The scientific community has come together in an unprecedented effort to find a COVID-19 vaccine. However, the success of any vaccine depends on the share of the population that gets vaccinated. We design a survey experiment in which a nationally representative sample of 3,133 adults in the U.S. state their intentions to vaccinate themselves and their children for COVID-19. In the experiment, we account for uncertainty about the severity and infectiousness of COVID-19, as well as inconsistencies in risk communication from government authorities, by varying these factors across treatments. We find that 20% of people in the U.S. would decline the vaccine. General vaccine hesitancy (including not having had a flu shot in the last two years), distrust of vaccine safety, and vaccine novelty are among the most important deterrents to vaccination. Further, our results suggest that inconsistent risk messages from public health experts and elected officials reduce vaccine uptake. We use our survey results in an epidemiological model to explore conditions under which a vaccine could achieve herd immunity. We find that in a middle-of-the-road scenario with central estimates of model parameters, a vaccine will benefit public health by saving many lives but nevertheless may fail to achieve herd immunity.
Ashworth, M, D Finnoff, S Newbold and L Thunström (2020), ‘Hesitancy towards a COVID-19 vaccine and prospects for herd immunity‘, COVID Economics 35, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-35#392514_392911_390546
We propose a simple method based on firms' balance sheets and sectoral predictions of sales growth to determine the firms that will become illiquid month by month as the Covid-19 crisis unfolds. We apply the method to the population of Italian incorporated businesses to the end of 2020. We find that at the peak, around 200,000 companies employing 3.3 million workers would become illiquid. The progression is fast, with 180,000 firms turning illiquid already by April. The liquidity shortage, defined as the "negative" liquidity stock of illiquid firms, amounts to 72 billion. We evaluate the Italian government liquidity decree, which provides guarantees for bank loans under four different facilities of increasing complexity. Assuming that firms have access to all the facilities, almost all firms are able to cover their liquidity shortfalls. The issue is the speed of implementation: the facilities supplying more liquidity are more complex to administrate, and many firms require these facilities to cover their liquidity shortfalls. Overall, we conclude that even in the case of a second wave after the summer, which would increase the liquidity shortfall substantially, firms' liquidity needs are manageable under the current schemes of liquidity provision.
Romano, G and F Schivardi (2020), ‘A simple method to estimate firms liquidity needs during the Covid -19 crisis with an application to Italy‘, COVID Economics 35, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-35#392514_392911_390554
This paper estimates the effect of non-pharmaceutical intervention (NPI) policies on public health during the recent COVID-19 outbreak by considering a counterfactual case for Sweden. Using a synthetic control approach, I find that strict initial lockdown measures played an important role in limiting the spread of the COVID-19 infection and that Swedish policymakers would have eventually reduced the infection cases by more than half had they followed those policies. As people dynamically adjust their behavior in response to information and policies, the impact of NPIs becomes visible with a time lag of around 5 weeks. An alternative difference-in-differences research design that allows for changes in behavioral patterns also confirms the effectiveness of a strict lockdown policy. Finally, extending the analysis to excess mortality, I find that the lockdown measures would have lowered excess mortality in Sweden by 23 percentage points, with a steep age gradient of more than 30 percentage points for the most vulnerable elderly cohort. The outcome of this study can help policymakers lay out future policies to further protect public health, as well as facilitate an economic plan for recovery.
Cho, S (2020), ‘Quantifying the Impact of Non-pharmaceutical Interventions (NPI) during the COVID-19 Outbreak -- The Case of Sweden‘, COVID Economics 35, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-35#392514_392911_390547
Sharp changes in consumer expenditure may bias inflation during the Covid-19 pandemic. Using public data from debit card transactions, I quantify these changes in consumer spending, update CPI basket weights and construct an alternative price index to measure the effect of the Covid-induced weighting bias on the Swiss consumer price index. I find that inflation was higher during the lock-down than suggested by CPI inflation. The annual inflation rate of the Covid price index was -0.4% by April 2020, compared to -1.1% of the equivalent CPI. Persistent â€œlow-touchâ€ consumer behaviour can further lead to inflation being underestimated by more than a quarter of a percentage point until the end of 2020.
