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Ariadne M. Checo

Francesco Grigoli

Jose Mota

January 14th, 2021

Targeted containment policies are not a panacea

0 comments | 12 shares

Estimated reading time: 10 minutes

Ariadne M. Checo

Francesco Grigoli

Jose Mota

January 14th, 2021

Targeted containment policies are not a panacea

0 comments | 12 shares

Estimated reading time: 10 minutes

Many countries have recently implemented lockdown measures to contain the pandemic’s spread. These measures slowed the growth of infections, relieved some of the pressure on health systems, and saved lives. However, they led to deep economic recessions and high levels of unemployment. At the same time, voluntary social distancing also chilled economic activity as people feared social interactions. With many countries experiencing resurgences in the number of infections, the need to weigh health and economic considerations has become pressing.

In this context, policymakers have considered containment policies that differentiate between low- and high-risk people. Given the evidence, which suggests that the virus is deadlier for older people (Figure 1), one such containment policy could be age-graded.

Figure 1: COVID-19 Mortality Rate by Age Group (Percent) 

 

Source: Centers for Disease Control and Prevention. Notes: Based on data for the United States between February 12 and March 16, 2020. The lower bound of the shaded area is estimated by using all cases within each age group as denominators, while the upper bound is estimated by using only cases with known information on each outcome as denominators. The line denotes the middle point between the lower and upper bound.

 

To evaluate the pros and cons of a targeted containment policy, we compare epidemiological and macroeconomic outcomes under such a policy to the ones obtained and under a blanket policy that treats individuals equally.

In our work, people differ in terms of mortality rates and contact rates, and the government maximises welfare by banning individuals from working. People, in turn, react to changes in the probability of getting infected by either curtailing consumption and working less when the virus is spreading, or the reverse when the virus is contained. We contend that modeling these dynamic changes is key to optimal containment.

We find that under a targeted policy the optimal containment reaches a larger portion of the population than under a blanket policy, and that the work ban is held in place for longer (panel a of Figure 2). This is because, in a blanket containment policy, it is costlier for the government to contain people with a lower average mortality rate compared to those with a higher mortality rate.

We also find that the optimal policy would prompt the government to contain some low-risk individuals before it contains all high-risk individuals (panel b of Figure 2). This is because, despite the relatively higher mortality rate of the latter, (i) the value of their discounted consumption is smaller and (ii) the government knows that containing low-risk individuals leads to fewer deaths among high-risk people (who can get infected by interacting within the household).

Figure 2: Optimal Containment

a) Blanket and Targeted Policy

(Percent of population, excluding retirees)

b) Targeted Policy, Low- and High-Risk Agents (Percent of population in each category, excluding retirees)

Notes: The figure shows the optimal containment over the first 150 weeks since the beginning of the pandemic.

 

From an epidemiological perspective, a containment policy aimed at high-risk individuals results in a sizeable decline of infections and deaths. Our results suggest that, in the case of the US, a targeted containment saves about 167,000 more lives than a blanket one (panel a of Figure 3). From a macroeconomic perspective, however, a targeted policy generates the deepest initial plunge in consumption as a larger fraction of people is contained (panel b of Figure 3).

Also, the recession lasts longer under a targeted containment, which reflects two key facts. First, restrictions remain in place for longer; and second, herd immunity is achieved later compared to a blanket policy. This protraction of the time until a society reaches herd immunity is particularly the case among high-risk individuals who have a stronger propensity to voluntarily self-distance in response to changes in the chances of infection. Yet, the smaller death count makes the targeted policy superior for welfare.

Figure 3: Epidemiological and Microeconomic Dynamics

Deaths (Percent of initial population)

Consumption (Percent deviation from steady state)

Notes: The figure shows the epidemiological and macroeconomic dynamics over the first 150 weeks since the beginning of the pandemic.

 

How practical are targeted containment measures? Government-mandated restrictions forcing people to stay at home may lead to an undesirable increase of contacts within the household, which is where most of the interactions between younger and older cohorts take place. Increasing the number of interactions at home diminishes the benefits of differentiating between low- and high-risk agents. Since individuals can now infect each other more easily, the optimal work ban has to extend to all high-risk individuals and a significantly larger portion of low-risk individuals.

In an alternative setting with given contact rates, a household structure featuring a higher share of contacts between low- and high-risk individuals makes it difficult for the government to implement a targeted containment due to the cross-risk category contamination at home. We therefore find that a sufficiently high share of contacts between low- and high-risk individuals makes the targeted policy equivalent to a blanket one.

This article gives the views of the authors, and not the position of the Social Policy Blog, nor of the London School of Economics.

About the author

Ariadne M. Checo

Ariadne M. Checo is an economist working at the Executive Secretariat of the Central American Monetary Council and the Central Bank of Dominican Republic. Her research focuses on macroeconomics, monetary policy, and forecasting. She holds a MS in Specialized Economic Analysis from Barcelona Graduate School of Economics and a MS in Mathematics from Instituto Tecnológico de Santo Domingo.

Francesco Grigoli

Francesco Grigoli is an Economist in the World Economic Studies Division of the IMF’s Research Department and an adjunct professor at Georgetown University. Previously, he worked in the IMF’s Fiscal Affairs and Western Hemisphere Departments and was a visiting scholar at Columbia University. He published extensively in academic journals on a wide range of topics in macroeconomics and international economics. His current research focuses on expectation formation mechanisms, monetary policy, uncertainty, and news shocks. Francesco received his PhD in Economics from the University of Insubria and holds a Master's in International Economics from the University of Sussex.

Jose Mota

Jose Mota is a PhD candidate in Economics at the University of Houston. His research focuses on macroeconomics, labor, and the economics of infectious diseases. He holds an MA in Economics from the Pontificia Universidad Católica de Chile and a BA in Economics from the Instituto Tecnológico de Santo Domingo (INTEC). He previously worked at the following institutions of the Dominican Republic: Central Bank, Ministry of Finance, and Superintendency of Banks. Also, he has taught undergraduate economic courses at the University of Houston and INTEC.

Posted In: COVID 19

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