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The Experimenters' Dilemma: Inferential Preferences over Populations

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  • Neeraja Gupta
  • Luca Rigotti
  • Alistair Wilson

Abstract

We compare three populations commonly used in experiments by economists and other social scientists: undergraduate students at a physical location (lab), Amazon's Mechanical Turk (MTurk), and Prolific. The comparison is made along three dimensions: the noise in the data due to inattention, the cost per observation, and the elasticity of response. We draw samples from each population, examining decisions in four one-shot games with varying tensions between the individual and socially efficient choices. When there is no tension, where individual and pro-social incentives coincide, noisy behavior accounts for 60% of the observations on MTurk, 19% on Prolific, and 14% for the lab. Taking costs into account, if noisy data is the only concern Prolific dominates from an inferential power point of view, combining relatively low noise with a cost per observation one fifth of the lab's. However, because the lab population is more sensitive to treatment, across our main PD game comparison the lab still outperforms both Prolific and MTurk.

Suggested Citation

  • Neeraja Gupta & Luca Rigotti & Alistair Wilson, 2021. "The Experimenters' Dilemma: Inferential Preferences over Populations," Papers 2107.05064, arXiv.org, revised Jul 2021.
  • Handle: RePEc:arx:papers:2107.05064
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    References listed on IDEAS

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    1. Felipe A. Araujo & Erin Carbone & Lynn Conell-Price & Marli W. Dunietz & Ania Jaroszewicz & Rachel Landsman & Diego Lamé & Lise Vesterlund & Stephanie W. Wang & Alistair J. Wilson, 2016. "The slider task: an example of restricted inference on incentive effects," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 2(1), pages 1-12, May.
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    Cited by:

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    2. Roggenkamp, Hauke C., 2024. "Revisiting ‘Growth and Inequality in Public Good Provision’—Reproducing and Generalizing Through Inconvenient Online Experimentation," OSF Preprints 6rn97, Center for Open Science.
    3. Heger, Stephanie A. & Slonim, Robert, 2022. "Giving begets giving: Positive path dependence as moral consistency," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 699-718.
    4. Jesper Akesson & Robert W. Hahn & Robert D. Metcalfe & Itzhak Rasooly, 2022. "Race and Redistribution in the United States: An Experimental Analysis," NBER Working Papers 30426, National Bureau of Economic Research, Inc.
    5. Tanja Artiga González & Francesco Capozza & Georg D. Granic, 2022. "Can Cognitive Dissonance Theory Explain Action Induced Changes in Political Preferences?," CESifo Working Paper Series 9549, CESifo.
    6. Aksoy, Billur & Carpenter, Christopher S. & Sansone, Dario, 2022. "Understanding Labor Market Discrimination against Transgender People: Evidence from a Double List Experiment and a Survey," IZA Discussion Papers 15542, Institute of Labor Economics (IZA).
    7. Abel, Martin & Brown, Willa, 2022. "Prosocial behavior in the time of COVID-19: The effect of private and public role models," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 101(C).
    8. Abel, Martin & Burger, Rulof, 2022. "Choice over Payment Schemes and Worker Effort," IZA Discussion Papers 15769, Institute of Labor Economics (IZA).
    9. Aksoy, Billur & Chadd, Ian & Koh, Boon Han, 2023. "Sexual identity, gender, and anticipated discrimination in prosocial behavior," European Economic Review, Elsevier, vol. 154(C).
    10. Billur Aksoy & Ian Chadd & Boon Han Koh, 2022. "(Anticipated) Discrimination against Sexual Minorities in Prosocial Domains," University of East Anglia School of Economics Working Paper Series 2021-08, School of Economics, University of East Anglia, Norwich, UK..
    11. Irene Maria Buso & Daniela Di Cagno & Lorenzo Ferrari & Vittorio Larocca & Luisa Lorè & Francesca Marazzi & Luca Panaccione & Lorenzo Spadoni, 2021. "Lab-like findings from online experiments," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 7(2), pages 184-193, December.
    12. Ned Augenblick & Eben Lazarus & Michael Thaler, 2021. "Overinference from Weak Signals and Underinference from Strong Signals," Papers 2109.09871, arXiv.org, revised Mar 2023.

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