Resumo:
Propsito do Trabalho:
In the last decades, microfinance has been achieving more and more prominence in the policy-making for the poor people (Morduch, 1999). Muhammad Yunus, the pioneer of microfinance, founded the Grameen Bank in 1976, and for that received the Nobel Peace Prize thirty years later. However, there is no much agreement in how should microfinance institutions (henceforth MFIs) be structured in order to achieve the best results in alleviating poverty.
There are advocates for both approaches of for-profit and not-for-profit MFIs. Morduch (2000) expressed those different points of view. Those that advocate for the profit-oriented approach of microfinance claim that with profits an MFI can achieve self-sustainability and expand their loan portfolio to help more people. They also claim that not-for-profit MFIs are less efficient and cannot survive without subsides. As Norell (2001) notes, a not-for-profit MFI can signal to their clients that the key concern is their well-being and not the financial return, what can lead to the opportunistic behavior of strategic default. However, those that advocates for the not-for-profit approach express their concerns that for-profit MFIs may explore the poor with higher interest rates and become the new moneylenders. They also claim that, as long as poverty exists, there will be subsides for MFIs, since reducing poverty is a key element in the policy-making of all governments.
There are examples of MFIs that were founded as not-for-profit, and then became for-profit enterprises. Schmidt (2013) cites two very well known cases: Compartarmos in Mexico and SKS in India.
Thus, our objective is to verify if the profit status of a MFI influences on its financial accounting measures.
Base da plataforma terica:
Compartamos was founded in 1990 as an NGO, but in 2007 it released its IPO. However, they did not create new shares in the IPO process, therefore they did not receive new funds. Several authors (Ashta & Hudon, 2012; Rosenberg et al., 2009; Schmidt, 2013) have criticized the IPO and the interest rates charged by Compartamos. The incentives alignment is controversial since the goal of open companies is to maximize the shareholders return, but MFIs clients are mostly poor from developing countries.
However, although there is credit elasticity for poor clients (Karlan & Zinman, 2008), there is evidence that the loans made by Compartamos, even with high interest rates, do achieve some positive outcomes (Angelucci et al., 2015). It was found that those loans had a modest positive impact in business size, trust, and female decision making, and a modest negative impact in depression and reliance on or need for aid.
The SKS MFI was founded in India in 1998 and released its IPO in 2010. SKS adopted, as Compartamos, the policy of high interest rates. This policy is credited for the crash of microfinance in India in 2010 (Wichterich, 2012).
During the crises the SKS shares price dropped 77%. The crises was originated in Andhra Pradesh, and even is suspected to cause a number of suicides (CGAP, 2010), resulted in the indebtedness of 82 percent of rural household (Wichterich, 2012), and over 35,000 people lost their jobs in Andhra Pradesh.
Therefore it seems that exists anecdotal evidence for the good and the bad side of for-profit high-interest MFIs. Zeller & Meyer (2002) argued for the triangle of microfinance that is: financial sustainability, outreach, and impact. Those three legs would only be achieved if the MFI had profits, in order to be self-sustainable and growth (achieving more people, and giving larger loans).
However, profit-oriented MFIs tend to have different goals than not-for-profit ones. Cull et al. (2007) shows that profit-oriented MFIs tends to lend to the richest of the poor, in order to achieve a higher level of profitability.
Armendriz & Morduch (2010) list five often used financial accounting ratios that are important for a MFI to be sustainable: Financial Self Sufficiency (FSS), Return on Assets (ROA), Portfolio at Risk after 30 days (PAR30) and Yield on Gross Loan Portfolio (Yield). Nevertheless, Cull et al. (2011) shows that the FSS ratio may be biased, since grants, donations, and other alternative ways of funding are not included in the FSS calculation, making not-for-profit institutions score lower in this financial ratio, although the funding for those institutions being stable over years (Morduch, 2000).
Mtodo de investigao:
Using a database of 198 MFIs (52 countries), with 661 observations, from 2010 to 2014 in a multilevel model, we expect to contribute to the discussion of those two different approaches and their impact on several different outcomes.
We used a database provided by the Microfinance Information eXchange, Inc. (MIX). The data is self-reported, what can create a problem if a MFI reports untruthful figures. However, MIX did an audit in a number of MFIs in 2013-2014 period, and we used only MFIs that were audited. Also only observations that contained non-missing data for key variables were used.
All the four financial accounting measures (ratios) used as dependent variables were taken from Armendriz & Morduch (2010) as follows: Yield on Gross Loan Portfolio, Return on Assets, Portfolio at Risk after 30 days, and Operational Self Sufficiency.
