Abstract
Are rural residents more likely to volunteer than those living in urban places? Although early sociological theory posited that rural residents were more likely to experience social bonds connecting them to their community, increasing their odds of volunteer engagement, empirical support is limited. Drawing upon the full population of rural and urban respondents to the United States Census Bureau’s current population survey volunteering supplement (2002–2015), we found that rural respondents are more likely to report volunteering compared to urban respondents, although these differences are decreasing over time. Moreover, we found that propensities for rural and urban volunteerism vary based on differences in both individual and place-based characteristics; further, the size of these effects differs across rural and urban places. These findings have important implications for theory and empirical analysis.
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Introduction
Folk wisdom has long suggested that rural people are more helpful than city people (Steblay, 1987). Traditional social science theory maintained that residents in rural areas and smaller communities share a high level of trust and strong social networks, which support collective activity and pro-social behavior. Early sociologists argued that the process of urbanization leads to a breakdown in social ties, social order, and an increase in anonymity from churches, family ties, and personal networks (Hofferth & Iceland, 1998). This wisdom seems to be supported by some evidence that rural residents are more likely to engage in informal helping behavior (Steblay, 1987) and volunteering than their urban peers (Balish et al., 2018; Forbes & Zampelli, 2014a; Fritz et al., 2003; Steblay, 1987; Svendsen & Svendsen, 2016; Tavares & Carr, 2013).
However, the evidence is far from conclusive, as other studies find no such difference in rural and urban civic engagement (Hooghe & Botterman, 2012; Sundeen, 1992; Wang et al., 2013). In fact, some suggest that higher levels of economic development in urban places allow individuals to devote more time to volunteering (Parboteeah et al., 2004). While a limited number of studies of volunteering control for contextual characteristics that may account for rural/urban differences, many of these occur at the national (Lim & MacGregor, 2012), regional (Glanville et al., 2016), or state levels (Rotolo & Wilson, 2012), which may obscure the effect of community characteristics on individual behaviors. Finally, few studies offer conceptual support for why rural/urban differences may shape volunteer behavior.
This paper examines two questions: (1) are there differences between urban and rural residents’ propensity to volunteer, and (2) if yes, what accounts for those differences? In line with the growing body of research that explores the contextual determinants of volunteering behavior, we posit that rural/urban context matters by shaping the socio-economic, organizational, and cultural characteristics (capitals) of a community. Furthermore, we propose that these community characteristics have different effects on the determinants of volunteering across rural and urban communities. Although community has many definitions, for the purposes of this paper we focus on community as a geographic place in which people interact with each other, regardless of whether they share a sense of identity (Flora et al., 2016). We tested this model using the full population of rural and urban respondents to the United States Census Bureau's Current Population Survey (CPS), which allows us to control for the county-level contextual effects of place on volunteering for all respondents.
The probit analysis found no statistically significant difference between rural and urban residents’ likelihood of volunteering, after controlling for individual and community characteristics. However, using an Oaxaca decomposition, we find that rural respondents are more likely to volunteer than urban respondents. Consistent with our conceptual model, place-based differences in the effect of contextual characteristics account for much of the difference in propensity to volunteer across rural and urban places.
Although traditional social science theory posits that pro-social behavior is more abundant in rural places, the conceptual and empirical evidence is far from conclusive. Our paper contributes to the growing scholarship on the place-based determinants of volunteering in several ways. First, scholars increasingly seek to identify the contextual determinants of volunteering behavior; however, existing studies of volunteerism often offer limited conceptual support for rural/urban differences. We offer a conceptual framework that integrates both traditional developmental theories and newer environmental frameworks for explaining why rural/urban differences still matter. Because the effects of contextual determinants are not the same across rural and urban places, our study also has implications for future empirical models that attempt to identify the contextual determinants of volunteering. Finally, our findings have important policy implications as there is growing evidence that volunteering provides numerous benefits to those who volunteer (Wilson et al., 2020), the organizations for which they volunteer (Haski-Leventhal et al., 2019; Ivonchyk, 2019), and the communities they serve (Putnam, 2000; Wilson & Son, 2018). Although volunteering has long played an important role in meeting the needs of rural communities (Wuthnow, 2019), depressed economic development, agricultural restructuring, dwindling support for public services and infrastructure, and an aging population challenge voluntary participation in rural places across the globe (Alston, 2002; Davies et al., 2018; Flora et al., 2016).
