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Over the past thirty years, disaster scholars have highlighted that communities with stronger social infrastructure - including social ties that enable trust, mutual aid, and collective action - tend to respond to and recover better from crisis. However, comprehensive measurements of social capital across communities have been rare. This study adapts Kyne and Aldrich’s (2019) county-level social capital index to the census-tract level, generating social capital indices from 2011 to 2018 at the census-tract, zipcode, and county subdivision levels. To demonstrate their usefulness to disaster planners, public health experts, and local officials, we paired these with the CDC’s Social Vulnerability Index to predict the incidence of COVID-19 in case studies in Massachusetts, Wisconsin, and Illinois. We found that social capital and social vulnerability predicted as much as 95% of the variation in COVID outbreaks, highlighting their power as diagnostic and predictive tools for combating the spread of COVID.
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Won't You Be My Neighbor?
Uncovering ties between Social Capital and COVID-19 Outcomes at Local Levels
Timothy Fraser
1
, Courtney Page-Tan
2
, & Daniel P. Aldrich
3
1. Timothy Fraser, PhD Candidate, Political Science Department, Northeastern University
Address: 960A Renaissance Park, 360 Huntington Avenue, Boston, MA 02115-5000 USA
Email: timothy.fraser.1@gmail.com
ORCID: 0000-0002-4509-0244
* Corresponding author
2. Courtney Page-Tan (Ph.D.), Assistant Professor of Human Resilience, Security and
Emergency Services Department, Embry-Riddle Aeronautical University
Address: Embry–Riddle Aeronautical University Global Campus, Daytona Beach, FL
Email: courtneypagetan@gmail.com
ORCID: 0000-0002-3584-3484
3. Daniel P. Aldrich (Ph.D.), Professor of Political Science, Public Policy and Urban Affairs and
Director of Security and Resilience Program, Northeastern University
Address: Dept. of Political Science, 215H Renaissance Park, 360 Huntington Avenue, Boston
MA 02115
Email: daniel.aldrich@gmail.com
ORCID: 0000-0002-4150-995X
Competing Interests: The authors declare no competing interests.
Correspondence: Correspondence and requests for materials should be addressed to Timothy
Fraser at timothy.fraser.1@gmail.com
Data availability: All code necessary for replicating this study will be made available for
replication on the Harvard Dataverse ( https://doi.org/10.7910/DVN/OSVCRC ).
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Won't You Be My Neighbor?
Uncovering ties between Social Capital and COVID-19 Outcomes at Local Levels
Abstract: Over the past thirty years, disaster scholars have highlighted that communities with
stronger social infrastructure - including social ties that enable trust, mutual aid, and collective
action - tend to respond to and recover better from crisis. However, comprehensive
measurements of social capital across communities have been rare. This study adapts Kyne and
Aldrich’s (2019) county-level social capital index to the census-tract level, generating social
capital indices from 2011 to 2018 at the census-tract, zipcode, and county subdivision levels. To
demonstrate their usefulness to disaster planners, public health experts, and local officials, we
paired these with the CDC’s Social Vulnerability Index to predict the incidence of COVID-19 in
case studies in Massachusetts, Wisconsin, and Illinois. We found that social capital and social
vulnerability predicted as much as 95% of the variation in COVID outbreaks, highlighting their
power as diagnostic and predictive tools for combating the spread of COVID.
Keywords : social capital; social vulnerability; disaster; resilience; COVID-19; index
1. Introduction
Why do some communities experience greater outbreaks of COVID-19? As of the time
of writing, COVID-19 has led to at least 27 million cases and 455,000 known deaths in the US,
and over 2.16 million deaths worldwide (Johns Hopkins 2021; Allen et al. 2021). Past studies
highlighted that pandemic outcomes, like many disaster resilience outcomes, depend on more
than the capacity of governments and health care workers to respond (Schoenbaum et al. 2011;
Farag et al. 2012) and the social vulnerability of residents by race, class, age, and gender (Cutter
et al. 2003; Yancy 2020; Wadhera et al. 2020; Chin-Hong et al. 2020). Crises outcomes correlate
with the strength of community resources such as social infrastructure accessible to members
(Borgonovi et al. 2020; Borgonovi & Andrieu 2020; Varshney & Socher 2020, Jean-Baptiste et
al. 2020). Social capital, the social ties that enable trust, reciprocity, and collective action
(Putnam 2000), serves as a key resource residents can draw upon before, during, and after crisis
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to ensure that they obtain mutual aid from family, friends, and neighbors and also gain access to
key public or semi-public goods from lawmakers (Szreter & Woolcock 2004, Kawachi et al.
2008). Past scholars measured social capital using regional level responses from the Global
Social Survey (Lee & Fraser 2019), custom neighborhood surveys in disaster zones (Iwasaki et
al. 2017), and aggregate measures at the county level (Kyne & Aldrich 2019). However, the
impact of COVID-19 varies widely by neighborhood. Scholars and local officials need a clearer
measure of social ties at increasingly hyperlocal levels.
This study introduces new measures for social capital for every census tract, zipcode, and
county subdivision in the United States from 2010 to 2018. These indices characterize the
strength of bonding, bridging, and linking social capital, as well as overall levels of social
capital. Rather than reinventing the wheel, these measures draw directly from the methodology
of Kyne and Aldrich’s (2019) validated county level Social Capital Indices, extending it to the
census tract level. Then, we average these census tract measures up to the zipcode and county
subdivision level to create broad coverage for measuring social capital. Finally, we apply these
indices to predict COVID-19 outcomes in different regions of the United States, showing
remarkably high associations with COVID test positivity rates.
