Associational Capital and Adult Charitable Giving: A Canadian Examination

Abstract

This article examines the relationship between associational capital, developed through participation in social networks, and charitable giving behavior in Canada. Empirical models are specified to determine whether a relationship exists between associational capital, formed in youth and adulthood, and secular and religious donation expenditures in adulthood. Tobit regression models are estimated using data from the 2010 Canada Survey of Giving, Volunteering and Participating (CSGVP). The results suggest that the formation of associational capital in youth and adulthood is related to larger donation expenditures, although the source of associational capital and the type of recipient organization matters. It is also found that those who participate in a variety of associations are more likely to make larger donations than those who participate in fewer types of associations. The results provide further insight into charitable giving behavior and have policy implications for public and nonprofit sectors concerned with increasing charitable donations.

Keywords

charitable giving associational capital Canada secular donations religious donations

Introduction

Canada has an enormous and vibrant charitable sector, with Canadians being among the most generous people in the world (Barr, 2013). In 2010, C$10.6 billion in financial donations were made by individuals to more than 170,000 charitable or nonprofit organizations in Canada (Imagine Canada, 2017; Turcotte, 2012). However, when one takes a closer look at who is making the donations and how much they are giving, some worrisome trends appear; most of the donations come from a small segment of the population. According to the 2010 Canada Survey of Giving, Volunteering and Participating (CSGVP), 25% of donors contributed 83% of total donations and 10% of donors contributed 63% of total donations in 2010 (Turcotte, 2012).¹ Similar degrees of concentration were found in previous surveys in 2007 and 2004 (Statistics Canada, 2008). In light of these disconcerting trends in donating behavior, this research attempts to gain greater insight into donating behavior with the purpose of informing public policy designed to increase support for the charitable and nonprofit sectors.

Several correlates of charitable giving behavior in Canada have been identified in past research such as income, employment, education, gender, age, marital status, having children, tax incentives, religiosity, and participation in volunteer activities (Apinunmahakul, Barham, & Devlin, 2009; Hossain & Lamb, 2012; Kitchen, 1992; Kitchen & Dalton, 1990). Also, research consistently shows that those who volunteer their time are more likely to make charitable donations and are more likely to make larger charitable donations than those who do not volunteer (Hossain & Lamb, 2012). One line of thought contends that those who volunteer their time become exposed to the need for donations for charities and nonprofits and thus are more compelled either to make a charitable donation or make a larger donation (Hossain & Lamb, 2012, 2015). A determinant related to volunteering is participation in associations, which has been labeled “associational capital” by sociologists Schervish and Havens (1997). Associational capital results from participation in social networks stemming from either invitation or a sense of obligation and would include volunteering. It has been suggested that an individual’s propensity to donate depends on the density and mix of voluntary associations (Schervish & Havens, 1997).

One stream of literature has studied the impact of associational capital, albeit under different labels, and found it to be a significant determinant of charitable giving behavior (Brooks, 2005; Brown & Ferris, 2007; Reddick & Ponomariov, 2013; Schervish & Havens, 1997; Wang & Graddy, 2008), although no studies have used Canadian data. This relationship is grounded in the belief that greater participation and involvement in societal issues will lead to greater awareness, concern, and ultimately larger charitable donations.

Another stream of literature examined the relationship between participation in associations in youth with positive outcomes in adulthood (Curtis & Perks, 2007; Ferguson, 2006; McGee, Williams, Howden-Chapman, Martin, & Kawachi, 2006; Perks & Haan, 2010; Smith, 1999; Zaff, Moore, Papillo, & Williams, 2003). The positive outcomes include adult activities such as informal and formal volunteering, membership in community associations, and voting participation in political elections.

The current research attempts to make a link between the two streams with the objective of showing that associational capital developed in youth and adulthood is positively related to charitable giving behavior in Canada. To our knowledge, this is the first article to examine the relationship between associational capital developed in youth and charitable giving behavior in adulthood. Given that much of the research in this area has focused on U.S. data sets, this study makes use of a Canadian data set to show that some results can be generalized to another culture. Following past research, separate models are developed for donations to secular organizations and donations to religious organizations based on the belief that motivations for secular giving may be different from motivations for religious giving (Brown & Ferris, 2007; Hossain & Lamb, 2012; Wang & Graddy, 2008).

Hypotheses

Hypothesis 1: The formation of associational capital in youth is positively related to charitable giving in adulthood in Canada. This hypothesis is grounded in the literature linking social capital in youth and community participation in adulthood (Curtis & Perks, 2007; Hanks & Eckland, 1978; Perks & Haan, 2010; Smith, 1999; Zaff et al., 2003).

Hypothesis 2: Associational participation in adulthood is positively related to charitable donations in adulthood in Canada, as has been shown in similar studies in the United States.

Hypothesis 3: Diversity in associational participation for both youths and adults has a positive relationship with donation expenditures. This hypothesis is based on past findings reporting that belonging to a range of voluntary associations tended to increase charitable giving (Jackson, Bachmeier, Wood & Craft, 1995; Schervish & Havens, 1997).