Conceptualizing Why “Rurality” Matters for Volunteering
Despite a growing interest in how context shapes volunteering (Rotolo & Wilson, 2012) and the enduring social science interest in rural/urban differences (Adua & Beaird, 2018), empirical and conceptual support for differences in rural/urban volunteering behavior is mixed. Steblay’s (1987) meta-analysis of helping behaviors provides a statistical review of early empirical studies of urban/rural differences in helping behavior and found that rural residents are more likely to help others. However, more recent studies using experimental methods have not consistently supported these differences (Grueter et al., 2020; Zwirner & Raihani, 2020). For example, in field experiments, Zwirner and Raihani (2020) found no differences between urban and rural residents in three forms of pro-social behavior—posting a lost letter, returning a dropped item, and stopping to let someone cross the road.
Recent empirical studies that test rural/urban differences in formal volunteering behavior also return mixed results. Forbes and Zampelli (2014) look at the effect of social, religious, and human capital on volunteerism, using data from the 2006 Social Capital Community Survey. Controlling for rurality, the authors found consistent evidence across several model specifications that rural respondents are more likely to volunteer than urban respondents.Similarly, using the 2010 Japanese General Social Survey, Taniguchi and Marshall (2016) found that rurality is positively and significantly associated with volunteering.
However, other research found no such differences. Glanville et al. (2016) studied the effects of both individual and contextual factors on volunteering behavior using data from the European Social Survey. Using multi-level modeling, the authors showed that while population density is positively associated with the likelihood of volunteering, the inclusion of social capital measures renders the relationship insignificant. Hooghe and Botterman (2012) found that population density has no statistically significant effect on individuals’ engagement in voluntary associations. Finally, Wang et al. (2013) studied how community context influences Hispanics’ decision to volunteer in the USA. Drawing upon three data sources (Current Population Survey (CPS), Philanthropic Module of the Panel Study of Income Dynamics (PSID), and the Arizona, Indiana, and Michigan Giving and Volunteering Survey (AIM)), they found inconsistent results across surveys and subsamples. For example, the CPS survey showed that while non-Hispanics in rural places are more likely to volunteer than urban respondents, rurality has no effect on Hispanic volunteering. Furthermore, the AIM survey found rural Hispanics to be less likely to volunteer than their urban counterparts.
As Wang et al. (2013) suggest, the inconsistent findings on rural/urban differences call for additional research. Clearly some of these variations may result from regional differences, sample differences, diverse measurements of the dependent variable, and the inclusion of additional contextual controls. However, many existing studies, in which rurality is often a control variable, provide little conceptual support for why rural/urban differences should matter for volunteering. In the following section, we review two perspectives on the relevance of place to voluntary behavior—the developmental model and the environmental model—and conclude by offering a conceptual model that integrates both. See Fig. 1 for a visual summary.
A place-based conceptual framework of volunteering
Developmental Model of Volunteering
Traditional social science theory (Goudy, 1990; Hofferth & Iceland, 1998) suggests that the rural propensity for helping behavior is a function of community dynamics, which are shaped by size. Individuals living in rural areas with lower populations are more likely to know each other and share a culture of mutual support and cooperative behavior. According to pioneering social scientists, such as Durkheim and Wirth, who suggested a developmental model (Goudy, 1990), urbanization, accompanied by increased density and heterogeneity, weakened the social bonds between people, reduced individuals’ attachment to their community, and dampened normative consensus.
In contrast, small community size and its face-to-face interactions provide rewards for cooperative behavior, leading to greater civility, higher levels of trust, and strong social networks, which in turn support collective activity and pro-social behavior. Individuals living in small communities are more likely to help others because they know each other, are directly asked, and are likely to have repeated contact with the same person (Grueter et al., 2020; Zwirner & Raihani, 2020). Fallah and Partridge (2007) found that people in small towns and rural areas are more likely to know and encounter one another, creating a sense of responsibility for their welfare. In small towns, “…everyone knows everyone else, and for better or worse, monitor and sanction each other’s behavior to ensure that trustworthiness and reciprocity are the norm” (Besser, 2009, p. 186). In contrast, the process of urbanization led to a breakdown in social ties, social order, and an increase in anonymity from churches, family ties, and personal networks (Hofferth & Iceland, 1998). Both sociology and economics emphasize that small communities are generally able to achieve high levels of cooperative behavior through their ability to select, sanction, and signal norms (Abascal & Baldassarri, 2015). Small size makes it is more difficult for individuals to defy community norms and “free ride.”
These interactions shape a shared moral community, which creates expectations for quality of life, norms of self- and family care, and support for community activities and projects (Wuthnow, 2019). Rural communities tend to value self-reliance, individualism, and informal responses to social needs (Sherman, 2006, 2013), often favoring voluntary aid over government programs. The developmental model, which describes the rural idyll as a “supportive, harmonious and safe environment” (Bernard, 2019, p. 43) compared to the “incivility” of urban places (Grueter et al., 2020), suggests a direct relationship between rurality and volunteering behavior. All else being equal, the developmental models suggests that individuals living in rural places are more likely to volunteer.