This study makes three main contributions to the literature. First, we extend Kyne &
Aldrich’s (2019) county level measures of social capital to the census tract, zipcode, and county
subdivision, enabling comparison of tens of thousands of communities in terms of bonding,
bridging, and linking social ties. This adds an important resource to scholars and policymakers
involved in disaster and pandemic response efforts, easily paired with the CDC’s Social
Vulnerability Index (Flanagan et al. 2018) and the Baseline Resilience Indicators (BRIC, Cutter
et al. 2010).
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Second, we find that these local level measures of social capital are closely correlated
with a key diagnostic measure of COVID spread, the percentage of COVID-19 tests returned
positive. Our models accounted for up to 90% of the variation in COVID spread. This builds on
recent research that suggests that social capital helps residents adopt new behavioral norms like
social distancing and masking and reduce COVID spread (Borgonovi et al. 2020; Borgonovi &
Andrieu 2020; Varshney & Socher 2020, Jean-Baptiste et al. 2020), while verifying it with a
measure that adjusts for testing capacity.
Third, we find that social capital has divergent effects depending on the type of social
capital present in a community and the local context of each city or region. First, each kind of
social capital index produces strong, significant associations with test positivity rates in multiple
cases, and bonding social capital is linked to reduced COVID spread in regions at large,
matching past studies (Borgonovi et al. 2020; Varshney & Socher 2020; Fraser, Page-Tan &
Aldrich 2020). However, in specific cities the effect of bonding social capital frequently changes:
communities with strong bonding ties promote COVID spread through insular social networks,
while strong bridging ties help reduce that spread in urban environments. This matches results
from disaster studies, which highlight that while bonding social capital can help some
communities, bridging ties aid most communities by fostering trust, reciprocity, and pro-social
behavioral changes across different racial, ethnic, religious, and political lines (Hawkins &
Maurer 2010, Aldrich 2012, Aldrich 2019).
2. Literature Review
Since the COVID-19 pandemic reached the US in early 2020 (or arguably earlier
(Basavaraju et al. 2020)), local, state, and federal government agencies have made tracking key
COVID-19 outcomes a top priority, including case rates, death rates, test positivity rates, excess
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deaths rates, and vaccination rates. States governments across the country have digitized
measurements of these new pandemic resilience outcomes, creating online dashboards for health
officials to more easily communicate with policymakers, the press, and the public, and to
engender greater trust and transparency in crisis information flow. While these dashboards
provoked early pushback from state government officials in denial about the scope of the crisis,
they have become powerful tools for highlighting hotspots of vulnerability, as it became clear
quickly that communities of color, working class neighborhoods, front line workers in crowded
areas, the aged, and residents with pre-existing conditions faced high health or financial risks
from COVID. While some dashboards (see for example this Alabama dashboard; Samford
University 2021), embraced the CDC's Social Vulnerability Index, other key indicators of
disaster resilience have been conspicuously absent in pandemic planning dashboards and data
monitoring. Below, we review past explanations from disaster scholarship for pandemic
resilience, including mobility, health care capacity, quality of health, governance capacity,
political partisanship, and social capital.
First, some communities might see higher rates of COVID spread due to greater
population mobility . While past crises, like SARS and the Avian flu (Bowen & Laroe 2006;
Smallman-Raynor & Clif 2006), highlighted that travel can spread viruses across borders, this
was complicated in the ongoing pandemic by the fact that many persons infected with COVID
remain asymptomatic (Lavezzo et al. 2020). Among symptomatic patients, 95% of individuals
manifest symptoms between 2-11 days after contact with COVID, with a median of 5 days (Guan
et al. 2020; Li et al. 2020; Lauer et al. 2020). Recent studies of COVID spread have shown that
lockdowns, compulsory (May 2020) or not (Yabe et al. 2020), greatly reduced mobility and
likely altered the course of the epidemic.
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Second, communities with better staffed and funded health care systems might respond
more adroitly to the pandemic. The capacity of health care systems, measured by better hospital
quality and larger physician workforces (Baicker & Chandra 2004; Goodman & Grumbach
2008), has been linked to better response quality both before (Schoenbaum et al. 2011) and
during COVID (Miller et al. 2020). Similarly, more capable governments which can effectively
allocate resources, purchase appropriate protective gear, manage information flow, enforce
lockdowns, and ensure high shares of residents with health insurance may see better health and
pandemic outcomes (May 2020; Bonaccorsi et al. 2020; Bollyky et al. 2019). However, some
health care systems have struggled more than others due to the overall quality of health of the
population. Diabetes, heart conditions, obesity, and smoking all rank among many common
comorbidities of COVID-19 (Richardson et al. 2020).
Third, communities may see different mobility patterns depending on their levels of
political partisanship . Several notable republican national and state election officials consistently
downplayed the COVID-19 pandemic, despite multiple superspreader events at the White House
in 2020 (Rosenberg 2020). These sentiments played out among the general public: By mid-July
2020, just 45% of Republican voters ranked COVID-19 as a major threat to the country
compared to 85% of Democrats (Tyson 2020). Multiple studies have highlighted that Republican
elected officials and voters have been less likely to adopt social distancing and other COVID-19
prevention measures than their Democratic peers (Grossman et al. 2020; Painter & Qiu 2020;
Clinton et al. 2020).
Fourth, some communities see worse resilience to crisis due to their levels of social
vulnerability (Cutter et al. 2003). In both pandemic and disaster studies, socially vulnerable
residents fare worse in terms of resilience to crisis due to employment challenges, limited
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mobility, and the fear of discrimination or retribution from neighbors or local authorities. Groups
more socially vulnerable to disaster including women and single parents (Enarson 1998),
families facing unemployment or poverty (Deng et al. 2020), the elderly (Salvati et al. 2018), the
LGBTQ+ community (Dominey-Howes et al. 2016), the disabled (Uscher-Pines et al. 2009), and
racial, religious, or ethnic minorities (Fussell et al. 2009). During COVID, Black Americans
have overwhelmingly felt a higher burden (Chin-Hong et al. 2020; Karaye et al. 2020; Wadhera
et al. 2020; Yancy 2020), with 1 in 1000 African Americans dead from COVID (Peck 2020) at
the same time as crushing financial loss, with over 40% of Black-owned businesses in the US
closed permanently this past year (da Costa 2020).
