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. Author manuscript; available in PMC: 2023 Nov 9.
Published in final edited form as: Socius. 2022 Apr 24;8:10.1177/23780231221090301. doi: 10.1177/23780231221090301

Sociospatial Disparities in “Third Place” Availability in the United States

Danielle Rhubart 1, Yue Sun 2, Claire Pendergrast 2, Shannon Monnat 2
PMCID: PMC10634631  NIHMSID: NIHMS1936989  PMID: 37946734

Abstract

Tertiary to home and work, “third places” serve as opportunity structures that transmit information and facilitate social capital and upward mobility. However, third places may be inequitably distributed, thereby exacerbating disparities in social capital and mobility. The authors use tract-level data from the National Neighborhood Data Archive to examine the distribution of third places across the United States. There were significant disparities in the availability of third places. Higher poverty rates were associated with fewer third places. Tracts with the smallest shares of Black and Hispanic populations had comparatively more third places. However, this racial disadvantage was not linear, suggesting potential buffering effects in places with the largest shares of Black and Hispanic populations. There was also a rural disadvantage, except in the most isolated rural tracts. This study demonstrates the value of conceptualizing and measuring third places to understand sociospatial disparities in the availability of these understudied opportunity structures.

Keywords: third places, social mobility, racial/ethnic disparities, socioeconomic disparities

The United States has remarkably high levels of racial/ethnic, socioeconomic, and geographic inequality in access to social capital and upward mobility (Murray et al. 2006; Smeeding 2005). Sociologists have long implicated placelevel factors in contributing to these inequalities (Entwisle 2007; Galster and Sharkey 2017; Lobao, Hooks, and Tickamyer 2007). This has included understanding how third places—the physical locations outside the home or workplace that provide opportunities for social interaction and social support (Finlay et al. 2019; Oldenburg 1999)—shape opportunity (Hickman 2013). Although third places have the capacity to support individual and community-level wellbeing and social opportunity (Cabras and Mount 2017; Stenstrom, Cole, and Hanson 2019), little research has examined the distribution of third places in the United States. Moreover, the existing research on third places focuses almost exclusively on urban contexts, with very little attention to the distribution of third places in rural communities (Klinenberg 2018; Small and McDermott 2006). Because third places may offer resources and social supports that facilitate social mobility, disparities in their availability risk perpetuating or exacerbating existing social cleavages. Therefore, it is important to determine if third place availability varies by rurality and by neighborhood racial/ethnic or socioeconomic status (SES) composition.

In this article, we build on work by Galster and Sharkey (2017) arguing that spatial opportunity structures create and perpetuate inequality because they provide the tools or attributes for social mobility but are not available equitably across disparate sociospatial contexts. We also build on previous work showing that third places act as spatial opportunity structures (e.g., Stenstrom et al. 2019; Small 2006; Walton 2014). We use tract-level data from the National Neighborhood Data Archive (NaNDA) to examine the distribution of third places in the United States. Our analyses identify census tract–level rural-urban continuum status and racial/ethnic and SES composition differences in the distribution of five specific types of third places: (1) free and publicly available third places; (2) organizations that provide social assistance, including child and youth services, services for older adults and persons with disabilities, community food services, child care centers, emergency and other relief services, and vocational rehabilitation services; (3) low-cost commercial, including coffee shops, bars, and fast food restaurants; (4) creative, athletic and entertainment, including spectator sports, fitness and recreation, bowling alleys, museums, and performance art; and (5) personal services, including salons and barbershops.

This research makes two substantial contributions to the literature. First, we propose a conceptual organization of third place types. Second, we use census tract–level data on these third place types to identify racial/ethnic, socioeconomic, and rural-urban disparities in third place availability across the United States. This is a substantial advancement on prior studies that relied on a limited scope of third places, urbancentric case studies, or county-level data.

Third Places

Tertiary to home (first place) and work (second place), “third places” such as coffee shops, bookstores, salons, bars, libraries, civic associations, and religious organizations provide physical spaces where friends, neighbors, and strangers can gather regularly, happily, and on common ground (Oldenburg 1999). The broad definition of third places (also known as a type of social infrastructure) encompasses a diverse range of organizations and institutions that serve the characteristic purpose of affording sociality in local environments (Latham and Layton 2019). Although there is no formal typology of third places in the literature, two key distinctions in third place purpose and function are worth noting: the requirement of payment for access to places and the type of activities and services supported by a place. These two distinctions may have implications for who uses third places and how third places affect individual and community outcomes (Finlay et al. 2019). The diversity of third place form and function, and the potential for racial/ethnic, socioeconomic, and rural-urban variation in availability, access, and use of specific types of third places, suggests that they should not be conceptualized as homogenous but instead as a set of types. On the basis of the literature, we propose five types of third places.

