by ‘Nobel prize’ winners Duflo and Banerjee do not tackle the real root causes of poverty Some go so far as to insist that development interventions should be subjected to the same kind of randomised control trials used in medicine, with “treatment” groups assessed against control groups. Such trials are being rolled out to evaluate the impact of a wide variety of projects – everything from water purification tablets to microcredit schemes, financial literacy classes to teachers’ performance bonuses … The real problem with the “aid effectiveness” craze is that it narrows our focus down to micro-interventions at a local level that yield results that can be observed in the short term. At first glance this approach might seem reasonable and even beguiling. But
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Lars Pålsson Syll considers the following as important: Economics
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‘Nobel prize’ winners Duflo and Banerjee do not tackle the real root causes of poverty
Some go so far as to insist that development interventions should be subjected to the same kind of randomised control trials used in medicine, with “treatment” groups assessed against control groups. Such trials are being rolled out to evaluate the impact of a wide variety of projects – everything from water purification tablets to microcredit schemes, financial literacy classes to teachers’ performance bonuses …
The real problem with the “aid effectiveness” craze is that it narrows our focus down to micro-interventions at a local level that yield results that can be observed in the short term. At first glance this approach might seem reasonable and even beguiling. But it tends to ignore the broader macroeconomic, political and institutional drivers of impoverishment and underdevelopment. Aid projects might yield satisfying micro-results, but they generally do little to change the systems that produce the problems in the first place. What we need instead is to tackle the real root causes of poverty, inequality and climate change …
If we are concerned about effectiveness, then instead of assessing the short-term impacts of micro-projects, we should evaluate whole public policies … In the face of the sheer scale of the overlapping crises we face, we need systems-level thinking …
Fighting against poverty, inequality, biodiversity loss and climate change requires changing the rules of the international economic system to make it more ecological and fairer for the world’s majority. It’s time that we devise interventions – and accountability tools – appropriate to this new frontier.
Angus Deaton James Heckman Judea Pearl Joseph Stiglitz et al.
Most ‘randomistas’ — not only Duflo and Banerjee — underestimate the heterogeneity problem. It does not just turn up as an external validity problem when trying to ‘export’ regression results to different times or different target populations. It is also often an internal problem to the millions of regression estimates that are produced every year.
Just as econometrics, randomization promises more than it can deliver, basically because it requires assumptions that in practice are not possible to maintain. And just like econometrics, randomization is basically a deductive method. Given the assumptions, these methods deliver deductive inferences. The problem, of course, is that we will never completely know when the assumptions are right. And although randomization may contribute to controlling for confounding, it does not guarantee it, since genuine randomness presupposes infinite experimentation and we know all real experimentation is finite. And even if randomization may help to establish average causal effects, it says nothing of individual effects unless homogeneity is added to the list of assumptions. Causal evidence generated by randomization procedures may be valid in ‘closed’ models, but what we usually are interested in, is causal evidence in the real-world target system we happen to live in.
‘Ideally controlled experiments’ tell us with certainty what causes what effects — but only given the right ‘closures.’ Making appropriate extrapolations from (ideal, accidental, natural or quasi) experiments to different settings, populations or target systems, is not easy. “It works there” is no evidence for “it will work here”. Causes deduced in an experimental setting still have to show that they come with an export-warrant to the target population/system. The causal background assumptions made have to be justified, and without licenses to export, the value of ‘rigorous’ and ‘precise’ methods — and ‘on-average-knowledge’ — is despairingly small.
Apart from these methodological problems, I do think there is also a rather disturbing kind of scientific naïveté in the Duflo-Banerjee approach to combatting poverty. The way they present their whole endeavour smacks of not so little ‘scientism’ where fighting poverty becomes a question of applying ‘objective’ quantitative ‘techniques.’ But that can’t be the right way to fight poverty! Fighting poverty and inequality is basically a question of changing the structure and institutions of our economies and societies.