What Can We Learn from Experiments? Understanding the Threats to the Scalability of Experimental Results
AbstractPolicymakers 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.
CitationAl-Ubaydli, Omar, John A. List, and Dana L. Suskind. 2017. "What Can We Learn from Experiments? Understanding the Threats to the Scalability of Experimental Results." American Economic Review, 107 (5): 282-86. DOI: 10.1257/aer.p20171115
- C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- C90 Design of Experiments: General
- D82 Asymmetric and Private Information; Mechanism Design