Financial permeation as a role of microfinance: has microfinance actually been a viable financial intermediary for helping the poor?

Abstract

This article represents a valuable contribution to the existing literature on the relationship between financial sector growth – specifically, of microfinance institutions (MFIs) – and poverty levels in developing countries. We propose a concept termed herein financial permeation to describe how expanding financial activity affects low-income households; just as water permeates dry sand, an increase in the use of and access to financial services may spread more money among the poor, meeting their credit needs and improving their levels of well-being. Another feature of the presented study is that it is among the first to apply the logit transformation to the poverty ratio, thereby eliminating some of the problems of standard regression models. We measure financial permeation by applying three indicators related to MFIs and use panel data for 76 developing countries from the period 1995 to 2008. We find that financial permeation has a statistically significant and robust effect on reducing the poverty ratio.

Keywords:

• financial permeation
• microfinance
• panel data
• poverty reduction

JEL Classification:

• G21
• O16
• O50

Acknowledgements

We are grateful to the two anonymous referees and the editor for their many helpful comments and suggestions.

Notes

¹Arestis and Demetriades (1997), Levine and Zervos (1998), Ram (1999), Shan and Morris (2002) and Bloch and Tang (2003) are among the related literature on financial development and economic growth.

²The positive impact of microfinance on poverty is found in Bangladesh (Pitt and Khandker, 1998; Khandker, 2005) and in Bolivia (Mosley, 2001).

³In addition, Banerjee et al. (2009) and Karlan and Zinman (2009) conclude that the effects of microcredit on borrowers vary depending on client characteristics and that they are not as significant as those claimed by proponents of the microfinance movement.

⁴These 76 countries are reported in Appendix A.

⁵Logistic regression has been applied to various fields of statistical analysis. See, for example, Hosmer and Lemeshow (2000) and Kleinbaum and Klein (2010).

⁶We multiply MFIRATIO by 10 000 in order to adjust the unit in our empirical analysis, i.e. MFIRATIO is equal to the ratio of the number of MFIs multiplied by 10 000 to the population.

⁷If the instrumental variables do not have large correlations with the endogenous variables, then we suffer from the problem of weak instruments (Bound et al., 1995). Thus, we check the correlation between the endogenous and instrumental variables and find that they are strongly correlated.

⁸We also use the education variable, namely secondary school enrolment rate, in the empirical analysis. We find that the coefficient of microfinance intensity is significantly negative in this case as well, which indicates that an increase in microfinance intensity reduces the poverty ratio.