Hypothesis 4: Associational capital related to religious activities in youth or adulthood is related to larger donations to religious organizations, which is grounded in previous findings linking religiosity to charitable giving in the United States (Brown & Ferris, 2007; Wang & Graddy, 2008) and in Canada (Hossain & Lamb, 2012).

The relevance of this research lies in its potential to provide further insight into charitable giving behavior. Individual donations are a critical source of funding for nonprofit organizations which provide many important goods and services to society. A deeper understanding of the factors influencing an individual’s willingness to donate is critical to the growth and financial sustainability of charitable organizations globally. If associational capital developed in youth and adulthood is related to more generous donations, then policy makers may want to consider devoting more time and resources to encouraging participation in associations.

This article is organized as follows. First, a literature review establishes the link between associational capital and charitable giving behavior, and between the development of associational capital in youth and positive outcomes in adulthood. Second, the “Method” section includes a description of the model, a discussion of the statistical issues and techniques, an explanation of the data, and a description of the variables. Then, a discussion of the results, policy implications, and conclusions will follow.

Literature Review

The Concept of Associational Capital

The term associational capital is not widely used, and thus warrants explanation. Associational capital can be understood as an outcome of what Schervish and Havens (1997) described as communities of participation which are “the networks of formal and informal relationships to which people are associated” (p. 240). Their concept of communities of participation is heterogeneous in that some require little voluntary activity while others require a substantial amount of voluntary activity as a condition of membership (Schervish & Havens, 1997). Others result from circumstances such as parents of school-aged children becoming involved in school and extracurricular programs. The common theme is that they all provide a means for individuals to form connections based on some commonalities and provide a basis for people to become aware of needs.

Associational capital can be thought of as a component of social capital, as illustrated in Bourdieu’s (1983) description of social capital as “the networks of association and norms of trust and reciprocity that facilitate collective action by ‘transforming contingent relations’ into relationships that imply ‘durable obligations subjectively felt’” (Brown & Ferris, 2007, p. 86). Bourdieu’s networks of association appear to be very similar to Schervish and Havens’s concept of communities of participation. The term organizational affiliation, defined as involvement in nonprofits, groups, and associations, has been described as representing a dimension of social capital to the extent that participation is motivated by a specific interest or concern or aids in relationship building and advancement of shared concerns (Reddick & Ponomariov, 2013). The relationship between participation in groups and social capital can be viewed as having a two-way causation such that some individuals may build relationships for the purpose of voluntarily entering into associations driven by a desire to address collective needs, while others may experience enhanced relationships as a result of participation in a group (King, 2004).

Associational Capital and Charitable Giving

The theory behind a proposed relationship between associational capital and charitable giving is based on two explanations. One proposes that participation in associations leads to the development of the social networks component of social capital. Here, the conceptualization of social capital is the “totality of linkages between individuals and their associations, and the resulting inclinations toward civic engagement” (Reddick & Ponomariov, 2013, p.1217). The implied process is that greater levels of organizational participation lead to greater levels of social involvement and subsequently a greater level of concern for the issues. Reddick and Ponomariov (2013) noted that nonprofits and charities are one of the most accessible means for addressing societal problems; thus, it is reasonable that there be a direct relationship between connectedness through participation in associations and charitable giving. This explanation is supported by Putnam (2000) who stated the following, “Social networks provide the channel through which we recruit one another for good deeds, and social networks foster norms of reciprocity that encourage attention to others’ welfare” (p. 117).

The second explanation draws on research supporting the contention that people tend to respond positively to direct requests for charitable donations (Statistics Canada, 2008; Turcotte, 2012). During the course of participating in an organization, invitations to donate often arise from someone an individual has come to know through participation (Schervish & Havens, 1997). Furthermore, the intensity and diversity of social networks appear to have a positive impact on the number of personal and organizational connections, leading to more information and knowledge about societal needs ultimately increasing the likelihood of donating (Schervish, 2005; Wang & Graddy, 2008). It has also been postulated that participation in associations tends to increase one’s belief in the system and regard for others leading to a rise in donative behavior (Brown & Ferris, 2007). Some of the literature specifically focuses on participation in religious associations based on the premise that religions teach their members to care for others (Jackson et al., 1995). Local congregations provide an organizational context that encourages and mobilizes helping behavior, such that the needs of the community are shared and social networks are used to recruit volunteers (Jackson et al., 1995).

Several studies have found the concept of associational capital to be directly related to charitable giving behavior (Brooks, 2005; Brown & Ferris, 2007; Jackson et al., 1995; Reddick & Ponomariov, 2013; Schervish & Havens, 1997; Wang & Graddy, 2008). Reddick and Ponomariov (2013) found that both the intensity and nature of involvement in groups had distinct effects on the likelihood of donating online using logistic regression analysis with U.S. survey data. Using U.S. survey data and multiple regression analysis, Schervish and Havens (1997) found that factors that generate communities of participation are associated with donations. Jackson et al. (1995) used ordinary least squares (OLS) regression analysis and data from 800 Indiana residents to find that membership in religious and secular associations is associated with increased charitable giving.

