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 finally got my hands on Lamia Karim’s Microfinance and Its Discontents: Women in Debt in Bangladesh, and it made for a great read. If you are interested in this field, you should check it out. I thoroughly enjoyed the narrative and appreciated her attention to detail in terms of laying out the context necessary to follow in her anthropologist’s footsteps, so to speak.
I think the biggest contribution this book makes to the field is providing another counter to the PR-ridden “microcredit is the silver bullet to poverty” storyline that has done as much harm to industry by setting up unrealistic expectations of what microfinance in general, and microcredit in particular, can and does do. For better or worse, anecdotes continue to play a strong role in shaping the perception of the utility of microcredit in the absence of rigorous quantitative proof either way, partly because pretty much every recent RCT has found no evidence of statistically-significant impact (as opposed to evidence of no statistically-significant impact …).
Check out Chapter 4 for the 7 narratives provided. 3 of them end up doing well, 4 of them – not so much. The complexity of each person’s life and the financial intermediation they have to undertake in the presence of other instruments amply illustrates the fallacy of relying on a linear narrative that draws a causal connection between providing credit and increased income.
Chapter 1 and Chapter 5 are nice contributions to discussions about the genesis of the NGO scene in Bangladesh, especially when it comes to those providing microcredit. There used to be four – Grameen, BRAC, ASA and Proshika. And then the fourth kinda went way overboard with what it was trying to do, and then there were three. Proshika’s story of demise is interesting in itself, but is also an example of the dangers of confronting interest groups within existing social hierarchies head on, as opposed to working with them as most others try to. Reading these chapters makes one appreciate the institutionalized impediments to development the microfinance industry had to overcome.
One should keep in mind a couple of things while reading this book though. The lion’s share of her work was done in 1997/98. So when the publisher says this book “offers a timely and sobering perspective on the practical, and possibly detrimental, realities for poor women inducted into microfinance operations” on the back cover, I’m not so sure about the “timely” bit. This is not to say that a lot of the societal dynamics are still not relevant today, but microfinance as an industry has come a long, long way in 15 years, as has the critical awareness of civil society and the media to developmental initiatives. It is virtually impossible to imagine that “house breaking,” for example, is sanctioned or possible on an industrial scale today.
There is also this sense of exploitation of women borrowers on an industrial scale, although this book’s various reviews are probably guiltier of overhyping this than the book itself. It is couched in a neoliberal narrative – one that has found particular traction amongst critics of microfinance. In so far as “neoliberal” denotes “more markets, less state,” microfinance is guilty of that charge. Unfortunately, Lamia Karim assigns predominantly negative characteristics to those who are successful in this “neoliberal” enterprise – they lived by the principles of “competition and rationality,” and “while NGOs construct female borrowers as entrepreneurs, the emergent neoliberal subjectivity that I encountered was that of the petty female moneylender. The female moneylender embodied all the competitive aspects of the neoliberal subject.” (p.p. 199-200)
She makes a similar case on how “introduction into private life has led to loss of social solidarity,” where “introducing loans into private life, NGOs have begun to weaken the kin-based bond of identification and family solidarity.” (p.g. 200). I think it’s fair to say that most practitioners would be very confused with the first statement, and point out that most processes of upward economic mobility has the effect of reducing family size and relationships becoming more nuclear. I won’t go through all the other things that I found similarly odd, but the chapter Conclusion is littered with them.
In summary, put a tinfoil hat on for the last chapter if you must, but the book is good – check it out.
Natural experiments are great for the social sciences, and has been exceedingly rare for microfinance. Then the AP crisis came along, and caused the kind of change in circumstances that make for just such an experiment.
Basically, microfinance institutions beat a hasty retreat, writing off their outstanding loans as losses to a large part. This caused a pretty significant vacuum in liquidity providers in local economies. Guess who filled it up. SHGs, to a certain extent, sure. But this was also the perfect opportunity for the traditional archenemy of microfinance – moneylenders – to make a roaring comeback!
