Organisation type (Orgtype)

A study by Kosmidou, Pasiouras, and Tsaklanganos (2007) for 19 Greek bank subsidiaries operating in 11 nations (including UK, USA, Canada and South Africa) covering the period 1995 to 2001 shows privately owned banks generate more profit than government owned banks. This argument is true in developing countries like those in South Asia (Perera et al., 2013) as most government bank lending is supported by government direct credit programmes, with interest rate ceilings and other bank-specific regulations imposed by the government. Varma (2005, p. 200) points out that “the ability of the domestic financial institutions to play a role in corporate governance is constrained by their ownership structure”.

Ownership types of MFIs varies as there are diverse legal incorporations in microfinance sector (Hartarska, 2005). MFIs in this study are also diversely incorporated and this circumstance is similar in other countries (Hartarska & Mersland, 2009). They are registered as NGOs, private banks, NBFIs or member- owned co-operatives. Hartarska (2005) and Strøm et al. (2014) use different types of MFI as a firm specific control variable to find the link between governance and MFI performance. It is appropriate to consider the impact of organisation type; whether the MFI is a non-profit organisation, for-profit organisation, member-based co-operative, or shareholder owned firm as a variable to control for firm heterogeneity.

Many policy papers report that the most appropriate ownership type for MFIs is a shareholder firm that can be regulated by the banking authorities and remain independent from donors (Christen & Rosenberg, 2000; Hardy, Holden, & Prokopenko, 2003; Jansson, Rosales, & Westley, 2004). Such MFIs are able to

119 benefit from corporate governance too. This underlines a need to transform non- profit MFIs to for-profit ownership (Ledgerwood & White, 2006). A trend toward MFIs commercialising their institutions from non-profit to for-profit, based on a belief that shareholder firms can perform better than non-profit organisations (Hardy et al., 2003; Ledgerwood & White, 2006), is apparent. It is further suggested they can provide low-cost credit to greater outreach (Varottil, 2012). However, Mersland (2009) and Sinclair (2012) highlight that there is minimal difference between shareholder owned MFI performance and other MFI performance. Mersland and Strøm (2009) reveal that ownership of MFIs does not directly impact performance. Due to the ambiguous evidence suggested in prior research, this study employs organisation type (Orgtype) variable as a control variable to mitigate its effect on the link between governance and MFI performance.

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Table 4.6-1: Definitions of dependent, independent and control variables

Variables Acronym Predicted

Sign

Definition Dependent Variables

Financial Performance Variables Operating self-sufficiency OSS

Operating self-sufficiency is the total financial revenue divided by the financial expenses, loan loss provision expenses and operating expenses.

Return on assets ROA Return on assets is the net income after tax and before donations

divided by the total assets. Yield on gross loan

portfolio YOGLP

Yield on gross loan portfolio is the interest on loan portfolio and fees and commissions on loan portfolio divided by the gross loan portfolio.

Operating expense OCR The operating expenses/cost is the operating expenses divided by

the total assets.

Capital to Asset CA Capital to asset is the total capital divided by the total assets. Portfolio at risk more than

30 days PAR

The portfolio at risk more than 30 days is the loans that are more than 30 days divided by the gross loan portfolio for borrowers. Outreach Variables

Breadth of Outreach Breadth The natural logarithm of the number of active borrowers in the MFI. Percentage of female

borrowers FemBorr

The ratio of female borrowers to total number of active borrowers.

Depth of Outreach Depth The average loan balance per borrower divided by the adjusted GNI

per capita. Independent Variables

Percentage of female

directors FemDir Positive (+)

The ratio of female directors to total number of directors on the board.

Female CEO FemCEO Positive (+) Dummy explanatory variable that takes a value of one if the CEO of the firm in a female.

Female chairperson FemChair Positive (+) Dummy explanatory variable that takes a value of one if the chairperson of the firm in a female.

Duality Duality Negative (-) Dummy explanatory variable that takes a value of one if the firm’s CEO and chairperson are same.

Board of directors who represent international and/or donors agencies of the firm

IntDorDir Positive (+)

Dummy explanatory variable that takes a value of one if the firm has at least one international and/or donor director on board.

Board of directors who represent

clients/borrowers of the firm

ClientDir Positive (+)

Dummy explanatory variable that takes a value of one if the firm has at least one director representing clients/borrowers of the firm.

Non-executive directors

on board IndDir Positive (+)

The ratio of non-executive directors on the board to total number of directors on the board.

Board size Bsize Positive (+) The total number of directors on the board.

Internal auditor IntAudit Positive (+) Dummy explanatory variable that takes a value of one if the firm has an internal auditor reporting to the board.

Control Variables Regulated by banking

authority Regbank

Dummy variable that takes a value of one if the firm regulated by banking authority in the country.

Firm Age Fage The natural logarithm of the number of years from the date of

establishment as an MFI.

Firm size Fsize The natural logarithm of the firm’s total assets. Leverage Lev The ratio of the firm's total debt to its total assets. Organisation type dummy

variables Orgtype

Dummy variables for each of the organisation type: NGO-MFIs, NBFCs, Co-operatives, Credit Unions, Rural Banks, Urban Co- operative Banks, Private Companies.

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4.7

Conclusion

Sustainability of MFIs emphasises not only the financial viability of the organisations but also transparent and efficient organisations that are accepted by all the stakeholders involved in the organisation. Most of these requirements can be achieved through good corporate governance. However, the empirical studies relating to good corporate governance practices of MFIs are still in their infancy and further studies are needed to find out how improved corporate governance practices may increase profitability and sustainability of MFIs.

This chapter provides a rational description of the corporate governance characteristics used in the empirical model and their relationship with firm performance by developing the nine hypotheses to be tested in three analysis chapters. These hypotheses will facilitate the understandings of the corporate governance mechanisms in MFIs in Sri Lanka and India. This chapter presents the conceptual framework of this study in relation to the developed hypotheses. Based on the conceptual framework, the dependent and control variables used in the study are explained. The next chapter demonstrates the data collection method and econometric methods that are used for testing the hypotheses.

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5

CHAPTER FIVE

DATA COLLECTION AND RESEARCH METHOD

5.1

Introduction

This chapter explains the research method used to investigate the research questions, the data collection process, the empirical model and research techniques used in this study. Corporate governance has been extensively investigated in for- profit sectors but there are limited prior studies focusing on the corporate governance of MFIs. The link between corporate governance and MFI performance remains unexplained. This study investigates empirically the nexus between corporate governance practices and MFI performance.

This study has adopted diverse research methods to determine an appropriate research method which is suitable for the study of corporate governance of MFIs. This enables researcher to decide the research philosophy, empirical data and analysis techniques to come from the findings of the research. A panel data analysis approach is used to search for patterns in MFI data which are collected over time for the same organisations and then a regression is run to identify the association between governance and performance of MFIs.