For decades, development economics was plagued by a fundamental problem: we couldn't tell what actually worked. Programs were evaluated through before-and-after comparisons, correlation studies, and expert intuition. Billions of dollars flowed based on assumptions that rarely faced rigorous testing.

Then came the randomized controlled trial revolution. By randomly assigning some communities to receive an intervention while others served as controls, researchers could finally isolate causal effects. The method that transformed medicine began reshaping how we think about poverty reduction.

But two decades into this revolution, a more nuanced picture has emerged. RCTs have delivered genuine insights while revealing their own limitations. Understanding both sides matters enormously for anyone designing or funding development interventions.

Before RCTs became widespread in development economics, evaluating whether a program worked was surprisingly difficult. If a microfinance organization reported that its borrowers' incomes rose, was that because of the loans? Or did motivated, entrepreneurial people simply select into borrowing?

This selection problem corrupted nearly every evaluation approach. Cross-sectional comparisons confused cause and effect. Before-and-after studies couldn't distinguish program impacts from broader economic trends. Instrumental variable approaches required assumptions that critics could always challenge.

Randomization cuts through this methodological thicket. When treatment and control groups are randomly assigned, any systematic differences between them reflect the intervention itself—nothing else. The logic is elegantly simple, even if implementation is complex.

The credibility gains proved substantial. Studies that seemed to support popular interventions sometimes showed null effects under rigorous testing. Conversely, randomized trials occasionally validated approaches that theory suggested shouldn't work. The deworming studies in Kenya, whatever their later controversies, demonstrated that health interventions could boost educational outcomes through unexpected channels. This wasn't ideology or intuition—it was evidence.

Takeaway

Randomization doesn't just improve precision; it transforms what questions we can credibly answer by eliminating the selection bias that haunts observational research.

Here's the uncomfortable reality: development interventions that succeed brilliantly in one context often disappoint elsewhere. A conditional cash transfer program that transformed outcomes in Mexico may produce modest effects in Indonesia. A teacher training approach that worked in Kenya might fail in India.

This external validity problem runs deeper than cultural differences or implementation quality. RCTs excel at answering a specific question: did this intervention work, in this place, at this time, implemented by these people? Generalizing beyond that context requires assumptions the experimental design cannot test.

The mechanisms matter enormously. When an intervention succeeds, why? Is it the specific design features, the implementing organization's capacity, the local economic conditions, or social norms that shaped how participants responded? An RCT can tell you that something worked without necessarily explaining why.

Some researchers have responded by conducting multiple trials across contexts, seeking patterns that hold broadly. Others emphasize understanding mechanisms over simply measuring average effects. Both approaches acknowledge that a single positive trial, however well-executed, provides weaker guidance for policy than we might hope.

Takeaway

Evidence from one context is not automatically evidence for another; the real work lies in understanding why interventions succeed or fail, not just whether they did.

Certain development questions simply cannot be randomized. You cannot randomly assign countries to adopt different institutions, trade policies, or governance structures. The most consequential determinants of development—the things that explain why some nations prospered while others stagnated—lie beyond experimental reach.

Even at smaller scales, practical and ethical constraints bind tightly. Many interventions cannot be randomized for political reasons. Others would be unethical to withhold from control groups. Some operate at scales where randomization becomes logistically impossible.

There's also the question of what effects we measure. RCTs typically track outcomes over months or a few years. But development is a generational process. The interventions that matter most might produce effects that take decades to materialize—effects no standard RCT timeline could capture.

Perhaps most importantly, RCTs are better at evaluating marginal improvements to existing systems than transformative changes. They can tell us whether distributing bed nets reduces malaria. They cannot tell us how to build the state capacity that would make bed net distribution unnecessary.

Takeaway

The experimental method illuminates certain questions with remarkable clarity while leaving the largest questions about development necessarily in shadow.

The RCT revolution delivered genuine gains. Development economics now has an empirical foundation it previously lacked. We know more about what works at the micro level than any previous generation of researchers.

But epistemic humility remains essential. RCTs are a tool, not a worldview. They answer some questions exceptionally well while remaining silent on others. The most sophisticated practitioners recognize this—they use experimental evidence where appropriate while drawing on other methods for questions randomization cannot reach.

The path forward isn't choosing between rigorous micro-evidence and attention to big-picture development processes. It's integrating both, understanding what each approach contributes and where each falls short.