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.