Seiler, P (2020), ‘Weighting bias and inflation in the time of Covid-19: Evidence from Swiss transaction data‘, COVID Economics 35, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-35#392514_392911_390548
This paper evaluates the dynamic impact of various policies adopted by US states on the growth rates of confirmed Covid-19 cases and deaths as well as social distancing behavior measured by Google Mobility Reports, where we take into consideration people's voluntarily behavioral response to new information of transmission risks. Our analysis finds that both policies and information on transmission risks are important determinants of Covid-19 cases and deaths and shows that a change in policies explains a large fraction of observed changes in social distancing behavior. Our counterfactual experiments suggest that nationally mandating face masks for employees on April 1st could have reduced the growth rate of cases and deaths by more than 10 percentage points in late April, and could have led to as much as 17 to 55 percent less deaths nationally by the end of May, which roughly translates into 17 to 55 thousand saved lives. Our estimates imply that removing non-essential business closures (while maintaining school closures, restrictions on movie theaters and restaurants) could have led to -20 to 60 percent more cases and deaths by the end of May. We also find that, without stay-at-home orders, cases would have been larger by 25 to 170 percent, which implies that 0.5 to 3.4 million more Americans could have been infected if stay-at-home orders had not been implemented. Finally, not having implemented any policies could have led to at least a 7 fold increase with an uninformative upper bound in cases (and deaths) by the end of May in the US, with considerable uncertainty over the effects of school closures, which had little cross-sectional variation.
Chernozhukov, V, H Kasahara and P Schrimpf (2020), ‘Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S.‘, COVID Economics 35, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-35#392514_392911_390550
This paper examines the individual records of patients treated for COVID-19 during the early phase of the epidemic in Ontario. We trace out daily transitions of patients through medical care of different intensity and address the right truncation in the database. We also examine the sojourn times and reveal duration dependence in the treatments for COVID-19. The transition model is used to estimate and forecast the counts of patients treated for COVID-19 in Ontario, while accommodating the right truncation and right censoring in the sample. This research is based on the Public Health Ontario (PHO) dataset from May 07, 2020.
Bandehali, M, A Djogbenou, C Gourieroux, J Jasiak and P Rilstone (2020), ‘Transition Model for Corona Virus Management‘, COVID Economics 35, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-35#392514_392911_390551
I examine the short-term labor market effects of the Great Lockdown in the United States. I analyze job losses by task content (Acemoglu & Autor 2011), and show that they follow underlying trends; jobs with a high non-routine content are especially well-protected, even if they are not teleworkable. The importance of the task content, particularly for non-routine cognitive analytical tasks, is strong even after controlling for age, gender, race, education, sector and location (and hence for differential demand shocks). Jobs subject to higher structural turnover rates are much more likely to be terminated, suggesting that easier-to-replace employees were at a particular disadvantage, even within sectors; at the same time, there is evidence of labor hoarding for more valuable matches. Individuals in low-skilled jobs fared comparatively better in industries with a high share of high-skilled workers.
Petroulakis, F (2020), ‘Task content and job losses in the Great Lockdown‘, COVID Economics 35, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-35#392514_392911_390552
Using high-frequency panel data for U.S. counties, I estimate the full dynamic response of COVID-19 cases and deaths to exogenous movements in mobility and weather. I find several important results. First, weather and mobility are highly correlated and thus omitting either factor when studying the COVID-19 effects of the other is likely to result in substantial omitted variable bias. Second, temperature is found to have a negative and significant effect on future COVID-19 cases and deaths, though the estimated effect is sensitive to which measure of mobility is included in the regression. Third, controlling for weather, overall mobility is found to have a large positive effect on subsequent growth in COVID-19 cases and deaths. The effects become significant around 2 weeks ahead and persist through around 8 weeks ahead for cases and around 9 weeks ahead for deaths. The peak impact occurs 4 to 6 weeks ahead for cases and around 8 to 9 weeks ahead for deaths. The effects are largest for mobility measured by time spent away from home and time spent at work, though significant effects also are found for time spent at retail and recreation establishments, at transit stations, at grocery stores and pharmacies, and at parks. Fourth, I find that public health non-pharmaceutical interventions affect future COVID-19 cases and deaths, but that their effects work entirely through, and not independent of, individuals' mobility behavior. Lastly, the dynamic effects of mobility on COVID-19 outcomes are found to be generally similar across counties, though there is evidence of larger effects in counties with high cases per capita and that reduced mobility relatively late.
Wilson, D (2020), ‘Weather, Social Distancing, and the Spread of COVID-19‘, COVID Economics 35, CEPR Press, Paris & London. https://cepr.org/publications/covid-economics-issue-35#392514_392911_390553