Social Capital and Generosity

Abstract

Although much is known about the individual-level predictors of volunteering, charitable giving, and informal helping, less is known about how the characteristics of communities shape generosity. In this article, we assess the predicted effects of both individual- and contextual-level social capital (social networks and generalized trust) on three forms of generous behavior using the European Social Survey, which provides complete data on over 30,000 respondents in 160 regions in 19 countries. The results suggest that regional-level trust is associated with more volunteering and donating to charities. In addition, regional-level social capital (the combination of trust and social ties) predicts greater volunteering. The relationship between contextual-level social capital and informal helping is weaker.

Keywords

social capital generosity volunteering charitable giving informal helping

Your corn is ripe today; mine will be so tomorrow. ‘Tis profitable for us both, that I should labour with you today, and that you should aid me tomorrow. I have no kindness for you, and know you have as little for me. I will not, therefore, take any pains upon your account; and should I labour with you upon my own account, in expectation of a return, I know I should be disappointed, and that I should in vain depend upon your gratitude. Here then I leave you to labour alone; You treat me in the same manner. The seasons change; and both of us lose our harvests for want of mutual confidence and security.

—David Hume (1740)

Ever since Hume’s (1740/1978) classic observation, philosophers and scholars have wondered what can produce generous individuals who cooperate, share, help, and overcome dilemmas of collective action. One strain of explanation focuses on the role that local-level social bonds play in producing individual action for the public good. Perhaps the most famous example is Robert Putnam’s (1993) Making Democracy Work, which argued that differences in civic life across Italian regions could be explained by the level of social capital held within each region. Social capital, or “features of social organization such as networks, norms, and social trust that facilitate coordination and cooperation for mutual benefit,” made the difference in regions that thrived (Putnam, 1995, p. 67). Without trust and social connections, the citizens of other regions were unable to work together to solve common problems, share resources, or generally overcome selfishness to produce any type of public good. Overall, there exists a long-term, established interest in understanding what local-level features promote sharing, generosity, and individual action for public benefit.

At the same time, recent research on individual actions such as volunteering time to voluntary associations and charities, as well as donating money to charities, has begun to answer a call to move beyond its traditional micro-level focus on individual-level determinants (Hodgkinson, 2003; Smith, 1994; Wiepking & Handy, 2015; Wilson, 2000). This new work, exemplified by Rotolo and Wilson (2012), Ruiter and De Graaf (2006), and Voicu and Voicu (2009), recognizes that there are processes that operate at the societal level in choices to volunteer or donate. “These processes are not reducible to the characteristics of the individuals living there” (Rotolo & Wilson, 2012, p. 453). Most of this work takes the nation-state as the aggregate unit of analysis and has begun to illuminate the role of democracy (Voicu & Voicu, 2009), the welfare state (Stadelmann-Steffen, 2011), and religion (Lim & MacGregor, 2012; Ruiter & De Graaf, 2006) in contributing to cross-national variation in volunteering. But rates of formal and informal volunteering and charitable giving also vary across regions within nations (Pichler & Wallace, 2007). Previous research has not yet examined whether characteristics of lower level localities, in particular if they have high levels of social capital, contribute to volunteering and other forms of generous behavior. The lack of research in this area is surprising given claims in the social capital literature regarding the fundamental importance of social capital in contributing to societal well-being (Putnam, 1995, 2000).

In this article, we assess the potential effects of individual- and contextual-level social capital on formal volunteering, charitable giving, and informal helping. We focus on multiple aspects of generosity, rather than only one, because studying pieces in isolation may obscure the full picture of how features of communities shape this syndrome of behaviors. Studying multiple forms of generosity simultaneously may also uncover crucial differences in their sources. We examine the role of social capital at the regional, rather than country, level. Almost all previous research that considers contextual influences on volunteering or donating addresses differences across countries. But theory suggests effects at lower aggregate levels (e.g., Paxton, 2007; Putnam, 1993). Regions more closely approximate this theory. We theorize that two classic measures of social capital at the individual level, trust and social ties, aggregated to the regional level, can be viewed as resources reflecting the nature of social relations within that locality. Accordingly, these features of local areas should influence individual decisions to behave generously. We assess the role of social capital in generosity through multilevel models using the European Social Survey (ESS).

Theoretical Background

Generosity

How can we best conceptualize “individual action for public benefit?” We use the concept of generosity, or “giving good things to others freely and abundantly” (Science of Generosity, 2012), to conceptualize a constellation of behaviors including volunteering, donating to charities, and informal helping. Generous acts are those that benefit another person or persons and cost the giver something, whether it is time, money, or energy. Generous behaviors, like volunteering, are essential to well-functioning societies; promote the well-being of individuals, communities, and nations; and played a critical part in our evolution as cooperative beings (Bowles & Gintis, 2011; Keltner, 2009; Musick & Wilson, 2008; Putnam, 2000; Tomasello, 2009). Studying individual behaviors in isolation does not provide the full picture of how features of communities shape the syndrome of behaviors we define as generosity. Thus, our work focuses on three of these distinct behaviors: volunteering, donating, and informal helping.

Volunteer work is unpaid work that is freely chosen, deliberate, extends over time, and is “engaged in without expectation of reward or other compensation and often through formal organizations,” and is “performed on behalf of causes or individuals who desire assistance” (Snyder & Omoto, 2008, p. 3). Donating, or charitable giving, is the voluntary transfer of money or cash equivalents to a cause or organization. Donating is performed with the intention of benefiting “others beyond one’s own family” (Bekkers & Wiepking 2011b, p. 925). Informal helping is “any assistance given directly—that is not through a formal organization to non-household individuals” (Lee & Brudney, 2012, p. 160) that is uncompensated. Informal helping usually occurs in direct response to a request and is likely to be directed toward known others and is therefore usually more spontaneous than formal volunteering (Wilson & Musick, 1997). For example, informal helping includes activities such as helping a friend move or taking a neighbor to the doctor. Wilson (2012) encourages broadening our conception of “volunteerism” to include such informal helping acts. Rather than substituting for one another, volunteering, and informal helping are positively related (Burr, Choi, Mutchler, & Caro, 2005; Lee & Brudney, 2012; Plagnol & Huppert, 2010; Wilson & Musick, 1997), as are volunteering and donating (Schervish & Havens, 1997).

