We study the dynamic interaction between COVID-19, economic mobility, and containment policy. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through traditional and narrative sign restrictions. We find that incidence shocks and containment shocks have large and persistent effects on mobility, morbidity, and mortality that last for one to two months. These shocks are the main drivers of the pandemic, explaining between 20 and 60 percent of the average and historical variability in mobility, cases, and deaths worldwide. The policy trade-off associated to nonpharmaceutical interventions is 1 pp less economic mobility per day for 8 percent fewer deaths after 3 months. (JEL C43, H51, I12, I18, O15)
{"title":"Disentangling COVID-19, Economic Mobility, and Containment Policy Shocks","authors":"Annika Camehl, Malte Rieth","doi":"10.2139/ssrn.3792425","DOIUrl":"https://doi.org/10.2139/ssrn.3792425","url":null,"abstract":"We study the dynamic interaction between COVID-19, economic mobility, and containment policy. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through traditional and narrative sign restrictions. We find that incidence shocks and containment shocks have large and persistent effects on mobility, morbidity, and mortality that last for one to two months. These shocks are the main drivers of the pandemic, explaining between 20 and 60 percent of the average and historical variability in mobility, cases, and deaths worldwide. The policy trade-off associated to nonpharmaceutical interventions is 1 pp less economic mobility per day for 8 percent fewer deaths after 3 months. (JEL C43, H51, I12, I18, O15)","PeriodicalId":368984,"journal":{"name":"HEN: Other Specific Diseases or Therapies (Sub-Topic)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134541607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
COVID-19 death rates per 100,000 vary widely across the nation. As of September 1, 2020, they range from a low of 4 in Hawaii to a high of 179 in New Jersey. Although academic research has been conducted at the county and metropolitan levels, no research has rigorously examined or identified the demographic and socioeconomic forces that explain state-level differences. This study presents an empirical model and the results of regression tests that help identify these forces and shed light on the role they play in explaining COVID-19 deaths. A stepwise regression model we tested exhibits a high degree of explanatory power. It suggests that two measures of density explain most of the state-level differences. Less significant variables included the poverty rate and racial/ethnic differences. We also found that variables relating to health, air travel, and government mandates were not significant in explaining COVID-19 deaths at the state level. This study also examines the elasticities of those variables we found significant. We measured both average and constant elasticities to determine the relationship between changes in COVID-19 deaths and percentage changes in the relevant explanatory variables. In an analysis of residuals, we found that the unexplained variation was found to be related mainly to factors site-specific to individual states. Unlike the empirical results of several academic studies, our model found that the density of a state is the most important factor explaining COVID-19 deaths. The role that density plays in the transmission of COVID-19 has important policy implications in responding to the challenges posed by the coronavirus and future pandemics.
{"title":"A Model to Explain Statewide Differences in COVID-19 Death Rates","authors":"J. Doti","doi":"10.2139/ssrn.3731803","DOIUrl":"https://doi.org/10.2139/ssrn.3731803","url":null,"abstract":"COVID-19 death rates per 100,000 vary widely across the nation. As of September 1, 2020, they range from a low of 4 in Hawaii to a high of 179 in New Jersey. Although academic research has been conducted at the county and metropolitan levels, no research has rigorously examined or identified the demographic and socioeconomic forces that explain state-level differences. This study presents an empirical model and the results of regression tests that help identify these forces and shed light on the role they play in explaining COVID-19 deaths. \u0000 \u0000A stepwise regression model we tested exhibits a high degree of explanatory power. It suggests that two measures of density explain most of the state-level differences. Less significant variables included the poverty rate and racial/ethnic differences. We also found that variables relating to health, air travel, and government mandates were not significant in explaining COVID-19 deaths at the state level. \u0000 \u0000This study also examines the elasticities of those variables we found significant. We measured both average and constant elasticities to determine the relationship between changes in COVID-19 deaths and percentage changes in the relevant explanatory variables. In an analysis of residuals, we found that the unexplained variation was found to be related mainly to factors site-specific to individual states. \u0000 \u0000Unlike the empirical results of several academic studies, our model found that the density of a state is the most important factor explaining COVID-19 deaths. The role that density plays in the transmission of COVID-19 has important policy implications in responding to the challenges posed by the coronavirus and future pandemics.","PeriodicalId":368984,"journal":{"name":"HEN: Other Specific Diseases or Therapies (Sub-Topic)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116800295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study provides multi-disciplinary assessment of the coronavirus pandemic transmission in Nigeria, magnitude of COVID-19 confirmed cases, recovery, deaths, and inventory of infected person with recovery lags. It applied the statistical outcomes in predicting spilling over to subsequent periods. It identifies economic sectors worst hit by COVID-19 triggered recession, simulate the estimates of potential fiscal and other macroeconomic impact of the pandemic in the country in short run alongside synthesis of restoration and sustainability strategies. Secondary data relating to coronavirus infection cases, spreads, recoveries and fatalities were assessed, using the susceptible-infected-recovered” (SIR) model in absence of mass testing and probable cessation from health crisis management. It identified economic sectors/activities being