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What Can We Learn from Experiments? Understanding the Threats to the Scalability of Experimental Results

Author

Listed:
  • Omar Al-Ubaydli
  • John A. List
  • Dana L. Suskind

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.

Suggested Citation

  • Omar Al-Ubaydli & John A. List & Dana L. Suskind, 2017. "What Can We Learn from Experiments? Understanding the Threats to the Scalability of Experimental Results," American Economic Review, American Economic Association, vol. 107(5), pages 282-286, May.
    • Handle: RePEc:aea:aecrev:v:107:y:2017:i:5:p:282-86
    Note: DOI: 10.1257/aer.p20171115
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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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