A little while back, we realized we were nipping at the heels of “big data” territory, arriving at near 1 terabytes of data. We being the team at Bankable Frontier Associates I work with, through a partnership with the Center for Emerging Markets Enterprises at Fletcher. Not quite a size that would titillate folks who drink Hadoop and sleep in Elastic Clouds, but it was a sobering moment that caused for some reflection on the limits of what we could do with all this information, even as we strained a pretty souped up machine to it’s limits.
Most of my concerns stem from the fact that big data has the disconcerting property of confessing to something – anything – under sufficient coercion. It’s a variation of the age-old problem of statistical correlation, aptly captured in the XKCD to the right –>
As the venerable Nassim Taleb points out, “We’re more fooled by noise than ever before, and it’s because … with big data, researchers have brought cherry-picking to an industrial level. … I am not saying here that there is no information in big data. There is plenty of information. The problem — the central issue — is that the needle comes in an increasingly larger haystack.“
When you’re dealing with data on tens of millions of accounts and billions of transactions from financial institutions serving clients in eight countries, that’s a rather massive haystack to get lost in. In situations like this, it is ever so important to have a set of null hypotheses that can be proved/disproved conclusively, thereby keeping us honest, instead of chasing spurious connections.
Which brings us to correlation vs causation. Yes, we have granular transaction data over a course of years for each account holder, meaning we know everything they are doing with that account. We can also have up to twenty characteristics of the client and the account type – age, gender, income, occupation, age of account, interest rate paid, etc.
But unless such studies are paired with detailed financial diaries, we know nothing of the individuals motivations for why they do what they do, or of the rest of their financial portfolio and financial tools at their disposal. This means we usually cannot say things like, “the average account holder saves Ksh X for her child’s school uniform”.
And that’s ok.
Causality in the social sciences is a hard problem. It’s not possible to hold “everything else constant” like we can with the hard sciences. Human free will allows for a mind-boggling array of choices, people may not always take the same decision despite being faced with the same choices, and somethings an effect may have multiple contributing causes.
Quantitative researchers do the best they can to account for all possible explanatory variables and then attribute degrees of causality to certain variables. Because we don’t have all possible explanatory variables when dealing with big data, we restrict ourselves to demonstrating strong correlations and usually end up indicating potential causal connections and let others take it from there – such as field researchers who can conduct focus groups to dig in deep.
This may not sound intellectually gratifying, but it is once you get into the thick of things. Let’s consider the example of two savings types: A, which is a short-term, low-balance almost transactional behavior, and B, which is accretionary savings over the course of a year leading to a decent balance. A is strongly correlated with ATM card usage, while B is strongly correlated with branch usage. 20-40% of all savings accounts seem to display A-type behavior, while about 1% display B-type behavior across many of the financial institutions we have looked at and I can talk about. What questions come to mind? How about:
- Do ATMs make it hard to save larger amounts over the long term because it’s just so easy to take money out? Do branches make it harder for folks to withdraw funds willy nilly and therefore save more over the long term?
- Or.. do clients self-select to use ATMs in cases where they need easy access to money and intermediate small amounts through that channel, leaving large amounts of transactions aimed at towards building that large lump sum for some purpose to happen at branches, not least because they don’t feel safe hauling a satchel of catch to an ATM in the middle of nowhere?
The implications of potential answer(s) can be profound. The first would imply that while we have celebrated ATMs as a successful de-congestion measure for banks, reducing staff load, client wait-times and operational expenses associated with physical branches, they have also caused people to save less, which can be antithetical to the cause of financial inclusion. On the other hand, the second would imply that branches still have certain benefits that are not being captured by other channels, and more effort needs to be made to address this convenience/security factor.
Of course, as with any complex system, the actual answer probably contains kernels of truth from both possibilities, and then some. Unless ridiculously fortuitous natural experiments present themselves with just the right incentives, say through subtle product rule changes intended to “nudge” a certain type of behavior, it’s well nigh impossible to seek answers to these kinds of questions irrespective of how big “big data” is.
(Btw, having 1% of accounts display a particular type of behavior across different banks in different countries is highly interesting in itself, since there is nothing definitional that would force this to happen. But that’s another story.)
I, for one, sleep peacefully at night knowing that often, all I can expect to get from “big data” are glorified correlations; anything else is gravy.
