September 12, 2013Doug Hadden
Doug Hadden, VP Products
Michael Clemens, a Senior Fellow at the Center for Global Development has an interesting article: The New Transparency in Aid Evaluation: The Millennium Villages Debacle Is a Good Sign of What We’re Learning. I made a few comments on the blog that I'm expanding here. Clemens describes recent advances in development economics that are helping to answer some questions about aid effectiveness.This "new transparency" involves new media like blogs and social media. The promise of transparency is better evidence about what works while eliminating "confirmation bias."
Confirmation Bias and Dogma
The razor's edge on aid transparency is the challenge to dogma. We're at this exciting inflection point thanks to the International Aid Transparency Initiative (IATI), AidInfo and others that increase accessible data. There is a real promise that this information will generate insights. Aid interventions will become comparable. Contextual factors will be better understood. Yet, the aid debate within politics seems to have become even more dogmatic and polarized where the notion that 'aid doesn't work' has become a troubling meme. Where confirmation bias plays a dominant role.
Could this "information overload" enhance confirmation bias? Generate even more 'facts' to support dogma? Much like the health care / climate change / gun control debates in the United States.
There is a world of difference between the politics of aid and the reality of aid. Transparency will inform decision-makers and be leveraged by implementers.
The difficulty with the aid / health care / climate change / gun control debates in the U.S. is about narrative, not facts. (For example, blaming aid recipients as if they deserve to be poor and sick.) It's emotional. It's based on views of what motivates people to act in different ways and what ought to be priorities. And, fear.
Micro and Macro Problem
Much of the evidence that aid works is at a micro level where smaller interventions are easy to manage and measure. There are few expected outcomes and few unexpected outcomes. Much of the evidence that aid doesn't work or does work is at a very high macro level. Economies that receive aid improve or do not improve. Theories abound. It is virtually impossible to prove that aid was materially responsible for every country meeting MDG objectives (or, that those objectives represent effective outcomes.) And, it is similarly almost impossible to prove that aid created perverse incentives so that MDG objectives were not met.
The data at the micro level can not be extrapolated, today, to broad conclusions. That's where big data comes in.
Big Data and the End of Narrative?
There is a "big data" backlash. (From experts of the pre-big data world. Much like the horse industry objecting the the automobile industry.) The primary criticism of big data is that it is no different than typically business intelligence and size of the data doesn't change effects. And, many argue that there is simply no way for big data to find unexpected insight because only experts can ask the right question. (The notion of "data scientist" is rejected because you can't get insight without specialist knowledge.)
My sense is that the big data will generate insight because specialists often filter out relevant information from other specializations.
The key behind big data is not that the data is big. It's that the data comes from multiple sources including reports, social media, and electronic monitoring devices. It is possible to understand the context coming from these sources. This will slowly cut through false and misleading narratives. Slowly because media remains dominated by narrative – especially cable news in the United States.
And, there seems to be a mad rush to lower aid or privatize aid or pontificate about aid during this window before we gain the next generation of insight. It's almost as if the dogmatic are trying to fill us with noise to drown out the potential problem when transparency demonstrates that they were wrong.
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