Modeling the distributional epidemic-economic effects of the Covid-19 pandemic
Abstract: Complexity economics models are characterized by the lack of equilibrium, that is they are not solved for a fixed-point as in traditional economics models, but simply iterated forward in time. This makes it easier to include realistic dynamics, networks, boundedly-rational decisions, heterogeneity and to initialize the models with detailed real-world data. In this talk we will provide a concrete example of the advantages of complexity models in the context of the economic impact of the Covid-19 pandemic. We introduce a data-driven, granular, Agent-Based Model that simulates epidemic and economic outcomes across industries, occupations, and income levels. The key mechanism coupling the epidemic and economic modules is the reduction in consumption demand of customer-facing industries, such as entertainment and restaurants, due to fear of infection. We calibrate the model to the first wave of Covid-19 in the New York metro area, showing that it reproduces key epidemic and economic statistics, and then examine counterfactual scenarios. We find that: a) high fear of infection, alike strict restrictions, harms the economy (especially low-income workers) but reduces infections (especially among low-income workers); b) closing non-customer-facing industries such as manufacturing and construction only marginally reduces deaths while greatly increasing unemployment; and c) when fear of infection is high, delaying the start of protective measures increases both deaths and unemployment. We anticipate that our model will help designing effective and equitable non-pharmaceutical interventions that minimize disruptions in the face of a novel pandemic.