The first type is free and publicly available third places, including libraries, religious organizations, and civic associations. These third places are free to access and provide public good. Libraries have been found to support socialization and facilitate social capital through educational programming, public talks, social gatherings, and community group meetings (Klinenberg 2018; Stenstrom et al. 2019). Religious organizations also provide space and programming to support social connection and support (Small and Gose 2020). Community centers, other civic organizations, and senior centers also shape community cohesion and social opportunities (Colistra, Bixler, and Schmalz 2019). Social service organizations are a second type of third place that typically have no cost associated with them but are focused primarily on promoting resources, opportunities, and social networks by connecting individuals with service providers, organizational networks, opportunities, and other participants/service recipients (Small, Jacobs, and Peeples Massengill 2008).

The three remaining third place types all fall under the umbrella of commercial spaces where goods and services are typically purchased. Low-cost commercial establishments where people share a drink or a meal are a ubiquitous type of third place, with distinct social benefits for different age groups, geographies, and income levels (Cabras and Mount 2017; Rosenbaum et al. 2007). Creative, athletic, or entertainment third places provide opportunities for social connection over creative activities and performances (Slater and Koo 2010) or through observing and participating in sporting events (Melnick 2016). Finally, personal services, including barbershops and salons, enable relationship building and the dissemination of information (Anderson, Cimbal, and Maile 2010; Mills 2013). Our analyses consider all five types.

Third Places as Spatial Opportunity Structures for Social Mobility

Galster and Sharkey (2017) argued that spatial opportunity structures provide the tools or attributes for social mobility but are not equitably available across sociospatial contexts. They defined spatial opportunity structure as the compilation of “natural and human-made systems that have a geographic connection and play important roles in people’s socioeconomic status achievements” (p. 7). The spatial opportunity structures of local environments enable sharing attributes that accumulate over time into a “bundle” that can provide pathways for social mobility and ultimately facilitate wellbeing (Galster and Sharkey 2017). Third places can help people acquire attributes that can subsequently shape SES. However, it is not known if third places are equitably available across different sociospatial contexts. In this section, we briefly review the evidence on how third places yield attributes that individuals can accumulate over time to improve SES. We then discuss the rationale for examining the sociospatial distribution of third places.

Third places connect people to networks of social capital, support, resources, and information that may lead to economic opportunities, professional connections, or support accessing services and benefits, particularly among historically marginalized and disadvantaged groups (Small 2006; Walton 2014). For example, libraries connect individuals to a range of resources and information, including job search support, learning materials, and literacy development (Stenstrom et al. 2019). Libraries, recreational facilities, and social services can also facilitate social capital, particularly for lowincome populations as individuals connect with resources and wider networks of support and relationships (Aabø 2009; Curley 2010). Similarly, routine organizations such as churches and childcare centers facilitate social connections for low-income populations that lead to information, services, and material goods (Small and Gose 2020). Social capital stemming from third places can also yield community-level benefits, such as facilitating community-scale entrepreneurial activity or producing social connections that spur local economic development (Cabras and Mount 2017; Sharp et al. 2002). The perceived quality of third places, along with other local services, can play a role in whether people want to remain in their communities. Accordingly, a lack of third places can reduce community well-being and sustainability (Erickson, Call, and Brown 2012).

Sociospatial Disparities in Third Place Access

Historical and contemporary processes of racial and economic segregation and urbanization have fundamentally altered the distribution of schools, employment opportunities, environmental hazards, and exposure to violence (Galster and Sharkey 2017). It is possible that these same processes have also shaped the distribution of third places. We briefly review the literature and knowledge gaps on the distribution of third places by rural-urban status and racial/ ethnic and socioeconomic composition.

Rural-Urban Status Variation in Third Places.