Finally, some communities, even facing high social vulnerability or weak health care
response, have managed better pandemic outcomes than others due to their social capital . Social
capital - the social ties that bind together a community and enable trust, reciprocity, and
collective action (Putnam 2000) - serves as a key resource for residents responding to disaster
(Aldrich 2012), closely correlated with health outcomes (Kawachi et al. 2008) and recovery
outcomes after crisis (Iwasaki et al. 2019; Page-Tan 2021). Recent survey and aggregator level
research has demonstrated that residents and communities with stronger social capital were more
likely to socially distance (Barrios et al. 2020), see fewer cases and fatalities (Borgonovi &
Andrieu 2020, Borgonovi et al. 2020), and slower spread of COVID-19 (Varshney & Socher
2020). However, disaster studies acknowledge the Janus-faced nature of social capital, in which
social capital can generate both positive and negative outcomes depending on the type of social
ties fosters (Aldrich et al. 2018). Indeed, recent studies of social capital highlight that vertical
social ties to government officials are especially key, relative to others (Fraser & Aldrich 2021;
Fraser, Aldrich, & Page-Tan 2020).
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Social capital comes in three forms: bonding, bridging, and linking social capital (Aldrich
& Meyer 2015). Bonding social capital refers to close ties between members of the same social
circles, facilitating trust and mutual aid among friends and family members (Cox et al. 2011;
Hawkins & Maurer 2010). Bridging social capital describes association ties between members of
different social groups, built through workplaces (Granovetter 1973), unions (Norris et al. 2008),
volunteering (Lee & Fraser 2019), sports clubs (Putnam 2000), and local associations (Smiley et
al. 2018). Bridging ties are the lifeblood of democracy, helping residents build shared stake in
their community and enabling close cooperation during crisis (Aldrich 2012). Finally, linking
social capital refers to vertical ties connecting residents to local, state, and national authorities
(Szreter & Woolcock 2004). These linking ties help residents access key public goods from
elected officials and instill trust in government (Tsai 2007, Aldrich 2019), which has been linked
to better public compliance with public health protocols during outbreaks (Funk et al. 2019),
including SARS (Tai & Sun 2007), Ebola (Vinck et al. 2019), and COVID-19 crises (Fraser &
Aldrich 2021). We might expect that linking and bridging ties aid COVID response, while
bonding, insular ties might limit the spread of information and lead to less resilient response
under certain conditions (Aldrich et al. 2018).
3. Methods
This study examines what kinds of communities experience greater outbreaks of
COVID-19, and to what degree local level measures of social capital and vulnerability can
predict those outbreaks. As our main outcome of interest, this study investigates COVID test
positivity rates (the percentage of tests that come back positive). While case incidence rates are
biased because some states test more than others, test positivity rates adjust for the number of
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total tests performed in an area. They are also now widely available in some cities and regions at
extremely local geographies - the census tract, zipcode, and county subdivision levels being the
focus of this paper. This study adapts existing, validated methodologies for measuring social
capital at the county level (Kyne and Aldrich 2019) to new measures at the census tract, zipcode,
and county subdivision levels. Then, to demonstrate their usefulness to scholars and local
officials, we show that social capital measures, when paired with social vulnerability indicators,
predict up to 95% of the variation in test positivity rates at the census tract, zipcode, and county
subdivision levels. Below, we 1) outline our methodology for adapting these measures to the
local level, and 2) describe our modeling procedures for case studies demonstrating the uses of
these indices in predicting COVID spread.
3.1 Measuring Social Capital at the Local Level
To measure social capital, we took the average of three subindices (bonding, bridging,
and linking) in each census tract, built out of 20 indicators total, listed in Table 1 . These
indicators were outlined and validated for the original county level social capital index (Kyne &
Aldrich 2019), but we describe them below as well.
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Table 1: Indicators for Census-Tract Level Social Capital Indices
10
Index
Concept
Indicator
Effect
on
Index
Level
Years
Missing
Data (%)*
Literature
Bonding
Race similarity
Race Fractionalization
(0 = homogeneity,
1 = heterogeneity)
-
Tract
2010-18
0.9%
Alesina et al.
1999
Ethnicity similarity
Ethnicity
Fractionalization
(0 = homogeneity,
1 = heterogeneity)
-
Tract
2010-18
0.9%
Alesina et al.
1999
Education equality
Negative absolute
difference between % of
residents with college
education vs. did not
graduate high school.
-
Tract
2010-18
1.4%
Norris et al.
2008; Morrow
2008
Race/income
inequality
Gini coefficient (0 =
equality, 1 = inequality)
-
Tract
2010-18
1.2%
Cutter et al. 2010
Employment
equality
Absolute difference
between % employed
and unemployed labor
force
+
Tract
2011-18
1.0%
Tierney et al.