Relevant section from the abstract:
Both studies validate the fact that the members of the community face issues raising credit in the absence of MFIs. Members of the community have reduced their spending on important aspects such as health, education and business because of non availability of adequate credit from alternative sources. Moneylenders are having a field day with the absence of MFIs. Members of the community are falling back to moneylenders who charge usurious rates of interest to meet their credit needs.
Here’s the full report: Andhra Pradesh MFI Crisis and Its Impact on Clients
And a related policy brief: What Are Clients Doing Post The AP MFI Crisis?
A friend of mine recently forwarded me a recent publication titled The Limitations of Microcredit for Promoting Microenterprises in Bangladesh that appeared in the Jan-Mar 2012 edition of the Economic Annals. I sort of feel bad for picking on this one study but it’s somewhat representative of a few others I’ve seen where a couple of things just bothered me a brick ton.. Here’s a sampling. Apologies in advance to the authors; and I’m sure my turn will come soon enough
Claim 1: The field survey shows that about 11.7% of the microcredit borrowers are this kind of potential or growing microentrepreneur (Abstract).
Except, the survey this paper is based on was not randomly sampled (p.g. 43):
Samples were selected from urban (32.4%), semi-urban (27.2%), and rural (40.4%) areas, to ensure that microborrowers of different sized loans engaged in various categories of economic operations in rural and urban settings were adequately represented. In the absence of full knowledge of the structure and distribution of the microcredit borrower population in the country, random sampling as representative sampling is neither possible nor desirable (Molla and Alam, 2011). Moreover, in many situations random sampling is neither effective nor cost effective in serving the purpose for which sample data are collected. Purposive or judgment sampling is effectively used in such cases. Accordingly, a judgment sampling procedure was thought more effective and appropriate for this survey.
A couple of things:
- The 11.7% is not representative of the universe of Bangladeshi microcredit recipients, but of the non-random sample used in the study. Indeed, unless the distribution of microcredit borrowers is exactly 32.4%:27.2%:40.4% over urban:semi-urban:rural areas, the number is anything but 11.7%. Not a lot of MFIs want to work in urban areas, specially slums, where a large proportion of the urban poor and ultra-poor live. Ask Shakti Foundation, one of the few MFIs that serve this challenging demographic. The real number could be 5%, it could be 20% – who knows..
- I don’t buy the excuse that random sampling is not appropriate. By the authors’ own admissions, there are 15 million microcredit borrowers. (I think that’s a low ball number, but let’s go with it for now.) There are 150 million people in the country, including infants, the middle class, the elderly – demographics which are not obvious target populations of MFIs. You are almost guaranteed to hit a MFI borrower if you throw a … pillow a few times. Purposive or judgemental sampling is done when you are either targeting a very specific group and you don’t care about being representative, or there are so few of those you want to talk to that you have to search them out with deliberation. I can’t figure out how that could possibly be the case here.
- I find the suggestion that “in the absence of full knowledge of the structure and distribution of the microcredit borrower population in the country, random sampling as representative sampling is neither possible nor desirable” quite counter-intuitive. Indeed, if one does not know the underlying distribution of borrowers, a random sample would have illuminated that unknown too, contributing to the findings of this paper. Also, one has to look at the big three – Grameen, BRAC and ASA- and one can guesstimate fairly accurately what the distribution is..
It would have made much more sense to present the findings in three silos – urban, semi-urban and rural, and share all the findings within those segments. It would have been more appropriate than as an aggregate too since, conceivably, the urban implications of microcredit on microenterprises is somewhat different from rural ones.
Claim 2: A sizeable chunk of all borrowers, microentrepreneurs or not, have issues with the terms of credit, which are inadequate for entrepreneurial purposes. (p.g. 47, my summary)
The entire para is as follows:
About 20.7% of all the borrowers and 15.4% of the microenterprise borrowers believe that they do not have the scope to effectively use the entire loan amount at the start of activities. In practice about 29.2% of all the borrowers and 20% of the microenterprise borrowers did not use the entire loan amount at the start of their business operations (Table 3). On the other hand, about 27.9% of all the borrowers and 55.4% of the microenterprise operators had to top-up the loan fund with personal or other borrowed funds to start operations. On top of that about 21.4% of all the borrowers and 8.6% of the microenterprise operators invested additional funds during the year, either from personal sources or from credits obtained from other microcredit providers. About 28.3% of all the sample clients and 40% of the microenterprise clients received multiple loans (2-3 or more) from 2-3 or more microcredit institutions.