Social Capital

Social capital refers to the idea that certain social relations can facilitate the production of individual or collective goods. Two key components of social capital are social networks and trust (Adler & Kwon, 2002; Glanville & Bienenstock, 2009; Paxton, 1999).¹ Social networks, combined with trust, or the expectation that others will behave with goodwill and that they intend to honor their commitments (Yamagishi & Yamagishi, 1994), have value. Social capital exists at multiple levels of the social structure. Individuals can access social capital which provides personal benefits such as social support or information that might be used in finding a job (Lin, 2001; Paxton, 1999). But, beyond individuals, social capital can exist at the level of groups and communities (e.g., Bourdieu, 1986; Coleman, 1988), and some have suggested even at the level of nations (Paxton, 2002; Putnam, 1993). At aggregate levels, social capital is said to reduce incentives for opportunism and thereby allow the “dilemmas of collective action to be resolved” (Putnam, 1995, p. 65). Both components of social capital, social networks and trust, are expected to operate at both the individual and contextual levels to influence information about opportunities and incentives for engaging in generous behaviors.

Hanifan’s (1916) early definition of social capital leads us toward an understanding of the relationship between social capital and generosity:

I do not refer to real estate, or to personal property or to cold cash, but rather to that in life which tends to make these tangible substances count for most in the daily lives of people, namely, goodwill, fellowship, mutual sympathy and social intercourse among a group of individuals and families who make up a social unit . . . If he may come into contact with his neighbor, and they with other neighbors, there will be an accumulation of social capital, which may immediately satisfy his social needs and which may bear a social potentiality sufficient to the substantial improvement of living conditions in the whole community. The community as a whole will benefit by the cooperation of all its parts, while the individual will find in his associations the advantages of the help, the sympathy, and the fellowship of his neighbors. (pp. 130-131)

Social Capital and Generosity at the Individual Level

The role of individual-level social capital in producing the generosity outcomes we examine is already well established (Bekkers, 2012; Brooks, 2005; Brown & Ferris, 2007; Forbes & Zampelli, 2014; Jones, 2006; Wang & Graddy, 2008; Wilson & Musick, 1997). Arguably one of the most pervasive observations in research on the predictors of formal volunteering is that persons with larger social networks volunteer more (Musick & Wilson, 2008). Larger networks promote volunteering because they increase information about volunteering opportunities and the likelihood of being asked to volunteer. Larger social networks are also likely to draw people into charitable giving through information and requests (Schervish & Havens, 1997),² as well as increase the likelihood of informal volunteering (Burr et al., 2005; Lee & Brudney, 2012).

Generalized trust is also an important contributor to generous behaviors through shaping motivation. A critical foundation for cooperative behavior (Gambetta, 1988; Hardin, 2002), trust “creates an expectation that others will act on behalf of the common good” (Neilson & Paxton, 2010, p. 9). In other words, higher trust leads to more prosocially oriented behavior as it is central to the development of a sense of interdependence with others and contributes to the belief that others will reciprocate. When individuals expect that others are contributing to public goods, they themselves are more likely to cooperate in public goods games (Fischbacher, Gächter, & Fehr, 2001), and trust has been previously linked to volunteering (Bekkers, 2012) and donating (Brooks, 2005; Wiepking & Maas, 2009).

Social Capital and Generosity at the Contextual Level

The mechanisms through which contextual-level social capital leads to generous behavior parallel, but go beyond, those at the individual level. Social capital at the contextual level is viewed as a public good that produces a “rainmaker” or “spillover” effect in which the resources produced from social capital diffuse beyond those who possess the social capital to others who live in regions with high social capital, even if they, themselves, do not have high levels of social capital (Putnam, Pharr, & Dalton, 2000; Ruiter & De Graaf, 2006; van der Meer, 2003). In our case, we hypothesize that residents of a socially integrated region with high levels of trust will be more likely to engage in generous behaviors than identical persons who reside in a lower social capital region. This is because information about opportunities and incentives to be generous are more prevalent in regions with higher levels of social capital, and both components of social capital at the regional level enhance the capacity for collective action.³

Indeed, while a great deal of research focuses on the link between individuals’ networks and volunteering, contextual-level networks should also be crucial to the spread of information about volunteering and giving opportunities. In more integrated communities, residents are more likely to be exposed to formal opportunities to donate time and money. This increased likelihood is not simply a matter of aggregation of the individual-level relationship. Instead, we hypothesize that regional-level networks operate in their own right. Even individuals with fairly minimal connections should be more likely to volunteer and donate if they reside in a local area high in connections than in an area with a lower level of connections because their own networks will be better connected in a high social capital area than in a low social capital area (see Paxton, 2007). Furthermore, social networks discourage free-riding (Lee & Brudney, 2009; Marwell & Oliver, 1993) while “community ties within a physical place draw people into common interests and public engagement” (Jones, 2006, p. 252).

We also expect that trust will operate at the contextual level. A higher level of trust within a region creates an expectation of “regular and honest behavior” by others that reduces the potential risks and uncertainty associated with others’ actions, and therefore aids in overcoming the free-rider problem in the pursuit of cooperative goals (Fukuyama, 1995, p. 153). In other words, in more trusting regions, individuals should behave more generously because they see evidence of trust and reciprocity in their daily lives, and consequently, they will have more confidence that their generosity will be well placed. For example, an individual will be more likely to contribute toward a neighborhood cleanup project when he or she believes that other neighbors will be doing so (Wilson & Musick, 1997). Hence, regardless of an individual’s level of trust, residence in a higher trust region should enhance his or her motivation to engage in generous behaviors.

Although ties and trust at the individual level are likely to shape donating, volunteering, and informal helping, we expect that contextual-level social capital should be particularly important in predicting charitable giving and volunteering in contrast to informal helping. Charitable giving and volunteering are generally directed toward collective goods (Bekkers & Wiepking, 2011b; Wilson & Musick, 1997), and social capital is thought to be particularly instrumental in resolving the dilemmas of collective action (Putnam, 1995). Informal helping, then, should be less shaped by social capital at the contextual level because the prevalence of opportunities and incentives to help persons to whom one is not directly connected or to get involved in collective action are not as relevant when considering whether someone helps a specific individual with whom they share a direct connection. Thus, while we investigate the role of regional-level social capital in informal helping, we anticipate that regional-level social capital will play either less of a role or no role in predicting informal helping.

