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 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 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 The Effect of Stay-at-Home Orders on COVID-19 Infections in the United States

2020 ◽  
Author(s):  
James H. Fowler ◽  
Seth J. Hill ◽  
Remy Levin ◽  
Nick Obradovich
Keyword(s):  
United States ◽  
Temporal Variation ◽  
Fixed Effects ◽  
National Level ◽  
The United States ◽  
Spatial And Temporal Variation ◽  
Full Potential ◽  
County Level ◽  
Fixed Effects Model ◽  
At Home

SummaryBackgroundIn March and April 2020, public health authorities in the United States acted to mitigate transmission of and hospitalizations from the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). These actions were not coordinated at the national level, which raises the question of what might have happened if they were. It also creates an opportunity to use spatial and temporal variation to measure their effect with greater accuracy.MethodsWe combine publicly available data sources on the timing of stay-at-home orders and daily confirmed COVID-19 cases at the county level in the United States (N = 124,027). We then derive from the classic SIR model a two-way fixed-effects model and apply it to the data with controls for unmeasured differences between counties and over time. This enables us to estimate the effect of stay-at-home orders while accounting for local variation in factors like health systems and demographics, and temporal variation in national mitigation actions, access to tests, or exposure to media reports that could influence the course of the disease.FindingsMean county-level daily growth in COVID-19 infections peaked at 17.2% just before stay-at-home orders were issued. Two way fixed-effects regression estimates suggest that orders were associated with a 3.9 percentage point (95% CI 1.2 to 6.6) reduction in the growth rate after one week and a 6.9 percentage point (2.4 to 11.5) reduction after two weeks. By day 27 the reduction (22.6 percentage points, 14.8 to 30.5) had surpassed the growth at the peak, indicating that growth had turned negative and the number of new daily infections was beginning to decline. A hypothetical national stay-at-home order issued on March 13, 2020 when a national emergency was declared might have reduced cumulative infections by 63.3%, and might have helped to reverse exponential growth in the disease by April 10.InterpretationAlthough stay-at-home orders impose great costs to society, delayed responses and piecemeal application of these orders generate similar costs without obtaining the full potential benefits suggested by this analysis. The results here suggest that a coordinated nationwide stay-at-home order might have reduced by hundreds of thousands the current number of infections and by tens of thousands the total number of deaths from COVID-19. Future efforts in the United States and elsewhere to control pandemics should coordinate stay-at-home orders at the national level, especially for diseases for which local spread has already occurred and testing availability is delayed. Since stay-at-home orders reduce infection growth rates, early implementation when infection counts are still low would be most beneficial.FundingNone.

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