Explanatory variables

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5.8.2.1.1. Female directors (D_Female directors)

The key variable of interest in this study is the female directors on boards. Many previous studies have used percentage of female directors on board as a measure of board gender diversity (Adams and Ferreira, 2009; Ahern and Dittmar, 2012). Other studies employ number of female directors or a dummy variable to weigh the influence of women directors based on the critical mass idea (Konrad et al, 2008; Simpson et al, 2010; Strom et al., 2014; Liu et al., 2014). We follow Strom et al., (2014) four different definitions to measure female director’s influence on corporate board. A dummy variable for the presence of one or more female directors, a percentage of female directors of all directors, number of female directors and a dummy to measure three or more female directors on a corporate board.

5.8.2.1.2. Board size (Board size)

The second explanatory variable is the board size, which is measured in log specification in most studies (Yermack, 1996; Wen et al., 2002; Mersland and Strom, 2009; Liu et al., 2014;). We follow Wen et al (2002) and Liu et al., (2014) board size measurement criteria that is, the natural logarithm of the number of board of directors in our regression.

5.8.2.1.3. Financial performance (ROA)

Following recent literatures on MFIs performances, (Hermes at al., 2011; Quayes, 2012; Bogan, 2012; Strom et al., 2014). We group our performance variables into two categories, namely, financial performance and social performance. This study used return on assets (ROA) as the financial performance variables. Return on assets is measured by adjusted net operating income divided by adjusted average total assets. It is assumed that firms with high return on assets will prefer internal sources of funding. Thus, return on asset is added to the model in order to extract any of these potential effects.

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5.8.2.1.4. Social performance (Female borrowers and Loan balance/GNI)

The second category is the social performance group. As far as MFIs are concerned, the standard way of focusing on their social performance is through the measures of outreach in various forms (Gutierrez-Nieto et al., 2009). Following the Mix Market standard definition, social performance is measured as the average loan size divided by GNI per capita that measures the depth of outreach. While number of active women borrowers divided by the adjusted number of active borrowers measures the breadth of outreach. This is wholly or partially consistent with many prior studies (Louis et al., 2013; Hermes at al., 2011; Gutierrez-Nieto et al., 2009; Cull et al., 2009).

5.8.2.1.5. Tangibility (TANG)

Following Tchuigoua (2014), tangibility is defined as the ratio of net fixed assets divided by MFIs total assets. Prior studies have found that asset specificity of firms determines the best type of capital structure choice firms employ (Williamson, 1987). This variable measures the MFIs collateral level. Alves and Ferreira (2011), and Campello and Giambona (2013) empirically observe a strong positive relationship between firm’s tangibility and capital structure. This is due to firm’s larger asset base, which makes them to have more capacity to issue debt since assets could be used as collateral. However, firm’s that either are unable to provide collateral will have to pay higher interest rate or will be forced to issue equity capital (Scott, 1977). Therefore, tangibility as firm-specific variable is measured as a ratio of net fixed assets divided by MFIs total assets.

5.8.2.1.6. Risk (RISK)

Risk is defined as the outstanding balance of portfolio on arrears over 30 days plus total gross outstanding renegotiated portfolio divided by adjusted gross loan portfolio. This information is

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provided in the MIX database and is consistent with prior studies on MFIs (Ahlin et al., 2011; Tchuigoua, 2014).

5.8.2.1.7. Profitability (PROF)

Profitability is defined as the net operating income divided by total assets. It measures MFIs capacity to use its assets to generate returns. Unlike the general capital structure literature that measures profitability using ROA (Booth et al., 2001; Demirguc-Kunt and Maksimovic, 2002; Gianetti, 2003). This study follow the measure of profitability proposed in the literature of microfinance, for example, Tchuigoua (2014).

5.8.2.1.8. Liquidity (LIQUI)

Liquidity is the total cash and cash equivalent divided by total assets. Unlike previous capital structure literature (de Jong et al., 2008; Gungoraydinoglu and Oztekin, 2011), that define liquidity as total current asset as a proportion of total current liabilities. Our definition is consistent with prior microfinance literature (Tchuigoua, 2014).

5.8.2.1.9. MFI-size (SIZE)

The natural logarithm of MFIs total assets is used as a proxy for size, given that it is the easiest way to control for outlier’s bias. Size has been identified by the corporate finance literature as an important determinant of capital structure decisions (Frank and Goyal, 2009). Du and Dai (2005) find a positive relationship between firm size and leverage. Larger firms tend to have less possibility of default risk, easier access to the capital market, and stronger negotiating power. This is applicable in the microfinance settings, where larger MFI tend to have the advantage of economies of scale compared to smaller MFIs.

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5.8.2.1.10. MFI-age (AGE)

This study classifies MFIs into three age categories (new, young and mature) based on the maturity of their microfinance operations given by the Mix Market database. It is calculated as the difference between the year they started their microfinance operations and the year of data submitted by the institutions.