Although most of my work is with microfinance institutions, I have the good fortune to catch up with great institutions doing quite innovative work every now and then. D.Net is one such institution, who I interned with way back in 2006. Most of their work is in the realm of ICT4D (ICT for Development) and over the years they have pushed out a couple of quite successful technology-reliant solutions.
One of the latest one they are working on is this concept called the Info Lady. It’s an entrepreneurial woman who is provided skills training and a notebook and other digital equipment, who goes around in a bike in her own community and those near it and provides livelihood information, usually for a fee. Sounds a little … far-fetched, right?
Turns out, it actually works and is a sustainable business model. The original idea was inspired by D.Net’s success with Pallitathya Kendras, or Village Information Kiosks where people would come to these centers and obtain all manners of livelihood information for a fee. The Info Lady model simply extends this idea by taking that information to the doorsteps of clients, literally.
There are 90 possible services that could be provided, but based on demand most Info Ladies provide 20 to 25 services. Offerings include everything from health services (blood pressure, pregnancy tests etc.) to information on income generation activities to assistance for workers seeking employment abroad to taking pictures or videos at weddings. In fact, the last one has turned out to be quite popular and is also a good source of income for the Info Ladies.
Info Ladies require a fair bit of training on domain knowledge and technical expertise, which requires an extensive training program over a couple of weeks. The program is being rolled out in a handful of districts. There are 57 Info Ladies in the field at the moment, with 105 undergoing training. The target is to have 300 Info Ladies offering their services by the end of the quarter.
There is an upfront cost to getting the Info Ladies set up. The total up-front cost can vary between $1,500 to $2,000 depending on the equipment options. D.Net is subsidizing a part of the costs, but the Info Lady is expected to come up with most of it on her own, or take responsibility for most of it. One option is a 3-yr loan from the public National Bank that comes with a 3-month grace period and a 9% interest rate (commercial interest rates are around 19%). The average earnings reported is about $150 per month, which makes a monthly loan servicing amount of $50-$60 conceivable. By the way, the highest consistent earnings reported is a little over $600; while this can’t be taken as the average scenario, it does show the earning potential of the practice.
D.Net is also trying to get the Bangladeshi Diaspora involved by getting them to sponsor Info Ladies – another hallmark of D.Net projects.
Here’s an article that came out in The Daily Star (link to an archive because the paper’s website is currently migrating to a new one..). And here’s a powerpoint that gives a bit more detail on what an Info Lady does:
There’s poverty everywhere …
We’ve spent a fair bit of time talking about various methods to study poverty and development in this blog, ranging from plain vanilla surveys to financial diaries, RCTs and portfolio analytics, and it has all been within the context of developing countries. It is a reality that poverty exists in the developed world too, including the most vibrant economy in the world, the US. This post will talk about the US Financial Diaries program.
Just to put things in context, here’s a map of the world showing poverty percentages compared to the national poverty line:
… including the US
This tells us between 10% and 20% of the population live below the poverty line in the US. The Oracle provides us with some more detail:
Poverty is a state of privation or lack of the usual or socially acceptable amount of money or material possessions. According to the U.S. Census Bureau data released Tuesday September 13, 2011, the nation’s poverty rate rose to 15.1% (46.2 million) in 2010, up from 14.3% (approximately 43.6 million) in 2009 and to its highest level since 1993. In 2008, 13.2% (39.8 million) Americans lived in relative poverty. In 2000, the poverty rate for individuals was 12.2% and for families was 9.3%. In November 2012 the U.S. Census Bureau said more than 16% of the population was impoverished, and almost 20% of American children live in poverty.
And details can be found in all its glory right from the horses mouth; some of the highlights are copy/pasta-ed below:
- In 2011, the family poverty rate and the number of families in poverty were 11.8 percent and 9.5 million, respectively, both not statistically different from the 2010 estimates.
- As defined by the Office of Management and Budget and updated for inflation using the Consumer Price Index, the weighted average poverty threshold for a family of four in 2011 was $23,021.
- The poverty rate for males decreased between 2010 and 2011, from 14.0 percent to 13.6 percent, while the poverty rate for females was 16.3 percent, not statistically different from the 2010 estimate.
- In spring 2012, 9.7 million young adults age 25-34 (23.6 percent) were additional adults in someone else’s household. The number and percentage were both unchanged from 2011.