Past studies investigating the impact of social capital on charitable donations in the United States have found social trust, social networks, and civic involvement to be significant determinants of charitable giving to both religious and secular organizations (Brooks, 2005; Brown & Ferris, 2007; Wang & Graddy, 2008). These three studies by Wang and Graddy (2008), Brown and Ferris (2007), and Brooks (2005) all use tobit regression models with U.S. data from the 2000 Social Capital Community Benchmark Survey.

Associational Capital in Youth

The literature examining the relationship between developing associational capital in youth and positive outcomes in adulthood is reviewed in this section. Many of the relevant studies look at the effect of the broader concept of social capital rather than the narrower concept of associational capital. With U.S. longitudinal data and using regression analysis, Hanks and Eckland (1978) reported that extracurricular activities in youth have a stronger impact on involvement in voluntary associations later in life than education, occupation, or income. Smith (1999) found that early connections to others was a significant predictor of political and civic involvement in adulthood with U.S. longitudinal data. Also using U.S. longitudinal data, Zaff et al. (2003) found that participation in extracurricular activities predicted academic achievement and prosocial behaviors such as voting and volunteering.

Two Canadian studies used data from the 1997 National Survey of Giving, Volunteering and Participating (NSGVP) to examine the relationship between youth community involvement and adult community participation (Curtis & Perks, 2007; Perks & Haan, 2010). Perks and Haan (2010) found that youth involvement in a religious organization was positively associated with adult participation in informal and formal volunteering, and community associational membership, based on Poisson regression analysis. Curtis and Perks (2007) were the first to include charitable giving among the different types of adult involvement in the community. Youth activities were found to be significantly and positively related to the number and amount of charitable donations in adulthood. Their measure of youth involvement represents social capital, a broader measure than associational capital proposed in the current research; thus, the results are not necessarily expected to be the same. The present study examines the relationship between associational capital formed in youth and charitable giving behavior in adulthood.

Method

Econometric Issues and Techniques

A tobit regression model is used to examine the relationship between associational capital and charitable donation expenditures in Canada, as it is considered to be most appropriate given that the dependent variable is censored left at zero representing the survey respondents who did not make donations (Brooks, 2007; McClelland & Kokoski, 1994).²

A tobit model examines the relationship between the nonnegative dependent variable, $Y_{i},$ and a set of independent variables, $X_{i}$ . The model assumes there is a latent (unobservable) variable, $Y_{i}^{*}$ , which linearly depends on $X_{i}$ , a vector of independent variables, via a set of parameters, $β$ . It is assumed that there is a normally distributed error term, $U_{i}$ , to capture the random influences on this relationship. The observable variable $Y_{i}$ is defined to be equal to the latent variable whenever the latent variable is above zero and zero otherwise.

Y_{i} = {\begin{matrix} Y_{i}^{*} & if Y_{i}^{*} > 0 \\ 0 & if Y_{i}^{*} \leq 0 \end{matrix},

where $Y_{i}^{*}$ is a latent variable such that

Y_{i}^{*} = {X'}_{i} β + U_{i}; U_{i} ~ N (0, σ^{2}) .

Following Amemiya (1973), the maximum likelihood estimator technique is used with observations weighted for different sampling probabilities for the parameter estimates to be consistent. Note that the above model assumes that zero and nonzero values of the dependent variable are taken from the same underlying distribution.

Data and Sample

The data are from the public use micro data files of the 2010 CSGVP, published by Statistics Canada. The objective of the survey was to collect data on charitable giving, unpaid volunteer activities, and participation in Canada (Statistics Canada, 2012). The target population for the 10 provinces was all persons 15 years of age and above, excluding full-time residents of institutions. Approximately 14,059 randomly selected respondents were interviewed between September and December 2010 in all 10 provinces. We restrict the sample to respondents of age 25 years and older as those in the younger age group (15-24) are most likely to be teens and students (Apinunmahakul et al., 2009; Perks & Haan, 2010). Given our research focuses on the relationship between youth experiences and donative behavior in adulthood, we must exclude the younger age group.

There are measurement challenges associated with all survey data, a couple of which particularly apply to this study, such as the tendency of respondents to give socially desirable responses to questions about charitable giving and the ability to accurately recall past behaviors such as past donations and youth experiences. While Statistics Canada has implemented numerous strategies to improve the quality of the data collected, survey bias cannot be totally eliminated and thus is a limitation to the research (Hall, 2001). Panel data are generally considered superior for studying behavior over time, although it comes with challenges such as high levels of attrition. Curtis and Perks (2007) reviewed data collected by both approaches (recall and panel) and found that they both yielded the same pattern of results. After the variables for the models were identified, observations with missing data were excluded, leaving a sample of 12,867 observations.³

Dependent Variables

The natural log of donation expenditures to religious organizations and secular organizations are the two dependent variables.⁴ To avoid the problem of taking the log of zero in the case of nondonors, one is added to all donation expenditures.

