country legal tradition, which we measure by a dummy variable that takes the value 1 if the

country adopts the common-law system. This variable comes from La Porta et al. (1997) and Djankov et al. (2007). 3.2.1.3. MFI-level variables. To control for MFI-specific characteristics a set of four variables is used. These variables come from MIX database. The size is measured by the logarithm of total assets and the risk by the portfolio at risk at 30 days. Asset tangibility is measured by the ratio of net fixed assets to total assets. Profitability is measured by the economic rate of return. 3.2.1.4. Control variables. Similar to other financial institutions, some MFIs, specifically deposits-taken ones, are subject to prudential regulation. It seems necessary to introduce prudential regulation as a control variable in order to account for the regulated nature of MFI activity. Regulation is measured by a dummy. The regulation variable (REG) takes the value 1 if the MFI is subject to prudential regulation and 0 otherwise. This variable is also available on the MIX database.

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H. Tchakoute Tchuigoua / Journal of Business Research xxx (2014) xxx–xxx

1 0.32 *** −0.13 *** 1 −0.16 *** −0.001 −0.10 ***

1 0.18 ***

1

Control variables also include country-level macroeconomic indicators, the annual growth of the GDP per capita. This variable was collected from the World Bank website (http://data.worldbank.org/data-catalog/ world-development-indicators). We also include the corruption index, which measures the country's level of corruption. This variable come from the world governance indicators provided by the World Bank and are available World Bank website (http://info.worldbank.org/ governance/wgi/sc_country.asp). Table 1 summarizes all variables used in the study. 3.2.2. Econometric model To answer the question of whether institutions affect the funding structure of MFIs, we use the panel data approach, controlling for MFIlevel variables, regulation, and macroeconomic environment. There were two main reasons we chose the method of panel data for the technical estimation. The first relates to the data structure, which consists of repeated observations of a cross-section of 292 MFIs. We use the same time periods of six years (2004–2009) for each cross-section. The second relates to the fact that panel data account for the presence of the unobserved MFI fixed effects, which we assume to be correlated with MFIlevel variables. We test the following specification, which includes year and MFIs fixed to account for unobserved heterogeneity at the MFI level:

1 0.02 0.01 0.003 0.03 −0.13 *** −0.02

0

***, ** and * indicate that the coefficient estimates are significantly different from zero at the 1%, 5%, and 10% levels.

1 0.03 −0.09 *** 0.35 *** −0.05 *** −0.02 −0.13 *** 0.15 *** −0.19 *** −0.16 *** 1 0.81 *** −0.08 *** 0.04 0.29 *** 0.001 0.01 −0.18 *** 0.06 *** −0.29 *** −0.16 *** 1 0.01 −0.04 * −0.03 0.03 0.02 0.28 *** −0.12 *** 0.03 −0.42 *** −0.15 *** 0.20 *** 1 −0.34 *** −0.19 *** −0.19 *** −0.04 * 0.09 *** −0.15 *** −0.32 *** 0.25 *** 0.09 *** 0.34 *** 0.35 *** −0.14 *** 1 −0.59 *** −0.20 *** 0.18 *** 0.23 *** 0.13 *** 0.001 0.10 *** 0.005 −0.17 *** −0.12 *** 0.04 −0.11 *** 0.04 1 0.31 *** 0.46 *** −0.63 *** 0.002 0.03 0.07 0.12 *** −0.07 *** −0.39 *** 0.11 *** −0.05 ** 0.47 *** 0.27 *** −0.16 ***

0

yit ¼ Constant þ βXjt þ γ MVit þ λ CVjt þ δt þ ci þ eit

1 0.44 *** 0.14 *** −0.04 0.002 −0.07 *** 0.11 *** 0.14 *** 0.07 ***

1.00 −0.08 *** −0.06 *** −0.001 0.01 −0.02 0.10 *** 0.16 ***

1 −0.16 *** −0.02 −0.20 *** −0.20 *** 0.13 ***

1 0.16 *** −0.07 *** −0.07 *** −0.35 ***

13 9

1. Leverage 2. Borrowings 3. Deposits 4. Donated equity 5. Domestic credit provided by banking sector 6. Domestic credit to private sector 7. Strength of legal rights 8. Legal Origin 9. Corruption index 10. Profitability 11. Portfolio at risk 12. Asset tangibility 13. Size 14. Regulation 15. Country Economic Growth

Table 3 Pearson correlation matrix.

