COVID-19: US shelter-in-place orders and demographic characteristics linked to cases, mortality, and recovery rates

Jillian Alderman (Pepperdine Graziadio Business School, Pepperdine University, Malibu, California, USA)
Maretno Harjoto (Pepperdine Graziadio Business School, Pepperdine University, Malibu, California, USA)

Transforming Government: People, Process and Policy

ISSN: 1750-6166

Article publication date: 26 November 2020

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Abstract

Purpose

This study aims to examine the relationship between the duration (in days) of states’ shelter-in-place orders; state demographic characteristics; and the rates of spread (cases), death (mortality), and recovery of COVID-19 in the USA.

Design/methodology/approach

State-level data across 50 states and Washington D.C. from January 23, 2020, to June 11, 2020, and a multivariate regression analysis were used to empirically investigate the impacts of the duration of shelter-in-place orders and state demographic characteristics on the rates of cases, mortality and recovery per capita of COVID-19.

Findings

This study finds that a longer duration of a shelter-in-place order is associated with lower cases and deaths per capita from COVID-19. This study also finds that demographic characteristics, such as the percentage of people who are unsheltered homeless, family size, percentage of individuals with health insurance, income inequality, unemployment rate, gender and race, are related to cases, mortality and recovery rates of COVID-19.

Social implications

This study offers policy implications for state and locality (e.g., city, region and country) lockdown decisions and salient demographics to consider curbing the spread and mortality rate of the COVID-19 pandemic. Study results are important to consider as the world braces for the anticipated resurgence of COVID-19.

Originality/value

This study reveals that the duration of shelter-in-place orders and demographics in states are related to the rates of spread, mortality, and recovery of COVID-19.

Keywords

Citation

Alderman, J. and Harjoto, M. (2020), "COVID-19: US shelter-in-place orders and demographic characteristics linked to cases, mortality, and recovery rates", Transforming Government: People, Process and Policy, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/TG-06-2020-0130

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited


1. Introduction

Initially identified in Wuhan, China, in late December 2019, the novel coronavirus (COVID-19) spread quickly throughout the world. On January 21, 2020, the US Centers for Disease Control and Prevention (CDC) reported the first case of COVID-19 in the USA (CDC, 2020a, January 21). On February 26, the CDC identified the first community spread of COVID-19 to a US resident who had not recently travelled to a foreign country (CDC, 2020b, February 26). On February 29, 2020, the first death due to COVID-19 was reported in the State of Washington (CDC, 2020c, February 29). During this period, the risk of COVID-19 to the American people was still considered relatively low.

Within 12 days, COVID-19 swiftly spread throughout the states. By March 12, 2020, 1,645 people from 47 states had tested positive for COVID-19. One day later, President Donald Trump declared the coronavirus pandemic to be a national emergency (The Whitehouse, 2020). A few weeks later, the US had the highest number of cases in the world (103,321 cases on March 29 according to WHO Situation Report 69) and highest number of deaths (20,444 deaths on April 13 according to WHO Situation Report 84) from COVID-19. Less than three months after the first case of COVID-19 was identified in the US, the government declared all 50 states under federal disaster due to COVID-19.

Initiated first by the State of California on March 19, 2020, several states implemented a lockdown or shelter-in-place order, limiting the number of people allowed to gather and placing travel restrictions on residents. By the second week of April 2020, all 50 US states and Washington D.C. had adopted some form of lockdown or shelter-in-place order (FINRA, 2020). However, the duration of shelter-in-place orders varied by state, and the political debate over the necessity and effectiveness of such orders has heightened. As the rates of daily cases and deaths across the states begin to level off, and states started to reopen, our timely study examines the effects of state shelter-in-place policy on spread (cases) and deaths (mortality) from COVID-19.

Using state-level data across 50 states and Washington D.C. from January 23, 2020, to June 11, 2020, we performed a multivariate regression analysis to empirically investigate the impact of the duration (in days) of shelter-in-place orders on the rates of cases, mortality and recovery per capita from COVID-19. In addition, we examined the relationship between state demographics (unsheltered homeless, family size, nursing homes, health insurance coverage, political leaning, religiosity, education level, unemployment rate, gender, and race) and rates of spread, mortality and recovery from COVID-19.

Answering the call for examining the role of government as the vanguard (Visvizi and Lytras, 2020), our study offers important policy implications to inform state and local government decisions to reduce the spread and mortality rate from COVID-19. In a sense, the independent lockdown decision by each state emulates an independent government authority (e.g. country) decision to implement a policy to curb the spread of the virus. Therefore, our study provides unique, relevant insights on the role of shelter-in-place orders duration, which can be applied not only to future policy-making decisions for viral outbreaks within the US, but could also have implications for other nations around the world facing similar challenges in limiting the spread of COVID-19 and future viral pandemics. As information and communication technology (ICT) advances, our study contributes to the growing literature by utilizing state-level demographic data to examine COVID-19 rates of spread and mortality. We find significant links between state demographics (unsheltered homeless, family size, percentage of individuals with health insurance, income inequality, unemployment rate, gender and race) and COVID-19 rates of spread, mortality and recovery. These results provide evidence on important factors to consider when assessing the risks and predicting the impact of future viral outbreaks in societies, which could aid policymakers in their efforts to minimize cases and deaths, and allocate resources.

2. Brief literature review and research questions

Existing literature has examined the pattern of epidemic spread, deaths, and recovery. Based on mathematical models, McKendrick (1912) examined the pattern of spread for malaria, and Ross (1916) and Ross and Hudson (1917) examined the rate of spread for epidemic diseases with various patterns and frequencies of outbreak (leprosy, measles, cholera, etc.). Lockdown and regional quarantine have been traditionally adopted to suppress the spread of epidemic infectious disease. Gensini et al. (2004) examined the adoption of quarantine as a health policy that has been widely instituted during pandemics from the 1377 Black Death Plague to the 2003 severe acute respiratory syndrome (SARS) outbreak. Research indicates that in the absence of an effective vaccine or cure, quarantine and shelter-in-place orders are the best preventive interventions to limit the spread of infectious disease (Peak et al., 2018).