Data and Method

The analyses are based on the first wave (2002) of the ESS which collected representative samples of non-institutionalized residents aged 15 years or older in 21 European countries (for details see www.europeansocialsurvey.org).⁴ The first wave of the ESS is well suited to our research interests because it includes information on multiple facets of both social capital and generosity. Also essential for our purposes, the survey was designed to facilitate the analyses of regional-level factors. In the first wave, 39,860 respondents in 174 regions and 21 European countries were surveyed in face-to-face interviews, although questions on volunteering and donating were not included in the surveys of Switzerland and the Czech Republic, which reduces the number of respondents to 36,460. After excluding respondents with missing values on any of the variables included in the analyses, the final sample for the volunteering and donating analyses is 33,062 individuals from 160 regions in 19 European countries, and the sample for the analysis of informal helping includes 35,384 individuals from 174 regions in 21 countries.⁵

The ESS provides sub-national regional indicators that correspond to the Nomenclature of Territorial Units for Statistics (NUTS) system developed by the Statistical Office of the European Communities (Eurostat, 2007). Institutional divisions as well as considerations of the “general character” of areas such as the predominant economic activity, language, ethnic origin, and shared history informed the NUTS designations. We use the NUTS 2 level, which corresponds to medium sized regions where populations range from 800,000 to 3 million, as our regional units of analysis.⁶

Dependent Variables

Volunteering is coded from a series of items that ask individuals whether they have done any voluntary work in the last 12 months for 12 different types of voluntary associations: (a) sports club or club for out-door activities; (b) an organization for cultural or hobby activities; (c) a trade union; (d) a business, professional, or farmers’ organization; (e) a consumer or automobile organization; (f) an organization for humanitarian aid, human rights, minorities, or immigrants; (g) an organization for environmental protection, peace, or animal rights; (h) a religious or church organization; (i) a political party; (j) an organization for science, education, or teachers and parents; (k) a social club, club for the young, the retired/elderly, women, or friendly societies; and (l) any other voluntary organization similar to the ones above. Volunteering is the count of the number of organization types for which the respondent indicated they had volunteered.⁷

Charitable giving is obtained from the same set of voluntary associations and is coded as the count of the number of organization types for which the respondent says they donated money in the last 12 months. Because they are highly skewed, volunteering and charitable giving are logged. Although volunteering and charitable giving are often dichotomized, using the continuous measures allows us to capture distinctions among those who volunteer for or give to only one organization and those who do so for more than one.⁸

The question “Not counting anything you do for your family, in your work, or within voluntary organizations, how often, if at all, do you actively provide help for other people?” provides our measure of informal helping. Respondents chose from seven response categories ranging from “every day” to “never.” We translated the response categories into the number of days per year that respondents engage in the behavior, ranging from 0 to 365. The multivariate analysis uses the logged transformation to correct the high level of skewness. A notable limitation of this measure is that it does not capture help extended to relatives outside of the respondent’s household as is more typical in studies of informal helping (e.g., Lee & Brudney, 2012; Wilson & Musick, 1997). However, because many instances of helping are directed toward friends and neighbors (Amato, 1990), the ESS measure still captures a good deal of informal helping behavior.

Descriptive statistics for all of the variables in the analyses are summarized in Table 1.

Table 1.

Descriptive Statistics (N = 33,062).

	Range	M	SD
Dependent variables
Volunteering	0-12	0.29	0.75
Volunteering (logged)	0-2.57	0.17	0.37
Charitable giving	0-12	0.50	1.01
Charitable giving (logged)	0-2.57	0.27	0.47
Informal helping	0-365	53.89	94.84
Informal helping (logged)	0-5.90	2.73	1.72
Individual-level variables
Trust	0-10	5.15	2.01
Social ties	0-365	126.03	122.87
Social ties (logged)	0-5.90	4.20	1.34
Children in household	0-1	0.41	0.49
Sex (female = 1)	0-1	0.52	0.50
Years of education	0-40	11.88	4.07
Age	14-109	46.08	18.09
Age²	196-11,881	2,451.06	177,1.76
Married	0-1	0.55	0.50
Unemployed	0-1	0.05	0.21
Minority	0-1	0.03	0.18
Institutional trust	0-10	4.75	2.08
Religious attendance (logged)	0-5.90	1.49	1.56
Catholic	0-1	0.36	0.48
Protestant	0-1	0.17	0.38
Orthodox	0-1	0.07	0.25
Other Christian	0-1	0.02	0.14
Other religion	0-1	0.02	0.13
Urban	0-1	0.33	0.47
Length of residence	0-98	23.57	18.99
Regional-level variables
Average trust	1.99-6.82	5.15	0.96
Average social ties	28.86-286.87	126.03	34.65
Average social ties (logged)	2.51-5.36	4.20	0.42
GDP (at current market prices) (/100)	0.34-2.55	1.15	0.42
Proportion high education	0-0.48	0.21	0.09
Population density	0.003-6.12	0.35	0.75
Proportion minority	0-0.22	0.03	0.03

Social Capital

The analyses investigate the role of two core aspects of social capital: generalized trust and social ties. Generalized trust is derived from an index (α = .77) of the respondent’s degree of agreement, on a 0 to 10 scale, with the following three statements: (a) “Most people can be trusted, or you can’t be too careful in dealing with people”; (b) “Most people would try to take advantage of you if they got the chance, or try to be fair”; and (c) “Most of the time people try to be helpful or they are mostly looking out for themselves.” Previous research on these items indicates that they are conceptually consistent and together provide a strong measure of latent generalized trust (Paxton, 1999).

Social ties are measured by how often the respondent meets socially with friends, relatives, or work colleagues. According to the questionnaire, “meet socially” implies meeting “by choice rather than for reasons or either work or pure duty.” Respondents chose from seven response categories ranging from “never” to “every day.” We recode the original ordinal variable to the number of days per year, from 0 to 365, and then log transform it.