1

2

3

4

5

6

7

8

10

11

12

14

15

8

ð1Þ

where i indexes MFIs, j indexes country, and t indexes year. yit is the vector of MFI capital structure variables for MFI i at a year t (leverage, borrowings, deposits, donated equity); Xjt is the vector of institutional variables for a country j at year t (strength of legal rights, financial sector development, corruption index, legal origin); MVit is the vector of MFIspecific variables (profitability, portfolio at risk, asset tangibility, size); CVjt is the vector of control variables that comprise the country rate of economics and the regulatory framework; and ci is the MFI's individual unobserved effects that capture the managers' ability and capabilities, which according to Degryse, De Goeij, and Kappert (2012) and Berger and Udell (2006) are crucial for firm financing. We assume this unobserved individual effect to be correlated with MFI-level variables (profitability, portfolio at risk, asset tangibility, size); δt is the year fixed effects; eit is the idiosyncratic error. We begin by estimating a pooled ordinary least squares (OLS) regression. The estimators provided by these regressions may be biased and inconsistent given that we assume the individual fixed effects are uncorrelated with the MFI-level explanatory variables. The model contains a time-invariant variable (regulation), which makes it difficult to estimate the fixed effects models. The fixed effects methods drop the time-invariant variable and seem not to accommodate time-invariant variables. We also estimate a random effects model that, compared to the fixed effect model, is better suited to tackle time-invariant explanatory variables. Compared to the pooled OLS, it assumes that the unobserved effects is uncorrelated with MFI-level variables but exploits the serial correlation in the composite error in a generalized least squared (GLS) framework (Wooldridge, 2010). Finally, we reestimate models by implementing the Hausman and Taylor (1981) estimator for error-components models in which MFIspecific variables are correlated with the unobserved effects ci. The Hausman–Taylor estimator also accounts for the presence of timeinvariant variables. The rewritten model is thus as follows: 0

0

yit ¼ Constant þ β X 1it þ β X2it þ γZi þ δt þ ci þ eit

ð2Þ

where ci is MFI individual unobserved effects; δt are year dummy variables; X1it is the vector of exogenous time-varying variables that are assumed to be uncorrelated with ci (creditors right, financial development, index of corruption, the country rate of economic growth, the regulatory

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H. Tchakoute Tchuigoua / Journal of Business Research xxx (2014) xxx–xxx

framework); X2it is the vector of time-varying variables that are assumed to be correlated with ci (profitability, portfolio at risk, asset tangibility, size); and Z1i is the vector of exogenous time invariants that are assumed to be uncorrelated with ci (the regulatory framework). 4. Results 4.1. Summary statistics We winsorized 5% of observations in order to limit the impact of outliers. Descriptive statistics and regressions reported are thus carried out from winsorized observations. Panel A of Table 2 provides summary statistics of the sample. The mean value of the leverage is 0.68. Donated equity represents an average of 11% of total assets. The average deposit ratio for the whole sample is 0.20. Non-deposit liabilities represent 39% of MFI assets. The creditor rights average index is 0.47. On average, MFIs operate in countries with lower creditor protection rights. Only 19% of the MFIs in our sample come from common-law countries. A majority of MFIs (63%) offer financial services in an environment where the microfinance activity is subject to prudential regulation. The average portfolio at risk is 0.05, below the 0.1 threshold (Bruett, 2005). We can conclude that the average loan portfolio in our sample is healthy. MFIs are profitable. The average value of profitability is 0.05. Descriptive evidence regarding MFI-level explanatory variables seems to be close to those of studies that use data provided by rating agencies. For example, Beisland and Mersland (2012) report an average portfolio at risk of 0.05 and a profitability ratio (ROA) of 0.03. Panel B of Table 2 provides information about the distribution of MFIs across the six main regions and the mean value of our main variables in each of these regions. The majority of MFIs comes from Latin America (106 MFIs). Deposits are the main source of funding in subSaharan Africa, whereas donated equity is predominant in the Middle East and North African regions. Borrowing is the main funding source in Latin America and in Eastern Europe and Central Asia. This might be explained by the fact that those two regions attract the majority of microfinance investments (Consultative Group to Assist the Poor (CGAP), 2011). Panel C reports the average value of capital structure and country-level variables by country. Prior to estimations, we assess the presence of multi-collinearity among explanatory variables (Table 3). Our diagnostics reveals that domestic credit provided by banking sector and domestic credit to private sector (0.81) are significantly and strongly correlated. The evaluation of the multi-collinearity tells us that all of the explanatory variables feature a variance inflation factor (VIF) below 10. Variables' domestic credit provided by banking sector and domestic credit to private sector have been separately included in the models in order to avoid the fact that the strong correlation disrupts estimation. Domestic credit to private sector has thus been initially excluded. The recalculated VIF is less than 10. The average is 1.27. The minimum and maximum are respectively 1.12 and 1.38. To check the robustness of the results and to assess the sensitivity of such results to a change in the variable measurement, domestic credit provided by banking sector has been replaced by domestic credit to private sector. The correlation between creditor right and common-law country is positive and significant, supporting La Porta et al. (1997), who argue that common-law countries provide better protection to investors and creditors.