I agree with the general thrust of the message. The rigid disbursement and repayment schedules are not conducive to the fluid needs of business, and borrowers often have to borrow from other sources to make up working capital shortfalls.
But the numbers I see here actually don’t seem that bad:
- If 20% of borrowers don’t believe they can use the entire loan amount right away, then 80% believe they can, right?
- Similarly, if almost 3/4 of borrowers and 1/2 of microentrepreneurs do not have to top-up funds right at the beginning, that’s not too bad, right?
- Also, similarly, if almost 80% of borrowers and > 90% of microentrepreneurs did not have to invest additional funds, that’s not terrible either, right?
- 40% of the clients borrowing from multiple MFIs could be seen as a bad thing, but we have to be careful not to equate miltiple-borrowing with overindebtedness. PotP is chock full of examples which clearly demonstrate how sophisticated the poor are in their financial management, and Bangladesh was one of the study countries too.
I mean, it looks like microcredit is able to satisfy funding needs at various levels for 75-80% of borrowers in general and 50-90% of microentrepreneurs in particular, more or less. If we demand more, are we not holding microcredit to an unrealistically high standard, given the realities of the products and distribution channels?
Again, the bone I pick is not with the underlying message, but that the numbers put forward seem to weaken the case being made.
Claim 3: (The) preference for women as clients for credit is found to be a strong methodological limitation of the microcredit delivery system in promoting microenterprises. (p.g. 45-46)
The authors make a compelling enough case, up to a point. Men tend to run businesses in Bangladesh, and their survey shows how the female clients simply pass on the microcredit to their male counterparts. The respondents note the following as reasons for dependence on men (p.g. 45):
- inability and lack of skill of the women borrowers,
- more investment opportunities in man-relevant activities,
- male-dominated family structure where male members maintain and control family,
- social environment and custom where business activities are considered to be men’s work, and
- women are not expected or respected in the domain of men’s activities (business activities)
So .. Why don’t MFIs simply lend to men? Blind ideology, or is this something based on reality?
Google “men microfinance” and you’ll get a ton of useful discussion, interspersed by a couple of good studies on this issue. The short answer is that we may not always know why, but men tend make for crappy borrowers. There is something in the woman-borrower/man-entrepreneur dynamic that “works.” (But may not always “work” in a way that is comforting – check out Lamia Karim’s work for societal dynamics gone bad.) Man-borrower-entrepreneur models don’t tend to work.
It is not constructive to simply take out the borrower intermediary when she clearly has something big to do with things.
It is also why SME lending has been so hard.
There are bunch of other things that gave me reason for pause, including:
- The study relies too much on the Grameen model. BRAC and specially ASA do not do things like Grameen, and the results might be quite different for them. The authors may find that the “stereotyped microcredit delivery system” may have considerable variation within it.
- There is no “counterfactual” to the claim that “microcredit is not sufficiently productive to generate enough revenue for interest payments if market rate wages are paid for family labour” for a significant portion of the borrowers. What if they did not borrow? Would they earn more? Would they starve?
- It calls 25%-65% interest rates exorbitant, citing Bangladesh Bank lending rates of 4-5%, and commercial lending rates of 10-12% (p.g. 42). 65% could be considered exorbitant, but 25%? And most importantly, there are very, very real reasons why microfinance interest rates are so high. And it’s not because Grameen/BRAC/ASA are wannabe loan-sharks.
- Its citations are .. unimpressive. One study used to comment on male-female gender dynamics is from 1996 – arguably an eternity in terms of the evolution of microfinance (p.g. 45). Commercial lending rates are quoted from 1997 (p.g. 42). More than half the references are the authors’ own, and the rest are mostly links to MFI reports.
Overall, this piece has decent analysis behind it. I think it gets into trouble trying to hammer out conclusions from it that are not adequately supported by the data.
