As more countries, states, and municipalities begin to reopen their businesses and public spaces in response to the ongoing COVID-19 pandemic, one constant refrain is the warning that we will just get back to square one, with the pandemic running its course and the death toll rising once again, as everyone will get back to normal. But will they? How far might people go in practicing precaution on their own by adjusting their social and economic behavior, without government stay-at-home orders, and how will that affect the economy and the dynamics of the pandemic?
To address this question, the authors developed a simple model based on other recent research, which includes agents (people) who are aware of infection and death risks if they continue to leave their homes to work and to shop, among other activities. Faced with these risks to their own health, they will adjust their behavior. This is a key element of economic models, and is a feature that is not part of standard epidemiological models.
Crucially and in departure from other economic models, the authors assume that the economy is composed of sectors that differ in their infection probabilities. This heterogeneity is simply illustrated, for example, by people’s choice to eat a pizza delivered to their home vs. in a restaurant, or to work at home rather than in an office (if they are among those able to work from home). This heterogeneity matters. The way people choose to “consume” public experiences—whether work, worship, or entertainment—has a profound impact on infection rates.
Broadly summarized, when the authors run their model without heterogeneity in infection risk across sectors, economic activity declines 10%. However, the introduction of heterogeneity mitigates much of that decline. Likewise, the majority of deaths are avoided after the first year, compared to the homogeneous sector version. Importantly, these results are realized without government intervention. One can think of these results as capturing some of the experiences with Sweden’s less-restrictive approach to COVID-19 management. Better, these results are indicative of the unfolding dynamics subsequent to re-opening: a modest rise in infection, a very persistent, but modest decline in economic activity, and a substantial and prolonged shift across sectors, which flexibility of labor markets needs to allow for. This is far from a return to normal, but it is a reasonably optimistic outlook nonetheless.
What explains these outcomes? The authors suggest that infections may decline due to the re-allocation of economic activity that people will make on their own, and the resulting and longer-lasting shift between sectors. For the rather benign outcome in the model and for successful sectoral shifts, it is key that workers can adjust rather quickly to the changing labor market. Food servers can become delivery drivers. Former shop clerks find employment in Amazon warehouses. Artists provide entertainment online. Jobs lost in some sectors get partly offset by recruitment in others.
The authors acknowledge that labor markets do not function as smoothly as they assume in their model. The authors stress that their results are not definitive in and of themselves; models are approximations of reality that depend greatly on the parameters applied by researchers. In this case, the authors concede that the results may appear Panglossian.
However, one need not wear rose-colored glasses to recognize that private incentives can shape behavior during a health pandemic. Most importantly, allowing the economy to succeed in shifting sectoral activities in response to these choices is key for mitigating both the economic as well as the health impact. Consideration of such incentives and sectoral shifts could be important as governments around the world consider strategies to reopen public activities.