We also include the presence of children in the household as an additional proxy for social ties at the individual level. Having children in the household is a consistent predictor of volunteering (Smith, 1994). Wilson and Musick (1997) argue that this association is present because children in the household lead to greater participation in community activities and consequently more social contacts.

The averages of trust and social ties at the regional level provide the contextual measures of social capital.

Controls

The models we present below control for several additional individual- and regional-level variables to rule out alternative explanations to the extent possible with cross-sectional data. First, we include a series of sociodemographic controls. Sex is controlled because rates of volunteering and helping differ significantly between men and women. On average, men participate in more voluntary associations than women in European societies (Curtis, Baer, & Grabb, 2001), while women are more involved in providing help in long-term close personal relationships (Beutel & Marini, 1995; Eagly & Crowley, 1986). We also include age, measured in years. Because age and volunteering are related in a curvilinear manner (Curtis et al., 2001; Ruiter & De Graaf 2006), the quadratic form of age is also included. Education is an important predictor of volunteering and charitable giving (Wiepking & Maas, 2009; Wilson & Musick, 1997 see Wilson, 2012, pp. 185-186, for a review) as well as social capital (Helliwell & Putnam, 2007) and is measured by the years of full-time education that respondents completed. We also include an indicator of whether the respondent is married or in a civil partnership. Individuals with fewer resources are less likely to be asked to volunteer (Musick, Wilson, & Bynum, 2000). Therefore, we also control for whether the respondent is unemployed and whether he or she identifies as belonging to a minority group.

In addition to the standard sociodemographic predictors, we control for the respondent’s trust in institutions. Institutional trust is measured by averaging the respondent’s trust in parliament, legal system, police, and politicians and correlates with giving and volunteering. Religious attendance and affiliation are also important individual-level controls.⁹ Religious attendance positively predicts volunteering or donating money for both religious and secular causes (Borgonovi, 2008; Putnam, 2000; Regnerus, Smith, & Sikkink, 1998). Church attendance is measured by the number of days per year that respondents attend religious services apart from special occasions and is logged. We distinguish five religious traditions: Catholic, Protestant, Orthodox, other Christian, and non-Christian. Non-religious respondents are the reference group. Previous research has observed that Protestants are more involved in volunteering activities compared with Catholics and Orthodox Christians (Borgonovi, 2008; Curtis et al., 2001).

Urban versus rural residence and residential stability may also shape individuals’ community identity and influence their decisions to volunteer or give (Wilson, 2000). We coded respondents’ description of their domicile as “a big city” or “the suburbs or outskirts of a big city” as “urban.” Residential stability is measured by the number of years respondents said she or he had lived in the area.

In better off areas, residents are more likely to focus some of their time on volunteering because their basic needs are being met (Parboteeah, Cullen, & Lim, 2004). To guard against the possibility that a positive relationship between regional-level social capital and generosity merely reflects greater levels of volunteering and so on in better off communities, we control for GDP and educational attainment at the regional level. Regional GDP was obtained from Eurostat. Regional educational attainment is the proportion of ESS respondents within the NUTS 2 region who completed at least the first stage of tertiary education. We also control for population density because higher population density has sometimes been linked to poorer social integration and lower proclivity toward prosocial behaviors (Putnam, 2000). Population density was obtained from Eurostat and is measured as inhabitants per square kilometers and is divided by 1,000. Finally, we control for the diversity of the region, which is calculated as the proportion of ESS respondents within the region who identify as belonging to a minority group.

Method

Although we are primarily interested in how regional-level social capital shapes generosity, it is important to use a modeling approach that allows us to rule out compositional explanations, that is, that any significant predicted effects of regional social capital might be attributed merely to differences in the attributes of individuals within regions, including in this case, individual-level ties and trust (Wiepking & Handy, 2015). Therefore, we utilize multilevel modeling (Raudenbush & Bryk, 2002), which allows the estimation of effects at multiple levels of analysis. We also incorporate country-level fixed-effects to control for differences between countries. The regional-level predictors are grand-mean centered, the individual-level variables of substantive interest are group-mean centered, and all other Level 1 non-dummy variables are grand-mean centered. Group-mean centering is appropriate for our research questions; Enders and Tofighi (2007) explain that to understand a predictor variable at both Level 1 and Level 2, the Level 1 variables should be group-mean centered.

Results and Discussion

Before examining whether, and the extent to which, regional stocks of social capital are associated with generosity, we first inspect regional variation in generosity. Figures 1a to 1c display the NUTS 2 regional means for formal volunteering, charitable giving, and informal helping. As the maps illustrate, there are considerable differences in the average levels of each type of generosity both across countries and within countries.¹⁰ Regions in the Nordic countries, western Germany, the Netherlands, and southern France, tend to have the highest rates of volunteering and donating, whereas regions in eastern and southern Europe have the lowest rates. Informal helping shows a somewhat different pattern. Respondents from Germany, Netherlands, and Austria are most generous in providing informal help, while those from some of the regions of Spain, Poland, France, and Italy are substantially less likely to do so. For all three behaviors, the differences between the regions of northern and southern Italy, the subject of Putnam’s (1993) seminal work on social capital and good governance, are particularly striking.

Figure 1.

Generosity across European regions.

Multivariate Results

Table 2 presents two models for each generosity outcome. Model 1 includes the social capital and control variables and Model 2 incorporates the interaction between the components of social capital. Consistent with previous research on individual-level social capital and generous behaviors, both trust and social ties are significantly associated with all three generous outcomes, supporting the expectation that individuals who are more trusting of others and who have larger social networks are more likely to engage in generous behaviors. To illustrate the magnitudes of these predicted coefficients, we compare them to the coefficients of education and religious attendance, two factors that are widely recognized as strong and important predictors of volunteering. Net of the other explanatory variables, for trust, a one standard deviation increase is associated with an approximately 1.4% increase in volunteering while a 10% increase in social ties is associated with a .25% increase in volunteering. For comparison, for educational attainment, the expected increase is 4% for a one standard deviation increase, and a 10% increase in religious attendance is associated with a .3% increase in volunteering. The coefficients of trust and social ties in the charitable donating model are of similar magnitude. Not surprisingly, social ties are somewhat more strongly related to informal helping. A 1% increase in social ties is associated with a .18% increase in informal helping. A more realistic 10% increase in social ties is associated with a 1.8% increase in informal helping. The associated percentage increases for one standard deviation increases in trust and education are 3.2% and 7.7%, respectively. A 10% increase in religious attendance is associated with a 1.5% increase in informal helping. Net of the other explanatory variables, including social ties, having children in the household is only marginally significant in the volunteering and donating models and is insignificant in the informal helping model.

