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The “who uses it” and “how much does it cost” of P9

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.

(By the way, full product details are here, and various other related research and analysis are here.)

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.

As of April 2012, 31% of the cycles lasted 30 days or less, 36% between 31 and 90 days, 30% were between 91 and 360 days, and a mere 3% more than 360 days.

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.

Shout Out

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.

Posted by Ashirul Amin

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  1. this is great analysis, and helpful insight! I especially appreciated the work you did in Feb 2010 reported on the P9 site, with the charts and graphs of data for the p( Hrishi product.
    Would love to see more!

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