Available online at www.sciencedirect.com
Social Science Research 37 (2008) 330–349
www.elsevier.com/locate/ssresearch
Social capital and civic action: A network-based approach
q
Joonmo Son *, Nan Lin
Department of Sociology, Box 90088, Duke University, Durham, NC 27708, USA
Available online 30 January 2007
Abstract
We propose that social capital, defined as resources embedded in individual and organizational networks, produces
expressive and instrumental civic actions. The 2000 Social Capital Benchmark Survey data were used to examine the
hypothesis. Structural equation modeling confirmed that (1) individual social capital was the consistent and significant predictor of both expressive and instrumental civic actions; (2) organizational social capital played the most important role in
predicting instrumental civic actions, although it was not significant in predicting expressive civic actions; and (3) civic
actions are gendered: women were more likely to be involved in expressive civic actions, but the female dominance disappeared in the realm of instrumental civic actions.
2006 Elsevier Inc. All rights reserved.
Keywords: Social capital; Individual and organizational social capital; Expressive and instrumental civic actions; Gendered civic actions;
Structural equation modeling
1. Introduction
Social capital, a salient area of social science research, has been seen as a useful perspective for understanding how social elements may enhance performance of individuals as well as collectives (Bourdieu, 1980, 1983/
1986; Coleman, 1988, 1990; Lin, 1982, 1997, 2001a). Studies on social capital have emerged in two research
traditions. One focuses on the utility of social capital for individual actors—how individuals access and mobilize resources embedded in social networks to attain personal goals, such as status attainment (Burt, 1992,
2000; Erickson, 1996; Flap, 1991). Another focuses on the utility of social capital for collective actors—
how participation in groups and associations enhances collective goals, such as participatory democracy or
social development (Bebbington and Perreault, 1999; McClenaghan, 2000; Paxton, 2002; Putnam, 1993,
2000; Putnam et al., 1993; Schafft and Brown, 2000). The two research traditions diverge at the level of analysis (micro- versus macro-level data) and the consequences of interest (individual achievement versus collective
q
We benefited from the helpful comments by the Social Science Research reviewers. We would also like to thank John Wilson, Miller
McPherson, Lynn Smith-Lovin, and participants of the Social Capital Group meetings at Duke University. Earlier versions of this paper
were presented at the 2004 Annual Meeting of the American Sociological Association, August 17, San Francisco, CA, and at the 2005
Sunbelt XXV International Social Network Conference, February 18, Redondo Beach, CA.
*
Corresponding author. Fax: +1 919 660 5623.
E-mail address: [email protected] (J. Son).
0049-089X/$ - see front matter 2006 Elsevier Inc. All rights reserved.
doi:10.1016/j.ssresearch.2006.12.004
J. Son, N. Lin / Social Science Research 37 (2008) 330–349
331
achievement); yet both traditions claim a common conceptual basis—that social networks afford the formation of social capital.
The individual-level analysis has employed a more precise definition of social capital and its measurement,
and cumulative evidence supporting the utility of social capital in the process of instrumental actions (e.g.,
attainment) has been consistent and uncontroversial (see reviews in: Burt, 2000, 2001a; Lin, 1999b, 2001b,
2005; Marsden and Gorman, 2001). On the other hand, the collective-level analysis with more elastic measurements has resulted in more equivocal and contentious findings. The research traditions have proceeded in a
parallel fashion, with a lack of dialogue between the two.
We argue that there is a conceptual and theoretical basis, shared by both traditions. The purpose of the
present paper is to take some preliminary but fundamental steps in resolving conceptual confusion and establish a consistent theoretical formulation useful in demonstrating the utility of social capital for both individual
and collective goals. In this paper, we will (1) clarify the conceptual differences and significance of civic engagement and social capital at the collective level, (2) identify elements of social capital at both individual and collective levels, and (3) test a theory that explains civic actions with both individual and collective social capital.
2. Civic engagement as a theoretical framework
Civic engagement has long been an intriguing notion for its possible implications for the functioning of
modern societies. For instance, Tocqueville ([1840] 2003)) regarded civic associations in nineteenth century
America as a principal factor that accounted for the uniqueness and success of her participatory democracy.
Civic engagement, in the Tocquevillian framework, may be interpreted as grasping both the mechanism (participation in associations) and the consequence (participatory democracy) it produced. The important aspect
of this thesis is that both the mechanism and the consequence are ‘‘engagement’’ in nature. Yet it has also
created confusion in subsequent interpretation and research, because civic engagement may represent multitude types of engagement as well as implying plausible cause-effect associations (i.e., some engagement
enhances some other engagement). Extending this framework forward to analyzing the second half of the
twentieth century in the United States, for example, Putnam focused on a variety of ‘‘engagements,’’ including
participation in various voluntary associations, and suggested that a declining civic engagement, aligned with
the other concurrent preceding factors such as surging distrust in government, intra- and inter-cohort generational social changes, and civic dropout from political mobilization, might signal a crisis of participatory
democracy, an essential aspect of American society (2000, pp. 31–47).
What is civic engagement, then? Surprisingly, there is a scarcity of formal definitions offered in the research
literature. Some scholars emphasize involvement and participation in the community (Putnam, 2000); while
others focus on actions initiated, by either individuals or groups, for collective benefits (Edwards and McCarthy, 2004; McAdam, 1986, 1989). One conventional definition seems to capture its diverse content well:
[Civic engagement consists of] individual and collective actions designed to identify and address issues of
public concern. Civic engagement can take many forms, from individual volunteerism to organizational
involvement to electoral participation. It can include efforts to directly address an issue, work with others in a community to solve a problem or interact with the institutions of representative democracy. Civic
engagement encompasses a range of activities such as working in a soup kitchen, serving on a neighborhood association, writing a letter to an elected official or voting. (Source: http://www.apa.org/ed/slce/
civicengagement.html, Pew Charitable Trusts).
This definition reflects well the essence of, as well as confusion about civic engagement. It is a collection of
empirical phenomena or elements, collectively identified as civic engagement. It takes on multiple forms—engagement can mean different things in different social contexts, and thus has multiple empirical indicators. The
diverse elements in the notion of civic engagement are also reflected in actual measures employed in research,
ranging from membership in voluntary organizations, percentage of civically-oriented establishments in a
community, voting, religious participation, indicators of philanthropic activities such as charity and leisure
dollars, workplace connections, or even team sports as summarized in Table 1.