- In 2011, 13.7 percent of people 18 to 64 (26.5 million) were in poverty compared with 8.7 percent of people 65 and older (3.6 million) and 21.9 percent of children under 18 (16.1 million).
- The South was the only region to show changes in both the poverty rate and the number in poverty. The poverty rate fell from 16.8 percent to 16.0 percent, while the number in poverty fell from 19.1 million to 18.4 million. In 2011, the poverty rates and the number in poverty for the Northeast, Midwest and the West were not statistically different from 2010. The poverty rate in the South was not statistically different from the rate in the West. In addition, the Northeast poverty rate was not statistically different from the rate in the Midwest.
More than one in eight individuals living in poverty is bad, right, given the relative abundance of wealth in the US? Consider the fact that 16 million (22%) of all children live in poverty – a number exacerbated along ethnic lines (38% of Black and 35% of Hispanic communities) and one can see how this is an important issue to address in the US. [Source for numbers.]
Enter the US Financial Diaries
There’s been much renewed interest in the lives of the poor since financial armageddon a few years ago. Michael Barr’s No Slack: The Financial Lives of Low-Income Americans is a recent publication that based on 1,000 in-depth surveys that elaborate various ways in which the financial system “fails the most vulnerable Americans”. The U.S. Financial Diaries project is driven by similar motivations – it wants to understand how low-income individuals and households manage their financial lives.
You’re probably familiar with the financial diaries methodology championed by PoTP – conducting repeated surveys exploring every detail of a HH’s financial life for an extended period of time. This provides unprecedented detail, specially into behaviors that are difficult to pick out in one-off surveys, such as extent of engagement with the informal sector, and savings intermediation habits. As one can imagine, the presence or absence of various financial instruments creates a completely different ecosystem than say what we see in Kenya or India or the Philippines.
Non-probability sampling techniques are used, so the results will not be representative of the entire US. “Sites have been chosen to ensure geographic, ethnic, and racial diversity of households and urban/rural communities,” according to the project website, with sites including “Cincinnati and surrounding areas, Northern Kentucky, Eastern Mississippi, San Jose and surrounding areas, Queens, and Brooklyn”.
The project is in the data gathering stage right now, and we can expect analysis to start coming out in 2013 itself. It’s got the heavyweights in this field behind it – it is being administered by New York University’s Financial Access Initiative (FAI), Bankable Frontier Associates (BFA) and The Center for Financial Services Innovation (CFSI), with funding from Ford Foundation
and Citi Foundation and the Omidyar Network.
I for one can’t wait to see what comes out of it – the chance to compare and contrast with similar studies around with world will be quite interesting, as will be any follow up work that utilizes this information to design interventions to address the poverty situation in the US.
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 finally got my hands on Lamia Karim’s Microfinance and Its Discontents: Women in Debt in Bangladesh, and it made for a great read. If you are interested in this field, you should check it out. I thoroughly enjoyed the narrative and appreciated her attention to detail in terms of laying out the context necessary to follow in her anthropologist’s footsteps, so to speak.
I think the biggest contribution this book makes to the field is providing another counter to the PR-ridden “microcredit is the silver bullet to poverty” storyline that has done as much harm to industry by setting up unrealistic expectations of what microfinance in general, and microcredit in particular, can and does do. For better or worse, anecdotes continue to play a strong role in shaping the perception of the utility of microcredit in the absence of rigorous quantitative proof either way, partly because pretty much every recent RCT has found no evidence of statistically-significant impact (as opposed to evidence of no statistically-significant impact …).
Check out Chapter 4 for the 7 narratives provided. 3 of them end up doing well, 4 of them – not so much. The complexity of each person’s life and the financial intermediation they have to undertake in the presence of other instruments amply illustrates the fallacy of relying on a linear narrative that draws a causal connection between providing credit and increased income.
Chapter 1 and Chapter 5 are nice contributions to discussions about the genesis of the NGO scene in Bangladesh, especially when it comes to those providing microcredit. There used to be four – Grameen, BRAC, ASA and Proshika. And then the fourth kinda went way overboard with what it was trying to do, and then there were three. Proshika’s story of demise is interesting in itself, but is also an example of the dangers of confronting interest groups within existing social hierarchies head on, as opposed to working with them as most others try to. Reading these chapters makes one appreciate the institutionalized impediments to development the microfinance industry had to overcome.
