- Social Interactions in Pandemics: Fear, Altruism, and Reciprocity
In SIR models, infection rates are typically assumed to be exogenous. However, individuals adjust their behavior. Using daily data for 89 cities worldwide, we document that mobility falls in response to fear, as approximated by Google search terms. Combining these data with experimentally validated measures of social preferences at the regional level, we find that stringency measures matter less if individuals are more patient and altruistic (preference traits), and exhibit less negative reciprocity (community traits). Modifying the homogeneous SIR and the SIR-network model with different age groups to incorporate agents‘ optimizing decisions on social interactions, we show that susceptible individuals internalize infection risk based on their patience, infected ones do so based on their altruism, and reciprocity matters for internalizing risk in SIR networks. Simulations show that the infection curve is flatter when agents optimize their behavior and when societies are more altruistic. A planner further restricts interactions due to a static and a dynamic inefficiency in the homogeneous SIR model, and due to an additional reciprocity inefficiency in the SIR-network model. Optimal age-differentiated lockdowns are stricter for risk spreaders or the group with more social activity, i.e., the younger.