Yes, this blog was in a bit of a hiatus. While I put together some more original stuff (and hopefully welcome additional contributors), let me share two maps with you that are pretty cool not just because they are quality products in their own right, but also because of how they were put together.
Source for everything in this post: 5 Maps That Could Help Solve Some of the World’s Most Daunting Problems
My favorite of the five shows the outreach of financial services in Nigeria:
… the Gates Foundation’s Financial Services for the Poor program launched an ambitious geospatial project in Nigeria. Armed with BlackBerrys and traveling in a grid pattern, 30 surveyors undertook a three-month campaign in Nigeria to pinpoint all of the services available—from bank branches to microfinance institutions to post offices and more. They logged GPS coordinates and took photos. … The resulting website gives the best picture yet of how financial services work in a developing economy. Jake Kendall, the Gates Foundation program officer who heads the project, hopes financial institutions will use the map to figure out where to open new locations. It can also guide public policy: Kendall’s team used this map to show the Nigerian central bank governor the need for mobile-based financial services, since it demonstrates that more than half of residents live outside a 3-mile radius of brick-and-mortar options.
A good map inspires curiosity. I find myself asking, how do you get to people who do not have mobile coverage? (Those are the non-purple parts.) Mobile money agents are often touted as a great list mile solution – yet, they often overlap with existing infrastructure, and in a fair few cases, are missing where other delivery channels, such as post offices, are present. Are mobile money agents really that redundant? Are there alternative delivery channels for financial services one should explore?
The second map is of the biggest slum in Kenya, Kiberia (the Wired piece lets you zoom into the map):
African maps are notoriously problematic. Much of the data is old; roads, particularly footpaths, languish unnamed. Africans often navigate by informal landmarks like bars or gas stations, places not represented on standard maps. The slums have it even worse: On Google Maps they figure as blank expanses, in keeping with their reputation as shadowy, marginal places.
Enter Spatial Collective and Map Kibera. These two organizations, a company and a nonprofit, are mapping a Kenyan mega-slum called Kibera—the name is derived from a word meaning “jungle”—according to how its 200,000 inhabitants actually navigate it. The maps started with crowdsourced landmarks important to locals: water taps, schools, pharmacies. Residents with Internet access were invited to add to an open source map; others contributed data by SMS or attended community workshops, where they wrote on giant empty maps.
Spatial Collective overlays these community-generated maps with official data. One project tacked on sewer-line data (depicted here) from Nairobi City Water to find the most valuable spots to build new public toilets. Another mapped community-reported crime data to help the World Bank understand where to place safety interventions like lamps. These kinds of projects make Kibera more legible to its inhabitants and to outsiders.
In addition to the ability to provide efficient placement of new services, the fact that this map was crowdsourced is particularly noteworthy. By collecting information deemed useful to the residents of the slums themselves, whatever work done based on this will presumably be more relevant to them. Incidentally, comments in the article suggest that this is built on OpenStreetMaps’ work in mapping Kibria (http://mapkibera.org/) – that in itself is worth checking out.
Although most of my work is with microfinance institutions, I have the good fortune to catch up with great institutions doing quite innovative work every now and then. D.Net is one such institution, who I interned with way back in 2006. Most of their work is in the realm of ICT4D (ICT for Development) and over the years they have pushed out a couple of quite successful technology-reliant solutions.
One of the latest one they are working on is this concept called the Info Lady. It’s an entrepreneurial woman who is provided skills training and a notebook and other digital equipment, who goes around in a bike in her own community and those near it and provides livelihood information, usually for a fee. Sounds a little … far-fetched, right?
Turns out, it actually works and is a sustainable business model. The original idea was inspired by D.Net’s success with Pallitathya Kendras, or Village Information Kiosks where people would come to these centers and obtain all manners of livelihood information for a fee. The Info Lady model simply extends this idea by taking that information to the doorsteps of clients, literally.
There are 90 possible services that could be provided, but based on demand most Info Ladies provide 20 to 25 services. Offerings include everything from health services (blood pressure, pregnancy tests etc.) to information on income generation activities to assistance for workers seeking employment abroad to taking pictures or videos at weddings. In fact, the last one has turned out to be quite popular and is also a good source of income for the Info Ladies.
Info Ladies require a fair bit of training on domain knowledge and technical expertise, which requires an extensive training program over a couple of weeks. The program is being rolled out in a handful of districts. There are 57 Info Ladies in the field at the moment, with 105 undergoing training. The target is to have 300 Info Ladies offering their services by the end of the quarter.
