scholarly journals The Influence of Metropolitan Statistical Areas on Direct-to-consumer Agricultural Sales of Local Food in the Northeast

2016 ◽  
Vol 45 (3) ◽  
pp. 539-562 ◽  
Author(s):  
Jeffrey K. O'Hara ◽  
Sarah A. Low
Keyword(s):  
United States ◽  
Panel Data ◽  
Income Elasticity ◽  
Metropolitan Areas ◽  
The United States ◽  
County Level ◽  
Ceteris Paribus ◽  
Metropolitan Statistical Areas ◽  
Direct To Consumer ◽  
Market Saturation

Direct-to-consumer (DTC) agricultural sales doubled in the United States between 1992 and 2007 and then plateaued between 2007 and 2012. It is not clear whether the plateau in sales was attributable to the recession, market saturation, an aging population, or other factors. We estimate the influence of socioeconomic factors in metropolitan areas on DTC agricultural sales between 1992 and 2012 in thirteen Northeast states using county-level panel data. We find that the income elasticity of DTC agricultural purchases ranged from 2.2 to 2.7 and that counties in metropolitan areas did not have higher DTC agricultural sales than other counties, ceteris paribus.

scholarly journals Spatial Clustering of County-Level COVID-19 Rates in the U.S

2021 ◽  
Vol 18 (22) ◽  
pp. 12170
Author(s):  
Marcus R. Andrews ◽  
Kosuke Tamura ◽  
Janae N. Best ◽  
Joniqua N. Ceasar ◽  
Kaylin G. Battey ◽  
...  
Keyword(s):  
United States ◽  
Health Disparities ◽  
Rural Areas ◽  
Metropolitan Areas ◽  
Spatial Clustering ◽  
The United States ◽  
County Level ◽  
Communities Of Color ◽  
States Data ◽  
The U.S

Despite the widespread prevalence of cases associated with the coronavirus disease 2019 (COVID-19) pandemic, little is known about the spatial clustering of COVID-19 in the United States. Data on COVID-19 cases were used to identify U.S. counties that have both high and low COVID-19 incident proportions and clusters. Our results suggest that there are a variety of sociodemographic variables that are associated with the severity of COVID-19 county-level incident proportions. As the pandemic evolved, communities of color were disproportionately impacted. Subsequently, it shifted from communities of color and metropolitan areas to rural areas in the U.S. Our final period showed limited differences in county characteristics, suggesting that COVID-19 infections were more widespread. The findings might address the systemic barriers and health disparities that may result in high incident proportions of COVID-19 clusters.

scholarly journals Differential Effects of Intervention Timing on COVID-19 Spread in the United States

2020 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Disease Transmission ◽  
Metropolitan Areas ◽  
Human Mobility ◽  
The United States ◽  
Aggressive Response ◽  
Control Measures ◽  
County Level ◽  
Social Distancing ◽  
Us Counties

Assessing the effects of early non-pharmaceutical interventions1-5 on COVID-19 spread in the United States is crucial for understanding and planning future control measures to combat the ongoing pandemic6-10. Here we use county-level observations of reported infections and deaths11, in conjunction with human mobility data12 and a metapopulation transmission model13,14, to quantify changes of disease transmission rates in US counties from March 15, 2020 to May 3, 2020. We find significant reductions of the basic reproductive numbers in major metropolitan areas in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same control measures been implemented just 1-2 weeks earlier, a substantial number of cases and deaths could have been averted. Specifically, nationwide, 61.6% [95% CI: 54.6%-67.7%] of reported infections and 55.0% [95% CI: 46.1%-62.2%] of reported deaths as of May 3, 2020 could have been avoided if the same control measures had been implemented just one week earlier. We also examine the effects of delays in re-implementing social distancing following a relaxation of control measures. A longer response time results in a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive response in controlling the COVID-19 pandemic.

scholarly journals Social Disadvantage, Politics, and Severe Acute Respiratory Syndrome Coronavirus 2 Trends: A County-level Analysis of United States Data

