Complexity economics models to estimate the economic impact of disasters: The Covid-19 pandemic and beyond

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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 disasters. We mostly focus on the Covid-19 pandemic. We first introduce a model in which agents are industries, connected through an input-output network, and an aggregate representative consumer. We calibrated this model with estimates of Covid-19 supply and demand shocks in April 2020, and were able to forecast the economic impact on the UK economy better than most competitors. We then couple this model with an epidemiological model in which, instead of an aggregate consumer, we have 400 thousand individuals whose mobility patterns are directly initialized from cell phone data. We show that this model can be used to address the most debated epidemic-economic tradeoffs, taking into account heterogeneous outcomes across socio-economic group. In the final part of the talk we discuss how these models can be used to understand the economic impacts of a cutoff in Russian gas and of climate disasters.