It is clear from such a definition and the rich literature, that civic engagement is generally understood to
represent a framework that contains a multitude of elements and measurements. It may implicate both
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Table 1
Measures of civic engagement in the recent studies
Studies
Measures of civic engagement
Tolbert et al. (1998)
•
•
•
•
•
Putnam (2000)
• Membership in civic associations
• Time diary of volunteering
• Consumer expenditure (leisure dollars)
Schofer and Fourcade-Gourinchas (2001)
• Membership in ten kinds of associations in 32 countries
Lyson et al. (2001)
•
•
•
•
•
Rosenfeld et al. (2001)
• Electoral participation
• Participation in a national voluntary organization
Messner et al. (2004)
•
•
•
•
•
•
•
Lee and Bartkowski (2004)
• Number of adherents to over 100 religious denominations per capita
Rotolo and Wilson (2004)
• Number of weeks of unpaid volunteer work in the past year
• Hours volunteered
Third places (retail firms) such as cafés, pubs, and barbershops
Small manufacturing establishments
Family farms
Total number of county associations
Percent population in civically engaged denominations
Labor force that works at home
Workforce that is self-employed
Number of small commercial establishments
Percentage of the population that belongs to church
Percentage of the population that voted in the 1980 or 1992 election
Social trust
Political engagement
Community social service
Religious participation
Workplace connections
Volunteering and charity
Team sports
potential causal and effectual elements, all of which bear on the general notion of ‘‘engagement,’’ but the ‘‘implied’’ potential theory has not been made explicit. By this definition and understanding, the term ‘‘civic
engagement’’ is informative if the intent of the research is to uncover different types of engagement and document their trends over time. It is not as informative as a guiding theory for the formulation and research since
the causal ordering of these ‘‘engaging’’ elements is not specified. Because of the lack of attention given to
these problems in conceptualization and explanations, studies relying on a multitude of measures of civic
engagement have generated equivocal findings and interpretations, and evoked controversies and criticisms
(Costa and Kahn, 2001; Durlauf, 2002; Foley and Edwards, 1999; Putnam, 2000; Wilson, 2001).
To rescue the notion of civic engagement from the complicated and hopeless entanglement of elements and
measures and to retain its conceptual utility, we propose several steps be taken in furthering the conceptual
development. First, we believe civic engagement is best used as a conceptual framework, not a theory. It suggests
that aspects of social activities and experiences may play critical roles in the development of public goods. An
engaging society is one where many individuals and groups participate in activities to preserve or promote
public goods. This framework points to the significance of social participation/action for a society as well
as hinting at its potential consequences on the society. It does not constitute a theory by itself, but speculates
that theories may be formulated to explain how individuals join or form groups for such purposes, and what
makes some of these groups more effective than others in preserving or promoting public goods. The second
step, therefore, is to formulate such theories.
The collection of empirical elements in the definition offered above does hint at a possible theoretical statement. It includes both participation and actions. One obvious but implicit proposition is that social participation of individuals in voluntary groups and organizations may facilitate actions taken by individuals or
groups to preserve or promote public goods. That is, social participation promotes civic actions (e.g., see
McAdam and Su (2002) for a theoretical development between protest participation and voting actions). Some
J. Son, N. Lin / Social Science Research 37 (2008) 330–349
333
studies may have assumed this linkage as a proposition, but they do not offer explicit theoretical mechanisms
to actually explicate this linkage (e.g., Smidt, 1999).
What makes participation propel action? The notion of social capital has been evoked as a possible explanatory mechanism. Putnam pointed out that the usefulness of the notion of social capital in the context of civic
engagement lies in its implication that ‘‘civic virtue is most powerful when embedded in a dense network or
reciprocal social relations’’ (2000, p. 19). In other words, density of interactions and therefore reciprocal social
relations, as properties of social networks, constitute ‘‘capital.’’ Participation in such supportive and mutually
reinforcing relations may provide a sufficient motivation for individual members to take action, and sufficient
resources for the groups to take action. That is, participation in voluntary organizations may induce civic
actions because such participation triggers or benefits from dense interactions and reciprocal relations,
enhancing the propensity to take action.
It should be noted that this ‘‘theory’’ has never been formally explicated. For example, in concept and measurement, voluntary organizations are implicitly assumed to be social networks. Also, it is assumed that such
networks are dense—all or most members engage in interactions with one another, and relations are reciprocal, perhaps providing one another with mutual and reinforcing support. In empirical research employing this
framework or ‘‘theory,’’ dense interactions or reciprocal relations are assumed rather than measured.
3. Voluntary organizations as social networks
Can organizations and, more specifically, voluntary associations, be considered as networks? A review of
the literature suggests that the response is affirmative. A formal organization can be characterized by four elements: (1) a set of social units (positions) that possess differential amounts of one or more types of valued
resources, that (2) are hierarchically related relative to authority (control of and access to resources), (3) share
certain rules and procedures in the use of the resources, and (4) are entrusted to occupants (agents) who act on
these rules and procedures (Lin, 2001b, pp. 33–36; Sewell, 1992). Network analysis highlights the interconnectedness among the positions, their occupants, and how such interconnections largely determine the access to
and use of resources embedded in the organization. For instance, the inter- and intra-network of corporate
organizations were demonstrated to be effective for the flow of new ideas and information (Burt, 2001b,
2004; McEvily and Zaheer, 1999; Walker et al., 1997). Likewise, the network metaphor has been employed
to analyze voluntary organizations. Galaskiewicz and his associates (Galaskiewicz, 1997; Galaskiewicz and
Burt, 1991; Galaskiewicz and Wasserman, 1989) have effectively demonstrated the dynamics of nonprofit
organizations as functioning networks and how they are related to local communities. In fact, voluntary organizations with reduced or minimal authority and rules and a flat rather than hierarchical structure tend to rely
more heavily on interconnectedness among the volunteering members. Further, such organizations, usually
without functional feature of economic production, are likely to rely on members’ contribution of resources
to sustain their existence and performance, and from this perspective individual actors are regarded as ‘‘potential laborers who can perform and fulfill an organization’s obligations in the institutional field’’ (Lin, 2001b,
190). They participate in and contribute to the productivity of the organization by offering their own resources. Thus voluntary organizations are social spaces where resources, much of which come from individual
members, are mobilized for collective purposes. We note that voluntary organizations in this research include
a wide range of social collectivities such as tax-exempt nonprofit organizations, nongovernmental associations, and community organizations (Appendix C).
4. Dense versus sparse networks
Are dense networks and reciprocal relations prerequisites or a uniform characteristic of all social networks or
social groups? There is no doubt that for most voluntary associations, some degree of density and reciprocity, by
definition, must be present. But do we need to assume that all voluntary associations are similarly endowed with
density and reciprocity, and that density and reciprocity are always the asset or capital useful for civic actions?
The answers to these questions can be gleaned from the individual-level research tradition on social capital. As
mentioned, both the individual-level and collective-level research traditions have postulated the importance
of social networks as the conduit for capital. However, at the individual-level conceptualization, a further
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clarification about ‘‘capital’’ has been identified—that it is the resources embedded in the networks that determine the utility of the network to the individual. Further, access to embedded resources may depend on two factors (1) the strength of originating position (e.g., socioeconomic positions) and (2) the strength of (weaker) ties
(Lin, 1982, 1990). Part of this explanatory mechanism, for example, causally links these elements: (1) network
properties (open networks and weaker ties), (2) resources accessed, and (3) outcomes (e.g., better jobs). Measurement of the embedded resources, both accessed in the networks and actually mobilized, is taken in research, sometimes along with openness of networks or strength of ties. This explanatory mechanism and corresponding
measurements have yielded consistent and verifiable results (e.g., finding a good job) (Lin, 1999a,b).
