Measuring Financial Inclusion in Africa with the MIX and SEEP Network

It’s hard to dispute that good decisions are based on good data. However, in the world of microfinance, good data is notoriously hard to come by. Early in December, The SEEP Network, a non-profit membership organization of international organizations committed to microenterprise activities to reduce poverty, hosted an online discussion on its blog to discuss recent data collection efforts by the Microfinance Information Exchange (MIX), a Washington, DC non-profit that provides microfinance performance data and analysis. The online conversation centered on financial inclusion in Africa.

On Day One of the discussion, MIX’s data was introduced. The dataset brings together information from over 60 distinct industry resources along with hundreds of individual institutions, covering some 23,000 providers of financial services reaching low-income populations in Africa. The research is presented as an interactive map showing the distribution of institutions that provide financial services to the poor throughout Africa in the context of social and economic factors, by country.

Sub-Saharan Africa has a diverse landscape of financial service providers, but the main challenge is that there is “little consistency of the data or frequency of collection.” Day Two of the discussion focused on how reliable data can be collected. It was recognized that “networks are our unique window onto this world.” Building up data among networks can support member organizations’ advocacy goals at a country level, can show potential investors where gaps in services exist, and can help MFIs expand their markets at a local level. State of the sector reports can also help members of networks put their work in context.

Although data collection is critically important in advancing the goals of the microfinance industry, particularly in Africa, it is also important that it is being leveraged by the right users. Day Three of the discussion focused on who uses the data and how it could be put to work. Data reporting and collection can be heavy burdens on institutions with low capacity, but it was noted that these same institutions have incentives for doing so. In the end, the discussion returned to the central role that networks should assume in data collection, data sharing and analysis in order to gain a more realistic understanding of the world.