Pandemics and Economic Activity: A Framework for Policy Analysis

This paper studies the interaction between epidemiological dynamics and the dynamics of economic activity in a simple model in the structuralist/post-Keynesian tradition. On the one hand, rising economic activity increases the contact rate and therefore the probability of exposure to a virus. On the other hand, rising infection lowers economic activity through both supply and demand channels. The resulting framework is well-suited for policy analysis through numerical exercises. We show that, first, laissez-faire gives rise to sharp fluctuations in activity and infections before herd immunity is achieved. Second, absent any restrictions on economic activity, physical distancing measures have rather limited mitigating effects. Third, lockdowns are effective, especially at reducing death rates while buying time before a vaccine is widely rolled out, at the cost of a slightly more pronounced downturn in economic activity compared with alternative policies. This casts some doubt on the so-called "lives versus livelihood" policy trade-off. However, we also highlight the importance of policies aimed at mitigating the effects of the epidemic on workers' income.

A Behavioural SIR Model: Implications for Physical Distancing Decisions

Early evidence during the first phase of the COVID-19 outbreak shows that individuals facing the risk of infection increased their levels of physical distancing even before relevant measures were imposed. Not taking individual behaviour into account can lead policy makers to overestimate the infection risks in absence of physical distancing measures and underestimate the effectiveness of measures. This paper proposes a behavioural-compartmental-epidemiological model with heterogenous agents who take physical distancing measures to reduce the risk of becoming infected. The level of these measures depends on the government's regulations and the daily new cases and is influenced by the individual perception of the infection risk. This approach can account for two important factors: (i) the limited information about the exact infection risks and (ii) the heterogeneity across individuals with regards to physical distancing decisions. We find that the intensity of measures required to reduce infections is directly related to the public perception of the risk of infection, and that harsher late measures are in general less effective than milder ones imposed earlier. The model demonstrates that the feedback effects between contagion dynamics and individual decisions make the extrapolation of out-of-sample forecasts from past data dangerous, in particular in a context with high uncertainty.