Table 2.

Unstandardized Coefficients From Multilevel Models Explaining Generous Behaviors.

	Volunteering^a		Charitable donating^a		Informal helping^b
	Model 1	Model 2	Model 1	Model 2	Model 1	Model 2
Individual-level social capital
Trust	0.007***	0.007***	0.010***	0.010***	0.016**	0.016**
Trust	(0.001)	(0.001)	(0.001)	(0.001)	(0.005)	(0.005)
Social ties	0.025***	0.026***	0.023***	0.023***	0.177***	0.178***
Social ties	(0.002)	(0.002)	(0.002)	(0.002)	(0.007)	(0.007)
Trust × Social ties		0.002 **		−0.001		0.006
Trust × Social ties		(0.001)		(0.001)		(0.004)
Children in household	0.009^†	0.009^†	0.010^†	0.010^†	0.014	0.014
Children in household	(0.005)	(0.005)	(0.006)	(0.006)	(0.021)	(0.021)
Regional-level social capital
Trust	0.039***	0.040***	0.043**	0.043**	0.104^†	0.103^†
Trust	(0.011)	(0.011)	(0.014)	(0.014)	(0.057)	(0.057)
Social ties	0.025	0.051	0.017	0.030	0.039	−.018
Social ties	(0.019)	(0.023)	(0.024)	(0.030)	(0.090)	(0.114)
Trust × Social ties		0.033*		0.017		−0.069
Trust × Social ties		(0.017)		(0.021)		(0.085)
Level 1 control
Sex (female = 1)	−0.035***	−0.035***	−0.012*	−0.012*	0.077***	0.077***
Sex (female = 1)	(0.004)	(0.004)	(0.005)	(0.005)	(0.017)	(0.017)
Years of education	0.010***	0.010***	0.019***	0.019***	0.019***	0.019***
Years of education	(0.001)	(0.001)	(0.001)	(0.001)	(0.003)	(0.003)
Age	0.006***	0.006***	0.008***	0.008***	0.054***	0.053***
Age	(0.001)	(0.001)	(0.001)	(0.001)	(0.003)	(0.003)
Age²	0.000***	0.000***	0.000***	0.000***	−0.001***	−0.001***
Age²	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
Married	0.016***	0.017***	0.023***	0.023***	−0.024	−0.024
Married	(0.005)	(0.005)	(0.006)	(0.006)	(0.021)	(0.020)
Unemployed	−0.032***	−0.032***	−0.059***	−0.059***	−0.023	−0.022
Unemployed	(0.009)	(0.009)	(0.012)	(0.012)	(0.043)	(0.043)
Minority	−0.007	−0.007	0.002	0.001	0.016	0.027
Minority	(0.011)	(0.011)	(0.014)	(0.014)	(0.053)	(0.050)
Institutional trust	0.004***	0.004***	0.007***	0.007***	−0.015**	−0.015**
Institutional trust	(0.001)	(0.001)	(0.001)	(0.001)	(0.005)	(0.005)
Religious attendance	0.030***	0.030***	0.032***	0.032***	0.147***	0.147***
Religious attendance	(0.002)	(0.002)	(0.002)	(0.002)	(0.007)	(0.007)
Catholic	−0.008	−0.008	0.013^†	0.013^†	−0.133***	−0.133***
Catholic	(0.006)	(0.006)	(0.008)	(0.008)	−0.027	(0.028)
Protestant	0.043***	0.043***	0.067***	0.067***	−0.064*	−0.064*
Protestant	(0.007)	(0.007)	(0.008)	(0.008)	(0.029)	(0.029)
Orthodox	−0.019	−0.021	−0.003	−0.004	0.055	0.061
Orthodox	(0.021)	(0.021)	(0.026)	(0.026)	(0.098)	(0.098)
Other Christian	0.046**	0.047**	0.100***	0.100***	0.066	0.066
Other Christian	(0.015)	(0.015)	(0.019)	(0.019)	(0.068)	(0.068)
Other religion	−0.036*	−0.036**	−0.009	−0.009	−0.004	−0.005
Other religion	(0.016)	(0.016)	(0.020)	(0.020)	(0.073)	(0.072)
Urban	−0.028***	−0.028***	−0.015**	−0.015**	−0.003	−0.004
Urban	(0.005)	(0.005)	(0.006)	(0.006)	−0.021	(0.021)
Length of residence	0.000***	0.000***	0.000	0.000	0.002***	0.002***
Length of residence	(0.000)	(0.000)	(0.000)	(0.000)	(0.001)	(0.001)
Level 2 control
GDP	0.020	0.019	−0.004	−0.004	0.059	0.058
GDP	(0.020)	(0.020)	(0.025)	(0.025)	(0.101)	(0.100)
Education	0.082	0.083	0.370***	0.370***	0.383	0.384
Education	(0.054)	(0.053)	(0.066)	(0.066)	(0.252)	(0.251)
Population density	−0.006	−0.006	0.004	0.004	0.012	0.011
Population density	(0.007)	(0.007)	(0.008)	(0.008)	(0.034)	(0.034)
Proportion minority	−0.062	−0.056	0.153	0.157	0.200	0.187
Proportion minority	(0.113)	(0.112)	(0.139)	−0.139	(0.531)	(0.531)
Variance component
Regional-level variance	0.038***	0.038***	0.047***	0.047***	0.193***	0.193***
Regional-level variance	(0.004)	(0.004)	(0.005)	(0.005)	(0.019)	(0.019)
Country-level variance	0.062***	0.059***	0.085***	0.085***	0.409***	0.409***
Country-level variance	(0.012)	(0.011)	(0.015)	(0.015)	(0.068)	(0.067)
Constant	0.137***	0.129***	0.162***	0.158***	2.553***	2.570***
Constant	(0.027)	(0.027)	(0.034)	(0.034)	(0.143)	(0.144)

Note. Standard errors are in parentheses.