Visvizi and Lytras (2020) argue that “while the nature of risks, threats and challenges that governments are exposed to today is qualitatively new and their scope unprecedented, a lot of governments’ capacity remains idle” (p. 333). Tognotti (2013) argues that the adoption of quarantine and shelter-in-place measures to control the spread of epidemic infectious diseases typically incites a controversy because such strategies raise political, ethical, and socioeconomic issues that require a careful balance between public interest and individual rights. The COVID-19 pandemic represents an unprecedented risk and decisions made on a state-by-state basis have led to a heated debate on the impact of government policy to adopt shelter-in-place or lockdown on saving lives versus saving the economy (Favero et al., 2020). The US Constitution, individual state constitutions, and centuries of legal precedent generally support states’ rights to implement policies that impact public health and safety (Davis, 2020). The 10th Amendment of the US Constitution indicates that “The powers not delegated to the USA by the Constitution, nor prohibited by it to the States, are reserved to the States respectively, or to the people”. Hence, each state adopted their own lockdown policies during the COVID-19 pandemic, with some states adopting shelter-in-place orders for a longer duration compared to others. This variation in duration of shelter-in-place orders and demographic characteristics among states creates a unique setting to examine the effectiveness of adopting shelter-in-place orders and the association of demographic variables on the spread, mortality and recovery rates of COVID-19.

Research on state government decisions to adopt the lockdown to reduce the spread of COVID-19 is developing rapidly. Hsiang et al. (2020) examined 1,700 local, regional, and national government policies across localities in China, South Korea, Italy, Iran, France and the USA, and find that anti-contagion policies significantly reduce the spread of COVID-19. Alvarez et al. (2020) examined the optimal lockdown and find that the absence of testing for COVID-19 increases the economic costs and causes the lockdown to end more abruptly. Palladino et al. (2020) find that a slower implementation of lockdown measures has caused significantly higher deaths and hospitalizations from COVID-19 in Italy. Conyon et al. (2020) examined the impact of lockdown in Denmark, Norway and Sweden and found that stricter lockdown policies in Denmark and Norway led to fewer deaths. Abouk and Heydari (2020) find that the statewide stay-at-home orders reduced social interactions and confirmed cases from COVID-19. Finally, a recent event study of county-level data links the timing of the adoption of social-distancing and shelter-in-place orders with reductions in COVID-19 cases in the USA (Courtemanche et al., 2020).

We explored the impact of shelter-in-place or stay-at-home orders on the infection, mortality, and recovery rates of COVID-19 across all states and Washington D.C. in the USA. We specifically asked the following research question:

RQ1.

Does the duration of shelter-in-place order affect the rates of spread (cases), mortality (deaths), and recovery of COVID-19?

Extant literature has also documented that regional demographics and political perspectives affect the spread of pandemic diseases. Abedi et al. (2020) find that variables such as education, income levels, and disability rates are positively associated with higher infection rates, while disability and poverty are positively related with higher mortality rates from COVID-19. Tellis, Sood, and Sood (2020) find that US state governors with a Democratic Party political leaning were more likely to adopt stay-at-home orders, and governors of states affected later by COVID-19 tended to act more quickly than those affected earlier. Barrios and Hochberg (2020) indicate that political leaning is significantly related to the perception of risk and the adoption of social distancing guidelines. Holm et al. (2020) and Painters and Qiu (2020) found that individuals with liberal and moderate political identities are more likely to adopt social distancing than conservatives.

Recent studies have also examined the impact of demographic factors such as race, gender, education, and homelessness on behavior during a pandemic. Bish and Michie (2010) found that gender and race are significantly related to the susceptibility and severity of the disease. Zhang et al. (2020) show that education level is positively related to willingness to self-isolate during the pandemic. Tsai and Wilson (2020) argue that people who are homeless are considered a vulnerable group for the spread of COVID-19 because they tend to live in congregate settings, they do not have regular access to basic hygiene, and they have pre-existing chronic physical and mental health conditions.

Several studies also examined the relationship between state characteristics (i.e. public health and unemployment rate) and COVID-19 cases and mortality rate. Benatia et al. (2020) found that three states (New York, New Jersey and Louisiana) have the highest infection rates of COVID-19. They concluded that the public health response to COVID-19 is dependent on the infection rate across different regions. Yilmazkuday (2020) find that the spread of COVID-19 in states is positively related to the unemployment rate.

Recent studies explored the relationship between race and confirmed cases and deaths from COVID-19. Mahajan and Larkin-Pettigrew (2020) find that racial minority groups (Asians and African Americans) are more positively related to confirmed cases and deaths from COVID-19. Shah et al. (2020) find that males, African Americans, and people with pre-existing health conditions are more likely to die from COVID-19.

We extend this literature by examining whether state demographic characteristics significantly relate to the rates of cases, mortality and recovery of COVID-19, and pose our second research question as the following:

RQ2.

Do state demographic characteristic (i.e., gender, race, political leaning, people who are homeless, etc.) affect the rates of spread (cases), mortality (deaths) and recovery of COVID-19?

3. Data, methodology and sample statistics

We investigated our two research questions using publicly available US data from the 50 states and Washington D.C. during the initial stage of COVID-19 from January 23, 2020, until June 11, 2020 (the period in which this article was initially written). Details on our exploration of these research questions are provided in this section, including descriptions of our dataset, analysis procedures, and sample statistics.

3.1 Data

We collected our data from various archival resources, utilizing the most recent data publicly available for our study. We began by collecting data on daily numbers of positive cases, deaths and recovery from the COVID-19 Tracking Project (2020), which started reporting this data publicly on January 23, 2020. We then collected data on the date when each state instituted a state-wide shelter-in-place order and the date when each state repealed the order, then calculated the duration of shelter-in place orders (Days Shelter-in-Plc). Four states did not explicitly announce a stay-at-home order (Arkansas, Iowa, North Dakota, and Nebraska). We assigned a zero value for the natural log of the length of shelter-in-place orders for these states. We use the natural log of the number of days of Shelter-in-Plc (LN(Shelter-in-Plc)) to reduce the skewness. Next, we collected the most recent available data on state-level demographics described in the Appendix.

