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!
Why, you can save through all of them, of course!
That was a key part of the intuition that gave rise to the three savings types outlined in BFA’s InFocus Note #3: Combining demand and supply side insights to build a better proposition for banks and clients. This post walks through a some of the highlights of this Note.
The Need for A New Savings Nomenclature
But, you may ask, why on earth do we need to come up with new types? Well, mostly because we didn’t find anything out there that did justice to the nuances in savings behavior we were seeing, and because we had tons and tons of data and so could segment at the granularity that client-based surveys could not accommodate. Systematic classification of savings types is sparse, and frankly, my favorite is still the oldie-but-goldie from Stuart Rutherford’s The Poor and Their Money. There is “saving up,” “saving down,” and “saving through.” You can read about this here, here and here, but basically the first is classic savings, the second is classic credit, and the third is a mixture of the two (like health insurance). Turns out voluntary savings accounts can display behavior that cannot be satisfactorily classified into one of these three.
We were also looking for pattern based matches solely based on account and balance information from the MIS, without any clue as to why savers were doing what they were doing. (We went on to combine this with client surveys afterwards, but that’s another story.) The patterns had to be sensible and discernible from each other, but they also had to be very precise to match the precision of the data we had on our hands. And on a personal level, it just fun to be able to craft software bots that crawl through the 0′s and 1′s to provide the kind of insights we gained!
X101: The A, B and C of Savings
Anyway, so coming back to our mattress->cow story… One can save a small amount, or a larger one. One can save it for a short period of time, or longer. And, one can save it in a form that allows ready access to cash, or in one that takes a bit of effort to liquidate. Generally speaking, one tends to store smaller amounts of money for a shorter period of time in a more liquid form at one end of the spectrum, and larger amounts of money for longer periods of time in rather illiquid forms.
Combining this intuition with our mattress-savings club-cow triptych gives us:
As self-explanatory as this graph is to you and me, it means absolute jack to Python, our programming language of choice. We needed a way to translate what you are seeing above to numerically defined filters that classified accounts based on one of more indicators.
We settled on the following rules for our pet algorithms through a process that relied largely on descriptive analytics of the underlying dataset and Daryl Collins‘ extensive experience with the financial lives of the poor – a process that was really part science, and part art.
Note that while clients may display all three types of behaviors, not all are welcome by banks. Type A are particularly expensive to maintain, since they not maintain adequate balances for the bank to book sufficient income on the float of that balance.
Not all accounts would fall into one of the three types. The two below captured the leftovers with some level of activity. Those which showed no activity are simply marked dormant.
The “Active but nor Savings” bucket contains accounts that display “dump and pull” behavior, where individuals use the account as a temporary repository between when cash inflows and outflows, and is typical of salary deposits or social grants.
We call this entire nomenclature “X101″. The genesis of this name involves thinking of this exercise as an X-ray that provides a basic-level dissection of savings accounts.
The X101 Wagon Wheel
Once we apply this nomenclature to the underlying savings accounts, we get breakdown that are specific for each of the financial institutions we looked at. One example is given below; it’ll give us a sense of the kind of information we can get from something even this aggregated. (Source: InFocus Note #3, page 10)
- A full half of the accounts are dormant! (Yes, it’s amusing how the number is exactly 50%..) Uptake followed by non-usage is a nagging problem for many of these institutions.
- About half of the accounts that are not dormant display A-, B- or C-type behavior. Seems like only a quarter of the accounts this institution services are really saving.
- B-type saving is hard to do! Recall that this is the one analogous to the savings clubs, which requires considerable discipline. But voluntary savings accounts do not have discipline enforcement mechanisms by definition, and few have incentives either.
- The rest are about evenly split between the “dump and pull”-ers and the folks who can maintain some kind of balance some of the time, but not all the time.
Is this what you would have expected, based on what you know about savings accounts?
Looking through the X101 Lens
Now that we have this classification of the accounts, we can look at existing information through a new lens, so to speak. Two examples are given below.
The first involves asking how much it costs to support each of these types of accounts. Below are the net revenue numbers in USD for one of the banks:
So.. other than Type B, all other types are losing money for the savings division.. Not so good from a financial sustainability point of view, specially considering Type Bs typically make up a small sliver of total savers. (These figures include the amortized customer acquisition costs and monthly maintenance charges, by the way.) This sort of analysis is the beginning of the discussion surrounding the business case of savings accounts, and how things can be different.
The second involves this thing called “channel dominance” – a creation of the venerable David Porteous. Financial institutions offer their services through different channels, such as branches, ATMs, agents, mobile vans, mobile phones etc. We consider an account to be displaying a certain channel dominance if the number of transactions the client conducts using that channel exceed those conducted through any other channel by at least 50%.
For one of the banks, the breakdown of channel dominance by X101 types looked like so (“Other” implies that the account did not fall in any one of the dominance buckets):
So .. we see that:
- Type A savers love ATMs! Easiest to withdraw cash, maybe?
- Type B savers really love branches! Could going to branches be providing some of the discipline needed for this kind of saving?
- Type C savers don’t really have a particular preference between ATMs or branches, but they sure don’t like agents… Maybe access to agents makes it hard to maintain balances over a long period of time?
- Balance Managers look like Type C savers as far as the channel distribution is concerned.. Perhaps they just need a nudge or three to become Type Cs?
Yes, the purported causal chains I casually drop above are purely speculative. But this line of thinking gave food for some great discussions with the institution in question, who know their clients really, really well.
The Big Picture
I think the X101 nomenclature has the potentially to materially impact the conversation around low-income savers and their savings accounts. It’s a rather quantitative approach that focuses on the how, which when married with the qualitative why provides fascinating insights into savings-oriented financial inclusion. This is important because saving is often hard for the client to do, and appropriate savings products are often challenging for the banks to design. X101 can inform this discussion, and we’ve been having some fascinating discussions indeed.
If more data is better than less, what could better than having access to … all the data?
Since around the beginning of the year, I’ve been researching it up with Bankable Frontier Associates, focusing on low income savers. There are two particular multi-year engagements going on, called InFocus and GAFIS, both supported by the Bill & Melinda Gates Foundation. As part of the engagements, we get data dumps from financial institutions around the world – 4 for InFocus and 5 for GAFIS. This includes client information, account information, records of every single transaction within the analysis window, and running account balance data.
Literally, all the data on accounts of savers which we could lay our hands on from the MIS systems.
My job is to beat this data till it decides to play nice and cough up useful information. (Given the effort that goes into cleaning and harmonizing data that often involves millions of accounts and hundreds of millions of transactions per institution, I assure you that this characterization is not overly dramatized!) This information is combined with all manners of other data, such as financial statements, demand-side surveys (i.e. surveys of the clients themselves), qualitative interviews, etc.
The latest round of findings are now available:
- InFocus Note #1: Do savings products at commercial banks really improve the lives of the poor?
- InFocus Note #2: How the Poor Use their Savings Accounts – A Supply Side View
- InFocus Note #3: Combining demand and supply side insights to build a better proposition for banks and clients
I should note that these are the sanitized cliff-note versions of the voluminous reports the individual institutions get. Part of the deal for them engaging with BFA was preservation of confidentiality, which makes the vast majority of the analysis and recommendations not publicly shareable. Still, what is in these three Notes should give a decent idea of both the theoretical basis as well as the general thrust of the analytics supporting the projects.
I’ll take a closer look at some of the things that I found fascinating in upcoming posts. In the meantime, dive into the Notes and lemme know what you find most interesting!
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.