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

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  • Belot, Michele
  • James, J

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.
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Suggested Citation

  • Belot, Michele & James, J, 2014. "A New Perspective on the Issue of Selection Bias into Randomized Controlled Field Experiments," Department of Economics Working Papers 39843, University of Bath, Department of Economics.
  • Handle: RePEc:eid:wpaper:39843
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    File URL: http://opus.bath.ac.uk/39843/1/23_14.pdf
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    References listed on IDEAS

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    1. Bruno Crépon & Esther Duflo & Marc Gurgand & Roland Rathelot & Philippe Zamora, 2013. "Do Labor Market Policies have Displacement Effects? Evidence from a Clustered Randomized Experiment," The Quarterly Journal of Economics, Oxford University Press, vol. 128(2), pages 531-580.
    2. Roland G. Fryer, 2011. "Financial Incentives and Student Achievement: Evidence from Randomized Trials," The Quarterly Journal of Economics, Oxford University Press, vol. 126(4), pages 1755-1798.
    3. Belot, Michèle & James, Jonathan, 2016. "Partner selection into policy relevant field experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 123(C), pages 31-56.
    4. Ernst Fehr & Lorenz Goette, 2007. "Do Workers Work More if Wages Are High? Evidence from a Randomized Field Experiment," American Economic Review, American Economic Association, vol. 97(1), pages 298-317, March.
    5. Esther Duflo & Pascaline Dupas & Michael Kremer, 2011. "Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya," American Economic Review, American Economic Association, vol. 101(5), pages 1739-1774, August.
    6. List, John A. & Rasul, Imran, 2011. "Field Experiments in Labor Economics," Handbook of Labor Economics, Elsevier.
    7. Oriana Bandiera & Iwan Barankay & Imran Rasul, 2007. "Incentives for Managers and Inequality among Workers: Evidence from a Firm-Level Experiment," The Quarterly Journal of Economics, Oxford University Press, vol. 122(2), pages 729-773.
    8. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics,in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097 Elsevier.
    9. Jessica Wisdom & Julie S. Downs & George Loewenstein, 2010. "Promoting Healthy Choices: Information versus Convenience," American Economic Journal: Applied Economics, American Economic Association, vol. 2(2), pages 164-178, April.
    10. Gary Burtless, 1995. "The Case for Randomized Field Trials in Economic and Policy Research," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 63-84, Spring.
    11. Malani, Anup, 2008. "Patient enrollment in medical trials: Selection bias in a randomized experiment," Journal of Econometrics, Elsevier, vol. 144(2), pages 341-351, June.
    12. Pieter A. Gautier & Bas van der Klaauw, 2012. "Selection in a field experiment with voluntary participation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(1), pages 63-84, January.
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    Cited by:

    1. Belot, Michèle & James, Jonathan, 2016. "Partner selection into policy relevant field experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 123(C), pages 31-56.
    2. 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, Department of Economics, University of Bristol, UK.

    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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