By the way, if 11.7% of the (non-random) sample are microentrepreneurs of some stripe, what about the remaining 88.3%? What are they using microcredit for? If microcredit has limitations for “promoting microenterprises in Bangladesh,” what is it overwhelmingly succeeding in doing?
Wouldn’t that be fun to know!
Microfinance Banana Skins 2012 is out! As always, it’s a good read, and gives one a sense of the shifting perceptions of risks in the microfinance industry.
We have a new entrant at #1 this year – overindebtednes:
This year, our survey has identified another worrying trend – a widespread perception that the industry could well find itself facing the kind of bad debt problem that many conventional financial institutions have had to cope with in the last few years. The reason is simple: too many clients of too many MFIs have taken on too much debt. Hard figures are difficult to come by – and some observers of the industry believe that the worst of the problem is actually behind us. But the most striking result of this year’s survey is clearly the very high risk ranking attached to over-indebtedness among MFI clients.
This is at least in part due to the Andhra Pradesh microfinance crisis. Over-saturated lending has been a rising concern across the world, and the highly publicized implosion of the industry followed by the heavy handed regulatory response in AP brought to the fore an issue that has been of increasingly grave concern for practioners.
(For all it’s worth, it used to be accounted for under “credit risk” but I guess it’s been so talked about now that it gets its own category.)
The part that interested me more is how the top-1o of the “banana skins” have changed over the last 4 years. Here’s a screenshot from page 46:
That’s .. a lot of movement.
Sure, the last few years have been transformative. There’s the global financial crisis, and microfinance isn’t quite as counter-cyclical as it used to be. And then there are the pains brought on by the industry as it matures – commercialization, over-saturated lending, predatory interest rates etc. etc. It makes sense that the risks have changed in response to these changing circumstances.
One has to wonder though .. how correlated are the perceived risks to “actual” risks in the industry? How influenced is it by the “noise” out there? Are MFIs in more of a holding pattern at the moment, or are aggressively affecting and being effected by change, resulting in the recalibration of risks as seen above?
Overall, the Banana Skins survey does a nice job of talking to various relevant stakeholders, and for an industry kinda prone to hype and hearsay, this is probably one of the closer feels we can get of the pulse of people closest to the ground, so to speak. Check it out.
How important are compulsory deposits, insurance, etc. to calculating Annual Percentage Rates (APRs)? I played around some more with the MFTransparency dataset on India I wrote my last post on to answer this question, and it seems the answer is, “it depends”.
The Pricing Data Report looks at APR including deposits and APR including insurance, but in addition to this, the web portal offers APR figures that include neither, and that include both. It then becomes a trivial exercise to figure out how much compulsory deposits and insurance contribute to APR.
Intuitively Speaking …
The graph below is shows what happens when you calculate APR by including neither of, one of or both of the two constituents – deposits and insurance:
The red and blue columnar bars were presented before; the grey one is kind of a baseline without neither deposits or insurance, and the green one includes both. The error bars are the maximum and minimum.
By seeing how the red and blue bars change compared to the grey, we get an intuitive sense of what is going on. For co-ops, there is a small change of about 1% from the base 23% when you include insurance, but when you include deposits, it shoots up to 35%. On the other hand, for for-profit public MFIs, there is no difference from the base value of ~30% when you include deposits, but on including insurance, this moves up about 3%.
More Formally …
Fortunately, the MFT site allows you to download all the data and come up with precise numbers for what the differentials with respect to deposits and insurance. The graph above is built off of columns A, B, C and D from the table below. Columns E and F give us the differentials.
This tells us that:
- Co-ops charge an additional 1000+ basis points on top of interest and fees by taking compulsory savings and deposits
- For profit public MFIs charge an additional 250+ basis points on top of interest and fees by charging for insurance
- Other types of MFIs charge between ~100 and ~250 additional basis points on top of interest and fees through insurance and deposits
How’s that for “it depends”?
This also goes to show why proper pricing is so important – if one were to look at the top line numbers reported for interest rates, one would very easily conclude that Co-ops were about 10% cheaper than other MFIs as a source of funds.
