LAUSR

Abstract With minimal statistical or theoretical assumptions, randomized controlled trials (RCTs) provide a necessary input for poverty analysis: credibly estimated causal relationships. But complexities arise when moving from RCT research results to anti-poverty policy, with unintended consequences. RCT evidence by itself offers an incomplete prediction of the effects of policy, due to heterogenous effects, spillovers and general equilibrium changes, macroeconomic and welfare effects, political economy reactions, and implementation challenges, when programs are scaled. We suggest strategies for tightening the link between development research and anti-poverty policy, for example, by changing the practice of RCTs to be more ambitious about what is randomized, and to combine the analysis of experimental data with other rigorous methods that go beyond estimating treatment effects. We describe our efforts to encourage and coordinate this type of work via a new research initiative.