One should keep in mind a couple of things while reading this book though. The lion’s share of her work was done in 1997/98. So when the publisher says this book “offers a timely and sobering perspective on the practical, and possibly detrimental, realities for poor women inducted into microfinance operations” on the back cover, I’m not so sure about the “timely” bit. This is not to say that a lot of the societal dynamics are still not relevant today, but microfinance as an industry has come a long, long way in 15 years, as has the critical awareness of civil society and the media to developmental initiatives. It is virtually impossible to imagine that “house breaking,” for example, is sanctioned or possible on an industrial scale today.
There is also this sense of exploitation of women borrowers on an industrial scale, although this book’s various reviews are probably guiltier of overhyping this than the book itself. It is couched in a neoliberal narrative – one that has found particular traction amongst critics of microfinance. In so far as “neoliberal” denotes “more markets, less state,” microfinance is guilty of that charge. Unfortunately, Lamia Karim assigns predominantly negative characteristics to those who are successful in this “neoliberal” enterprise – they lived by the principles of “competition and rationality,” and “while NGOs construct female borrowers as entrepreneurs, the emergent neoliberal subjectivity that I encountered was that of the petty female moneylender. The female moneylender embodied all the competitive aspects of the neoliberal subject.” (p.p. 199-200)
She makes a similar case on how “introduction into private life has led to loss of social solidarity,” where “introducing loans into private life, NGOs have begun to weaken the kin-based bond of identification and family solidarity.” (p.g. 200). I think it’s fair to say that most practitioners would be very confused with the first statement, and point out that most processes of upward economic mobility has the effect of reducing family size and relationships becoming more nuclear. I won’t go through all the other things that I found similarly odd, but the chapter Conclusion is littered with them.
In summary, put a tinfoil hat on for the last chapter if you must, but the book is good – check it out.
The concept of financial inclusion has been around for a while, and digital approaches to furthering financial inclusion has received a lot of attention because it is a potentially relatively low cost way to bring a basket of financial services to the doorstep of those who have been ill-served by the formal financial sector. Dan Radcliffe and Rodger Voorhies at the Bill & Melinda Gates Foundation recently published a paper titled A Digital Pathway to Financial Inclusion that does a nice job of capturing some of discussion and evidence surrounding digital approaches to financial inclusion.
In their own words:
We depict what digital financial inclusion would look like and present a growing body of evidence which suggests that connecting poor people to a digital financial system will generate sizable welfare benefits. We argue that countries will not bridge the cash-digital divide in one giant leap. Instead, they will likely pass through four stages of market development along the pathway to an inclusive digital economy.
This image is from page 9 of the paper that captures the central thesis:
You hear about all these digital-based interventions, from cell phones to mobile money to digital deposits of government-to-person (G2P) payments and sometimes it’s overwhelming to try to categories which does what and how it matters in the grand scheme of things. I think this 4-stage schema does a pretty good job at providing one framework for all that.
Who knew dissertation proposals took so long to pump out … Will blame the lull in blogging on that, though there’s been a lot going on in the background that I’d love to share “soon” on the blog.
In the meantime, let me just put this interview out there. Jonathan Morduch noted in his Household Savings in Developing Economies: An Annotated Reading List in 2008 that there are three main types of contributors to the literature on microfinance: 1) academic economists, 2) practitioners, and 3) anthropologists and sociologists. Lamia Karim is an anthropologist who has a new book out called Microfinance and Its Discontents:Women in Debt in Bangladesh, and am waiting eagerly to get my hands on it via Illyiad.
Btw, spoiler alert: she’s part of the “microcredit is bad for poor people, mmkay?” crowd. Don’t let that detract from her message though – this stuff is worth keeping in mind so that we don’t have to deal with another AP-style disaster again.
Natural experiments are great for the social sciences, and has been exceedingly rare for microfinance. Then the AP crisis came along, and caused the kind of change in circumstances that make for just such an experiment.
Basically, microfinance institutions beat a hasty retreat, writing off their outstanding loans as losses to a large part. This caused a pretty significant vacuum in liquidity providers in local economies. Guess who filled it up. SHGs, to a certain extent, sure. But this was also the perfect opportunity for the traditional archenemy of microfinance – moneylenders – to make a roaring comeback!