Existing research has provided important insights about the role third places play in maintaining community cohesion and promoting overall well-being in urban areas of the United States (Latham and Layton 2019; Mouratidis 2018) as well as in rural communities outside the United States (Cabras and Mount 2017; Mair 2009). We build on this literature by considering rural-urban disparities in third place availability in the United States. Access to third places may be especially important for social and economic life in rural communities (Cabras and Mount 2017; Flaherty and Miller 2016; Mair 2009). Shaped by broader global and national processes (e.g., deindustrialization, automation, and urbanization), rural areas have higher rates of poverty and precarious employment, larger shares of older adults, and smaller shares of people with at least some college compared with urban areas (Jensen et al. 2020; Schafft and Biddle 2014; Slack 2014). In addition, research shows that rural counties with aging populations have comparatively low availability of essential services (Thiede et al. 2017). Meanwhile, disinvestment in the public service sector across many parts of the United States as a result of the devolution of social welfare programs may have reinforced the importance of third places as local resources for social development in rural areas (Sharp and Parisi 2003). Some types of third places, such as religious organizations, may have been somewhat protected from these trends, given higher rates of church attendance, for example, in the rural South (Dillon and Savage 2006). For these reasons, we hypothesize that rural communities may have comparatively less third place infrastructure.

Socioeconomic Composition.

Third places can fulfill important functions for low-income communities and residents (Hickman 2013; Small and Gose 2020). However, the density and types of third places may vary depending on place-level socioeconomic composition. For example, some types of third places (e.g., civic and social organizations, bowling alleys) are more likely to be located in counties with higher income levels (Rupasingha, Goetz, and Freshwater 2000). Prior research has shown that place-level financial hardship is associated with disinvestment in the arts and recreation (Scott 2013). Conversely, previous research on 331 U.S. metropolitan areas showed that there were slightly more commercial establishments1 in higher poverty metro areas (Small and McDermott 2006). Research encompassing the entire United States found no association between poverty and availability of fast food restaurants (James et al. 2014). Although findings vary by geography and type of establishments, we hypothesize that the types of third places that are predominantly profit driven or require substantial initial private investment, such as low-cost commercial establishments, personal services, and creative, athletic, and entertainment third places are less prevalent in high-poverty neighborhoods.

Racial/Ethnic Composition.

Third places play important roles in social mobility for historically marginalized racial/ethnic groups. For example, Black barber shops can serve as spaces for social connection and entrepreneurial opportunity (Mills 2013). Higher densities of churches and civic organizations are associated with lower homicide rates among Blacks (Lee and Ousey 2005). Businesses and civic organizations in predominantly Black and predominantly Hispanic neighborhoods can also help mediate the effects of natural and public health disasters (e.g., Finucane et al. 2020; Klinenberg 2002). Yet given the history of racist policies and institutions in the United States, including redlining, blockbusting, and disinvestment (Adelman and Gocker 2007; Lichter et al. 2007a; Lichter et al. 2007b), neighborhoods with larger shares of racial/ethnic minorities have less availability of health care, quality schooling, and other spatial opportunity structures (Caldwell et al. 2017; García 2020). Therefore, there is reason to expect less availability of third places in communities with larger shares of Blacks and Hispanics. However, there is almost no research on the role of placelevel racial/ethnic composition on third place availability. Small and McDermott (2006) found that metropolitan areas with larger shares of Black residents had fewer establishments (see note 1). Rupasingha, Goetz, and Freshwater (2006) found that ethnic fragmentation (the probability that two randomly chosen residents from the same county would be of two different races) is associated with lower levels of county-level social capital, but they did not examine racial composition differences in the availability of different types of third places.

In sum, third places play important roles in facilitating upward social mobility at both the individual and community levels. Third places have the potential to serve as equalizers in disadvantaged and marginalized communities, but if they are not available, their absence could exacerbate existing disadvantages in these communities. However, it is unclear how the distribution of third places varies by rural-urban status and by community-level racial/ethnic and socioeconomic composition. We fill this gap.

Methods

Data and Measures

Our analyses include all census tracts in the United States (N = 72,760). Census tracts are standard geographic units that can approximate neighborhoods and have a relatively narrow range in population sizes compared with larger units of analyses (e.g., counties). Census tracts also approximate neighborhoods better than county or ZIP code delineations (Forrest 2019). We used publicly available data from the NaNDA, housed by the Inter-University Consortium for Political and Social Research, to examine the spatial distribution of third places (Esposito et al. 2020a, 2020b; Finlay et al. 2020a, 2020b, 2020c). NaNDA data include annual counts of various establishment types taken from the National Establishment Time Series database, which provides records for private forprofit and nonprofit establishments and government agencies (Walls and Associates 2017). Establishments are classified according to the North American Industry Classification System codes (see Appendix A for a full list). We used census tract–level establishment counts from 2017, the most recent year of available data, from the following NaNDA data sets: eating and drinking places; religious, civic, and social organizations; parks; personal services; arts, entertainment, and recreation; and social service organizations. We aggregated types of third places into the five theoretically informed types discussed earlier:

  1. Free and publicly available third places: religious organizations, civic organizations, and libraries

  2. Social services: organizations that provide social assistance such as child and youth services, services for the elderly and persons with disabilities, other individual and family services, community food services, childcare centers, emergency and other relief services, and vocational rehabilitation services.

