Daniel Aaronson, Scott A. Brave, R. Butters, Daniel W. Sacks, Boyoung Seo
We leverage an event-study research design focused on the seven costliest hurricanes to hit the US mainland since 2004 to identify the elasticity of unemployment insurance filings with respect to search intensity. Applying our elasticity estimate to the state-level Google Trends indexes for the topic “unemployment,” we show that out-of-sample forecasts made ahead of the official data releases for March 21 and 28 predicted to a large degree the extent of the Covid-19 related surge in the demand for unemployment insurance. In addition, we provide a robust assessment of the uncertainty surrounding these estimates and demonstrate their use within a broader forecasting framework for US economic activity.
{"title":"Using the Eye of the Storm to Predict the Wave of COVID-19 UI Claims","authors":"Daniel Aaronson, Scott A. Brave, R. Butters, Daniel W. Sacks, Boyoung Seo","doi":"10.2139/ssrn.3561298","DOIUrl":"https://doi.org/10.2139/ssrn.3561298","url":null,"abstract":"We leverage an event-study research design focused on the seven costliest hurricanes to hit the US mainland since 2004 to identify the elasticity of unemployment insurance filings with respect to search intensity. Applying our elasticity estimate to the state-level Google Trends indexes for the topic “unemployment,” we show that out-of-sample forecasts made ahead of the official data releases for March 21 and 28 predicted to a large degree the extent of the Covid-19 related surge in the demand for unemployment insurance. In addition, we provide a robust assessment of the uncertainty surrounding these estimates and demonstrate their use within a broader forecasting framework for US economic activity.","PeriodicalId":13563,"journal":{"name":"Insurance & Financing in Health Economics eJournal","volume":"158 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76493908","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}
We present a theoretical model for performance-based testing. In this model, the doping agency observes either high or low performance from heterogeneous athletes and then decides whom to test. We show that there is an interior equilibrium with a positive testing probability for high and low performers. Counter-intuitively, low performers will be tested more often than high performers. We show that less-able athletes will dope more often than more-able athletes. Furthermore, we show that the overall doping abuse is independent of the distribution of talent, but only depends on the agency’s benefits and costs of doping tests.
{"title":"Does High Performance Bespeak Doping Abuse? A Performance Related Strategy for Anti-Doping Agencies","authors":"Christian Salzmann","doi":"10.2139/ssrn.3632792","DOIUrl":"https://doi.org/10.2139/ssrn.3632792","url":null,"abstract":"We present a theoretical model for performance-based testing. In this model, the doping agency observes either high or low performance from heterogeneous athletes and then decides whom to test. We show that there is an interior equilibrium with a positive testing probability for high and low performers. Counter-intuitively, low performers will be tested more often than high performers. We show that less-able athletes will dope more often than more-able athletes. Furthermore, we show that the overall doping abuse is independent of the distribution of talent, but only depends on the agency’s benefits and costs of doping tests.","PeriodicalId":13563,"journal":{"name":"Insurance & Financing in Health Economics eJournal","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80007118","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}
Viktor Stojkoski, Z. Utkovski, Petar Jolakoski, Dragan Tevdovski, L. Kocarev
The magnitude of the coronavirus disease (COVID-19) pandemic has an enormous impact on the social life and the economic activities in almost every country in the world. Besides the biological and epidemiological factors, a multitude of social and economic criteria also govern the extent of the coronavirus disease spread in the population. Consequently, there is an active debate regarding the critical socio-economic determinants that contribute to the resulting pandemic. In this paper, we contribute towards the resolution of the debate by leveraging Bayesian model averaging techniques and country level data to investigate the potential of 35 determinants, describing a diverse set of socio-economic characteristics, in explaining the coronavirus pandemic outcome.
