The final GAFIS report is out. “Big banks can’t serve low-income clients because small balance accounts just do not offer a sustainable business proposition” has been a truism since forever, and for good reason. This report takes a good stab at how that doesn’t always have to be the case, and how some of the biggest banks in sizeable developing countries are finding creative, segmented solutions to expanding the envelope of financial inclusion.
Between 2010 and 2013, the GAFIS project engaged five banks – Bancolombia (Colombia), BANSEFI (Mexico), Equity Bank (Kenya), ICICI Bank (India), and Standard Bank (South Africa). Together, they serve 77 million clients, and have combined assets of $250 billion, many of whom are the low-income segment. Obviously not all low-balance accounts belong to low-income clients, but the vast preponderance of low-income clients will have low-balance accounts; hence the focus on making them sustainable.
The final impact was described as follows (p.g. 38):
Collectively, the GAFIS banks have opened more than 4.2 million new accounts in GAFIS-linked products in less than three years. More importantly, they now serve approximately 420,000 “new, poor savers” as a result of the project (see Box I). This number is measured according to the GAFIS project definition, which requires evidence of both savings activity (870,238 accounts) and the poverty status of the account holders (543,119 new accounts meet both criteria); and which also weights the level of attribution to the project according to how directly GAFIS was involved (reducing the 543,119 to 419,654). Even with delays in launching new products at several banks, these growing numbers provide early indication that large banks can achieve scale outreach with their new propositions.
Very importantly, the business case for these 420k new, poor saver accounts was improved significantly through a combination of strategies:
- Costing methodology adjusted to product and channel specifications: It doesn’t make sense to load up branch costs into an acquisition expense if the product is mostly mobile phone based, for example.
- Using agent channels, where agents are essentially mom-and-pop stores set up to function as mini-banks: Transactions cost about a fifth as much with agents, where the agent is paid a commission. Account origination costs are in the $1-$6 range at agents, while those at branches are $16-$25.
- Increasing cross-sell: This might mean serving credit needs of clients, or fees from intermediating inflows from government or other sources of credit payments.
Here’s a summary of what each bank did to improve the business case (p.g. 22):
Still, all this does not make the business case for the accounts a positive one. It does significantly improve it though, from losing $2.79 per account per month, to losing $1.02 per account per month (p.g. 32):
Lot’s more details in the report – do check it out!
Mobile money is all the rage these days in inclusive finance circles, and for good reason. Mobile penetration has increased dramatically across the developing world over the previous decade, providing convenient rails for financial services to piggy-back on. It’s cheaper than Western Union and MoneyGram for the client, and cheaper than having to erect brick-and-mortar branches for banks. It’s faster than virtually any other method to send money to a relative in a pinch at the other end of the country, and safer than carrying cash around. And it has spawned an incredible array of services.
One of those is M-Shwari. It’s a savings-and-loan product offered by Safaricom in Kenya, in partnership with the Commercial Bank of Africa (CBA). M-Pesa clients can save into their M-Shwari account and earn interest, and take out short-term loans for a fee. It’s been hailed as a revolutionary product and discussed by the likes of CGAP, GSMA, NextBillion etc. highlighting fascinating aspects of mobile money adoption. And Safaricom promotes it as seamlessly blending convenience, safety and affordability.
I am totally sold on the “convenience” and “safety” elements of M-Shwari, but to buy the claim of “affordability” requires a somewhat unconventional understanding of that word.
But First, the Conventional Take …
So, how much does M-Shwari cost?
Q: Do you get charged interest on your M-Shwari loan?
A: The M-Shwari loan DOES NOT attract any interest. The 7.5% charged is a loan facilitation fee payable only once for each loan taken.
Q: If you have not paid your loan within 30 days, what will happen?
A: Your loan repayment period will be extended for an additional 30 days and you will be charged an additional 7.5% facilitation fee on your outstanding loan balance.
Q: If you pay your loan before the due date, will you still be charged the loan facilitation fee of 7.5% on the loan amount?
A: Yes, the 7.5% is a facilitation fee charged on the cost of processing the loan. Early repayment will increase your future loan limit qualification. Remember your loan limit is dependent on your previous loan repayment behaviour and usage of other Safaricom services such as Voice, DATA and M-PESA.
That’s a 7.5% flat charge on the nominal loan amount, with a maximum term of a month. The nominal and effective Annual Percentage Rates (APRs), “annualized” interest rates if you like, are:
- Nominal APR: 7.5% * 12 = 90%
- Effective APR: (1 + 7.5%)¹² – 1 = 138%
For additional details/nuances on APR voodoo please refer to MFTransparency or Wikipedia. XIRR comes to 146%, assuming the 7.5% is collected when the loan is repaid, and 164% if collected when disbursed.