Relevant section from the abstract:
Both studies validate the fact that the members of the community face issues raising credit in the absence of MFIs. Members of the community have reduced their spending on important aspects such as health, education and business because of non availability of adequate credit from alternative sources. Moneylenders are having a field day with the absence of MFIs. Members of the community are falling back to moneylenders who charge usurious rates of interest to meet their credit needs.
Here’s the full report: Andhra Pradesh MFI Crisis and Its Impact on Clients
And a related policy brief: What Are Clients Doing Post The AP MFI Crisis?
A friend of mine recently forwarded me a recent publication titled The Limitations of Microcredit for Promoting Microenterprises in Bangladesh that appeared in the Jan-Mar 2012 edition of the Economic Annals. I sort of feel bad for picking on this one study but it’s somewhat representative of a few others I’ve seen where a couple of things just bothered me a brick ton.. Here’s a sampling. Apologies in advance to the authors; and I’m sure my turn will come soon enough
Claim 1: The field survey shows that about 11.7% of the microcredit borrowers are this kind of potential or growing microentrepreneur (Abstract).
Except, the survey this paper is based on was not randomly sampled (p.g. 43):
Samples were selected from urban (32.4%), semi-urban (27.2%), and rural (40.4%) areas, to ensure that microborrowers of different sized loans engaged in various categories of economic operations in rural and urban settings were adequately represented. In the absence of full knowledge of the structure and distribution of the microcredit borrower population in the country, random sampling as representative sampling is neither possible nor desirable (Molla and Alam, 2011). Moreover, in many situations random sampling is neither effective nor cost effective in serving the purpose for which sample data are collected. Purposive or judgment sampling is effectively used in such cases. Accordingly, a judgment sampling procedure was thought more effective and appropriate for this survey.
A couple of things:
- The 11.7% is not representative of the universe of Bangladeshi microcredit recipients, but of the non-random sample used in the study. Indeed, unless the distribution of microcredit borrowers is exactly 32.4%:27.2%:40.4% over urban:semi-urban:rural areas, the number is anything but 11.7%. Not a lot of MFIs want to work in urban areas, specially slums, where a large proportion of the urban poor and ultra-poor live. Ask Shakti Foundation, one of the few MFIs that serve this challenging demographic. The real number could be 5%, it could be 20% – who knows..
- I don’t buy the excuse that random sampling is not appropriate. By the authors’ own admissions, there are 15 million microcredit borrowers. (I think that’s a low ball number, but let’s go with it for now.) There are 150 million people in the country, including infants, the middle class, the elderly – demographics which are not obvious target populations of MFIs. You are almost guaranteed to hit a MFI borrower if you throw a … pillow a few times. Purposive or judgemental sampling is done when you are either targeting a very specific group and you don’t care about being representative, or there are so few of those you want to talk to that you have to search them out with deliberation. I can’t figure out how that could possibly be the case here.
- I find the suggestion that “in the absence of full knowledge of the structure and distribution of the microcredit borrower population in the country, random sampling as representative sampling is neither possible nor desirable” quite counter-intuitive. Indeed, if one does not know the underlying distribution of borrowers, a random sample would have illuminated that unknown too, contributing to the findings of this paper. Also, one has to look at the big three – Grameen, BRAC and ASA- and one can guesstimate fairly accurately what the distribution is..
It would have made much more sense to present the findings in three silos – urban, semi-urban and rural, and share all the findings within those segments. It would have been more appropriate than as an aggregate too since, conceivably, the urban implications of microcredit on microenterprises is somewhat different from rural ones.
Claim 2: A sizeable chunk of all borrowers, microentrepreneurs or not, have issues with the terms of credit, which are inadequate for entrepreneurial purposes. (p.g. 47, my summary)
The entire para is as follows:
About 20.7% of all the borrowers and 15.4% of the microenterprise borrowers believe that they do not have the scope to effectively use the entire loan amount at the start of activities. In practice about 29.2% of all the borrowers and 20% of the microenterprise borrowers did not use the entire loan amount at the start of their business operations (Table 3). On the other hand, about 27.9% of all the borrowers and 55.4% of the microenterprise operators had to top-up the loan fund with personal or other borrowed funds to start operations. On top of that about 21.4% of all the borrowers and 8.6% of the microenterprise operators invested additional funds during the year, either from personal sources or from credits obtained from other microcredit providers. About 28.3% of all the sample clients and 40% of the microenterprise clients received multiple loans (2-3 or more) from 2-3 or more microcredit institutions.