The individual-level analysis thus points to two areas where understanding and measurement for social capital at the collective level may be improved. First, dense network/reciprocal relations are not necessarily
always the more desirable network properties to achieve outcomes, as the individual-level analysis postulates
the possible utility of open networks and weaker relations, because such extensive relations afford reaching
others with richer and more diverse resources. That is, social networks can be characterized by the extent that
they are dense/closed or sparse/open (e.g., with ties to other groups and social circles). Sparse or open networks access more diverse and richer resources (Burt, 2000, 2001a; Granovetter, 1973; Lin, 1999a, 2005).
In the social capital literature, two types of network outcomes have been identified: expressive versus instrumental actions. Expressive actions are intended to preserve resources (e.g., values, living standard, and welfare) in the individual, group, or in the community. Therefore, a closed or dense network that may be the
source of bonding social capital (Putnam, 2000) should enhance the cohesion of the group and solidarity
among members to defend prevailing resources. In contrast, instrumental actions seek more resources to
improve the chances of certain success for the individual, the group, or the community. To acquire such additional resources, a more open or sparse network extending beyond the group (i.e., bridging social capital) will
be more helpful. Theoretically we may thus speculate that for expressive civic actions the dense or closed networks in a group would be more useful; and that for instrumental actions the open or sparse networks in a
group would be more beneficial.
5. Conceptual reformulation and design implications
The discussions above lead to a reformulation of social capital as an explanatory mechanism for both individual and collective actors. First, social capital may be captured by both individuals and collectivities, to be
identified as individual and organizational social capital. Second, it may be postulated that both individual
and organizational social capital may affect action outcomes for both individuals and collectivities. Further,
the individual and organizational network features that enhance social capital may be specified. The ultimate
utility of network features and subsequent social capital may also be dependent on richness and diversity of
existing resources embedded, as well as the intended actions—whether to maintain and preserve existing
resources, or to seek additional resources. The theoretical model is illustrated in Fig. 1.
An ideal design would include all the elements discussed above: the number of voluntary associations participated in, the network features (closeness/openness and reciprocity/extensity of each organization participated in), individual social capital (embedded resources in the networks at the individual level),
organizational social capital (both internal and external social capital for the organization), and civic actions
for both individuals and the organizations. To our knowledge, no such data set exists. However, given these
understandings of how social capital may theoretically shed light on civic actions, steps must be taken to
improve upon research designs and subject them to systematic examination. The remainder of the paper will
put some of the ideas to a preliminary examination. The examination will necessarily be limited to the extent
that the existing data and measures permit.
6. Data, model, and measures
6.1. Data
We used the 2000 Social Capital Benchmark Survey (SCBS) data provided by the Roper Center for Public
Opinion Research and developed at the John F. Kennedy School of Government at Harvard University (full
J. Son, N. Lin / Social Science Research 37 (2008) 330–349
335
Organizational
SocialCapital
Civic Actions for
Organizations
Organizational
Network Features
Civic Actions for
Individuals
Individual
Network Features
Individual
SocialCapital
Fig. 1. Conceptual Model of Social Capital and Civic Action.
details of the survey and data sets available at http://www.cfsv.org/communitysurvey/index.html). The SCBS
was conducted using a large-scale RDD telephone survey. It is composed of two different sets of cross-sectional samples on which the same module of survey was administered: the national sample (n = 3003) was selected
from a national sampling frame while the community samples of a total of 26,230 respondents were taken
from 40 communities across 29 continental US states. Proportionate random sampling was used to select
households for both the national and the community samples.
Despite the large sample sizes, the SCBS had relatively low response rates even though up to ten additional
callbacks after the initial trials of telephone interview were made. On the whole, 42.3% of those contacted for
the national survey cooperated to complete the interview by which 28.7% of all persons in the national sampling frame responded; and quite similarly, 41.6% of those asked to participate in the community surveys completed the interview so that 28.9% of all the persons in the sampling frames of 40 communities responded. The
impact of nonresponse on the data sets is not known.
We selected the SCBS in spite of its low response rates because, to our knowledge, there is currently no other
data set with large sample sizes that offers plentiful information regarding memberships in manifold voluntary
organizations, a variety of volunteering activities, and social networks of respondents (to identify actual use
of such information from the data sets, see Aguilera, 2002; Messner et al., 2004; Subramanian et al., 2002).
Thus we decided to employ the SCBS but cope with the low response rates by a reliability test between the
national and the pooled community samples. Specifically, we take the SCBS national sample as our target sample
of the analysis in the study. However, we run the exact same models on the pooled community sample that combined a variety of 40 communities in the United States. Hence, we assume that the pooled community sample
should produce similar results to the national sample because they reflect diverse and heterogeneous geographical
regions in the United States (see the list of the communities at http://www.cfsv.org/communitysurvey/studied.html). Given that the two data sets were produced based on independent sampling frames—the national
frame was made by the survey firm TNS Intersearch, while the size and sampling geography of each community
sample were determined by the sponsoring community foundations in the 40 communities—we should see disorderly patterns of correlations among the key variables between the national and the pooled community samples
if the low response rates harmed the representativeness of the samples. We acknowledge that this is not an ideal
test of sample reliability, but we argue that our models are at least falsifiable when we get inconsistent results from
the two samples. In brief, we focus on the results from the national sample, but also report parallel results from the
pooled community sample to test the reliability of the results from the former. In addition, as a partial solution for
the unbalanced samplings from various social groups, we employ a sample-balancing weight variable provided
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J. Son, N. Lin / Social Science Research 37 (2008) 330–349
by the SCBS in all the following analyses that enabled us to adjust the samples to reflect their target populations in
terms of age, gender, race, and education.
6.2. Model
With the available data and measures, we devised a model that contains five elements: an organizational
network feature, organizational social capital (only internal), individual social capital, expressive civic actions,
and instrumental civic actions, as shown in Fig. 2.
The empirical model imposes a causal order among the elements in accordance with the theoretical model,
even though the data are cross-sectional. At the collective-level, more extensive networks are postulated to lead
to better organizational social capital. Unfortunately, the data set did not provide individual-level measures of
network feature (e.g., number of friends, family members, or acquaintances). A compromise, to be discussed
shortly, had to be made in the measurement, resulting in a single measure of individual social capital.
We expect that individual and organizational social capital measures will produce both expressive and
instrumental civic actions. However, the data only contained items which could be construed as civic actions
for individuals. No data were available on collective civic actions. We thus propose that the diversity of
embedded resources of both individuals and organizations, in which the individuals participated, will enhance
the individuals’ likelihood of engaging in civic actions.
It should be clearly noted that this model represents compromises relative to the theoretical model depicted
in Fig. 1. The empirical model does not contain individual-level network measure of extensity, nor civic
actions for organizations due to data and measurement constraints.