  3. Low-cost commercial third places: coffee shops, bars, and fast food restaurants

  4. Creative, athletic, and entertainment third places: spectator sports, fitness and recreation, bowling alleys, museums, and performance art

  5. Personal services: salons, barbershops, and weight loss counseling

We used 2012–2016 American Community Survey fiveyear estimates for census tract–level demographic and socioeconomic measures (U.S. Census Bureau 2019). We used the percentage of the population who fall at or below the federal poverty line as the measure of tract-level SES. We used percentage non-Hispanic Black and percentage Hispanic as measures of racial composition. Because the racial composition variables are highly skewed, we created quartiles for both percentage non-Hispanic Black and percentage Hispanic.

We used the census tract rural-urban commuting area (RUCA) codes from the U.S. Department of Agriculture Economic Research Service (ERS 2020) to operationalize rural-urban continuum status. We aggregated RUCA codes into four groups on the basis of previous classifications used by Rural Health Research Center (n.d.): urban, large rural, small rural, and isolated. Urban RUCAs are those in metropolitan areas (RUCAs 1–3) or outside of metropolitan areas, but with secondary commuter flows of 30 percent to 50 percent to an urbanized area (RUCAs 4.1, 5.1, 6.1, 7.1, 8.1, and 10.1). Large rural includes tracts in micropolitan areas with secondary commuter flows of less than 30 percent to an urbanized area (RUCAs 4.0, 5.0, and 6.0). Small rural includes tracts in small town areas and with secondary commuter flows of less than 30 percent to an urbanized area (RUCAs 7.0, 7.2, 8.0, 8.2, and 9.0). Isolated rural includes tracts in rural areas with no primary flows to urbanized areas or clusters (RUCAs 10.0, 10.2, and 10.3).

Analyses

Although census tracts provide a better representation of neighborhood-level measures of the built environment than counties, many people travel outside of their home census tract on a daily basis for work, school, shopping, and recreation. Therefore, measuring availability of third places requires attention to neighboring tracts, especially for aerially small census tracts. We account for potential spatial spillover of third place availability, by using a distance-based third place count. We use the spatial window sum approach to calculate the total number of third places available, for each third place type, within 5 miles for urban host census tracts or 10 miles for rural host census tracts. Median, minimum, and maximum counts for each third place type are reported in Table 1.

Table 1.

Median Counts (Minimum, Maximum) of Third Places by Racial/Ethnic and Poverty Composition and RUCA Code for All U.S. Census Tracts, 2017.

Free and Publicly Available Low-Cost Commercial Creative, Athletic, and Entertainment Personal Services Social Services
Percentage non-Hispanic Black
 Ql 62 (0, 7,331) 30 (0, 4,278) 14 (0, 3,001) 31 (0, 4,625) 35 (0, 4,459)
 Q2 163 (0, 6,303) 100 (0, 4,282) 60 (0, 2,973) 105 (0, 4,037) 115 (0, 3,754)
 Q3 242 (0, 6,168) 144 (0, 4,270) 86 (0, 2,992) 152 (0, 4,060) 165 (0, 3,736)
 Q4 369 (0, 6,291) 167 (0, 4,247) 94 (0, 2,971) 190 (0, 4,046) 218 (0, 3,755)
Percentage Hispanic
 Q1 79 (0, 7,331) 35 (0, 4,142) 15 (0, 2,945) 36 (0, 4,625) 39 (0, 4,459)
 Q2 145 (0, 6,102) 75 (0, 4,259) 42 (0, 2,973) 78 (0, 3,926) 88 (0, 3,708)
 Q3 246 (0, 6,303) 138 (0, 4,282) 87 (0, 3,001) 156 (0, 4,037) 169 (0, 3,754)
 Q4 376 (0, 6,280) 217 (0, 4,278) 127 (0, 2,992) 246 (0, 4,060) 245 (0, 3,755)
Percentage poverty
 Q1 188 (0, 7,331) 113 (0, 4,270) 79 (0, 2,973) 125 (0, 4,625) 134 (0, 4,459)
 Q2 148 (0, 6,183) 85 (0, 4,282) 47 (0, 3,000) 87.5 (0,4,037) 95 (0, 3,754)
 Q3 148 (0, 6,291) 75 (0, 4,241) 36 (0, 2,992) 77 (0, 4,060) 85 (0, 3,736)
 Q4 313 (0, 6,303) 153 (0, 4,247) 74 (0, 3,001) 160 (0, 4,046) 187 (0, 3,755)
RUCA code
 Urban 259 (0, 6,303) 147 (0, 4,282) 88 (0, 3,001) 161 (0, 4,060) 175 (0, 3,755)
 Large rural 76 (0, 437) 31 (0, 209) 12 (0, 138) 31 (0, 293) 37 (0, 270)
 Small rural 37 (0, 766) 12 (0, 364) 4 (0, 287) 13 (0, 354) 15 (0, 574)
 Isolated rural 18 (0, 7,331) 5 (0, 3,730) 2 (0, 2,028) 6 (0, 4,625) 6 (0, 4,459)
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Note: N = 72,760. Q = quartile; RUCA = rural-urban commuting area.

