scholarly journals Contextualizing COVID-19 spread: a county level analysis, urban versus rural, and implications for preparing for the next wave

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 418
Author(s):  
J. Sunil Rao ◽  
Hang Zhang ◽  
Alejandro Mantero
Keyword(s):  
Disease Spread ◽  
County Level ◽  
Deep Dive ◽  
Public Dataset ◽  
Contextual Determinants ◽  
Social Environmental ◽  
Urban Versus ◽  
Level Analysis ◽  
The U.S ◽  
Over Time

Background: Contextual determinants of health including social, environmental, healthcare and others, are a so-called deck of cards one is dealt. The ability to modify health outcomes varies then based upon how one’s hand is played. It is thus of great interest to understand how these determinants associate with the emerging pandemic coronavirus disease 2019 (COVID-19). Methods: To this end, we conducted a deep-dive analysis into this problem using a recently curated public dataset on COVID-19 that connects infection spread over time to a rich collection of contextual determinants for all counties of the U.S and Washington, D.C. Results: Using random forest machine learning methodology, we identified a relevant constellation of contextual factors of disease spread which manifest differently for urban and rural counties. Conclusions: The findings also have clear implications for better preparing for the next wave of disease.

scholarly journals Contextualizing covid-19 spread: a county level analysis, urban versus rural, and implications for preparing for the next wave

2020 ◽  
Author(s):  
J. Sunil Rao ◽  
Hang Zhang ◽  
Alejandro Mantero
Keyword(s):  
Disease Spread ◽  
County Level ◽  
Deep Dive ◽  
Public Dataset ◽  
Contextual Determinants ◽  
Social Environmental ◽  
Urban Versus ◽  
Level Analysis ◽  
The U.S ◽  
Over Time

AbstractParaphrasing [Morano and Holt, 2017], contextual determinants of health including social, environmental, healthcare and others, are a so-called deck of cards one is dealt. The ability to modify health outcomes varies then based upon how one’s hand is played. It is thus of great interest to understand how these determinants associate with the emerging pandemic covid-19. To this end, we conducted a deep-dive analysis into this problem using a recently curated public dataset on covid-19 that connects infection spread over time to a rich collection of contextual determinants for all counties of the U.S and Washington, D.C. Using random forest machine learning methodology, we identified a relevant constellation of contextual factors of disease spread which manifest differently for urban and rural counties. The findings also have clear implications for better preparing for the next wave of disease.

Incarceration and Crime

2016 ◽  
Vol 664 (1) ◽  
pp. 196-220 ◽  
Author(s):  
Magnus Lofstrom ◽  
Steven Raphael
Keyword(s):  
Cohort Analysis ◽  
Property Crime ◽  
Crime Rates ◽  
County Level ◽  
Auto Theft ◽  
Incarceration Rate ◽  
Synthetic Cohort ◽  
Study Support ◽  
Level Analysis ◽  
The U.S

Recent reforms in California caused a sharp and permanent reduction in the state’s incarceration rate. We evaluate the effects of that incarceration decline on local crime rates. Our analysis exploits the large variation across California counties in the effect of this reform on county-specific prison incarceration rates. We find very little evidence that the large reduction in California incarceration had an effect on violent crime, and modest evidence of effects on property crime, auto theft in particular. These effects are considerably smaller than existing estimates based on panel data for periods of time when the U.S. incarceration rate was considerably lower. We corroborate these cross-county results with a synthetic-cohort analysis of state crime rates in California. The statewide analysis confirms our findings from the county-level analysis. In line with with previous research, the results from this study support the hypothesis of a crime-prison effect that diminishes with increased reliance on incarceration.

scholarly journals U.S. county level analysis to determine If social distancing slowed the spread of COVID-19

2020 ◽  
Vol 44 ◽  
pp. 1
Author(s):  
Tannista Banerjee ◽  
Arnab Nayak
Keyword(s):  
United States ◽  
Cell Phone ◽  
The United States ◽  
County Level ◽  
Difference In Difference ◽  
Social Distancing ◽  
The Public ◽  
Risks And Benefits ◽  
Level Analysis ◽  
The U.S

Objective. To analyze the effectiveness of social distancing in the United States (U.S.). Methods. A novel cell-phone ping data was used to quantify the measures of social distancing by all U.S. counties. Results. Using a difference-in-difference approach results show that social distancing has been effective in slowing the spread of COVID-19. Conclusions. As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19. These results might help policymakers to make the public understand the risks and benefits of the lockdown.

scholarly journals Partisan differences in physical distancing are linked to health outcomes during the COVID-19 pandemic.