6.3. Outcome variables
6.3.1. Expressive and instrumental civic actions
The data offered ten kinds of voluntary actions in which the respondents engaged in the past year of the
interview (see Appendix A). We grouped these items into two categories, expressive and instrumental civic
Organizational
Social Capital:
Diversity of
embedded resources
0.78***
-0.03
Expressive
Civic Actions
0.56***
Organizational
Network Feature:
Network extensity
0.21***
0.22***
0.30***
0.13**
Instrumental
Civic Actions
0.18***
Individual
Social Capital:
Diversity of
embedded resources
Fig. 2. SEM of Social Capital and Civic Action (the National Sample) Notes: All estimates are standardized; Structural coefficients are
from Model 5s in Tables 6 and 7 based on the national sample; Model Fit Statistics: CFI = 1.0, TLI = 1.0, WRMR = 0.00; R2 = 0.46
(Expressive Civic Actions), R2 = 0.27 (Instrumental Civic Actions); N = 2,225; *: p < .05, **: p < .01, ***: p < .001 (two-tailed).
J. Son, N. Lin / Social Science Research 37 (2008) 330–349
337
actions. The standard of the categorization was whether an action was performed to preserve preexisting collective goods in a community (expressive civic actions), or to seek resources through collective mobilization to
improve status quo of either a community or a broader social entity (instrumental civic actions). Then confirmatory and exploratory factor analyses were conducted. The results by both analyses supported the categorization with the two-factor solution in which the ten items were divided in the same fashion (Table 2).
Based upon the categorization, we created the summated scales of expressive civic actions (0–7) and instrumental civic actions (0–3).
6.4. Explanatory variables
6.4.1. Individual social capital
Individual social capital was measured based on 11 kinds of personal friendship ties (see Appendix B).
Although the items are apparently not systematic indices of individual social capital, they capture a variety
of characteristics in friendship ties (i.e., bridging and bonding aspects of social capital) including some occupations, economic status, and race of a respondent’s friends. In other words, the items represent both diversity
and richness of embedded resources residing in the friendship networks in terms of socioeconomic status. Thus
measures out of the items may not just show a simple count of equivalent friendship ties, but reveal the structure of social network. Depending on the information of personal friends of respondents, we made a summated scale of friendship ties (0–11) called individual social capital. Given that the questions did not limit the
boundary of the friendship network to closest friends, it should measure a relatively wide range of friendship
diversity and resources.
6.4.2. Organizational network extensity
The data sets enabled us to obtain the membership information of the respondents in the 19 types of varied
voluntary organizations (Appendix C). We conceptualized earlier that voluntary organizations may be seen as
a form of social networks. Individual memberships in such organizations should thus inform of a person’s
location in the organizational social networks. This seems equivalent to knowing how many personal friends
a respondent has at the individual level. In the same way, a person’s number of memberships in voluntary
organizations reveals the person’s network extensity in a social space composed of voluntary organizations
and their members. Thus we created a summated scale of individual memberships in voluntary organizations
(0–19), and name it organizational network extensity because it shows a firsthand feature of organizational
social network. The reason why we view the summated scale as an organizational network feature rather than
as a social capital measure is that it does not have any direct connection to resources embedded in the organizational social network.
6.4.3. Organizational social capital
The measures of organizational social capital consist of four indices that represent diversity of embedded
resources in the organizational network—‘‘organizational diversity,’’ ‘‘range of organizational diversity,’’ ‘‘organizational resources,’’ and a consolidated measure of ‘‘diversity of embedded resources’’ that combines the
three prior indices. There were no such built-in measures in the SCBS data sets. Therefore, we constructed the
Table 2
Summated scales of expressive and instrumental civic actions
Expressive civic actions (0–7)
Instrumental civic actions (0–3)
Worship
Health care
Youth programs
Helping needy
Arts
Neighborhood group
Community project
Petition
Rally
March
Notes. Both confirmatory and exploratory factor analyses produced the same categorization of civic actions. Appendix A shows the full
list of these voluntary actions with the questions asked to the respondents.
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J. Son, N. Lin / Social Science Research 37 (2008) 330–349
measures for each of the 19 voluntary organizations using a two-step approach. In the first step, we selected
five sociodemographic and SES variables that represent the uneven distribution of resources: age, gender, race,
education, and income:
Age.
Gender.
Race.
Education.
Age is measured in years.
Gender is a dummy variable with female equal to 1 and male to 0.
Race is a dummy variable with whites equal to 1 and nonwhites to 0.
There are seven categories of education level: (1) less than high school, (2) high school diploma
(including GED), (3) some college, (4) associate degree (2 years) or specialized technical training,
(5) bachelor’s degree, (6) some graduate training, and (7) graduate or professional training.
Income. Income is 1999 household income, and has four categories: (1) under $30,000, (2) $30,000–
$75,000, (3) $75,000–$100,000, and (4) over $100,000.
We then generated diversity scores of the five variables for the 19 types of voluntary organizations. Specifically, we identified all respondents who belonged to a particular type of organization (e.g., PTA) and constructed diversity scores on the five characteristics (e.g., education) by aggregating the characteristics of the
members in the organization. For the binary variables of gender and race, we employed the absolute value
of the percentage of a minor category, ranging from .0 (minimal diversity) to .5 (maximal diversity). For
the other three variables (age, education, and income), standard deviations were used (e.g., the greater the
S.D.s, the more diverse a focal organization in terms of age, education, and income).
For instance, the upper panel of Table 3 shows the diversity scores of gender by organizations. The political
type of voluntary organizations recorded the greatest gender diversity among all types of organizations in both
the national and the pooled community samples; the gender ratio was almost 1 to 1. In the national sample,
the second least diverse organization (‘‘PTA’’) with regard to the social characteristic of gender had a score of
.32, indicating that males comprised only 32% of its members.
As is shown in the lower panel of Table 3, we used standard deviations for the nondichotomous variables.
For instance, internet groups and ethnic organizations showed most diverse composition in terms of education
among all types of voluntary organizations in the national sample, whereas veterans’ organizations showed the
least education diversity in both the national and the pooled community samples. Likewise, we created the
diversity scores for the other three social characteristics—age, race, and income—for the 19 types of voluntary
organizations.
For each respondent we assigned scores of specific diversities (e.g., gender diversity) of the organizations in
which the person had memberships (e.g., if a person had memberships in three organizations, this person was
Table 3
Organizational diversity in the national and community samples
National sample (n = 3003)
Pooled community sample (n = 26,230)
Organization
Gender diversity (%)
Organization
Gender Diversity (%)
1. Political
2. Sports
3. Other
...
18. PTA
19. Art
0.49
0.48
0.48
...
0.32
0.31
1. Political
2. Other
3. Professional
...
18. Art
19. PTA
0.50
0.49
0.48
...
0.34
0.32
Organization
Education diversity (SD)
Organization
Education diversity (SD)
1. Internet
2. Ethnic
3. Other
...
18. Labor
19. Veterans
2.06
1.95
1.93
...
1.76
1.74
1. Ethnic
2. PTA
3. Charity
...
