Mental Wellbeing and Economics of Crime: A County Level Analysis in 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.

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

Revista Panamericana de Salud Pública ◽

10.26633/rpsp.2020.90 ◽

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.

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

F1000Research ◽

10.12688/f1000research.23903.1 ◽

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.

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

10.1101/2020.04.24.20078204 ◽

2020 ◽

Cited By ~ 1

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.

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Meddling in the Ballot Box

10.1093/oso/9780197519882.001.0001 ◽

2020 ◽

Cited By ~ 1

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.

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

Political Research Quarterly ◽

10.1177/10659129211006791 ◽

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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COVID-19 Vaccination Rates Vary By Community Vulnerability: A County-level Analysis

Vaccine ◽

10.1016/j.vaccine.2021.06.038 ◽

2021 ◽

Author(s):

Clare C. Brown ◽

Sean G. Young ◽

George C. Pro

Keyword(s):

County Level ◽

Community Vulnerability ◽

Vaccination Rates ◽

Level Analysis

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Timing of social distancing policies and COVID-19 mortality: county-level evidence from the U.S.

Journal of Population Economics ◽

10.1007/s00148-021-00845-2 ◽

2021 ◽

Author(s):

Catalina Amuedo-Dorantes ◽

Neeraj Kaushal ◽

Ashley N. Muchow

Keyword(s):

New York ◽

Political Ideology ◽

Death Rate ◽

The United States ◽

County Level ◽

Level Data ◽

Level Information ◽

Alternative Measures ◽

The U.S ◽

Pharmaceutical Interventions

AbstractUsing county-level data on COVID-19 mortality and infections, along with county-level information on the adoption of non-pharmaceutical interventions (NPIs), we examine how the speed of NPI adoption affected COVID-19 mortality in the United States. Our estimates suggest that adopting safer-at-home orders or non-essential business closures 1 day before infections double can curtail the COVID-19 death rate by 1.9%. This finding proves robust to alternative measures of NPI adoption speed, model specifications that control for testing, other NPIs, and mobility and across various samples (national, the Northeast, excluding New York, and excluding the Northeast). We also find that the adoption speed of NPIs is associated with lower infections and is unrelated to non-COVID deaths, suggesting these measures slowed contagion. Finally, NPI adoption speed appears to have been less effective in Republican counties, suggesting that political ideology might have compromised their efficacy.

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