Crop insurance participation and cover crop use: Evidence from Indiana county‐level data

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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Abstract P636: Social Determinants of Stroke Hospitalization and Mortality in the United States

Stroke ◽

10.1161/str.52.suppl_1.p636 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Randhir Sagar Yadav ◽

Durgesh Chaudhary ◽

Shima Shahjouei ◽

Jiang Li ◽

Vida Abedi ◽

...

Keyword(s):

United States ◽

Income Inequality ◽

Social Determinants ◽

Hemorrhagic Stroke ◽

The United States ◽

Care Physician ◽

County Level ◽

Medicare Beneficiaries ◽

Level Data ◽

Control And Prevention

Introduction: Stroke hospitalization and mortality are influenced by various social determinants. This ecological study aimed to determine the associations between social determinants and stroke hospitalization and outcome at county-level in the United States. Methods: County-level data were recorded from the Centers for Disease Control and Prevention as of January 7, 2020. We considered four outcomes: all-age (1) Ischemic and (2) Hemorrhagic stroke Death rates per 100,000 individuals (ID and HD respectively), and (3) Ischemic and (4) Hemorrhagic stroke Hospitalization rate per 1,000 Medicare beneficiaries (IH and HH respectively). Results: Data of 3,225 counties showed IH (12.5 ± 3.4) and ID (22.2 ± 5.1) were more frequent than HH (2.0 ± 0.4) and HD (9.8 ± 2.1). Income inequality as expressed by Gini Index was found to be 44.6% ± 3.6% and unemployment rate was 4.3% ± 1.5%. Only 29.8% of the counties had at least one hospital with neurological services. The uninsured rate was 11.0% ± 4.7% and people living within half a mile of a park was only 18.7% ± 17.6%. Age-adjusted obesity rate was 32.0% ± 4.5%. In regression models, age-adjusted obesity (OR for IH: 1.11; HH: 1.04) and number of hospitals with neurological services (IH: 1.40; HH: 1.50) showed an association with IH and HH. Age-adjusted obesity (ID: 1.16; HD: 1.11), unemployment (ID: 1.21; HD: 1.18) and income inequality (ID: 1.09; HD: 1.11) showed an association with ID and HD. Park access showed inverse associations with all four outcomes. Additionally, population per primary-care physician was associated with HH while number of pharmacy and uninsured rate were associated with ID. All associations and OR had p ≤0.04. Conclusion: Unemployment and income inequality are significantly associated with increased stroke mortality rates.

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Habit Formation in Voting: Evidence from Rainy Elections

American Economic Journal Applied Economics ◽

10.1257/app.20140533 ◽

2016 ◽

Vol 8 (4) ◽

pp. 160-188 ◽

Cited By ~ 35

Author(s):

Thomas Fujiwara ◽

Kyle Meng ◽

Tom Vogl

Keyword(s):

Voter Turnout ◽

Presidential Elections ◽

Habit Formation ◽

County Level ◽

Level Data ◽

Social Spillovers ◽

Consumption Value

We estimate habit formation in voting—the effect of past on current turnout—by exploiting transitory voting cost shocks. Using county-level data on US presidential elections from 1952–2012, we find that rainfall on current and past election days reduces voter turnout. Our estimates imply that a 1-point decrease in past turnout lowers current turnout by 0.6–1.0 points. Further analyses suggest that habit formation operates by reinforcing the direct consumption value of voting and that our estimates may be amplified by social spillovers. (JEL D72, D83, N42)

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Racial demographics explain the link between racial disparities in traffic stops and county-level racial attitudes

10.31234/osf.io/znhuf ◽

2021 ◽

Author(s):

Pierce D. Ekstrom ◽

Joel Michel Le Forestier ◽

Calvin K. Lai

Keyword(s):

Racial Disparities ◽

Racial Attitudes ◽

County Level ◽

White Americans ◽

Traffic Stops ◽

Arrest Rates ◽

Black And White ◽

Level Data ◽

Implicit And Explicit ◽

Racial Demographics

Disparities in the treatment of Black and White Americans in police stops are pernicious and widespread. We examine racial disparities in police traffic stops by leveraging data on traffic stops from hundreds of U.S. counties from the Stanford Open Policing Project and corresponding county-level data on implicit and explicit racial attitudes from the Project Implicit research website. We find that Black-White traffic stop disparities are associated with county-level implicit and explicit racial attitudes and that this association is attributable to racial demographics: counties with a higher proportion of White residents had larger racial disparities in police traffic stops. We also examined racial disparities in several post-stop outcomes (e.g., arrest rates) and found that they were not systematically related to racial attitudes, despite evidence of disparities. These findings indicate that racial disparities in counties’ traffic stops are reliably linked to counties’ racial attitudes and demographic compositions.

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The evolution of infant mortality inequality in the United States, 1960–2016

Science Advances ◽

10.1126/sciadv.aba5908 ◽

2020 ◽

Vol 6 (29) ◽

pp. eaba5908

Author(s):

Nick Turner ◽

Kaveh Danesh ◽

Kelsey Moran

Keyword(s):

United States ◽

Infant Mortality ◽

The United States ◽

Mortality Rates ◽

County Level ◽

Level Data ◽

Antipoverty Programs ◽

The Relationship ◽

Over Time ◽

Mortality Inequality

What is the relationship between infant mortality and poverty in the United States and how has it changed over time? We address this question by analyzing county-level data between 1960 and 2016. Our estimates suggest that level differences in mortality rates between the poorest and least poor counties decreased meaningfully between 1960 and 2000. Nearly three-quarters of the decrease occurred between 1960 and 1980, coincident with the introduction of antipoverty programs and improvements in medical care for infants. We estimate that declining inequality accounts for 18% of the national reduction in infant mortality between 1960 and 2000. However, we also find that level differences between the poorest and least poor counties remained constant between 2000 and 2016, suggesting an important role for policies that improve the health of infants in poor areas.

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Mapping Clovis: Projectile Points, Behavior, and Bias

American Antiquity ◽

10.7183/0002-7316.76.1.107 ◽

2011 ◽

Vol 76 (1) ◽

pp. 107-126 ◽

Cited By ~ 39

Author(s):

Mary M. Prasciunas

Keyword(s):

Spatial Patterning ◽

County Level ◽

Archaeological Research ◽

Projectile Point ◽

Point Distribution ◽

Projectile Points ◽

Level Data ◽

Colonization Process ◽

Modern Population ◽

Point Visibility

The distribution of Clovis projectile points across North America demonstrates distinct spatial patterning that has the potential to inform on many aspects of the colonization process. However, before accurate inferences regarding prehistoric behavior can be drawn from projectile point distributional databases, it is necessary to account for biases potentially affecting point visibility. Using county-level data for a sample of states from the western and southeastern U.S., this paper demonstrates that Clovis projectile point distribution is significantly related to modern population density, cultivated acreage, intensity of archaeological research, and measures of environmental productivity. Interpreting Clovis projectile point distribution is therefore more complex than frequently assumed.

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