2001
Gender income
similarity*
Gender income
fractionalization
(0 = homogeneity,
1 = heterogeneity)
-
Tract
2010-18
3.4%
Norris et al. 2008
Language
competency
% Proficient English
Speakers
+
Tract
2010-18
1%
Morrow 2008
Communication
capacity
% Households with
telephone
+
Tract
2010-18
1.5%
Cutter et. al 2010
Non-elder
population
% Below 65 years of age
+
Tract
2010-18
1%
Morrow 2008
Bridging
Religious
Organizations
Religious organizations
per 10,000 persons
+
Zipcode
2012-16
0.4%
Chamlee-Wright
2010
Civic
Organizations*
Civic organizations per
10,000 persons
Social Advocacy
organizations per 10,000
persons
+
Zipcode
2012-16
0.7%
(9.3%)
2.5%
(22.0%)
Cutter et al. 2016
Social
embeddedness -
charitable ties*
Charitable organizations
per 10,000 persons
+
Zipcode
2012-16
2.3%
(20.7%)
Norris et al. 2008
Social
embeddedness -
Fraternal ties**
Member of fraternal
order (% of total)
+
County
2010
0%
Norris et al. 2008
Social
embeddedness -
Union ties
Unions per 10,000
persons
+
Zipcode
2012-16
3%
Norris et al. 2008
Linking
Political Linkage
% of voting age citizens
eligible for voting
+
Tract
2010-18
1%
Morrow 2008
Local government
linkage
% local government
employees (per capita)
+
Tract
2010-18
1%
Murphy 2007
State government
linkage
% state government
employees (per capita)
+
Tract
2010-18
1%
Murphy 2007
Federal
government linkage
% federal government
employees (per capita)
+
Tract
2010-18
1%
Murphy 2007
Political
linkage-political
activities**
% Attended political
rally, speech, or
organized protest
+
County
2010
0%
Tierney 2001
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First, to represent bonding social capital, we use 9 indicators describing how similar
residents in a community are in terms of race, age, class, gender, language, and communication
capacity, because homophilous communities tend to have strong bonding social ties (Mouw
2006; Pretty 2003). To represent race similarity , we used a fractionalization approach to measure
how fractionalized a community is into different racial categories, where 0 represents
homogeneity and 1 represents heterogeneity (Alesina et al. 1999). We repeated this approach for
ethnicity similarity , using the share of residents which identified as Latino or Hispanic, or not
(Alesina et al. 1999), as well as similarity between genders by income (Norris et al. 2008). To
represent educational equality , we calculated the negative absolute difference between the share
of residents with a college education compared to those which did not graduate high school
(Morrow 2008). Each of the aforementioned negative measures were then reverse-coded, so that
a low value denotes low heterogeneity while high values denote homogeneity. Finally, we
measured four more positive measures of homogeneity. To represent employment equality , we
calculated the absolute difference between the share of the employed and unemployed labor
force, because this indicates that most of the labor force is similarly employed (Tierney et al.
2001). To represent language competency , we used the percentage of residents who speak
English proficiently; to represent communication capacity , we used the share of households with
a telephone; while to represent age, we used that share of residents below 65 years of age
(Morrow 2008; Cutter et al. 2010).
Second, to represent bridging social capital, we use 6 indicators of membership in
associations that can bridge different parts of society. We measured the number of religious
organizations (Chamlee-Wright 2010), civic organizations , social advocacy organizations
(Cutter et al. 2016), charitable organizations, and unions (Norris et al. 2008), normalized per
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10,000 residents. Each of these was obtained from the Zipcode Business Patterns census, and
then averaged to the census tract level. Finally, we supplement this with one county tally not
available at the zipcode level, the percentage of residents participating in a fraternal order .
Third, to represent linking social capital, we use 5 indicators of connection and
representation. To represent political linkages , we gathered the share of voting age eligible
citizens (Morrow 2008), while to represent government linkages , we measured the share of local ,
state , and federal government employees per capita (Murphy 2007). We supplemented these
census tract level measures with one county level measure of political activities , namely the
share of residents who attended a political rally, speech, or organized protest that year (Tierney
2001).
This culminated in four indices, including bonding, bridging, and linking social capital,
as well as overall social capital, which represents the average of its three subindices. Each index
was measured at the census tract level, and their distributions are visualized across US Census
Divisions in Figure 1 to demonstrate their degree of geographic variation.
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Figure 1: Geographic Distribution of Social Capital Indices at the Census-Tract Level
Caption: Violins depict distributions of social capital indices for each of the 9 US Census-bureau
designated geographic divisions in the US. The bridging social capital scores of two outlier
census tracts (values 0.2 and 0.4) were truncated to ensure clear visualization.
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3.2 Missing Data
While 15 out of 20 indicators were missing 1% of data points or less, 3 indicators were
missing considerable amounts of data. These variables included social advocacy groups (missing
22%), charitable organizations (20.7%), and civic organizations (9.3%). While complete data is
obviously preferable, these associational measures are vital to the measurement of bridging
social capital. Despite these missing data points, most of these census tracts were surrounded by
other census tracts where this data was widely available. As a result, we use a two-stage
imputation process, first imputing missing data for these five variables alone with each census
tract’s county mean value. This reduces missing data considerably, and ensures that our indices
are geographically consistent. After county mean imputation, social advocacy groups lack only
2.5% of data points, charitable organizations lack just 2.3%, and civic organizations lack just
0.7%. These are much better levels of missing data.
To fill in remaining data points, we leverage the statistical power of our dataset of 72,877
census tract observations from 2010 to 2018, using multiple imputation using the Amelia II
software in R. Using time-series imputation and melding results across 5 imputed datasets
improves imputation for missing data over time, because it fills in data points from, for example,
2013, by taking into consideration sensible values given that census tract’s values in 2012 and
2014. While complete data is preferable, this two-handed strategy helps keep missing data points
geographically similar to those in their county and temporally similar to observations of the same
census tract over time. Our indices reflect local level variation considerably better than would a
local level index that excludes these indicators.