Calling it a “facilitation fee” doesn’t change anything – for a month-long loan, the effective annual cost to the client is 138% or so.
In fact, that it’s a flat fee makes things worse. There is a pretty good reason why the 7.5% can’t be called an interest rate, since interest rates are time-defined. If the cost was actually 7.5% per month, someone borrowing for a fortnight only would be liable for a 3.75% charge. Not so with M-Shwari – you could repay in a day, but you’d still pay 7.5%. The nominal and effective APRs for a twice-a-month engagement comes out to be 180% and 462% respectively; anything shorter, and the APR is astronomically higher.
We’re hovering dangerously close to the “u” word, don’t you think?¹
Conventional Carrot-and-Stick Included
Two other “features” jumped out from the FAQs:
Q: What is the loan duration?
A: The loan is payable within 30 days. However, you can repay the loan before the due date and borrow again. If you pay the loan in less than 30 days your loan limit qualification will increase.
Q: If you have saved Kshs 5000 in your M-Shwari and have a loan of Kshs 2000 and do not repay within the loan duration (30 days), what happens to the money in your deposit account?
A: – When you borrow the Ksh 2,000, the money in your savings account will be frozen to the loan amount and the loan fee (loan amount Ksh 2,000 loan plus a facilitation fee of Ksh 150).
- You will only be able to access any balance above the frozen amount. The frozen amount will be accessible once you pay the loan. However you can continue to deposit money. Note: During the period the frozen savings will continue to earn interest which will be paid into your M-Shwari at the end of calendar quarter.
M-Shwari thus encourages faster loan repayment, offering an increase in the loan limit as the carrot. CBA sweetens the language even further by noting, “The earlier you repay your loan the better your chances of getting your credit limit increased!”
M-Shwari also seems to be fully collateralized, with both principal and “interest” held in escrow. Good incentive for borrowers to repay, also because they run the danger of being reported as delinquent 90 days after the loan is due, and potentially ineligible for another loan in 7 years. (See CBA FAQ, pages 6-7.)
This is all well and good from a profitability and prudential lending point of view, but it could be argued that simultaneously making the current loan costly and increasing future indebtedness on one hand, and conducting a complete bait-and-switch between savings and credit balances so that there is no net increase in availability of funds to the saver on the other, are not inclusion-friendly “features”.²
The “Affordable” Case
There is no math that can known down the glaring APR of 138%. Context, however, can help explain why M-Shwari has 2.4 million active users within 1 year of operations. Here are some of the reasons I think it’s been so :
- M-Shwari is primarily a savings product, with an option to take an emergency loan, as promoted by Safaricom and CBA. The maximum amount one can save is KES 100,000, but the maximum loan amount is KES 20,000.
- People are willing to pay fees on short-term loans akin to a service fee. It’s why payday loans can charge $15 for every $100 borrowed in the US. People are also willing to pay a fixed, simple fee compared to a more complicated, constantly adjusting rate, which is why microcredit interest is often charged at a flat rate. There may also be an element of pay-as-you-go, where bank clients are willing to pay a transaction fee each time they use a service, as opposed to pay a fixed monthly ledger fee.
- APR is not particularly useful for short-term loan products. No one expects a borrower to roll over 12 times in one year, bleeding KES 90 to access KES 100 through the year – we know even the unbanked are more sophisticated financial users than that.
- But most importantly, it’s convenient – it’s on that phone that is attached to your hips, it’s on the M-PESA rails that is ubiquitous in Kenya, and it’s near instant, secure and private. I know I would pay a 7.5% premium for a service like that.
M-Shwari therefore seems to be “affordable” despite having an APR of 138%. With no cash-handling or client interaction costs at branches, a relatively cheap source of capital in deposits from the same clients³, and non-performing loans at around a low 3.8%, this is one partnership that must make for quite nice margins for CBA and Safaricom.
¹ The “u” word is usury, in case you’re still wondering.
² It’s not clear if one can borrow more than one’s savings amount – the product pages and FAQs are deliberately vague.
³ Tiered interest rate is in the 2-5% range per annum.
Saving in a Lending-to-save Product
We know that folks who have to deal with incomes that are low, irregular and uncertain have to resort adapting available financial instruments to meet their idiosyncratic needs. This is another post on one of my favorite datasets – P9 – that illustrates a simple but powerful adaptation. (You can read previous post here.)
You’ll recall that P9 is a lending-to-save product, where a certain proportion of the earmarked amount is held back as savings, which is then replaced with cash flow from the client once the loan portion has been paid off. This implies that you have to pay off the loan amount first, before you can really save. If nothing else, the discipline of paying off the loan in small increments is transferred to saving in small amounts towards a large lump sum.
Except, what if you only wanted to save, and didn’t need or want the loan?