Environmental Model of Volunteering
In contrast, the environmental perspective suggests that rural/urban differences are not just a function of the civility promoted by small population size but that these differences are rooted in the diverse resources embedded in communities [i.e., community capitals (et al., 2004, Flora, 2016)]. These contextual resources, above and beyond individual characteristics, shape individuals’ capacity and incentives to help others (Grueter et al., 2020; Sampson, 2012; Zwirner & Raihani, 2020), join organizations (Rotolo, 2000), and volunteer (Rotolo & Wilson, 2012). Studies of the environmental determinants of volunteerism focus on how differences in a variety of place-based resources, including socioeconomic resources, cultural capital, and organizational density, affect volunteering behavior.
Socio-Economic Resources: Education and Income
While many studies have found a positive relationship between individual socio-economic status and volunteering/prosocial behavior (Wilson, 2012a, 2012b), place-based socio-economic resources, such as education and income, also support this dynamic (Grueter et al., 2020; Helliwell & Putnam, 1999; Safra et al., 2016; Zwirner & Raihani, 2020). Wilson’s (2012a, 2012b) theory of “neighborhood social isolation” posits that poor neighborhoods are socially detached from middle- and higher-income communities, which decreases individuals’ motivations, opportunities, and invitations to participate in voluntary activities. Adolescents raised in poor neighborhoods are less likely to be socialized to participate, because they are less likely to be exposed to people, events, and discussions that invite participation (Gimpel et al., 2003). Similarly, education results in higher levels of trust (Helliwell & Putnam, 1999) and an advanced understanding of reciprocity and social contracts, which are necessary for engagement (Bjørnskov, 2007). From a contagion perspective, in high-status neighborhoods many individual can serve as role models for productive behavior and exert greater social control over others (Mayer & Jencks, 1989). From a practical perspective, individuals living in communities with higher levels of development are less preoccupied with meeting immediate material needs and more willing to invest in cooperative behavior with no immediate return (Grueter et al., 2020; Parboteeah et al., 2004). In contrast, individuals living in more disadvantaged communities are more likely to be present-oriented and less focused on the long-term benefits of helping others (Lettinga et al., 2020). Higher status communities might also encourage quality of life values and self-expression (Parboteeah et al., 2004). Meanwhile, socio-economic resources are not equally distributed across place: Rural places have comparatively lower levels of economic development and education (Adua & Beaird, 2018; Goetz & Rupasingha, 2004).
Cultural Capital: Racial Cohesion & Religiosity
Cultural capital refers to the attitudes, knowledge, and preferences that shape how people view their immediate and wider world and impart a shared understanding of standards, norms, and acceptable behavior (Flora et al., 2016; J. Wilson & Musick, 1997; Wuthnow, 2019). In the study of volunteerism, two of the most common cultural dimensions are racial cohesion and religiosity. A growing body of research suggests that racial homogeneity supports cooperative behavior, while diversity dampens cooperation and participation in community life (Alesina & La Ferrara, 2000; Rotolo, 2000; Uslaner & Brown, 2005; Putnam, 2007). At the most basic level, diverse communities produce fewer homophilous ties that bring people into organizations (Rotolo, 2000), weakening connections and shared values and lowering their willingness to sanction free riders (Habyarimana et al., 2007; Putnam, 2000; Scott & Storper, 2015).
Another vital dimension of cultural capital is community religiosity (Lim & MacGregor, 2012; Paxton et al., 2014; Ruiter & De Graaf, 2006), which may have a spillover effect, increasing the likelihood that even non-religious individuals will volunteer. Community religiosity encourages the shared values of compassion common to most religious traditions (Parboteeah et al., 2004) and social regulations that support volunteering norms. Religious communities also provide the social connections that lead to invitations to volunteer and role models of helping behavior (Lim & MacGregor, 2012; Ruiter & De Graaf, 2006).
Some of the stereotypes of rural places as close-knit and cohesive are supported by the racial homogeneity of rural places. Historically, rural areas in the USA have not been as diverse as urban places, although rural communities in the USA are increasingly becoming more diverse (Flora et al., 2016). Similarly, there are “prevailing” beliefs that people living in rural places are more religious than urban residents and that traditional rural value systems support religiosity (Luck, 2010).