18. Youth
19. Veterans
1.88
1.87
1.86
...
1.80
1.77
Note. Refer to Appendix C for the full list of the 19 organizations.
J. Son, N. Lin / Social Science Research 37 (2008) 330–349
339
given three different gender diversity scores). Some clarifications in regard to this approach are necessary. In
essence, we assumed that the respondents who belonged to a given type of voluntary organization constituted
a sample of participants in that particular type of group. There were no validity checks possible, as no such
information exists at the national level. In the second step we created two indices with the diversity scores
related to organizational diversity:
Organizational diversity. For each respondent, we set the most diverse scores of the five variables among
the affiliated organizations. For instance, if a respondent had memberships in political, PTA, and art groups
in the national sample, the gender diversity score of the political group (0.49) was given to that person (see
the upper panel in Table 3). Likewise, we assigned the respondents the maximum organizational diversity
scores with regard to the five variables. Then for parsimony we conducted factor analysis of the five organizational diversity measures that produced a one-factor solution, which we named organizational diversity
(see Appendix D).
Range of organizational diversity. We then created another set of indices that accounted for the range of the
diversity scores in the five variables. We constructed the indices by subtracting the minimum diversity scores
from the maximum diversity scores for each respondent. Specifically, according to Table 3, if a respondent was
affiliated in political, PTA, and art groups in the national sample, then the person was given 0.18 point of the
range of gender diversity score by taking out the minimum diversity score (Art: 0.31) from the maximum
diversity score (Political: 0.49). We performed the same procedure for the other four variables, producing
the five indices of the range of organizational diversity. We subsequently did factor analysis on the five measures whereby the one-factor solution, range of organizational diversity, was created (Appendix D).
Nonetheless, it was not possible with the prior two measures of organizational diversity to take into
account how resourceful the voluntary organizations were. Thus we created measures of resourcefulness in
the voluntary organizations in a similar way as the two-step construction of organizational diversity measures.
First, we selected education and income variables because these two variables are starkly related to gauging
socioeconomic resources. Then we sorted the means of each variable from the greatest to the smallest by
the 19 voluntary organizations as shown in Table 4.
Organizational resources. Finally, the respondents who had any memberships in the organizations were given the highest scores of organizational education and income among their affiliated organizations. Further, we
conducted factor analysis on organizational education and income variables so that a single factor solution of
organizational resources was produced (Appendix D).
To this point we created three aggregated indices of organizational diversity and resources as the measures
of organizational social capital. In order to achieve the best possible parsimony in the actual analysis, we tried
to combine all three indices of organizational social capital to make an overarching factor.
Table 4
Organizational resources in the national and community samples
National sample (n = 3003)
Pooled community sample (n = 26,230)
Organization
Education resource (categories: 1–7)
Organization
Education Resource (categories: 1–7)
1. Political
2. Professional
3. Ethnic
...
18. Veterans
19. Senior
4.59
4.56
4.46
...
3.50
3.44
1. Professional
2. Political
3. Ethnic
...
18. Senior
19. Veterans
4.78
4.65
4.59
...
3.67
3.66
Organization
Income resource (categories: 1–4)
Organization
Income resource (categories: 1–4)
1. Political
2. Professional
3. Neighborhood
...
18. Religious
19. Senior
2.43
2.38
2.29
...
1.99
1.88
1. Professional
2. Political
3. Sports
...
18. Self-help
19. Senior
2.49
2.38
2.32
...
2.01
1.89
Note. Refer to Appendix C for the full list of the 19 organizations.
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J. Son, N. Lin / Social Science Research 37 (2008) 330–349
Diversity of embedded resources. We ran factor analysis on the three indices of organizational social capital
– organizational diversity, range of organizational diversity, and organizational resources – and it produced a
single factor solution that we call diversity of embedded resources (Appendix D).
6.4.4. Controls
We used the same set of sociodemographic and SES variables that were used to create the aggregated organizational social capital indices as the control variables, but added age squared term to capture potential nonlinear effects of age on civic actions. Thus the controls are age, age2, gender, race, education, and household
income.
The descriptive information of these key variables can be checked in Table 5; it shows consistent associations of univariate statistics across the national and the pooled community samples.
7. Method
We employed structural equation modeling (SEM) to estimate model parameters using maximum-likelihood method in the Mplus 3, taking the covariance matrices as input (Bollen, 1989; Muthén and Muthén,
2004). Our general model and hypothetical relationships are presented in Fig. 2. As explicated, the SEM
has five main constructs: expressive and instrumental civic actions, individual and organizational social capital, and organizational network extensity. We aim primarily to identify the effects of individual and organizational social capital on the two different types of civic actions. In particular, the effect of individual social
Table 5
Variables in the analyses (means)
National sample
Expressive civic actions (voluntary actions taken past year: 0–7)
Instrumental civic actions (voluntary actions taken past year: 0–3)
Individual social capital (number of friendship ties: 0–11)
Organizational network extensity (number of affiliated organizations: 0–19)
Organizational social capital
Organizational diversitya (standard deviation or percentage)
Age diversity (11.56–18.52/11.55–17.91)b
Gender diversity (0.31–0.49/0.32–0.50)b
Race diversity (0.22–0.48/0.18–0.48)b
Education diversity (1.74–2.06/1.77-s-1.88)b
Income diversity (0.87–1.02/0.88-1.01)b
1.80
0.58
6.09
3.57
(2.01)
(0.79)
(2.67)
(2.84)
Pooled community sample
1.74
0.61
6.18
3.51
17.08
0.44
0.35
1.87
0.99
16.92
0.45
0.27
1.85
0.99
Range of organizational diversitya (maximum diversity—minimum diversity)
Range of age diversity (6.96/6.36)b
Range of gender diversity (0.18/0.18)b
Range of race diversity (0.26/0.30)b
Range of education diversity (0.32/0.11)b
Range of income diversity (0.15/0.13)b
2.97
0.10
0.08
0.09
0.07
2.68
0.09
0.07
0.04
0.05
Organizational resourcesa (highest mean among affiliated organizations)
Organizational education (3.44–4.59/3.66–4.78)b
Organizational income (1.88–2.43/1.89p–2.49)b
4.15
2.25
4.35
2.32
Controls
Age (18–92/18–118)b
Female (0 = male, 1 = female)
White (0 = nonwhite, 1 = white)
Education (seven categories: 1–7)
Income (1999 household income, four categories: 1–4)
N
44.63
0.52
0.78
3.30
2.01
3003
(17.24)
(0.49)
(0.42)
(1.89)
(0.94)
Notes. Standard deviations are in parentheses in the second and third columns.
a
In the following analyses, factors are used instead of multiple indices of the measures (see Appendix D).
b
(National sample/pooled community sample).
44.70
0.52
0.76
3.33
2.01
26,230
(1.95)
(0.79)
(2.68)
(2.83)
(17.34)
(0.50)
(0.43)
(1.75)
(0.93)
J. Son, N. Lin / Social Science Research 37 (2008) 330–349
341
capital on civic actions has not been tested in the literature. We then seek to check whether diversity of embedded resources in the organizational network produced civic actions using the four factors of organizational
social capital. To keep the results as conservative as possible, we report only the fully saturated structural coefficients after controlling all the five latent variables by the same set of control covariates.
