Contemporary Issues in Sustainable Finance by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9783030402488
Publisher: Springer International Publishing

6.3 Data and Methods

In this section of the chapter, we present the selected data and methods employed to examine our main hypotheses. The indicators that we use in this research are selected on the basis of data availability, quality, and previous usage in scientific research. After a careful selection of the data, the hypothesized relationships between microfinance and socio-economic development in sub-Saharan Africa are examined through two sets of regressions. In the first set of models, we assess the relationship between microfinance and economic development. In the second set of models, we assess the relationship between microfinance and social development. In order to add robustness to our results, we test the hypothesized nexus separately with two different indicators of microfinance: active number of borrowers and gross loan portfolio. Unless otherwise specified, the selected data is collected from the World Bank’s World Development Indicators dataset (World Bank 2017).

To measure economic development, we simply select the most popular indicator of economic development: GDP/capita. Therefore, in the first set of regression models, in which we estimate the effect of microfinance on economic development, our dependent variable is economic development, measured by the natural logarithm of GDP/capita at constant US dollars (2010). Conversely, in order to measure social development, we use Penn World Table’s Human Capital Index (Feenstra et al. 2015). This widely used measure of human capital is based on the average years of schooling in a given country. Hence, in the second set of regression models, in which we estimate the effect of microfinance on social development, our dependent variable is human capital, measured by the abovementioned Human Capital Index.

To measure microfinance, we select two indicators from the MIX Market database (MIX 2019b). The first indicator of microfinance is the natural logarithm of active borrowers, measured as a share of total population. The second indicator of microfinance is the natural logarithm of gross loan portfolio, measured as a share of GDP/capita. Natural log transformation is used to reduce the skewness of the data and the impact of outlier values. MIX Market is the largest database in the world with worldwide cross-national data on microfinance. Despite it outplays other databases on the topic, its data has some limitations that must be acknowledged when interpreting our findings. Above all, MIX Market does not include data on all MFIs because MFIs are not obliged to report their information to MIX. Thus, the database contains data only on MFIs that voluntarily report their information to MIX. While the MIX Market database has an extensive coverage of microfinance customers worldwide (Cull, Demirgüç-Kunt, and Morduch 2007) and it has been employed in several studies on microfinance (e.g., Hermes 2014; Lopatta and Tchikov 2016; Bangoura et al. 2016), we cannot exclude the possibility that the data is biased, for instance, toward large and well-established MFIs that are more likely to report their figures to MIX. We acknowledge that this is a limit of our study but we know as well that it is a limit of all extant large-N

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