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.
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.
Formerly seen as the golden child of microfinance, SKS Microfinance has recently suffered from a plight of bad press. In July 2010, SKS became the first Indian microfinance institution to go public. The deal was oversubscribed by 13 times at the top of its price band. Since this time, several events have called into question its future success such as the unexpected firing of its CEO, a rash of suicides by loan holders, and pending government regulations. Throughout this time, SKS’ stock has seen large price swings. Given this situation, it seemed appropriate to reevaluate that company’s stock price.
A number of factors support SKS’ high stock price. First, SKS is the clear market leader within India and established a model that is easily scalable. Second, the Indian market has only begun to be tapped. Currently, microfinance demand is estimated at US$51.4 million with only US$4.3 million supplied, representing huge market potential. Third, SKS’ operating expenses, while low internationally, are high in the Indian environment and could be reduced to increase margins.
Yet, several investment concerns are also present. First and foremost, SKS’ initial valuation was widely considered high. This overly optimistic valuation means that SKS’ extraordinary growth will have to continue to support its high price. If growth slows or does not meet expectation, the price will fall. Second, the company already has a high leverage ratio and could be incentivized to over leverage to meet its growth expectations. Lastly, SKS’ management has made some questionable decisions raising concerns about its capabilities and bringing negative publicity to the organization.
SKS’ operations may be impacted by two important risks. First, leadership risk is significant. Vikram Akula is the face of the organization, but should something happen to him it is unclear if the organization could maintain its fast paced growth. Second, regulatory risk is an immediate and pressing issue. Access to priority sector lending is a key funding source, which may be made off limits to for-profit institutions such as SKS. Any forced reduction in interest rates would also be damaging.
In valuing the company, comparative analyses were used, specifically price to book value (P/BV) and price to earnings ratio (P/E). In both instances, the ratios appeared high even with SKS’ phenomenal past success and future prospects. Looking first at P/BV, this ratio is used to compare a stock’s market value to the book value of equity. Utilizing FY2010 financial information, the P/BV was 6.27 and 7.53 post-dilution on October 15, 2010. Several analysts felt that this high ratio was inconsistent with SKS’ return on equity and that it should be in line with private sector banks (P/BV of 2.5 – 4x). P/E is used to compare a stock’s market value to the per-share earnings. With the same financial information, the P/E was 34.53 and 41.45 post-dilution. Looking at Compartamos (the only other public MFI) with a P/E of 26, these multiples appear unrealistic, especially considering Compartamos’ ROE is nearly double SKS’.
An updated stock price of Rs. 855.82 was calculated, representing a 24% decrease from the current stock price. This valuation was calculated with a P/BV of 4.75, which is in line with private banks with a slight increase to represent SKS’ potential growth, and a P/E of 26, which is Compartamos’ current ratio. While this valuation may be disappointing for current stock holders, it too may be high given SKS’ uncertain future.
For a full analysis, please refer to the “SKS: Poor Prospects ” report attached.
SKS: Poor Prospects
SKS chief Vikram Akula cancelled his scheduled visit to Tufts University.
He was going to talk about this new book, A Fistful of Rice. It’s unfortunate that we won’t get to pick his brain regarding the debate generated by SKS’ recent IPO, and about the developing situation in Andhra Pradesh (AP).
Meanwhile, two pieces worth checking out:
- An Intellicap White Paper, Indian Microfinance Crisis of 2010: Turf War or a Battle of Intentions?, that analyzes the crisis in AP. It’s timely, and a great read. How’s this for context:
If we may be permitted a whimsical moment, the Indian microfinance story offers an irresistible parallel to a familiar Bollywood plot: in the Indian microfinance potboiler, the SHG model is the elder brother in an Indian joint family while the MFIs play the part of an aggressive younger brother. The elder brother struggles to uphold tradition and retain his leadership position, while the maverick younger brother tries to break free (using new financial and technology tools), often overenthusiastically, and sometimes recklessly, in pursuit of the same goals. This script bears many similarities to a classic Bollywood family drama. Unfortunately, the conflict between the two brothers often leaves the family destitute. As the AP microfinance drama plays out in the national media and the world watches, it is mainly the poor, forced into choices not of their making, who will suffer.
This paper strengthens the uneasy feeling many were having – that the regulations are more good politics than good policy. Barring door-to-door servicing – really?