Our interest independent variable is Profit, which received the value of 1 if the MFI was registered as profit-oriented and 0 otherwise (not-for-profit). This variable will be interpreted as the difference in the dependent variable for the MFIs that are seeking to profit and those that do not. We used a number of control variables: size (small, medium, large), age (new, young, mature), clients (percentage of female borrowers and retention rate of borrowers) and staff turnover rate. We used the categories small (outreach less than 10,000 clients) and new (1 to 4 years) as baseline in the regressions.
We chose to use the hierarchical model because it allow us to verify the heterogeneity of the firms across several levels. The research problem requires us to use controls in the estimation, since we know that hierarchical structure cannot sustain the assumption of independence (Raudenbush & Bryk, 2002). For instance, since MFIs are in the same region they are susceptible to similar elements, as we will see in the size of ICCs (Intra-Class Correlations) for region. Moreover, this technique allows identify how much of variance can be explained by each one of the three hierarchical levels as we will see in the next section. Also, since all the variables are endogenous, the multilevel model with random intercepts allows each MFI and each region to have its own intercept what controls for endogeneity at the MFI and region levels, as Hanchane & Mostafa (2012) noted.
Resultados, concluses e suas implicaes:
The MFI level explains the most part of variance explained for all the dependent variables which implies that a hierarchical model can be useful see the variability of each MFI. Figure 1 shows it intuitively. This is consistent to our goal in order to observe the difference between profit and non-profit driven MFIs. The region, besides the lower variance, allow us to control for the heterogeneity in the data that can lead to biased estimation results.
The effects of different regions are shown to be quite small, accounting for only 5% of overall variance. This shows that the intrinsic characteristics of firms have the biggest explanatory power and region characteristics explain a small portion of the financial indexes. This is an interesting finding since this shows that MFIs in different regions, but with equal intrinsic characteristics are quite comparable. This result gives robustness to experiments that are conducted across different regions such as Banerjee et al. (2015), Banerjee , Karlan and Zinman (2015) and Cull et al. (2009).
The results indicate that in all models the profit dummy variable was not significant, except in Model 2 and Model 3. Interesting to note is that in Model 2 the coefficient is 7 significant at 1% level, though after including the controls it remained significant at 5% (Model 3). When we included the control variables the number of observations decrease due to missing data. We can infer no difference between profit and non-profit oriented MFIs for three out of four most common financial accounting measures used to compare the performance of microfinance institutions (Armendriz & Morduch, 2010). Hence, the fact of profit maximizing behavior of firms are not contradictory of the main goal of microfinance.
It is natural to expect that small MFIs require more profits (comparative to larger MFIs) to sustain their activity in order to be self-sustainable and grow its business, given its revenues are smaller. Moreover, larger MFIs have economies of scale due to fixed costs that can be dissipated in a larger portfolio (Gonzalez, 2007), so they may charge smaller interest rates (therefore reducing its yield on gross portfolio). The results for these estimations are presented in Table 3. The robustness of our findings was scrutinized with a 1-to-1 nearest neighbor propensity score matching with replacement. It was performed in order to make small MFIs comparable to large MFIs in the all observable variables but the profit dummy variable. The bias reduction achieved by this approach is shown at Table 4 and Figure 3.
The results occurred as theorized. The difference in average yield ratio, that can be interpreted as interest rates charged, it is prominent in smaller MFIs. This result is interpreted as follows: smaller profit-oriented MFIs need higher yield on gross portfolio (therefore higher interest rates) to maintain its growth and expand their portfolio (i.e., making more loans). However, there is no difference between profit-oriented and not-for-profit MFIs in the larger subgroup (right half of Table 3).
What drives this effect? We theorize that smaller not-for-profit MFIs can rely on alternative sources of financing (such as grants and donations) while for-profit MFIs need to self-finance themselves by increasing its revenues, since their fixed costs can only be distributed to a small number of clients. The larger profit-oriented MFIs can distribute their fixed costs in a larger portfolio, what enables them to charge smaller interest rates in comparison to the smaller ones (Gonzalez, 2007).
Further research must be done, but is relevant to hypothesize that the best approach for the MFIs is to be not-for-profit when smaller and after growth to change its nature to profit-oriented. This approach would allow smaller yields in all sizes: large and small. Unfortunately, few MFIs changed its profit status during time, what makes difficult to estimate its effects with a statistical approach.
Referncias bibliogrficas:
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Armendriz, B. & Morduch, J. (2010). The economics of microfinance. MIT press.
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