{"title":"The socio-economic determinants of the coronavirus disease (COVID-19) pandemic","authors":"Viktor Stojkoski, Z. Utkovski, Petar Jolakoski, Dragan Tevdovski, L. Kocarev","doi":"10.2139/ssrn.3576037","DOIUrl":"https://doi.org/10.2139/ssrn.3576037","url":null,"abstract":"The magnitude of the coronavirus disease (COVID-19) pandemic has an enormous impact on the social life and the economic activities in almost every country in the world. Besides the biological and epidemiological factors, a multitude of social and economic criteria also govern the extent of the coronavirus disease spread in the population. Consequently, there is an active debate regarding the critical socio-economic determinants that contribute to the resulting pandemic. In this paper, we contribute towards the resolution of the debate by leveraging Bayesian model averaging techniques and country level data to investigate the potential of 35 determinants, describing a diverse set of socio-economic characteristics, in explaining the coronavirus pandemic outcome.","PeriodicalId":13563,"journal":{"name":"Insurance & Financing in Health Economics eJournal","volume":"114 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82749544","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}
We combine GPS data on changes in average distance traveled by individuals at the county level with COVID-19 case data and other demographic information to estimate how individual mobility is affected by local disease prevalence and restriction orders to stay-at-home. We find that a rise of local infection rate from 0% to 0.003% is associated with a reduction in mobility by 2.31%. An official stay-at-home restriction order corresponds to reducing mobility by 7.87%. Counties with larger shares of population over age 65, lower share of votes for the Republican Party in the 2016 Presidential Election, and higher population density are more responsive to disease prevalence and restriction orders.
{"title":"Staying at Home: Mobility Effects of COVID-19","authors":"S. Engle, John Stromme, Anson Zhou","doi":"10.2139/ssrn.3565703","DOIUrl":"https://doi.org/10.2139/ssrn.3565703","url":null,"abstract":"We combine GPS data on changes in average distance traveled by individuals at the county level with COVID-19 case data and other demographic information to estimate how individual mobility is affected by local disease prevalence and restriction orders to stay-at-home. We find that a rise of local infection rate from 0% to 0.003% is associated with a reduction in mobility by 2.31%. An official stay-at-home restriction order corresponds to reducing mobility by 7.87%. Counties with larger shares of population over age 65, lower share of votes for the Republican Party in the 2016 Presidential Election, and higher population density are more responsive to disease prevalence and restriction orders.","PeriodicalId":13563,"journal":{"name":"Insurance & Financing in Health Economics eJournal","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86968809","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}
The coronavirus did not cause the weakness of the economy but painfully exposed it. The ‘underlying condition’ which made our collective economic mind-body so vulnerable was caused by another virus. This one isn’t a pathogen but a mental bug in the form of the mechanical worldview that infected mainstream economics many years ago. It basically views the economy as a machine, the market as an automaton, and humans as robots. What this ignores is the fact that humans have conscious minds which, via trading and technologies, extend into real and financial markets. Problems thus start when such a view is turned into practice via mechanical policies, regulations and strategies. Specifically, they interfere with the chain of (e.g. price) discovery that sustains the economic system for the benefit of society. Such discovery relies on conscious minds. Crucially, there is nothing mechanical about consciousness or discovery. In fact, treating (extended) conscious minds in a mechanical way is very damaging. It goes against their nature and at some point this leads to reality checks. Reality checks are ontological, and sometimes also existential. In 2008 we experienced our first. This paper argues to make this our last by curing economics’ paradigm.