Table 4 Results of the pooled ordinary least squared. Variables

Leverage

Borrowings

Deposits

Donated equity

_cons

−0.03 (−0.25) −0.01 (−0.20) −0.06 (−0.95) 0.08* (1.70) −0.03 (−1) −1.37*** (−6.79) 0.37 (1.59) −0.21 (−0.64) 0.05*** (6.40) 0.05** (2.04) −0.87** (−2.03) Yes 1752 0.35 27.51***

0.33** (2.07) 0.16* (1.69) 0.18*** (3.04) −0.05 (−0.93) 0.02 (0.35) −0.24 (−0.91) −0.96*** (−3.52) −0.38 (−0.82) −0.001 (−0.35) −0.06 (−1.35) 0.62 (1.54) Yes 1752 0.12 10.79***

−0.50*** (−2.97) −0.18* (−1.91) −0.20*** (−3.16) 0.11** (2.21) −0.05 (−0.89) −1.16*** (−5.08) 1.40*** (4.22) 0.38 (0.94) 0.05*** (4.37) 0.12** (2.38) −1.21** (−2.41) Yes 1752 0.37 28.26***

0.64*** (6.49) 0.04 (0.70) 0.03 (0.61) −0.001 (−0.04) 0.001 (0.15) 0.76*** (3.90) −0.33 (−1.44) 0.09 (0.39) −0.04*** (−5.81) −0.01 (−0.44) 0.72* (1.94) Yes 1752 0.26 10.33***

Domestic credit provided by banking sector Strength of legal right Legal origin Corruption index Profitability Portfolio at Risk Asset tangibility Size Regulation Country economic growth Year fixed effects Number of observations R2 F_stat

***, **, * indicate that the coefficient estimates are significantly different from zero at the 1%, 5%, and 10% levels.

MFI characteristics. The following discussions are based on the Hausman–Taylor estimation results given that it accommodates timeinvariant variables and accounts for heterogeneity. In the total debt model (Table 6), findings show that the strength of legal rights is not significantly correlated with leverage. We cannot therefore support Hypothesis 1a. The country legal origin and the level of the banking sector development signs are in the expected direction.

Table 5 Results of the random effects estimation. Variables

Leverage

Borrowings

Deposits

Donated equity

_cons

−0.20 (−1.21) −0.003 (−0.01) 0.05 (1.01) 0.07 (1.58) 0.01 (0.43) −0.56*** (−3.97) 0.24* (1.94) −0.14 (−0.63) 0.05*** (4.68) 0.06** (2.21) −0.59*** (−3.07) Yes 1752 0.31 187.91***

−0.19 (−1.05) 0.08** (2.34) 0.14** (2.24) −0.002 (−0.06) 0.03 (0.65) −0.40*** (−2.73) −0.12 (−0.86) −0.20 (−0.93) 0.03*** (3.03) −0.08* (−1.94) −0.41* (−1.95) Yes 1752 0.05 279.92***

0.10 (0.75) −0.03* (−1.74) 0.001 (0.01) 0.05 (0.77) 0.01 (0.50) −0.04 (−0.68) 0.16** (2.02) 0.28 (1.63) −0.00 (−0.25) 0.19*** (4.48) 0.08 (0.81) Yes 1752 0.14 88.71***

0.79*** (6.90) 0.07*** (2.72) −0.01 (−0.19) −0.01 (−0.34) −0.05 (−1.54) 0.27** (2.57) −0.03 (−0.21) −0.08 (−0.43) −0.04*** (−5.83) −0.02 (−0.62) 0.38* (1.64) Yes 1752 0.22 99.82***