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3.3 Aggregation
Finally, we aggregated these census tract measures to the zipcode and county subdivision
levels as well. Since these geographies do not overlap perfectly, for each zipcode or county
subdivision, we identified all census tracts that overlap with these jurisdictions and took the
mean index score for each index. We repeated this process with the CDC's 2018 Social
Vulnerability Index and its four subcomponents, to enable comparisons between social capital
and social vulnerability indices at the census tract, zipcode, and county subdivision levels.
3.4 Variables
Finally, to demonstrate the value of these granular social capital indices, we modeled the
effects of social capital on COVID spread in several case studies, to be discussed further below.
This study uses the following variables to represent key factors in COVID spread.
As our outcome variable, we used the test positivity rate - the percentage of tests that
return positive. This is a useful outcome to measure, because it controls for the amount of testing
in a locale, whereas straight case rates and death rates tend to miss unidentified cases. (Other
ideal measures, such as excess deaths rates, are not currently available below the county level).
Since this outcome is right skewed, we log transformed it and applied linear models with fixed or
random effects, as discussed below. Generally, random effects are preferred for time-series data,
but we report both fixed and random effects in Appendix A for transparency. Because our
outcome is a percentage, these models already adjust in part for the size of the population.
For each case study, we tested the effects of bonding, bridging, and linking social capital
on test positivity rates, controlling for the CDC's for 4 social vulnerability sub-indices. These
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include 1) socioeconomic status , 2) minority status and language , 3) household composition and
disability , and 4) housing type and transport (CDC GRASP, 2018; Flanagan et al. 2018).
Additionally, we controlled for population mobility, measured over time at the county
level, using the average daily change in workplace mobility, estimated with Google android user
movement. We averaged movement between two and eleven days prior to each observation,
since 95% of individuals manifest symptoms between 2-11 days after contact with COVID
(Guan et al. 2020; Li et al. 2020; Lauer et al. 2020).
Finally, we also controlled for several other constant county level traits. These include
health care capacity , which we measured by averaging two rescaled indicators, including the
number of primary care physicians per 100,000 residents in 2017 (Baicker & Cahndra 2004) and
the number of preventable hospital stays per 100,000 residents in 2017, which we reverse scaled
(Brumley et al. 2007). We also measured overall quality of health by averaging seven indicators.
These include the precentage of residents identified as current smokers in 2017, drinking
excessively in 2017, who reported being physically inactive in 2016, who had diabetes in 2016,
who were obese in 2016, who reported experiencing poor physical health over 14 days in a
month in 2017, and the age-adjusted premature mortality rate (deaths under age 75) between
2016 and 2018. These measures were gathered from the County Health Rankings (University of
Wisconsin Population Health Institute 2019). We also control for governance capacity, measured
by the number of municipal employees per capita from the American Community Survey in
2018, and for partisanship, measured by the share of residents who voted for the Democratic
presidential candidate in 2016 ( MIT Elections Lab ).
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3.5 Case Studies
We applied this array of variables to two types of case studies, including 1) cases within
counties and 2) cases across counties. These cases were located in the Northeast, Mid-Atlantic,
and Midwest, to highlight indices in different regions and geographic levels, where COVID
outcome data had been reported at these granular levels. (While it is rare for entire states to
report these outcomes, many cities do).
First, we examine variation across counties at our three levels of interest, drawing from
1) weekly test positivity rates over a 7-day period in 1393 Wisconsin census tracts from April 6,
2020 to November 16, 2020, 2) daily test positivity rates over a 7 day period in 178 New York
City zipcode tabulation areas within the five boroughs from May 18 to October 28, 2020, and 3)
weekly test positive rates over a 14 day period in 175 Massachusetts county subdivisions from
September 30 to November 19, 2020. For these cases, we model the effect of social capital
indices, controlling for social vulnerability indices, at the census tract, zipcode, or county
subdivision, and we then control for county-level traits including mobility, health care capacity,
overall health quality, governance capacity, and partisanship. For each model, we compare fixed
effects by date, random effects by jurisdiction, and random effects by jurisdiction nested within
counties. The advantage here is that by nesting within multiple counties, we can control for
county wide traits.
Second, we examine variation within counties at the zipcode level and census tract level
(there are usually too few county subdivisions to do this otherwise). We draw from 77 census
tracts in Madison, WI from April 6, 2020 to November 16, 2020, from 60 zipcodes in Chicago,
IL from March 7 to November 14, 2020, and from 44 zipcodes in Manhattan, 25 in the Bronx, 37
in Brooklyn, and 60 in Queens between May 18 and October 28, 2020. (Staten Island could not
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be modeled individually due to considerable collinearity between multiple social capital and
vulnerability indicators). Each model includes social capital and social vulnerability indicators,
with fixed effects by date or random effects by jurisdiction, as well as county mobility over time
when modeling random effects. Since other county variables are time-invariant, they cannot be
included in these models, which are intended to use as few variables as possible, to demonstrate
the considerable predictive power of these main indices together.
Finally, we repeated all nine models ( within counties and across counties ) as simple OLS
models for each timestep to demonstrate the high predictive power of social capital and controls
in each timestep. We use the R
2
statistic, the percentage of variance explained in the outcome, to
represent this, and plot this over time for each sample in Figure 3.
Finally, several models had highly collinear variables, usually between social capital and
social vulnerability variables. We performed a series of transformations on these variables, which
reduced collinearity for each variable, measured by the variance inflation factor, to below 10, the
indicator for problematic levels, and near 2.5, the gold standard. In a handful of cases,
problematic covariates like governance capacity were dropped when transformations would not
suffice. These steps are described in Appendix A.
As shown in Figure 2A and 2B , these case studies demonstrated considerable variation
in overall social capital compared to the national mean, with urban areas showing especially
troublingly low levels of social capital. Below we explore how this variation might shape
resilience to COVID-19.
