Conclusiones

5.4.4.1 Test for heteroskedasticity

In an OLS regression, the error term is assumed to be homoscedastic, which means variance of the error term is constant across observations and it cannot be violated.

143 Therefore, heteroskedasticity means a situation where the variance of the error term is not constant and varies across all the levels of independent variables (Stock & Watson, 2007). Most often, this arises with cross-sectional data and provides biased standard errors. Heteroskedasticity can be detected by using Breusch-Pagan/Cook- Weisberg Test for Heteroskedasticity or White’s General Test for Heteroskedasticity. The null hypothesis that the error variances are all constant is tested by using the Heteroskedasticity test. However, the most common and trustworthy method to respond to the presence of heteroscedasticity is to estimate robustness standard errors as it relaxes the assumption that the errors are independent and identically distributed. This study uses the robustness standard error [VCE (robust)] option in STATA statistical package to obtain heteroskedasticity-robustness standard errors (also known as Huber/White or sandwich estimators).

5.4.4.2 Test for regressor endogeneity

It is important to test for the endogeneity of the regressors used in the model. A Durbin-Wu-Hausman (DWH) test for endogeneity provides a way to test whether a regressor is endogenous (Cameron & Trivedi, 2010). The test is under the null hypothesis that the specified endogenous regressors can actually be treated as exogenous regressors (Baum, Schaffer, & Stillman, 2007, p. 16). If there is a significant difference between the two coefficient vectors then the regressor is endogenous, otherwise it is exogenous. However, Hartarska (2005, p. 1632) states that the empirical evidence is not always supported the hypothesis that various governance mechanisms are endogenously determined.

5.4.4.3 Test for over-identifying restrictions

In a system GMM approach, the large collection of generated instruments can be suspected. The consistency of the estimators are highly dependent on the validity of instruments used. Therefore, it is important to diagnose the validity of over- identified instruments in an over-identified model to ensure that the parameters of the model are estimated using optimal GMM (Cameron & Trivedi, 2010, p. 185). Different tests can be used to check the validity of the IVs, such as the Hansen-J

144 test of over-identification restrictions and the Hausman specification test (Arellano & Bond, 1991). In this study, the validity of IVs used in the system GMM estimator is verified through the use of the Hansen-J test of over-identification as a standard test for joint validity of the IVs (Roodman, 2009a). The null hypothesis that the instruments are valid instruments cannot be rejected when the p-value is greater than 0.05 level. This confirms that all the instruments employed in the model are appropriate (Baum et al., 2007).

5.4.4.4 Stepwise regression

A stepwise regression is used to identify the most important corporate governance variables for the microfinance sector. This procedure is used only as a robustness option to check with the OLS regression outcomes to guarantee that this study has not missed any important variables in the model. As a result, both OLS and stepwise regression models have identified the same set of variables as significantly important variables to determine MFI financial performance and outreach. However, stepwise regression method does not apply to select best independent variables for the study, because this method is not recommended for testing the significance of a relationship between certain variables or a particular variable.

5.5

Conclusion

Prior research and anecdotal evidence largely suggests that there is a relationship between corporate governance and firm performance, but less is known about the microfinance sector. Based on the indicators provided in earlier empirical studies in developed and developing countries, this research explores the impact of corporate governance on MFIs’ financial performance and outreach. Does it lead to better services to poor people?

This chapter presents the framework of the empirical analysis that is used to analyse the link between corporate governance and MFI performance. First, it describes the research method of this study. Second, the data collection method is described in relation to MFI definition. Third, the sample selection procedure, data cleaning, editing, transformation and normality test are all described. Lastly, the methodology

145 of the study with regards to model specification and data analysis techniques is discussed. Data analysis techniques consist of univariate, bivariate and multivariate analysis techniques and specification tests.

The next chapter presents the findings of significant corporate governance variables for financial performance and outreach of MFIs in Sri Lanka, applying the data analysis techniques described in this chapter.

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6

CHAPTER SIX

CORPORATE GOVERNANCE AND

PERFORMANCE IN MFIS IN SRI LANKA: AN

EMPIRICAL INVESTIGATION

6.1

Introduction

Empirical findings regarding the relationship between corporate governance practices and firm performance of Sri Lankan MFIs are presented in this chapter. First, this chapter provides a background of the Sri Lankan microfinance sector focusing on different organisation types and lending methods. Second, it provides an interpretation of the descriptive statistics relating to the Sri Lankan sample in order to visualise the behaviour of the dataset in a more meaningful way. Third, the study reports the relationships between nine key corporate governance variables and MFI performance, both financial and outreach. A panel data technique is employed as the main analytical framework to identify the corporate governance- performance relationship of MFIs in Sri Lanka. The chapter concludes pointing to those corporate governance practices that appear most significant for improving the performance of MFIs in Sri Lanka.