A Tri-Variate Nexus of Microfinance-Growth-Inequality: The South-Asian Experience

A Tri-Variate Nexus of Microfinance-Growth-Inequality: The South-Asian Experience

Sovik Mukherjee, Asim Kumar Karmakar
DOI: 10.4018/978-1-5225-5240-6.ch012
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Policymakers throughout the world have actively been in pursuit of improvements of financial markets in the developing regions, but often with disappointing results. In this background, this chapter claims that microfinance can play a significant role in financial development, and that by concentrating on microfinance, development policy can consolidate the links between financial development, growth, and thus inequality reduction. The inequality computation used is based on the generalized entropy index (standard assumption of weight equal to 2 is applicable). Panel cointegration and panel causality are the techniques that have been applied in a vector error correction mechanism (VECM) set-up using panel data for fifteen years across seven South-Asian nations (i.e., Bhutan, India, Afghanistan, Bangladesh, Nepal, Pakistan, and Sri Lanka). The findings validate the existence of a cointegrated relation coupled with a tri-variate causality linking the focus variables in this model in the way that microfinance initiatives, their outreach is beneficial for the reduction of inequality but that inequality reduction does not promote economic growth per se.
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…Strikingly, 30 years into the microfinance movement we have little solid evidence that it improves the lives of clients in measurable ways. (Roadman & Morduch, 2014)

Microfinance, the indispensable multiplier. Money to pay for a cow. Fresh milk and something wondrous called ‘income’. The cow became a diary, then a milk distribution business. (The Award Committee awarding the 2008 Hilton Humanitarian Prize to BRAC as cited in Bateman, 2010)

How is inequality generated and does it impact growth? In this regard, can financial inclusion be of any significance? In fact, there is ambiguity to a certain extent on the impact of microfinance on growth across countries. But if one can bring in inequality then the story becomes interesting. Numerous researchers have tried to answer the first question over the years but examining this trivariate nexus has remained subservient. In the beginning, economists paid attention to factors that determine income inequality. To start off, Kuznets (1955) in his study analyzed the extent of influence of the distribution of income on economic growth. Empirically, in a panel set-up, Kuznets (1963) found an inverted U-shaped relation between income inequality and GNP per capita. This historical result highlighted the role that the distribution of income has on the transition of an economy from an agrarian based one to an industrialized one. The reason often cited for this “inverted U” relation is the sectoral shift that takes place in an economy i.e. the share of the industrial sector in GDP increases in comparison to the primary sector. However, there has been a lot of debate regarding the measure of capturing inequality. That is to say, can any one voice forth: should it be wealth, income or land inequality? There exist arguments in the literature for and against the relevance of this general relation proposed by Kuznets (1955) (see, for example, Ferreira, 1999; Arjona et al., 2003). Delving deep into the recent literature, Ravallion (2004) in his empirical analysis surprisingly gets zero correlation between the change in inequality and economic growth. In contrast to Ravallion (2004), this paper shows that for South Asian countries economic growth has been accompanied by a reduction in inequality over a span of 15 years from 2002 onwards. Interestingly, neither does this result lend support to the Kuznets relationship nor does it validate the idea of zero correlation.

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