Based on state population, we calculated the daily per capita or rate of cases (%Cases), deaths or mortality rate (%Deaths) and recovery rate (%Recovery) by dividing the daily numbers of cases, deaths, and recovery with corresponding state’s population data. We also calculated the change in daily per capita rates of cases (%ΔCases), deaths or mortality (%ΔDeaths), and recovery (%ΔRecovery) to measure any changes in the trends (rising or declining). After merging these various state-level data, our final sample consists of 4,938 observations from January 23, 2020, until June 11, 2020, across 50 states and Washington D.C.

3.2 Methodology

First, we provide descriptive statistics using charts to display daily trends of cases, mortality, and recovery rates from COVID-19. Since there are multiple factors that could affect the state-by-state rate of spread, mortality, and recovery of COVID-19 simultaneously, we used a multivariate regression that allowed us to specifically isolate the impact of the duration of shelter-in-place order on the rates of spread, mortality, and recovery of COVID-19 while holding the state demographics constant. Consistent with existing literature in epidemiology and public policies (Rothman et al., 2008; Spellman et al., 2010), we analyzed the association of state demographics with the rates of spread, mortality, and recovery of COVID-19 while holding the duration of shelter-in-place constant in a multivariate regression. We also used state-level clustering for the standard errors to account for the variations across different states.

3.3 Sample statistics

Figure 1 shows the trends in the average daily rates of cases, mortality, and recovery per capita across all 50 states and Washington D.C., from January 23, 2020, until June 11, 2020. We find overall that the daily rates of cases and mortality continued to rise, while the recovery rate shows a decline in the month of June. Figure 2 shows trends for changes in the daily rates of cases, mortality, and recovery across all 50 states and Washington D.C., during the same time period. Changes in daily cases and deaths per capita increased sharply in the month of March and continued to rise in the month of April. Changes in daily recovery rates per capita increased sharply in the month of May. At the end of the time period examined in this study, there is a sign of resurgence in the rate of change in daily cases per capita, while the change in daily recovery rates per capita seems to have been in decline nationally.

Figures 3–5 display the box plot of state-by-state rates of cases, deaths, and recovery. We find that New York, New Jersey, RI, and Massachusetts have significantly higher rates of cases, deaths, and recovery while Alaska, HI, and Montana have significantly lower rates of cases, deaths, and recovery from COVID-19.

Next, we analyzed average cases, mortality, and recovery rates on a state-by-state basis. Table 1 shows the averages of daily cases per capita (%Cases), daily deaths per capita or mortality rate (%Deaths), and daily recovery per capita (%Recovery) and averages across all 50 states and Washington D.C. are presented at the bottom of Table 1. New York and New Jersey have the highest averages of daily %Cases (1.118% and 0.982%) and %Deaths (0.0668% and 0.0627%), while Hawaii has the lowest daily averages of %Cases and %Deaths (0.032% and 0.0007%). Louisiana and Delaware have the highest averages of daily %Recovery (0.2775% and 0.1952%) while the State of Washington has the lowest average daily %Recovery (0.0023%).

California and Hawaii adopted the longest shelter-in-place orders while Arkansas and Wyoming had the shortest shelter-in-place orders (Shelter-in-Plc). California and Hawaii also have the highest percentages of unsheltered homeless while North Dakota and Wisconsin have the lowest percentages of unsheltered homeless (Unsheltered). Hawaii, UT, and California have the highest number of family size (FamilySize) while Maine and Vermont are the lowest. Iowa and South Dakota have the highest number of nursing homes per capita (NursingHome) while Mississippi is the lowest. Massachusetts and Vermont have the highest percentages of population with health insurance coverage (HealthInsured) while Texas represents the lowest. Washington D.C. has the highest percentage of population with Democratic political leaning (Democratic) while Wyoming represents the lowest. The rest of state-by-state characteristics are presented in part 2 of Table 1.

Mississippi and Alabama have the highest percentages of population that indicated they believe in God (Religious) while Massachusetts represents the lowest. Washington D.C. and Massachusetts have the largest percentages of population with bachelor degrees or higher (Bachelor+) while West Virgina represents the lowest. Washington D.C. and New York have the highest income inequality (Gini) index while Utah has the lowest. Nevada has the highest unemployment rate (Unemp) during the COVID-19 outbreak while Connecticut has the lowest. Washington D.C. and New Mexico have the highest proportions of women population (Women) while Arkansas and Texas have the lowest. Maine and Vermont have the highest proportions of Caucasians (White) population while Hawaii has the lowest. Washington D.C. and Mississippi have the highest proportions of African American population (AfricanAmer) while Montana has the lowest. Hawaii and California have the highest proportions of Asian population (Asian) while West Virginia has the lowest. Alaska and New Mexico have the highest proportions of American Indian and Alaska Native (AIAN) population while West Virginia has the lowest. Hawaii has the highest proportion of Native Hawaiian and Pacific Islander (NHPI) population while West Virginia has the lowest. Hawaii also has the highest proportion of multiracial population (Multirace) while Mississippi has the lowest.

Table 2 provides descriptive statistics of our overall sample in the US across 50 states and Washington D.C. On average, the daily %Cases, %Deaths and %Recovery are 0.238%, 0.012% and 0.058%, respectively. The average changes in daily %Cases, %Deaths, and %Recovery are 0.006%, 0.0003%, and 0.002%, respectively. On average, the states implemented shelter-in-place (Shelter-in-Plc) orders for 49.81 days. The average percentage of people who are homeless with no shelter (Unsheltered) is 34.5% of total homeless and the average family size (FamilySize) is approximately three individuals. The number of nursing home beds per capita (NursingHome) is 0.005 and 86% of individuals have health insurance coverage (HealthInsured).