{"title":"Reality Check 2: Corona, Contagion, and the Economic Mind-Body; A Story of Two Bugs","authors":"Patrick Schotanus","doi":"10.2139/ssrn.3567796","DOIUrl":"https://doi.org/10.2139/ssrn.3567796","url":null,"abstract":"The coronavirus did not cause the weakness of the economy but painfully exposed it. The ‘underlying condition’ which made our collective economic mind-body so vulnerable was caused by another virus. This one isn’t a pathogen but a mental bug in the form of the mechanical worldview that infected mainstream economics many years ago. It basically views the economy as a machine, the market as an automaton, and humans as robots. What this ignores is the fact that humans have conscious minds which, via trading and technologies, extend into real and financial markets. Problems thus start when such a view is turned into practice via mechanical policies, regulations and strategies. Specifically, they interfere with the chain of (e.g. price) discovery that sustains the economic system for the benefit of society. Such discovery relies on conscious minds. Crucially, there is nothing mechanical about consciousness or discovery. In fact, treating (extended) conscious minds in a mechanical way is very damaging. It goes against their nature and at some point this leads to reality checks. Reality checks are ontological, and sometimes also existential. In 2008 we experienced our first. This paper argues to make this our last by curing economics’ paradigm.","PeriodicalId":13563,"journal":{"name":"Insurance & Financing in Health Economics eJournal","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74776457","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}
Pub Date : 2020-04-02DOI: 10.1553/POPULATIONYEARBOOK2021.RES1.1
D. Bloom, Victoria Fan, Vadim Kufenko, O. Ogbuoji, K. Prettner, G. Yamey
Per capita GDP has limited use as a well-being indicator because it does notcapture many dimensions that imply a “good life”, such as health and equality ofopportunity. However, per capita GDP has the virtues of being easy to interpret andto calculate with manageable data requirements. Against this backdrop, there is aneed for a measure of well-being that preserves the advantages of per capita GDP,but also includes health and equality. We propose a new parsimonious indicatorto fill this gap, and calculate it for 149 countries. This new indicator could beparticularly useful in complementing standard well-being indicators during theCOVID-19 pandemic. This is because (i) COVID-19 predominantly affects olderadults beyond their prime working ages whose mortality and morbidity do notstrongly affect GDP, and (ii) COVID-19 is known to have large effects on inequalityin many countries.
{"title":"Going Beyond GDP with a Parsimonious Indicator: Inequality-Adjusted Healthy Lifetime Income","authors":"D. Bloom, Victoria Fan, Vadim Kufenko, O. Ogbuoji, K. Prettner, G. Yamey","doi":"10.1553/POPULATIONYEARBOOK2021.RES1.1","DOIUrl":"https://doi.org/10.1553/POPULATIONYEARBOOK2021.RES1.1","url":null,"abstract":"Per capita GDP has limited use as a well-being indicator because it does notcapture many dimensions that imply a “good life”, such as health and equality ofopportunity. However, per capita GDP has the virtues of being easy to interpret andto calculate with manageable data requirements. Against this backdrop, there is aneed for a measure of well-being that preserves the advantages of per capita GDP,but also includes health and equality. We propose a new parsimonious indicatorto fill this gap, and calculate it for 149 countries. This new indicator could beparticularly useful in complementing standard well-being indicators during theCOVID-19 pandemic. This is because (i) COVID-19 predominantly affects olderadults beyond their prime working ages whose mortality and morbidity do notstrongly affect GDP, and (ii) COVID-19 is known to have large effects on inequalityin many countries.","PeriodicalId":13563,"journal":{"name":"Insurance & Financing in Health Economics eJournal","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81950134","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}
New York City has been rightly characterized as the epicenter of the coronavirus pandemic in the United States. Just one month after the first cases of coronavirus infection were reported in the city, the burden of infected individuals with serious complications of COVID-19 has already outstripped the capacity of many of the city’s hospitals. As in the case of most pandemics, scientists and public officials don’t have complete, accurate, real-time data on the path of new infections. Despite these data inadequacies, there already appears to be sufficient evidence to conclude that the curve in New York City is indeed flattening. The purpose of this report is to set forth the evidence for – and against – this preliminary but potentially important conclusion. Having examined the evidence, we then inquire: if the curve is indeed flattening, do we know what caused to it to level off?
