An Economic Framework of Scaling
Economic models provide theoretical frameworks for understanding complex systems. We are proud to have developed one such model that helps enhance understanding of why promising interventions often fail to deliver the same effects when moved from a research setting to population-wide implementation.
Understanding why this phenomenon occurs is the first step in reversing the trend and ensuring that evidence-based programs and policies can benefit more people.
Our economic model of scaling was introduced in the 2017 paper, “What Can We Learn from Experiments? Understanding the Threats to the Scalability of Experimental Results,” by Omar Al-Ubaydli, John List, and Dana Suskind.
As explained in that paper, the authors use economic modeling to explore the changes in the magnitude of a theoretical intervention’s per-capita net treatment effect when it moves from a research setting to population-wide implementation. This allows them to identify the specific sources of the scale-up effect, which they sort into four key categories:
- Errors in Statistical Inference
- Properties of the population
- Properties of the situation
- Spillover and general equilibrium effects
You can read the original paper and related publications, which include detailed discussions of what this model reveals for researchers and policymakers, below.