scholarly journals The Immediate Effect of COVID-19 Policies on Social Distancing Behavior in the United States

2020 ◽  
Author(s):  
Rahi Abouk ◽  
Babak Heydari
Keyword(s):  
Social Interactions ◽  
Public Awareness ◽  
State Level ◽  
The United States ◽  
Social Distancing ◽  
Level Data ◽  
Causal Impact ◽  
Testing Practices ◽  
Policy Mechanisms ◽  
At Home

Anecdotal evidence points to the effectiveness of COVID-19 social distancing policies, however, their effectiveness vis-a-vis what is driven by public awareness and voluntary actions have not been studied. Policy variations across US states create a natural experiment to study the causal impact of each policy. Using a difference-in-differences methodology, location-based mobility, and daily state-level data on COVID-19 tests and confirmed cases, we rank policies based on their effectiveness. We show that statewide stay-at-home orders had the strongest causal impact on reducing social interactions. In contrast, most of the expected impact of more lenient policies were already reaped from non-policy mechanisms. Moreover, stay-at-home policy results in a steady decline in confirmed cases, starting from ten days after implementation and reaching a 37% decrease after fifteen days, consistent with the testing practices and incubation period of the disease.

scholarly journals The Immediate Effect of COVID-19 Policies on Social-Distancing Behavior in the United States

2021 ◽  
Vol 136 (2) ◽  
pp. 245-252 ◽  
Author(s):  
Rahi Abouk ◽  
Babak Heydari
Keyword(s):  
United States ◽  
Early Stage ◽  
Public Awareness ◽  
The United States ◽  
School Closure ◽  
Grocery Stores ◽  
Social Distancing ◽  
Percentage Points ◽  
Before And After ◽  
At Home

Objective Although anecdotal evidence indicates the effectiveness of coronavirus disease 2019 (COVID-19) social-distancing policies, their effectiveness in relation to what is driven by public awareness and voluntary actions needs to be determined. We evaluated the effectiveness of the 6 most common social-distancing policies in the United States (statewide stay-at-home orders, limited stay-at-home orders, nonessential business closures, bans on large gatherings, school closure mandates, and limits on restaurants and bars) during the early stage of the pandemic. Methods We applied difference-in-differences and event-study methodologies to evaluate the effect of the 6 social-distancing policies on Google-released aggregated, anonymized daily location data on movement trends over time by state for all 50 states and the District of Columbia in 6 location categories: retail and recreation, grocery stores and pharmacies, parks, transit stations, workplaces, and residences. We compared the outcome of interest in states that adopted COVID-19–related policies with states that did not adopt such policies, before and after these policies took effect during February 15–April 25, 2020. Results Statewide stay-at-home orders had the strongest effect on reducing out-of-home mobility and increased the time people spent at home by an estimated 2.5 percentage points (15.2%) from before to after policies took effect. Limits on restaurants and bars ranked second and resulted in an increase in presence at home by an estimated 1.4 percentage points (8.5%). The other 4 policies did not significantly reduce mobility. Conclusion Statewide stay-at-home orders and limits on bars and restaurants were most closely linked to reduced mobility in the early stages of the COVID-19 pandemic, whereas the potential benefits of other such policies may have already been reaped from voluntary social distancing. Further research is needed to understand how the effect of social-distancing policies changes as voluntary social distancing wanes during later stages of a pandemic.

The Rise of the Commuter Student: Changing Patterns of College Attendance for Students Living at Home in the United States, 1960–1980

2011 ◽  
Vol 113 (5) ◽  
pp. 1031-1066
Author(s):  
Dongbin Kim ◽  
John L. Rury
Keyword(s):  
Higher Education ◽  
United States ◽  
College Students ◽  
State Level ◽  
The United States ◽  
Commuter Students ◽  
Individual Level ◽  
Level Data ◽  
The Individual ◽  
At Home