We then used negative binomial regression models to predict third place counts using rural-urban status, neighborhood racial/ethnic composition, and poverty composition (Table 2). We offset each model with the natural log of the tract-level population to account for tract-level differences in population size. All models control for U.S. census region. We conducted sensitivity analyses by replicating the models using 2015 NaNDA data and 2010–2014 ACS data (Appendix B). Results from the sensitivity analyses were consistent with results presented in Table 2. We conducted all regression analyses in Stata 17.0.

Table 2.

Incident Rate Ratios and Confidence Intervals from Negative Binomial Models Predicting Census Tract–Level Third Place Counts, 2017.

Free and Publicly Available Low-Cost Commercial Creative, Athletic, and Entertainment Personal Services Social Services
IRR 95 Percent CI IRR 95 Percent CI IRR 95 Percent CI IRR 95 Percent CI IRR 95 Percent CI
Percentage non-Hispanic Black (reference: Q1)
 Q2 .115*** .108–.122 .117*** .109–.124 .120*** .112–.128 .116*** .109–.123 .120*** .112–.128
 Q3 .140*** .131–.149 .134*** .126–.143 .137*** .128–.146 .134*** .125–.142 .144*** .135–.154
 Q4 .236*** .221–.251 .199*** .186–.213 .197*** .184–.211 .213*** .199–.227 .235*** .220–.251
Percentage Hispanic (reference: Q1)
 Q2 .091*** .086–.097 .099*** .093–.106 .111*** .104–.118 .100*** .094–.107 .100*** .094–.106
 Q3 .108*** .101–.114 .121*** .114–.129 .137*** .129–.146 .124*** .117–.132 .118*** .111–.126
 Q4 .137*** .129–.146 .161*** .151–.172 .182*** .171–.194 .174*** .164–.186 .144*** .135–.153
Percentage poverty .995*** .994–.996 .995*** .994–.996 .991*** .990–.992 .992*** .991–.993 .994*** .993–.995
RUCA code (reference: urban)
 Large rural .124*** .119–.130 .093*** .088–.097 .059*** .056–.062 .088*** .084–.093 .092*** .087–.096
 Small rural .051*** .048–.055 .030*** .028–.032 .018*** .016–.019 .032*** .030–.034 .032*** .030–.034
 Isolated rural 3.207*** 3.003–3.424 3.140*** 2.930–3.365 3.429*** 3.191–3.685 3.247*** 3.032–3.477 3.265*** 3.050–3.496
Constant 27.847*** 26.872–28.858 16.455*** 15.850–17.083 10.066*** 9.681–10.466 18.278*** 17.612–18.969 17.507*** 16.871–18.167
α 2.875*** 2.852–2.899 3.146*** 3.120–3.173 3.320*** 3.291–3.348 3.083*** 3.058–3.109 3.075*** 3.049–3.101
AIC 1,073,849 969,038 882,279 980,758 991,860
BIC 1,073,987 969,175 882,416 980,896 991,998
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Note: N = 72,760. Counts are offset by the natural log of the census tract population, and models control for U.S. census region. AIC = Akaike information criterion; BIC = Bayesian information criterion; CI = confidence interval; IRR = incidence rate ratio; Q = quartile; RUCA = rural-urban commuting area.