33,062 observations in 160 regions in 19 countries.

35,384 obervations in 174 regions in 21 countries.

†

p < .10. *p < .05. **p < .01. ***p < .001.

Turning now to regional-level social capital, a one-unit increase (which is roughly equivalent to a one standard deviation increase, SD = .96) in regional trust is associated with about a 4% increase in both volunteering and charitable giving. The magnitude of the regional trust coefficient is not trivial. A one standard deviation increase in education at the individual level is associated with an increase in the volunteering index of a similar magnitude. Regional trust is also marginally significant in the informal helping model. In contrast to regional levels of trust, regional levels of social ties are not associated with any of the generosity outcomes net of the other explanatory variables in the model.

But theory suggests that the two components of social capital work in conjunction (Glanville & Bienenstock, 2009; Paxton, 1999). Model 2 shows that, at the individual level, the interaction between trust and ties is significant in the model for volunteering. More interesting, we see that, at the regional level, the associations of trust and social ties with volunteering are conditional on one another. Figure 2 illustrates that, at low levels of regional trust (one standard deviation below the mean), the slope of the regression line for social ties is almost flat, but at higher levels of regional trust (one standard deviation above the mean) a greater number of connections within the region are associated with more volunteering of individual residents. In other words, greater regional connections are not associated with more volunteering on their own. However, in conjunction with regional trust, they are associated with greater volunteering. In contrast to the results for volunteering, regional trust and ties do not interact significantly in the models for charitable giving or informal helping. Overall, social capital at the community level appears to be most important in affecting volunteering, somewhat less important in fostering charitable giving and still less influential for informal helping.

Figure 2.

Interaction of regional ties with regional trust in predicting volunteering.

The results for the control variables are generally consistent with previous literature. On average, women volunteer for fewer types of organizations than men in European countries, but they help informally more frequently. Greater education is positively associated with all three generous behaviors. Age follows a curvilinear pattern whereby older adults engage in more generosity, up to a point at which these efforts decrease. Married or partnered individuals volunteer and give more than their non-married counterparts, but they do not engage in more frequent informal helping. Not surprisingly, institutional trust is positively associated with volunteering and charitable giving. Interestingly, individuals who are less trusting of societal institutions are more likely to engage in informal helping. Religious attendance is positively related to the generosity behaviors. But different religious denominations show different patterns. Being unemployed and living in an urban area are negatively related to volunteering and giving but not informal helping, while residential stability is associated with increases in volunteering and informal helping.

With the exception of regional education in the charitable donations model, the regional control variables are not significant predictors in our models. In examining additional models we found that GDP and population density were positively associated with volunteering as expected. It is the inclusion of the regional-level social capital variables that renders their estimated effects smaller and statistically insignificant, indicating that regional social capital may serve as a mediator between the socioeconomic status of communities and volunteering. (Results available upon request.)

Conclusion

Scholars across a broad range of disciplines have long been interested in why people overcome narrow self-interest to pursue cooperative goals. We focus on an important subset of cooperative behaviors, generosity, which is a collection of activities such as volunteering, donating to charities, and informal helping. Our understanding of the individual characteristics that increase generosity, such as human capital, direct requests, and empathy is well developed (Wilson, 2000). Much less is known about how context influences generous behaviors (Hodgkinson, 2003; Smith, 1994; Wilson, 2000). We suggest that social capital is an important contextual influence on generosity because community integration and trust increase information about opportunities for generous activities and the motivation for participating in these activities.

We found that residents in high-trust regions volunteer for and give money to more organizations than similar residents in less trusting regions, which is consistent with a “spillover” effect of contextual-level social capital (Putnam et al., 2000; Ruiter & De Graaf, 2006; van der Meer, 2003) on volunteering and giving. The predicted associations between regional trust and volunteering and donating compare favorably with other important predictors of volunteering and giving, such as educational attainment and religious attendance. In contrast, the other component of regional-level social capital, social ties, was not statistically significant on its own in any of the models of generous behaviors we examined. However, the interaction suggested that regional-level ties work in combination with regional-level trust to facilitate greater volunteering.

Why do we see that regional-level trust is more related to generosity than regional-level social ties? A stronger test of the potential role of regional-level networks in enhancing generosity would include measures of networks that better tap the structure of community networks. The extent to which social circles within localities overlap is theoretically crucial in fostering the flow of information about opportunities to volunteer and donate (Paxton, 2007). Consequently, the lack of a significant coefficient of regional-level ties in this study does not necessarily suggest that contextual-level ties are unimportant to volunteering and donating. Future research that can include better measures of local networks may well find more evidence for the importance of this component of social capital.

Another limitation is that the effects of contextual-level social capital may manifest themselves at lower levels of aggregation than regions within countries, and there may be geographic variation in social capital within regions. Nonetheless, the regions in our analyses are smaller aggregate units than those of most research interested in aggregate-level social capital effects. Relatedly, social capital measured at the community level may mask internal differences in social capital among subgroups (Edwards & Foley, 1998; Portes & Landolt, 1996). Thus, there may be internal inequalities or externalities that remain unmeasured in the aggregate.¹¹

Finally, endogeneity is an ever-present concern in analyses of prosocial behavior, particularly with cross-sectional data. For example, the individual-level relationship between social ties and volunteering is likely reciprocal as volunteering strengthens existing ties or leads to new acquaintances. But Bekkers (2012) investigated the individual-level relationship between trust and volunteering over time and found that trust leads to volunteering and not vice versa. It seems reasonable to surmise that same would be true at the regional level, where endogeneity, although still possible, is less of a concern.

Despite these limitations, the present study is an important preliminary step in shedding greater light on why levels of generosity vary across individuals and communities. Our multilevel models and multidimensional view of generosity help us identify not only the individual characteristics associated with generosity but also the larger social forces that shape generosity. Specifically, we suggest that a fuller understanding of the sources and origins of generosity requires assessing both individual- and contextual-level social capital. Our findings help address the enduring question of whether positive features of community can resolve social dilemmas, and promote sharing, generosity, and individual action for public benefit.

