Lending group sizes: Read something recently stating that repayment rates were highest among a studied selection of lending groups when the group had 14 people in it. I find this fascinating – is it something like Dunbar’s number for close friends, instead of general social connections? What I don’t totally understand is why the number would be as large as 14. It seems to me that the social pressure exerted by any one individual on others would decrease linearly with the number of group members – that is, I can put more pressure on 5 other people to repay their loans than on 14, because of my own time constraints. Or perhaps the limits on social pressure are outweighed by the implicit pressure of having 13 other examples of people successfully repaying their loans, instead of just 4? Freakonomics mentioned some research recently about the power of implicit social pressure (or sanction) in guiding behavior, with regard to people seeing examples or merely believing that a majority of others were acting in a certain way. Perhaps one could think of living up to an example (of loan repayment, in this case) as a way of earning social capital, instead of actively spending it by pressuring other group members to repay their loans.
Social determinants of lending group access: Another predictable but still interesting fact is that self-selecting lending groups are sometimes reluctant to take on members who are too poor – lacking social capital as well as financial (and isn’t that a metaphor for poverty in general?). It does make sense to view current poverty as deterministic of future entrepreneurial success, from the POV of another lending group member, but I also wonder if group members would view occasional poverty (i.e. brought on by a recent illness) differently than chronic poverty. How long does someone have to be abjectly poor before a lending group is more likely to reject them? That’s an interesting question. [NB: Can’t find citation for this, although I think it may have come from Understanding Poverty.)
Monthly vs. weekly repayment schedules: This was a wonderful study – an analysis of whether Indian MFI clients were more likely to repay their loans if they made weekly repayments, monthly repayments, or monthly repayments with weekly group meetings regardless. Repayments didn’t actually vary in a statistically significant manner across any of the three repayment schemes, although the monthly group actually had the lowest rate of missed payments according to the raw data. I say “wonderful” because this knowledge is a great step towards designing lending programs that are better suited to the variable and unpredictable incomes of the poor – it’s quite valuable to understand that time-consuming weekly repayments aren’t necessary to pressure clients into repaying, which may give them more flexibility with their repayments (and use of loans).
I’ve been reading a great deal recently about the linkages between food availability, intra-household resource allocation, and nutritional status, and it’s made me wonder about time-specific determinants of resource allocation. That is, it’s clear that there are some systematic, long-term differences in allocation connected to overall education levels, overall income, and gender. What I’m curious about are short-term, potentially more idiosyncratic effects: for instance, are women more or less likely to command adequate nutrition if they fall ill? Are there differences between the amount of food received at home by, say, a young child in school (potentially also benefiting from a school lunch program) and an older child who drops out to care for younger ones or help in the fields? How much would one discount future education (for the young child) versus immediate ability to do work in that case, and how might one be able to change that calculus if it weren’t a long-term beneficial one?
The obvious connection to pro-poor financial services lies in the oft-noted social “shock” of women suddenly receiving access to credit when they previously had none, which disrupts existing allocation schemes and may result in tension or violence between women and their relatively disempowered husbands. I can think of a few specific predictors of violence or generally negative reactions in this case – a history of prior violence being the most obvious one, or a husband’s unemployment, or a cultural injunction against women handling money – but I wonder if there are other traits that could be used to predict whether men might react poorly to women’s credit, and perhaps develop plans to defuse this scenario.
Landscape, Murambi District, Rwanda
Thinking some more about the geographic scale of different agricultural markets has led me to consider how information availability and pricing might differ between them. Mobile phones are still a fairly rare commodity in rural Rwanda (outside of the towns), and if some set of extremely poor farmers were only selling extra produce hyperlocally, it seems that perhaps prices would either be set in total isolation from regional or national prices, or might be determined exclusively by a few people with mobiles. (I wonder if outside information would be convincing in this scenario – if the phone guy says to the farmer, “this cassava is 5 francs cheaper in Nyamata,” would the farmer accept this as a negotiating tactic, or assume that he’s lying? Perhaps it’s connected to how easy it is for the phone guy to actually access the cheaper cassava in Nyamata – the credibility of his threat.)
Then again, even with mobile technology to help information access along, its helpfulness still seems fundamentally predicated on A) physical mobility and B) social networks. Information about great prices in a town up the road will be less useful to a farmer if he still can’t reach it easily, and receiving the information in the first place is still connected to one’s actual social network (and ability to pay for airtime, of course). I wonder if the differing availability of mobiles to rural residents of different socioeconomic statuses may actually increase the vulnerability and exclusion of the poorest of the poor, rather like differing levels of access to microinsurance might actually push healthcare farther out of reach of the poorest.