Independent Variables

The explanatory variables include a set of variables most commonly used in past related studies with the addition of several variables representing measures of associational capital. The explanatory variables from past research include the tax incentive, household income, education, age, gender, marital status, children, and religiosity. In Canada, the tax incentive is in the form of a tax credit, different from the more common tax deduction. Following Hossain and Lamb (2015), the tax incentive is measured with a proxy variable indicating whether the tax incentive is a motivating factor in the donation decision. The specification of these variables is summarized in Table 1.

Table 1.

Description of Independent Variables.

Variable	Name	Description
Tax incentive	credit	Respondents who state the tax credit as a motive for donating (yes = 1; no = 0).
Education (3 variables)	some post-secondary post-secondary diploma post-secondary degree	Respondents whose highest level of educational attainment is as stated (1 = yes; 0 = no). Default is maximum high school graduation.
Age (4 variables)	35-44 years of age 45-54 55-64 65+	Respondents in the stated age range (1 = yes; 0 = no). Default is age 25 to 34.
Gender	male	Gender of the respondent (male = 1; female = 0).
Marital status	married	Respondent is married (1 = yes; 0 = no).
Pre-school children	pre-school	Respondent has at least one preschool child in the household (1 = yes; 0 = no).
School-age children	school-age	Respondent has at least one school-age child in the household (1 = yes; 0 = no).
Health status	hstatus	Respondent’s self-reported health status (1 if excellent, very good, or good; 0 if fair or poor)
Life satisfaction	lsat	Respondent’s degree of self-reported life satisfaction (1 if very satisfied; 0 if somewhat satisfied, somewhat dissatisfied, or very dissatisfied)
Adulthood associational capital
Volunteering	volunteer	Respondent has volunteered time in the past 12 months (1 = yes; 0 = no).
Religious attendance	religious	Respondent has attended a religious meeting or service at least weekly in the past 12 months (1 = yes; 0 = no).
Serve in committees	committees	Respondent served a committee or board in the past 12 months (1 = yes; 0 = no)
Family group participation	group	Respondent was involved in group activities with family members in the past 12 months (1 = yes; 0 = no)
Youth associational capital
Participated in youth sports	youth sports	Respondent participated in an organized team sport as a youth (1 = yes; 0 = no)
Participated in youth government	youth government	Respondent participated in student government in youth (1 = yes; 0 = no)
Participated in youth groups	youth group	Respondent was active in a youth group (1 = yes; 0 = no)
Participated in youth religious group	youth religious	Respondent was active in a religious organization in youth (1 = yes; 0 = no)
Indices
Adult indices (3 variables)	aindex	Respondent was involved in one, two, or more than two (if one aindex1 = 1; else = 0; if two aindex2 = 1; else = 0; if more than two aindex3 = 1; else = 0)
Youth indices (3 variables)	aindex	Respondent was involved in one, two, or more than two (if one yindex1 = 1; else = 0; if two yindex2 = 1; else = 0; if more than two yindex3 = 1; else = 0)

There are two categories of variables measuring associational capital: those measuring associational capital formed in adulthood, and those measuring associational capital formed in youth. Four variables specified to measure associational capital in adulthood include volunteering, frequent attendance at religious meetings or services, serving as a member of a committee or board, and participation in group activities with family members. Associational capital formed in youth is measured with the following four variables: participation in an organized team sport; participation in student government; participation in a youth group such as guides, scouts, 4H, or choir; and participation in a religious organization. The estimated coefficient of alpha is .602, providing some support for internal consistency of the measures of associational capital in youth and adulthood as representations of the overarching construct of associational capital.⁵

Associational capital indices were created to examine the impact of diversity of sources of associational capital on charitable giving, as shown in Table 1. Three adult indices were created as follows: (a) If a respondent is involved in one of the four adult associational capital measures, the index variable aindex1 is coded 1, otherwise 0. Similarly if a respondent is involved in two measures, variable aindex2 is coded 1, otherwise 0. If the respondent is involved in more than two adult measures, variable aindex3 is coded 1, otherwise 0. Similarly, three variables (yindex1, yindex2, and yindex3) are specified to measure the diversity of youth associational capital. Specification of the associational capital indices are shown in Table 1.

It is important to note that people with certain characteristics may be more likely to participate in associations and also participate in charitable giving. For instance, it is plausible that individuals who are relatively healthy and happy are more likely to participate in both associations and charitable giving than those who are not. For this reason, we include variables measuring health status and life satisfaction in the models. We acknowledge that there may be other variables affecting both participation in associations and charitable giving which we have not identified.

Descriptive Statistics

Table 2 shows the distribution of respondents by donation and by different measures of adult and youth associational capital. Close to 91% of the sample are donors, with 80% donating to secular organizations and 44% to religious organizations. The distribution of respondents with respect to adult associational capital is 56% participation in volunteering, 19% with frequent attendance at religious meetings and services, 22% serving on committees and boards, and 14% participating in family group activities. On the contrary, the distribution of respondents with respect to youth associational capital is 53% participation in organized youth sports, 19% participation in student government, 56% participation in youth group activities, and 37% participation in religious organizations.

