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The Show Must Go On: How to Elicit Lablike Data on the Effects of COVID-19 Lockdown on Fairness and Cooperation

Author

Listed:
  • Irene Maria Buso
  • Daniela Di Cagno
  • Sofia De Caprariis
  • Lorenzo Ferrari
  • Vittorio Larocca
  • Francesca Marazzi
  • Luca Panaccione
  • Lorenzo Spadoni

Abstract

Given the impossibility of having participants in the lab during the COVID-19 lockdown, we introduce a novel methodology based on a multi-platform architecture that brings experimental subjects in a “Lab on the Web”. This methodology allows us to study the effects of Covid-19 lockdown in Italy on preferences for fairness and cooperation. Results from sessions of standard Ultimatum and linear Public Good games validate our methodology. Moreover, we show that the circumstances in which participants lived the lockdown significantly affect their behavior in the two games. In particular, participants are more selfish in the ultimatum bargaining and contribute more to public good when lockdown is longer and social isolation is stronger. We interpret these results as evidence of “social embeddedness” to compensate for “social distancing”.

Suggested Citation

  • Irene Maria Buso & Daniela Di Cagno & Sofia De Caprariis & Lorenzo Ferrari & Vittorio Larocca & Francesca Marazzi & Luca Panaccione & Lorenzo Spadoni, 2020. "The Show Must Go On: How to Elicit Lablike Data on the Effects of COVID-19 Lockdown on Fairness and Cooperation," Working Papers CESARE 2/2020, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    • Handle: RePEc:lui:cesare:2002
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Covid-19; economic experiment; fairness; voluntary contribution mechanism; cooperation;
    All these keywords.

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games

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