I wish I had a better intuition on the question of the social impact of large-scale agriculture on smallholder farmers. I’m guessing the deciding factor is the strength of their existing market connections – whether they’re strong or weak, and central to livelihoods or a supplemental source of income – but I can’t seem to get at it on Google with specific regard to Rwanda.
This is probably analogous to any form of large-scale, cost-efficient production – the potential for socioeconomic displacement depends on the product you’re displacing. And I suppose that’s also a commentary on the target market. I’d guess that the vast majority of Rwandese agricultural production is by smallholder farmers for either hyperlocal (within a few miles) or local (say half a day’s walking distance) markets, and that only certain commodities are ever going to be linked to the Kigali low-income or high-income markets. (There’s not really a hell of a lot of local stuff being sold to high-income people in Kigali anyway, except maybe the specialty vegetables at Simba. The wealthy mzungus & Rwandese have their own totally separate selection of imports, whose prices correspondingly reflect good information about international pricing and transport costs. Which is to say, they cost an arm and a leg.)
So I’m wondering if perhaps there are many smallholder farmers who are producing the same types of commodities as might be produced commercially, but if they’re really not linked into the same markets that large-scale production would target. It seems like this might be more of a threat to medium-scale enterprises that already serve perhaps a regional or national (Rwandese) market, but then that’s a difficult ethical issue from threatening the livelihoods of smallholders who have almost nothing to start with. It’s a persistant challenge of doing business in poor countries – weighing the social benefits of increased food production & lower prices (marginal improvements to the well-being of many relatively poor consumers) vs. the possibility of undercutting the incomes of medium-scale farmers (major detriments to well-being of a few relatively poor producers). As an aside, I think there’s also an interesting lesson about innovation in there – that its effects will go in different directions depending on whether it’s creating an improved version of an existing product or service, or whether it’s creating something so novel that new markets develop around it. “Innovation” always seems to be referred to as a uniform category of activities, without regard for its differing impacts, at least in popular parlance.
The insight of pay-as-you-go schemes as a response to variable incomes is a great one. Its applicability to things like mobile phone minutes is obvious, and I’m very curious as to how it could be applied to financial services for the poor. The big stumbling blocks appear to be the questions of credibility and enforceability. Or, rather, the fact that enforceability tends to engender credibility – banks believe their clients when they say they’ll pay, as they can easily check up on their payments and enforce them if they fall behind. A pure pay-as-you-go plan in microcredit – say, a three-month loan that can be repaid in any amount at any time before the three-month period is up – would be unenforceable over the period of payment, as the bank couldn’t demand payment at any given time before the deadline.
Actually, that’s a separate (and interesting) question – how different types of enforcement may affect the repayment of loans. Perhaps you could look at peer pressure in lending groups, vs. occasional visits from a loan officer, vs. frequent visits from a loan officer, vs. financial incentives for timely payment, or something like that. That might shed some more light on designing systems of incentives for ultimate on-time payment of a pay-as-you-go loan.
Loan repayment book, WomensTrust Microfinance, Pokuase, Ghana
I think I missed an important point in my last musings on microfinance – that ex-post savings does actually make sense if the loan is given for investment rather than consumption. So the salient factor in assessing the potential of microloans to help or harm their recipients is also connected to their intentions for the use of the loan, as well as their existing income level.
But this does come back to the broader point currently being raised by many people, about the apparent contradiction between views of the poor as necessarily innovative, and the fact that most people, rich or poor, aren’t good at being individual entrepreneurs. I do wonder if the “poor as inherent innovators” view doesn’t suffer from some confirmation bias. There certainly are many memorable examples of entrepreneurship born from poverty, but, as in the developed world, it seems that there’s a subset of people who are actually responsible for innovation. The demands of living in poverty do include hard work and often creativity, but while those characteristics are also necessary for market-based entrepreneurs, they’re not by themselves sufficient. Even investment-oriented microloans can turn into consumption-oriented ones, in practice, if their recipients don’t have adequate business strategies and market knowledge for their use. Perhaps it’s not quite as bad at the level of larger microfinance banks with better data-gathering operations, but at the level of the small organizations at which I’ve worked, there seems to be a lot of fuzziness around the use of loans and their ultimate impacts. I worry that this is one of the situations where a lack of data may actually end up harming people who really can’t afford to be harmed, in improperly distributing loans.
From Sen’s perspective of freedom, I suppose the ultimate question of entrepreneurship is whether self-determined employment (but perhaps a lower wage) or higher-waged corporate employment (but less career self-determination) is more conducive to personal freedom, in the end. For the majority of people, I’m guessing it will be the latter, once you average the benefits of a higher wage over all the categories that it affects.