Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
The new Nobel prize winners have expertly popularized randomized controlled trials (RCTs) as the “tool-of-choice” for empirical research. The award is a good opportunity to reflect on the role of RCTs in development-policy evaluation. Unbiasedness is the tool’s main virtue; transparency is another. Practitioners should also be aware of some limitations. First, an RCT assigns the treatment in a different way to most real-world policies, which use purposive selection; given heterogeneous impacts, one is evaluating a different intervention. Second, the tool may only be feasible for non-random subsets of both the relevant populations and the policy options, biasing assessments of overall development effectiveness. Third, given budget-constraints and a bias-variance trade-off, a non-RCT may allow a larger sample size, making its trials often closer to the truth. There is a continuing need for a broad range of research methods for addressing pressing knowledge gaps in fighting poverty.