The Trials and Triumphs of Fighting Poverty - What "Gold Standard" Research Can and Can't Tell Us
By: Ebony D. Johnson
Every day, dedicated organizations around the world wage war on poverty through various economic development programs. But how can we really know which strategies are effective and which are spinning their wheels? That's where a research approach called randomized controlled trials (RCTs) comes in. However, these "gold standard" studies also have some stark limitations we need to understand.
The "Gold Standard" for Proving Cause and Effect
In the medical field, RCTs are considered the pinnacle of rigorous testing because of their ability to demonstrate causation, not just correlation. By randomly assigning participants into treatment and control groups, researchers can isolate the intervention as the sole differing factor. Any benefits that emerge in the treatment group can then be definitively attributed to the drug, therapy, etc. being tested rather than outside variables.
This "randomization" process, combined with techniques like double-blinding (where neither participants nor researchers know who gets the real treatment) and triple-blinding (where even the data analysts are kept in the dark), allows RCTs to prove legitimate cause-and-effect relationships. That's why they are deemed the "gold standard" for causal inference in epidemiology and clinical trials.
The Scientific Method Meets Economic Development
More recently, the RCT framework has been applied to evaluate anti-poverty initiatives and international development programs. Participants are randomly sorted into a treatment group that receives the program being studied, like job training or cash payments, and a control group that doesn't. Any differences that emerge between the two groups can then be attributed to the program itself.
Proponents rave about how RCTs cut through the noise to reveal clear facts about "what works" before scaling up costly initiatives. And their impartial data has indeed exposed some development sacred cows as ineffective while elevating surprising successes.An Oversimplified View of Human Nature
However, RCTs are far from a perfect tool. At their core, they tend to make some pretty big assumptions about how human beings think and behave. Like outdated economic models, RCTs often view people as purely rational "wealth-maximizers" free from complex emotional and social factors.
For example, one RCT in Malawi offered cash incentives to encourage people to maintain their HIV negative status for a year. While it helped reduce risky sexual behavior for women through abstinence, men paradoxically had more unprotected sex after getting the cash payments - potentially raising HIV transmission risks in the community.
Proving Correlation, Not Causation
Another key limitation of economic RCTs is that they can only prove correlation between the intervention and observed outcomes - not definitive causation. Just because cash payments coincided with higher abstinence rates for women doesn't necessarily mean the payments directly caused the abstinence. There could be unseen external factors or self-selection biases muddying the results.
Without understanding the intricate causal mechanisms at play, economic RCTs provide an incomplete picture. They can show if an outcome changed, but not clearly why. This makes it difficult to generalize findings or tweak programs to make them more effective.
One Size Doesn't Fit All
Even when RCTs produce robust data in their contained study settings, a major question is whether those results will translate to the real world (low "external validity"). Economic development programs interact with countless messy variables - local policies, cultural norms, environmental factors, and more. An RCT artificially controls for all this normal heterogeneity in human lives and contexts.
So while RCTs may be internally valid, their results could change when scaling up initiatives to the beautifully chaotic reality on the ground. Their artificial controls make it difficult to generalize findings across geographic regions or populations.
Scratching the Surface Rather Than Digging Deep
A major criticism of the RCT approach is that it fails to address the deeper, systemic drivers of poverty itself. These studies can tell us if a specific program, like conditional cash transfers, changes certain behaviors. But they don't shed light on the underlying societal barriers, inequalities, and power dynamics that keep impoverished populations trapped in desperate circumstances to begin with.
An RCT may show that subsidizing job training increases employment rates in the short-term, for example. But it can't reveal or alter the racial, gender, and class biases that restrict education and opportunity for marginalized groups. By their very nature, RCTs are too narrowly focused to dismantle entrenched, structural injustices.
A Valuable Tool, But Not a Crystal Ball
To be clear, RCTs absolutely have an important role to play in the fight against poverty. Their experimental approach has already reshaped how we prioritize and invest in development programs. However, we must wield RCTs judiciously and humbly, without treating them as a utopian crystal ball.
On their own, RCTs cannot answer crucial questions about why a program succeeded or failed in different contexts. Nor can they holistically capture ripple effects or weigh societal opportunity costs. To drive real progress, they must be combined with other tools and wisdom - like qualitative field research, historical analysis, and community engagement.
The quest to uplift our world's most vulnerable people is both vitally important and enormously complex. While RCTs have revolutionary potential, they are just one piece in solving the perpetual puzzle of sustainable economic development. Like any instrument, they have limits. When used wisely alongside other evidence, however, they can help guide our path to overcoming humanity's most daunting challenges.