- An Indian Express article, Andhra’s small-debt trap, that reports on the reality faced by micro-loan borrowers in a village in AP. Quite important to keep the caveats on media reports mentioned in the Intellicap White Paper in mind when reading this. Some of the anecdotes mentioned in the article are quite disturbing – fearful female borrowers assembling outside their houses at 7 a.m. sharp for the loan recovery agents to show up and having to pay late fees if they were even 5 mins late, 147 of the 150 households having multiple loans, and the men stopping going to work as farm laborers on getting loans instead of redirecting income towards investments.
In my last post, we saw how SKS’ write-offs levels went up from 0.29% in FY2008 to 0.60% in FY2009, and then up again to 0.86% in FY2010, and that PAR numbers followed a similar trend. Today, we’re going to look into this in more detail, and then ask why this might be an issue for SKS.
Tracking the increase
Before we go any further, let’s cross-check the numbers we got from MixMarket. Their numbers are generally very good, and allowing for reporting period disparities, differences in interpretation of similar terms, or differences in how something is bucketed, there is broad agreement in most cases with the numbers one gets from MFIs.
This is no different for SKS, which notes the following for it’s write-off figures in its Annual Report for 2009-2010:
(Source: Schedule 19, SKS Annual Report 2009-2010, p.g. 82)
Ok, SKS agrees that write-offs levels increased between FY2009 and FY2010. What’s their story for FY2008 to FY2009?
Turn to page 203 of the Red Herring, and you’ll learn that the increase in bad debt write offs was a whooping 2,205.8%!
Yes, that’s right, bad debt write-offs were up 22 times in FY2009, compared to FY 2008. Umm… what!?
And let me take this opportunity to say again why I am really happy about getting my hands on the Red Herring document – SKS has gone out of the way to provide an abundance of information, and it’s all very informative. There are two primary reasons that account for this, albeit partly:
- Under their provisioning policy, they used to write off 50% of the loans overdue between 25 to 50 weeks (or 6 to 12 months, roughly) in FY2008. In FY2009, they wrote off 100%, i.e. the entire amount. Makes sense that this increased write-off, since this change doubled the number of loans they had to bump off the books for that category of loans, all other things remaining the same.
- The portfolio grew during this period, to the tune of 81.5%. Again, makes sense that this increased write-offs – if you give out more loans, proportionately more loans will also have to be written-off, all other things remaining the same.
Here’s what’s interesting – once you normalize for these changes, (essentially deflating the 2,205.8% number by 100% and then by 81.5% again to account for the explanations) the write-offs still go up by approximately 600% … ! Gonna have to say it again – umm… what!?
Something is pushing up SKS’s bad loan count, and pushing it beyond what seems to be the average for Indian MFIs. As we saw in the previous post, the average write-off for the market was 0.52% in 2009; SKS’ figure was 0.73% for the corresponding period.
SKS needs to identify the drivers behind bad loans. I could hazard a couple of guesses:
- Quality of staff – any any given point, three-quarters of SKS’ staff has been around for less than year. This is partly because of phenomenal growth leading to continuous hirings, and partly because of high staff turnover.
- Characteristics of new markets – borrower demographics, existing competitors etc.
- Credit policy – particularly those related to due diligence on loan disbursal and procedures for loan recovery
- Resource focus on growth, as opposed to consolidation
Without looking at more detailed numbers or talking to SKS staff though, we’d be hard pressed to know which of these, if any, are key drivers of deteriorating portfolio quality.
Additional Mitigating Factors
Irrespective of the underlying drivers, there are two effects to keep in mind that make deteriorating portfolio quality a particularly pernicious issue for MFIs enjoying rapid growth:
- Lag Effect: Loan vintages (i.e. loans given out during the same period – say a month) do not display full delinquency levels until a few months after disbursement. Given that most SKS loans have a 50-week term, a short 3 month catch-up period would mean that ¾ of the portfolio are displaying full delinquency levels, while a 6 month one would imply that only ½ of them were doing so – assuming 0% portfolio growth. This also means that if, for some reason, the overall portfolio quality deteriorates, it will not manifest itself fully for a good couple of months.
- Dilution Effect: Of course, SKS’ portfolio is not growing at 0%. It grew 99% between FY2009 and FY2010, and according to the 2009-2010 Annual Report, has a CAGR of 150% over the last 4 years. Consider what this means for the lag effect. The earlier vintages that are now displaying full delinquency levels are essentially watered down, and the low or non-existent delinquency levels of newer vintages have a higher per-rupee weight. How much watering down happens is dependent on the vintage disbursement amounts, but in general, the dilution effect depresses the delinquency levels further.
