From the beginning of modern microcredit, its most controversial dimension has been the interest rates charged by microlenders—often referred to as microfinance institutions (MFIs). These rates are higher, often much higher, than normal bank rates, mainly because it inevitably costs more to lend and collect a given amount through thousands of tiny loans than to lend and collect the same amount in a few large loans. Higher administrative costs have to be covered by higher interest rates. But how much higher? Many people worry that poor borrowers are being exploited by excessive interest rates, given that those borrowers have little bargaining power, and that an ever-larger proportion of microcredit is moving into for-profit organizations where higher interest rates could, as the story goes, mean higher returns for the shareholders.
Several years ago CGAP reviewed 2003–2006 financial data from hundreds of MFIs collected by the Microfinance Information Exchange (MIX), looking at interest rates and the costs and profits that drive those interest rates. The main purpose of that paper (Rosenberg, Gonzalez, and Narain 2009) was to assemble empirical data that would help frame the question of the reasonableness of microcredit interest rates, allowing a discussion based more on facts and less on ideology.
In this paper, we review a better and fuller set of MIX data that runs from 2004 to 2011. Though we defer most discussion of methodology until the Annex, one point is worth making here at the beginning. The earlier CGAP paper used data from a consistent panel: that is, trend analysis was based on 175 profitable microlenders that had reported their data each year from 2003 through 2006. This approach gave a picture of what happened to a typical set of microlenders over time.
This paper, by contrast, mainly uses data from MFIs that reported at any time from 2004 through 2011. Thus, for example, a microlender that entered the market in 2005, or one that closed down in 2009, would be included in the data for the years when they provided reports. We feel this approach gives a better picture of the evolution of the whole market, and thereby better approximates the situation of a typical set of clients over time. The drawback is that trend lines in this paper cannot be mapped against trend lines in the previous paper, because the sample of MFIs was selected on a different basis. (We did calculate panel data for a consistent set of 456 MFIs that reported from 2007 through 2011; we used this data mainly to check trends that we report from the full 2004–2011 data set.)