Table 2.

Distribution of Respondents by Types of Donation and Associational Capital.

Variables	Frequency (%)
Donor	91
Secular donor	80
Religious donor	44
Adult measures
Volunteer in past 12 months	56
Attend religious meeting/service ≥ weekly	19
Serve on committees/boards in past 12 months	22
Participate in family group activity in past 12 months	14
Youth measures
Organized youth sports	53
Student government	19
Youth group	56
Religious group	37

As shown in Table 3, the average donation to a secular organization is C$337 and to a religious organization is C$578. Table 3 also illustrates how the presence or absence of different measures of associational capital influence average donation expenditures to secular and religious charities. The average donation to secular organizations is higher for all measures of associational capital. For instance, adults who volunteer their time give an average donation of C$389 compared with C$176 from those who do not volunteer. With the exception of participating in organized sports in youth, the same trend holds for average donations to religious organizations. The largest difference is for those who attend religious meetings and services at least weekly, donating C$719 more than those who attend less frequently. These descriptive statistics suggest that associational capital formed in adulthood as well as in youth may influence donation expenditures in adulthood. The partial correlation coefficients for the measures of associational capital, as shown in Table 4, are quite low with the highest being .47 between volunteering and serving as a member of a committee or board. The relatively low correlations imply that multicollinearity is not an issue in the regression analysis.

Table 3.

Average Donation Expenditures.

Associational capital	Average donation (C$)
Associational capital	Secular donation (C$)	Religious donation (C$)
Average donation expenditures	337.08	578.12
Adult measures
Volunteer
Yes (7,256)	388.56	322.54
No (5,611)	175.79	110.63
Attend religious meetings
Yes (2,481)	362.13	810.42
No (10,386)	279.92	91.51
Serve on committees/boards
Yes (2,890)	491.90	435.66
No (9,977)	238.96	170.60
Participate in family group activities
Yes (1,767)	477.20	398.88
No (11,100)	266.89	203.27
Youth measures
Organized youth sports
Yes (6,792)	329.70	219.16
No (6,075)	257.85	242.40
Student government
Yes (2,423)	446.90	306.16
No (10,444)	260.71	212.50
Youth group
Yes (7,151)	348.07	276.00
No (5,716)	230.35	172.76
Religious group
Yes (4,816)	360.52	395.36
No (8,051)	257.04	131.30

Note. Number of respondents in brackets.

Table 4.

Correlations Between Associational Capital Measures.

Variables	Volunteer	Religious	Committee	Family group	Youth sports	Youth group	Youth government	Youth religious
Volunteer	1.000
Religious	.156	1.000
Committee	.473	.130	1.000
Family group	.351	.087	.252	1.000
Youth sports	.107	−.034	.082	.088	1.000
Youth group	.139	.083	.120	.090	.277	1.000
Youth government	.161	.038	.146	.115	.196	.209	1.000
Youth religious	.116	.290	.101	.093	.100	.297	.177	1.000

Results

Three tobit models are estimated for both secular and religious donations. Model 1 is the basic model consisting of the previously tested independent variables, plus health status and life satisfaction, and excluding the associational capital variables. Model 2, the full model, includes the variables from the basic model and all the adult and youth associational capital variables. Model 3, the indices model, includes the variables from the basic model and the six adult and youth indices. The average marginal effects of the explanatory variables on secular donations and religious donations are presented in Tables 5 and 6, respectively.

Table 5.

Average Marginal Effects For Secular Donation Expenditures.

Explanatory variables	Model 1: Basic model	Model 2: Full model with associational capital	Model 3: Full model with associational capital indices
Tax credit incentive	0.981 (0.070)***	0.887 (0.067)***	0.884 (0.068)***
Income1	0.988 (0.149)***	0.952 (0.145)***	0.965 (0.146)***
Income2	1.522 (0.164)***	1.433 (0.159)***	1.442 (0.159)***
Income3	2.047 (0.157)***	1.886 (0.153)***	1.907 (0.154)***
Income4	2.636 (0.169)***	2.426 (0.165)***	2.457 (0.165)***
Education1	0.664 (0.145)***	0.370 (0.138)***	0.360 (0.139)***
Education2	0.514 (0.097)***	0.282 (0.093)***	0.275 (0.094)***
Education3	0.946 (0.106)***	0.613 (0.104)***	0.599 (0.104)***
Age1	0.525 (0.138)***	0.482 (0.133)***	0.475 (0.133)***
Age2	0.772 (0.146)***	0.749 (0.141)***	0.736 (0.141)***
Age3	1.004 (0.151)***	0.976 (0.144)***	0.966 (0.143)***
Age4	1.446 (0.160)***	1.430 (0.153)***	1.412 (0.153)***
Male	−0.385 (0.075)***	−0.353 (0.074)***	−0.349 (0.072)***
Married	0.013 (0.100)	−0.004 (0.096)	−0.011 (0.096)
Preschool children	−0.037 (0.122)	−0.058 (0.117)	−0.064 (0.118)
School-age children	0.093 (0.113)	0.186 (0.110)*	0.185 (0.110)*
Health status	0.016 (0.109)	−0.070 (0.107)	−0.076 (0.107)
Life satisfaction	0.436 (0.079)***	0.284 (0.076)***	0.286 (0.076)***
Religious attendance/association	0.002 (0.102)		−0.789 (0.115)***
Adult associational capital
Religious attendance/association		−0.284 (0.105)***
Volunteer		0.801 (0.082)***
Serve in committees		0.231 (0.072)***
Group participation		0.278 (0.085)***
Youth associational capital
Participation in organized sports		0.295 (0.077)***
Participation in student government		0.145 (0.087)*
Participation in youth groups		0.288 (0.078)***
Participation in religious association		0.164 (0.074)**
Adult indices
aindex1			0.676 (0.087)***
aindex2			1.089 (0.088)***
aindex3			1.513 (0.123)***
Youth indices
yindex1			0.293 (0.114)***
yindex2			0.666 (0.103)***
yindex3			0.732 (0.111)***
Log pseudolikelihood	−49,190,900	−48,505,147	−48,524,922
LR test statistics	—	11,371,506.6	1,331,954.9
F statistics	61.26	6 58.63	60.17