devastated by COVID-19 induced recession, provides interim estimates adverse impact based on economic peak and down-turn cycle method. The study also measured the magnitude of macroeconomic shocks in Nigeria’s economy using a standard global computable general equilibrium model and exploration of sustainability strategies based on synthesis of extant reports were employed. These data-sets were obtained from the Nigerian sources and partly from global sources. Furthermore, it utilized trend analysis derived from on empirical data of extant daily confirmed cases, discharges and hospitalized person together with tentative projection of additional confirmed cases as from July–September, 2020. Results revealed that confirmed cases in Nigeria will increase steadily from 25694 (in June) to around 74825 by the end September and expected to reach 121000 by end of year 2020. This suggests that the pandemic is likely to persist up to the second quarter of 2021. Education, transport (aviation), hospitality, tourism and sports businesses; trade (informal sector) in the services sector; petroleum exploration in mining sub-sector are most severely contracting activities industries in the economy. Given the prevailing intensity of recession, the result indicates that a reduction of about 5-to-7% in GDP will be recorded in 2020. Result of variance analysis of fiscal budget estimates indicates adverse increase of -2% in overall fiscal deficit balances during the periods, which may aggravate debt burden with decline of about -5.7 percent and up to -7 percent in nominal GDP. Health, education, agriculture, petroleum exploration; petroleum refining and petrochemical industries, manufacturing (particularly pharmaceuticals), energy and power generation should be given priority in the sustainability programme.
{"title":"Coronavirus (COVID-19) Pandemic, Economic Consequences and Strategies for Ameliorting Macroeconomic Shocks in Nigeria’s Economy","authors":"C. Alozie, A. O. Ideh, I. Ifelunini","doi":"10.2139/ssrn.3697109","DOIUrl":"https://doi.org/10.2139/ssrn.3697109","url":null,"abstract":"This study provides multi-disciplinary assessment of the coronavirus pandemic transmission in Nigeria, magnitude of COVID-19 confirmed cases, recovery, deaths, and inventory of infected person with recovery lags. It applied the statistical outcomes in predicting spilling over to subsequent periods. It identifies economic sectors worst hit by COVID-19 triggered recession, simulate the estimates of potential fiscal and other macroeconomic impact of the pandemic in the country in short run alongside synthesis of restoration and sustainability strategies. Secondary data relating to coronavirus infection cases, spreads, recoveries and fatalities were assessed, using the susceptible-infected-recovered” (SIR) model in absence of mass testing and probable cessation from health crisis management. It identified economic sectors/activities being devastated by COVID-19 induced recession, provides interim estimates adverse impact based on economic peak and down-turn cycle method. The study also measured the magnitude of macroeconomic shocks in Nigeria’s economy using a standard global computable general equilibrium model and exploration of sustainability strategies based on synthesis of extant reports were employed. These data-sets were obtained from the Nigerian sources and partly from global sources. Furthermore, it utilized trend analysis derived from on empirical data of extant daily confirmed cases, discharges and hospitalized person together with tentative projection of additional confirmed cases as from July–September, 2020. Results revealed that confirmed cases in Nigeria will increase steadily from 25694 (in June) to around 74825 by the end September and expected to reach 121000 by end of year 2020. This suggests that the pandemic is likely to persist up to the second quarter of 2021. Education, transport (aviation), hospitality, tourism and sports businesses; trade (informal sector) in the services sector; petroleum exploration in mining sub-sector are most severely contracting activities industries in the economy. Given the prevailing intensity of recession, the result indicates that a reduction of about 5-to-7% in GDP will be recorded in 2020. Result of variance analysis of fiscal budget estimates indicates adverse increase of -2% in overall fiscal deficit balances during the periods, which may aggravate debt burden with decline of about -5.7 percent and up to -7 percent in nominal GDP. Health, education, agriculture, petroleum exploration; petroleum refining and petrochemical industries, manufacturing (particularly pharmaceuticals), energy and power generation should be given priority in the sustainability programme. <br>","PeriodicalId":368984,"journal":{"name":"HEN: Other Specific Diseases or Therapies (Sub-Topic)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129878014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wasiu Adekunle, Feyisayo Oyolola, O. Atolagbe, Abdulhameed A. Ademola, Taiwo H. Odugbemi, Yusuff O. Ashiru
Virtually all economies of the world were caught up unawares with the sudden outbreak and rapid spread of coronavirus pandemic from Wuhan City of China to the rest of the world. A number of studies have been conducted to investigate the drivers of the spread of the viral infection. To differ with these studies which were mostly epidemiological-based, we employed a cross-sectional quantile regression approach to uncover both the common and region-specific socio-economic conditions that are instrumental in the spread of the pandemic across four continents of the world including Africa, Asia, America and Europe. Across the four continents, we observed that age characteristics proxied by life expectancy and the size of the aged population, as well as, overall spending on the health sector have significant impact on the spread of COVID-19. We also noted the significant roles of out-of-pocket spending on healthcare in the case of Africa, net migration in the case of America and tourism attraction in the case of Europe in driving the prevalence of coronavirus. We therefore draw policy implications in terms of the need for improved spending on health sector across continents and the need to intensify health checks for travelers and immigrants, and also the need to emphasize regular check-ups for all individuals across continents since current realities have shown that no age-group is spared of contracting the viral infection.