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Figure 2A: Variation in Overall Social Capital among Case Studies (Cities)
Figure 2B: Variation in Overall Social Capital among Case Studies (States)
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4. Results
This study generated social capital indices at the census tract, zipcode, and county
subdivision levels, and applied them to case studies within and across counties in Wisconsin,
New York, Illinois, and Massachusetts. Below, we summarize findings from these models.
Model tables in Appendix A report the projected increase in log-test positivity rates given an
increase of one standard deviation in the predictor; all effect sizes are comparable among
predictors.
These models explained considerable amounts of variation in the outcome. For models
across counties , this includes 18-41% just from the main variables, and 46-88% including
random and fixed effects. This is considerable especially considering the great differences
between different kinds of counties involved (eg. all of Wisconsin). For models within counties ,
this includes anywhere from 20-90% of variation in the outcome, and 40-95% including fixed or
random effects. This is a very large amount of variation explained by just social capital, social
vulnerability, and mobility. Generally, our fixed effects models were best at prediction,
explaining greater levels of variation, but when interpreting the effects of specific variables,
random effects models are preferable, both indicated by Hausman tests and by the sheer number
of observations over time.
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Figure 3: Variation Explained by Model Covariates Over Time
Figure 3 contextualizes this by displaying daily versions of these models among each
case study. While our New York City models explain the model variation (60~80% in most
counties), all models others explained nearly 50% of variation at some point in the study period.
This analysis produces three general trends, highlighted in Figure 4 using marginal
effects, calculated holding all covariates at their means while varying each specific type of social
capital. The bands depict 95% confidence intervals for each type of social capital as it increases
from -2 to 0 to 2 standard deviations from the mean level in each sample.
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Figure 4: Marginal Effects of Social Capital on COVID-Test Positivity Rates
First, after adjusting for social vulnerability, health care capacity and conditions,
governance capacity, and partisanship, we find that linking social capital, a close proxy for trust
in government, is negatively associated with text positivity rates. This is consistent in all models
across counties except New York City, and persists only in Queens county among within county
models.
Second, we see considerably mixed track records for bonding and bridging social capital.
All models across counties reveal that communities with strong bonding social ties saw lower
test positivity rates. Recent research on matched samples of US counties showed similar results,
where bonding social capital seemed to help families and friends shelter and reinforced norms to
wear masks and physical distance (Fraser, Aldrich, and Page-Tan 2020). This trend is largely
matched in the within county models. Only the Bronx and Brooklyn saw positive associations
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between bonding social capital and test positivity rates. Indeed, these communities were hard hit
by COVID early; Brooklyn is home to Spring Creek Towers, the subsidized housing
development which suffered an outbreak leading their zipcode to face the highest death rate from
COVID in New York in the first three months of the outbreak, while the Bronx overall faced the
highest rates for COVID cases, hospitalizations, and deaths in the first three months (Schwirtz
and Cook 2020).
Yet this is not necessarily a contradictory finding. Past studies of disaster indicate that
bonding social capital is fickle (Aldrich 2012); it can help family, friends, and members of the
same social circle team up and share resources, but it can also promote insular social circles and
inhibit the spread of quality information. In other words, we might expect that in historically
marginalized communities where trust in local and national authorities is limited, that bonding
social capital might not help these communities as much as bridging social capital.
Third, bridging social capital too shows mixed effects, associated with lower test
positivity rates in Massachusetts, Wisconsin, and Chicago, but greater rates in New York City.
When we zoom into New York City counties, however, the trend diverges. In the Bronx and
Brooklyn, where bonding ties increased COVID spread, bridging ties were associated with
decreases in test positivity rates. However, in most other cities, including Madison, Queens, and
Manhattan, the trend was flipped. Here, bonding ties hindered COVID spread, while bridging
ties were linked to increases in test positivity rates.
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Figure 5: COVID-19 Test Positivity Rates in New York City
Caption: Zipcodes of 5 boroughs based on whether above or below the median level of social
capital in the NYC area. Blue lines depicting countries, a color scale depicting distance from the
median COVID-19 test positivity rate (shaded white), averaged over time.
Figure 5 highlights these trends in three panels, which highlight using black borders the
zipcodes with the highest bonding (left panel), bridging (center panel), and linking social capital
(right panel), compared to the COVID-19 test positivity rate, colored from blue (far below
median) to white (median) to red (far above median). We see that bridging ties aided some parts
of the city: many Bronx and Brooklyn zipcodes, with summarily low bridging social ties, saw
high COVID outbreak levels, while Manhattan zipcodes with strong bridging ties saw low
COVID outbreaks. In contrast, Staten Island experienced high test positivity rates on average,
despite having strong bonding, bridging, and linking social capital, possibly leading to New York
City’s overall skewed, positive effects of bridging and linking social capital on COVID spread.
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These findings about bonding and bridging social capital at first glance defy past findings
in disaster studies, where after adjusting for bonding, bridging, and linking social capital,
bridging ties, fostered through associations, typically encourage greater resilience to crisis than
bonding, in-group ties. However, the nature of COVID-19 is especially challenging to bridging
social capital. While linking social ties, as usual, help promote trust in public health and disaster
response, bridging ties are usually indicated by connectedness to multiple different parts of
society. In this case, that makes bridging residents in a community both capable of great good,
reaching out to neighbors and supporting each other, but also capable of spreading the virus to
new groups.
However, as highlighted by bridging social capital's negative effects on Covid spread
within the Bronx, Brooklyn, Chicago, and Massachusetts and Wisconsin at large, many
communities with strong bridging social ties are developing important innovations to respond to
COVID, ranging from virtual religious services and clubs to masked volunteer outreach and
volunteering by teens out of school. For example, The Jewish Center located in Manhattan’s
Upper West Side, which typically can accommodate 500 of its faithful, now can only
accommodate 60 members who must pre-register and have their temperature taken on site before
entering. Similarly, the Dar Al-Dawah mosque in Astoria, Queens now limits their in-person
attendance to 64 people, requiring temperature checks and hand sanitization at the door for entry.
