What Can We Learn from Experiments? Understanding the Threats to the Scalability of Experimental Results
Omar Al-Ubaydli, John A. List, Dana L. Suskind
American Economic Review · 2017 · Volume 107 · Issue 5
Abstract
Policymakers often consider interventions at the scale of the population, or some other large scale. One of the sources of information about the potential effects of such interventions is experimental studies conducted at a significantly smaller scale. A common occurrence is for the treatment effects detected in these small-scale studies to diminish substantially in size when applied at the larger scale that is of interest to policymakers. This paper provides an overview of the main reasons for a breakdown in scalability. Understanding the principal mechanisms represents a first step toward formulating countermeasures that promote scalability.
Keywords
Single Equation Models, Single Variables: Cross-Sectional Models, Spatial Models, Treatment Effect Models, Quantile Regressions, Design of Experiments: General, Asymmetric and Private Information, Mechanism Design
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