Note. Standard errors are shown in brackets. The religious association variable is listed twice, once in the basic model and again as a measure of adult associational capital. LR = likelihood ratio.

Significance at 10% level. **Significance at 5% level. ***Significance at 1% level.

Table 6.

Average Marginal Effects For Religious Donation Expenditures.

Explanatory variables	Model 1: Basic model	Model 2: Full model with associational capital	Model 3: Full model with associational capital indices
Tax credit incentive	1.316 (0.172)***	1.167 (0.170)***	1.224 (0.171)***
Income1	0.716 (0.305)**	0.721 (0.302)**	0.619 (0.304)**
Income2	1.354 (0.323)***	1.313 (0.323)***	1.231 (0.322)***
Income3	1.268 (0.330)***	1.199 (0.331)***	1.051 (0.320)***
Income4	1.428 (0.361)***	1.320 (0.362)***	1.151 (0.360)***
Education1	0.131 (0.318)	−0.030 (0.309)	0.028 (0.314)
Education2	0.313 (0.201)	0.246 (0.197)	0.283 (0.196)
Education3	0.635 (0.232)***	0.454 (0.230)**	0.454 (0.229)**
Age1	1.182 (0.291)***	1.097 (0.286)***	1.099 (0.288)***
Age2	1.164 (0.289)***	1.111 (0.282)***	1.077 (0.283)***
Age3	1.730 (0.320)***	1.616 (0.312)***	1.616 (0.312)***
Age4	2.644 (0.305)***	2.486 (0.299)***	2.550 (0.298)***
Male	−0.120 (0.155)	0.088 (0.157)	−0.033 (0.154)
Married	0.264 (0.214)	0.148 (0.213)	0.208 (0.211)
Preschool children	−0.563 (0.262)**	−0.552 (0.258)**	−0.566 (0.259)**
School-age children	−0.614 (0.215)***	−0.418 (0.215)**	−0.500 (0.216)**
Health status	−0.388 (0.211)*	−0.395 (212)*	−0.356 (0.212)*
Life satisfaction	0.050 (0.165)	−0.122 (0.163)	−0.0873 (0.164)
Religious attendance/association	6.329 (0.151)***		5.474 (0.179)***
Adult associational capital
Religious attendance/association		5.604 (0.159)***
Volunteer		0.631 (0.180)***
Serve in committees		0.288 (0.178)
Group participation		0.980 (0.195)***
Youth associational capital
Participation in organized sports		−0.389 (0.159)***
Participation in student government		0.165 (0.186)
Participation in youth groups		−0.606 (0.161)***
Participation in religious association		1.515 (0.165)***
Adult indices
aindex1			0.427 (0.207)**
aindex2			1.049 (0.215)***
aindex3			1.974 (0.259)***
Youth indices
yindex1			−0.731 (0.238)***
yindex2			−0.330 (0.231)
yindex3			0.170 (0.240)
Log pseudolikelihood	−31,769,728	−31,470,955	−31,614,927
LR test statistic	—	597,547.12	309,601.48
F statistics	132.73	111.35	117.00

Significance at 10% level. **Significance at 5% level. ***Significance at 1% level.

Secular Donation Results

The data fits both Models 1 and 2 as indicated by the F statistics. The coefficients for the tax incentive, income, education, and age variables are positive and significant (p < .01), indicating that these variables are positively related to secular donations. The results indicate that income has the strongest association with donations to secular charitable organizations, with the marginal effect increasing with level of income. The marginal effects of the four age variables also increase successively, suggesting that donation expenditures to secular charities increase with age. The age variable can be interpreted as a proxy for wealth. Females appear to contribute more than males to secular charities, as indicated by the negative and significant (p < .01) coefficient of the gender variable. The coefficient of life satisfaction is positive and significant (p < .01), implying that those respondents who are very satisfied with their life tend to contribute more to secular charities than those with less life satisfaction. Health status is not significant.