We used three indices of model goodness-of-fit to check whether the models produced valid and reliable
results from the covariance matrices. The goodness-of-fit for an acceptable model should be approximately:
(1) CFI (Comparative Fit Index) >.95, (2) TLI (Tucker-Lewis Index) >.95, and (3) WRMR (Weighted
Root-Mean-square Residual) <1.0 (Bentler, 1990; Steiger, 1990; Yu, 2002).
8. Results
We conducted a set of SEMs on both the national and the pooled community samples to verify whether we
can reach unbiased results from the national sample. The SEMs allowed us to retain the two ultimate endogenous variables, expressive and instrumental civic actions, in a model and run it simultaneously. Next, we set
individual and organizational social capital and organizational network extensity as exogenous variables (see
Fig. 2). Acknowledging that the data sets were cross-sectional, we do not assume any causal relationship
between individual and organizational social capital. In order to identify the effects of the four factor indices
of organizational social capital on civic actions, we present the results one by one, with each measure included
in the models individually.
Table 6 shows the SEM results regressing expressive civic actions on individual and organizational social
capital measures and control covariates. It is shown that individual social capital was a significant predictor of
facilitating expressive civic actions in both national and pooled community samples. We also found that organizational network extensity was the biggest contributor to expressive civic actions according to the standardized coefficients, regardless of the differences of the data sets. However, the indices of organizational social
capital in Models 2–5 were not significant in predicting expressive civic actions in the national sample. Further, other than range of organizational diversity, three indices of organizational social capital among the four
measures showed negative associations with expressive civic actions. It thus seems that organizational social
capital did not help to facilitate expressive civic actions in the national sample. But the pooled community
sample indicates somewhat different results; other than the index of organizational resources the three other
indices were shown to be significant and positive predictors of expressive civic actions in Models 2–5. Nonetheless, the magnitudes of the significant indices were all under 0.10, which indicates that the impact of the
indices may be relatively weak. It should also be noted that the magnitude of diversity of embedded resources
in Model 5, the overarching index of organizational social capital was smaller than its components of organizational diversity and range of organizational diversity in the previous models. Further, it should be warned
that the large sample size of the pooled community sample might explain some portion of the reason that we
see the significant coefficients of the three indices with small magnitudes. Therefore, we conclude that the effect
of organizational social capital may be either insignificant if solely based on the national sample, or negligible
when considering the parallel results from the pooled community sample.
The control covariates delineated a meaningful pattern of individual sociodemographic and SES impacts
on expressive civic actions. Being female was the most important contributor to expressive civic actions among
the controls in both the national and the pooled community samples. This is quite understandable because
most of the indicators of expressive civic actions were composed of communal voluntary actions, which are
more likely to be taken by women than by men (see Table 2). Next, the results show that more-educated people were more likely to take expressive civic actions than the less educated. Subsequently, whites tended to
volunteer more for expressive civic actions than did nonwhites. Age and income did not produce consistent
results across the two data sets. The model fit indices confirmed the goodness of fit of the SEMs. Additionally,
it should be noted that model R2 was the greatest in Model 1, where no index of organizational social capital
was included as a mediator, but it decreased to some extent consistently across Models 2–5 in both the national and the pooled community samples, which demonstrates that the indices of organizational social capital did
not result in any positive impacts on the explained variances of expressive civic actions.
According to Table 7, it seems that the effect of individual social capital was again significant but reduced a
little in predicting instrumental civic actions in Models 1 through 5. However, it was organizational network
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J. Son, N. Lin / Social Science Research 37 (2008) 330–349
Table 6
SEMs of expressive civic actionsa on individual and organizational social capital
National sample
Model 1
Individual social
capitalb
Organizational
network
extensityc
Model 2
Model fit
CFI
TLI
WRMR
R2
N
Model 4
Model 5
Model 1
Model 2
Model 3
Model 4
Model 5
0.20***
0.21***
0.20***
0.21***
0.21***
0.19***
0.19***
0.19***
0.19***
0.19***
0.57***
0.58***
0.51***
0.54***
0.56***
0.56***
0.47***
0.45***
0.53***
0.47***
Organizational social capital
Organizational —
0.07
diversity
Range of
—
—
organizational
diversity
Organizational —
—
resources
Diversity of
—
—
embedded
resourcesd
Controls
Age
Age2
Female
White
Education
Income
Pooled community sample
Model 3
0.07
0.01
0.10***
0.05**
0.07**
0.01
1.00
1.00
0.00
0.50
2535
0.08
0.00
0.10***
0.06**
0.07**
0.01
1.00
1.00
0.00
0.46
2225
—
0.03
—
—
—
—
—
—
—
—
—
—
—
—
—
0.02
—
—
0.05
0.02
0.10***
0.06**
0.06*
0.01
1.00
1.00
0.00
0.46
2225
0.08*** —
—
—0.03
0.05
0.02
0.10***
0.06**
0.07**
0.01
1.00
1.00
0.00
0.46
2225
0.06
0.02
0.10***
0.06**
0.07**
0.01
1.00
1.00
0.00
0.46
2225
0.02
0.10**
0.10***
0.04***
0.07***
0.03***
1.00
1.00
0.00
0.49
22,164
0.02
0.11**
0.11***
0.04***
0.06***
0.03***
1.00
1.00
0.00
0.44
19,734
—
—
0.09*** —
—
0.01
0.07***
—
0.00
0.08*
0.11***
0.04***
0.07***
0.03***
1.00
1.00
0.00
0.44
19,734
—
0.02
0.11***
0.11***
0.04***
0.07***
0.04***
1.00
1.00
0.00
0.44
19,734
0.01
0.10**
0.11***
0.04***
0.06***
0.03***
1.00
1.00
0.00
0.44
19,734
Notes: All estimates are standardized; CFI, Comparative Fit Index; TLI, Tucker-Lewis Index; WRMR, Weighted Root-Mean-square
Residual; *p < .05; **p < .01; ***p < .001 (two-tailed).
a
Number of expressive civic actions (0–7).
b
Number of friendship ties (0–11).
c
Number of affiliated organizations (0–19).
d
One-factor solution of organizational diversity, range of organizational diversity, and organizational resources.
extensity that experienced drastic reduction in its explanatory power; the magnitudes of organizational network
extensity in predicting instrumental civic actions decreased by 42–77% in the national sample when compared
with the parallel coefficients in predicting expressive civic actions (see Table 6). In some sense, the reduction in
the explanatory power of organizational network extensity may be caused by the fact that the instrumental civic
actions (petition, rally, and march) were not so deeply embedded in the organizational context as the expressive
civic actions.
