"Understanding impact: how to improve policy design for economic development"

The field of development economics has changed radically in the last twenty years with an ever-increasing focus on causal identification using experimental and quasi-experimental micro-level data. Yet identifying causal relationships in tightly controlled settings is not sufficient for useful policy advice for at least two reasons: (i) interventions that are strictly controlled by the research team often have low generalizability; that is, we do not know whether such interventions would generate similar effects in other contexts, thereby compromising scalability; and (ii) policy responses might be very different depending on the mechanisms that underlie the causal chain. In this lecture I will explain how we can do better, by identifying and evaluating relevant low-cost, scalable alternatives to standard programs, and build-in tests to discriminate between competing explanations. I will use examples of new research I’m conducting with my team in the Middle East and North Africa region and central Asia to show how these approaches are expected to help to understand impact, increase research uptake by policymakers and can lead to more inclusive sustainable development. 

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