{"title":"Modelling the Global Prevalence of COVID-19: An Econometric Approach","authors":"Wasiu Adekunle, Feyisayo Oyolola, O. Atolagbe, Abdulhameed A. Ademola, Taiwo H. Odugbemi, Yusuff O. Ashiru","doi":"10.2139/ssrn.3761296","DOIUrl":"https://doi.org/10.2139/ssrn.3761296","url":null,"abstract":"\u0000 Virtually all economies of the world were caught up unawares with the sudden outbreak and rapid spread of coronavirus pandemic from Wuhan City of China to the rest of the world. A number of studies have been conducted to investigate the drivers of the spread of the viral infection. To differ with these studies which were mostly epidemiological-based, we employed a cross-sectional quantile regression approach to uncover both the common and region-specific socio-economic conditions that are instrumental in the spread of the pandemic across four continents of the world including Africa, Asia, America and Europe. Across the four continents, we observed that age characteristics proxied by life expectancy and the size of the aged population, as well as, overall spending on the health sector have significant impact on the spread of COVID-19. We also noted the significant roles of out-of-pocket spending on healthcare in the case of Africa, net migration in the case of America and tourism attraction in the case of Europe in driving the prevalence of coronavirus. We therefore draw policy implications in terms of the need for improved spending on health sector across continents and the need to intensify health checks for travelers and immigrants, and also the need to emphasize regular check-ups for all individuals across continents since current realities have shown that no age-group is spared of contracting the viral infection.","PeriodicalId":368984,"journal":{"name":"HEN: Other Specific Diseases or Therapies (Sub-Topic)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123269518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using monthly zip-code level data on credit card transactions covering 16 U.S. cities, this paper investigates changes in consumption at local commercial places during the coronavirus disease 2019 (COVID-19). Since using aggregate-level data can suppress valuable information on consumption patterns coming from zip codes, the main contribution is achieved by estimating common factors across zip codes that are controlled for factors that are zip-code and time specific as well as those that are zip-code and sector specific. Whereas raw data for the median zip code suggest that spending on all sectors (except for grocery) has decreased amid COVID-19, the estimation results based on common factors across zip codes rather show that relative consumption of products and services that can be consumed at home (e.g., grocery, pharmacy, home maintenance) has increased up to 56% amid COVID-19 compared to the previous year, whereas relative consumption of products and services that cannot be consumed at home (e.g., fuel, transportation, personal care services, restaurant) has decreased up to 51%. Similarly, after controlling for the corresponding factors, online shopping has relatively increased up to 21%, while its expenditure share has relatively increased by up to 16% compared to the pre-COVID-19 period.