I agree with the general thrust of the message. The rigid disbursement and repayment schedules are not conducive to the fluid needs of business, and borrowers often have to borrow from other sources to make up working capital shortfalls.
But the numbers I see here actually don’t seem that bad:
- If 20% of borrowers don’t believe they can use the entire loan amount right away, then 80% believe they can, right?
- Similarly, if almost 3/4 of borrowers and 1/2 of microentrepreneurs do not have to top-up funds right at the beginning, that’s not too bad, right?
- Also, similarly, if almost 80% of borrowers and > 90% of microentrepreneurs did not have to invest additional funds, that’s not terrible either, right?
- 40% of the clients borrowing from multiple MFIs could be seen as a bad thing, but we have to be careful not to equate miltiple-borrowing with overindebtedness. PotP is chock full of examples which clearly demonstrate how sophisticated the poor are in their financial management, and Bangladesh was one of the study countries too.
I mean, it looks like microcredit is able to satisfy funding needs at various levels for 75-80% of borrowers in general and 50-90% of microentrepreneurs in particular, more or less. If we demand more, are we not holding microcredit to an unrealistically high standard, given the realities of the products and distribution channels?
Again, the bone I pick is not with the underlying message, but that the numbers put forward seem to weaken the case being made.
Claim 3: (The) preference for women as clients for credit is found to be a strong methodological limitation of the microcredit delivery system in promoting microenterprises. (p.g. 45-46)
The authors make a compelling enough case, up to a point. Men tend to run businesses in Bangladesh, and their survey shows how the female clients simply pass on the microcredit to their male counterparts. The respondents note the following as reasons for dependence on men (p.g. 45):
- inability and lack of skill of the women borrowers,
- more investment opportunities in man-relevant activities,
- male-dominated family structure where male members maintain and control family,
- social environment and custom where business activities are considered to be men’s work, and
- women are not expected or respected in the domain of men’s activities (business activities)
So .. Why don’t MFIs simply lend to men? Blind ideology, or is this something based on reality?
Google “men microfinance” and you’ll get a ton of useful discussion, interspersed by a couple of good studies on this issue. The short answer is that we may not always know why, but men tend make for crappy borrowers. There is something in the woman-borrower/man-entrepreneur dynamic that “works.” (But may not always “work” in a way that is comforting – check out Lamia Karim’s work for societal dynamics gone bad.) Man-borrower-entrepreneur models don’t tend to work.
It is not constructive to simply take out the borrower intermediary when she clearly has something big to do with things.
It is also why SME lending has been so hard.
There are bunch of other things that gave me reason for pause, including:
- The study relies too much on the Grameen model. BRAC and specially ASA do not do things like Grameen, and the results might be quite different for them. The authors may find that the “stereotyped microcredit delivery system” may have considerable variation within it.
- There is no “counterfactual” to the claim that “microcredit is not sufficiently productive to generate enough revenue for interest payments if market rate wages are paid for family labour” for a significant portion of the borrowers. What if they did not borrow? Would they earn more? Would they starve?
- It calls 25%-65% interest rates exorbitant, citing Bangladesh Bank lending rates of 4-5%, and commercial lending rates of 10-12% (p.g. 42). 65% could be considered exorbitant, but 25%? And most importantly, there are very, very real reasons why microfinance interest rates are so high. And it’s not because Grameen/BRAC/ASA are wannabe loan-sharks.
- Its citations are .. unimpressive. One study used to comment on male-female gender dynamics is from 1996 – arguably an eternity in terms of the evolution of microfinance (p.g. 45). Commercial lending rates are quoted from 1997 (p.g. 42). More than half the references are the authors’ own, and the rest are mostly links to MFI reports.
Overall, this piece has decent analysis behind it. I think it gets into trouble trying to hammer out conclusions from it that are not adequately supported by the data.
By the way, if 11.7% of the (non-random) sample are microentrepreneurs of some stripe, what about the remaining 88.3%? What are they using microcredit for? If microcredit has limitations for “promoting microenterprises in Bangladesh,” what is it overwhelmingly succeeding in doing?
Wouldn’t that be fun to know!
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.