Organizational Density: The Size of the Nonprofit and Religious Sector
While the study of helping behavior has largely focused on the characteristics of people, there is growing recognition that the organizational environment also shapes individual behavior. Organizations are not just derivative of community; they actually produce it (Gilster, 2017; McQuarrie & Marwell, 2009; Sampson, 2012; Sharkey & Faber, 2014; Tolbert et al., 2002) and shape voluntary behavior (Rotolo & Wilson, 2012). First, organizations build human capital by addressing basic social needs (Sampson & Graif, 2009), attracting external resources to a community (Sampson, 2012; Small & Stark, 2005), and providing opportunities to learn leadership skills and participate in democratic life (Skocpol, 2013). Second, organizations represent the institutional dimension of social capital (Putnam, 2000; Sampson & Graif, 2009). Place-based organizations provide opportunities for individuals to come together for social, civic, and political purposes. Serving as physical gathering spaces where people interact with each other (Boyd et al., 2016; Tolbert et al., 2002), organizations promote togetherness and identity (Wuthnow, 2019). As McQuarrie and Marwell (2009) described, “In churches, corner stores, coffee houses, schools, community centers, political clubs, and workplaces, neighborhood residents interact to produce shared meanings, mutually intelligible practices, and identities, all of which refer to and reproduce a shared experience of place” (p. 457).
However, organizations are not distributed equally across place. Although rural places may have a higher density of informal organizations, particularly religious ones (Wuthnow, 2019), urban places may have a higher concentration of formal organizations—such as housing, human services, health providers, day care, and early childhood education centers (Allard, 2019).
In summary, an environmental perspective leads us to expect that community socio-economic resources, cultural capital, and organizational assets are all positively related to individual behavior. However, the level of these resources differs across rural and urban communities: Urban communities enjoy higher levels of socio-economic resources and organizational assets, while rural communities lead in cultural capital.
Integrating the Developmental and Environmental Models of Volunteering
While these environmental differences may partially account for the rural/urban divide, we assert that both the developmental and environmental models contribute to understanding the potential differences in volunteering behavior across rural and urban places. To accept both explanations, we must recognize possible inherent differences in rural and urban expectations of volunteering behavior and realize that the environmental determinants of volunteering may “work differently” across each setting. Drawing upon the developmental assumption that the face-to-face, repeated interactions of rural areas have traditionally supported strong norms of self-reliance and collective action and made free riding more difficult, we posit that individuals living in rural places, all else being equal, are more likely to volunteer than individuals in urban places. Integrating the development model and the environmental model leads us to expect that contextual characteristic of place—socio-economic resources, organizational infrastructure, and cultural capital—will have a stronger positive effect on the volunteering behavior of rural residents than on urban residents (Fig. 1).
As an example, let’s consider the effect of community education levels on individual volunteering. All else being equal, although community education levels are lower in rural places, we expect that community education levels will have a stronger positive effect on rural residents’ volunteer behavior. Because better-educated communities create role models and expectations for participation, these factors will exert an even stronger influence on individual behavior in more intimate rural communities. Similarly, we expect that while rural places will be more racially homogenous than urban places, this dynamic will have a stronger effect on rural residents’ volunteer behavior, as homogeneity reinforces the shared values and norms of rural communities.
Methodology
To test our conceptual model, we used the Current Population Survey’s (CPS) September volunteering supplementfrom 2002 through 2015. The CPS is a monthly survey conducted by the U.S. Census Bureau and the Bureau of Labor Statistics to collect data on labor force participation. Along with individual and household demographic data, the volunteering supplement asks questions about the incidence, intensity, location, and other specifics of volunteer service.
The CPS is a two-stage stratified probability sample of US households designed to be representative of the nation and each state. Approximately 56,000 households are interviewed every month, and data are collected on all household members ages 15 and up. Our pooled dataset represents 1,072,000 respondents to the volunteering questions, approximately 90,000 individuals per year. The unit of analysis for our study is individuals.
Each respondent has a corresponding county FIPS (the Federal Information Processing Standards) code; however, many respondents in the publicly available data (approximately 59.44% of the sample) do not have county codes available due to confidentiality concerns. Those respondents without county geographic identifiers in the publicly available dataset reside primarily in rural areas. To locate the geographic identifiers of rural respondents, we accessed the full dataset which provides each respondent’s county FIPS code through a secure Census Bureau Research Data Center.
Although the American federal government provides many different definitions of rural (Flora et al., 2016), for the purpose of this paper, we use the United States Department of Agriculture’s (USDA) rural/urban continuum code to define rural counties. We coded counties at 1, 2, or 3 on this continuum as urban, which includes those located in large and small metropolitan regions and adjacent to metropolitan areas. We classified all other non-metropolitan counties as rural. The USDA distinguishes between metropolitan and non-metropolitan areas based both upon population size and geographical connection to a larger metropolitan labor market. Metropolitan statistical areas (MSA) contain a core urban center, and geographically adjacent communities have a high degree of economic and social integration with the urban center. Non-metropolitan counties include open countryside, rural towns, and urban areas that are less than 50,000 population and not part of a larger MSA (https://www.ers.usda.gov/topics/rural-economy-population/rural-classifications/#map). This rural designation accounts for 19% of respondents in the sample.