***

p < .001.

Results

Descriptive Statistics

Table 1 presents the median counts of available third places for each third place type by racial/ethnic composition, poverty composition, and RUCA code for all U.S. census tracts. Across all third place types, tracts with larger relative shares of non-Hispanic Blacks had higher median counts of third places. Similarly, tracts with larger relative shares of Hispanics had higher median counts of third places for all types. Across all third place types, tracts with the highest (quartile 4) and lowest (quartile 1) poverty rates had the highest median counts of third places. The median count of third places declined with increasing rurality across all third place types.

Neighborhood Disparities in Third Places

Table 2 presents the incidence rate ratios and confidence intervals from negative binomial models predicting third place counts. The findings show that there are fewer third places in tracts with larger shares of Blacks and larger shares of Hispanics. Compared with tracts with the smallest shares of non-Hispanic Blacks and the smallest shares of Hispanics (in effect “White” census tracts), all other tracts have fewer average available third places across all third place types, net of other model variables. For example, compared with tracts with the smallest shares of Blacks, those with the highest shares (quartile 4), have between 76 percent and 80 percent fewer third places, depending on type. Compared to tracts with the smallest shares of Hispanics, those with the highest shares (quartile 4), have between 82 percent and 86 percent fewer third places, depending on type. However, the relationship is not linear across quartiles. For all third place types, the disadvantage was largest for tracts ranked in the second and third quartiles for shares of non-Hispanic Blacks and Hispanics. Higher poverty rates were associated with significantly fewer third places across all types. For most types of third places, a 1 percentage point increase in the poverty rate is associated with a 1 percentage point decline in the third place count. In terms of the rural-urban continuum, large and small rural tracts had significantly and substantially fewer third places per capita compared to urban tracts (between 88 percent and 98 percent fewer, on average, depending on type). However, isolated rural tracts had over three times the per capita count of third places across all types.

Discussion

Following home and work, third places such as coffee shops, barber shops, bars, libraries, civic associations, and religious organizations are physical spaces where people can meet and gather (Oldenburg 1999). Third places serve as opportunity structures that facilitate social capital formation and transmit knowledge, information, and aspirations that enable upward social mobility at both the individual and community levels. As such, third places have the potential to serve as equalizers in disadvantaged and marginalized communities, such as rural areas and communities with large shares of racial/ethnic minorities and/or high poverty rates. In this study, we proposed a typology for third places and examined the distribution of third places across this typology: free and publicly available third places; social services; low-cost commercial third places; creative, athletic, and entertainment third places; and personal services. We found that third places are unequally distributed across all third place types. Our results point to several important takeaways with implications for research and policy.

First, we found that poverty is negative associated with third place availability. This finding is not surprising. These high-poverty neighborhoods (census tracts) are home to residents with fewer resources and less political capital to advocate for these types of community resources. Less availability of third places in these neighborhoods is concerning because these are the very same places with the least robust safety nets in place to support and lift up low-income populations. Reduced availability of third places risks exacerbating the already subpar spatial opportunity structures in high-poverty communities in the United States. Local and state governments should support targeted policies and programs that facilitate a robust third place landscape through small business and nonprofit development as a means of promoting social capital and upward mobility.

We also found that tracts with the smallest shares of non-Hispanic Blacks had the most third places. These findings align with prior research showing that neighborhoods with larger concentrations of racial/ethnic minorities have less availability of health care, quality schooling, and other spatial opportunity structures (Caldwell et al. 2016; García 2020). Research on the broader historical and spatial context of racial residential segregation and apartheid shows a legacy of racist policies and practices that drove disinvestment in communities of color (Lichter et al. 2007a; Lichter et al. 2007b; Logan and Parman 2015; Soss, Fording, and Schram 2011) and resulted in blacks’ being clustered in persistently poor neighborhoods with limited opportunities for upward mobility (Slack and Jensen 2002). However, the relationship we found between third place count and percentage non-Hispanic Black was not linear. In particular, tracts with the largest relative shares of non-Hispanic Blacks were the least disadvantaged in comparison with tracts with moderate shares of non-Hispanic Blacks (quartiles 2 and 3). This may reflect the long history of grassroots organizing and community-based cooperatives created by and present in Black communities (Boston 2006; Green, Green, and Kleiner 2011; Lewis and Trulear 2008), which could be buffering this relationship.