Background/Context American higher education witnessed rapid expansion between 1960 and 1980, as colleges and universities welcomed millions of new students. The proportion of 19- and 20-year-old students living in dormitories, rooming houses, or other group quarters fell from more than 40% to slightly less than a third. At the same time, the proportion of students in this age group living at home with one or two parents increased from about 35% to nearly 47%, becoming the largest segment of the entering collegiate population in terms of residential alternatives. While growing numbers of high school graduates each fall headed off to campus dormitories, even more enrolled in commuter institutions close to home, gaining their initial collegiate experience in circumstances that may not have differed very much from what they had experienced in secondary school. The increased numbers of commuter students, whether they attended two-year or four-year institutions, however, have received little attention from historians and other social scientists. Purpose/Objective/Research Question/Focus of Study This study focuses on students aged 19 and 20 who lived with parents and commuted from home during the years from 1960 to 1980, when commuters became the largest category of beginning college students. It also addresses the question of how this large-scale change affected the social and economic profile of commuter students in the United States. In this regard, this study can be considered an evaluation of policy decisions intended to widen access to postsecondary institutions. Did the growing number of students living at home represent a democratic impulse in higher education, a widening of access to include groups of students who had previously been excluded from college? The study approaches this question by examining changes in the characteristics and behavior of commuter students across the country. Recognizing the variation in enrollment rates and other educational indices by state or region, this study also focuses on how the individual behavior at the point of college entry is affected by these and other characteristics of the larger social setting, particularly from a historical perspective. Research Design To grasp the larger picture of historical trends in college enrollment during the period of study, particularly in the growth of commuter students, the first part of the study utilizes state-level data and identifies changes in the number of entering college students who were commuters. In the process, descriptive statistics and ordinary least squares regression are used to identify factors associated with the proportion of college students living with their parents across states. In the second stage of analysis, hierarchical generalized linear modeling, utilizing both state- and individual-level data, is used to consider different layers of contextual effects on individual decisions to enroll in college. Data Collection and Analysis At the individual level, the principal sources of information are from 1% Integrated Public Use Microdata Samples (IPUMS) for 1960 and 1980. These are individual-level census data that permit consideration of a wide range of variables, including college enrollment. State-level variables are drawn from the published decennial census volumes, from National Center for Education Statistics reports on the number of higher education institutions, and from aggregated IPUMS data. Conclusions/Recommendations This study finds that commuter students in the United States appear to have benefited from greater institutional availability, the decline of manufacturing, continued urbanization, and a general expansion of the middle class that occurred across the period in question. It was a time of growth for this sector of the collegiate population. Despite rhetoric about wider access to postsecondary education during the period, however, the nation's colleges appear to have continued to serve a relatively affluent population, even in commuter institutions. Although making postsecondary institutions accessible to commuter students may have improved access in some circumstances, for most American youth, going to college appears to have remained a solidly middle- and upper-class phenomenon.

scholarly journals Human mobility trends during the early stage of the COVID-19 pandemic in the United States

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241468
Author(s):  
Minha Lee ◽  
Jun Zhao ◽  
Qianqian Sun ◽  
Yixuan Pan ◽  
Weiyi Zhou ◽  
...  
Keyword(s):  
Large Scale ◽  
Early Stage ◽  
Human Mobility ◽  
Public Awareness ◽  
The United States ◽  
Health Crisis ◽  
Social Distancing ◽  
Tangible Evidence ◽  
The Government ◽  
At Home

In March of this year, COVID-19 was declared a pandemic, and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. from January 2020 to early April 2020. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations and teleworking trends regarding the pandemic propagation and the non-pharmaceutical mobility interventions. All metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states before the stay-at-home mandates implemented and becomes more stable after the order with a smaller range of fluctuation. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. As the estimated teleworking rates also continue to incline throughout the study period, the teleworking trend can be another driving factor for the growing stay-at-home population. We confirm that there exists overall mobility heterogeneity between the income or population density groups. The study suggests that public mobility trends are in line with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.

scholarly journals State-level Stay-at-home Orders and Objectively Measured Movement in the United States During the COVID-19 Pandemic

2020 ◽  
Author(s):  
Kyle J. Bourassa
Keyword(s):  
Public Health ◽  
United States ◽  
Public Health Intervention ◽  
State Level ◽  
Large Degree ◽  
The United States ◽  
Total Change ◽  
Social Distancing ◽  
Objectively Measured ◽  
At Home

Objective: Social distancing has been one of the primary interventions used to slow the spread of COVID-19. State-wide stay-at-home orders received a large degree of attention as a public health intervention to increase social distancing, but relatively little peer-reviewed research has examined the extent to which stay-at-home orders affected people’s behavior. Method: This study used GPS-derived movement from 2,858 counties in the United States from March 1 to May 7, 2020 to test the degree to which changes in state-level stay-at-home orders were associated with movement outside the home. Results: From the first week of March to the first week of April, people in counties within states that enacted stay-at-home orders decreased their movement significantly more than people in counties within states that did not enact state-level stay-at-home orders. From the first week of April to the first week of May, people in counties within states that ended their stay-at-home orders increased their movement significantly more than people in counties within states whose stay-at-home orders remained in place. The magnitude of change in movement associated with state-level stay-at-home orders was many times smaller than the total change in movement across all counties over the same periods of time in both cases. Conclusions: Stay-at-home orders are likely insufficient to reduce people’s movement outside the home without additional public health actions. Existing research on behavior change would be useful to determine what additional interventions could support social distancing behaviors during the COVID-19 pandemic if becomes necessary to reduce movement in the future.

scholarly journals That Woman from Michigan: How Gender Resentment Shapes the Efficacy of Stay-at-Home Policies