There is an upfront cost to getting the Info Ladies set up. The total up-front cost can vary between $1,500 to $2,000 depending on the equipment options. D.Net is subsidizing a part of the costs, but the Info Lady is expected to come up with most of it on her own, or take responsibility for most of it. One option is a 3-yr loan from the public National Bank that comes with a 3-month grace period and a 9% interest rate (commercial interest rates are around 19%). The average earnings reported is about $150 per month, which makes a monthly loan servicing amount of $50-$60 conceivable. By the way, the highest consistent earnings reported is a little over $600; while this can’t be taken as the average scenario, it does show the earning potential of the practice.
D.Net is also trying to get the Bangladeshi Diaspora involved by getting them to sponsor Info Ladies – another hallmark of D.Net projects.
Here’s an article that came out in The Daily Star (link to an archive because the paper’s website is currently migrating to a new one..). And here’s a powerpoint that gives a bit more detail on what an Info Lady does:
There’s poverty everywhere …
We’ve spent a fair bit of time talking about various methods to study poverty and development in this blog, ranging from plain vanilla surveys to financial diaries, RCTs and portfolio analytics, and it has all been within the context of developing countries. It is a reality that poverty exists in the developed world too, including the most vibrant economy in the world, the US. This post will talk about the US Financial Diaries program.
Just to put things in context, here’s a map of the world showing poverty percentages compared to the national poverty line:
… including the US
This tells us between 10% and 20% of the population live below the poverty line in the US. The Oracle provides us with some more detail:
Poverty is a state of privation or lack of the usual or socially acceptable amount of money or material possessions. According to the U.S. Census Bureau data released Tuesday September 13, 2011, the nation’s poverty rate rose to 15.1% (46.2 million) in 2010, up from 14.3% (approximately 43.6 million) in 2009 and to its highest level since 1993. In 2008, 13.2% (39.8 million) Americans lived in relative poverty. In 2000, the poverty rate for individuals was 12.2% and for families was 9.3%. In November 2012 the U.S. Census Bureau said more than 16% of the population was impoverished, and almost 20% of American children live in poverty.
And details can be found in all its glory right from the horses mouth; some of the highlights are copy/pasta-ed below:
- In 2011, the family poverty rate and the number of families in poverty were 11.8 percent and 9.5 million, respectively, both not statistically different from the 2010 estimates.
- As defined by the Office of Management and Budget and updated for inflation using the Consumer Price Index, the weighted average poverty threshold for a family of four in 2011 was $23,021.
- The poverty rate for males decreased between 2010 and 2011, from 14.0 percent to 13.6 percent, while the poverty rate for females was 16.3 percent, not statistically different from the 2010 estimate.
- In spring 2012, 9.7 million young adults age 25-34 (23.6 percent) were additional adults in someone else’s household. The number and percentage were both unchanged from 2011.
- In 2011, 13.7 percent of people 18 to 64 (26.5 million) were in poverty compared with 8.7 percent of people 65 and older (3.6 million) and 21.9 percent of children under 18 (16.1 million).
- The South was the only region to show changes in both the poverty rate and the number in poverty. The poverty rate fell from 16.8 percent to 16.0 percent, while the number in poverty fell from 19.1 million to 18.4 million. In 2011, the poverty rates and the number in poverty for the Northeast, Midwest and the West were not statistically different from 2010. The poverty rate in the South was not statistically different from the rate in the West. In addition, the Northeast poverty rate was not statistically different from the rate in the Midwest.
More than one in eight individuals living in poverty is bad, right, given the relative abundance of wealth in the US? Consider the fact that 16 million (22%) of all children live in poverty – a number exacerbated along ethnic lines (38% of Black and 35% of Hispanic communities) and one can see how this is an important issue to address in the US. [Source for numbers.]
Enter the US Financial Diaries
There’s been much renewed interest in the lives of the poor since financial armageddon a few years ago. Michael Barr’s No Slack: The Financial Lives of Low-Income Americans is a recent publication that based on 1,000 in-depth surveys that elaborate various ways in which the financial system “fails the most vulnerable Americans”. The U.S. Financial Diaries project is driven by similar motivations – it wants to understand how low-income individuals and households manage their financial lives.
You’re probably familiar with the financial diaries methodology championed by PoTP – conducting repeated surveys exploring every detail of a HH’s financial life for an extended period of time. This provides unprecedented detail, specially into behaviors that are difficult to pick out in one-off surveys, such as extent of engagement with the informal sector, and savings intermediation habits. As one can imagine, the presence or absence of various financial instruments creates a completely different ecosystem than say what we see in Kenya or India or the Philippines.
Non-probability sampling techniques are used, so the results will not be representative of the entire US. “Sites have been chosen to ensure geographic, ethnic, and racial diversity of households and urban/rural communities,” according to the project website, with sites including “Cincinnati and surrounding areas, Northern Kentucky, Eastern Mississippi, San Jose and surrounding areas, Queens, and Brooklyn”.
