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A New Perspective on the Issue of Selection Bias into Randomized Controlled Field Experiments

Author

Listed:
  • Michele Belot

    (University of Edinburgh)

  • Jonathan James

    (University of Bath)

Abstract

Many randomized controlled trials require participants to opt in. Such self-selection could introduce a potential bias, because only the most optimistic may participate. We revisit this prediction. We argue that in many situations, the experimental intervention is competing with alternative interventions participants could conduct themselves outside the experiment. Since participants have a chance of being assigned to the control group, participating has a direct opportunity cost, which is likely to be higher for optimists. We propose a model of self-selection and show that both pessimists and optimists may opt out of the experiment, leading to an ambiguous selection bias.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Michele Belot & Jonathan James, 2014. "A New Perspective on the Issue of Selection Bias into Randomized Controlled Field Experiments," Department of Economics Working Papers 23/14, University of Bath, Department of Economics.
    • Handle: RePEc:eid:wpaper:39843
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    References listed on IDEAS

    as
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    3. repec:hal:pseose:halshs-00840901 is not listed on IDEAS
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    3. Maurizio Canavari & Andreas C. Drichoutis & Jayson L. Lusk & Rodolfo M. Nayga, Jr., 2018. "How to run an experimental auction: A review of recent advances," Working Papers 2018-5, Agricultural University of Athens, Department Of Agricultural Economics.
    4. Michael Sanders & Aisling Ní Chonaire, 2015. "“Powered to Detect Small Effect Sizes”: You keep saying that. I do not think it means what you think it means," The Centre for Market and Public Organisation 15/337, The Centre for Market and Public Organisation, University of Bristol, UK.

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    More about this item

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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