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.
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.