Those who worship in-person are required to wear a mask and bring their own prayer rugs and
place them in designated spots that allow for six feet of separation. Dar Al-Dawah has also
offered additional services during religious holidays to accommodate higher demand for
in-person, community worship (Estrin 2020).
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In other words, while strong bridging ties may not mean an automatic transition to
resilience to the pandemic, they represent an important reservoir that city and health care
officials can draw on to mobilize communities, provide aid, and address the "twin pandemics" of
COVID's outright effects and its disproportionate effects on Black Americans.
5. Discussion
This study measured bonding, bridging, and linking social capital for each census tract,
zipcode, and county subdivision between 2010 and 2018 in the United States. Then, we applied
these indices to predicting COVID-19 test positivity rates in Massachusetts, New York, Illinois,
and Wisconsin, zooming in further to several urban areas, including Chicago, IL, Madison, WI,
and each of the five boroughs of New York City. Broadly, we found that linking social capital
broadly predicts COVID outcomes, while bonding and bridging social demonstrate mixed effects
depending on the underlying social and racial histories of these communities.
In particular, we find that the effects of social capital are not uniform but divergent
depending on whether those social ties link in-groups (bonding), bridge different groups
(bridging), or engender trust in officials (linking), and the local context in which they are built.
First, we find that social capital indices are strong, significant predictors of COVID
spread rates. Across counties, communities with stronger bonding social capital see lower rates
of COVID spread. This largely confirms the trends found in past studies (Borgonovi et al. 2020;
Varshney & Socher 2020), including those which use robust metrics like excess death rates
(Fraser, Aldrich, & Page-Tan 2020). It is worth noting that overall, when analyzed descriptively
in Figure 6 s scatterplots, each type of social capital was negatively correlated with COVID
spread.
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Figure 6: Social Capital (mostly) Reduces COVID Spread
Caption: Bivariate Scatterplots of social capital index scores compared to test positivity rates
over, with lines of best fit depicting weak-to-strong negative associations for all except linking
social capital in New York City.
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The main exception in Figure 6 is New York City, where linking social capital was
related to worse spread. There are several reasons why this might be the case. First, Staten Island
is home to 5% of New York City's population, but residents accounted for a quarter of
COVID-19 fatalities in late 2020. The politically conservative borough has struggled with low
resident participation in public health measures, including difficulty enforcing restrictions on
indoor dining, residents refusing to wear masks (Farinaci 2020). And in Brooklyn, public health
messaging has had difficulty penetrating some communities despite strong social ties. For
example, tensions between Mayor Bill DeBlasio and the Orthodox Jewish community over
COVID-19 have flared since March 2020, when the mayor sent police to break up a local rabbi's
funeral and disperse crowds to prevent COVID transmission. Such confrontations have been
attributed to a decline in trust in public health measures and government response, and helped
circulate rumors discouraging COVID-19 testing (Bellafante 2020).
However, after applying statistical controls, we found that each type of social capital
produced varying effects depending on geography. When we zoomed into urban settings,
bonding social ties took on mixed effects. In four out of six urban areas studied, communities
with stronger bonding ties on average saw higher rates of COVID spread, matching past findings
in disaster studies, where bonding social ties can promote insular response (Aldrich 2012).
Further, bridging social capital showed varying effects, leading to lower spread in
Massachusetts, Chicago, Brooklyn, the Bronx, and Wisconsin (although these last two effects
were not statistically significant). We theorize that bridging ties help ameliorate the deleterious
effects of bonding social capital when present, but that since many bridging ties are formed in
civil society groups and associations, they were occasionally the cause of early superspreader
events. This study suggests that local planners should carefully inventory their community
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resources when responding to pandemics, in order to proactively channel bridging and linking
social capital early into better public response.
Finally, this study came with several limitations. First, we relied on aggregate indicators
sourced annually from the American Community Survey to make inferences about the state of
social capital in communities. Future studies should ground-truth these measures by comparing
them with survey responses in local communities. Second, this study also relied on a handful of
indicators at the zipcode or county level, which were then averaged down onto census tracts, due
to lack of availability. This primarily affected the bridging and linking indices, which fortunately
still retain much variability within counties. Third, as discussed in the methods section, we relied
on county averages to fill in some indicators of bridging social capital at the census tract level;
future studies should identify comparable correlates or principal components to approximate
such measures. Fourth, this study focused on data from 2010 to 2018, due to the limited
availability of bridging and linking indicators, but future studies should extend this backwards to
2000. Despite these limitations, these local level indices produce a considerably granular picture
of the state of social capital in the US, far more so than previously available, and are a powerful
tool for predicting the spread and abatement of COVID-19.
6. Conclusion
In summary, this study analyzed the state of social capital in the United States between
2010 and 2018, generating estimates for bonding, bridging, and linking social capital in 72,877
census tracts over 9 years. Through geographic aggregation, we have produced estimates at the
census tract, zipcode, and county subdivision level, which can serve as a resource for scholars
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and policymakers involved in disaster and pandemic response and recovery efforts. Further, this
study identified through 3 regional studies and 6 urban case studies the considerable statistical
power of these indices in predicting the spread of COVID-19.
We found considerable evidence that bonding social capital reduces COVID-19 spread at
large, but especially in our urban case studies, this trend reversed. Instead, several cities
displayed divergent trends, known as the Janus-faced nature of social capital (Aldrich et al.