Strength of legal rights Domestic credit provided by banking sector Legal origin Corruption index Profitability Portfolio at risk Asset tangibility Size Regulation

4.2. Multivariate results Table 4 presents the result of the pooled OLS with control for year fixed effects. Table 5 reports the results of the random effect estimation also with control for year fixed effects. In both the pooled OLS and the random effects model, standards errors are clustered at the country level in order to account for heteroskedasticity. Table 6 reports the result of the Hausman–Taylor estimation. All models are controlled for

9

Country rate of economic growth Year fixed effects N R2-overall Wald Chi2

***, **, * indicate that the coefficient estimates are significantly different from zero at the 1%, 5%, and 10% levels.

Please cite this article as: Tchakoute Tchuigoua, H., Institutional framework and capital structure of microfinance institutions, Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.01.008

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H. Tchakoute Tchuigoua / Journal of Business Research xxx (2014) xxx–xxx

Table 6 Results of the Hausman–Taylor estimation. Variables

Leverage

Borrowings

Deposits

Donated Equity

_cons

−0.35*** (−3.01)

−0.82*** (−5.87)

0.36*** (4.76)

1.10*** (10.18)

−0.001 (−0.01) 0.06* (1.86) 0.02 (0.90) −0.59*** (−4.30) Yes

0.05* (1.65) 0.10** (2.50) 0.03 (1.12) −0.43*** (−2.63) Yes

−0.02 (−1.11) 0.03 (1.12) 0.02 (1.31) 0.10 (1.19) Yes

0.09*** (3.62) −0.02 (−0.62) −0.06*** (−3.41) 0.35*** (2.75) Yes

−0.40*** (−4.20) 0.21** (2.47) −0.09 (−0.58) 0.06*** (7.93)

−0.42*** (−3.70) −0.01 (−0.12) −0.05 (−0.29) 0.08*** (8.64)

0.00 (0.04) 0.11** (2.17) 0.22** (2.25) −0.02*** (−4.30)

0.13 (1.46) 0.04 (0.56) −0.20 (−1.33) −0.06*** (−9.10)

0.06 ** (2.56) 0.07 *** (2.67) 1752 576.29 ***

−0.13 *** (−3.89) 0.01 (0.35) 1752 437.38 ***

0.22 *** (6.70) 0.04 (1.04) 1752 91.26 ***

0.001 (0.16) −0.02 (−0.90) 1752 525.73 ***

Exogenous time varying Strength of legal right Domestic credit provided by banking sector Corruption index Country economic growth Year fixed effects Endogenous time varying Profitability Portfolio at risk Asset Tangibility Size

Exogenous time invariant Regulation Legal origin Number of observations Wald Chi2

***, **, * indicate that the coefficient estimates are significantly different from zero at the 1%, 5%, and 10% levels.

MFIs in common-law countries are more leveraged. At this stage our results seem to support Hypothesis 2a. Results provide weak evidence about the relationship between the level of the banking sector's development and the leverage. This relationship is positive and significant only at 10% level. This evidence does not strongly support Hypothesis 3a. After splitting leverage into deposits and borrowing (non-deposit liabilities or external debt), findings suggest that institutional environment variables never drive deposits. Institutional framework has a very limited effect on the amount of deposits that MFIs can raise. Moreover, coefficients of the sign of institutional variables are in the expected direction in the borrowing (external debt, non-deposits liabilities) model. Consistent with previous studies (Bae & Goyal, 2009; Giannetti, 2003; González & González, 2008; Qian & Strahan, 2007), we find a positive and significant relationship between the strength of legal rights and external debt. However, the relationship is significant only at the 10% level, indicating that creditor rights have a limited impact on the ability of MFIs to raise external debt. This evidence seems to support Hypothesis 1a. Consistent with Hypothesis 3a, the level of financial development has a significant positive impact on borrowing. The development of the bank seems to complement the microfinance sector given that a highly developed banking sector is significantly associated with external debt. This result is consistent with the conclusions of the law and finance literature (Antoniou et al., 2008; Demirgüç-Kunt & Maksimovic, 1999; Fan et al., 2012; Giannetti, 2003) and those regarding the banking sector (Brewer, Kaufman, & Wall, 2008). As for the donated equity model, the sign of the coefficients are in the expected direction. Consistent with Hypothesis 1b, we find that donated equity is positively and significantly correlated with the strength of legal rights. MFIs in countries with better creditor protection rules seem to attract more grants and subsidies. Consistent with Hypothesis 2b, the country legal origin is not associated with donated equity. Evidence does not support Hypothesis 3b, although the sign of the coefficient is negative. We cannot claim that in countries where the banking sector is well developed, for example,