It seems that a certain portion of the clients at the Hrishipara site (P9 is offered in two sites – Hrishipara and Kalyanpur) have adapted the product to this end by paying off the loan within the first day of disbursement presumably using the same amount they had taken out, and then spend the next few weeks or months saving up. Clients thus seem to have taken the conscious decision to do away with the lending half of the “lending-to-save” model but have voluntarily taken on the discipline expected of them as they save up towards the amount held in escrow on their behalf.
Tracking Down the “Only Savers”
The first clue that something was not going exactly according to plan was this plot:
This plot tells us what percentage of the tranche is paid off as the first payment. To fully grasp what this is showing, let’s first set some expectations. Say you decide to pay off an outstanding amount of Tk 1,000 in 10 equal installments of Tk. 100. How much of the tranche are you paying off per payment? Why, 10% of course (Tk. 100/Tk 1,000). What if you decided to pay it off in 20 installments of Tk. 50? Each payment would then constitute 5% (Tk. 50 / Tk. 1,000).
Of course, this can also be calculated by taking the reciprocal of the number of payments as a percentages – 1/10 = 10%, 1/20 = 5%, and so on. We wouldn’t expect the first payment to be anything different per se from the “average” payment, so our expectation of the size of that first payment would also be 10%, 5% or x% depending on whether we expect 10, 20 or n payments, where x = 1 / n as a percentage.
Thus, the graph above tells us that in 56% of the tranches, the first payment is 10% of less than the entire disbursement amount – something we would expect. But check that 27% in the blue circle – these folks have paid off around half the disbursement amount through the first payment. And the clients in the green circle – the 5% – have paid off almost all, or all, of the disbursement amount right at the first payment!
What is going on with the folks in the red circle!?
The examples are pretty self-explanatory. The table below is for the blue “Save Only” folks – you can see the almost-equal amounts for the loan and the repayment made, with the delta essentially being a fee of Tk. 10-100.
And the table below is for the green “Ramp Up” folks – you can see that the repayments are equal to the disbursement amount:Yes, clients are paying off the entire tranche amount. This is generally done because you have to cycle through smaller tranches before you are earmarked a larger tranche, and these guys have simply decided to do that cycling in one go. Most clients will cycle through one or two such tranches, but one particularly adept client went through 7 tranches in 8 days, cycling from Tk. 3,000 to Tk. 13,000.
I have to say, it’s not often that a pattern jumps out like this – if only portfolio analytics was generally this readily discoverable!
Adaptation Behavior Over Time
How consistent is this “savings only” behavior? Do they do the same thing tranche after tranche, or do they go back to taking advantage of the loan option? If you consider the blue circle folks as “Saving Only” and the green circle folks as “Ramp Up” clients, with the remaining as “Neither”, you can envision a 3 x 3 transition matrix between each tranche where a client in any of the three “states” can choose to be at any of the other three “states”.
The complete state transition figures are given below as a percentages of the number of accounts that have gone through that tranche. We stop at the 20th tranche because less than 50 accounts have gone through more, resulting in a lot of noise.
That’s a lot of numbers.. so let’s just focus on these three rows: “Neither -> Neither”, “Neither -> Save Only” and “Save Only -> Save Only”. The first goes from 74% to 44%, the second fluctuates between 2% and 14%, and the third goes from 8% to 26%. Thus, fewer and fewer clients continue the lending-to-save model, and more and more save only.
A closer snapshot of this dynamic is given below by focusing on the two states of “Neither” and “Save Only” and looking at the 2nd, 10th and 20th tranche:
What Does This All Mean?
Well, at the end of the day it’s fairly simple – P9 at Hrishipara has certain rules that its clients found a way to serve their need better when they were interested in saving only. Quantifying the phenomenon gives us a sense of how widespread it is, and allows product designers to account for deviations from expected behaviors. (I haven’t looked at the P9 Kalyanpur data yet but my sense is that the product there is more flexible and accommodates this behavior already.)
One subtlety that you’ll probably appreciate is this usage behavior indicates the preference clients have of having the option to draw down a loan amount even if they do not exercise that option all the time – in fact, around the 20th tranche, about a tenth of the tranches exercise the option to draw down after saving only in the previous tranche.
The write-up on which this post is based can be found at the P9 Databank. It benefited greatly from Stuart Rutherford’s feedback.
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!
Not much, at face value. One could even claim that lotteries are quite antithetical to the spirit of savings – how could one, in good conscience, compare potentially reckless and addictive gambling with the perseverance and self-discipline that savings demands?
Funny thing is, it turns out that in some cases, they are not very different mathematically at all.
I recently read this fascinating paper titled Savings and Chance: Inclusive Finance and The Haitian Lottery on the Haitian lottery institution surrounding borlettes. Participants bet on the numbers drawn in U.S. state lotteries at kiosks, and payouts are made based on some combination of two to five digits.
