Hibernating a complex system is a formidable task: No wonder that human hibernation is still science fiction! The economy, which is a system at least as complex as a human being, makes no exception. Yet, perhaps for the first time, we face the problem of how to put the economy into hibernation in the least disruptive way. Indeed, to stop the spread of COVID-19, we need to reduce economic activity to a minimum for as long as necessary. At the same time, to avoid further suffering due to poverty and unemployment, we need to jump-start the global economy as soon as the pandemic is over. As this is a first time, economic theory offers little guidance on how to effectively hibernate and then restart the economy. In this post, I will argue that Agent-Based Models are the best tool to address this issue, as they represent the complexity of the economy in a more faithful way than traditional equilibrium models.
If the economy wasn’t a complex system, hibernating it could be straightforward. Imagine an economy with households-employees, firms, banks, a government and a central bank. Suppose that at a certain time all firms shut down (except essential ones such as health care, food, utilities, transports, telecommunications). Households stop buying all non-essential goods and services, firms stop producing and paying their employees. All loan and mortgage repayments are suspended and banks also shut down. The government and central bank provide all households with a basic income, which they use to consume the essential goods and services that are still produced . Once the pandemic is over, firms reopen, households go back to work, banks’ loans and mortgages are repaid, fiscal and monetary policies go back to normal. If this scenario was plausible, we would face a few months of hibernation, and then the economy would restart as if nothing happened. While the practical difficulties with implementing such a plan would be enormous, conceptually it would be quite simple.
Unfortunately, it’s not that easy. The economy is an interconnected web of work, trade and financial linkages, where beliefs, hysteresis and lags play a key role. Let me mention a few examples of what could happen when restrictions are lifted.
(1) Pessimistic expectations. Households may hold pessimistic expectations about the state of the economy, and thus reduce their consumption out of precautionary motives.
(2) Frictions in re-hiring. If firms lay off workers so that they can receive unemployment insurance, re-establishing work relations with them may be difficult, in particular if demand drops due to households’ pessimistic expectations.
(3) Lags in supply chains. In supply chain management, the devil is in the details. To have firms produce at full steam once restrictions are lifted would require an enormous coordination effort, both nationally and internationally. The currently asynchronous response to the health crisis across sectors and countries suggests that shipment of intermediate goods could face substantial delays. So, most manufacturing firms would have to remain closed for longer.
(4) Credit markets. Workers in those firms would remain unemployed for longer, depressing aggregate consumption and potentially being unable to repay their mortgages. Firms unable to operate would fail to repay their loans. Banks would not open new credit lines facing much higher risk of bankruptcy.
(5) Stock markets. Stock markets would crash due to a combination of pessimistic beliefs and real problems, leading to lower consumption through wealth effects and lower credit through the financial accelerator mechanism.
All these effects would be magnified if the economy was not put into hibernation in the first place.
None of these five effects is explicitly included in the model by McKibbin and Fernando that international organizations are using to estimate the economic impacts of the COVID-19 pandemic. This is a dynamic stochastic general equilibrium model with 24 countries and regions, 6 aggregate sectors, a representative household for each country and a government. In this model, households and firms behave optimally given their beliefs about current and future economic outcomes, and their beliefs are consistent with outcomes (rational expectations) . McKibbin and Fernando model the impact of the pandemic in five dimensions: (a) reduction in labor supply due to illness, caregiving and school closures; (b) increase in aggregate equity risk premia; (c) disruptions to supply chains at the 6-sector level, averaged over a quarter (e.g, reduction in the supply of goods from “mining” to “durable manufacturing” over three months); (d) shocks to consumer demand, differentially across sectors, during the lockdown; (e) increase in government expenditure to compensate for economic losses. With these effects, economic activity would reduce by up to 10% in 2020, depending on countries and scenarios. By 2021, it would largely return to 2019 levels. 
I argue that effects (1) to (5) can potentially reduce output by much more, and more permanently, as they impact the structure of the economy at a more fundamental level than the sector-aggregate transitory shocks (a) to (e). It should not be surprising that many analyses and policy proposals (e.g., see here and here) are aimed at tackling the “microeconomic” effects (1) to (5), by discouraging layoffs, guaranteeing most of the income of workers, providing long-term loans at no interest to firms to help with cash flows, and providing liquidity to banks. Some proposals even consider having the government pay firms for maintenance costs, utilities, interest and other costs. Unsurprisingly, these policies are very expensive, so it would be ideal to make them as targeted as possible. At the same time, it would be great to know which mix of policies aimed at addressing effects (1) to (5) is most effective.
Unfortunately, it is impossible to use the McKibbin and Fernando model for this goal, as it lacks most of the heterogeneity, networks and detailed time structure that would be necessary. Mainstream economics has thought about all these effects, but one-at-a-time, and often not embedded in a macroeconomic model. This is not a criticism: as mentioned at the beginning, this situation is new, and a model cannot include everything. However, I think that standard macroeconomic models will have hard time including these effects, as respecting equilibrium conditions with heterogenous households, firms and banks who have very different balance sheets is mathematically and computationally untractable. The analyses and policy proposals mentioned above come out of the intuition of economists, rather than from quantitative models.
Macroeconomic Agent-Based Models (ABMs) could include microeconomic effects much more easily, as they are simply solved recursively without the need to satisfy equilibrium constraints. For example, the ABM by Caiani et al. explicitly models balance sheets of firms and banks, so it could be used to test policies aimed at providing liquidity. The Keynes meets Schumpeter ABM developed in Sant’Anna by my new colleagues, in its various incarnations, can be used to test the effect of policies aimed at keeping workers employed in firms during the pandemic, at preventing pessimistic expectations, at avoiding financial crises induced by firm bankruptcies. While the above are theoretical models that are not directly calibrated on real-world data (unlike McKibbin and Fernando), Poledna et al. are the first to build an ABM that is calibrated on real-world data and used for forecasting. As Poledna et al. represent the full population of households and firms, one could test policies that target individual firms depending on their liquidity shortages (link in Italian).
Results may come too late to inform the current policy debate, as policy makers need to make decisions in a few weeks. However, modeling the economics effects of the COVID-19 pandemic would be useful at least academically, for our understanding of the economy under extreme circumstances. It would also be useful in case there is a second wave of the COVID-19 pandemic and we need to hibernate the economy again. Finally, theoretical guidance on how to restart the economy after hibernation could be useful in the future should we need to put similar measures in place, e.g. in face of climate risks.
I think that complexity economics and agent-based modeling, by being particularly good at capturing heterogeneity, networks, and non-linear dynamics, have a good shot at providing insights into the current economic crisis. Having an important role in the policy debate would be a great signal for the maturity of the field.
 This is clearly a caricature of the economy. It does not consider, for example, that many service workers can work effectively from remote, and that certain factories cannot completely shut down as some machinery can be damaged if switched off (e.g., industrial furnaces).
 McKibbin and Fernando claim that 70% of firms do not follow rational expectations, rather they follow “rule-of-thumb” behavior (see the description of the model in a 2018 paper). However, non-rational expectations behavior means adjusting slowly to rational expectations (see Eqs. 13 and 14 in the appendix). Likewise, a fraction of households consume a fixed fraction of their income, irrespective of their expectations (Eq. 20). None of these modeling assumptions allows for animal spirits and pessimistic expectations.
 The authors admit that scenarios could be much worse, but it is unclear if their model can endogenously produce worse scenarios.