Microfinance and Poverty—A Macro Perspective

Summary

We test the hypothesis that microfinance reduces poverty at the macro level using cross-country and panel data which are constructed by the Microfinance Information Exchange data on Microfinance Institutions (MFIs) and the World Bank data. Taking account of the endogeneity associated with MFIs’ loans, we show that a country with higher MFIs’ gross loan portfolio per capita tends to have lower levels of poverty indices. Contrary to recent micro evidence, our results suggest that microfinance significantly reduces poverty at macro level and thus reinforce the case for channeling funds from development finance institutions and governments of developing countries into MFIs.

Introduction

Most of the recent studies of the impact of microfinance on poverty or income have relied on micro-level evidence based on household data or entrepreneurial data (e.g., Hulme and Mosley, 1996, Imai et al., 2010a, Imai et al., 2010b, Khandker, 2005, Mosley, 2001). Due to the scarcity of reliable macro data on microfinance, macro-level studies of the impact of microfinance on poverty are rather limited. However, there are a few recent works that investigate the relationship between the macro economy and microfinance activities and/or performance, such as Ahlin et al., 2011, Ahlin and Lin, 2006 and Kai and Hamori (2009), among others. The thrust of these studies is either to examine the environmental context in which microfinance operates, or investigate the potential effect of microfinance on key macroeconomic variables, such as gross domestic product or inequality. The findings of a significant relationship between operations of Microfinance Institutions (MFIs) and the macro economy corroborate the recent evidence based on household data sets which posits that microfinance has a poverty reducing effect (e.g., Gaiha and Nandhi, 2009, Imai et al., 2010a, Imai et al., 2010b, Khandker, 2005).

This study redirects the attention to macro studies given the mixed results of microfinance impact studies at the micro level in recent years. As the separation of causal effects of microcredit from selection effects is unsatisfactory in many of the micro-level studies, Armendariz and Morduch (2005) pointed to a potential bias arising in the impact of microfinance in these studies. In view of this, studies that have recently emerged have used one of the following three approaches: (i) randomized control trials (Banerjee et al., 2009, Feigenberg et al., 2010, Karlan and Zinman, 2010); (ii) financial diaries/portfolios of the poor (Collins, Morduch, Rutherford, & Ruthven, 2009) and (iii) use of other variants of quasi-experimental estimation techniques such as treatments effect and propensity score matching in both cross-sectional and panel data setting (Imai et al., 2010a, Imai et al., 2010b). Evidence from such studies remains mixed due to different microfinance outcome measures and/or different methodologies adopted by these studies, leading to the perception that microfinance is likely to have little impact on poverty.¹ Our econometric analysis points to robust poverty reducing effects of microfinance, as elaborated below.

The challenges for empirical macro studies of microfinance include: (a) identifying an appropriate measure of microfinance activities, in terms of “availability” or “intensity”; (b) identifying the effects of “performance,” distinguished from “presence” and “scale” of microfinance on macro indicators; and (c) examining the robustness of coefficient estimates related to microfinance. Building on the small but emerging literature on analyzing the impacts of microfinance from a macro perspective, the present study aims to examine the relationship between MFI’s gross loan portfolio per capita and FGT class of poverty indices.² The results would be useful for development agencies, governments, and other investors, as there are important implications for microfinance’s potential role in reducing poverty at macro level. Our counterfactual simulations illustrate the possible effects on aggregate poverty expected from the decrease in MFIs’ gross loan portfolio per capita, GDP per capita, or domestic credit which may be caused as a consequence of global recession or financial crisis.

Drawing upon econometric estimations of the cross-country data—including a panel—we find consistently that a country with higher MFIs’ gross loan portfolio per capita tends to have lower levels of FGT class of poverty indices, which corroborates the poverty reducing role of microfinance. It is notable that microfinance loans per capita are negatively associated with not only the poverty headcount ratio, but also with the poverty gap and squared poverty gap, implying that even the poorest benefit from them.

The rest of the paper is organized as follows. The next section provides a brief explanation of the data which the present study draws upon. Econometric specifications are discussed in Section 3. The main results and simulations are given in Sections 4 Results, 5 Simulations, respectively. The final section offers concluding observations.