The results for Model 2, with the associational capital variables, suggest it is a better fit with the data than Model 1, as indicated by the likelihood ratio (LR) test statistic.⁶ That is, if adult and youth associational capitals are not included, the model is likely to suffer from specification bias, which may result in biased and inefficient parameter estimates. The marginal effects of all four measures of adult associational capital are positive and significant (p < .01), except for religious association which is negative and significant (p < .01), suggesting that weekly attendance at religious gatherings is negatively related to secular donations. The positive signs of the other adult associational variables imply that participation in volunteering, serving on committees or boards, and family group activities is positively associated with larger contributions to secular charities compared with nonparticipants of such activities. For example, the marginal effect of volunteering is 0.801, implying that those who volunteer are likely to donate C$2.23 more on average than those who do not volunteer.⁷

The marginal effects of all four youth associational capital variables are positive and statistically significant, suggesting that developing associational capital in youth is directly related to secular donations in adulthood. For instance, the coefficient of youth sports is 0.295, implying that those who were involved in youth sports are likely to contribute C$1.34 more on the average than those who did not participate in youth sports.

Model 3 includes the six indices of associational capital to determine if a relationship exists between diversity of associational capital and secular donations. Model 3 is a better fit than Model 1, as indicated by the LR test statistics. All three indices of adult associational capital are statistically significant (p < .01) and positive. Furthermore, the magnitude of the marginal effects of the adult associational indices increases successively, implying that greater diversity of adult associational capital is associated with larger donations to secular charities. The three youth associational indices are also significant (p < .01) and positive with an increasing trend, suggesting that diversity of associational capital in youth is associated with secular giving in adulthood.

Religious Donation Results

Table 6 presents average marginal effects for the three tobit models for religious donations. The parameter estimates in all three models are robust and stable. The estimation of Model 1 shows that the marginal effects of the tax incentive, income, age, and religious attendance are all positive and statistically significant. Religious involvement has the greatest marginal effect with a value of 6.33, indicating that respondents who attend religious meetings or services at least weekly are expected to donate C$561 more to religious charities than those who attend less frequently. The marginal effects of the four age variables increase successively indicating that donation expenditures tend to rise with age. Having a postsecondary degree is the only statistically significant education variable, unlike the effect on secular donations. The marginal effects for children are negative and significant (p < .05), implying that those with children are likely to donate less to religious organizations than those without children. In contrast to the secular model, life satisfaction is not significant but health status is significant (p < .1) and negative, indicating that those reporting good or excellent health tend to make smaller religious donations than those with fair or poor health.

The data fit Model 2 better than Model 1 as indicated by the LR test statistic. Of the four adult associational capital variables, all are statistically significant (p < .01) and positive except for serving on a committee or board. Of the four measures of youth associational capital, three are statistically significant (p < .01) with participation in organized sports and youth groups having negative signs, implying that those involved are likely to make smaller religious donations in adulthood than those not involved in these associations. Those involved in religious associations and student government in youth are more likely to give more to religious organizations in adulthood than those who were not.

The results for Model 3 reveal that all three indices of adult associational capital are significant determinants of religious giving. The marginal effects of the three adult associational indices increase successively, implying that the amount of religious donations increases with the rise in diversity of adult associational capital. On the contrary, the marginal effects of the first youth index (yindex1) is significant and negative, implying that greater diversity in associational capital is associated with smaller religious donations in adulthood.

Discussion, Policy Implications, and Conclusions

The empirical results provide support for all four hypotheses. In regard to the first hypothesis, the formation of associational capital in youth is positively related to charitable giving in adulthood in Canada, although the source of associational capital and the type of recipient charity or nonprofit matters. In this research, two broad categories of recipient charities and nonprofits are examined: secular organizations and religious organizations. While all four measures of youth associational capital are positively related to donations to secular organizations in adulthood, these results do not hold true for donations to religious organizations. In support of Hypothesis 4, association in religious organizations in youth is positively related to religious donations in adulthood. In addition, student government is positively associated with religious donations, while participation in organized sports and nonreligious groups in youth is negatively associated with religious donations.

The results support the second hypothesis that associational capital formed in adulthood is related to larger charitable donations in Canada. All measures of adult associational capital are found to have a significant and positive relationship with secular donations except for religious association, which has a negative relationship with secular donations. It could be that those with religious associations are substituting secular donations with religious donations. Except for serving on committees and boards, the other three measures of adult associational capital, including religious activities, are positively associated with donations to religious organizations, thus providing additional support for Hypothesis 4.