Why is this an issue? Well, SKS cannot continue to grow at a CAGR of 150% for too many more years. The Indian MFI market is getting increasingly saturated, and the industry as a whole will need to slow down. As growth slows, the delinquency and write-off levels will catch up. If SKS does not anticipate this catch-up and prepare accordingly, investor confidence will take a blow, along with portfolio quality.
Vintage-level Analysis Has Some Answers
One can get a sense of what will happen in the future by literally seeing how loans in SKS’ portfolio age. Loans given out during the same period, usually a month, are said to be in the same vintage. I have not found any public information on SKS vintages, let alone their delinquency profiles. It’s generally not very helpful to guesstimate on this because such profiles can vary greatly between MFIs with similar top-level PAR and write-off numbers, and can differ substantially for different products even within the same MFI, so I’m not going to dive into a modeling exercise.
It’s not too difficult to figure out the lag and dilution effect though, if one has access to granular portfolio data. SKS would have to take each vintage, figure out its delinquency profile, derive historic trends by superposition, and account for differences based on product characteristics, branch office location or any other salient factors. It can then combine projected performance of existing vintages with internal growth targets, and see where the PAR and write-off numbers end up at.
Sure, the past is not necessarily the best predictor of the future, but given the purported out-of-the-box nature of SKS products in particular and microfinance products in general, and the short loan lifecycles that reveal profile changes relatively quickly, this should be a pretty useful exercise.
This is the first of a series of posts that will take a look at the numbers behind MFI operations. I find it to be quite an instructive exercise to wade through MFI data in the rare instances where they are available in any level of detail beyond mere institution-level aggregation, as presented in annual reports and the like.
We’ll first look at delinquencies and write-offs, and use the Indian MFI SKS as a case study of sorts. This is partly because the Red Herring released prior to its IPO provides a wealth of information that allows for more meaningful and in-depth analysis, and in general, affords a rare look at the inner workings of an institution enjoying prodigious growth while carving out a place in a rapidly evolving market. SKS is also the largest MFI in India, followed by Spandana and SHARE, and according to 2009 MixMarket data, serves about a fifth of the Indian MFI market. What we might glean from this is therefore relevant to a large chunk of the Indian microfinance market too.
PAR and Write-off as Measures of Delinquency
Well-run MFIs fastidiously maintain high portfolio quality. Many MFIs have primarily lent to women, who traditionally have excellent repayment rates. Many adhere to the Grameen group-lending model because they rely on peer support and pressure to enforce regular repayment habits. Very low delinquency and default rates have made this sector a favourite for investors, domestic and international.
Portfolio-at-Risk (PAR) is one of the standards of measuring delinquency for microfinance loan portfolios. It is defined as the total outstanding principal balance of loans with any amount of arrears due. Thus, if a $100 loan still has $45 in outstanding principal, and the $2 of that was due last week is not paid up, the PAR amount is noted as $45. In a way, PAR is the most conservative measure of how much loss the portfolio would suffer, since this is the maximum value that the MFI would lose from this loan if nothing more was ever paid back.
PAR is usually associated with a number of days count, where PAR30 would mean the total outstanding principal balance with any amount of arrears due for over 30 days, PAR60 for 60 days, and so on. Many MFIs will typically provision against 100% of the PAR120 amount, thus assuming that none of it can be recovered.
Once a loan is written off, it is no longer on the books of the MFI.
SKS Delinquency Profile
The unweighted averages for PAR30 and write-offs were 1.84% and 0.52% respectively for Indian MFIs with more than USD 10m in gross loan portfolio. When one considers the weighted averages, the corresponding figures are 0.59% and 0.54%. (The weight applied is principal outstanding.) We are interested in looking at the weighted average, by the way, because it gives us a sense of how every dollar (or rupee) in the portfolio is doing, on average, as opposed to every loan, which would be the case for the unweighted measure. Comparing the two PAR30s tells us that larger portfolios have much lower PAR amounts on average – this is quite interesting, and we’ll come back to why a little later.
This provides some context to PAR and write-off data for SKS for the last 5 years:
(Screenshot Source: MixMarket. Note that FY2010 denotes fiscal year ended Mar 31, 2010.)
Two things of interest jump out at me from this data:
- Write-offs levels went up from 0.29% in FY2008 to 0.60% in FY2009, and then up again to 0.86% in FY2010.
- The recent PAR30 and PAR90 rates are much less than the write-off rates.
Rising write-offs are an obvious issue. The PAR numbers also follow a similar trend. We’ll take a closer look at this in my next post.
Do you think it’s normal that the PAR numbers are less than write-offs, by the way? It’s quite an interesting phenomena, and we’ll focus on it on the third post of this series.
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