We found a similar pattern for percentage Hispanic. Like Black residential patterns, Hispanic residential patterns are also shaped by past and present racial residential segregation as well as settlement patterns of Hispanic immigrants (Lichter and Johnson 2018). On the other hand, high rates of residential segregation among Hispanics (Lichter, Parisi, and Taquino 2016) may drive entrepreneurship among Hispanics themselves, leading to the creation of establishments (e.g., salons, fast food restaurants, entertainment venues) to serve their own communities (Bates, Jackson, and James Johnson 2007), potentially explaining the smaller disadvantage in tracts with the largest relative shares of Hispanics. Purposeful migration patterns to locations that have the resources and opportunity structures desired for upward mobility among Hispanic populations may also be explaining the smaller disadvantage in tracts with the largest relative shares of Hispanic populations. Qualitative research is needed to understand whether and how third places facilitate opportunity in historically marginalized communities, such as those with large Black and Hispanic populations, including in new Hispanic destinations.

It is important to point out that availability does not necessarily mean access. Residents of these neighborhoods may face cost and transportation barriers to accessing the third places that are available. For low-income Blacks and Hispanics in these neighborhoods, additional social and linguistic barriers could still play a role in limiting access to third places.

Third, we found a rural disadvantage in third place availability across all third place types, except in the case of the most isolated rural tracts. Small and large rural tracts had substantially fewer third places than their urban counterparts. This means that residents of many rural communities must travel further to access these spatial opportunity structures that can provide social support and the exchange of knowledge. Rural residents are also less likely to have access to public transportation if they do not have a personal vehicle (Litman 2017) and face reduced access to legal and health care services (Douthit et al. 2015; Pruitt et al. 2018; Statz and Termuhlen 2020), potentially compounding barriers to wellbeing and upward social mobility. However, rural places are not homogenous. The most isolated tracts had significantly more third places than urban tracts (and their small and large rural peers). This may reflect a basic minimum or bottom floor of third places required to support a community, resulting in higher relative counts when all other factors are held constant. These findings have implications for community-based efforts to promote individual- and community-level social mobility in rural areas (Green 2019).

Finally, this study also makes an important conceptual contribution. Specifically, we found that the proposed typology is useful for conceptually organizing third place types but that certain types of places (e.g., high-poverty tracts) are disadvantaged regardless of the type of third place being considered. Third place disadvantage seems to cluster. This suggests that third places, an important spatial opportunity structure, are unlikely to be substitutable, resulting in compounding challenges in creating spaces that promote social mobility and overall well-being. We encourage researchers to use the third place typologies we have identified in this paper to identify how the spatial distribution of different types of third places are related to various social and health outcomes. Future research should work to understand if and how each of these types of third places shape social mobility and well-being. For example, do third places help explain differential economic or health outcomes among populations from low-income neighborhoods?

Limitations

The present findings should be considered in light of some limitations. First, NaNDA uses the National Establishment Time Series database as its data source for establishments and organizations. These data have some inaccuracies (Finlay et al. 2019) and do not capture organizations or groups that do not have a fixed location (e.g., a local philanthropy group that meets in the local school or library). Moreover, approximately 5 percent of the data do not include a specific address that can be geocoded and assigned to a census tract but rather denotes a ZIP code. For those instances, NaNDA compilers assigned the establishment to the census tract that has the largest overlap with the ZIP code.

Second, we used Euclidean distance for our distance buffer calculations. Therefore, we were unable to account for physical barriers that may limit access (e.g., placement of roads, mountain ranges, rivers, etc.).

Third, for third places to yield attributes that facilitate upward mobility and well-being, not only do they need to be present (supply), but there must also be an active decision among individuals to use the third places (demand), and third places must be equitably accessible. This latter point has received some attention in the literature, suggesting that discrimination and language barriers can prevent disadvantaged minority groups from accessing some types of community spaces (Knollenberg et al. 2021; Sharaievska et al. 2010). In addition to availability, cost, and differences in preferences or service needs across groups and communities, transportation and time barriers may also produce disparities in the social and economic benefits third places offer (Small 2006; Williams and Hipp 2018).