countries that offer opportunities for MFIs to diversify their source of external financing, the amount of subsidy significantly decreases. Moreover, MFIs in countries with a specific microfinance regulation framework have higher debt ratio. Being regulated enables MFIs to access additional funding sources and thus to diversify their financing choices. The relationship between regulation and borrowings is negative and significant although being regulated has significant positive effect on deposits. Being regulated helps MFIs access additional funding. Their regulated status helps them attract more deposits than borrowings. 4.3. Robustness checks To gauge the robustness of the results, we assessed the sensitivity of results to a change in the measurement of variables. We thus replaced domestic credit by private credit, leaving other variables unchanged given that private credit and domestic credit provided by the banking sector are highly correlated. Except for the coefficient of the strength of legal rights that loses its significance in the borrowing regression, results reported in Table 7 yield consistent evidence and improve the reliability of the results. We then test for the predictive validity of the Hausman–Taylor regression. To assess the external validity of the results, we randomly split the initial sample into two sub-groups representing respectively 40% and 60% of the initial sample. Findings in this holdout sample validation, reported in Table 8, strengthen the results obtained from the whole sample and indicate that the model seems to be able to better predict the effect of institutional variables on the financial structure of MFIs. 5. Conclusion The main objective of this article is to answer the question of whether institutional frameworks have an effect on the capital structure of

Table 7 Replacing the domestic credit provided by the banking sector by the Domestic credit to private sector in the Hausman–Taylor model. Variable

Leverage

Borrowings

Deposits

Donated equity

_cons

−0.33*** (−2.84)

−0.79*** (−5.64)

0.37*** (4.86)

1.11*** (10.18)

−0.001 (−0.14) 0.13*** (2.83) 0.02 (0.90) −0.58*** (−4.27) Yes

0.05 (1.50) 0.22*** (3.73) 0.03 (1.13) −0.41** (−2.56) Yes

−0.02 (−1.16) 0.05 (1.56) 0.02 (1.37) 0.10 (1.22) Yes

0.09*** (3.64) 0.001 (0.03) −0.06*** (−3.40) 0.36*** (2.82) Yes

−0.39*** (−4.12) 0.20** (2.31) −0.11 (−0.70) 0.06*** (7.64)

−0.41*** (−3.62) −0.04 (−0.40) −0.07 (−0.37) 0.07*** (8.26)

0.01 (0.09) 0.10** (2.05) 0.21** (2.17) −0.02*** (−4.42)

0.13 (1.45) 0.04 (0.50) −0.18 (−1.23) −0.07*** (−9.17)

0.06*** (2.62) 0.07*** (2.88) 1752 583.76***

−0.13*** (−3.98) 0.02 (0.61) 1752 446.95***

0.22*** (6.69) 0.04 (1.10) 1752 92.05***

0.01 (0.36) −0.02 (−0.97) 1752 521.69***

Exogenous time varying Strength of legal right Domestic credit to private sector Corruption index Country economic growth Year fixed effects Endogenous time varying Profitability Portfolio at risk Asset Tangibility Size

Exogenous time invariant Regulation Legal origin Number of observations Wald Chi2

***, **, * indicate that the coefficient estimates are significantly different from zero at the 1%, 5%, and 10% levels.

Please cite this article as: Tchakoute Tchuigoua, H., Institutional framework and capital structure of microfinance institutions, Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.01.008

H. Tchakoute Tchuigoua / Journal of Business Research xxx (2014) xxx–xxx

MFIs. Our study concentrates on three institutional variables: the strength of legal rights, the country's legal tradition, and the development of the financial sector. We tested this hypothesis over a sample of 292 MFIs with data drawn between 2004 and 2009. Our results offer supportive evidence that the institutional environment has an impact on the capital structure of MFIs. The institutional environment never drives deposit financing. Our results are consistent with the results obtained in the non-financial sector. Indeed, we find

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that the strength of legal rights is a driver of MFI external debt and donated equity. The banking sector seems to complement the microfinance sector given that MFIs in countries with developed banking sectors are more leveraged. However, the size of the banking sector has a negative non-significant effect on donated equity. There is no strong evidence that in a well-developed banking sector, MFIs substitute external debt to equity. Finally, leverage appears to be more important in common-law countries.