2020 ◽  
Author(s):  
Ahmad Mourad ◽  
Nicholas A Turner ◽  
Arthur W Baker ◽  
Nwora Lance Okeke ◽  
Shanti Narayanasamy ◽  
...  
Keyword(s):  
United States ◽  
Severe Acute Respiratory Syndrome ◽  
Metropolitan Areas ◽  
Belief Systems ◽  
The United States ◽  
Social Disadvantage ◽  
County Level ◽  
The Us ◽  
Black Residents ◽  
Social Demographic

Abstract Background Understanding the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for public health control efforts. Social, demographic, and political characteristics at the United States (US) county level might be associated with changes in SARS-CoV-2 case incidence. Methods We conducted a retrospective analysis of the relationship between the change in reported SARS-CoV-2 case counts at the US county level during 1 June–30 June 2020 and social, demographic, and political characteristics of the county. Results Of 3142 US counties, 1023 were included in the analysis: 678 (66.3%) had increasing and 345 (33.7%) nonincreasing SARS-CoV-2 case counts between 1 June and 30 June 2020. In bivariate analysis, counties with increasing case counts had a significantly higher Social Deprivation Index (median, 48 [interquartile range {IQR}, 24–72]) than counties with nonincreasing case counts (median, 40 [IQR, 19–66]; P = .009). Counties with increasing case counts were significantly more likely to be metropolitan areas of 250 000–1 million population (P < .001), to have a higher percentage of black residents (9% vs 6%; P = .013), and to have voted for the Republican presidential candidate in 2016 by a ≥10-point margin (P = .044). In the multivariable model, metropolitan areas of 250 000–1 million population, higher percentage of black residents, and a ≥10-point Republican victory were independently associated with increasing case counts. Conclusions Increasing case counts of SARS-CoV-2 in the US during June 2020 were associated with a combination of sociodemographic and political factors. Addressing social disadvantage and differential belief systems that may correspond with political alignment will play a critical role in pandemic control.

scholarly journals The Effects of Workforce Creativity on Earnings in U.S. Counties

2007 ◽  
Vol 36 (1) ◽  
pp. 71-83 ◽  
Author(s):  
Todd M. Gabe ◽  
Kristen Colby ◽  
Kathleen P. Bell
Keyword(s):  
United States ◽  
Metropolitan Areas ◽  
Descriptive Analysis ◽  
The United States ◽  
Wage Premium ◽  
County Level ◽  
Urban Context ◽  
Labor Earnings

This paper examines the effects of local workforce creativity on county-level earnings. Descriptive analysis of the data shows that most of the high-creativity counties in the United States are part of metropolitan areas, and that employee earnings are high in these places. Regression results indicate that, other things being equal, workforce creativity enhances county-level labor earnings. However, the returns to creativity that we found can be confirmed only in the urban context. An extension of the analysis suggests that the creative workforce wage premium may be capturing the effects of “technical workforce creativity” on earnings.

scholarly journals The Association of Opening K-12 Schools and Colleges with the Spread of COVID-19 in the United States: County-Level Panel Data Analysis

2021 ◽  
Author(s):  
Victor Chernozhukov ◽  
Hiroyuki Kasahara ◽  
Paul Schrimpf
Keyword(s):  
United States ◽  
Growth Rate ◽  
Panel Data ◽  
Structural Model ◽  
Panel Data Analysis ◽  
Positive Association ◽  
The United States ◽  
County Level ◽  
Subsequent Increase ◽  
K 12

AbstractThis paper empirically examines how the opening of K-12 schools and colleges is associated with the spread of COVID-19 using county-level panel data in the United States. Using data on foot traffic and K-12 school opening plans, we analyze how an increase in visits to schools and opening schools with different teaching methods (in-person, hybrid, and remote) is related to the 2-weeks forward growth rate of confirmed COVID-19 cases. Our debiased panel data regression analysis with a set of county dummies, interactions of state and week dummies, and other controls shows that an increase in visits to both K-12 schools and colleges is associated with a subsequent increase in case growth rates. The estimates indicate that fully opening K-12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the positive association of K-12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools. These results have a causal interpretation in a structural model with unobserved county and time confounders. Sensitivity analysis shows that the baseline results are robust to timing assumptions and alternative specifications.

