The unequal effects of the health-economy tradeoff during the COVID-19 pandemic (preprint)

The potential tradeoff between health outcomes and economic impact has been a major challenge in the policy making process during the COVID-19 pandemic. Epidemic-economic models designed to address this issue are either too aggregate to consider heterogeneous outcomes across socio-economic groups, or, when sufficiently fine-grained, not well grounded by empirical data. To fill this gap, we introduce a data-driven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations, and income levels with geographic realism. The key mechanism coupling the epidemic and economic modules is the reduction in consumption demand due to fear of infection. We calibrate the model to the first wave of COVID-19 in the New York metropolitan area, showing that it reproduces key epidemic and economic statistics, and then examine counterfactual scenarios. We find that: (a) both high fear of infection and strict restrictions similarly harm the economy but reduce infections; (b) low-income workers bear the brunt of both the economic and epidemic harm; (c) closing non-customer-facing industries such as manufacturing and construction only marginally reduces the death toll while considerably increasing unemployment; and (d) delaying the start of protective measures does little to help the economy and worsens epidemic outcomes in all scenarios. We anticipate that our model will help designing effective and equitable non-pharmaceutical interventions that minimize disruptions in the face of a novel pandemic.

In and out of Lockdown: Propagation of Supply and Demand Shocks in a Dynamic Input-Output Model (preprint)

Economic shocks due to COVID-19 were exceptional in their severity, suddenness and heterogeneity across industries. To study the upstream and downstream propagation of these industry-specific demand and supply shocks, we build a dynamic input-output model inspired by previous work on the economic response to natural disasters. We argue that standard production functions, at least in their most parsimonious parametrizations, are not adequate to model input substitutability in the context of COVID-19 shocks. We use a survey of industry analysts to evaluate, for each industry, which inputs were absolutely necessary for production over a short time period. We calibrate our model on the UK economy and study the economic effects of the lockdown that was imposed at the end of March and gradually released in May. Looking back at predictions that we released in May, we show that the model predicted aggregate dynamics very well, and sectoral dynamics to a large extent. We discuss the relative extent to which the model's dynamics and performance was due to the choice of the production function or the choice of an exogenous shock scenario. To further explore the behavior of the model, we use simpler scenarios with only demand or supply shocks, and find that popular metrics used to predict a priori the impact of shocks, such as output multipliers, are only mildly useful.

In and out of lockdown: Propagation of supply and demand shocks in a dynamic input-output model (preprint)

Economic shocks due to Covid-19 were exceptional in their severity, suddenness and heterogeneity across industries. To study the upstream and downstream propagation of these industry-specific demand and supply shocks, we build a dynamic input-output model inspired by previous work on the economic response to natural disasters. We argue that standard production functions, at least in their most parsimonious parametrizations, are not adequate to model input substitutability in the context of Covid-19 shocks. We use a survey of industry analysts to evaluate, for each industry, which inputs were absolutely necessary for production over a short time period. We calibrate our model on the UK economy and study the economic effects of the lockdown that was imposed at the end of March and gradually released in May. Looking back at predictions that we released in May, we show that the model predicted aggregate dynamics very well, and sectoral dynamics to a large extent. We discuss the relative extent to which the model's dynamics and performance was due to the choice of the production function or the choice of an exogenous shock scenario. To further explore the behavior of the model, we use simpler scenarios with only demand or supply shocks, and find that popular metrics used to predict a priori the impact of shocks, such as output multipliers, are only mildly useful.

Modeling Simultaneous Supply and Demand Shocks in Input-Output Networks (preprint)

Natural and anthropogenic disasters frequently affect both the supply and demand side of an economy. A striking recent example is the COVID-19 pandemic which has created severe industry-specific disruptions to economic output in most countries. Since firms are embedded in production networks, these direct shocks to supply and demand will propagate downstream and upstream. We show that existing input-output models which allow for binding demand and supply constraints yield infeasible solutions when applied to pandemic shocks of three major European countries (Germany, Italy, Spain). We then introduce a mathematical optimization procedure which is able to determine best-case feasible market allocations, giving a lower bound on total shock propagation. We find that even in this best-case scenario network effects substantially amplify the initial shocks. To obtain more realistic model predictions, we study the propagation of shocks bottom-up by imposing different rationing rules on firms if they are not able to satisfy incoming demand. Our results show that overall economic impacts depend strongly on the emergence of input bottlenecks, making the rationing assumption a key variable in predicting adverse economic impacts. We further establish that the magnitude of initial shocks and network density heavily influence model predictions.

Simultaneous supply and demand constraints in input-output networks: The case of Covid-19 in Germany, Italy, and Spain (preprint)

Natural and anthropogenic disasters frequently affect both the supply and demand side of an economy. A striking recent example is the Covid-19 pandemic which has created severe disruptions to economic output in most countries. These direct shocks to supply and demand will propagate downstream and upstream through production networks. Given the exogenous shocks, we derive a lower bound on total shock propagation. We find that even in this best case scenario network effects substantially amplify the initial shocks. To obtain more realistic model predictions, we study the propagation of shocks bottom-up by imposing different rationing rules on industries if they are not able to satisfy incoming demand. Our results show that economic impacts depend strongly on the emergence of input bottlenecks, making the rationing assumption a key variable in predicting adverse economic impacts. We further establish that the magnitude of initial shocks and network density heavily influence model predictions.

Production networks and epidemic spreading: How to restart the UK economy? (preprint)

We analyse the economics and epidemiology of different scenarios for a phased restart of the UK economy. Our economic model is designed to address the unique features of the COVID-19 pandemic. Social distancing measures affect both supply and demand, and input-output constraints play a key role in restricting economic output. Standard models for production functions are not adequate to model the short-term effects of lockdown. A survey of industry analysts conducted by IHS Markit allows us to evaluate which inputs for each industry are absolutely necessary for production over a two month period. Our model also includes inventory dynamics and feedback between unemployment and consumption. We demonstrate that economic outcomes are very sensitive to the choice of production function, show how supply constraints cause strong network effects, and find some counter-intuitive effects, such as that reopening only a few industries can actually lower aggregate output. Occupation-specific data and contact surveys allow us to estimate how different industries affect the transmission rate of the disease. We investigate six different re-opening scenarios, presenting our best estimates for the increase in R0 and the increase in GDP. Our results suggest that there is a reasonable compromise that yields a relatively small increase in R0 and delivers a substantial boost in economic output. This corresponds to a situation in which all non-consumer facing industries reopen, schools are open only for workers who need childcare, and everyone who can work from home continues to work from home.

Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective (preprint)

We provide quantitative predictions of first order supply and demand shocks for the U.S. economy associated with the COVID-19 pandemic at the level of individual occupations and industries. To analyze the supply shock, we classify industries as essential or non-essential and construct a Remote Labor Index, which measures the ability of different occupations to work from home. Demand shocks are based on a study of the likely effect of a severe influenza epidemic developed by the US Congressional Budget Office. Compared to the pre-COVID period, these shocks would threaten around 22% of the US economy's GDP, jeopardise 24% of jobs and reduce total wage income by 17%. At the industry level, sectors such as transport are likely to have output constrained by demand shocks, while sectors relating to manufacturing, mining and services are more likely to be constrained by supply shocks. Entertainment, restaurants and tourism face large supply and demand shocks. At the occupation level, we show that high-wage occupations are relatively immune from adverse supply and demand-side shocks, while low-wage occupations are much more vulnerable. We should emphasize that our results are only first-order shocks -- we expect them to be substantially amplified by feedback effects in the production network.