Before continuing, it is important to note the growing difficulty in analyzing rural/urban differences. While “rural” was traditionally used to describe places that were “small” and “isolated,” these characteristics are often less relevant today. Our definition is based upon regional metropolitan exchange patterns and population size, and many metropolitan counties contain many residents who live in small communities. Furthermore, once-isolated rural communities are increasingly connected to larger metropolitan areas through information technology and commuting patterns. In addition, many counties contain both rural and urban places. Unfortunately, our data only contain a county identifier, preventing distinctions based upon smaller geographic units (such as town or village). Additionally, some counties may grow over time, while others lose population. Our analysis of the full population of US counties, however, suggests that between 2000 and 2010, only 3% of the counties switched from one classification to the other and some of those changes may result from changes in the USDA classification schemes. However, a robustness check of our models excluding those counties that changed rural classification does not change our results.
The dependent variable in this study is whether an individual volunteered through or for an organization during the previous year; this measure was created from two survey questions. The first reads: “Since September 1st of last year, have you done any volunteer activities through or for an organization?” Then, a follow-up prompt asks: “Sometimes people don’t think of activities they do infrequently or activities they do for children’s schools or youth organizations as volunteer activities. Since September 1st of last year, have you done any of these types of volunteer activities?” Respondents who answered yes to either question were coded as 1 on the volunteering variable; other respondents were coded as 0. This variable includes volunteering for either religious or secular organizations.
The individual variables in this study included age, gender (0 if male, 1 if female), race (white = 1, 0 otherwise), ethnicity (0 for non-Latinx, 1 for Latinx), education (0 for no bachelor’s degree, 1 for bachelor’s degree or greater), household income level, and homeowner (0 if not, 1 if so), citizenship (0 if not a US citizen, 1 if so), marital (0 if not, 1 if so), and employment (0 if not, 1 if so) status. These variables are the most used socio-demographic predictors of individual volunteering behavior (Musick & Wilson, 2007; Wilson, 2012a, 2012b).
Our county-level measures of socio-economic resources included a variable capturing the proportion of the county with a bachelor’s degree and the median household income for the county (www.census.gov).
We included two measures of cultural capital: racial homogeneity and religious adherents. We used Census Bureau data to create a racial homogeneity index (a Herfindahl index), created by summing the squared proportion of each race group in the county:
\({\text{Racial}}\;\;{\text{homogeneity}} \left( {{\text{Herfindahl}}} \right) = \sum \left( {p_{{{\text{White}}}}^{2} + p_{{{\text{Black}}}}^{2} + + p_{{{\text{Asian}}}}^{2} + p_{{{\text{Native}}\;{\text{American}}}}^{2} + p_{{{\text{Pacfic }}\;{\text{Islander}}}}^{2} + p_{{{\text{other}}}}^{2} } \right)\) The religious adherent rate for the county represents the number of religious adherents in the county for every 10,000 people (Association of Religious Data Archives). To capture organizational density, we included the number of religious congregations (Association of Religion Data Archives) and nonprofit organizations per 10,000 people in the county (National Center for Charitable Statistics Business Master File). Finally, we captured US region (0 if not in the southern portion, 1 if yes), as the southern region of the USA is thought to have historical traditions, rooted in the legacy of slavery, that limit association activity (Putnam, 2000).
To examine the volunteering gap between rural and urban areas, we began by using a standard probit non-linear regression model, running one model for the full sample and then splitting the sample between rural and urban respondents. To address the difficulty in comparing coefficients across split samples in probit models, we used an extension of the traditional Oaxaca-Blinder decomposition technique, which uses estimates from a probit model to compare two samples of unequal sizes (Fairlie, 2005). This technique tracks the differences between two groups in a dataset, to determine whether those differences stem from variations in the levels of the independent and control variables in the model or from their effects. This method, often used to determine causes of the gender wage gap (bias against female employees vs. differing levels of education, tenure, and other variables), gives a coefficient for each of the two groups examined; the difference between those coefficients represents the gap between the groups. Our rural dummy variable was used as the grouping variable for the decomposition.
This decomposition yielded the difference in the means of the dependent variable (volunteering) across two different groups (rural and urban respondents). This difference is comprised of three components. The endowment effect is the proportion of the rural/urban volunteering gap attributable to the differences in the magnitude of the explanatory variables across rural and urban areas. The coefficient effect is the proportion of this gap attributable to the difference in the coefficients between rural and urban respondents. The third—the interaction effect—is the difference resulting from the interaction between differences in endowments and differences in coefficients.