Footnotes

Acknowledgements

We thank participants of the University of Iowa’s Department of Sociology Theory Workshop and three anonymous reviewers for comments on an earlier version. Glanville and Paxton contributed equally.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Science of Generosity Initiative of the University of Notre Dame, supported by The John Templeton Foundation.

Notes

Author Biographies

Jennifer L. Glanville is an associate professor of sociology at the University of Iowa. Her research interests include social capital, trust, and prosocial behavior.

Pamela Paxton is centennial professor of sociology and public affairs at the University of Texas. Her research interests include social capital, trust, and gender and politics.

Yan Wang is an assistant professor of sociology at Nankai University. Her research interests include cross-national research, environmental sociology, and political sociology.

References

Abraham

K. G.

Helms

Presser

(2009). How social processes distort measurement: The impact of survey nonresponse on estimates of volunteer work in the United States. American Journal of Sociology, 114, 1129-1165.

Adler

P. S.

Kwon

(2002). Social capital: Prospects for a new concept. The Academy of Management Review, 27, 17-40.

Amato

(1990). Personality and social network involvement as predictors of helping behavior in everyday life. Social Psychology Quarterly, 53, 31-43.

Bekkers

(2012). Trust and volunteering: Selection or causation? Evidence from a 4 year panel study. Political Behavior, 34, 225-247.

Bekkers

(2015). Regional differences in philanthropy. In Harrow

Jung

Phillips

(Eds.), Routledge companion to philanthropy. London, England: Routledge.

Bekkers

Wiepking

(2011a). Accuracy of self-reports on donations to charitable organizations. Quality & Quantity, 45, 1369-1383.

Bekkers

Wiepking

(2011b). A literature review of empirical studies of philanthropy: Eight mechanisms that drive charitable giving. Nonprofit and Voluntary Sector Quarterly, 40, 924-973.

Beutel

A. M.

Marini

M. M.

(1995). Gender and values. American Sociological Review, 60, 436-448.

Borgonovi

(2008). Divided we stand, united we fall: Religious pluralism, giving, and volunteering. American Sociological Review, 73, 105-128.

10.

Bourdieu

(1986). The Forms of Capital. In Richardson

J. G.

(Ed.), Handbook of Theory and Research for the Sociology of Education (pp. 241-258). New York: Greenwood Press.

11.

Bowles

Gintis

(2011). A cooperative species: Human reciprocity and its evolution. Princeton, NJ: Princeton University Press.

12.

Brooks

A. C.

(2005). Does social capital make you generous? Social Science Quarterly, 86, 1-15.

13.

Brown

Ferris

J. M.

(2007). Social capital and philanthropy: An analysis of the impact of social capital on individual giving and volunteering. Nonprofit and Voluntary Sector Quarterly, 36, 85-99.

14.

Burr

J. A.

Choi

N. G.

Mutchler

J. E.

Caro

F. G.

(2005). Caregiving and volunteering: Are private and public helping behaviors linked? The Journals of Gerontology Series B: Psychological Sciences & Social Sciences, 60, S247-S256.

15.

Coleman

J. S.

(1988). Social capital in the creation of human capital. The American Journal of Sociology, 94, 95-120.

16.

Curtis

J. E.

Baer

D. E.

Grabb

E. G.

(2001). Nations of joiners: Explaining voluntary association membership in democratic societies. American Sociological Review, 66, 783-805.

17.

Eagly

A. H.

Crowley

(1986). Gender and helping behavior: A meta-analytic review of the social psychological literature. Psychological Bulletin, 100, 283-308.

18.

Edwards

Foley

M. W.

(1998). Civil society and social capital beyond Putnam. American Behavioral Scientist, 42, 124-139.

19.

Enders

C. K.

Tofighi

(2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12, 121-138.

20.

Eurostat. (2007). Regions in the European Union: Nomenclature of territorial units for statistics. Retrieved from https://censimentoindustriaeservizi.istat.it/rete/fileadmin/documenti/materiali_di_approfondimento/nomenclature_of_territorial_units.pdf

21.

Fischbacher

Gächter

Fehr

(2001). Are people conditionally cooperative? Evidence from a public goods experiment. Economics Letters, 71, 397-404.

22.

Forbes

K. F.

Zampelli

E. M.

(2014). Volunteerism: The influences of social, religious, and human capital. Nonprofit and Voluntary Sector Quarterly, 43, 227-253.

23.

Fukuyama

(1995). Trust: The social virtues and the creation of prosperity. New York, NY: Free Press.

24.

Gambetta

(1988). Trust: Making and breaking Cooperative relations. Oxford: Blackwell.

25.

Glanville

J. L.

Bienenstock

E. J.

(2009). A typology for understanding the connections among different forms of social capital. American Behavioral Scientist, 52, 1507-1530.

26.

Hanifan

L. J.

(1916). The rural school community center. Annals of the American Academy of Political and Social Science, 67, 130-138.

27.

Hardin

(2002). Trust and trustworthiness. New York: Russell Sage Foundation.

28.

Helliwell

J. F.

Putnam

R. D.

(2007). Education and social capital. Eastern Economic Journal, 33, 1-19.

29.

Hodgkinson

V. A.

(2003). Volunteering in global perspective. In Dekker

Halman

(Eds.), The values of volunteering: Cross-cultural perspectives (pp. 35-53). New York, NY: Kluwer Academic.

30.

Hume

(1978). A treatise of human nature, 2nd Edition. Oxford: Oxford University Press. (Original work published 1740).

31.

Jones

K. S.

(2006). Giving and volunteering as distinct forms of civic engagement: The role of community integration and personal resources in formal helping. Nonprofit and Voluntary Sector Quarterly, 35, 249-266.

32.

Keltner

(2009). Born to be good: The science of a meaningful life. New York, NY: W.W. Norton.

33.

Lee

Y.-J.

Brudney

J. L.

(2009). Rational volunteering: A benefit-cost approach. International Journal of Sociology and Social Policy, 29, 512-530.

34.

Lee

Y.-J.