On average, the states are 43.1% Democratic Party leaning (Democratic) and 39.2% of Republican Party. Approximately, 63.1% of the population indicated that they believe in God (Religious) and 30.7% of the population have a bachelor’s degree or higher (Bachelor+). The average income inequality (Gini) index is 0.468 and the average unemployment rate (Unemp) is 13.5%. Women represent over half of the population, and Caucasians (White) represent 78.1% of the population. African American (AfricanAmer), Asian (Asian), American Indian and Alaska Native (AIAN), Native Hawaiian and Pacific Islander (NHPI), and multiracial (Multirace) represent 11.8%, 4.6%, 2%, 0.4%, and 3.1% of the population, respectively.

4. Regression results

We further analyzed our data using multivariate ordinary least squares regression, with heteroscedasticity correction and state clustered standard errors. The first three columns of Table 3 present the multivariate regression results to examine the impact of shelter-in-place orders and state demographics on the daily per capita of the rate of cases (%Cases), deaths or mortality (%Deaths), and recovery (%Recovery). We include the one-day lag of the dependent variables (%Cases(t−1), %Deaths(t−1), and %Recovery(t−1)) to control for the trend and serial correlation on the dependent variables. We find that the natural log of the duration (in days) that the state instituted shelter-in-place reduces percentages of daily cases and mortality by 0.0019% and 0.0001% respectively, or approximately 1% of the means of percentages of daily cases and deaths per capita in our sample. This implies that states that adopted the shelter-in-place early, and maintained a longer duration of the shelter-in-place order, were able to reduce the spread and mortality rate per capita from COVID-19. Thus, we find empirical evidence to answer our first research question (RQ1), indicating that increasing the duration of a shelter-in-place order reduces the percentages of daily cases and mortality from COVID-19.

Further examination of state demographics indicates that a higher percentage of people who are unsheltered homeless (Unsheltered) significantly increases the rates of cases and mortality by 7.16% and 0.42% respectively. These magnitudes are quite significant, which implies that states need to consider providing shelter to people who are unsheltered homeless in order to reduce the spread and deaths from COVID-19. We also find that larger average household family sizes (FamilySize) are linked to higher daily rates of cases, mortality, and recovery. This finding is not surprising, since larger numbers of individuals in a household likely increase the chances of spread to other individuals residing in the same household. Interestingly, we find that states with a higher percentage of the population with health insurance coverage (HealthInsured) tend to have higher cases, mortality, and recovery rates. We believe this is due to the fact that individuals with health coverage have greater access to healthcare services to identify whether they are or are not infected by COVID-19. Hence, their cause of death is more clearly attributed to COVID-19 and they have a higher likelihood of receiving treatment to recover from COVID-19.

Furthermore, we find that states with a higher percentage of the population with higher education, measured as a bachelor’s degree or higher (Bachelor+), tend to have higher recovery rates. We also find that states with higher income inequality, measured by the Gini index, and a higher unemployment rate tend to have higher rates of cases and deaths. These findings are consistent with the existing literature that indicates a negative relationship between income inequality and health conditions (Pickett and Wilkonson, 2014; Subramanian and Kawachi, 2004), and a positive relationship between unemployment rate and poorer health conditions (Stewart, 2001). Results also indicate that the number of nursing homes (beds) per capita and religiosity do not significantly relate to cases, mortality, and recovery rates. States with Democratic political leaning and those with higher education tend to have a higher percentage of recovery rates.

Examining state demographics, we find that states with a higher percentage of Asian or AIAN populations tend to have higher rates of cases and deaths, while those with higher multiracial populations tend to have lower rates of cases and deaths relative to states with higher percentages of Caucasians. States with higher percentages of AIAN and NHPI tend to have a higher recovery rate, while states with higher percentages of women and multiracial populations tend to have lower recovery rate percentages. Overall, we find evidence that various state-level demographic factors are related to rates of spread, mortality, and recovery of COVID-19.

We further examine the impacts of shelter-in-place orders and state demographics on the changes in the trends of daily per capita rates of cases (Δ%Cases), mortality (Δ%Deaths), and recovery (Δ%Recovery). The results presented in the last three columns of Table 3 display similar outcomes as the first three columns of Table 3, with a few exceptions. We find that family size does not significantly affect the change in mortality rates (Δ%Deaths), while states with higher nursing homes per capita are positively related to higher changes in daily recovery rates (Δ%Recovery). We also find that states with higher percentages of religiosity have lower changes in the percentage of daily deaths per capita, while states with higher percentages of African Americans tend to be positively related with the change in recovery rates. Overall, we find evidence to support our second research question (RQ2) that state demographics are indeed significantly related to the rates of spread, mortality, and recovery of COVID-19.

We conducted a robustness test by excluding four states that did not clearly declare the stay-at-home orders (Arkansas, Iowa, North Dakota, and Nebraska) and re-ran all our regressions, and the results are similar to the results presented in Table 3. Some states also do not clearly report recovered patients and therefore we excluded those states and re-ran our regression, and our results in Table 3 remain robust. Since the rates of cases, mortality, and recovery started to rise in the month of March (indicated in Figures 1 and 2) and started to slow down in June, we also examined the subsample period from March until May, and our untabulated results are consistent with the results presented in Table 3.

5. Conclusions

COVID-19 has brought swift and devastating effects on health and has taken more than one millions lives around the world. The US has the highest number of cases and deaths from COVID-19. Using US state level data, our study poses two research questions to examine whether the duration of state shelter-in-place orders and state demographic characteristics are related to the rates of spread, mortality, and recovery of COVID-19.

In contrast to Tellis et al. (2020), our findings indicate that states that instituted a shelter-in-place order for the longest duration were able to reduce the daily rate of spread and mortality rate from COVID-19. As countries around the world are scrambling to avoid a second lockdown (Mueller and Specia, 2020; Toua, 2020), our findings suggest that state and local government authorities should consider adopting shelter-in-place policies longer in the event of a second wave or a rampant rate of increase in COVID-19 cases. Furthermore, we find that higher percentages of unsheltered homeless are linked to significantly large magnitudes of daily rates of cases and deaths. This finding suggests that at-risk states and localities with high percentages of unsheltered homeless could focus on providing temporary shelter to people who are homeless to reduce COVID-19 spread and mortality rates. In contrast to extant studies (Holm et al., 2020; Painters and Qiu, 2020), we find that a state’s political leaning does not significantly relate to daily cases and deaths. Our finding is also consistent with the fact that recent increases in COVID-19 cases have occurred in states with a Democratic political leaning (e.g. California) and a Republican political leaning (e.g. Florida and Texas).

