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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 List
  • Dana 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.
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Suggested Citation

  • Omar Al-Ubaydli & John List & Dana Suskind, 2017. "What Can We Learn From Experiments? Understanding the Threats to the Scalability of Experimental Results," Natural Field Experiments 00607, The Field Experiments Website.
    • Handle: RePEc:feb:natura:00607
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    References listed on IDEAS

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    1. Linda Babcock & George Loewenstein, 1997. "Explaining Bargaining Impasse: The Role of Self-Serving Biases," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 109-126, Winter.
    2. Omar Al-Ubaydli & John A. List, 2015. "Do Natural Field Experiments Afford Researchers More or Less Control than Laboratory Experiments? A Simple Model," NBER Working Papers 20877, National Bureau of Economic Research, Inc.
    3. Roland G. Fryer & Steven D. Levitt & John A. List, 2008. "Exploring the Impact of Financial Incentives on Stereotype Threat: Evidence from a Pilot Study," American Economic Review, American Economic Association, vol. 98(2), pages 370-375, May.
    4. Vernon L. Smith, 1962. "An Experimental Study of Competitive Market Behavior," Journal of Political Economy, University of Chicago Press, vol. 70, pages 322-322.
    5. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    6. Diane Paulsell & Sarah Avellar & Emily Sama Martin & Patricia Del Grosso, "undated". "Home Visiting Evidence of Effectiveness Review: Executive Summary," Mathematica Policy Research Reports 5254a2ab30e146ce900220dbc, Mathematica Policy Research.
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    8. Omar Al-Ubaydli & John A. List, 2015. "Do Natural Field Experiments Afford Researchers More or Less Control Than Laboratory Experiments?," American Economic Review, American Economic Association, vol. 105(5), pages 462-466, May.
    9. Neal S Young, 2008. "Why Current Publication May Distort Science," Working Papers id:1757, eSocialSciences.
    10. Neal S Young & John P A Ioannidis & Omar Al-Ubaydli, 2008. "Why Current Publication Practices May Distort Science," PLOS Medicine, Public Library of Science, vol. 5(10), pages 1-5, October.
    11. Steven D. Levitt & John A. List, 2007. "What Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World?," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 153-174, Spring.
    12. Zacharias Maniadis & Fabio Tufano & John A. List, 2014. "One Swallow Doesn't Make a Summer: New Evidence on Anchoring Effects," American Economic Review, American Economic Association, vol. 104(1), pages 277-290, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    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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