Results
Table 1 displays descriptive statistics for our dataset for both the full sample and the split samples of rural and urban respondents. Overall, 30.1% of all respondents report volunteering, with 32.2% of rural respondents reporting volunteering and 29.7% of rural respondents volunteering. All the means of the demographic variables were significantly different between rural and urban respondents (p < 0.01). Rural respondents tended to be slightly older and married. Rural and urban respondents also exhibit different racial and ethnic characteristics. White respondents comprised 88.6% of the rural sub-sample and 82.7% percent of the urban subsample. Almost 5% of rural respondents identified as Latinx, compared to nearly 13% of urban respondents. A higher proportion of rural respondents were citizens (97.7% vs. 92.3%) and owned their own homes (77.2% vs. 71%). However, urban respondents had higher levels of education and income and were more likely to be employed.
There were also contextual differences across counties. Twenty-one percent of rural counties were in the south (vs. 24% of urban counties). Rural counties had higher levels of religious adherents, congregations, and more nonprofits per capita. In contrast, urban counties demonstrated higher levels of education and median household income.
To show the differences in volunteering across rural and urban places, we begin by tracking this activity over time. Figure 2 graphs the percent of respondents that report volunteering, broken down by rurality. The percentage of respondents that report volunteering is declining among both rural and urban respondents. The decline appears steepest among rural volunteers and by 2015 urban/rural volunteering differences are no longer significant.
Trends in volunteering
Table 2 displays the results of three regression models, one focusing on the entire dataset and the other two focusing separately on the rural and urban split samples. Most of the demographic variables behaved as expected in the regression using the full dataset, though age and age-squared were not statistically significant predictors of volunteering. Importantly, although the likelihood of volunteering differs across rural and urban places, being from a rural county does not have a statistically significant effect on the likelihood of volunteering when all other individual and community level variables are considered.
Although it is difficult to compare coefficients across nonlinear models, the results for the split sample regressions suggest the predictors of volunteering may vary for individuals living in rural and urban communities. Other than the age variable, the sign and significance of the individual coefficients in the rural and urban regressions followed the same patterns. However, there were notable differences in the effects of the county-level variables across rural and urban respondents. County rates of religious adherence increased the likelihood of volunteering for rural respondents but decreased the likelihood of volunteering for urban residents (βrural = 0.0002, p < 0.001, βurban = 0.–0.0003, p < 0.01). While there was a negative relationship between median county income and the likelihood of volunteering for urban respondents, county income had no significant relationship to rural volunteering. Similarly, county homogeneity displayed a positive effect on the likelihood of volunteering for urban respondents but no effect on the likelihood of rural volunteering. Similarly, nonprofit density demonstrated a positive effect on the likelihood of volunteering for rural respondents, while congregational density had a positive effect on urban volunteering. Living in the south decreased the volunteering of rural respondents more than for urban respondents (βrural = − 0.22, p < 0.001, βurban = 0.–0.072, p < 0.001).
Taken together, these results suggest several explanations for differences between rural and urban volunteering. First, while the development model might suggest that rurality is positively related to volunteering, rurality does not have a direct effect on volunteering behavior when other individual and environmental variables are included, providing no support for the developmental model. Second, the socio-demographic characteristics of people and place differ across rural and urban contexts. Third, while the individual service predictors across rural and urban places are generally similar, the effects of county-level characteristics on the likelihood of volunteering differ across rural and urban places. These last two observations suggest that any gap between rural and urban volunteering rates is best explained by the integration of the developmental and environmental models, leading us to our Oaxaca-Blinder decomposition (Table 3).
The Oaxaca-Blinder decomposition parses out how the differences in individual and place-based characteristics and coefficients account for differences in volunteering across rural and urban respondents. The decomposition analysis estimates the underlying factors that explain the rural/urban gap by matching subsets of rural and urban respondents on mean values of explanatory characteristics (individual and contextual) and estimating the extent to which the change in the predicted probabilities of the dependent variable (volunteering) are due to rural/urban differences in the values of the explanatory measures or differences in the coefficients of these measures. These gaps represent the differences in the volunteer rates between rural and urban respondents (in contrast to the probit model which explores differences in the likelihood of volunteering). The mean volunteering rate for urban respondents was 0.297 (meaning 29.7% of urban respondents reported volunteering during the prior year), and the mean for rural respondents was 0.322 (32.2% of rural respondents volunteered during the prior year). The rural/urban volunteering gap (rural volunteering—urban volunteering) is 0.025, a statistically significant difference. The Oaxaca-Blinder decomposition breaks that difference down into the proportion explained by the characteristics of the respondents and the places that they live (0.054) and the proportion explained by the differences in the effect of these individual and contextual factors on the likelihood to volunteer (0.056). A variable’s positive and significant endowment effect indicates that its differing magnitudes across rural and urban counties provide a rural advantage and would increase urban volunteering relative to rural if an urban community had similar levels of endowment. Similarly, a positive sign on the coefficients effect indicates that the effect of that variable on volunteering will increase urban volunteering relative to the rural rate. In both cases, a positive sign suggests that variable provides a “rural advantage.” Our results suggests that the rural/urban difference in the volunteering rates results from both differences in characteristics and different effects of those characteristics on rural vs. urban respondents. If urban respondents and their places shared the characteristics of rural respondents, urban volunteering rates would increase by 0.05. If the coefficients in the model had the same effect in urban places as they did in rural places, then urban volunteering rates would increase by 0.06. It is important to note that because this technique may be sensitive to the order in which variables are introduced into the model, we replicated the model by reversing the order of introducing the independent variables (Fairlie, 2005). Overall, the results were not substantially different from the reported estimates.