5.8.2.2. Institutional-specific variables

In the analysis of international capital structure of MFIs, this study employs different types of institutional-specific variables to test their impact on MFIs capital structure. The study makes use a number of variables characterizing legal, financial sector development and macro-economic of countries. All the institutional-specific variables are averaged over the study period. The selections of the institutional-specific variables were based on the previous microfinance literatures and are defined as follows.

5.8.2.2.1. Creditors rights index (CREDRIGHT)

The strenght of creditor’s rights index measures the degree to which collateral and bankruptcy laws protect borrower and lender rights and thus faclitate lending. Unlike previous studies by (Qian and Strahan, 2007; Bae and Goyal, 2009), which used the five-point-scale built by La Porta (1997) and further extended by Djankov et al (2007). This study follow Ahlin et al (2011) and Tchugoua (2014) which used the new version of the index that ranges from 0 to 10 as provided by the Doing Business database. The choice of creditors right provided by Doing Businness 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 this study (2004–2014).

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5.8.2.2.2. Creditors information index (CREDINFO)

The credit information index measures rules and practices affecting the coverage, scope and accessibility of credit information available through either a credit bureau or a credit registry. A good credit registry may aid MFIs to access a qualitative credit information in the market. This study follows Ahlin et al (2011) and use the new version of this index that ranges from 0 to 8, with higher values indicating the availability of more credit information, from either a credit bureau or a credit registry, to facilitate financing decisions. This information is provided by the Doing Business database.

5.8.2.2.3. Financial sector development (FINDEV)

Given that domestic credit market seems to be a funding source for MFIs, the level of development in country’s financial sector may facilitate access to funding sources. Following recent studies on microfinance (Vanroose and D'Espallier, 2013; Tchuigoua, 2014), this study uses domestic credit provided by the private sector percentage of GDP to measure the financial sector development, that is taken from World Development Indicators provided by the World Bank.

5.8.2.2.4. Legal origin (LEGAL)

Consistent with prior studies in microfinance (Tchuigoua, 2014), this study measures country legal tradition by a dummy variable that takes the value of 1 if the country legal origin is common-law system and 0 otherwise. This variable is gotten from La Porta et al. (1997) and Djankov et al. (2007).

5.8.2.2.5. Corruption (CORRUP)

Reflects perceptions 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

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interests. In the case of microfinance, corruption may affects their operations and create barriers to their expansion. Unlike previous studies by Alves and Ferreira (2011) and Fan et al. (2012) which used transparency international corruption index, this study follows Ahlin et al (2011) and Tchuigoua (2014) which used the Kauffman et al (2009) governance indicators index that ranges between -2.5 to 2.5. A positive number reflects an ideal institutional outcome, meaning to say less level of corruption and vice-versa. This information is provided by the World Governance Indicator provide by the World Bank database.

5.8.2.2.6. Political stability (POLSTAT)

Political stability and absence of violence measures the perception of the likelihood of political instability and/ or politically motivated violence, including terrorism. Instability contributes to the uncertainty of returns for external investors, hence investors may reduce the likelihood and cost of any subsequent exposures by reducing the amount investments in that particular country. MFIs operating in a stable country may attrack external financing that can be channeled to higher-growth endevours. This study follow Ahlin et al (2011) to measure political stability provided by the Kaufmann et al., (2009) governance indicators.

5.8.2.2.7. Regulation (REG)

Similar to other financial institutions, most deposit taking MFIs are subject to prudential regulations (REG). It is therefore important to account for MFIs regulatory activities, which is consistent with prior studies of MFIs financing policies (Bogan, 2012; Tchugoua, 2014). A dummy variable of 1 is created for countries with regulatory framework for MFIs in place and 0 otherwise.

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5.8.2.2.8. Economic growth (GDP)

The study finally controls for differences in growth opportunities across countries using economy- wide growth rates in gross domestic product (GDP). Following recent studies on microfinance (Ahlin et al., 2011; Tchuigoua, 2014), this study focus on real per capita growth as arguably the most informative single indicator of economic progress. It can be considered as an approximate summary statistic for the various institutional, technological and factor accumulation related ingredients of development.

5.8.2.2.9. Human development index (HDI)

Aguilera and Jackson (2010) show that country specific tradition and institutions plays an important role in corporate finance studies. Cultural and economic differences are likely to be found between countries. The institutional variable that controls for gender inequality and other cultural differences in our regression is human development Index gotten from UN’s development programme.

5.9. Summary

This chapter presents details of the research design and focus of the present study. In particular, the chapter has outlined in details the models and empirical research technique employed in the analysis. The variables used were broadly defined and measured while the sample selection procedure was justified. Without these design, the present study could not be realised in its current form.

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