Our study also finds that income inequality (Gini), unemployment, health insurance, gender and race are related to cases, mortality and recovery rates of COVID-19. We believe that our study contributes to the literature, offers policy implications and adds to the ongoing debate examining how demographics impact the rates of spread, mortality, and recovery of COVID-19. As states in the US and countries around the world have reopened, our study provides a greater understanding of salient demographic characteristics to consider when attempting to curb COVID-19 rates of spread and mortality, and sparks further government policy debates at all levels regarding stay-at-home orders. Although the world may have been unprepared for the first wave of COVID-19, these findings are important to consider as the US and the rest of the world brace for the second wave or a return of rampant rates of increase in COVID-19 cases and deaths.

Figures

Daily trends for %Cases, %Deaths, and %Recovery from COVID-19 (national-level data)

Figure 1.

Daily trends for %Cases, %Deaths, and %Recovery from COVID-19 (national-level data)

Daily Trends for changes in %Cases, %Deaths, and %Recovery from COVID-19 (national-level data)

Figure 2.

Daily Trends for changes in %Cases, %Deaths, and %Recovery from COVID-19 (national-level data)

State-by-state box plot for %Cases

Figure 3.

State-by-state box plot for %Cases

State-by-state box plot for %Deaths

Figure 4.

State-by-state box plot for %Deaths

State-by-state box plot for %Recovery

Figure 5.