To account for the specific factors that drive these differences, we used a detailed composition to decompose the contribution of individual and contextual variables. As Fig. 3 suggests, contextual variables account for most of the variation in rural/urban volunteering rates. If urban respondents had the same individual characteristics as rural respondents, volunteering rates among urban respondents would decrease by 0.01, but the effect of these individual characteristics on the likelihood of volunteering is stronger for rural respondents and accounts for a difference of 0.04 (p < 0.05). However, this difference requires cautious interpretation as it may not meet the more rigorous significance levels appropriate for a large data set.
Decomposition of the rural/urban volunteering gap
When we reduce the contextual factors to specific county level variables, we find that if urban places had the same congregational density as rural places, the likelihood of urban respondents volunteering would increase by 0.067 (p < 0.001). Similarly, if urban places were as homogenous as rural places, volunteering among urban respondents would increase by 0.01 (p < 0.001). However, congregational density and homogeneity both have a stronger positive effect on urban volunteering, reducing the gap between rural and urban respondents by 0.042 (p < 0.001) and 0.043 (p < 0.001), respectively. Although difference in the density of nonprofits does not statistically account for differences in volunteering, nonprofit density has a stronger positive effect on volunteering in rural places, providing a rural advantage of 0.024 (p < 0.001). What is most notable, however, is the strong effect of place-based religiosity (measured as the density of religious adherents) on explaining the gap. If religiosity had as strong a positive effect on volunteering in urban places as it does in rural places, urban volunteering would increase by 0.096 (p < 0.001).
The effect of education and income is less clear. Levels and effect size of county median income provide an advantage to rural places, but in an unexpected way. Because county income is negatively associated with the likelihood of volunteering in urban places, the lower median income of rural places provides an advantage to rural places. Furthermore, the negative effect of county income, strongest in urban places, widens the rural/urban volunteering gap. In contrast, lower levels of education in rural counties and the weaker effect of education on the likelihood of volunteering reduce the rural/urban gap. Overall, the characteristics of place, as well as differences in the size of effects across place, support higher rates of volunteerism in rural places. Figure 3 visually summarizes these results.
In summary, several important patterns emerge. First, differences in rural and urban residents’ likelihood of volunteering are not the result of differences in the characteristics of people across place, which explains only 0.011 of the gap and actually provides an urban advantage. Instead, the rural volunteering advantage can be explained by differences in the effects of individual determinants of volunteering across rural and urban areas (0.043), differences in the level of place-based resources (0.064), and the differing effects of place-based resources (0.092). In general, the endowments of place contribute to the rural volunteering advantage. However, the patterns of the differences in the effects of community resources are not as clear. Some contribute to the rural advantage (community income, adherent rate, and the density of nonprofit organization), while others reduce the rural/urban volunteering gap (education rates, homogeneity, and congregational density).
A close look at the interaction effects, specifically the simultaneous effect of endowments and coefficients, tells another important story, as the joint effect (− 0.08) is larger than individual coefficients and contextual endowments. Although researchers often ignore the interaction effect, in our case it accounts for a significant proportion of the gap. The interaction term can be interpreted as the differential effect of the change in coefficients as the endowments change: what would happen if urban residents’ endowments and coefficients changed to match that of rural residents? Congregational density accounts for almost 80% of the interaction effect. While rural counties have a higher density of religious counties, the interaction suggests that as the density of religious organizations in rural places increases, it dampens volunteering propensity, potentially reducing the rural/urban volunteer gap. A similar pattern exists in the effects of racial homogeneity. We will discuss the implications of these findings for theory development in the next section.
Discussion
Our study contributes to the growing body of research that suggests that community context matters for volunteering, and not merely as proxy for characteristics of individuals. Our findings provide evidence of the rural volunteering advantage and demonstrate how community context shapes differences in the volunteering behavior of individuals living in rural and urban places. In this section we will discuss the implication for theories of volunteering, empirical modeling, and practice.
