Methodologies used in previous studies

Several empirical studies have attempted to assess the determinants of firm’s capital structure. All studies in the reviewed literature used quantitative data analysis to investigate the firm-level such as board gender diversity and institutional level determinants on capital structure. For example, Campbell and Vera (2010) conducted a study on quoted Spanish firms using GMM models with 408 observations. This study however, was biased because it examined only firms listed in the Spanish stock exchange. The capital structure definition is limited (market value of stock and the book value of debt divided by the book value of total assets). The number of observations was also too small to achieve statistically reliable conclusions and, therefore, generalizability is questionable. Huang and Kisgen (2013) examine corporate financial and investment decision made by female executive compared with male executives using the difference-in-differences framework to control for endogeneity. Although this method is intended to mitigate the effect of extraneous factors and selection bias, it may still be subject to biases such as reverse causality and omitted variable bias.

Alvez et al., (2015) studied the composition of board of directors and capital structure of non- financial firms from 33 countries. The methodological weakness of this study is that, they assume that board gender diversity and financing sources variables are unlikely to be endogenously determined which is against prior evidences suggesting that capital structure shocks cause changes in governance mechanisms in microfinance sector (Strom et al., 2014). Moreover, conclusions drawn from non-financial firms cannot be generalized to financial firms. Unlike non-financial firms, financial firms call for distinctive regulatory treatment and this generates novel challenges for corporate governance (Gulanhussen and Santa, 2015).

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Mersland and Urgeghe (2013), examined the determinants of MFI financing using data from 319 MFIs in 68 developing countries. The dataset has a certain sample selection bias as only rated MFIs are included. This study used simple pooled probit regression in which the coefficient of the explicative variables cannot be interpreted as marginal effects on the dependent variable, and their signs only show whether the corresponding variables influences the likelihood for the dependent variable to equal 1. However, binary outcome variable does not contain any direct information a non-constant variance. For example, the variance of capital structure is tied directly to the probability of board composition. This implies that any inference about changes in this variable must come from observed changes in the probability of a board composition.

The study by Adams and Ferreira (2009) was set to explain the significant impact of female directors on firm outcomes. Specifically, the study was meant to show how female directors improve governance quality and firm performance. Their study was built on many previous works Farrell and Hersch (2005) which fail to address the endogeneity problems that arise because of differences in unobservable characteristics across firms or reverse causality. Instead of a single method analysis, they introduced comprehensive analysis of the consequences of gender diversity of boards. Their study was based on data obtained mainly from Investor Responsibility Research Centre which does not cover microfinance institutions.

Mersland and Strom (2009) and Strom et al., (2014), have used multiple regression models with a relatively moderate sample compared to previous studies. Using self-constructed global dataset of MFIs collected from third party rating agencies, Mersland and Strom (2009) employ random effect panel data estimation without consideration for endogeneity issues. Following this trend, Strom et al (2014) expand the gender diversity variables to three female leadership categories. This study used Heckman endogenous dummy variable model to control for any possible endogeneity

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problem. This study however, focused only on the effect of board gender diversity on governance and performance. Thus, the role of female directors on MFIs capital structure is not covered in this study.

On the impact of institutional specific factors on capital structure, prior studies by (Booth et al., 2001; Giannetti, 2003; Fan et al., 2012) use simple multivariate analysis for cross-section of firms in different countries and sectors excluding MFIs. Although this research has identified several empirical regularities, it suffers from erroneous assumptions, weak results and more importantly some econometric problems. By reporting the estimated coefficients for firm-level determinants of leverage per country, these studies, on the one hand, acknowledge that the impact of firm-level determinants does differ in terms of significance levels across countries. On the other hand, in the analysis of institutional-specific determinants of corporate leverage, these studies also make use of country dummies in pooled firm-year regressions, thus forcing the firm-specific coefficients to have the same value. With an extremely large number of firm-year observations, it is more likely for this procedure to produce statistically significant results for many country-specific variables. However, de Jong et al., (2008) utilizes an alternative regression framework where a single average capital structure for each country is used as an observation, they hardly find strong evidence on this issue.

Therefore, as an additional contribution of this study, we show the invalidity of this implicit assumption as examined by Tcuigoua (2014) in the microfinance settings. This study adopts de Jong et al (2008) regression framework by decomposing the analysis into stages. In the first stage, comparative analyses using simple ordinary least square regression is run to determine the impact of firm-specific factors on MFIs capital structure on a country-by-country basis. A simple statistical analysis was run to test whether the coefficient of firm level determinants is equal across

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countries. In the second stage, the use of country dummies can be a potential solution in the analysis of the impact of institutional factors on capital structure, where each country should serve as a particular observation in the second stage weighted least square regression, rather than pooling samples from all firm across countries.

In the joint determinants of capital structure variables of deposit and borrowing. This study borrows from Yan (2006) and Dzolkarnaini (2009) methodology. They examine the firm-specific joint determinants of leases and debt using the generalised method of moments (GMM) technique developed by Arellano and Bond (1991) and Arellano and Bover (1995). This study however, focus most on firm-level joint determinants of leases and debts. Thus, other institutional factors that could jointly determine deposits and borrowing were not covered in this study. With this method in place, a re-examination of joint determinants of deposits and borrowing using microfinance experience is necessary. In the next section we present the methodologies used in our study.