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If it needs a power calculation, does it matter for poverty reduction?
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DOI：10.1016/j.worlddev.2019.104815.png)
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Abstract
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A key critique of the use of randomized experiments in development economics is that they largely have been used for micro-level interventions that have far less impact on poverty than sustained growth and structural transformation. I make a distinction between two types of policy interventions and the most appropriate research strategy for each. The first are transformative policies like stabilizing monetary policy or moving people from poor to rich countries, which are difficult to do, but where the gains are massive. Here case studies, theoretical introspection, and before-after comparisons will yield good enough results. In contrast, there are many policy issues where the choice is far from obvious, and where, even after having experienced the policy, countries or individuals may not know if it has worked. I argue that this second type of policy decision is abundant, and randomized experiments help us to learn from large samples what cannot be simply learnt by doing. (C) 2019 The International Bank for Reconstruction and Development/The World Bank. Published By Elsevier Ltd. All rights reserved.
Keywords:
Research methods
Statistical inference
Poverty alleviation
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