Randomized Evaluation in Development
Also known as: Randomized Controlled Trials, Field Experiments in Development, RCTs in Development Economics, Randomized Field Trials, Experimental Impact Evaluation
Randomized evaluation applies the logic of the controlled experiment to development policy: an intervention — a school grant, a deworming pill, an insurance product — is assigned at random to some units and withheld from others, so that any subsequent difference in outcomes can be attributed causally to the intervention rather than to confounding. Championed from the early 2000s by the Abdul Latif Jameel Poverty Action Lab (J-PAL) and Innovations for Poverty Action (IPA), the approach earned its leading proponents — Esther Duflo, Abhijit Banerjee, and Michael Kremer — the 2019 Nobel Memorial Prize in Economics for transforming how anti-poverty programmes are tested.
Key highlights
- Random assignment eliminates selection bias by construction, giving the cleanest available identification of a causal effect with minimal modelling assumptions.
- Results are transparent and credible to non-specialists: the headline estimate is a simple difference in group averages.
- Pre-analysis plans and registration discipline the analysis, reducing the risk of specification searching and publication bias.
- The method has built a cumulative, replicable evidence base across countries and sectors, enabling cost-effectiveness comparisons of competing programmes.
Intuition
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How it works
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When to use it
Use a randomized evaluation when the central question is whether a specific, deliverable intervention causes a change in measurable outcomes, when ethical and operational conditions permit withholding or phasing in the programme, and when the population is large enough to detect a policy-relevant effect with adequate statistical power. It is the design of choice for testing programme prototypes and mechanisms. It is poorly suited to evaluating economy-wide policies, unique large infrastructure, or interventions whose effects unfold over decades, and it cannot by itself explain why a programme worked — questions better addressed by theory-based or mixed-methods approaches.
Strengths & limitations
- Random assignment eliminates selection bias by construction, giving the cleanest available identification of a causal effect with minimal modelling assumptions.
- Results are transparent and credible to non-specialists: the headline estimate is a simple difference in group averages.
- Pre-analysis plans and registration discipline the analysis, reducing the risk of specification searching and publication bias.
- The method has built a cumulative, replicable evidence base across countries and sectors, enabling cost-effectiveness comparisons of competing programmes.
- External validity is not guaranteed: an effect measured in one context, scale, or population may not transfer to another (the site-selection and scale-up problem).
- Many of the largest policy questions — macro policy, institutions, exchange rates — cannot be randomised at all.
- Spillovers, general-equilibrium effects, and Hawthorne/John Henry behavioural responses can bias estimates if the design ignores them.
- Trials are expensive and slow, and ethical or political constraints may forbid withholding treatment from a control group.
Common pitfalls
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Applications
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Frequently asked
What is the difference between intention-to-treat and treatment-on-the-treated effects?
The intention-to-treat (ITT) effect compares everyone assigned to treatment against everyone assigned to control, regardless of whether they actually took up the programme; it reflects the effect of offering the programme under realistic non-compliance. The treatment-on-the-treated (ToT), recovered as a local average treatment effect (LATE) via instrumental variables, divides the ITT by the take-up rate to estimate the effect on those who actually complied. ITT is policy-relevant for roll-out decisions; ToT speaks to the effect of the treatment itself on participants.
Why randomise at the cluster level instead of the individual level?
Cluster randomisation — assigning whole villages, schools, or clinics — is used when treatment effects spill over between nearby individuals (e.g., deworming reduces transmission to untreated neighbours) or when the intervention is naturally delivered at a group level. It protects the comparison from contamination, but it reduces statistical power because outcomes within a cluster are correlated, so standard errors must be clustered and sample-size calculations must account for the intra-cluster correlation.
Does a successful trial mean the programme will work everywhere?
Not necessarily. A randomized evaluation has strong internal validity — it credibly estimates the effect in the studied context — but external validity, the transfer of that effect to other populations, scales, or settings, must be argued separately. Replication across sites, attention to the mechanism behind the effect, and theory about which contextual features matter are needed before generalising. Effects can also change at scale through general-equilibrium and implementation effects that a small trial cannot capture.
Sources
- 1.Banerjee, A. V., & Duflo, E. (2009). The Experimental Approach to Development Economics. Annual Review of Economics, 1, 151–178.
- 2.Duflo, E., Glennerster, R., & Kremer, M. (2007). Using Randomization in Development Economics Research: A Toolkit. Handbook of Development Economics, 4, 3895–3962.
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Cite this page
ScholarGate. (2026, June 22). Randomized Evaluation in Development. ScholarGate. https://scholargate.app/development-studies/randomized-evaluation-development