{"title":"The Coronavirus Epidemic Curve is Already Flattening in New York City","authors":"J. Harris","doi":"10.2139/ssrn.3563985","DOIUrl":"https://doi.org/10.2139/ssrn.3563985","url":null,"abstract":"New York City has been rightly characterized as the epicenter of the coronavirus pandemic in the United States. Just one month after the first cases of coronavirus infection were reported in the city, the burden of infected individuals with serious complications of COVID-19 has already outstripped the capacity of many of the city’s hospitals. As in the case of most pandemics, scientists and public officials don’t have complete, accurate, real-time data on the path of new infections. Despite these data inadequacies, there already appears to be sufficient evidence to conclude that the curve in New York City is indeed flattening. The purpose of this report is to set forth the evidence for – and against – this preliminary but potentially important conclusion. Having examined the evidence, we then inquire: if the curve is indeed flattening, do we know what caused to it to level off?","PeriodicalId":13563,"journal":{"name":"Insurance & Financing in Health Economics eJournal","volume":"99 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86140390","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}
The coronavirus pandemic has restricted people’s physical movement but not the exchange of information, knowledge, and other digitally delivered services The p
冠状病毒大流行限制了人们的身体活动，但没有限制信息、知识和其他数字服务的交流
{"title":"A Pivot to a Services Trade Agenda Can Help Economic Growth","authors":"C. McDaniel","doi":"10.2139/ssrn.3592994","DOIUrl":"https://doi.org/10.2139/ssrn.3592994","url":null,"abstract":"The coronavirus pandemic has restricted people’s physical movement but not the exchange of information, knowledge, and other digitally delivered services The p","PeriodicalId":13563,"journal":{"name":"Insurance & Financing in Health Economics eJournal","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73327070","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}
For several reasons the death rate from an epidemic (deaths per infected persons) is difficult to determine before the epidemic ends. I proposed a procedure to calculate the death rate mid-epidemic by estimating the relationship between daily reported deaths and the daily time lag structure for reported cases associated with deaths. The structure is the spread of days over which cases are associated with deaths. The death rate is calculated by dividing the deaths by a weighted average of cases in that spread. I calculate the lag structure for COVID-19 cases worldwide, excluding China and Korea. This produces an estimated death rate of approximately 4.5 percent of reported cases.
{"title":"Calculating Death Rates for the COVID-19 Virus during an On-Going Epidemic","authors":"T. Marvell","doi":"10.2139/ssrn.3565815","DOIUrl":"https://doi.org/10.2139/ssrn.3565815","url":null,"abstract":"For several reasons the death rate from an epidemic (deaths per infected persons) is difficult to determine before the epidemic ends. I proposed a procedure to calculate the death rate mid-epidemic by estimating the relationship between daily reported deaths and the daily time lag structure for reported cases associated with deaths. The structure is the spread of days over which cases are associated with deaths. The death rate is calculated by dividing the deaths by a weighted average of cases in that spread. I calculate the lag structure for COVID-19 cases worldwide, excluding China and Korea. This produces an estimated death rate of approximately 4.5 percent of reported cases.","PeriodicalId":13563,"journal":{"name":"Insurance & Financing in Health Economics eJournal","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91359262","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}
The COVID-19 pandemic is a major shock to the American economy Indeed, we are already seeing signs that the United States will experience a severe recession M
新冠肺炎大流行对美国经济是一个重大冲击。事实上，我们已经看到美国将经历严重衰退的迹象
{"title":"Reforming the Fed’s Toolkit and Quantitative Easing Practices: A Plan to Achieve Level Targeting","authors":"Scott Sumner, P. Horan","doi":"10.2139/ssrn.3592952","DOIUrl":"https://doi.org/10.2139/ssrn.3592952","url":null,"abstract":"The COVID-19 pandemic is a major shock to the American economy Indeed, we are already seeing signs that the United States will experience a severe recession M","PeriodicalId":13563,"journal":{"name":"Insurance & Financing in Health Economics eJournal","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82328499","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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