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      Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective

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          Abstract

          We provide quantitative predictions of first-order supply and demand shocks for the US economy associated with the COVID-19 pandemic at the level of individual occupations and industries. To analyse the supply shock, we classify industries as essential or non-essential and construct a Remote Labour Index, which measures the ability of different occupations to work from home. Demand shocks are based on a study of the likely effect of a severe influenza epidemic developed by the US Congressional Budget Office. Compared to the pre-COVID period, these shocks would threaten around 20 per cent of the US economy’s GDP, jeopardize 23 per cent of jobs, and reduce total wage income by 16 per cent. At the industry level, sectors such as transport are likely to be output-constrained by demand shocks, while sectors relating to manufacturing, mining, and services are more likely to be constrained by supply shocks. Entertainment, restaurants, and tourism face large supply and demand shocks. At the occupation level, we show that high-wage occupations are relatively immune from adverse supply- and demand-side shocks, while low-wage occupations are much more vulnerable. We should emphasize that our results are only first-order shocks—we expect them to be substantially amplified by feedback effects in the production network.

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          Estimates of the severity of coronavirus disease 2019: a model-based analysis

          Summary Background In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases. Methods We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation. Findings Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9–19·2) and to hospital discharge to be 24·7 days (22·9–28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56–3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23–1·53), with substantially higher ratios in older age groups (0·32% [0·27–0·38] in those aged <60 years vs 6·4% [5·7–7·2] in those aged ≥60 years), up to 13·4% (11·2–15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4–3·5] in those aged <60 years [n=360] and 4·5% [1·8–11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39–1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0–7·6) in those aged 80 years or older. Interpretation These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death. Funding UK Medical Research Council.
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            How Many Jobs Can be Done at Home?

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              The possible macroeconomic impact on the UK of an influenza pandemic.

              Little is known about the possible impact of an influenza pandemic on a nation's economy. We applied the UK macroeconomic model 'COMPACT' to epidemiological data on previous UK influenza pandemics, and extrapolated a sensitivity analysis to cover more extreme disease scenarios. Analysis suggests that the economic impact of a repeat of the 1957 or 1968 pandemics, allowing for school closures, would be short-lived, constituting a loss of 3.35 and 0.58% of GDP in the first pandemic quarter and year, respectively. A more severe scenario (with more than 1% of the population dying) could yield impacts of 21 and 4.5%, respectively. The economic shockwave would be gravest when absenteeism (through school closures) increases beyond a few weeks, creating policy repercussions for influenza pandemic planning as the most severe economic impact is due to policies to contain the pandemic rather than the pandemic itself.Accounting for changes in consumption patterns made in an attempt to avoid infection worsens the potential impact. Our mild disease scenario then shows first quarter/first year reductions in GDP of 9.5/2.5%, compared with our severe scenario reductions of 29.5/6%. These results clearly indicate the significance of behavioural change over disease parameters. Copyright © 2009 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                Journal
                ecopol
                Oxford Review of Economic Policy
                Oxford University Press (UK )
                0266-903X
                1460-2121
                29 August 2020
                : graa033
                Affiliations
                [1 ] Institute for New Economic Thinking at the Oxford Martin School and Mathematical Institute, University of Oxford
                [2 ] Institute for New Economic Thinking at the Oxford Martin School, Smith School of Environment and Enterprise, and School of Geography and Environment, University of Oxford
                [2a ] Bennett Institute for Public Policy, University of Cambridge
                [3a ] Complexity Science Hub Vienna
                [5 ] Santa Fe Institute and Complexity Science Hub Vienna
                Author notes
                Article
                graa033
                10.1093/oxrep/graa033
                7499761
                70c18d6c-115c-4644-9c5d-ada934bf4427
                © The Author(s) 2020. Published by Oxford University Press. For permissions please e-mail: journals.permissions@oup.com

                This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

                This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

                History
                Page count
                Pages: 44
                Funding
                Funded by: Office of the Director of National Intelligence, DOI 10.13039/100011038;
                Funded by: Intelligence Advanced Research Projects Activity, DOI 10.13039/100011039;
                Categories
                Article
                Jel/I15
                Jel/J21
                Jel/J23
                Jel/J63
                Jel/O49
                AcademicSubjects/SOC00720
                Custom metadata
                PAP

                covid-19,shocks,economic growth,unemployment
                covid-19, shocks, economic growth, unemployment

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