Further, the additional reductions in the magnitudes of this coefficient from Model 1 to the following
models were called forth by the inclusion of the indices of organizational social capital. The first thing
to note is that all four indices of organizational social capital were positively associated with instrumental
civic actions in both the national and the pooled community samples, which was not the case for expressive
civic actions. It then seems that the stepwise inclusion of the three different indices of organizational social
capital in both the national and the pooled community samples mediated the effect of organizational network extensity on instrumental civic actions, which is verified by the further reductions in magnitudes of
organizational network extensity in Models 2–4. Moreover, the overarching factor, diversity of embedded
J. Son, N. Lin / Social Science Research 37 (2008) 330–349
343
Table 7
SEMs of Instrumental civic actionsa on individual and organizational social capital
National sample
Model 1
Individual social
capitalb
Organizational
network
extensityc
Model 2
Model fit
CFI
TLI
WRMR
R2
N
Model 4
Model 5
Model 1
Model 2
Model 3
Model 4
Model 5
0.19***
0.15***
0.16***
0.15***
0.18***
0.18***
0.13***
0.14***
0.14***
0.17***
0.33***
0.24***
0.25***
0.25***
0.13**
0.35***
0.22***
0.26***
0.28***
0.11***
Organizational social capital
—
0.07
Organizational
diversity
Range of
—
—
organizational
diversity
Organizational —
—
resources
Diversity of
—
—
embedded
resourcesd
Controls
Age
Age2
Female
White
Education
Income
Pooled community sample
Model 3
0.06
0.03
0.01
0.07**
0.13***
0.03
1.00
1.00
0.00
0.29
2535
0.11
0.02
0.00
0.05*
0.10***
0.03
1.00
1.00
0.00
0.21
2225
—
0.05
—
—
—
—
—
—
—
—
—
—
0.22*** —
—
—
0.07*
—
—
—
0.13
0.05
0.00
0.05*
0.10***
0.03
1.00
1.00
0.00
0.21
2225
0.15*** —
—
0.12
0.04
0.00
0.04
0.09**
0.02
1.00
1.00
0.00
0.21
2225
0.07
0.03
0.01
0.06*
0.11***
0.03
1.00
1.00
0.00
0.27
2225
0.05
0.13**
0.00
0.04***
0.10***
0.01
1.00
1.00
0.00
0.26
22,164
0.03
0.10*
0.01
0.04***
0.07***
0.00
1.00
1.00
0.00
0.20
19,734
—
—
0.07*** —
—
0.09*** —
0.28***
—
0.00
0.07
0.01
0.04***
0.08***
0.00
1.00
1.00
0.00
0.19
19,734
0.02
0.08
0.00
0.03***
0.06***
0.00
1.00
1.00
0.00
0.19
19,734
0.04
0.11*
0.00
0.04***
0.07***
0.00
1.00
1.00
0.00
0.25
19,734
Notes: All estimates are standardized; CFI, Comparative Fit Index; TLI, Tucker-Lewis Index; WRMR = Weighted Root-Mean-square
Residual; *p < .05; **p < .01; ***p < .001 (two-tailed).
a
Number of instrumental civic actions (0–3).
b
Number of friendship ties (0–11).
c
Number of affiliated organizations (0–19).
d
One-factor solution of organizational diversity, range of organizational diversity, and organizational resources.
resources, in Model 5 in both the samples mediated approximately two-thirds of the impact of organizational network extensity in Model 1 so that organizational social capital presented itself as the biggest contributor to instrumental civic actions in Model 5. Thus we conclude that diversity of embedded resources in the
organizational network is more important than mere network extensity of organizations in generating more
instrumentally oriented civic actions.
According to the control covariates, education was shown to be the most important individual social character that triggered more instrumental civic actions in both the national and the pooled community samples.
Being white was generally a significant predictor of taking more instrumental civic actions. However, being
female turned insignificant in producing instrumental civic actions, although it was the biggest contributor
to expressive civic actions. Thus we conclude from the relevant results in Tables 6 and 7 that women are more
bound in communal and expressive civic actions than men, but the female dominance no longer exists when it
comes to instrumental civic actions. All the models in Table 7 were supported by the satisfactory goodness of
the model fit indices.
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J. Son, N. Lin / Social Science Research 37 (2008) 330–349
Having verified that the results from the SEMs were quite consistent between the national and the pooled
community samples in predicting the two types of civic actions, it is necessary to identify the structural relations among the key exogenous and endogenous variables. As shown in Fig. 2 only based on the national sample, organizational network extensity is shown to be a significant predictor of organizational social capital
(0.78). Then organizational social capital was the strongest predictor of instrumental civic actions mediating
the effect of organizational network extensity in the process as well, although it was not significant in generating expressive civic actions. Individual social capital was the consistent and significant contributor for both
types of civic actions.
9. Conclusion and discussion
In the present study we aimed to test whether civic actions are the product of diversity of embedded
resources in individual and organizational social networks. As elaborated, we constructed measures of individual and organizational social capital. In particular, we created several indices of organizational social capital that reflect the structural features of diversity of embedded resources in the organizational network by
aggregating relevant social characteristics of members of each type of civic association. We could not generate
corresponding indices of individual social capital because of the lack of pertinent measures, but were able to
give rise to a summated scale of individual social capital that represents resource diversity in friendship network. We then regressed two types of civic actions—expressive and instrumental civic actions—on the social
capital measures in a series of SEMs. The SEM results evidenced that (1) both types of civic actions were generated by individual social capital to a significant extent; and (2) instrumental civic actions were best predicted
by diversity of embedded resources in organizational network, whereas the expressive type of civic actions
received almost insignificant impact from the same measure of organizational social capital. We further identified from the control covariates that civic actions were gendered in the sense that women were more likely to
be involved in the domain of expressive civic actions, but such female dominance disappeared in the realm of
instrumental civic actions.
As acknowledged, the study offers only preliminary support for the proposed theoretical framework, as the
data sets had limitations: (1) they are cross-sectional samples, so that causal attributions tend to be tentative;
(2) measurements of organizational social capital had to be aggregated from respondent data rather than from
actual organizational census or surveys, and the variables for such construction were limited to certain social
characteristics; (3) measurements of individual social capital were limited to what was available from the survey; and (4) measurements of civic actions were restricted to individual actions, and no collective actions were
available for the analyses. Further, because the response rate of the SCBS was relatively low, we conducted the
cross-checking of the results between the national sample and the pooled community samples. Despite such
constraints, the data and our analyses provide sufficient warrant for further theoretical and empirical
explorations.
One future research direction should elaborate social capital at the organizational level. As Lin proposed (forthcoming), social capital for organizations may be measured with internal social capital (i.e.,
resources contributed from members in the organization) and external social capital (i.e., resources contributed by other organizations to which the focal organization is connected). The data sets employed in
the present study offer only potential internal social capital. External social capital thus deserves future
research attention.
As evidenced in our analyses, another direction points to the goals of organizations as an important
focus. Individual social capital in the labor market has identified two types of returns: instrumental
and expressive returns. Instrumental returns involve the seeking and gaining of additional or new
resources, whereas expressive returns involve the preservation and maintenance of existing resources.