2018), where the insular, homophilous social networks fostered by bonding social capital were
associated with greater COVID-19 spread, but the bridging networks, trust, and reciprocity
fostered by bridging social capital were associated with reduced COVID-19 spread. Finally, this
study found continued evidence that linking social capital is usually negatively associated with
COVID-19, matching past findings that trust in government and public health officials is key to
pandemic prevention and response (Fraser & Aldrich 2021, Fraser, Aldrich, & Page-Tan 2020,
Tai & Sun 2007, Vinck et al. 2019, Funk et al. 2019).
These findings highlight that while horizontal social ties usually aid in community
resilience, local policymakers should pay special attention to encouraging trust and reciprocity
among residents. This is especially vital considering that rising political polarization has been
accompanied by pandemic denial from some state officials and rejection of social distancing and
mask mandates among residents (Grossman et al. 2020, Painter & Qiu 2020, Clinton et al. 2020).
Future studies should apply these indices to examine how social ties affected the
movements and patterns of residents, economic activity, and physical and mental health during
the pandemic - all important adjacent indicators of risk and resilience during the COVID-19
crisis. Further, scholars and policymakers should use these indices to identify communities as
similar as possible to their own in terms of social capital in order to make projections about their
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communities’ recovery trajectories. Finally, scholars should apply these indices to diagnosing
and explaining a wide variety of other community resilience, highlighting the close relationships
between social capital and policy-relevant social outcomes, including health (Kawachi et al.
2008), political polarization (Grossman et al. 2020), adaptation to climate change (Fraser,
Cunningham, & Nasongo 2021), and resilience to future disasters (Aldrich 2019; Page-Tan
2020;; Page-Tan 2021). We hope that these indices galvanize social capital scholars to examine
the role of social capital in their communities’ recovery and to encourage greater attention to the
role of residents and community planning in recovery and response to crisis.
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Appendix A: Methods (continued)
Table A1: Modeling Social Capital with Controls
in Massachusetts, New York, & Wisconsin
This analysis required several transformations to remove collinearity. For Massachusetts
models, we log-transformed our index for Socioeconomic Status. For New York City models,
socioeconomic status and minority status & language were log-transformed. Further, health care
capacity was strongly correlated with mobility, government capacity, and partisanship, and the
latter three had to be removed to reduce collinearity. Finally, for Wisconsin models, no
transformations were necessary.
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Table A2: Effect of Social Capital given different levels of controls
in Massachusetts, New York, & Wisconsin
Further, we break these overall nested random effects models down to show just the
effect of social capital, then with vulnerability controls, and finally with all controls. These help
clarify that the social capital effects we find statistically significant in our final analysis are not
statistical noise but rather consistent effects across modeling strategies.
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Table A3: Modeling Social Capital with Controls within Counties
This analysis also required several transformations to remove collinearity, particularly
Chicago models. Here, we squared socioeconomic status, took the reciprocal transformation of
household composition and disability, took the reciprocal transformation of the square of
minority status, and took the log of bonding social capital. For the Bronx, we simplified bonding
and linking social capital into six quantiles, dropped socioeconomic status, and broke minority
status into four quantiles and cubed it to break the association. (It was important to still control
for vulnerability by race and ethnicity in the Bronx.) In Brooklyn, we took the log of
socioeconomic status. In Queens, we took the log of socioeconomic status and minority status,
while in Manhattan, we broke bonding social capital into six quantiles and squared it.While such
heavy transformations are generally to be avoided, they were necessary to reduce strong
collinearity between bonding social capital, minority status, and socioeconomic status, which are
deeply intertwined in American cities.
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... Certaines études se sont penchées plus spécifiquement sur le rôle des trois types de liens dans le cadre de la pandémie et notamment sur la transmission de la COVID-19. Si le capital social d'une manière générale va permettre l'adoption de nouveaux comportements et favoriser la distanciation, les trois types de liens ne vont pas avoir le même effet (Fraser et al., 2021). Au début de la pandémie, les régions au Japon ayant un plus haut taux de capital social de type linking et bridging avaient plus de cas de COVID-19 . ...
... Ces mêmes chercheurs se sont penchés sur les liens sociaux dans différentes régions des États-Unis. Ils démontrent que les liens de types linking sont associés à un faible taux de COVID-19 (Fraser et al., 2021). Si la transmission est aussi généralement plus faible quand les liens de type bonding sont forts, il y a des exceptions dans certains quartiers où ces liens ne sont pas associés à d'autres types de lien (bridging ou linking). ...
... Cela démontre l'aspect imprévisible de ces liens intracommunautaires qui, s'ils sont élevés et ne sont pas compensés par des liens de bridging et linking, peuvent avoir des effets négatifs, notamment dans certaines communautés marginalisées. Le capital social de type bridging va quant à lui venir contrebalancer les effets négatifs du bonding quand celui-ci est élevé, mais également être facteur de transmission quand le bonding est faible (Fraser et al., 2021). ...
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... This quantifies investment in each community's organic response capacity to crisis. Finally, to represent social capital effects, this study used new social capital indices measured at the county subdivision level from Fraser et al. (2021c), building on the measurement approach used in Kyne and Aldrich (2020) county-level Social Capital Index and Fraser's (2021) citylevel social capital index internationally. These measure bonding, bridging, and linking social capital for each county subdivision on a scale from 0 (weakest) to 1 (strongest). ...
... The next logical step would be to compare these results with Facebook mobility data from a series of other hurricanes, first in Florida, then in Louisiana and up the East Coast, and finally with typhoons in the Pacific and elsewhere. I expect that the protective effect of linking social capital will reappear elsewhere; we have already seen that greater trust in government has improved for compliance with emergency orders in other disasters (Fraser et al. 2021a), including the pandemic (Fraser and Aldrich 2021;Fraser et al. 2021c). ...
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