scholarly journals Dynamic Panel Data Modeling and Surveillance of COVID-19 in Metropolitan Areas in the United States: Longitudinal Trend Analysis

10.2196/26081 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e26081
Author(s):  
Theresa B Oehmke ◽  
Lori A Post ◽  
Charles B Moss ◽  
Tariq Z Issa ◽  
Michael J Boctor ◽  
...  
Keyword(s):  
United States ◽  
Panel Data ◽  
Data Model ◽  
Metropolitan Areas ◽  
The United States ◽  
Dynamic Panel Data ◽  
Dynamic Panel ◽  
Dynamic Panel Data Model ◽  
Extreme Behavior ◽  
Second Wave

Background The COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence. Objective The goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level. Methods Using a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. Results Minneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week. Conclusions Three of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

scholarly journals The association of opening K–12 schools with the spread of COVID-19 in the United States: County-level panel data analysis

2021 ◽  
Vol 118 (42) ◽  
pp. e2103420118
Author(s):  
Victor Chernozhukov ◽  
Hiroyuki Kasahara ◽  
Paul Schrimpf
Keyword(s):  
United States ◽  
Panel Data ◽  
Growth Rates ◽  
Fixed Effects ◽  
Panel Data Analysis ◽  
The United States ◽  
Dynamic Panel Data ◽  
County Level ◽  
Past Infection ◽  
K 12

This paper empirically examines how the opening of K–12 schools is associated with the spread of COVID-19 using county-level panel data in the United States. As preliminary evidence, our event-study analysis indicates that cases and deaths in counties with in-person or hybrid opening relative to those with remote opening substantially increased after the school opening date, especially for counties without any mask mandate for staff. Our main analysis uses a dynamic panel data model for case and death growth rates, where we control for dynamically evolving mitigation policies, past infection levels, and additive county-level and state-week “fixed” effects. This analysis shows that an increase in visits to both K–12 schools and colleges is associated with a subsequent increase in case and death growth rates. The estimates indicate that fully opening K–12 schools with in-person learning is associated with a 5 (SE = 2) percentage points increase in the growth rate of cases. We also find that the association of K–12 school visits or in-person school openings with case growth is stronger for counties that do not require staff to wear masks at schools. These findings support policies that promote masking and other precautionary measures at schools and giving vaccine priority to education workers.

scholarly journals Perspectives from the Field: Adaptions in CSA Models in Response to Changing Times in the U.S.

2019 ◽  
Vol 11 (11) ◽  
pp. 3115 ◽  
Author(s):  
Diane Smith ◽  
Weiwei Wang ◽  
Lisa Chase ◽  
Hans Estrin ◽  
Julia Van Soelen Kim
Keyword(s):  
United States ◽  
The United States ◽  
Early Years ◽  
Community Supported Agriculture ◽  
Economic Factors ◽  
Direct To Consumer ◽  
Fresh Food ◽  
Consumer Markets ◽  
Changing Times ◽  
Market Saturation

Representing three states in the United States, the authors describe approaches and practices of direct-to-consumer markets from their combined experience of 40 plus years of working with Community Supported Agriculture (CSA), beginning in the early years of skepticism about the CSA model to the periods of rapid growth and optimism followed by today’s challenges regarding market saturation, competition from mainstream foods, complex logistics, and cultural disconnect. Through Cooperative Extension appointments in California, Vermont, and Washington, the authors have supported farmers as they have adopted CSA models and then adapted these models in response to changing consumer demand. This article examines the term and concept of CSA and how it has evolved in practice in different parts of the United States and at times been misused and co-opted for marketing purposes. We explore recent variations on the CSA model, including Farm Fresh Food Boxes (F3B), and discuss economic factors, marketing considerations, environmental stewardship, and community connections. The article concludes with projections for the future of CSA and the importance of maintaining authentic and beneficial relationships between farmers and consumers.

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