No post on this blog will I write with less objectivity than this one. CGD and the Financial Access Initiative have just released a working paper by Jonathan Morduch and myself that critiques what have been the leading studies of whether microcredit reduces poverty. We recreate three well-known analyses of survey data collected in Bangladesh in the 1990s. They seem to show that microcredit either increased household spending or reduced its volatility (meaning there is enough to eat more of the time). In our view, they fail to show either benefit. I've already blogged the general problem: without introducing an artificial random element in what you study---without experimenting---it is really hard to prove that A causes B instead of B causing A. If prosperity and borrowing go hand in hand, which causes which? Economists do what they can to mathematically distinguish competing theories but I have found that they succeed less than they realize.
Another highlight: when we re-run the complicated regression that is the source of a statistic Muhammad Yunus has cited, that "5 percent of the Grameen borrowers get out of poverty every year," we get the opposite sign. Seemingly, lending to women makes families poorer...but I just told you how much credence we put on such claims about cause and effect.
Bottom line: the academic evidence that microcredit reduces poverty is really weak.
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From my point of view, the story goes like this:
1991--92. With funding from the World Bank, and in cooperation with the Bangladesh Institute for Development Studies, economists Mark Pitt and Shahidur Khandker field a survey of some 1,800 households in Bangladeshi villages, visiting each three times, in three successive seasons.
1996. Pitt and Khandker (PK) circulate a World Bank working paper analyzing this data using complex mathematics and concluding that microcredit increases household spending, especially when given to women.
1998. The study appears in the prestigious Journal of Political Economy and becomes the leading analysis of the impact of microcredit. "[A]nnual household consumption expenditure increases 18 taka for every 100 additional taka borrowed by women…compared with 11 taka for men.” But a young economist named Jonathan Morduch circulates a draft paper that applies much simpler methods to the data and reaches different conclusions. Microcredit does not seem to increase spending, but it does appear to smooth it out from season to season. Morduch questions key assumptions in PK.
1999. Pitt retorts, seeming to rebut Morduch's criticisms one by one. Neither Pitt nor Morduch uses the other's methods, so no direct confrontation between the seemingly contradictory results occurs. For interested bystanders, the exchange is as enlightening as two nuclear engineers arguing over obscure properties of plutonium isotopes. Meanwhile in Bangladesh, surveyors revisit the households of 1991--92 to collect more data.
2003. Khandker attempts to rise above the old fray by studying the superior, augmented data set.
2005. Khandker's paper appears in the World Bank Economic Review.
2007. In my drive to understand what we know about the impacts of microfinance, I determine to get to the bottom of the unresolved, confusing methodological debates by recreating the old studies. I write a computer program that makes it much easier for people to perform regressions (statistical analyses) like those in PK. in time, I collaborate with Jonathan Morduch. Rerunning old regressions and applying new statistical tests, we show why economists should doubt the positive conclusions in the previous Pitt, Khandker, and Morduch papers. The arguments are inherently technical. Most of the number crunching is new, but much of the conceptual critique traces to Jonathan's earlier paper.
2009. I write, "in my view it was for decades essentially correct to say that we have zero solid studies of whether microfinance makes clients better off on average." Now you see why.
Note well: I am not saying that microcredit, much less microfinance as a whole, is bad for poor people. For me, the take-home lesson is that social scientists and promoters of social programs respond to incentives to overestimate and exaggerate the power of mathematics to enlighten us about causality in social systems. Math does not substitute for wiser reflections on the nature of development and how financial services can contribute to it.