2020 ◽  
Vol 16 (4) ◽  
pp. 983-990
Author(s):  
Nicole Kalaf-Hughes ◽  
Debra Leiter
Keyword(s):  
Government Policy ◽  
The United States ◽  
Election Study ◽  
Female Leaders ◽  
Political Leaders ◽  
Social Distancing ◽  
National Election Study ◽  
American National Election Study ◽  
Using Data ◽  
At Home

AbstractThe United States has the highest number of COVID-19 cases, yet many Americans have responded indifferently toward policies designed to combat the spread of the virus. While nearly all 50 states have implemented some type of stay-at-home policy to encourage social distancing, there has been high variation in the degree of compliance. We argue that this variance is partly driven by gender resentment. Gender resentment reduces trust in female political leaders and thus decreases compliance with government policy and recommendations. Using data from SafeGraph and the 2016 American National Election Study, we demonstrate that the effect of stay-at-home policies on social distancing is reduced when gender resentment increases in states with female leaders. However, when gender resentment is low, there is no difference in the effect of policies on behavior. This research has important implications for understanding unseen barriers that can mediate the efficacy of female political leaders.

Distribution and capacity utilization in the United States: evidence from state-level data

2020 ◽  
Vol 8 (2) ◽  
pp. 240-267
Author(s):  
Luke Petach
Keyword(s):  
United States ◽  
Distribution System ◽  
Capacity Utilization ◽  
State Level ◽  
The United States ◽  
Coordination Failure ◽  
Wage Policy ◽  
Us States ◽  
Level Data ◽  
Strategy I

Applying previously unused regional data to the problem of wage- versus profit-led growth, this paper estimates a demand-and-distribution system for a panel of US states for the years 1974 to 2014. Using variation in minimum-wage policy across states as an instrument for the labor share, I find that – at a regional level – the United States is strongly wage-led. In the absence of a satisfactory econometric identification strategy, I estimate the distributive curve non-parametrically. The results suggest the presence of significant non-linearities, with US states exhibiting profit-squeeze dynamics at low levels of capacity utilization and wage-squeeze dynamics at high levels. These results suggest difficulties for wage-led policy akin to a coordination failure.

scholarly journals The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets (Preprint)

2020 ◽  
Author(s):  
Paiheng Xu ◽  
Mark Dredze ◽  
David A Broniatowski
Keyword(s):  
United States ◽  
South Carolina ◽  
Social Mobility ◽  
State Level ◽  
The United States ◽  
Mobility Index ◽  
Social Distancing ◽  
Mobility Data ◽  
Response Planning ◽  
Twitter Data

BACKGROUND Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and “flattens the curve” so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. OBJECTIVE The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. METHODS We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. RESULTS We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. CONCLUSIONS We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.

scholarly journals Predictive Modeling on the Number of Covid-19 Death Toll in the United States Considering the Effects of Coronavirus-Related Changes and Covid-19 Recovered Cases

2020 ◽  
Vol 5 (6) ◽  
pp. 1140-1155 ◽  
Author(s):  
Hoang Pham
Keyword(s):  
United States ◽  
United Kingdom ◽  
Policy Decision ◽  
The United States ◽  
Social Distancing ◽  
Death Toll ◽  
The United Kingdom ◽  
First Case ◽  
Staying At Home ◽  
At Home

COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University. With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: • How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way? In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavirus cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study. The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020. The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020. The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.

scholarly journals The COVID States Project #26: Trajectory of COVID-19-related behaviors

2021 ◽  
Author(s):  
David Lazer ◽  
Mauricio Santillana ◽  
Roy H. Perlis ◽  
Alexi Quintana ◽  
Katherine Ognyanova ◽  
...  
Keyword(s):  
State Level ◽  
The United States ◽  
Contact Tracing ◽  
Upper Midwest ◽  
Social Distancing ◽  
Current State ◽  
Change Behavior ◽  
Current Prevalence ◽  
The Us ◽  
National Trends

The current state of the COVID-19 pandemic in the United States is dire, with circumstances in the Upper Midwest particularly grim. In contrast, multiple countries around the world have shown that temporary changes in human behavior and consistent precautions, such as effective testing, contact tracing, and isolation, can slow transmission of COVID-19, allowing local economies to remain open and societal activities to approach normalcy as of today. These include island countries such as New Zealand, Taiwan, Iceland and Australia, and continental countries such as Norway, Uruguay, Thailand, Finland, and South Korea. These successes demonstrate that coordinated action to change behavior can control the pandemic. In this report, we evaluate how the human behaviors that have been shown to inhibit the spread of COVID-19 have evolved across the US since April, 2020.Our report is based on surveys that the COVID States Project has been conducting approximately every month since April in all 50 US states plus the District of Columbia. We address four primary questions:1) What are the national trends in social distancing behaviors and mask wearing since April?2) What are the trends among particular population subsets?3) What are the trends across individual states plus DC?4) What is the relationship, at the state level, between social distancing behaviors and mask wearing with the current prevalence of COVID-19?

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