Francesco Bogliacino

development and international economics seminar

Francesco Bogliacino

Universidad Nacional de Colombia
Negative economic shocks and the compliance to social norms
Co-écrit avec
Rafael Charris, Camilo Gomez, Felipe Montealegre
Lieu

MEGA Salle de conf

MEGA - Salle de conférence

Maison de l'économie et de la gestion d'Aix
424 chemin du viaduc
13080 Aix-en-Provence

Date(s)
Vendredi 29 octobre 2021| 12:00 - 13:15
Contact(s)

Timothée Demont : timothee.demont[at]univ-amu.fr
Eva Raiber : eva.raiber[at]univ-amu.fr

Résumé

Social norms prescribe and proscribe. They are scripts that guide behaviour and save cognitive resources for the decision-maker in normal times. But in the course of a lifetime subjects experience major negative economic shocks (NES), losses in income or accumulated assets. Shocks can occur in both developed and developing countries, as a result of natural disasters, violence and conflicts, health and trauma, macroeconomic crises and recessions. NES alter the cost-benefit profile of norms-following: decreasing marginal utility of income makes avoiding free-riding or bearing the cost of punishment or retaliation more costly. This implies that norm compliance decrease and norms evolve. Or this is what should occur. Whether and to what extent this will occur is the object of this paper. Through a model where norms enter the utility function and participants are heterogeneous in their psychological cost of compliance, we derive the predictions for a set of anti-social and pro-social tasks. In all these settings, participants should decide whether to harm the counterpart. Sometimes this action is prescribed by the norm, as in punishment and retaliation. Sometimes this action is proscribed by the norm, as in cheating or cooperation. The model predicts that we should observe more norm violations, in presence of shocks. Since the predictions are conditional on social norms, we elicit the normative expectations within each situation. To assess the predictions, we design four experiments and use data from our previous work, where we manipulate NES by inducing strong losses on the earnings from a Real Effort Task.