Discriminación Escolar

This section introduces data used in Part II of the study and the statistical methods used for analysis. The data set represents largely the global microfinance industry statistics, covering 435 MFIs, 68 investors and 112 partners as at the time of this study. Its made available by the microfinance information database (Microfinance Information eXchange, MIX) and accessed at http://www.mixmarket.org (Cull et al., 2008).

The MIX MARKET™ is the world’s largest microfinance database containing outreach and impact data, financial data and audited financial statements in addition to country relevant macro- economic and social development indicators (Cull et al., 2008; Cull et al., 2007). Part II of the study therefore used cross-country data of 103 African MFIs sampled from the MIX MARKET™ web- based database. See list of sample MFIs in Appendix D.

This database is particularly interesting to the study because it consists of firms that are keen in wooing investors. It gives an opportunity to post information necessary to lenders and other social investors, themselves in search for investment MFIs. In this sense it is seen as a way of exposing the microfinance sector to would-be commercial lenders so that the latter can play a more active role in the sector’s development (CGAP, 1997; CGAP, 2002c). The argument is that potential investors do not have sufficient information to make lending decisions and MFIs are not aware of

potential development partners besides their main donors, yet they need to entice other players in the industry with capacity to offer vast amounts of finance (Hattel & Halpern, 2002; McKee, 2001a).

3.3.1 Part II: Data collection and sample description

In this section, the data collection procedures for Part II of the study and sample formation is described. The sampling frame consists of the total population of African MFIs posting data on the MIX between 1998 to the end of the calendar year 2003. This constituted 188 African firms. Following Ozkan (2001), Peyer and Shivdasani (2001), Hendricks and Singhal (2001), and Laittinen (2002) the sampling criterion for firm inclusion in the model was defined as those MFIs with consecutive three-year financial data between 1998 and 2003. This definition resulted in a final sample of 103 MFIs and 309 observations after dropping firms with missing observations or those with non-continuous data series (Hasan, Wang & Zhou, 2009). This represented 55 per cent of total population of all 188 Africa firms drawn from 21 countries.

3.3.2 Part II: Measuring success in commercialisation: conceptualisation of the dependent variables

The measure of success in commercialisation was one of the challenges of this study. However, getting a uniform measure was necessary, firstly, to use it as a prediction rating rule in commercial success, and secondly, to use it as a useful information guide for investors in assessing MFI viability in Africa (Hartungi, 2007). This study explored two measures of the likelihood of success in commercialisation at two levels, constructed in the following manner:

3.3.3 Level I: Measure of success: leverage multiplier added

Success in Level 1 was measured by a single cardinal measure for gauging the probability of success in tapping the commercial markets. This measure was defined as equity multiplier (EM) which is the basic ratio of total assets to equity (sometimes called capital ratio). It represents the amount of assets supported by each shilling of equity/capital. A typical MFI balance sheet, as shown below in Formula 3.1, usually contains four major financing items to the asset side (Jansson, 2003). Item 4 is negligible in most MFI balance sheets while item 1 is just emerging as a source of capital (Jansson, 2003; Christen, 2000; Carlos & Carlos, 2001; Cull et al., 2008). This item may include a portion of non-interest-paying liabilities such as soft loans and guaranteed debt instruments that are difficult to isolate. Using the traditional balance sheet equation, total assets are financed by either equity (items 2 and 3 below) or liabilities (items 1 and 4 in the box below).

...(3.1) This leads to the second formulation:

$$&$ '('&$ ' )*&+ ...(3.2)

According to the asset growth model (Upneja & Dalbor, 2001; Watson & Wilson, 2002), an increase in (A) must be financed by some source, (L) or (E). The equity multiplier (EM) is expressed as total assets (A) divided by total equity (E).

, / ...(3.3)

This ratio is the inverse of the capital ratio used by banks to evaluate financial distress and capital adequacy (Demirguc-Kunt & Maksimovic, 1998; Pille & Paradi, 2002; Metwally, 1997; Ozkan, 2001). An increase in EM indicates a higher level of commercial financing (L) or debt financing (Cull et al., 2008). When this ratio is 2:1, it represents 50 per cent of financing by interest paying liabilities (debt). The ratio therefore indicates the degree of financial leverage or, as elsewhere defined as intake of interest-bearing debt (Pollinger et al., 2007; Hartungi, 2007). If an MFI has no debt (L tends to zero), the EM is equal to [1], and it rises as more debt is taken into the balance sheet (Cull et al., 2008: 11). This study defines the increase in financial leverage over time as LMA (leverage multiplier added) formulated as follows:

EM Rating 1 EM Rating 3 LMA ...(3.4)

The equity multiplier rating (EMR) is by itself a summary measure of how successful an MFI has managed to attract commercial financing. This indicates commercialisation in progress as the higher the LMA, the greater the effort in commercialisation (defined as access to commercial funding or increase in L relative to E) all other things being equal. This measure however represents a ‘weak form’ of commercialisation as it may include collaterised debt or soft loans that are not at fully market rates (Bystrom, 2007). Commercial interest rates are difficult to determine in practice because they depend on where the market sets the rates, particularly in a cross-country study (Cull et al., 2008).

1. INTEREST PAYING LIABILITIES