In 2006, Columbia University economist Jeffrey Sachs launched one of the most ambitious anti-poverty experiments ever attempted. The Millennium Villages Project would transform a dozen African villages through coordinated investments in health, education, agriculture, and infrastructure. The theory was elegant: poverty is a trap with multiple interlocking causes, so only a comprehensive "big push" could break the cycle.

Over the following decade, roughly $120 million flowed into these villages. Clinics were built. Fertilizers were distributed. Teachers were trained. And indeed, many indicators improved. But when independent researchers tried to determine whether the project actually caused these improvements, they encountered a fundamental problem: the evaluation design made it nearly impossible to know.

This isn't a story about failure in the traditional sense. It's a more instructive story about how even well-funded, well-intentioned development programs can be structured in ways that prevent us from learning whether they work—and why that matters for every dollar spent fighting poverty.

The intellectual foundation of the Millennium Villages Project was compelling. Sachs and his team argued that poverty operates as a system of interconnected constraints. A farmer can't increase yields without fertilizer, can't afford fertilizer without credit, can't get credit without collateral, can't build collateral without savings, can't save while paying for malaria treatment, can't avoid malaria without bed nets, and so on. Attack one problem and another constraint immediately binds.

This "poverty trap" theory suggested that small, incremental interventions would always fail. Instead, communities needed a coordinated package of investments across multiple sectors simultaneously—what economists call a big push. The project would provide improved seeds, fertilizers, malaria prevention, clean water, school meals, and business training all at once.

The villages selected were meant to represent different African agricultural zones. Each would receive approximately $120 per person per year for five years, later extended to ten. Local coordinators would adapt the standard package to local conditions. The goal wasn't just immediate improvement but triggering self-sustaining growth that would continue after external support ended.

This approach had intuitive appeal and historical precedent—the Marshall Plan had arguably demonstrated that concentrated investment could transform economies. But there was a tension built into the design from the beginning: the very comprehensiveness that made the theory compelling also made it extraordinarily difficult to test.

Takeaway

When interventions address multiple problems simultaneously, they may be more realistic about how poverty works, but they also become harder to evaluate and replicate—creating a tradeoff between theoretical coherence and practical learning.

The most basic question in program evaluation is: what would have happened without the intervention? This counterfactual is impossible to observe directly, so rigorous evaluations create comparison groups—similar communities that don't receive the program. The difference between treatment and comparison groups estimates the program's causal effect.

The Millennium Villages Project initially had no comparison villages at all. When critics pointed this out, the project team selected comparison sites after the fact. But these sites differed systematically from the treatment villages in ways that made comparison unreliable. Some comparison villages were farther from roads, had different ethnic compositions, or faced different climate conditions.

Even more problematic, the project reported progress by comparing villages to their own baseline conditions—a "before and after" design that confuses program effects with background trends. During the project period, much of Africa experienced economic growth, expanded health services, and declining malaria rates. Many comparison villages improved on similar metrics without any intervention.

When external evaluators from the UK's Overseas Development Institute examined the evidence, they found that most claimed improvements either weren't statistically distinguishable from comparison areas or couldn't be attributed to the project with confidence. The Lancet published studies from the project, but critics noted that even positive findings were undermined by design limitations that the project's own team acknowledged.

Takeaway

Before-and-after comparisons without proper control groups can make almost any intervention look successful, because they attribute background trends and natural variation to the program being evaluated.

The Millennium Villages experience reveals a fundamental tension in development practice. Donors and implementing organizations face pressure to show results quickly, which discourages the rigorous evaluation designs that take longer and might reveal failure. Simultaneously, comprehensive programs are politically attractive because they promise transformation rather than incremental progress.

One lesson is that evaluation must be built in from the start, not retrofitted when critics demand evidence. Randomized selection of treatment communities, while sometimes ethically or practically challenging, provides the cleanest causal inference. When randomization isn't possible, carefully matched comparison groups selected before implementation can still generate useful evidence.

Another lesson concerns scale and replicability. The Millennium Villages invested roughly $120 per person annually in communities that also received intensive attention from Columbia University researchers and international visitors. Even if the model worked, could governments replicate it at national scale without the research infrastructure and external attention? The project never tested this.

Perhaps most importantly, the experience highlights the value of testing components separately before bundling them. The development field now has rigorous evidence that specific interventions—bed nets, deworming, conditional cash transfers—produce measurable benefits. Starting with proven components and testing how they interact may generate more useful knowledge than launching comprehensive packages that resist evaluation.

Takeaway

Designing development programs that can actually be evaluated requires upfront investment in comparison groups and may mean accepting simpler interventions that produce clearer evidence over ambitious packages that resist attribution.

The Millennium Villages Project wasn't a scandal or a fraud. It was something more common and perhaps more instructive: a well-resourced effort by serious people that was structured in a way that prevented learning. Millions of dollars produced improvements that may or may not have exceeded what would have happened anyway.

The development field has since moved toward more rigorous evaluation standards, partly in response to this experience. Organizations like J-PAL and IPA now promote randomized evaluations as a default approach, and major funders increasingly require credible impact measurement.

The deeper lesson isn't that comprehensive approaches are wrong—poverty really is multidimensional. It's that our desire to solve complex problems quickly can lead us to skip the difficult work of building evidence. Good intentions without good evaluation isn't development—it's hope with a budget.