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.
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.