Table 8 Split sample validation. We draw two random samples of MFIs. Hausman–Taylor regression for the first random sample which represent 40% of the initial sample. Variables Leverage _cons −0.20 (−0.84) Exogenous time varying Strength of Legal right Domestic credit provided by banking sector Corruption index Country economic growth Year fixed effects Endogenous time varying Profitability Portfolio at risk Asset tangibility Size

Exogenous time invariant Regulation Legal origin Number of observations Wald Chi2

Borrowing −0.56* (−1.95)

Deposits 0.32** (2.23)

Donated equity 1.03*** (5.16)

0.06 (1.44) 0.03 (0.66) −0.04 (−1.23) −0.75*** (−2.90) Yes

0.14*** (2.82) 0.16** (2.37) −0.01 (−0.26) 0.03 (0.08) Yes

−0.01 (−0.34) −0.05 (−1.29) −0.001 (−0.19) −0.09 (−0.56) Yes

0.09*** (2.61) 0.07 (1.63) −0.07*** (−2.58) 0.55** (2.46) Yes

−0.42** (−2.41) 0.05 (0.28) 0.03 (0.11) 0.05*** (3.06)

−0.64*** (−3.04) −0.05 (−0.26) 0.09 (0.26) 0.05*** (2.89)

0.14 (1.40) −0.09 (−0.90) 0.24 (1.52) −0.02* (−1.68)

0.28* (1.90) −0.02 (−0.15) −0.15 (−0.64) −0.06*** (−4.85)

0.07** (2.20) 0.06* (1.94) 699 210.90***

−0.12*** (−2.69) −0.05 (−1.11) 699 135.57***

0.21*** (5.53) 0.08* (1.79) 699 61.66***

0.02 (0.65) −0.02 (−0.81) 699 200.78***

−0.89*** (−5.07)

0.35*** (3.61)

1.08*** (7.81)

−0.03 (−0.73) 0.06 (1.57) 0.01 (0.37) −0.57*** (−3.20) Yes

0.02 (0.46) 0.11** (2.06) 0.06* (1.82) −0.47** (−2.25) Yes

−0.02 (−0.81) 0.02 (0.76) 0.01 (0.42) 0.12 (1.10) Yes

0.10*** (3.05) −0.03 (−0.74) −0.05** (−2.11) 0.41** (2.52) Yes

−0.40*** (−3.01) 0.26** (2.33) −0.18 (−0.79) 0.06*** (6.58)

−0.19 (−1.23) −0.02 (−0.16) 0.11 (0.42) 0.08*** (7.32)

−0.05 (−0.58) 0.22*** (3.15) 0.12 (0.86) −0.02*** (−3.35)

−0.15 (−1.20) 0.03 (0.32) −0.15 (−0.76) −0.06*** (−6.75)

0.06** (2.09) 0.07** (2.51) 1053 339.60***

−0.14*** (−3.55) 0.04 (0.76) 1053 281.08***

0.23*** (6.34) 0.03 (0.62) 1053 67.51***

−0.01 (−0.26) −0.02 (−0.91) 1053 305.28***

Hausman–Taylor regression for the first random sample which represent 60% of the initial sample. _cons −0.41*** (−2.76) Exogenous time varying Strength of Legal right Domestic credit provided by banking sector Corruption index Country economic growth Year fixed effects Endogenous time varying Profitability Portfolio at risk Asset tangibility Size

Exogenous time invariant Regulation Legal origin Number of observations Wald Chi2

***, **, * indicate that the coefficient estimates are significantly different from zero at the 1%, 5%, and 10% levels.

Please cite this article as: Tchakoute Tchuigoua, H., Institutional framework and capital structure of microfinance institutions, Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.01.008

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H. Tchakoute Tchuigoua / Journal of Business Research xxx (2014) xxx–xxx