Likewise, for organizations we expect that some organizations are more instrumental in orientation,
seeking changes in the allocation and reallocation of resources, while others are more expressive, seeking to preserve possessed resources (Lin, Forthcoming). Or some organizations may engage in instrumental and expressive actions relative to different issues. Therefore, it is important to understand and
J. Son, N. Lin / Social Science Research 37 (2008) 330–349
345
build such alternative returns in the formulation of specific research design and measurement for social
capital. For example, instrumental actions may benefit from extended and open networks linking to
varied and richer embedded resources (i.e., the bridging function of social capital), while expressive
actions may rely on dense network among close rank of members (i.e., the bonding function of social
capital).
One last thing we should note is that we did not deal with interpersonal and generalized trust in this
study given that its main focus was to identify the network-basedness of social capital. As Cook (2005)
recently proposed, trust relations may or may not transform themselves into social capital contingent on
macro-institutional conditions. More specifically, she argues that trust may be an important intervening variable when the macro-institutional condition is characterized as uncertain. Thus, in this conceptualization,
trust is not social capital but a contingent factor. We agree with Cook’s analysis and conceptualization separating trust from social capital. Future studies may consider incorporating trust as a moderator between
network features and individual/organizational social capital. These modifications and extensions, we argue,
offer potentially fruitful advances of social capital as a cohesive and comprehensive concept and a unifying
and powerful theory in capturing social dynamics among individuals as well as organizations in a variety of
social contexts.
Appendix A. Survey questions regarding civic actions
[Questions 1–6] Tell me whether you have done any volunteer work for each in the past twelve months
(Yes = 1; No = 0); [Questions 7–10] Which of the following things have you done for the past twelve
months? (Yes = 1; No = 0):
1. For your place of worship (‘‘worship’’).
2. For health care or fighting particular diseases (‘‘health care’’).
3. For school or youth program (‘‘youth programs’’).
4. For any organization to help the poor or elderly (helping needy).
5. For any arts or cultural organizations (‘‘arts’’).
6. For any neighborhood or civic group (‘‘neighborhood group’’).
7. Worked on a community project? (‘‘community project’’)
8. Signed a petition? (‘‘petition’’)
9. Attended a political meeting or rally? (‘‘rally’’)
10. Participated in any demonstrations, protests, boycotts, or marches? (‘‘march’’)
Appendix B. Survey questions regarding individual social capital
Thinking now about everyone that you would count as a PERSONAL FRIEND, not just your closest
friend—do you have a personal friend who. . . (Yes = 1; No = 0):
1. Owns their own business? (‘‘business’’)
2. Is a manual worker? (‘‘worker’’)
3. Has been on welfare? (‘‘welfare’’)
4. Owns a vacation home? (‘‘vacation home’’)
5. Has a different religion than you? (‘‘different religion’’)
6. Is white? (‘‘white’’)
7. Is Latino or Hispanic? (‘‘Latino’’)
8. Is Asian? (‘‘Asian’’)
9. Is Black or African American? (‘‘African American’’)
10. Is gay or lesbian? (‘‘gay’’)
11. Is a community leader? (‘‘leader’’)
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J. Son, N. Lin / Social Science Research 37 (2008) 330–349
Appendix C. Survey questions regarding organizational network extensity
Memberships in voluntary organizations
Now I’d like to ask about . . . groups and organizations. I am going to read a list; just answer YES if you
have been involved in the past 12 months with this kind of group:
1. Besides your local place of worship, any organization affiliated with religion, such as the Knights of
Columbus or B’nai B’rith, or a Bible study group (‘‘religious’’)
2. An adult sports club or league, or an outdoor activity club (‘‘sports’’).
3. A youth organization like youth sports leagues, the scouts, 4-H clubs, and Boys and Girls Clubs (‘‘youth’’)
4. A parents’ association, like the PTA or PTO, or other school support or service groups (‘‘PTA’’).
5. A veterans group (‘‘veterans’’).
6. A neighborhood association, like a block association, a homeowner or tenant association, or a crime
watch group (‘‘neighborhood’’).
7. Clubs or organizations for senior citizens or older people (‘‘senior’’).
8. A charity or social welfare organization that provides services in such fields as health or service to the
needy (‘‘charity’’).
9. A labor union (‘‘labor’’).
10. A professional, trade, farm, or business association (‘‘professional’’).
11. Service clubs or fraternal organizations such as the Lions or Kiwanis or a local women’s club or a
college fraternity or sorority (‘‘fraternity’’).
12. Ethnic, nationality, or civil rights organizations, such as the National Organization for Women, the
Mexican American Legal Defense, or the NAACP (‘‘ethnic’’).
13. Other public interest group, political action groups, political clubs, or party committees (‘‘political’’).
14. A literary, art, discussion or study group, or a musical, dancing, or singing group (‘‘art’’).
15. Any other hobby, investment, or garden clubs or societies (‘‘hobby’’).
16. A support group or self-help program for people with specific illnesses, disabilities, problems, or
addictions, or for their families (‘‘self-help’’).
17. Any group that meets only over the Internet (‘‘Internet’’).
18. Any other kinds of clubs or organizations (‘‘other’’).
19. Any local church, synagogue, or other religious or spiritual community (‘‘church’’).
Appendix D. Factor structures of indices of organizational social capital
Variable
Organizational diversity
Factors eigenvalues
Factor I
Factor II
Factor III
Factor IV
Factor V
Factor scoring on Factor Ia
Age diversity
Gender diversity
Race diversity
National sample
(N = 2638)
Pooled community
sample (N = 23,303)
2.65
0.84
0.82
0.38
0.28
2.42
0.97
0.86
0.43
0.32
0.20
0.28
0.24
0.20
0.28
0.27
J. Son, N. Lin / Social Science Research 37 (2008) 330–349
347
Appendix D (continued)
Variable
National sample
(N = 2638)
Pooled community
sample (N = 23,303)
0.33
0.31
0.33
0.33
3.67
0.53
0.33
0.29
0.18
3.50
0.57
0.44
0.27
0.21
0.22
0.24
0.24
0.24
0.23
0.24
0.25
0.21
0.23
0.26
1.95
0.05
1.93
0.07
Factor scoring on Factor Ia
Organizational education
Organizational income
0.51
0.51
0.51
0.51
Diversity of embedded resources
Factors eigenvalues
Factor I
Factor II
Factor III
2.60
0.30
0.10
2.49
0.40
0.10
0.37
0.36
0.34
0.38
0.38
0.34
Education diversity
Income diversity
Range of organizational diversity
Factors eigenvalues
Factor I
Factor II
Factor III
Factor IV
Factor V
Factor scoring on Factor Ia
Range of age diversity
Range of gender diversity
Range of race diversity
Range of education diversity
Range of income diversity
Organizational resources
Factors eigenvalues
Factor I
Factor II
Factor scoring on Factor Ia
Organizational diversity
Range of organizational diversity
Organizational resources
a
Principal component, minimal eigenvalue of 1, and varimax rotation.
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