The empirical results provide support for the contention that associational capital formed in youth and adulthood is positively related to an individual’s decision of how much to give to charitable organizations. It should be noted that while the marginal effects of associational capital are significant, the size of the effects are quite small. It is also interesting to note that the type of associational activity matters. For instance, involvement in student government as a youth is associated with larger secular donations but not to religious donations. It may be surprising to some that involvement in a religious organization as a youth is positively related to donations to both religious and secular organizations, while participation in a secular association is positively related to secular giving but not religious giving. It may be the case that participation in youth associations such as sports teams and secular clubs increases the awareness of charitable need in areas other than religion. It is also noteworthy to mention that the secular donation category, including everything from health to environment to international recipients, is quite heterogeneous compared with the religious donation category, which may play a role in the results. Future research might disaggregate the secular donation category to examine further differences in donation behavior based on the type of recipient organization.

The results offer support for the third hypothesis that diversity in associational participation is related to larger donations. The marginal effects of the adult indices are significant and positive, thereby providing support for the premise that involvement in a variety of different types of association is linked with larger donations to both secular and religious organizations. The results of the youth indices with secular donations follow the same trend as the adult indices; however, the relationship with religious donations is contrary to the hypothesis, such that involvement in a variety of different types of association is related to a decrease in religious donations. Perhaps youth involved in a variety of associations are exposed to societal needs other than religious ones, leading them to donate less to religion organizations and more to secular organizations as adults.

It is interesting to observe how the magnitude of the effects of other explanatory variables changes when associational capital is included in the models. For instance, in the secular model, the effects of income, education, and, to some extent, age are reduced with the inclusion of the associational capital variables. This same trend is evident in the religious model, except for education, where it holds true only for those with a postsecondary degree. These results are compatible with the findings of Brown and Ferris (2007) who reported that social capital reduced the magnitude of human capital on charitable donations.

Note that religious association in adulthood is included in the basic model (Model 1) to be consistent with past research (Brooks, 2007; Hossain & Lamb, 2012). When other measures of association capital are added to the models (Models 2 and 3), the size of the effect of religious association on religious donations falls. In the secular model, religious association is not significant in the basic model (Model 1), but is significant and negative in the full models (Models 2 and 3), implying that it is associated with a reduction in the amount of secular donations.

The results imply that charitable behavior begins to form at early ages and may be enhanced by the formation of associational capital in youth and adulthood. The private and societal benefits of participation in associations have been widely documented, but now an additional benefit has come to light.

In sum, this research makes three contributions to the literature. First, the inclusion of associational capital, formed in youth and in adulthood, improves the explanatory power of the model for charitable donations in Canada, suggesting future research should include these variables. Second, certain types of associational capital may be positively or negatively related to donations to specific types of charities. Third, diversity in associational capital may have varying effects on donations depending on the donation sector.

There are a few limitations of this research. First, there are omitted variables, due to data constraints, that might influence both participation in associations and donating, such as personal traits like empathy and extraversion, to name a couple. Second, another limitation of the data set is illustrated by the Cronbach’s alpha of .60, which is not high enough to demonstrate good internal reliability of the variables measuring associational capital. Third, further research is needed to establish causation between associational capital and charitable giving in Canada, which requires either panel or longitudinal data.

Although the results do not establish causation, the positive relationships found between participation in associations and charitable donations may provide public policy makers with an additional reason to encourage youth participation in various associations. In 2013, the Canadian government initiated a children’s fitness tax credit to encourage physical activity for youth. Policy makers might want to consider broadening the tax credit to include a wider range of youth activities. The notion of providing a tax incentive for associational participation among adults has not received much attention. For adults, the hours devoted to paid work have a negative impact on the likelihood of participating in associations, implying that some form of compensation would reduce the cost of volunteering, thereby increasing the likelihood of forming associational capital. It is noted that the existing literature on such a tax incentive does not reach a consensus about the expected effectiveness of such a tax incentive (Frey & Goette, 1999; Lamb, 2011). In addition, governments and nonprofits could develop campaigns to promote volunteerism in general, as well as to target specific populations. For instance, the Government of British Columbia requirement that high school students must complete at least 30 hr of work experience and/or community service is one method of encouraging community service. Nonprofits and charities can do their part to promote participation in associations to further their fundraising goals with the knowledge that the development of associational capital begins at early ages.

Footnotes

Acknowledgements

The authors would like to extend their thanks to the three anonymous referees for their insightful comments.

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) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Belayet Hossain is an associate professor of economics at Thompson Rivers University, British Columbia, Canada. His research interests include nonprofit sector, development economics, and issues of Aboriginal Economics. He earned his PhD in economics at North Carolina State University, USA. He has published as many as 20 papers in peer reviewed journals including Applied Economics, Education Economics, Applied Economics Letters Journal of Developing Areas, Economic Papers, and International Review of Applied Economics.

Laura Lamb, PhD, is an associate professor of economics at Thompson Rivers University. Her research interests and publications are in the areas of volunteer and charitable giving behaviors, sustainable community economic development, personal finance behavior, economics education, and public policy. She researches in the areas of public policy, nonprofits, and sustainable community development. She has published several book chapters and articles in peer reviewed journals such as Applied Economic Letters, Economic Papers: A journal of applied economics and policy, and the Canadian Journal of Nonprofit and Social Economy Research.

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