Conclusion

As the United States continues to struggle with high levels of racial, ethnic, economic, and geographic inequality (Murray et al. 2006; Smeeding 2005), social scientists must broaden our understanding of how the built environment propels or stymies movement up the social ladder. Third places can play a role in creating opportunities for social interaction, economic development, and community-scale entrepreneurial activities that stem from strong social capital ties (Cabras and Mount 2017; Sharp et al. 2002). In this paper, we argue that an understudied type of spatial opportunity structure—third places—may have the potential to offset or compensate for the negative effects of low access to other spatial opportunity structures in historically disadvantage communities in the United States. As expected, tracts with the lowest rates of poverty and smallest shares of Blacks and Hispanics have the most third places. However, the relationship for Blacks and Hispanics is not linear. This means that third places may play a role in buffering historical legacies of marginalization by facilitating social capital, support, and the exchange of resources and information in tracts with the largest relative shares of Blacks and Hispanics that could lead to economic opportunities, professional connections, or access to services (Small 2006; Stenstrom et al. 2019; Walton 2014). In addition, given escalating trends of rural population aging and decline (Jensen et al. 2020), it is essential to shore up access to third places to prevent further erosion of the rural spatial opportunity structure. Rather than disinvesting in third places and risking continued loss of important opportunity structures (Finlay et al. 2019), communities should invest in and leverage the existing network of organizations and small businesses that facilitate social capital formation and social mobility opportunities and fill the gaps left by other types of spatial opportunity structures especially in low-income and rural communities.

Supplementary Material

supp_appendix

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: two research networks funded by the National Institute on Aging (R24 AG065159 and 2R24 AG045061), the National Institute on Aging-funded Center for Aging and Policy Studies at Syracuse University (P30AG066583), the Eunice Kennedy Shriver National Institute of Child Health and Human Development-funded Population Research Institute at Penn State (P2CHD041025), the U.S. Department of Agriculture Agricultural Experiment Station Multistate Research Project: W4001, Social, Economic and Environmental Causes and Consequences of USDA-supported Rural Population research Network, and the Syracuse University Lerner Center for Public Health Promotion.

Biographies

Danielle Rhubart is an assistant professor of biobehavioral health and director of the Rural Health Disparities Lab. She is trained as a rural sociologist and demographer and studies rural population health and the social determinants of health. She uses large data sets to determine how place shapes health and wellbeing differently across groups of people. She has published 12 peer-reviewed journal articles, including articles in Public Policy and Aging Report, Population Research and Policy Review, and Population and Environment. She has also published research briefs and reports through the Carsey School of Public Policy (University of New Hampshire), the Lerner Center for Public Health Promotion (Syracuse University), and the Center for Rural Pennsylvania.

Yue Sun is a PhD student in the Department of Sociology at Syracuse University. She holds a master of sociology from Zhejiang University and a bachelor of public administration from East China University of Science and Technology. Her research interests include spatial disparities in health and mortality, rural demography, and political economy of health and environment. Her recent research focuses on coronavirus disease 2019 mortality, vaccination, and spatial modeling. Her work has been published in the Journal of Rural Health and Population and Environment.

Claire Pendergrast is a PhD student in the Department of Sociology at Syracuse University and a graduate fellow at the Syracuse University Lerner Center for Public Health Promotion. Her research focuses on aging, social policy, health disparities, and the public health impacts of disasters. She has published 11 peerreviewed papers in public health, aging, and sociology journals, including Ageing & Society, the Journal of Applied Gerontology, and Health Security.

Shannon Monnat is an associate professor of sociology and director of the Lerner Center for Public Health Promotion at Syracuse University. Her research examines trends and geographic differences in health and mortality, with a particular focus on rural-urban and within-rural health disparities. She has more than 60 peerreviewed journal articles and book chapters and has presented to numerous public, academic, and policy audiences, including the United Nations Office on Drugs and Crime, the National Institutes of Health, the National Academy of Sciences, the Aspen Institute, the U.S. Office of National Drug Control Policy, and at congressional briefings. She was a member of the National Academy of Sciences Consensus Committee that released the 2021 report “High and Rising Mortality Rates among Working-Age Adults.” Her research has been funded by several federal and foundation grants, including through the National Institutes of Health, U.S. Department of Agriculture, U.S. Department of Justice, Robert Wood Johnson Foundation, and Institute for New Economic Thinking.

Footnotes

Human Subjects Protections

The data used in this study are publicly available county-level data. The study is exempt from human subjects review.

Supplemental Material

Supplemental material for this article is available online.

1

Establishments included banks and credit unions, daycare centers, convenience stores, hardware stores, laundromats, grocery stores and pharmacies, barbershops, nail salons and beauty salons, and restaurants (Small and McDermott 2006)

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