Studies on the international funding of microfinance show that debt is the main funding instrument through which MIVs invest in microfinance. These investments primarily benefit MFIs of Latin America and Caribbean as well as those of Europe and Central Asia (Consultative Group to Assist the Poor (CGAP), 2011; Galema et al., 2011; MicroRate, 2011). The attractiveness of these regions is partly explained by the fact that the commercialization of microfinance is more pronounced and therefore results in the high quality of institutions in these two regions. Investigating the question of whether the quality of institutions drives the flow of microfinance investments by MIVs in those regions is an avenue for future research. Another possible research avenue is to examine the structure of the debt of MFIs and explore the determinants of the maturity of commercial debts in MFIs. We hope that data will be available in the future so that this study could be extended in these directions. Finally, the buffer view of the capital structure of financial institutions supposes that meeting minimum capital requirements or capital adequacy requirements determines the funding strategies of financial institutions. It thus seems important to extend this study by examining how MFI capital buffers vary according to institutional features. Acknowledgment The author thanks C. Faugere for his helpful and valuable comments, C. Micolino for her proofreading as well as the two anonymous referees whose comments and suggestions have undoubtedly helped improve the quality and precision of the article. References Acharya, V., Amihud, Y., & Litov, L. (2011). Creditor rights and corporate risk-taking. Journal of Financial Economics, 102, 150–166. Aghion, P., & Bolton, P. (1992). An incomplete contracts approach to financial contracting. Review of Economic Studies, 59, 473–494. Ahlin, C., Lin, J., & Maio, M. (2011). Where does microfinance flourish? Microfinance institutions performance in macroeconomic context. Journal of Development Economics, 95, 105–120. Antoniou, A., Guney, Y., & Paudyal, K. (2008). The determinants of capital structure: Capital market-oriented versus bank-oriented institutions. Journal of Financial and Quantitative Analysis, 43, 59–92. Armendáriz de Aghion, B., & Morduch, J. (2004). Microfinance: Where do we stand? In C. A. E. Goodhart (Ed.), Financial development and economic growth (pp. 135–148). Basingstoke, UK: Palgrave Macmillan. Armendáriz de Aghion, B., & Morduch, J. (2010). The economics of microfinance (2nd ed.). Cambridge, MA: MIT Press. Bae, K. H., & Goyal, V. K. (2009). Creditor rights, enforcement, and bank loans. Journal of Finance, 64, 823–860. Battilana, J., & Dorado, S. (2010). Building sustainable hybrid organizations: The case of commercial microfinance organizations. Academy of Management Journal, 53(6), 1419–1440. Becchetti, L., & Castriota, S. (2011). Does microfinance work as a recovery tool after disasters? Evidence from the 2004 tsunami. World Development, 39(6), 898–912. Beck, T., Demirgüç-Kunt, A., & Maksimovic, V. (2006). The influence of financial and legal institutions on firm size. Journal of Banking and Finance, 30, 2995–3015. Beck, T., Demirgüç-Kunt, A., & Maksimovic, V. (2008). Financing patterns around the world: Are small firms different? Journal of Financial Economics, 89, 467–487. Beisland, L. A., & Mersland, R. (2012). Do microfinance rating assessments make sense? An analysis of the drivers of the MFI ratings. Nonprofit & Voluntary Sector Quarterly, 41, 213–231. Berger, A. N., & Udell, G. F. (2006). A more conceptual framework for SME finance. Journal of Banking and Finance, 30(11), 2945–2966. Bogan, V. (2012). Capital structure and sustainability: An empirical study of microfinance institutions. Review of Economics and Statistics, 94(4), 1045–1058. Booth, L., Aivazian, V., Demirgüç-Kunt, A., & Maksimovic, V. (2001). Capital structures in developing countries. Journal of Finance, 56, 87–130. Brewer, E., Kaufman, G. G., & Wall, L. D. (2008). Bank capital ratios across countries: Why do they vary? Journal of Financial Service Research, 34, 177–201. Brockman, P., & Unlu, E. (2009). Dividend policy, creditor rights, and the agency costs of debt. Journal of Financial Economics, 92, 276–299. Bruett, T. (2005). Measuring performance of microfinance institutions: A framework for reporting analysis, and monitoring. SEEP Network, USAID. Byrne, J., & O'Connor, T. (2012). Creditor rights and the outcome model of dividends. Quarterly Review of Economics and Finance, 52(2), 227–242. Consultative Group to Assist the Poor (CGAP) (2003). Microfinance consensus guidelines Definitions of selected financial terms, ratios, and adjustments for microfinance (3rd ed.). The World Bank. Consultative Group to Assist the Poor (CGAP) (2010). Apexes: An important source of local funding. CGAP Brief.

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Please cite this article as: Tchakoute Tchuigoua, H., Institutional framework and capital structure of microfinance institutions, Journal of Business Research (2014), http://dx.doi.org/10.1016/j.jbusres.2014.01.008