2020 ◽  
Author(s):  
Anton Gollwitzer ◽  
Cameron Martel ◽  
William J. Brady ◽  
Philip Pärnamets ◽  
Isaac Freedman ◽  
...  
Keyword(s):  
Population Density ◽  
Presidential Election ◽  
Median Income ◽  
County Level ◽  
Hillary Clinton ◽  
Donald Trump ◽  
Fox News ◽  
Conservative Media ◽  
The U.S ◽  
Over Time

Numerous polls suggest that COVID-19 is a profoundly partisan issue in the U.S. Using the geotracking data of 15 million smartphones per day, we found that U.S. counties that voted for Donald Trump (Republican) over Hillary Clinton (Democrat) in the 2016 presidential election exhibited 14% less physical distancing between March and May 2020. Partisanship was more strongly associated with physical distancing than numerous factors, including counties’ median income, COVID-19 cases, population density, and racial and age demographics. Contrary to our predictions, the observed partisan gap strengthened over time and remained when stay-at-home orders were active. Additionally, county-level consumption of conservative media (Fox News) related to reduced physical distancing. Finally, the observed partisan differences in distancing were associated with subsequently higher COVID-19 infection and fatality growth rates in pro-Trump counties. Taken together, these data suggest that U.S. citizens’ responses to COVID-19 are subject to a deep—and consequential—partisan divide.

Transocean and the History of Tax Inversions

2018 ◽  
Vol 34 (1) ◽  
pp. 1-12
Author(s):  
Susan M. Albring ◽  
Randal J. Elder ◽  
Mitchell A. Franklin
Keyword(s):  
Multinational Corporations ◽  
Economic Impacts ◽  
Income Taxes ◽  
Tax Rates ◽  
Corporate Tax ◽  
Corporate Income Taxes ◽  
History Of ◽  
Jobs Act ◽  
The U.S ◽  
Over Time

ABSTRACT The first tax inversion in 1983 was followed by small waves of subsequent inversion activity, including two inversions completed by Transocean. Significant media and political attention focused on transactions made by U.S. multinational corporations that were primarily designed to reduce U.S. corporate income taxes. As a result, the U.S. government took several actions to limit inversion activity. The Tax Cuts and Jobs Act of 2017 (TCJA) significantly lowered U.S. corporate tax rates and one expected impact of TCJA is a reduction of inversion activity. Students use the Transocean inversions to understand the reasons why companies complete a tax inversion and how the U.S. tax code affects inversion activity. Students also learn about the structure of inversion transactions and how they have changed over time as the U.S. government attempted to limit them. Students also assess the tax and economic impacts of inversion transactions to evaluate tax policy.

Meddling in the Ballot Box

2020 ◽  
Author(s):  
Dov H. Levin
Keyword(s):  
Statistical Analysis ◽  
Theoretical Model ◽  
Preliminary Analysis ◽  
Great Powers ◽  
Archival Research ◽  
Micro Level ◽  
Election Results ◽  
Level Analysis ◽  
The U.S ◽  
Scholarly Attention

This book examines why partisan electoral interventions occur as well as their effects on the election results in countries in which the great powers intervened. A new dataset shows that the U.S. and the USSR/Russia have intervened in one out of every nine elections between 1946 and 2000 in other countries in order to help or hinder one of the candidates or parties; the Russian intervention in the 2016 U.S. elections is just the latest example. Nevertheless, electoral interventions receive scant scholarly attention. This book develops a new theoretical model to answer both questions. It argues that electoral interventions are usually “inside jobs,” occurring only if a significant domestic actor within the target wants it. Likewise, electoral interventions won’t happen unless the intervening country fears its interests are endangered by another significant party or candidate with very different and inflexible preferences. As for the effects it argues that such meddling usually gives a significant boost to the preferred side, with overt interventions being more effective than covert ones in this regard. However, unlike in later elections, electoral interventions in founding elections usually harm the aided side. A multi-method framework is used in order to study these questions, including in-depth archival research into six cases in which the U.S. seriously considered intervening, the statistical analysis of the aforementioned dataset (PEIG), and a micro-level analysis of election surveys from three intervention cases. It also includes a preliminary analysis of the Russian intervention in the 2016 U.S. elections and the cyber-future of such meddling in general.

Political Competition and Right-Wing Terrorism: A County-Level Analysis of the United States

2021 ◽  
pp. 106591292110067
Author(s):  
Stephen C. Nemeth ◽  
Holley E. Hansen
Keyword(s):  
Comparative Politics ◽  
Political Competition ◽  
The United States ◽  
County Level ◽  
Right Wing ◽  
Social Changes ◽  
Electoral Outcomes ◽  
Economic Anxiety ◽  
Political Economic ◽  
Level Analysis

While many previous studies on U.S. right-wing violence center on factors such as racial threat and economic anxiety, we draw from comparative politics research linking electoral dynamics to anti-minority violence. Furthermore, we argue that the causes of right-wing terrorism do not solely rest on political, economic, or social changes individually, but on their interaction. Using a geocoded, U.S. county-level analysis of right-wing terrorist incidents from 1970 to 2016, we find no evidence that poorer or more diverse counties are targets of right-wing terrorism. Rather, right-wing violence is more common in areas where “playing the ethnic card” makes strategic sense for elites looking to shift electoral outcomes: counties that are in electorally competitive areas and that are predominantly white.

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