By the way, the “Unexplained” in the last column is essentially the difference between the kitchen-sink APR as reported by MFT, and the kitchen-sink APR that we get when we reconstruct it from its constituent parts. That all the numbers are less than 1% is a good sign – we’re not missing some big chunk of data when we do the differentials.
While there is a fair degree of divergence on what constitutes APR, it turns out that the averages for each type of organization is within a narrow band of 32.3% and 36.7%. Not surprising, since the Indian microfinance sector has many players and is increasingly saturated, leading to a leveling of the marketplace when it comes what a financial institution can charge a for a fairly similar service to a fairly similar market segment.
It would be interesting to see how the reported interest rates diverge from this APR, and hopefully that will be the topic of the next post.
Skin Your Own Cat
I’ll briefly outline how to get the data I used to pull together the tables and graphs above, since it’s a little involved:
- Go to: http://www.mftransparency.org/data/countries/in/data/
- Under Filter Graph Results on the right, select the kind of Calculation Method you want to use (there are four types – stick to one)
- Also under Filter Graph Results on the right, select the kind of Institution Type you want to use (there are six types)
- Make sure the Loan Size and Number of Clients boxes are empty (the site fills them up with some defaults – this may filter your results. Also, do not press “All” button under the graph – it reverts to some kind of default …)
- Hit Filter Graph
- From the page that comes up, select the table only, and paste it into an Excel worksheet As Text
- Go back to step 3, choose another kind of institution and follow steps to #6, pasting in a new worksheet
- Once all institution types are done, go back to step 2, choose another calculation method, then go through #3 to #7
- Once all calculation methods are done, throw in your formulae in Excel
Yes, you will have to pull in 24 feeds from the site to get all the data, so take care as you consolidate data. Wouldn’t it be nice if you could have one extra column for institution type, and then a column each for each of the types of APR calculations.. Probably wouldn’t fit on the website, but that being available even as a CSV would have made life so much easier.
I should also note that the numbers I got from the raw data are slightly different than the average, maximum and minimum values that are in the report, but I couldn’t tell you why.
MFTransparency does some awesome work surrounding pricing transparency of micro-loans. One of their latest initiatives involved pricing microfinance loans in India. The report is available here, and you can play around with the data they collected using a pretty nifty dashboard-like tool here.
Transparent pricing is necessary because the terms that come with relatively innocuous looking micro-loans can be quite involved. The Annual Percentage Rate (APR) is considered to be the “true” price of a loan, and is often used to make apples-to-apples comparisons between loan products. If all the loan involves is taking out a principal amount of say 10,000 [insert your favorite currency (YFC) here] at an interest rate of 24%, and is paid down in weekly installments using a declining balance method, its a fairly simple calculation.
However, a range of “features” are often tied to the product and this can make calculating APR a less than trivial task, not least because not all MFIs are equally diligent when it comes to properly disclosing them to their clients. Some examples of such “features” would be:
- Principal and interest payments being paid down on a straight-line schedule
- Paying over 52 weeks but in 50 installments, allowing 2 weeks for national holidays
- Having a compulsory savings amount of 25 YFCs, payable every week
- Having a compulsory insurance premium of 10 YFCs, payable every week
- Having an origination fee equal to 1.5% of initial principal balance of the loan
- First installment being due on the day the loan is disbursed
… and so on and so forth.
MFTransparency takes this smorgasbord of product offerings and harmonizes them into APRs so that we can compare all the MFIs they talked to on. Because the 82 MFIs sampled represent about 80% of both the gross loan outstanding and the number of borrowers, it’s a very nice representative sample of the Indian microfinance market.
The Answer Is …
Anyway, enough background info. The top-line number, the APR for Indian MFIs sampled, is:
32.78% .. or 32.61%
Yes, it’s perfectly fine to round that to 33%, and leave it at that.
If you’re wondering why there are two numbers, it turns out there is a “it depends” surrounding the supposedly-ultimate-harmonizer, APR, also.
