Feeling quite comfortable on my nice leather chair, sitting at my desk in front of the computer, I try to figure out how I could change human destiny… And it seems difficult to me, I have to admit, to keep some of my thoughts on poor individuals’ daily struggle for life when I take a look out of my BlueOrchard office over pretentious, seven story-tall Geneva buildings. Needless to say, most articles on this blog help me to understand better the numbers I am handling with by recalling me what kind of people are behind these numbers and giving me some part of reality I am desperately missing.
But since I cannot provide any miracle story of a single mother being able, thanks to a microcredit, to build up her own business and thereby to send her children to school (and I really like these stories), I settle for giving some revealing figures out of my “Universe of Numbers”. Last time, I touched already on the subject of social performance, arguing that the great challenge for the microfinance sector should be the measurement of its impact on poverty. It is essentially this point which puts the cherry on the cake for the investor. For BlueOrchard, as a social fund manager, this issue is crucial if we want to create a niche in financial markets big enough to be profitable. But while the financial return for the shareholder is quite easy to measure (expressed as the interest rate on her investment), there is still no clear concept how you want to put a figure on a story of poverty. And although I believe that investors do not necessarily follow strict economic ideas based on monetary return and risk, communication of social impact should be made as simple as possible, best in a quantitative way to facilitate comparison across institutions, countries and regions. It is in this context that microfinance rating agencies (like M-Cril, a company working in particular in South Asia) try to establish a common framework for social rating and an indicative list of dimensions and indicators for social performance. This includes different categories, describing the services offered by the MFI, evaluating its mission, investigating its social responsibility towards clients and the environment and finally measuring the real outreach of its activities. Whereas it is complicated to assess the different visions of a MFI, at least its outreach can be calculated and compared. For this purpose, different indicators have been proposed (% of clients living below the poverty line, % of clients in areas with lower than average socio-economic development,…), however, data on these values are either tricky to gather or little reliable (especially when you do not work for a microfinance information platform or for the World Bank). As a result, researchers suggest several proxies, among others the “Average loan size / GNI per capita”. The reasoning behind this indicator is quite simple: Assuming a more or less stable relationship between an individual’s credit and its wealth, it states that people asking for smaller loan amounts are supposed to be poorer. To compare the loan sizes across countries, they are standardized by dividing through the GNI per capita, hence transforming the figure into a measurement of relative poverty. Doing this you advantage relative rich regions because they can claim to fight against distress by disbursing loans going up to $4000 – $5000 and which are certainly relative less costly than smaller loans. It does not seem to be perfect and can lead to biased decisions when you want to get a first idea of the issue.
Nevertheless, lacking concrete information on the social performance of MFIs (and impatiently waiting for some results of social rankings), we decided to research on the basis of available data (which is essentially the MIX market data base) to see how well our own clients score. In defiance of all possible measurement errors and unadjusted figures, but strong believing in the law of large numbers I founded my study on a sample of more than 750 MFIs for 2006. Trying to correct for the shortcomings of the concept of relative poverty, I calculated the average loan size in PPP, that is to say in purchasing power parity which allows evaluating performance across regions and creating an indicator of absolute poverty. Taking the medians I got the following results:
Furthermore, I added the Financial Self-Sustainability to measure the financial performance over regions (coming from the MIX). Even if this chart isn’t based on an academic rigorous research study, it can give you a hint about major trends. And there are several lessons I learnt from using this method. First of all, loan amounts vary largely across regions: while the bulk of loans in East Europe are tailored between $2500 and $6500, most south Asian MFIs disburse credits of less than $500 on average. Thus, saying that we only disburse credits to MFIs having less than $1000 average loan size would discriminate against clients from our main regions East Europe, Central Asia and South America. On the other hand, asking for a good impact on relative poverty (defined as an average loan size / GNI per capita of less than 50%) would penalise Africa in particular.
This brief example illustrates quite well the difficulty of defining poverty (my second lesson from the project) and raises the question of which kind of poverty you want to eradicate. There are three regions performing fine in both ways: Central America, East Asia and South Asia. Reason enough to justify a rectification of BlueOrchard portfolio? Even now, our clients are well represented in these areas, except of South Asia because of legal obstacles. And a little bit surprisingly for sceptics, our “median” client in each region performs only slightly worse than the overall sample. So, what is about the famous trade-off between social and financial performance? Having a look on the chart you only find some correlation between absolute poverty and FSS, however, this issue needs to be more seriously investigated, especially to detect the main external drivers of average loan sizes (e.g. population density). This would help to qualify the big differences in poverty impact across regions and to take right social investment decisions.
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