Mapping meets microlending, two of my favorite topics! Having previously ranked the quality of Kiva microloan partners (and now, for several months, having used the rankings to steer my own loans), I thought I would do some quick and dirty visualization of the results. As a bonus I’ve churned out some visualizations of worldwide Kiva lending, poverty rates, and the relative penetration of Kiva into the neediest countries in the world.
My default quick-and-dirty visualization workhorse is IBM ManyEyes – lacking in design elegance and configurability, but free, interactive and very versatile. Whether in color density or bubble mode (click the link to try that out), I’m a little underwhelmed by the results. I’ve only got 35 loans to work with, so the starting data are thin, but nonetheless, the graphic looks a little too spartan for my tastes.
I’ve also tried a Kiva dashboard page using Nick Felton’s Daytum service (at right); slick but more of a curiosity than anything else. (Nick and Ryan are now on their way over to work for Facebook, but I’m still holding out hope of WordPress-embeddable Daytum panes, and some point!)
Visualization-wise, my best results came by simply adding locations to GoogleMaps, along with judicious selection of a good contrast background (the terrain map). This does (hopefully) a better job at pulling out my preference towards funding women borrowers, showing my interest in lending to Mongolia (spurred by both a visit there, and the admirable social lending practices of XacBank, including both microsavings reinvestment, and green loans) as well as several countries along the former Silk Roads (derivative Central Asian interest from the Mongolia thing, plus some empathy from the locals for surviving millenia of cross-cultural political intrigue).
Of course, my own loans are of interest to, well, no one but me. More interesting is the distribution of all Kiva microloans to date. Most interesting is this distribution compared with the worldwide occurrence of poverty. For the latter, I’ve pulled data from the World Bank, specifically, country-by-country percentages of population living on $2 (USD) per day, or less. Combining that with raw population data converts percentages to poor population estimates – the upper panel in the graphic below:
Clicking the graphic will take you to the live visualization at ManyEyes.org (where you can also visualize many other parameters). In the lower panel above, color intensity shows where Kiva lending has made the “biggest dent” in various countries’ poorest populations (Kiva dollars loaned per “poor” individual). The imbalance between microloan need (upper panel) and Kiva supply (lower panel) begins to emerge. A bubble representation highlights this better than color depth, with the disconnect in Africa being most evident (inverse pattern from South America):
In the highest tier, Lebanon, Ukraine, Azerbaijan and Paraguay show the highest penetration … and it is very high. The approximate equivalent* of more than better than 1 in 100 of these countries’ poorest people have been served by Kiva microloans. Mongolia and a host of Central and South American countries come next (Nicaragua, Costa Rica, Ecuador, Peru, Bolivia) within equivalent penetration rates of 1-in-150 to 1-in-250. In order, Armenia, El Salvador, Tajikistan, Cambodia, the Dominican Republic, Togo, Kyrgyzstan, Chile and Moldova round out uppermost tier, up to 1-in-1000 equivalent penetration.
Back to visualization – the ManyEyes shaded map is still less than satisfactory to me. Rearranging the data as a treemap may communicate better; in the graphic below, countries are binned by region, the boxes are scaled by the absolute size of the <$2/day population, and the color intensity shows the total amount of money loaned through Kiva.
Personally, I think the treemap approach does a better job at communicating relative need and gaps.
Of course for lending “decision support”, I’m more interested in even more finely tuned assessment of need. Comparing poverty rates (x-axis) with lending penetration (y-axis) and throwing in a bubble scaling effect to denote absolute magnitude, brings out some patterns.
This is helpful for understanding (many countries with high Kiva “penetration” have comparatively small absolute populations in poverty) but less useful for quick and dirty decision support when it comes time to make loans. So instead, I converted both the absolute poverty incidence (blue below, from the $2/day data) and the Kiva penetration data (or rather, its inverse; green below), percentile ranked all the countries, and combined the two into one “need ranking”:
In practice, I’ll be comparing this with my MFI rankings from last November. Part of the reason some countries are underserved is due to shakier partner institutions.
Finally, by binning the “need ranking” into high, medium and low categories, and returning to trusty old Google Maps, I can provide a (perhaps) clearer picture of Kiva’s penetration worldwide:
Simplistic, to be sure, but hopefully the analysis and visualization drive home the need for continued penetration of microfinance (whether Kiva or otherwise) into African and Southeast Asian markets.
My final caveat is that “need”, of course, continues to exist worldwide. Personally, I will continue to lend to “green” countries above, as they have mature microfinance networks and, perhaps, “manageably” sized poorer populations, where microlending can make a dent. However, I will also now go out of my way to look for reasonably decent MFI partners (based on my earlier rankings) in “red” countries, where the need is demonstrably greater.
Technical note: To estimate “equivalent penetration” statistics, i.e., “1 in 100 poor people served”, I have taken the inverse of (total dollar amount loaned to a country, divided by the estimated size of the population living on <$2/day, divided by the average size of a Kiva loan [$749]). I.e., I estimate total loans per poor-capita, and take the inverse to get “1 in x” statistics. I call this “equivalent” since actual loan sizes vary, since loan recipients are often repeat customers, once they pay back their initial loans, and since recipients don’t necessarily originate from the lowest tier of wage owners ($2/day is a proxy for overall microloan need). Even with the uncertainties, I think it really highlights the power of Kiva. The fact that countries that develop strong MFI partner networks can reach “1 in hundreds“, not “1 in millions“, type penetration, is both surprising and inspirational.