According to MFT, there are four possible interpretations:
APR India (Int + Fees + Deposit) : Interest + Fees + Security Deposit APR (excluding insurance) : Interest + Fees APR (Int + Fees + Ins) : Interest + Fees + Insurance APR (including security deposit) : Interest + Fees + Insurance + Security Deposit
Throughout their report, they use the two that are bolded. The one called “APR India” that includes deposits such as compulsory savings but does not include insurance is the one used by India’s Microfinance Institutions Network (MFIN). The one called “APR” is the more “international” one used by MFT generally, and includes insurance but does not include deposits.
It makes a difference on which one you use, as you can see from the graph below:
(The information presented in this graph is pulled from two graphs/tables in the report – figures 17 and 18 on pages 29 and 30 respectively.)
The implication of using one or the other is as follows:
- If you include insurance but not deposits (APR), co-ops have a much lower average interest rate, compared to other types of MFIs.
- If you include deposits but not insurance (APR India), public for-profit MFIs have the lowest average interest rate, but all types are pretty much at par with one another.
This is a result of the fact that co-ops use member savings and other forms of compulsory deposits as a source of funds to a greater degree than their peers.
Sadly, the report does not present APR values when you include all four – interest, fees, insurance and security deposit. It is available on their website though, but one needs to use the filters to pull out numbers for one type of institutions and one type of APR at a time. (Unlike MiX, you can’t get a ginormous Excel dump that includes all the data, as far as I can tell.)
I’ll try to pull all that together soon; in the meantime, the answer is 33%, more or less.
Gambling to Save
The Haitian lottery stalls are indeed fascinating (see Amin post). They are still going strong despite the earthquake, cropping up like weeds throughout the tent cities.
Their popularity is understandable.
Borlettes are a pretty good place to save, when you look at the alternatives. In Haiti, the poorest people were fleeced by credit unions in 2004. Depositors had sold their assets – cows, goats, and bicycles – to put their money into the Caisses Populaires, which were promising ridiculously high and unsupportable returns. Huge numbers of Caisses collapsed from fraud, leaving depositors with nothing. No wonder playing the borlette seems like a good option. A 20% negative return is better than a 100% negative return.
Turning Thrill-Seekers into Thrift-Seekers
Understanding gambling impulses is key to designing safer financial services. Many financial institutions (BRI for example) look at the prize aspect of the lottery and incorporate elements of chance into their savings products. But, product design is only a piece of the puzzle. Savvy businesses and banks might make dull products sizzle if they paid heed to what their shadier cousins down the street were doing. For example, in Haiti, an entire value chain has sprung up around the borlette culture, which involves paying for the divination of lucky numbers. It’s big business and shows how engaged users are in choosing their bets.
But, I digress. Here are some ground rules for banks and MFIs whose savings services are just a bit too bland:
Rule 1 – Take your cues from culture. If people are using dreams to help them win the lottery, then market savings product that ask users to chase their dreams. If they are consulting astrology experts to win, then offer them astrological consulting your savings product. If lottery stalls are painted in bright colors, then paint your branches in bright colors.
Rule 2 – Make your product crystal clear. If users like the transparency of the lottery (and they do because they know ahead of time exactly how the numbers are drawn and what they payout will be), then make your incredibly complicated savings product incredibly simple. Forget compound interest. Offer them something better – airtime minutes, lottery tickets, whatever is easy to calculate.
Rule 3 – Be ubiquitous. If people like the ubiquity of the lottery (they can get rid of any loose change frequently) then find a way to make your product ubiquitous. Sell savings through savings resellers. We know people will pay to save, and M-PESA is proving this.
And It’s Not Just Haiti
It turns out that Haiti is not the only country where local gambling stalls are chock-a-block. For example, in Ghana, more newspapers are dedicated to the lottery than to any other topic, catering to masses of players. The Dominican Republic has an estimated 35,000 stalls equivalent to that of Haiti. The Fahfee in South Africa is ever popular and in India private lotteries are spreading like wildfire. Even Bhutan – yes Bhutan – has lottery kiosks in shopping malls. So get out there and see why people would rather put their coins into games of chance and not into safer savings. Then make and market a more exciting product. Convert a few punters headed for the gambling stall into a few depositors headed for the savings stall.