The project is in the data gathering stage right now, and we can expect analysis to start coming out in 2013 itself. It’s got the heavyweights in this field behind it – it is being administered by New York University’s Financial Access Initiative (FAI), Bankable Frontier Associates (BFA) and The Center for Financial Services Innovation (CFSI), with funding from Ford Foundation
and Citi Foundation and the Omidyar Network.
I for one can’t wait to see what comes out of it – the chance to compare and contrast with similar studies around with world will be quite interesting, as will be any follow up work that utilizes this information to design interventions to address the poverty situation in the US.
I found some pretty nifty Python code online that allows one to calculate Excel-like XIRR, and used the publicly available P9 data as meat for the grinder. This post shares the goodies that came out through the other end.
P9 is a pretty cool savings-and-loan product managed by Start Rutherford and SafeSave. Clients take a certain amount out and commit a significant portion of it to a sort of savings escrow. First, they pay down the loan, and then accumulate up to the amount of savings that is held in that escrow. This mechanism provides an immediate access to cash in the short term, and builds up savings in the longer term.
There are a couple of things that stand out about P9, two of which particularly piqued my interest:
- Clients can take however long they want to pay back the drawn down amount, and they can pay back as often (or not-so-often) as they want, and
- There is no interest rate associated with the draw down, only an up-front charge of 1% or 3%.
So … how long do clients take to pay back? And, how much are they paying for this service in effective interest rates (EIR)? Let’s take a look.
Keeping it short and sweet
P9 has about 800 clients, and they have collectively gone through almost 5,000 cycles. Each of those cycles are counted separately (and not all the cycles are counted here – see fine print below). The overall distribution is like so:
Do you see something interesting here? There are relative peaks around the 30, 60 and 90 day marks. They’re not massive, but they are accentuated by the troughs on either side. There is nothing in the product design that would reinforce a 30-, 60- or 90-day cycle, so there must be some kind of external cash flow event these line up would, unless the client is self-enforcing this regularity. Possible candidates could be salaries, remittance inflows and other microfinance institution (MFI) disbursements that do enforce periodicity – but I’m just guessing here.
Thus, 2/3 of the clients pay back within 90 days, and virtually all do so within the year.
This is good news, in that not only does P9 preserve its capital, but manages to cycle it multiple times within a year. The range of cycle lengths also suggests that there is demand for flexible-duration loan products – a feature that products offered by MFIs sorely lack.
But.. (yes, there’s always a “But..”) if clients are going through multiple cycles, they are also paying the up-front fee multiple times. And by the laws of compound interest, 2% and 2% tends to add up to more than 4%.
No Surprises with the EIR
How bad could it get? Well, the extreme case is someone going through 1-day cycles of 1%-fee drawdowns. This gives a EIR of 3,500%. You’ve also probably seen pay-day loans carrying EIRs of hundreds of percents. So hypothetically at least, it can get pretty bad.
This is what it looks like for P9:
The EIRs for the shortest cycles are pretty high, as expected, and tapers off rapidly as cycle lengths get longer.This relationship holds at all percentiles, also as expected:
If you’re worried about the 156% in the 90th percentile, note that this is for “30 days or less” bucket, and involves cycles which are a couple of days long, at most.
There is a certain amount of variability in the repayments, as allowed by design, so the EIRs aren’t exactly what one would expect with a uniform paydown. If more of the payments happen earlier on, the EIR is bumped; if more of the payments happen later on, the EIR is reduced.
Words of Caution
First, this analysis doesn’t take into consideration all cycles clients have gone through. It ignores the about 1,000 cycles that are involved with top-ups, and another 200 that were discarded for various reasons. This leaves about 3,700 cycles for this analysis. Top-ups were ignored because it requires extra-special care when stitching consecutive cycles, and I’ll do it when I have some more time.
Second, while EIRs are very useful for analytical purposes for apples-to-apples comparisons, they tend to lose their utility a bit when very short time frames are involved. By virtue of their compounding nature, they assume that all returns will be reinvested continually too, in addition to principal, which is hardly the case in real life from the client’s point of view. Thus, the 156% we picked on above very, very probably has no connection to anything in reality in that client’s life.
Special thanks to MFTransparency’s Tim Langeman who shared the Python code needed to calculate the EIR using cashflow discounting, just like Excel’s XIRR function, in this post. His work is based off of Skipper Seabold’s post here. It saved me a lot of time being able to re-engineer their work for my needs.
The CEME Inclusive Commerce Blog went dormant about a year ago. Graduations, comprehensive exams, various life-altering events – you know how it goes. But, we’re back, and hopefully will have more posts on more interesting topics from more contributors than ever before!
Welcome to the Center for Emerging Market Enterprises’ Inclusive Commerce Blog!