{"title":"Changes in Consumption amid COVID-19: Zip-Code Level Evidence from the U.S.","authors":"H. Yilmazkuday","doi":"10.2139/ssrn.3658518","DOIUrl":"https://doi.org/10.2139/ssrn.3658518","url":null,"abstract":"Using monthly zip-code level data on credit card transactions covering 16 U.S. cities, this paper investigates changes in consumption at local commercial places during the coronavirus disease 2019 (COVID-19). Since using aggregate-level data can suppress valuable information on consumption patterns coming from zip codes, the main contribution is achieved by estimating common factors across zip codes that are controlled for factors that are zip-code and time specific as well as those that are zip-code and sector specific. Whereas raw data for the median zip code suggest that spending on all sectors (except for grocery) has decreased amid COVID-19, the estimation results based on common factors across zip codes rather show that relative consumption of products and services that can be consumed at home (e.g., grocery, pharmacy, home maintenance) has increased up to 56% amid COVID-19 compared to the previous year, whereas relative consumption of products and services that cannot be consumed at home (e.g., fuel, transportation, personal care services, restaurant) has decreased up to 51%. Similarly, after controlling for the corresponding factors, online shopping has relatively increased up to 21%, while its expenditure share has relatively increased by up to 16% compared to the pre-COVID-19 period.","PeriodicalId":368984,"journal":{"name":"HEN: Other Specific Diseases or Therapies (Sub-Topic)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129503656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT The COVID-19 pandemic and the associated economic crisis are posing huge challenges, raising many unknowns, and imposing wrenching trade-offs. Both crises are global, but their impacts are deeply local. The policy response to both crises needs to be rapid, even if it is rough around the edges. But countries cannot pull this off on their own—the global crises require global solidarity and coordination. Governments must dramatically overhaul policies and invest in public health, economic stimulus, and social safety nets, to help countries recover faster from the COVID-19 pandemic. The economic report warns that a patchwork of preexisting solutions won’t work and points out those governments must coordinate with each other to hasten the recovery. This is a global crisis and working in silos is not an option, it says. The report `Position Note on the Social and Economic Impacts of COVID-19 in Asia-Pacific` calls on countries in the region to avoid returning to the pre-pandemic environmentally unsustainable development path, and to capitalize on the opportunity to build a better future. The study covers the primary data collected related to the topic and primary data was collected through Google forms sample size of 78. The collected primary data was analyzed by using Paired sample test, KMO and Bartlett`s Test, and Factor Analysis with the help of SPSS.
{"title":"Economic Impact of COVID-19","authors":"N. Akbulaev, I. Mammadov, V. Aliyev","doi":"10.2139/ssrn.3649813","DOIUrl":"https://doi.org/10.2139/ssrn.3649813","url":null,"abstract":"ABSTRACT The COVID-19 pandemic and the associated economic crisis are posing huge challenges, raising many unknowns, and imposing wrenching trade-offs. Both crises are global, but their impacts are deeply local. The policy response to both crises needs to be rapid, even if it is rough around the edges. But countries cannot pull this off on their own—the global crises require global solidarity and coordination. Governments must dramatically overhaul policies and invest in public health, economic stimulus, and social safety nets, to help countries recover faster from the COVID-19 pandemic. The economic report warns that a patchwork of preexisting solutions won’t work and points out those governments must coordinate with each other to hasten the recovery. This is a global crisis and working in silos is not an option, it says. The report `Position Note on the Social and Economic Impacts of COVID-19 in Asia-Pacific` calls on countries in the region to avoid returning to the pre-pandemic environmentally unsustainable development path, and to capitalize on the opportunity to build a better future. The study covers the primary data collected related to the topic and primary data was collected through Google forms sample size of 78. The collected primary data was analyzed by using Paired sample test, KMO and Bartlett`s Test, and Factor Analysis with the help of SPSS.","PeriodicalId":368984,"journal":{"name":"HEN: Other Specific Diseases or Therapies (Sub-Topic)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115309757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I use data on cumulative tests, positive tests, and deaths for the coronavirus in South Korea and the U.S. lower-48 states during April 2020 to estimate the extent of infection and the unidentified share of the infected population in each state and in the U.S. as a whole on April 11, 2020. I find that 3.8 million people, or 1.2% of the U.S. population, were infected in the U.S., with rates of infection that ranged from 0.1% in more rural states to 7.0% in New York state. I estimate that only 20% of all U.S. cases were identified later through testing. The unidentified share of total cases ranged from 61% to 83% across the states. I estimate that 38% of all cases are asymptomatic, which is consistent with the high shares of unidentified cases.
{"title":"An Estimate of Unidentified and Total U.S. Coronavirus Cases by State on April 11, 2020","authors":"T. Breton","doi":"10.2139/ssrn.3583941","DOIUrl":"https://doi.org/10.2139/ssrn.3583941","url":null,"abstract":"I use data on cumulative tests, positive tests, and deaths for the coronavirus in South Korea and the U.S. lower-48 states during April 2020 to estimate the extent of infection and the unidentified share of the infected population in each state and in the U.S. as a whole on April 11, 2020. I find that 3.8 million people, or 1.2% of the U.S. population, were infected in the U.S., with rates of infection that ranged from 0.1% in more rural states to 7.0% in New York state. I estimate that only 20% of all U.S. cases were identified later through testing. The unidentified share of total cases ranged from 61% to 83% across the states. I estimate that 38% of all cases are asymptomatic, which is consistent with the high shares of unidentified cases.","PeriodicalId":368984,"journal":{"name":"HEN: Other Specific Diseases or Therapies (Sub-Topic)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134028710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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