Not much, at face value. One could even claim that lotteries are quite antithetical to the spirit of savings – how could one, in good conscience, compare potentially reckless and addictive gambling with the perseverance and self-discipline that savings demands?
Funny thing is, it turns out that in some cases, they are not very different mathematically at all.
I recently read this fascinating paper titled Savings and Chance: Inclusive Finance and The Haitian Lottery on the Haitian lottery institution surrounding borlettes. Participants bet on the numbers drawn in U.S. state lotteries at kiosks, and payouts are made based on some combination of two to five digits.
Operators add their twists to this, but here’s how one of the simplest forms of this works – choose three numbers between 1 and 100, bet $1 (or 1 gourde, the local currency) on each, and wait for the radio to announce the U.S. state lottery numbers. If your first number corresponds to the lottery, you get a $50 payout. If your second number corresponds, you get a $20 payout. And if its your third number, its a $10 payout.
On any given day, for every $1 coming in, the expected payout for a kiosk is therefore ($1 x 0.5 + $1 x 0.2 + $1 x 0.1) = $0.80. This is also the expected return for folks who play the borlette over a long enough period of time (and many do – they play a small amount with a high degree of regularity). That’s a -20% return, on average.
In what universe does a -20% return seem like a good ROI on savings, you may ask – mathematically, at that. Surely folks would save better if they simply saved under their mattress?
Paying to Save
This is where the caveat, “some cases,” comes in. Sure, the plain vanilla ROSCAs where n members save $m per meeting and hand $(n x m) to one member each meeting has a 0% return, ignoring time value of money. And savings groups that have the luxury of depositing their funds in a bank will actually make a positive return.
Many other savings setups that are common do have a cost element though. Consider the following two types that are widespread:
Organizer takes one payout: Groups often need a promoter who shepherds a complete payout cycle or two. As remuneration, the promoter takes one payout, often the first. Thus, if there are n members, the savers will save for (n+1) cycle. The return in these cases will be -100%/(n+1).
This example from a Women’s World Banking Report is a bit dated, but the general thrust holds true across regions of the world where savings groups are formed via promoters:
On February 26th, 2003, Bethania finished a ROSCA which had five participants, each of whom contributed RD$100 for 60 days, equivalent to RD$6,000 each or RD$30,000 for the entire ROSCA. The payout was RD$5,000 every ten days and the pay out sequence was determined by lot. Bethania, as the ROSCA organizer, was entitled to the first payout, so she was able to gather this lump sum just ten days after she had organized the ROSCA. She received this without contributing any money to the ROSCA. This was her fee for organizing and managing it.
Bidding ROSCAs: In these ROSCAs, members submit sealed bids for the right to receive money in every meeting. This effectively serves as an interest payment on the savings of others, since the member gets the pot minus his or her bid amount. There are various ways of running this, and here is one example outlined in a recent paper by Tanaka and Nguyen that looks at Vietnam:
A winning bid turns into a discount to the other bidders who have not received the pool. In each meeting, the one who submits the highest sealed bid wins the pot, and the members who have not won the pool pay the full fixed amount minus the winning bid. Those who win the pot in earlier meetings get no discount, thus contribute the full amounts. The winner receives the pot, and pays a commission to the host. The cycle ends when the last member receives the pool. The winning bid of the last receiver is zero since he/she is the only bidder. Thus, the last member receives the full amount of contribution from each of other members.
Since members in such bidding ROSCAs determine their own price for the pot, and it varies from round to round, the cost varies quite a bit. In one of the examples cited in the paper above, they found that “the daily interest rates of the first receivers in these ROSCA are 0.90%, 0.88%, 0.56%, 0.17% and 0.10%, respectively” (p.g. 6).
There are other examples of where people will pay a premium to save. Yes, it denotes a negative return on investment, but it is still better than no return at all. Lest we forget, saving is hard, specially when one is talking about small amounts of income that is often uncertain, or irregular.
So how are they similar again?
The borlettes essentially function like a ROSCA with multiple payouts that occur in a random sequence, where the organizer charges a fee.
