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!
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.
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!
Bangladesh, with a population of nearly 160 million and a landmass of 147,570 square kilometers, is among the most densely-populated countries in the world. It remains a low-income country, with a per capita income of US$ 652 in FY09 and 40 percent of its population living in poverty. Despite periods of political turmoil and frequent natural disasters, in the past decade Bangladesh has been marked by sustained growth(with nearly six percent on an average in past one decade), stable macroeconomic management, significant poverty reduction, rapid social transformation and human development. Foreign remittances sent by expatriate Bangladeshis remain one of the most consistent sources of foreign currency in Bangladesh, which earned Bangladesh 7th position among the top remittance-receiving countries in 2010, as reported byMigration and Remittance Fact Book 2011 of the World Bank.
Still, the majority of the Bangladeshis are unbanked, and access to financial services is very limited. Commercial banks have very low penetration in rural areas, where over 75 percent of the population lives. There are less than two ATMs for every 100,000 adults in the country. All of these characteristics make it a good business case for expanding accessibility to financial services through innovations.
Mobile networks have expanded quite rapidly in Bangladesh over the past decade. Currently, there are six mobile network operators (MNOs) in Bangladesh covering more than 90 percent of the geographic territory and 99 percent (coverage wise) of the population in Bangladesh. Consumer demand in Bangladesh makes the mobile market one of the fastest growing in the world. For instance, over the past 15 months, Bangladesh recorded nearly 1.4 million subscribers per month. The total number of mobile phone active subscribers reached about 73 million at the end of March 2011, with about a 45 percent penetration rate in the whole country. The Government and the Central Bank now recognize that the mobile banking is as a unique opportunity for the banks to increase their presence in rural and remote areas of the country and serve a huge unbanked population.
Banglalink, the fastest growing telecom operator in recent years, is the pioneer in testing out mobile mone initiatives in Bangladesh. The products it has recently launched along with other partners (such as banks and post offices) are as follows: (i) M-remittance (International and Local): which allows people to receive international remittance in the m-wallet account; enables local fund transfer P2P from one m-wallet to another m-wallet; facilitates cash deposit from “cash points” and other sources; and provides for cash withdrawal from “cash points”; (ii) M-payment (Utility): allows payment of utility bills using m-wallet; and (iii) M-collection: facilitates the purchase of train tickets.
M-remittance services are meant to address widespread issues such as inability to send money frequently, delays experienced while sending money, and issues related to insecure distribution and inconsistent delivery methods. It is also helping small entrepreneurs (agents) use part of the remittances which the receivers often do not withdraw all at once. The use of m-remittance services offered by Banglalink is picking up gradually. Around 1000 transactions are being reported across the country at the Banglalink mobile money agents/points. Users like the service for its fast and cost effective disbursement, as Banglalink found in its own market study.
Among many recent initiatives is the creation of a new organization called bKash — a scalable mobile money platform that will allow poor Bangladeshis to store, transfer and receive money safely via mobile phones. bKash is a joint venture of BRAC Bank Limited and Money in Motion LLC, USA, created out of a generous $10m grant from the Bill and Melinda Gates Foundation to ShoreBank International, an international consulting firm. The grant forms part of the Foundation’s $500m pledge over the next five years to expand savings and build a “new financial infrastructure” to bring savings services to the poor.
With mobile density of 45 percent and mobile retail density averaging 0.5 in each village, like many other countries such as Kenya, South Africa and the Philippines, Bangladesh also has enormous opportunity for financial inclusion through creating a solid “mobile money ecosystem”. This arguably will contribute to greater efforts at poverty reduction and economic development in Bangladesh.
(The writer/blogger is a Graduate student of Development Economics and International Finance at the Fletcher School of Law & Diplomacy, Tufts University)
When I first heard about Inclusive Business, I immediately realized how “Fletcheresque” of an idea this was – multi-disciplinary in terms of business and development, cross-sectorial in terms of actors and stakeholders, and producing a two-fold outcome in terms of financial and social rewards. In other words, a new type of business model for Multi-national Corporations (MNCs) which engages not only the traditionally affluent and the top and middle of the economic pyramid but also focuses on the poor and the bottom of the pyramid (BOP) to make profit and deliver social change is Inclusive Business. Originally coined by the World Business Council for Sustainable Development (WBCSD) and SNV, a Dutch International Development organization, the term has now become ubiquitous among all of the major international organizations, development consultancies, NGOs and civil societies. As much as the buzz-word has spread like wild fire, so has the related research, incubation and piloting initiative by major MNCs throughout the world.
Inclusive Business model promises to be fundamentally sustainable than what critics would consider as just a ‘fad’. Reasons are simple. As any core course in business would teach (Professor Jacque’s Corporate Finance course at Fletcher for instance) the basic objective of a corporation is to make profit and sustain its free cash flow for as long possible. Inclusive Business model is not just a philanthropic activity for a firm’s corporate social responsibility (CSR) initiative, but it vows to be bigger and better, and most importantly, profitable and sustainable. Compared to a philanthropic activity which would increase and decrease during economic upturn and downturn, an inclusive business isn’t just a “step child” that can simply be cutoff during harsher times. Inclusive Business is a model in which a project or a firm is built on, based on the inclusion of BOP as a part of the value chain – whether it be through directly employing low-income individuals, partnering with low income suppliers and entrepreneurs or by offering affordable goods and services to the BOP market.
While Inclusive Business promises wonders, it is definitely not free of challenges and shortcomings. Firstly, Inclusive Business model adoption means exposure to unknown political and social risks and, as we have seen time and again that this is a tough nut to crack for MNC’s. Secondly, measuring impact of these types of projects is cumbersome, often times subjective and hence no one standard has been agreed upon yet. Without a proper impact assessment method, returns from investing in an Inclusive Business are not properly quantified. Thirdly, company indices and ratings that go beyond the bottom line is still in its infancy and not common across industry.
Realizing these shortcomings, international organizations and aid agencies have actively partnered with MNCs and other state and non-state actors to promote these models. For instance, United Nations has incorporated Inclusive Business models as ways to achieving several of the Millennium Development Goals. In general, advocacy to adopt these models by businesses throughout the world has increased many folds in recent years. However, as long as impact remains only as an asterix (*) in the footnotes of financial proformas, the potential of this model will only be overlooked. Particularly, executives often fail to look beyond the model’s idealist social impact, which they believe is not-aligned with their firm’s goal. As a result, the golden opportunity of an Inclusive Business to partner with the hardest working individuals and to reach newer and larger markets, obvious dream targets for any businesses, is also largely overlooked.
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.