State-by-state box plot for %Recovery

State-by-State descriptive statistics

State State Name Obs. %Cases %Deaths %Recovery Shelter-in-Plc Unsheltered FamilySize NursingHome HealthInsured Democratic
AK Alaska 94 0.038 0.001 0.025 28 0.364 3.39 0.002 0.810 0.32
AL Alabama 94 0.150 0.005 0.046 27 0.222 3.21 0.005 0.852 0.35
AR Arkansas 95 0.102 0.002 0.064 0 0.441 3.1 0.008 0.816 0.38
AZ Arizona 99 0.116 0.005 0.024 46 0.610 3.29 0.002 0.820 0.39
CA California 96 0.117 0.004 0.041 134 2.618 3.56 0.003 0.821 0.49
CO Colorado 96 0.213 0.011 0.027 32 0.353 3.16 0.004 0.864 0.42
CT Connecticut 95 0.602 0.051 0.077 61 0.127 3.12 0.006 0.919 0.5
DC Washington DC 96 0.560 0.028 0.082 60 0.861 3.38 0.003 0.921 0.73
DE Delaware 94 0.434 0.016 0.195 70 0.078 3.17 0.005 0.891 0.55
FL Florida 98 0.130 0.005 0.023 33 0.527 3.31 0.003 0.785 0.44
GA Georgia 99 0.209 0.009 0.032 28 0.353 3.31 0.003 0.808 0.41
HI Hawaii 94 0.032 0.001 0.025 130 2.250 3.61 0.003 0.923 0.51
IA Iowa 96 0.249 0.006 0.127 0 0.057 2.99 0.014 0.898 0.4
ID Idaho 89 0.086 0.002 0.049 36 0.383 3.24 0.004 0.841 0.32
IL Illinois 98 0.401 0.018 0.023 70 0.142 3.21 0.006 0.864 0.48
IN Indiana 97 0.237 0.014 0.045 56 0.091 3.09 0.008 0.866 0.37
KS Kansas 93 0.136 0.003 0.007 36 0.157 3.11 0.009 0.874 0.31
KY Kentucky 90 0.094 0.004 0.031 99 0.161 3.06 0.006 0.843 0.43
LA Louisiana 96 0.488 0.032 0.278 54 0.205 3.3 0.006 0.816 0.43
MA Massachusetts 91 0.754 0.048 0.024 76 0.119 3.16 0.006 0.959 0.56
MD Maryland 97 0.364 0.018 0.025 44 0.201 3.27 0.004 0.876 0.55
ME Maine 95 0.080 0.003 0.048 61 0.068 2.85 0.007 0.905 0.47
MI Michigan 101 0.359 0.030 0.126 70 0.060 3.268 0.004 0.891 0.47
MN Minnesota 96 0.154 0.007 0.106 54 0.257 3.09 0.007 0.917 0.46
MO Missouri 96 0.108 0.005 0.025 30 0.144 3.05 0.008 0.867 0.42
MS Mississippi 96 0.227 0.010 0.120 26 0.163 3.27 0.0001 0.879 0.42
MT Montana 96 0.032 0.001 0.024 32 0.272 3.0 0.007 0.818 0.3
NC North Carolina 98 0.106 0.003 0.044 54 0.189 3.1 0.004 0.828 0.43
ND North Dakota 96 0.142 0.003 0.090 0 0.004 2.95 0.010 0.886 0.33
NE Nebraska 96 0.264 0.003 0.039 0 0.055 3.05 0.011 0.868 0.36
NH New Hampshire 97 0.147 0.007 0.073 81 0.107 3.03 0.005 0.880 0.44
NJ New Jersey 98 0.982 0.063 0.113 80 0.164 3.27 0.004 0.860 0.51
NM New Mexico 94 0.155 0.006 0.048 69 0.568 3.28 0.004 0.781 0.48
NV Nevada 98 0.139 0.006 0.005 38 1.220 3.33 0.002 0.765 0.46
NY New York 99 1.118 0.067 0.184 80 0.208 3.24 0.003 0.887 0.53
OH Ohio 97 0.135 0.008 0.021 59 0.119 3.05 0.008 0.877 0.4
OK Oklahoma 94 0.079 0.004 0.057 29 0.280 3.16 0.008 0.828 0.4
OR Oregon 98 0.051 0.002 0.015 52 1.935 3.05 0.003 0.846 0.47
PA Pennsylvania 96 0.283 0.018 0.082 64 0.125 3.04 0.005 0.880 0.46
RI Rhode Island 101 0.615 0.025 0.045 39 0.063 3.19 0.008 0.876 0.48
SC South Carolina 97 0.105 0.004 0.054 28 0.280 3.18 0.004 0.858 0.39
SD South Dakota 92 0.244 0.003 0.168 36 0.250 3.05 0.012 0.856 0.37
TN Tennessee 95 0.152 0.003 0.085 30 0.310 3.13 0.005 0.861 0.36
TX Texas 96 0.094 0.002 0.052 30 0.378 3.5 0.004 0.754 0.4
UT Utah 96 0.142 0.001 0.070 35 0.126 3.61 0.003 0.856 0.3
VA Virginia 96 0.202 0.006 0.026 66 0.097 3.18 0.003 0.875 0.39
VT Vermont 97 0.107 0.006 0.051 52 0.179 2.88 0.006 0.930 0.57
WA Washington 141 0.124 0.006 0.029 69 1.167 3.09 0.003 0.864 0.44
WI Wisconsin 99 0.128 0.005 0.063 50 0.048 2.97 0.006 0.903 0.42
WV West Virginia 97 0.053 0.002 0.029 42 0.128 2.99 0.007 0.854 0.41
WY Wyoming 93 0.076 0.001 0.052 15 0.187 3.05 0.007 0.848 0.25
State State Name Obs. Religious Bachelor+ Gini Unemp Women White AfricanAmer Asian AIAN NHPI Multirace
AK Alaska 94 0.55 0.29 0.432 0.129 0.482 0.651 0.038 0.067 0.155 0.014 0.075
AL Alabama 94 0.82 0.245 0.486 0.129 0.510 0.695 0.265 0.015 0.007 0.001 0.017
AR Arkansas 95 0.77 0.22 0.485 0.102 0.497 0.792 0.157 0.017 0.010 0.004 0.022
AZ Arizona 99 0.62 0.284 0.461 0.126 0.495 0.830 0.050 0.036 0.052 0.003 0.028
CA California 96 0.54 0.326 0.491 0.155 0.506 0.718 0.066 0.154 0.016 0.005 0.040
CO Colorado 96 0.55 0.394 0.456 0.113 0.486 0.872 0.045 0.034 0.016 0.002 0.031
CT Connecticut 95 0.54 0.384 0.501 0.079 0.505 0.800 0.120 0.049 0.006 0.001 0.024
DC Washington DC 96 0.55 0.566 0.524 0.111 0.523 0.459 0.462 0.044 0.006 0.001 0.028
DE Delaware 94 0.61 0.31 0.459 0.143 0.513 0.697 0.228 0.041 0.007 0.001 0.027
FL Florida 98 0.64 0.285 0.489 0.129 0.512 0.775 0.168 0.029 0.005 0.001 0.022
GA Georgia 99 0.74 0.299 0.482 0.119 0.509 0.609 0.320 0.043 0.005 0.001 0.021
HI Hawaii 94 0.62 0.32 0.445 0.223 0.501 0.254 0.022 0.378 0.004 0.102 0.241
IA Iowa 96 0.66 0.277 0.441 0.102 0.507 0.907 0.040 0.027 0.005 0.001 0.019
ID Idaho 89 0.62 0.268 0.445 0.115 0.490 0.932 0.009 0.016 0.017 0.002 0.025
IL Illinois 98 0.61 0.334 0.485 0.164 0.513 0.764 0.150 0.059 0.006 0.001 0.020
IN Indiana 97 0.63 0.253 0.451 0.169 0.505 0.852 0.098 0.025 0.004 0.001 0.021
KS Kansas 93 0.66 0.323 0.463 0.112 0.515 0.864 0.061 0.031 0.012 0.001 0.030
KY Kentucky 90 0.75 0.232 0.479 0.154 0.515 0.876 0.084 0.016 0.003 0.001 0.020
LA Louisiana 96 0.75 0.234 0.494 0.145 0.516 0.634 0.323 0.018 0.008 0.001 0.017
MA Massachusetts 91 0.4 0.421 0.488 0.151 0.522 0.808 0.090 0.071 0.005 0.001 0.025
MD Maryland 97 0.64 0.39 0.454 0.099 0.513 0.583 0.314 0.067 0.006 0.001 0.029
ME Maine 95 0.48 0.303 0.452 0.106 0.508 0.946 0.016 0.012 0.007 0.0003 0.018
MI Michigan 101 0.63 0.281 0.468 0.227 0.511 0.793 0.140 0.034 0.007 0.0004 0.025
MN Minnesota 96 0.56 0.348 0.454 0.081 0.496 0.842 0.067 0.051 0.014 0.001 0.025
MO Missouri 96 0.7 0.282 0.466 0.097 0.505 0.830 0.118 0.021 0.006 0.002 0.023
MS Mississippi 96 0.82 0.213 0.483 0.154 0.5 0.603 0.367 0.011 0.006 0.001 0.013
MT Montana 96 0.64 0.307 0.454 0.113 0.494 0.890 0.006 0.009 0.066 0.001 0.028
NC North Carolina 98 0.73 0.299 0.478 0.122 0.505 0.710 0.219 0.031 0.016 0.001 0.023
ND North Dakota 96 0.64 0.289 0.443 0.085 0.486 0.871 0.034 0.018 0.055 0.001 0.022
NE Nebraska 96 0.66 0.306 0.449 0.083 0.499 0.883 0.051 0.027 0.015 0.001 0.023
NH New Hampshire 97 0.43 0.36 0.453 0.163 0.503 0.932 0.017 0.030 0.003 0.0004 0.017
NJ New Jersey 98 0.6 0.381 0.484 0.153 0.518 0.724 0.146 0.100 0.006 0.001 0.023
NM New Mexico 94 0.63 0.269 0.489 0.113 0.525 0.820 0.026 0.018 0.109 0.002 0.026
NV Nevada 98 0.59 0.237 0.469 0.282 0.487 0.747 0.099 0.086 0.017 0.008 0.044
NY New York 99 0.56 0.353 0.513 0.145 0.514 0.695 0.175 0.093 0.010 0.001 0.026
OH Ohio 97 0.67 0.272 0.467 0.168 0.513 0.820 0.128 0.025 0.003 0.001 0.023
OK Oklahoma 94 0.71 0.248 0.469 0.137 0.505 0.743 0.078 0.023 0.093 0.002 0.062
OR Oregon 98 0.57 0.323 0.458 0.142 0.498 0.868 0.022 0.048 0.018 0.005 0.039
PA Pennsylvania 96 0.61 0.301 0.475 0.151 0.508 0.820 0.117 0.037 0.004 0.001 0.021
RI Rhode Island 101 0.6 0.33 0.47 0.17 0.513 0.839 0.083 0.036 0.011 0.002 0.028
SC South Carolina 97 0.74 0.27 0.476 0.121 0.505 0.685 0.272 0.017 0.005 0.001 0.019
SD South Dakota 92 0.69 0.278 0.445 0.102 0.494 0.845 0.024 0.017 0.090 0.001 0.024
TN Tennessee 95 0.78 0.261 0.478 0.147 0.513 0.780 0.176 0.019 0.005 0.001 0.019
TX Texas 96 0.69 0.287 0.482 0.128 0.483 0.789 0.128 0.052 0.010 0.001 0.020
UT Utah 96 0.61 0.325 0.427 0.097 0.499 0.908 0.014 0.027 0.015 0.010 0.026
VA Virginia 96 0.67 0.376 0.475 0.106 0.504 0.694 0.199 0.069 0.005 0.001 0.031
VT Vermont 97 0.41 0.368 0.447 0.156 0.508 0.942 0.014 0.020 0.004 0.0004 0.020
WA Washington 141 0.55 0.345 0.457 0.154 0.499 0.791 0.042 0.092 0.019 0.008 0.047
WI Wisconsin 99 0.56 0.29 0.448 0.141 0.498 0.871 0.067 0.030 0.012 0.001 0.020
WV West Virginia 97 0.77 0.199 0.474 0.152 0.509 0.934 0.036 0.009 0.003 0.0003 0.018
WY Wyoming 93 0.66 0.267 0.456 0.092 0.489 0.926 0.013 0.011 0.027 0.001 0.022