First, our findings offer a conceptual framework that rural/urban differences result from variations in resource levels across rural and urban communities, and the differing effects of these resources across place. We find that differences in volunteering behavior occur not because rural places are inherently more civil, but because small places, by nature, have different levels of endowments to support volunteerism and activate these community resources in different ways. These findings provide support for an integrated model of volunteering that draws upon both traditional development and environmental explanations.
Second, while this integration of the developmental and environmental models may seem novel to volunteering scholars, it appears in studies of other forms of cooperative behavior. For example, Lettinga et al. (2020) suggested that cooperative behavior is more risky in the face of environmental adversity (economic deprivation and resource scarcity). However, they concluded that the risk of cooperating in adverse contexts may be lower in smaller communities, where a decreased likelihood of stranger interactions reduces opportunities for cheating and freeriding.
Some of our unexpected findings offer opportunities for additional research. For example, contrary to Lettinga et al. (2020), we found that community income provides a rural advantage, but not because rural communities have higher levels of income, or because community income has a stronger positive effect on rural volunteering, as we might expect. Rather, community income dampens volunteering in urban communities, but has no effect in rural communities. Our models cannot provide a clear explanation for this result. While Lettinga et al. (2020) posited that resource scarcity increases the “risk” of helping others, it may be that resource munificence in urban areas is accompanied by greater competition for one’s time. Individuals in wealthier urban communities with higher costs of living may face greater pressure to work longer hours or they may have more opportunities to engage in leisure and social activity in commercial spaces—such as coffee shops, gyms, and movie theaters.
Other patterns in our findings are important to note because of their implications for future research. Though we expected all resources to have a stronger positive effect on rural behavior, this was not always the case. Community religiosity did exert a stronger effect in rural places, possibly because rural religious traditions are inherently more conservative (Chalfant & Heller, 1991), but more likely because the religious spillover effect may be stronger in rural places, where religious and secular connections are more likely to overlap. However, the positive effects of population homogeneity and congregational density on individual volunteering are strongest in urban places, where these resources may promote social interactions and shared norms. In rural communities, individuals may be more likely to meet randomly (Fallah & Partridge, 2007) and know each other in multiple contexts (Painter & Paxton, 2014). Those forces (religious institutions and population homogeneity) that mimic the attributes of “small community” in urban places may serve to close the gap in rural/urban volunteering behavior. Additional research should explore the differences in how community resources are activated across rural and urban places. Finally, while we observe in Fig. 1 that volunteering rates are declining rapidly in rural places, possibly affecting their advantage, our results are not able to explain these declines. Our results provide strong evidence that community matters, future research should explore how changes in community context shape individual volunteering behavior.
These findings also have implications for volunteerism research methods. Our study is unique in that, through access to confidential data, we were able to draw upon a nationally representative sample of both rural and urban respondents. While individual characteristics explain only a small percentage of rural/urban differences, our results confirm the importance of including contextual variables in studies of volunteerism. Since place matters for volunteerism, and perhaps other forms of philanthropic and civic action, empirical studies that attempt to identify the determinants of voluntary action, but do not include both rural and urban respondents or account for context, may return results that are not generalizable across respondents and places. Second, our findings offer important insights into the rural/urban volunteering divide. At the most basic level, the determinants of volunteering differ across rural and urban places. As our results suggest, merely including a rural/urban control may mask the complexity of rural/urban differences.
Our research posed important questions for policy and practice. Rural communities across the globe have been experiencing dramatic demographic and economic change, including depressed economic development, aging populations, increased diversity, and declining religiosity (Flora et al., 2016). These changes can alter when and how rural residents interact with each other, how they develop social networks, and their capacity to volunteer. Our findings, combined with other studies that report declines in social capital (Besser, 2009) and decreases in voluntary association membership and participation (Painter & Paxton, 2014) in rural places, raise alarms for the future of rural voluntary action. In the face of decreased government responsibility for social needs, declines in volunteerism in rural places that already have less access to government and commercial services are particularly problematic. Few studies specifically address the challenges of rural volunteering and suggest resolutions. Additional attention should be paid to both the contextual drivers of voluntary action and how volunteerism can be sustained amid changes in rural places.
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Funding
This material is based upon work funded by the Office of Research and Evaluation at the Corporation for National and Community Service (CNCS) under Grant No. 18RE207108 through the National Service and Civic Engagement research grant competition. Opinion or points of view expressed in this document are those of the authors and do not necessarily reflect the official position of, or a position that is endorsed by, CNCS. Any opinions and conclusions expressed herein are those of the author(s) and do not necessarily represent the views of the U.S. Census Bureau. This research was performed at a Federal Statistical Research Data Center under FSRDC Project Number 1833. All results have been reviewed to ensure that no confidential information is disclosed.
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