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Selection in the Lab: A Network Approach

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  • Aleksandr Alekseev

    (Economic Science Institute, Chapman University)

  • Mikhail Freer

    (ECARES, Université libre de Bruxelles)

Abstract

We study the selection problem in economic experiments by focusing on its dynamic and network aspects. We develop a dynamic network model of student participation in a subject pool, which assumes that students’ participation is driven by the two channels: the direct channel of recruitment and the indirect channel of student interaction. Using rich recruitment data from a large public university, we find that the patterns of participation and biases are consistent with the model. We also find evidence of both short- and long-run selection biases between males and females, as well as between cohorts of students. Males tend to have higher participation rates than females, and participation rates tend to decrease with a cohort’s age. Our empirical findings confirm that dynamic and peer effects play an important role in shaping the selection problem. Our model allows us to reconcile some of the mixed results in previous studies.

Suggested Citation

  • Aleksandr Alekseev & Mikhail Freer, 2018. "Selection in the Lab: A Network Approach," Working Papers 18-13, Chapman University, Economic Science Institute.
  • Handle: RePEc:chu:wpaper:18-13
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    File URL: https://digitalcommons.chapman.edu/esi_working_papers/251/
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    References listed on IDEAS

    as
    1. Armin Falk & James J. Heckman, 2009. "Lab Experiments are a Major Source of Knowledge in the Social Sciences," Working Papers 200935, Geary Institute, University College Dublin.
    2. Johannes Abeler & Daniele Nosenzo, 2015. "Self-selection into laboratory experiments: pro-social motives versus monetary incentives," Experimental Economics, Springer;Economic Science Association, vol. 18(2), pages 195-214, June.
    3. Alexander W. Cappelen & Knut Nygaard & Erik Ø. Sørensen & Bertil Tungodden, 2015. "Social Preferences in the Lab: A Comparison of Students and a Representative Population," Scandinavian Journal of Economics, Wiley Blackwell, vol. 117(4), pages 1306-1326, October.
    4. Blair Cleave & Nikos Nikiforakis & Robert Slonim, 2013. "Is there selection bias in laboratory experiments? The case of social and risk preferences," Experimental Economics, Springer;Economic Science Association, vol. 16(3), pages 372-382, September.
    5. Jackson, Matthew O. & Lã“Pez-Pintado, Dunia, 2013. "Diffusion and contagion in networks with heterogeneous agents and homophily," Network Science, Cambridge University Press, vol. 1(1), pages 49-67, April.
    6. Slonim, Robert & Wang, Carmen & Garbarino, Ellen & Merrett, Danielle, 2012. "Opting-In: Participation Biases in the Lab," IZA Discussion Papers 6865, Institute of Labor Economics (IZA).
    7. Joseph Henrich & Steve J. Heine & Ara Norenzayan, 2010. "The Weirdest People in the World?," RatSWD Working Papers 139, German Data Forum (RatSWD).
    8. Armin Falk & Stephan Meier & Christian Zehnder, 2013. "Do Lab Experiments Misrepresent Social Preferences? The Case Of Self-Selected Student Samples," Journal of the European Economic Association, European Economic Association, vol. 11(4), pages 839-852, August.
    9. 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.
    10. Slonim, Robert & Wang, Carmen & Garbarino, Ellen & Merrett, Danielle, 2013. "Opting-in: Participation bias in economic experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 90(C), pages 43-70.
    11. Michal Krawczyk, 2011. "What brings your subjects to the lab? A field experiment," Experimental Economics, Springer;Economic Science Association, vol. 14(4), pages 482-489, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    selection problem; laboratory experiments; external validity; networks; diffusion; peer effects;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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