Descriptive statistics for all states

Variable Obs. Mean SD Minimum Maximum
%Cases 4,938 0.238 0.346 0 1.958
%Deaths 4,938 0.012 0.022 0 0.140
%Recovery 4,938 0.058 0.107 0 0.729
Δ%Cases 4,938 0.006 0.009 0 0.110
Δ%Deaths 4,938 0.0003 0.001 0 0.008
Δ%Recovery 4,938 0.002 0.011 0 0.349
Shelter-in-Plc 4,938 49.81 26.64 0 134
Unsheltered 4,938 0.345 0.224 0.008 0.957
FamilySize 4,938 3.175 0.167 2.85 3.61
NursingHome 4,938 0.005 0.003 0.0005 0.017
HealthInsured 4,938 0.860 0.042 0.754 0.959
Democratic 4,938 0.431 0.082 0.25 0.73
Religious 4,938 0.631 0.094 0.40 0.82
Bachelor+ 4,938 0.307 0.061 0.199 0.566
Gini 4,938 0.468 0.020 0.427 0.524
Unemp 4,938 0.135 0.038 0.079 0.282
Women 4,938 0.504 0.010 0.482 0.525
White 4,938 0.781 0.128 0.259 0.942
AfricanAmer 4,938 0.118 0.105 0.006 0.462
Asian 4,938 0.046 0.054 0.009 0.378
AIAN 4,938 0.020 0.030 0.003 0.155
NHPI 4,938 0.004 0.014 0.000 0.102
Multirace 4,938 0.031 0.031 0.013 0.241

Multivariate regression

%Cases %Deaths %Recovery Δ%Cases Δ%Deaths Δ%Recovery
%Cases(t−1) 0.9688 (387.68)***
%Deaths(t−1) 0.9729 (279.35)***
%Recovery(t−1) 0.9596 (228.40)***
Δ%Cases(t−1) 0.5253 (13.13)***
Δ%Deaths(t−1) 0.5679 (12.91)***
Δ%Recovery(t−1) 0.0285 (1.69)*
LN(Shelter-in-Plc) −0.0019 (2.82)*** −0.0001 (1.69)* 0.0000 (0.03) −0.0005 (2.84)*** −0.00001 (1.84)* 0.0001 (0.39)
Unsheltered 7.1634 (2.24)** 0.4167 (2.11)** 0.5540 (0.66) 1.9308 (2.27)** 0.1382 (2.37)** 0.2910 (0.52)
FamilySize 0.0108 (1.79)* 0.0006 (1.80)* 0.0042 (2.26)** 0.0037 (2.13)** 0.0002 (1.64) 0.0031 (2.21)**
NursingHome −0.0485 (0.13) −0.0121 (0.58) 0.1437 (0.70) 0.1496 (1.47) 0.0053 (0.84) 0.2592 (1.96)*
HealthInsured 0.0979 (3.70)*** 0.0067 (4.20)*** 0.0381 (3.59)*** 0.0190 (2.75)*** 0.0016 (2.67)** 0.0251 (3.24)***
Democratic 0.0042 (0.40) −0.0001 (0.12) 0.0111 (1.90)* 0.0028 (0.83) 0.0001 (0.34) 0.0066 (1.52)
Religious −1.8521 (1.02) −0.1808 (1.57) −0.5073 (0.45) −0.7346 (1.55) −0.0922 (2.24)** −0.6990 (0.96)
Bachelor+ 0.0041 (0.24) −0.0004 (0.32) 0.0250 (3.47)*** 0.0038 (1.00) −0.0000 (0.13) 0.0182 (3.37)***
Gini 0.1922 (4.67)*** 0.0142 (5.45)*** 0.0094 (0.38) 0.0403 (3.00)*** 0.0041 (3.82)*** −0.0072 (0.39)
Unemp 0.0441 (2.23)** 0.0023 (1.99)* 0.0002 (0.03) 0.0075 (1.47) 0.0005 (1.40) 0.0000 (0.00)
Women 0.0292 (0.41) −0.0000 (0.01) −0.0708 (2.97)*** 0.0107 (0.51) −0.0003 (0.20) −0.0452 (2.56)**
AfricanAmer −0.0090 (0.96) −0.0010 (1.61) 0.0089 (1.57) 0.0010 (0.48) −0.0001 (0.95) 0.0073 (1.94)*
Asian 0.1368 (2.50)** 0.0067 (1.90)* 0.0061 (0.31) 0.0395 (2.66)** 0.0019 (1.76)* 0.0067 (0.52)
AIAN 0.0934 (3.26)*** 0.0048 (3.02)*** 0.0385 (3.06)*** 0.0254 (3.14)*** 0.0014 (2.94)*** 0.0277 (2.96)***
NHPI 0.0290 (0.15) −0.0034 (0.27) 0.1768 (1.71)* 0.0234 (0.55) 0.0001 (0.04) 0.1441 (1.93)*
Multirace −0.2377 (2.74)*** −0.0095 (1.81)* −0.1104 (2.47)** −0.0757 (3.57)*** −0.0031 (2.08)** −0.0930 (2.77)***
Intercept −0.2309 (4.81)*** −0.0145 (4.80)*** −0.0102 (0.54) −0.0553 (4.60)*** −0.0040 (4.66)*** −0.0008 (0.06)
Observations 4938 4938 4938 4887 4887 4887
R-squared 0.9595 0.9653 0.9290 0.4641 0.5174 0.1416
Notes:

***;

**, and

*represent statistically significant at 1%, 5% and 10%, respectively. The number of observations for the Δ%Cases, Δ%Deaths and Δ%Recovery regression are 51 less because we use the lag of the changes (Δ%Cases(t−1), Δ%Deaths(t−1), and Δ%Recovery(t−1)) as control variables in the last three columns of Table 3

Data sources

Data Sources
#Cases, #Deaths, #Recovered The COVID-19 Tracking Project https://covidtracking.com/data/us-daily
Population by state US Census Bureau www2.census.gov/programs-surveys/popest/tables/2010-2019/state/totals/nst-est2019-01.xlsx
Shelter-in-place order FINRA www.finra.org/rules-guidance/key-topics/covid-19/shelter-in-place
#Homeless unsheltered HUD Exchange www.hudexchange.info/resources/documents/2007-2019-HIC-Counts-by-State.xlsx
#Family size US Census Bureau https://data.census.gov/cedsci/table?q=household&hidePreview=false&tid=ACSDP1Y2018.DP02&t=Household%20and%20Family&vintage=2018
#Nursing home Kaiser Family Foundation (KFF) www.kff.org/other/state-indicator/number-of-nursing-facilities/ and Skilled Nursing Facilities (SNF) data www.snfdata.com/state_statistics.html
%Individuals with health insurance US Census Bureau www.census.gov/content/dam/Census/library/publications/2019/demo/p60-267.pdf
Political leaning
(Democratic)
PEW Research Center www.pewforum.org/religious-landscape-study/compare/party-affiliation/by/st
Religious PEW Religious Landscape Study www.pewforum.org/religious-landscape-study/state/
Education attainment
(Bachelor+)
US Census Bureau www.census.gov/data/tables/2019/demo/educational-attainment/cps-detailed-tables.html
Income inequality (Gini) US Census Bureau www.census.gov/library/publications/2019/acs/acsbr18-01.html
Unemployment rate US Bureau of Labor Statistics www.bls.gov/web/laus/laumstrk.htm#laumstrk.f.p
Gender and race US Census Bureau www2.census.gov/programs-surveys/popest/tables/2010-2019/state/detail/SCPRC-EST2019-18+POP-RES.xlsx

Appendix

Table A1

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Lyu, W. and Wehby, G.L. (2020), “Community use of face masks and COVID-19: evidence from a natural experiment of state mandates in the US”, Health Affairs, Vol. 39 No. 8, pp. 1419-1425.

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World Health Organization (WHO) (2020b), “Coronavirus disease 2019 (COVID-19) situation report – 84”, 13 April, available at: www.who.int/docs/default-source/coronaviruse/situation-reports/20200413-sitrep-84-covid-19.pdf?sfvrsn=44f511ab_2

Acknowledgements

The authors acknowledge and thank anonymous reviewers for their constructive comments and recommendations. They thank the Guest Editor, Anna Visvizi, for her comments, recommendations and support. Harjoto acknowledges the financial support and release time from the 2019–2021 Denney Academic Chair Endowment at Pepperdine Graziadio Business School for financial support and release time for this research project. They also thank Rebecca Dettorre, MA, for her editing support. All remaining errors are their own. Authors declare that they have no conflict of interest.

Corresponding author

Maretno Harjoto can be contacted at: maretno.harjoto@pepperdine.edu

About the authors

Jillian Alderman is an Assistant Professor of Accounting at Pepperdine University Graziadio Business School. Jillian’s research interests involve the study of human behavior and decision-making, with a focus on topics such as accounting and financial reporting policies, fraud prevention and detection, investors' use of financial statements, auditor liability and professional ethics. Jillian has published in the Contemporary Accounting Research, Advances in Accounting, Journal of Information Systems, Current Issues in Auditing, International Journal of Accounting Information Systems, Accounting, Auditing and Accountability Journal and Journal of Accountancy.

Maretno Harjoto is a Professor of Finance at Pepperdine University Graziadio Business School. His research focuses on corporate governance, board diversity, corporate social responsibility and the impacts of COVID-19. He has published in various journals, such as Journal of Business Research, Journal of Banking and Finance, Financial Management, Journal of Corporate Finance, Journal of Business Ethics, Corporate Governance: The International Journal of Business in Society, Business Ethics: The European Review and Advances in Accounting.