Sunday, July 19, 2015

JPAL Executive Education : Evaluating Social Programs

Do you know about Confirmation Bias ? People more likely to believe information that fits their pre-existing beliefs, but they’re also more likely to go looking for such information. Hence, we are stuck with wrong design of the development programs dooming millions of investment. So, we do not let evidence from the ground guide the policy? Unfortunately, it is hard to get clear-cut evidence of causality. Using evidence to guide aid and social work is crucial to ensuring the efficient use of limited resources. For years, policymakers have debated different approaches to helping the poor and now they have published research paper after a nine-year, six-country study, offers resounding evidence for a strategy that works. Proponents of randomized program evaluation argued more field experiments were needed to learn what worked.

There are also critiques like economist Angus Deaton who suspects that an average bureaucrat might take the results in controlled environment too serious. Any aid to poor really ought to be decided by democratic discussion  between stakeholders while RCTs are often done on the poor without any partnership is hardly an encouraging sign.

This led my interest in the executive education program offered by Abdul Latif Jameel Poverty Action Lab (J-PAL). I was among 31 executives selected for the course held on July 2015. This five-day program on evaluating social programs provided a thorough understanding of randomized evaluations and pragmatic step-by-step training for conducting one’s own evaluation. RCT measures impact of the program by comparing a treatment group to a control group, where the people who get the treatment are drawn randomly by lottery. There was emphasis on building theory of change for seeking an impact of the program.

I enjoyed great atmosphere of learning in those five days. Thanks to Sharanya Chandran, Dechen Zem, Diwaker Basnet, Chandra Shekhar Gowda, Rajeev Kumar and Rajesh Jain. And I learn a valuable lesson that ideas should be funded based on evidence that they work — not hope.



How to build a theory of change for an impact evaluation


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