Herd immunity is a central concept in epidemiology. It refers to a situation where the amount of recovered and immune individuals is high enough to protect susceptibles from contracting the disease. Herd immunity can be obtained naturally when individuals recover from the disease, or artificially after the administration of an appropriate vaccine. The present paper addresses the question of the amount of public spending in medical research to obtain a vaccine which maximizes welfare. Public spending is assumed to reduce the waiting time to discover a vaccine against an infectious disease evolving according to a SIR model. Both linear and logarithmic preferences are considered with and without time discounting. Worth to note, we show that if an economy has a sufficiently performing technology, then the government should invest as much as the initial public budget allows.
Bosi, S, C Camacho and D Desmarchelier (2020), ‘Optimal vaccination and herd immunity‘, COVID Economics 41, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-41#392514_392917_390581
This paper reviews the literature on incorporating behavioural elements into epidemiological models of pandemics. While modelling behaviour by forward-looking rational agents can provide some insight into the time paths of pandemics, the non-stationary nature of Susceptible-Infected-Removed (SIR) models of viral spread makes characterisation of resulting equilibria dicult. Here I posit a shortcut that can be deployed to allow for a tractable equilibrium model of pandemics with intuitive comparative statics and also a clear prediction that effective reproduction numbers (that is, R) will tend towards 1 in equilibrium. This motivates taking ^R = 1 as an equilibrium starting point for analyses of pandemics with behavioural agents. The implications of this for the analysis of widespread testing, tracing, isolation and mask-use is discussed.
Gans, J (2020), ‘The Economic Consequences of R= 1: Towards a Workable Behavioural Epidemiological Model ofPandemics‘, COVID Economics 41, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-41#392514_392917_390582
Pandemics have heterogeneous effects on the health and economic outcomes of members of the population. To stay in power, politician-policymakers have to consider the health vulnerability- economic vulnerability (HVâ€“EV) profiles of their coalition. We show that the politically optimal pandemic policy (POPP) reveals the HVâ€“EV profile of the smallest, rather than the largest, group in the coalition. The logic of political survival dictates that the preferences of the least loyal, most pivotal, members of the coalition determine policy.
Desierto, D and M Koyama (2020), ‘Health vs. Economy: Politically Optimal Pandemic Policy‘, COVID Economics 41, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-41#392514_392917_391050
The goal of this paper is to study the impact of the lockdown policy on voting behaviour, during the COVID-19 pandemic. We focus on France, where a differential lockdown was implemented across departments, based on the local diffusion of the disease. In particular, the country has been divided in two areas, red and green, subject to a â€œhardâ€ and a â€œsoftâ€ lockdown, respectively. To measure voting behaviour, before and after the policy, we rely on 2020 French municipal elections: the first round took place before the introduction of the restrictions, while the second round was delayed after the end of the lockdown. We estimate a Spatial Regression-Discontinuity-Design model comparing the difference in outcomes between the two electoral rounds, at the border of red and green areas. The main results suggest that lockdown regulations significantly affected electoral outcomes. First, in localities under a harder lockdown, the incumbent's vote share is higher as well as the consensus for Green parties. Second, voter turnout is larger where more stringent restrictions are adopted. These results suggest that lockdown measures strongly lead citizens to rally around the local incumbent politicians.
Giommoni, T and G Loumeau (2020), ‘Lockdown and Voting Behaviour: A Natural Experiment on Postponed Elections during the COVID-19 Pandemic‘, COVID Economics 41, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-41#392514_392917_390583
We consider an SIR model where the probability of infections between infected and susceptible individuals are viewed as Poisson trials. The probabilities of infection between pairwise susceptible-infected matches are thus order statistics. This implies that the reproduction rate is a random variable. We derive the first two moments of the distribution of Rt conditional on the information available at time t?1 for Poisson trials drawn from an arbitrary parent distribution with finite mean. We show that the variance of Rt is increasing in the proportion of susceptible individuals in the population, and that ex ante identical populations can exhibit large differences in the path of the virus. This has a number of implications for policy during pandemics. We provide a rationale for why shelter-in-place orders may be a better containment measure than mandating the use of masks because of their impact on the variance of the reproduction rate.
Holden, R and D Thornton (2020), ‘The Stochastic Reproduction Rate of a Virus‘, COVID Economics 41, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-41#392514_392917_390584
While the coronavirus spreads, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the economic shocks. To achieve higher effectiveness with such policy measures, it is key to identify the industries that are most in need of support. In this study, we introduce a data-mining approach to measure industry- specific risks related to COVID-19. We examine company risk reports filed to the U. S. Securities and Exchange Commission (SEC). This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it allows for a real-time analysis of risk assessments. Preliminary findings suggest that the companiesâ€™ awareness towards corona-related business risks is ahead of the overall stock market developments. Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing, we identify corona-related risk topics and their perceived relevance for different industries. The preliminary findings are summarised as an up-to-date online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool is up- dated weekly. It could provide relevant empirical data to inform models on the economic effects of the crisis. Such complementary empirical information could ultimately help policymakers to effectively target financial support in order to mitigate the economic shocks of the crisis.
Braesemann, F, P Darius, L Neuhaeuser, F Stephany, N Stoehr and O Teutloff (2020), ‘The CoRisk-Index: A data-mining approach to identify industry-specific risk assessments related to COVID-19 in real-time‘, COVID Economics 41, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-41#392514_392917_390585