We analyze the externalities that arise when social and economic interactions transmit infectious diseases such as COVID-19. Public health measures are essential because individually rational agents do not internalize that they impose infection externalities upon others. In an SIR model calibrated to capture the main features of COVID-19 in the US economy, we show that private agents perceive the cost of an additional infection to be around $80k whereas the social cost including infection externalities is more than three times higher, around $286k. This misvaluation has stark implications for how society ultimately overcomes the disease: individually rational susceptible agents act cautiously to atten the curve of infections, but the disease is not overcome until herd immunity is acquired, with a deep recession and slow recovery lasting several years. By contrast, the socially optimal approach in our model isolates the infected and quickly contains the disease, producing a much milder recession. If the infected and susceptible cannot be targeted independently, then containment is far costlier: it remains optimal for standard statistical values of life but not if only the economic losses from lost lives are counted.
Bethune, Z and A Korinek (2020), ‘COVID-19 Infection Externalities: Pursuing Herd Immunity or Containment?‘, COVID Economics 11, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-11#392514_392887_390406
The COVID-19 pandemic and the subsequent lockdown brought about a massive slowdown of the economy and an unparalleled stock market crash. Using US data, this paper explores how firms with high Environmental and Social (ES) ratings fare during the first quarter of 2020 compared to other firms. We show that stocks with high ES ratings have significantly higher returns, lower return volatilities, and higher trading volumes than other stocks. Firms with high ES ratings and high advertising expenditures perform especially well during the crash. This paper highlights the importance of ES policies in making firms more resilient during a time of crisis.
Albuquerque, R, Y Koskinen, S Yang and C Zhang (2020), ‘Love in the Time of COVID-19: The Resiliency of Environmental and Social Stocks‘, COVID Economics 11, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-11#392514_392887_391022
I develop an extension of a canonical epidemiology model in which the policy in place determines the probability of transmission of an epidemic disease. I use the model to evaluate the effects of isolating symptomatic individuals, of increasing social distancing and of tests of different quality: a poor quality test that can only discriminate between healthy and infected individuals (such as polymerase chain reaction 'PCR' or Rapid Diagnostic Test), and a high quality test that is able to discriminate between immune and vulnerable healthy, and infected individuals (such as a serology test like Neutralization Assay). I find that isolating symptomatic individuals has a large effect at delaying and reducing the pick of infections. The combination of this policy with the poor quality test represents only a negligible improvement, whereas with the high quality test there is an additional delaying and reduction in the pick of infections. Social distancing alone cannot achieve similar effects without incurring in enormous output losses. I explore the combined effect of social distancing at early stages of the epidemic with a following period of tests and find that the best outcome is obtained with a light reduction of human interaction for about three months together with a subsequent test of the population over 40 days.
Xiao, K (2020), ‘Saving Lives versus Saving Livelihoods: Can Big Data Technology Solve the Pandemic Dilemma?‘, COVID Economics 11, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-11#392514_392887_390408
This paper studies the effectiveness of big data technology in mitigating the economic and health impacts of the COVID-19 outbreak. I exploit the staggered implementation of contact-tracing apps called "health code" in 322 Chinese cities during the COVID-19 pandemic. Using high-frequency variations in population movements and greenhouse gas emissions across cities before and after the introduction of health code, I disentangle the effect of big data technology from confounding factors such as public sentiments and government responses. I find that big data technology significantly improves the tradeoff between human toll and economic costs. Cities adopting health code experience a significant increase in economic activities without suffering from higher infection rates. Overall, big data technology creates an economic value of 0.5%-0.75% of GDP during the COVID-19 outbreak in China.
Obiols-Homs, F (2020), ‘Precaution, Social Distancing and Tests in a Model of Epidemic Disease‘, COVID Economics 11, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-11#392514_392887_390407
Public response to rising deaths from COVID-19 was immediate and, in many cases, drastic, leading to substantial economic and institutional costs. In this paper, I focus on mortality from COVID-19. Using crosscountry evidence and controlling for a variety of contributing factors, I find that increasing the number of hospital beds has a significant and quite substantial impact on mortality rates. Hospital beds likely capture the capacity of ICU, laboratories, and other hospital-related equipment. Facing a potential second or third wave of infection following an exit from lockdown policies, countries short on medical infrastructures should increase them immediately.
Sussman, N (2020), ‘Time For Bed(s) - hospital capacity and mortality from COVID-19‘, COVID Economics 11, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-11#392514_392887_390809
The COVID-19 pandemic has rattled the global economy and has required governments to undertake massive fiscal stimulus to prevent the economic fallout of social distancing policies. In this paper, we compare the fiscal response of governments from around the world and its main determinants. We find sovereign credit ratings as one of the most critical factors determining their choice. First, the countries with one level worse rating announced 0.3 percentage points lower fiscal stimulus (as a percentage of their GDP). Second, these countries also delayed their fiscal stimulus by an average of 1.7 days. We identify 22 most vulnerable countries, based on their rating and stringency, and find that a stimulus equal to 1 percent of their GDP adds up to USD 87 billion. In order to fight the pandemic, long term loans from multilateral institutions can help these stimulus starved economies.
Balajee, A, S Tomar and G Udupa (2020), ‘COVID-19, Fiscal Stimulus, and Credit Ratings‘, COVID Economics 11, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-11#392514_392887_390409