The borlettes also allow participants to mobilize small amounts of funds into a transformational amount of 50x.
One crucial element here is the frequency of payouts. If these functioned as the NY Lottery that they draw their numbers from, where the player has a bat’s chance in hell of getting a payout, this would not work. Participants actually count on these payouts to undertake costly projects, such as home repairs.
And finally, borlettes re-direct funds away from under the magic mattresses which often simply make savings … disappear.
Well, not exactly – it is somewhat unlikely that “micro-lotteries” will follow in the footsteps of micro-credit, micro-savings, micro-insurance, micro-mortgages etc. and be transplanted to other countries and settings. Borlettes are a very Haitian institution, and are a unique product of the need for a way to mobilize funds where there are very few options, disenchantment with savings schemes which turned out to be just that – schemes, and the juxtaposition with dreams and aspirations. (If that last bit seems like a non sequitur, check out the paper – its very relevant.)
Nevertheless, these and other practices arise from the desire of individuals and communities to put aside small amounts of money at regular intervals to receive a lump sum payout at a future date – a service for which they are willing to pay a premium. Borlettes make for a fascinating case study of a locally-relevant, highly scalable response to that desire.
Formerly seen as the golden child of microfinance, SKS Microfinance has recently suffered from a plight of bad press. In July 2010, SKS became the first Indian microfinance institution to go public. The deal was oversubscribed by 13 times at the top of its price band. Since this time, several events have called into question its future success such as the unexpected firing of its CEO, a rash of suicides by loan holders, and pending government regulations. Throughout this time, SKS’ stock has seen large price swings. Given this situation, it seemed appropriate to reevaluate that company’s stock price.
A number of factors support SKS’ high stock price. First, SKS is the clear market leader within India and established a model that is easily scalable. Second, the Indian market has only begun to be tapped. Currently, microfinance demand is estimated at US$51.4 million with only US$4.3 million supplied, representing huge market potential. Third, SKS’ operating expenses, while low internationally, are high in the Indian environment and could be reduced to increase margins.
Yet, several investment concerns are also present. First and foremost, SKS’ initial valuation was widely considered high. This overly optimistic valuation means that SKS’ extraordinary growth will have to continue to support its high price. If growth slows or does not meet expectation, the price will fall. Second, the company already has a high leverage ratio and could be incentivized to over leverage to meet its growth expectations. Lastly, SKS’ management has made some questionable decisions raising concerns about its capabilities and bringing negative publicity to the organization.
SKS’ operations may be impacted by two important risks. First, leadership risk is significant. Vikram Akula is the face of the organization, but should something happen to him it is unclear if the organization could maintain its fast paced growth. Second, regulatory risk is an immediate and pressing issue. Access to priority sector lending is a key funding source, which may be made off limits to for-profit institutions such as SKS. Any forced reduction in interest rates would also be damaging.
In valuing the company, comparative analyses were used, specifically price to book value (P/BV) and price to earnings ratio (P/E). In both instances, the ratios appeared high even with SKS’ phenomenal past success and future prospects. Looking first at P/BV, this ratio is used to compare a stock’s market value to the book value of equity. Utilizing FY2010 financial information, the P/BV was 6.27 and 7.53 post-dilution on October 15, 2010. Several analysts felt that this high ratio was inconsistent with SKS’ return on equity and that it should be in line with private sector banks (P/BV of 2.5 – 4x). P/E is used to compare a stock’s market value to the per-share earnings. With the same financial information, the P/E was 34.53 and 41.45 post-dilution. Looking at Compartamos (the only other public MFI) with a P/E of 26, these multiples appear unrealistic, especially considering Compartamos’ ROE is nearly double SKS’.
An updated stock price of Rs. 855.82 was calculated, representing a 24% decrease from the current stock price. This valuation was calculated with a P/BV of 4.75, which is in line with private banks with a slight increase to represent SKS’ potential growth, and a P/E of 26, which is Compartamos’ current ratio. While this valuation may be disappointing for current stock holders, it too may be high given SKS’ uncertain future.
For a full analysis, please refer to the “SKS: Poor Prospects ” report attached.
SKS: Poor Prospects