Abstract
We combine two datasets to examine whether the presence of banks affects the profitability and outreach of microfinance institutions. We find evidence that competition matters. Greater bank penetration in the overall economy is associated with microbanks pushing toward poorer markets, as reflected in smaller average loans sizes and greater outreach to women. The evidence is particularly strong for microbanks relying on commercial-funding and using traditional bilateral lending contracts (rather than group lending methods favored by microfinance NGOs). We consider plausible alternative explanations for the correlations, including relationships that run through the nature of the regulatory environment and the structure of the banking environment, but we fail to find strong support for these alternative hypotheses.
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Notes
Data are from IMF Financial Access Survey (fas.imf.org, accessed 12/11/12). Mexico, for comparison, had 14.9 ATMs and 45.8 commercial bank branches per 100,000 adults in 2011.
Armendáriz and Morduch (2005) describe the economics of microfinance, and Cull, Demirgüç-Kunt, and Morduch (2009) describe recent trends and data. Data on Grameen Bank loan balance and customer size are from Grameen Bank (http://www.grameen-info.org/index.php?option=com_content&task=view&id=453&Itemid=527 accessed December 6, 2012).
These calculations are for a sample of leading microfinance institutions that serve 18 million borrowers. Loans are defined as delinquent if they are at least 30 days overdue.
See also Hermes et al. 2011, for a further refinement of their stochastic frontier analysis using data from MFIs.
See, for example, Cull, Demirguc-Kunt, and Morduch (2011) on the differential effects of regulation and supervision on these two groups.
This is a substantial increase over the MIX database used in Cull, Demirgüç-Kunt, and Morduch (2007), which contained information from 124 MFIs in 49 countries. That data set was a variant of the so-called MBB 9 database. In this paper, we use a variant of the MBB 10 database. There are 540 observations in our database because some MFIs report information for multiple years.
These include adjustments for inflation, the cost of subsidized funding, current-year cash donations to cover operating expenses, donated goods and services, write-offs, loan loss reserves and provisioning, a reclassification of some long-term liabilities as equity, and the reversal of any interest income accrued on non-performing loans.
The financial self-sufficiency ratio is adjusted financial revenue divided by the sum of adjusted financial expenses, adjusted net loan loss provision expenses, and adjusted operating expenses. It indicates the institution’s ability to operate without ongoing subsidy, including soft loans and grants. The definition is from Microbanking Bulletin (2005), p. 57.
We also clustered standard errors at the country level and derived similar qualitative results. We do not present those results here.
Commercial funding includes deposits and commercial borrowing which is divided by total funding. Total funding also includes donations, non-commercial borrowing (i.e., at non-market rates), and equity, which tends not to reflect true commercial investment for most microfinance institutions (Cull, Demirguc-Kunt, and Morduch, 2009).
Real portfolio yield = (nominal portfolio yield – inflation rate)/(1 + inflation rate). Nominal portfolio yield = (interest on loan portfolio + fees and commissions on loan portfolio)/gross loan portfolio.
In the regressions that interact the individual lending dummy with the bank penetration variables, we change the omitted category to include both the solidarity group lenders and the village banks.
We report correlations at the country level rather than the MFI level so as not to artificially inflate significance levels.
Positive results for the real portfolio yield variable in the FSS and ROA regressions in Table 3 would again appear to contradict results from Vanroose and D’Espallier (2013), who find that headline lending and deposit rates at the country level are negatively associated with profitability for MFIs. However, we include information on interest rates in the form of real yields for each MFI in our regression. We also include the (MFI-specific) ratio of capital costs to total assets in the regressions and find that is strongly negatively associated with MFI profitability. That variable could be picking up the negative effect of interest rates on profitability found in Vanroose and D’Espallier (2013). Because our variables on real yields and capital costs are computed at the MFI level, they are likely to offer more precise estimates than country level interest rate variables.
We note that even if selection is driving our results, the pattern suggests that those that make smaller loans and lend more to women are fitting themselves into high density markets, and thus are complementary to the banks that are already there. They are therefore making a contribution to expanded outreach, though they might not be competing directly with banks.
See Clarke et al. (2003) for a discussion of the role of foreign banks in developing countries.
We acknowledge, however, that we can only reject the hypothesis that the relationship between penetration and the share of lending to women is zero for commercially funded institutions when we use the geographic penetration measure (see F-test, model 7). In that sense, results are a bit weaker than when the bank ownership and concentration variables are not included in the regression.
It might be possible that penetration is associated with less stringent supervision if, for example, extensive branch networks reflect a laissez faire approach to expansion and other aspects of supervision, though this seems unlikely to us.
The breakdown of MFI clients by rural areas, small cities, and large cities was determined by the MIX.
The number of product offerings is reported in the MIX and is equal to the sum of the number of loan products, savings products, and non-financial products.
The geographic penetration variable does achieve significance when MFI borrowers as a share of the population appears in the regression (not shown), but that reduces our sample and renders coefficients in the portfolio yield regressions insignificant.
Other authors have included a dummy variable to identify countries that have Scandinavian legal origin. None of the microfinance institutions in our sample come from such a country, and thus that variable is dropped from our regressions.
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The views are those of the authors and not necessarily those of the World Bank or its affiliate institutions. Morduch is grateful for funding from the Bill and Melinda Gates Foundation through the Financial Access Initiative. The Mix Market provided data through an agreement with the World Bank Development Economics Research Group. Confidentiality of institution-level data has been maintained. We thank Isabelle Barres, Joao Fonseca, and Peter Wall of the Microfinance Information Exchange (MIX) for their substantial efforts in assembling both the adjusted data and the qualitative information on microfinance institutions for us. Ippei Nishida, Mircea Trandafir and Yeon Soo Kim provided expert data analysis, and Varun Kshirsagar provided additional assistance. All views and any errors are ours only.
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Cull, R., Demirgüç-Kunt, A. & Morduch, J. Banks and Microbanks. J Financ Serv Res 46, 1–53 (2014). https://doi.org/10.1007/s10693-013-0177-z
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DOI: https://doi.org/10.1007/s10693-013-0177-z