Operators add their twists to this, but here’s how one of the simplest forms of this works – choose three numbers between 1 and 100, bet $1 (or 1 gourde, the local currency) on each, and wait for the radio to announce the U.S. state lottery numbers. If your first number corresponds to the lottery, you get a $50 payout. If your second number corresponds, you get a $20 payout. And if its your third number, its a $10 payout.
On any given day, for every $1 coming in, the expected payout for a kiosk is therefore ($1 x 0.5 + $1 x 0.2 + $1 x 0.1) = $0.80. This is also the expected return for folks who play the borlette over a long enough period of time (and many do – they play a small amount with a high degree of regularity). That’s a -20% return, on average.
In what universe does a -20% return seem like a good ROI on savings, you may ask – mathematically, at that. Surely folks would save better if they simply saved under their mattress?
Paying to Save
This is where the caveat, “some cases,” comes in. Sure, the plain vanilla ROSCAs where n members save $m per meeting and hand $(n x m) to one member each meeting has a 0% return, ignoring time value of money. And savings groups that have the luxury of depositing their funds in a bank will actually make a positive return.
Many other savings setups that are common do have a cost element though. Consider the following two types that are widespread:
Organizer takes one payout: Groups often need a promoter who shepherds a complete payout cycle or two. As remuneration, the promoter takes one payout, often the first. Thus, if there are n members, the savers will save for (n+1) cycle. The return in these cases will be -100%/(n+1).
This example from a Women’s World Banking Report is a bit dated, but the general thrust holds true across regions of the world where savings groups are formed via promoters:
On February 26th, 2003, Bethania finished a ROSCA which had five participants, each of whom contributed RD$100 for 60 days, equivalent to RD$6,000 each or RD$30,000 for the entire ROSCA. The payout was RD$5,000 every ten days and the pay out sequence was determined by lot. Bethania, as the ROSCA organizer, was entitled to the first payout, so she was able to gather this lump sum just ten days after she had organized the ROSCA. She received this without contributing any money to the ROSCA. This was her fee for organizing and managing it.
Bidding ROSCAs: In these ROSCAs, members submit sealed bids for the right to receive money in every meeting. This effectively serves as an interest payment on the savings of others, since the member gets the pot minus his or her bid amount. There are various ways of running this, and here is one example outlined in a recent paper by Tanaka and Nguyen that looks at Vietnam:
A winning bid turns into a discount to the other bidders who have not received the pool. In each meeting, the one who submits the highest sealed bid wins the pot, and the members who have not won the pool pay the full fixed amount minus the winning bid. Those who win the pot in earlier meetings get no discount, thus contribute the full amounts. The winner receives the pot, and pays a commission to the host. The cycle ends when the last member receives the pool. The winning bid of the last receiver is zero since he/she is the only bidder. Thus, the last member receives the full amount of contribution from each of other members.
Since members in such bidding ROSCAs determine their own price for the pot, and it varies from round to round, the cost varies quite a bit. In one of the examples cited in the paper above, they found that “the daily interest rates of the first receivers in these ROSCA are 0.90%, 0.88%, 0.56%, 0.17% and 0.10%, respectively” (p.g. 6).
There are other examples of where people will pay a premium to save. Yes, it denotes a negative return on investment, but it is still better than no return at all. Lest we forget, saving is hard, specially when one is talking about small amounts of income that is often uncertain, or irregular.
So how are they similar again?
The borlettes essentially function like a ROSCA with multiple payouts that occur in a random sequence, where the organizer charges a fee.
The borlettes also allow participants to mobilize small amounts of funds into a transformational amount of 50x.
One crucial element here is the frequency of payouts. If these functioned as the NY Lottery that they draw their numbers from, where the player has a bat’s chance in hell of getting a payout, this would not work. Participants actually count on these payouts to undertake costly projects, such as home repairs.
And finally, borlettes re-direct funds away from under the magic mattresses which often simply make savings … disappear.
Well, not exactly – it is somewhat unlikely that “micro-lotteries” will follow in the footsteps of micro-credit, micro-savings, micro-insurance, micro-mortgages etc. and be transplanted to other countries and settings. Borlettes are a very Haitian institution, and are a unique product of the need for a way to mobilize funds where there are very few options, disenchantment with savings schemes which turned out to be just that – schemes, and the juxtaposition with dreams and aspirations. (If that last bit seems like a non sequitur, check out the paper – its very relevant.)
Nevertheless, these and other practices arise from the desire of individuals and communities to put aside small amounts of money at regular intervals to receive a lump sum payout at a future date – a service for which they are willing to pay a premium. Borlettes make for a fascinating case study of a locally-relevant, highly scalable response to that desire.