Brudney

J. L.

(2012). Participation in formal and informal volunteering: Implications for volunteer recruitment. Nonprofit Management & Leadership, 23, 159-180.

35.

Lewis

V. A.

MacGregor

C. A.

Putnam

R. D.

(2013). Religion, networks, and neighborliness: The impact of religious social networks on civic engagement. Social Science Research, 42, 331-346.

36.

Lim

MacGregor

C. A.

(2012). Religion and volunteering in context: Disentangling the contextual effects of religion on voluntary behavior. American Sociological Review, 77, 747-779.

37.

Lin

(2001). Social capital: A theory of social structure and action. Cambridge, UK: Cambridge University Press.

38.

Marwell

Oliver

(1993). The critical mass in collective action. Cambridge, UK: Cambridge University Press.

39.

Musick

M. A.

Wilson

(2008). Volunteers: A social profile. Bloomington: Indiana University Press.

40.

Musick

M. A.

Wilson

Bynum

W. B.

(2000). Race and formal volunteering: The differential effects of class and religion. Social Forces, 78, 1539-1570.

41.

Neilson

L. A.

Paxton

(2010). Social capital and political consumerism: A multilevel analysis. Social Problems, 57, 5-24.

42.

Parboteeah

K. P.

Cullen

J. B.

Lim

(2004). Formal volunteering: A cross-national test. Journal of World Business, 39, 431-441.

43.

Paxton

(1999). Is social capital declining in the United States? A multiple indicator assessment. The American Journal of Sociology, 105, 88-127.

44.

Paxton

(2002). Social capital and democracy: An interdependent relationship. American Sociological Review, 67, 254-277.

45.

Paxton

(2007). Association memberships and generalized trust: A multilevel model across 31 countries. Social Forces, 86, 47-76.

46.

Pichler

Wallace

(2007). Patterns of formal and informal social capital in Europe. European Sociological Review, 23, 423-435.

47.

Plagnol

A. C.

Huppert

F. A.

(2010). Happy to help? Exploring the factors associated with variations in rates of volunteering across Europe. Social Indicators Research, 97, 157-176.

48.

Portes

Landolt

(1996). The downside of social capital. The American Prospect, 26, 18-22.

49.

Putnam

R. D.

(1993). Making democracy work: Civic traditions in modern Italy. Princeton, NJ: Princeton University Press.

50.

Putnam

R. D.

(1995). Bowling alone: America’s declining social capital. Journal of Democracy, 6, 65-78.

51.

Putnam

R. D.

(2000). Bowling alone: The collapse and revival of American community. New York, NY: Simon & Schuster.

52.

Putnam

R. D.

Pharr

Dalton

R. J.

(2000). What’s troubling the trilateral democracies? In Pharr

Putnam

R. D.

(Eds.), Disaffected democracies: What’s troubling the trilateral countries (pp. 3-27). Princeton, NJ: Princeton University Press.

53.

Raudenbush

S. W.

Bryk

A. S.

(2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.

54.

Regnerus

M. D.

Smith

Sikkink

(1998). Who gives to the poor? The influence of religious tradition and political location on the personal generosity of Americans toward the poor. Journal for the Scientific Study of Religion, 37, 481-493.

55.

Rotolo

Wilson

(2012). State-level differences in volunteerism in the United States: Research based on demographic, institutional, and cultural macrolevel theories. Nonprofit and Voluntary Sector Quarterly, 41, 452-473.

56.

Ruiter

De Graaf

N. D.

(2006). National context, religiosity and volunteering: Results from 53 countries. American Sociological Review, 71, 191-210.

57.

Schervish

P. G.

Havens

J. J.

(1997). Social participation and charitable giving: A multivariate analysis. Voluntas, 8, 235-260.

58.

Science of Generosity. (2012). What is generosity? Retrieved from http://generosityresearch.nd.edu/more-about-the-initiative/what-is-generosity/

59.

Smith

D. H.

(1994). Determinants of voluntary association participation and volunteering: A literature review. Nonprofit and Voluntary Sector Quarterly, 23, 243-263.

60.

Snyder

Omoto

A. M.

(2008). Volunteerism: Social issues perspectives and social policy implications. Social Issues and Policy Review, 2, 1-36.

61.

Stadelmann-Steffen

(2011). Social volunteering in welfare states: Where crowding out should occur. Political Studies, 59, 135-155.

62.

Tomasello

(2009). Why we cooperate. Cambridge, MA: MIT Press.

63.

Traunmüller

(2011). Moral communities? Religion as a source of social trust in a multilevel analysis of 97 German regions. European Sociological Review, 27, 346-363.

64.

van der Meer

(2003). Rain or fog? An empirical examination of social capital’s rainmaker effects. In Hooghe

Stolle

(Eds.), Generating social capital: Civil society and institutions in comparative perspective (pp. 133-152). New York, NY: Palgrave Macmillan.

65.

Voicu

(2009). Volunteers and volunteering in central and Eastern Europe. Sociologica, 41, 539-563.

66.

Wang

Graddy

(2008). Social capital, volunteering, and charitable giving. Voluntas, 19, 23-42.

67.

Wiepking

Bekkers

Osili

U. O.

(2014). Examining the association of religious context with giving to non-profit organizations. European Sociological Review, 30, 640-654.

68.

Wiepking

Handy

(2015). Explanations for cross-national differences in philanthropy. In Wiepking

Handy

(Eds.), Generous people, generous nations: A comparative study of global giving. The Palgrave companion to global philanthropy. Hampshire, UK: Palgrave Macmillan.

69.

Wiepking

Maas

(2009). Resources that make you generous: Effects of social and human resources on charitable giving. Social Forces, 87, 1973-1995.

70.

Wilson

(2000). Volunteering. Annual Review of Sociology, 26, 215-240.

71.

Wilson

(2012). Volunteerism research: A review essay. Nonprofit and Voluntary Sector Quarterly, 41, 176-212.

72.

Wilson

Musick

M. A.

(1997). Who cares? Toward an Integrated theory of volunteer work. American Sociological Review, 62, 694-713.

73.

Yamagishi

(1994). Trust and commitment in the United States and Japan. Motivation and Emotion, 18, 129-166.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB